System

Abstract

To provide a system according to an embodiment that converts words a hearing-impaired person utters to an ordinary voice.SOLUTION: A system according to an embodiment comprises: a collection unit; an analysis unit; a conversion unit; and an output unit. The collection unit is configured to collect voices. The analysis unit is configured to analyze voice data collected by the collection unit. The conversion unit is configured to convert the voice data analyzed by the analysis unit to an ordinary voice. The output unit is configured to output the voice converted by the conversion unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional techniques have not adequately converted speech uttered by hearing-impaired people into normal speech, and there is room for improvement.

[0005] The system according to the embodiment aims to convert words spoken by a hearing-impaired person into normal speech. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a conversion unit, and an output unit. The collection unit collects voice. The analysis unit analyzes the voice data collected by the collection unit. The conversion unit converts the voice data analyzed by the analysis unit into normal voice. The output unit outputs the voice converted by the conversion unit. Effect of the Invention

[0007] The system according to the embodiment can convert words spoken by a hearing-impaired person into normal speech. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed 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 and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process program 56 executed on the RAM 30.

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

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the 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. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The voice conversion system according to the embodiment of the present invention is a system that converts words uttered by a hearing-impaired person into normal voice. In this voice conversion system, words uttered by a hearing-impaired person are collected by a microphone, and AI analyzes the collected voice data and converts it into normal voice. The converted voice is output through a speaker. This system is shaped like a bow tie, is easy to wear, and is unobtrusive, making it easy to use in daily life. For example, words uttered by a hearing-impaired person are collected by a microphone. In this case, the microphone is built into the bow tie and collects the voiced voice with high accuracy. For example, when a hearing-impaired person utters "hello," the voice is collected by the microphone. Next, the collected voice data is analyzed by AI. The AI ​​analyzes the collected voice data and identifies the uttered word. For example, the word "hello" is identified from the collected voice data. Then, the AI ​​converts the identified word into normal voice. For example, the word "hello" is converted into normal voice. This conversion is performed based on voice data that the AI ​​has learned in advance. Finally, the converted voice is output through a speaker. The speaker is also built into the bow tie and outputs the converted voice clearly. For example, a normal voice saying "hello" is output from a speaker. This device is shaped like a bow tie, is easy to wear, and is unobtrusive, making it easy to use in daily life. For example, by wearing it during a meeting or presentation, the device can convert words spoken by a hearing-impaired person into normal voice, thereby realizing smooth communication. In this way, the voice conversion system can convert words spoken by a hearing-impaired person into normal voice, thereby realizing smooth communication.

[0029] The voice conversion system according to the embodiment includes a collection unit, an analysis unit, a conversion unit, and an output unit. The collection unit collects words uttered by a hearing-impaired person. The collection unit, for example, has a built-in microphone and collects the uttered voice with high accuracy. For example, when a hearing-impaired person utters "hello", the collection unit can collect the voice with the microphone. The analysis unit analyzes the voice data collected by the collection unit. The analysis unit, for example, uses AI to analyze the collected voice data and identifies the uttered words. For example, the analysis unit can identify the word "hello" from the collected voice data. The conversion unit converts the voice data analyzed by the analysis unit into normal voice. The conversion unit, for example, uses AI to convert the identified words into normal voice. For example, the conversion unit can convert the word "hello" into normal voice. The output unit outputs the voice converted by the conversion unit. The output unit, for example, has a built-in speaker and clearly outputs the converted voice. For example, the output unit can output the normal voice of "hello" from the speaker. As a result, the voice conversion system according to the embodiment can convert words spoken by a hearing-impaired person into normal voice, thereby realizing smooth communication.

[0030] The collection unit collects words uttered by the hearing-impaired person. The collection unit, for example, has a built-in microphone and collects the uttered voice with high accuracy. Specifically, the collection unit uses a highly sensitive microphone and is equipped with noise canceling technology to reduce ambient noise. This allows for accurate collection of even the most subtle voices uttered by the hearing-impaired person. Furthermore, the collection unit can not only collect voices, but also capture the mouth movements and facial expressions of the speaker with a camera. This allows for more accurate analysis by combining voice data and video data. For example, if a hearing-impaired person utters "hello," not only the voice is collected with a microphone, but also the mouth movements and facial expressions are captured with a camera, allowing the intention and emotion of the utterance to be more accurately understood. The collection unit transmits these data to a central database in real time, allowing the analysis unit to access them quickly. Furthermore, the collection unit can identify the position and direction of the speaker by linking multiple microphones and cameras, and provide an optimal collection environment. This allows the collection unit to collect words uttered by the hearing-impaired person with high accuracy and in a multifaceted manner, thereby improving the performance of the entire system.

[0031] The analysis unit analyzes the voice data collected by the collection unit. The analysis unit, for example, uses AI to analyze the collected voice data and identify the spoken words. Specifically, the analysis unit uses voice recognition technology to convert the collected voice data into text data. AI analyzes the waveform of the voice data, identifies phonemes and syllables, and combines them to recognize words. For example, if a hearing-impaired person speaks "hello," the analysis unit analyzes the voice data, identifies the phonemes "ko," "n," "ni," "chi," and "ha," and combines them to recognize the word "hello." In addition, the analysis unit can also analyze the collected video data and improve the accuracy of voice recognition based on the speaker's mouth movements and facial expressions. For example, if the speaker's mouth movements match "ko," "n," "ni," "chi," and "ha," the results of voice recognition can be reinforced. The analysis unit is required to process these data in real time and quickly output the results. The analysis unit can also learn past voice data and speech patterns to build a voice recognition model specialized for each speaker. This enables the analysis unit to analyze the collected voice data with high accuracy and quickly and accurately identify the spoken words.

[0032] The conversion unit converts the voice data analyzed by the analysis unit into normal voice. The conversion unit converts, for example, words identified using AI into normal voice. Specifically, the conversion unit converts text data into natural voice using voice synthesis technology. The AI ​​uses a voice synthesis model to reproduce the voice quality and intonation of the speaker and generate natural pronunciation. For example, if the analysis unit identifies the word "hello," the conversion unit generates a natural voice based on the text data. Furthermore, the conversion unit can also generate a voice that reflects the speaker's emotions and intentions. For example, if the speaker says "hello" with a smile, the conversion unit generates a bright voice that reflects the speaker's emotions. The conversion unit generates these voice data in real time and transmits them to the output unit. In addition, the conversion unit can also support multiple languages ​​and convert words spoken in different languages ​​into an appropriate language. As a result, the conversion unit converts the voice data analyzed by the analysis unit into high-quality normal voice, thereby realizing smooth communication.

[0033] The output unit outputs the voice converted by the conversion unit. The output unit, for example, has a built-in speaker and outputs the converted voice clearly. Specifically, the output unit uses a high-quality speaker and reproduces the converted voice with clear and natural sound quality. Furthermore, the output unit has a function of adjusting the volume and tone of the voice, and can provide optimal voice output according to the environment and situation. For example, in a quiet room, the voice is output at a low volume, and in a noisy environment, the voice is output at a high volume. The output unit can also realize a wide range of voice output by linking multiple speakers. As a result, the output unit can output the converted voice at a clear and appropriate volume, and support smooth communication between the hearing impaired person and the people around them. Furthermore, the output unit can provide not only voice output, but also other feedback means such as text display and vibration notification. For example, at the same time as the voice output, the converted words are displayed on a text display device to provide visual feedback. As a result, the output unit can provide various communication means between the hearing impaired person and the people around them, and realize smooth information transmission.

[0034] The collection unit has a built-in microphone and can improve the accuracy of the voiced voice. The collection unit has a built-in microphone, for example, and collects the voiced voice with high accuracy. For example, the collection unit can use noise canceling technology to remove ambient noise and improve the accuracy of the voiced voice. The collection unit can also improve the accuracy of the voiced voice by adjusting the sensitivity of the microphone. For example, the collection unit can collect even a small voice by increasing the sensitivity of the microphone. As a result, the voiced voice is collected with high accuracy, and the analysis accuracy is improved. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the voice data collected by the microphone to the generation AI and cause the generation AI to improve the accuracy of the voice data.

[0035] The analysis unit can analyze the collected voice data and identify the spoken words. The analysis unit can analyze the collected voice data using, for example, AI and identify the spoken words. For example, the analysis unit can identify the word "hello" from the collected voice data using a voice recognition algorithm. The analysis unit can also identify the spoken words using a dictionary database. For example, the analysis unit can match the collected voice data with a dictionary database and identify the spoken words. By doing so, the conversion accuracy is improved by identifying the spoken words. A part 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 the collected voice data to a generation AI and cause the generation AI to identify the spoken words.

[0036] The conversion unit can convert the specified words into normal voice. The conversion unit converts the specified words into normal voice, for example, using AI. For example, the conversion unit can convert the word "hello" into normal voice. The conversion unit converts the specified words into normal voice based on voice data previously learned by the AI. For example, the conversion unit can convert the word "hello" into normal voice using voice data learned by the AI. As a result, the specified words are converted into normal voice, and the speech of the hearing impaired person is output as normal voice. A part or all of the above-mentioned processing in the conversion unit may be performed, for example, using AI, or may be performed without using AI. For example, the conversion unit can input the specified words into the generation AI and cause the generation AI to execute the conversion into normal voice.

[0037] The output unit can output the converted voice clearly. The output unit, for example, has a built-in speaker and outputs the converted voice clearly. For example, the output unit can output a normal voice saying "hello" from the speaker. The output unit can also use noise canceling technology to output the converted voice clearly. For example, the output unit can use noise canceling technology to remove ambient noise and output the converted voice clearly. As a result, the converted voice is output clearly, so that the speech of the hearing impaired person is transmitted clearly. A part or all of the above-mentioned processing in the output unit may be performed, for example, using AI, or may be performed without using AI. For example, the output unit can input the converted voice data to a generation AI and cause the generation AI to clarify the voice output.

[0038] The speech conversion system has a bow tie shape, is easy to wear, and has low visibility. The bow tie shape is easy to wear and unnoticeable, making it easy to use in daily life. For example, by wearing it during a meeting or presentation, the words spoken by a hearing-impaired person are converted into normal speech, realizing smooth communication. The bow tie shape can be made less visible by, for example, changing the color, shape, and size. For example, the bow tie can be made less noticeable by making the color of the bow tie the same as the clothing. Also, the bow tie shape can be made less visible by simplifying its shape. As a result, the bow tie shape makes it easy to use in daily life.

[0039] The collection unit can analyze the surrounding environmental sound in real time and collect the voice while performing noise canceling. For example, when the surrounding noise is large, the collection unit strengthens the noise canceling and collects the voice. For example, when the surrounding noise is large, the collection unit strengthens the noise canceling to collect a clear voice. In addition, the collection unit can minimize the noise canceling in a quiet environment to collect a natural voice. For example, in a quiet environment, the collection unit can minimize the noise canceling to collect a natural voice. In addition, when a sudden sound occurs, the collection unit can cancel the sound in real time and collect the voice. For example, when a sudden sound occurs, the collection unit can cancel the sound in real time to collect a clear voice. This makes it possible to collect a clear voice by noise canceling. A part or all of the above-mentioned 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 surrounding environmental sound data to the generation AI and cause the generation AI to adjust the noise canceling.

[0040] The collection unit can learn the user's speech pattern and automatically adjust the collection timing. For example, when the user speaks slowly, the collection unit adjusts the collection timing to match the pace. For example, when the user speaks slowly, the collection unit can adjust the collection timing to match the pace, thereby performing optimal voice collection. In addition, when the user speaks quickly, the collection unit can adjust the collection timing to match the pace. For example, when the user speaks quickly, the collection unit can adjust the collection timing to match the pace, thereby performing optimal voice collection. In addition, when the user speaks with a pause, the collection unit can adjust the collection timing taking into account the pause. For example, when the user speaks with a pause, the collection unit can adjust the collection timing taking into account the pause, thereby performing optimal voice collection. As a result, the accuracy of voice collection is improved by adjusting the collection timing according to the user's speech pattern. A part or all of the above-mentioned 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 speech pattern data to the generation AI and cause the generation AI to adjust the collection timing.

[0041] The collection unit can preferentially collect highly relevant voices in consideration of the geographical location information of the user when collecting voices. For example, when the user is in a specific location, the collection unit preferentially collects voices related to the location. For example, when the user is in a specific location, the collection unit can perform optimal voice collection by preferentially collecting voices related to the location. In addition, when the user is moving, the collection unit can also preferentially collect voices related to the destination. For example, when the user is moving, the collection unit can perform optimal voice collection by preferentially collecting voices related to the destination. In addition, when the user is participating in a specific event, the collection unit can also preferentially collect voices related to the event. For example, when the user is participating in a specific event, the collection unit can perform optimal voice collection by preferentially collecting voices related to the event. As a result, highly relevant voices can be preferentially collected by voice collection based on geographical location information. A part or all of the above-mentioned 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 geographic location information data to the generation AI and have the generation AI determine the priority of the audio.

[0042] The collection unit can analyze the user's social media activity and collect related voices when collecting voices. The collection unit, for example, collects voices related to the contents that the user is talking about on social media. For example, the collection unit can perform optimal voice collection by collecting voices related to the contents that the user is talking about on social media. The collection unit can also collect voices related to the statements of people that the user follows on social media. For example, the collection unit can perform optimal voice collection by collecting voices related to the statements of people that the user follows on social media. The collection unit can also collect voices related to the topics of groups in which the user participates on social media. For example, the collection unit can perform optimal voice collection by collecting voices related to the topics of groups in which the user participates on social media. This makes it possible to collect highly relevant voices by collecting voices based on social media activities. Part or all of the above-mentioned 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 social media activity data to the generation AI and cause the generation AI to execute the determination of the priority order of the voices.

[0043] The analysis unit can remove background noise from the voice data during analysis to improve the accuracy of the spoken words. The analysis unit can, for example, remove surrounding noise during analysis to improve the accuracy of the voice data. For example, the analysis unit can improve the accuracy of the voice data by removing surrounding noise during analysis. The analysis unit can also remove wind sounds during analysis to improve the accuracy of the spoken words. For example, the analysis unit can improve the accuracy of the spoken words by removing wind sounds during analysis. The analysis unit can also remove echoes during analysis to improve the accuracy of the voice data. For example, the analysis unit can improve the accuracy of the voice data by removing echoes during analysis. As a result, the accuracy of the spoken words is improved by removing the background noise. A part 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 background noise from the voice data to the generation AI and cause the generation AI to perform noise removal.

[0044] The analysis unit can improve the analysis accuracy by taking into account the user's speech pattern during analysis. For example, when the user speaks slowly, the analysis unit improves the analysis accuracy according to the pace. For example, when the user speaks slowly, the analysis unit can perform optimal analysis by improving the analysis accuracy according to the pace. In addition, when the user speaks quickly, the analysis unit can improve the analysis accuracy according to the pace. For example, when the user speaks quickly, the analysis unit can perform optimal analysis by improving the analysis accuracy according to the pace. In addition, when the user speaks with a pause, the analysis unit can improve the analysis accuracy by taking into account the pause. For example, when the user speaks with a pause, the analysis unit can perform optimal analysis by improving the analysis accuracy by taking into account the pause. As a result, the analysis accuracy is improved by the analysis based on the user's speech pattern. Part 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 the user's speech pattern data to the generation AI and cause the generation AI to execute improvement of the analysis accuracy.

[0045] The analysis unit can determine the priority of the analysis based on the collection time of the voice data at the time of analysis. For example, when the collected voice data is new, the analysis unit preferentially analyzes the data. For example, when the collected voice data is new, the analysis unit can perform optimal analysis by analyzing the data preferentially. In addition, when the collected voice data is old, the analysis unit can also analyze the data later. For example, when the collected voice data is old, the analysis unit can perform optimal analysis by analyzing the data later. In addition, the analysis unit can also determine the priority of the analysis based on the importance of the collected voice data. For example, the analysis unit can perform optimal analysis by determining the priority of the analysis based on the importance of the collected voice data. As a result, important data can be analyzed preferentially by determining the priority of the analysis based on the collection time. Part 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 the collection time data of the voice data to the generation AI and cause the generation AI to execute the determination of the priority of the analysis.

[0046] The analysis unit can adjust the order of analysis based on the relevance of the voice data during analysis. For example, when the collected voice data is highly relevant, the analysis unit prioritizes the analysis of the data. For example, when the collected voice data is highly relevant, the analysis unit can perform optimal analysis by analyzing the data preferentially. In addition, when the collected voice data is less relevant, the analysis unit can also analyze the data later. For example, when the collected voice data is less relevant, the analysis unit can perform optimal analysis by analyzing the data later. In addition, the analysis unit can adjust the order of analysis based on the contents of the collected voice data. For example, the analysis unit can perform optimal analysis by adjusting the order of analysis based on the contents of the collected voice data. This enables efficient analysis by adjusting the order of analysis based on the relevance. Part 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 relevance data of the voice data to the generation AI and cause the generation AI to adjust the order of analysis.

[0047] The conversion unit can learn the user's speech pattern at the time of conversion and perform optimal speech conversion. For example, when the user speaks slowly, the conversion unit converts the speech to match the pace. For example, when the user speaks slowly, the conversion unit can perform optimal speech conversion by converting the speech to match the pace. In addition, when the user speaks quickly, the conversion unit can also convert the speech to match the pace. For example, when the user speaks quickly, the conversion unit can perform optimal speech conversion by converting the speech to match the pace. In addition, when the user speaks with a pause, the conversion unit can also convert the speech taking into account the pause. For example, when the user speaks with a pause, the conversion unit can perform optimal speech conversion by converting the speech taking into account the pause. As a result, the speech conversion based on the user's speech pattern improves the conversion accuracy. A part or all of the above-mentioned processing in the conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversion unit can input the user's speech pattern data to the generation AI and cause the generation AI to execute the speech conversion.

[0048] The conversion unit can add a multilingual conversion function for handling different languages ​​at the time of conversion. For example, when the user speaks English, the conversion unit converts the voice into Japanese. For example, when the user speaks English, the conversion unit can perform optimal voice conversion by converting the voice into Japanese. In addition, the conversion unit can also convert the voice into English when the user speaks French. For example, when the user speaks French, the conversion unit can perform optimal voice conversion by converting the voice into English. In addition, the conversion unit can also convert the voice into French when the user speaks Spanish. For example, when the user speaks Spanish, the conversion unit can perform optimal voice conversion by converting the voice into French. This enables voice conversion of different languages ​​by the multilingual conversion function. Part or all of the above-mentioned processing in the conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversion unit can input voice data of different languages ​​to the generation AI and cause the generation AI to execute the multilingual conversion.

[0049] The conversion unit can adjust the conversion algorithm based on the collection location of the voice data at the time of conversion. For example, when converting a voice collected by a user outdoors, the conversion unit adjusts the conversion algorithm taking into account the sound of the wind. For example, when converting a voice collected by a user outdoors, the conversion unit can perform optimal voice conversion by adjusting the conversion algorithm taking into account the sound of the wind. In addition, when converting a voice collected by a user indoors, the conversion unit can also adjust the conversion algorithm taking into account the echo. For example, when converting a voice collected by a user indoors, the conversion unit can perform optimal voice conversion by adjusting the conversion algorithm taking into account the echo. In addition, when converting a voice collected by a user inside a car, the conversion unit can also adjust the conversion algorithm taking into account the engine sound. For example, when converting a voice collected by a user inside a car, the conversion unit can perform optimal voice conversion by adjusting the conversion algorithm taking into account the engine sound. As a result, optimal voice conversion is possible by adjusting the conversion algorithm based on the collection location. A part or all of the above-mentioned processing in the conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversion unit can input data on the location where the voice data was collected to the generation AI and cause the generation AI to adjust the conversion algorithm.

[0050] The conversion unit can analyze the user's social media activities and perform relevant voice conversion at the time of conversion. The conversion unit, for example, converts voice related to the content that the user is talking about on social media. For example, the conversion unit can perform optimal voice conversion by converting voice related to the content that the user is talking about on social media. The conversion unit can also convert voice related to the statement of a person that the user follows on social media. For example, the conversion unit can perform optimal voice conversion by converting voice related to the statement of a person that the user follows on social media. The conversion unit can also convert voice related to the topic of a group in which the user participates on social media. For example, the conversion unit can perform optimal voice conversion by converting voice related to the topic of a group in which the user participates on social media. This enables highly relevant voice conversion by voice conversion based on social media activities. Part or all of the above-mentioned processing in the conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversion unit can input the user's social media activity data to the generation AI and cause the generation AI to execute voice conversion.

[0051] The output unit can analyze the surrounding environmental sound in real time at the time of output and adjust the audio output. For example, when the surroundings are noisy, the output unit increases the volume of the audio output. For example, when the surroundings are noisy, the output unit can perform optimal audio output by increasing the volume of the audio output. In addition, when the surroundings are quiet, the output unit can also lower the volume of the audio output. For example, when the surroundings are quiet, the output unit can perform optimal audio output by lowering the volume of the audio output. In addition, when a sudden sound occurs, the output unit can analyze the sound in real time and adjust the audio output. For example, when a sudden sound occurs, the output unit can analyze the sound in real time and perform optimal audio output. This enables clear audio output by audio output based on the surrounding environmental sound. Part or all of the above-mentioned processing in the output unit may be performed using, for example, AI, or may be performed without using AI. For example, the output unit can input the surrounding environmental sound data to the generation AI and cause the generation AI to adjust the audio output.

[0052] The output unit can improve the accuracy of the voice output by considering the user's speech pattern at the time of output. For example, when the user speaks slowly, the output unit adjusts the voice output to match the pace. For example, when the user speaks slowly, the output unit can perform optimal voice output by adjusting the voice output to match the pace. In addition, when the user speaks quickly, the output unit can also adjust the voice output to match the pace. For example, when the user speaks quickly, the output unit can perform optimal voice output by adjusting the voice output to match the pace. In addition, when the user speaks with a pause, the output unit can also adjust the voice output taking into account the pause. For example, when the user speaks with a pause, the output unit can perform optimal voice output by adjusting the voice output taking into account the pause. As a result, the output accuracy is improved by the voice output based on the user's speech pattern. A part or all of the above-mentioned processing in the output unit may be performed using, for example, AI, or may be performed without using AI. For example, the output unit can input the user's speech pattern data to the generation AI and cause the generation AI to execute the adjustment of the voice output.

[0053] The output unit can perform optimal audio output in consideration of the geographical location information of the user when outputting audio. For example, when the user is in a specific location, the output unit preferentially outputs audio related to the location. For example, when the user is in a specific location, the output unit can perform optimal audio output by preferentially outputting audio related to the location. In addition, when the user is moving, the output unit can also preferentially output audio related to the destination. For example, when the user is moving, the output unit can perform optimal audio output by preferentially outputting audio related to the destination. In addition, when the user is participating in a specific event, the output unit can also preferentially output audio related to the event. For example, when the user is participating in a specific event, the output unit can perform optimal audio output by preferentially outputting audio related to the event. As a result, audio output based on geographical location information can preferentially output audio with high relevance. Part or all of the above-mentioned processing in the output unit may be performed using, for example, AI, or may be performed without using AI. For example, the output unit can input the geographical location information data of the user to the generation AI and cause the generation AI to execute adjustment of the audio output.

[0054] The output unit can analyze the user's social media activity and perform related audio output at the time of audio output. The output unit, for example, outputs audio related to the content that the user is talking about on social media. For example, the output unit can perform optimal audio output by outputting audio related to the content that the user is talking about on social media. The output unit can also output audio related to the statement of a person that the user follows on social media. For example, the output unit can perform optimal audio output by outputting audio related to the statement of a person that the user follows on social media. The output unit can also output audio related to the topic of a group in which the user participates on social media. For example, the output unit can perform optimal audio output by outputting audio related to the topic of a group in which the user participates on social media. This makes it possible to output highly relevant audio by audio output based on social media activity. Part or all of the above-mentioned processing in the output unit may be performed using, for example, AI, or may be performed without using AI. For example, the output unit can input the user's social media activity data to the generation AI and cause the generation AI to adjust the audio output.

[0055] The shape of the bow tie can be improved by changing the material of the bow tie to improve the fit and durability. The shape of the bow tie can be improved by using a soft material, for example. For example, the shape of the bow tie can be improved by using a soft material. The shape of the bow tie can also be made to withstand long-term use by using a highly durable material. For example, the shape of the bow tie can be made to withstand long-term use by using a highly durable material. The shape of the bow tie can also be made to provide a comfortable fit by using a material with good breathability. For example, the shape of the bow tie can be made to provide a comfortable fit by using a material with good breathability. As a result, the fit and durability are improved by changing the material. Part or all of the above-mentioned processing in the shape of the bow tie may be performed, for example, using AI, or may be performed without using AI. For example, the shape of the bow tie can be made by inputting material selection data into the generation AI and causing the generation AI to change the material.

[0056] The bow tie shape can incorporate additional functions in the bow tie, for example, a solar panel for extending the battery life. The bow tie shape can incorporate, for example, a solar panel for extending the battery life. For example, the bow tie shape can incorporate a solar panel to extend the battery life. The bow tie shape can also incorporate a battery pack to enable long-term use. For example, the bow tie shape can incorporate a battery pack to enable long-term use. The bow tie shape can also add a wireless charging function to eliminate the trouble of charging. For example, the bow tie shape can add a wireless charging function to eliminate the trouble of charging. This allows the additional function to extend the battery life and enable long-term use. Some or all of the above-mentioned processing in the bow tie shape may be performed, for example, using AI, or may be performed without using AI. For example, the bow tie shape can input design data of the additional function into the generation AI, and have the generation AI execute the incorporation of the additional function.

[0057] The shape of the bow tie can be applied to other accessories. The shape of the bow tie can be applied to, for example, a brooch or a necklace. For example, the shape of the bow tie can be applied to a brooch to expand the range of wear. The shape of the bow tie can also be applied to a necklace to enhance fashionability. For example, the shape of the bow tie can be applied to a necklace to enhance fashionability. The shape of the bow tie can also be applied to a hair accessory to accommodate a variety of styles. For example, the shape of the bow tie can be applied to a hair accessory to accommodate a variety of styles. This expands the range of wear by applying it to other accessories. Some or all of the above-mentioned processing for the shape of the bow tie may be performed, for example, using AI, or may be performed without using AI. For example, the shape of the bow tie can be applied to other accessories by inputting application data to the generation AI, and the generation AI can execute the application.

[0058] The shape of the bow tie can make the design of the bow tie customizable and allow the user to change it to suit his / her own preferences. The shape of the bow tie can, for example, allow the user to select a design, providing a bow tie tailored to an individual's preferences. For example, the shape of the bow tie can allow the user to select a design, providing a bow tie tailored to an individual's preferences. The shape of the bow tie can also allow the user to select a color, providing a bow tie tailored to an individual's style. For example, the shape of the bow tie can allow the user to select a color, providing a bow tie tailored to an individual's style. The shape of the bow tie can also allow the user to select a material, providing a bow tie tailored to an individual's fit. For example, the shape of the bow tie can allow the user to select a material, providing a bow tie tailored to an individual's fit. This allows the design to be customized to suit the user's preferences. Some or all of the above-mentioned processing in the shape of the bow tie may be performed, for example, using AI, or may be performed without using AI. For example, the shape of the bow tie can be customized by inputting customization data into a generation AI, and causing the generation AI to execute the customization of the design.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The voice conversion system can further include a function of learning the user's speech pattern and automatically adjusting the sensitivity of the collection unit. For example, if the user speaks slowly, the collection unit can adjust the sensitivity to match the pace and perform optimal voice collection. Also, if the user speaks quickly, the sensitivity can be adjusted to match the pace. Furthermore, if the user speaks with pauses, the sensitivity can be adjusted to take these pauses into account. This improves the accuracy of voice collection by adjusting the sensitivity according to the user's speech pattern.

[0061] The analysis unit can further include a function of learning the user's speech pattern and automatically adjusting the analysis algorithm. For example, if the user speaks slowly, the analysis unit can adjust the analysis algorithm to match the pace and perform optimal analysis. Also, if the user speaks quickly, the analysis algorithm can be adjusted to match the pace. Furthermore, if the user speaks with pauses, the analysis algorithm can be adjusted to take these pauses into account. This improves the analysis accuracy by adjusting the analysis algorithm according to the user's speech pattern.

[0062] The conversion unit may further include a multi-language conversion function for handling different languages. For example, if the user speaks English, the voice can be converted into Japanese. If the user speaks French, the voice can also be converted into English. If the user speaks Spanish, the voice can also be converted into French. Thus, the multi-language conversion function enables voice conversion between different languages, facilitating international communication.

[0063] The output unit can further include a function for analyzing surrounding environmental sounds in real time and adjusting the audio output. For example, when the surroundings are noisy, the volume of the audio output can be increased. Also, when the surroundings are quiet, the volume of the audio output can be decreased. Furthermore, when a sudden sound occurs, the sound can be analyzed in real time and the audio output can be adjusted. This enables clear audio output based on the surrounding environmental sounds.

[0064] The voice conversion system may further include a function for collecting and analyzing voice in consideration of the geographic location information of the user. For example, when the user is in a specific location, the collection unit may preferentially collect voice related to the location. Also, when the collected voice data is related to a specific location, the analysis unit may preferentially analyze the data. Furthermore, the conversion unit may adjust the conversion algorithm based on the collection location. This allows voice collection and analysis based on geographic location information to preferentially process highly relevant voice.

[0065] The process flow of the first embodiment will be briefly described below.

[0066] Step 1: The collection unit collects words uttered by the hearing-impaired person. The collection unit, for example, has a built-in microphone and collects the uttered voice with high accuracy. For example, when the hearing-impaired person utters "hello," the collection unit can collect the voice with the microphone. Step 2: The analysis unit analyzes the voice data collected by the collection unit. The analysis unit, for example, uses AI to analyze the collected voice data and identify the spoken words. For example, the analysis unit can identify the word "hello" from the collected voice data. Step 3: The conversion unit converts the voice data analyzed by the analysis unit into normal voice. The conversion unit converts, for example, words identified using AI into normal voice. For example, the conversion unit can convert the word "hello" into normal voice. Step 4: The output unit outputs the voice converted by the conversion unit. The output unit, for example, has a built-in speaker and outputs the converted voice clearly. For example, the output unit can output a normal voice saying "Hello" from the speaker.

[0067] (Example 2) The voice conversion system according to the embodiment of the present invention is a system that converts words uttered by a hearing-impaired person into normal voice. In this voice conversion system, words uttered by a hearing-impaired person are collected by a microphone, and AI analyzes the collected voice data and converts it into normal voice. The converted voice is output through a speaker. This system is shaped like a bow tie, is easy to wear, and is unobtrusive, making it easy to use in daily life. For example, words uttered by a hearing-impaired person are collected by a microphone. In this case, the microphone is built into the bow tie and collects the voiced voice with high accuracy. For example, when a hearing-impaired person utters "hello," the voice is collected by the microphone. Next, the collected voice data is analyzed by AI. The AI ​​analyzes the collected voice data and identifies the uttered word. For example, the word "hello" is identified from the collected voice data. Then, the AI ​​converts the identified word into normal voice. For example, the word "hello" is converted into normal voice. This conversion is performed based on voice data that the AI ​​has learned in advance. Finally, the converted voice is output through a speaker. The speaker is also built into the bow tie and outputs the converted voice clearly. For example, a normal voice saying "hello" is output from a speaker. This device is shaped like a bow tie, is easy to wear, and is unobtrusive, making it easy to use in daily life. For example, by wearing it during a meeting or presentation, the device can convert words spoken by a hearing-impaired person into normal voice, thereby realizing smooth communication. In this way, the voice conversion system can convert words spoken by a hearing-impaired person into normal voice, thereby realizing smooth communication.

[0068] The voice conversion system according to the embodiment includes a collection unit, an analysis unit, a conversion unit, and an output unit. The collection unit collects words uttered by a hearing-impaired person. The collection unit, for example, has a built-in microphone and collects the uttered voice with high accuracy. For example, when a hearing-impaired person utters "hello", the collection unit can collect the voice with the microphone. The analysis unit analyzes the voice data collected by the collection unit. The analysis unit, for example, uses AI to analyze the collected voice data and identifies the uttered words. For example, the analysis unit can identify the word "hello" from the collected voice data. The conversion unit converts the voice data analyzed by the analysis unit into normal voice. The conversion unit, for example, uses AI to convert the identified words into normal voice. For example, the conversion unit can convert the word "hello" into normal voice. The output unit outputs the voice converted by the conversion unit. The output unit, for example, has a built-in speaker and clearly outputs the converted voice. For example, the output unit can output the normal voice of "hello" from the speaker. As a result, the voice conversion system according to the embodiment can convert words spoken by a hearing-impaired person into normal voice, thereby realizing smooth communication.

[0069] The collection unit collects words uttered by the hearing-impaired person. The collection unit, for example, has a built-in microphone and collects the uttered voice with high accuracy. Specifically, the collection unit uses a highly sensitive microphone and is equipped with noise canceling technology to reduce ambient noise. This allows for accurate collection of even the most subtle voices uttered by the hearing-impaired person. Furthermore, the collection unit can not only collect voices, but also capture the mouth movements and facial expressions of the speaker with a camera. This allows for more accurate analysis by combining voice data and video data. For example, if a hearing-impaired person utters "hello," not only the voice is collected with a microphone, but also the mouth movements and facial expressions are captured with a camera, allowing the intention and emotion of the utterance to be more accurately understood. The collection unit transmits these data to a central database in real time, allowing the analysis unit to access them quickly. Furthermore, the collection unit can identify the position and direction of the speaker by linking multiple microphones and cameras, and provide an optimal collection environment. This allows the collection unit to collect words uttered by the hearing-impaired person with high accuracy and in a multifaceted manner, thereby improving the performance of the entire system.

[0070] The analysis unit analyzes the voice data collected by the collection unit. The analysis unit, for example, uses AI to analyze the collected voice data and identify the spoken words. Specifically, the analysis unit uses voice recognition technology to convert the collected voice data into text data. AI analyzes the waveform of the voice data, identifies phonemes and syllables, and combines them to recognize words. For example, if a hearing-impaired person speaks "hello," the analysis unit analyzes the voice data, identifies the phonemes "ko," "n," "ni," "chi," and "ha," and combines them to recognize the word "hello." In addition, the analysis unit can also analyze the collected video data and improve the accuracy of voice recognition based on the speaker's mouth movements and facial expressions. For example, if the speaker's mouth movements match "ko," "n," "ni," "chi," and "ha," the results of voice recognition can be reinforced. The analysis unit is required to process these data in real time and quickly output the results. The analysis unit can also learn past voice data and speech patterns to build a voice recognition model specialized for each speaker. This enables the analysis unit to analyze the collected voice data with high accuracy and quickly and accurately identify the spoken words.

[0071] The conversion unit converts the voice data analyzed by the analysis unit into normal voice. The conversion unit converts, for example, words identified using AI into normal voice. Specifically, the conversion unit converts text data into natural voice using voice synthesis technology. The AI ​​uses a voice synthesis model to reproduce the voice quality and intonation of the speaker and generate natural pronunciation. For example, if the analysis unit identifies the word "hello," the conversion unit generates a natural voice based on the text data. Furthermore, the conversion unit can also generate a voice that reflects the speaker's emotions and intentions. For example, if the speaker says "hello" with a smile, the conversion unit generates a bright voice that reflects the speaker's emotions. The conversion unit generates these voice data in real time and transmits them to the output unit. In addition, the conversion unit can also support multiple languages ​​and convert words spoken in different languages ​​into an appropriate language. As a result, the conversion unit converts the voice data analyzed by the analysis unit into high-quality normal voice, thereby realizing smooth communication.

[0072] The output unit outputs the voice converted by the conversion unit. The output unit, for example, has a built-in speaker and outputs the converted voice clearly. Specifically, the output unit uses a high-quality speaker and reproduces the converted voice with clear and natural sound quality. Furthermore, the output unit has a function of adjusting the volume and tone of the voice, and can provide optimal voice output according to the environment and situation. For example, in a quiet room, the voice is output at a low volume, and in a noisy environment, the voice is output at a high volume. The output unit can also realize a wide range of voice output by linking multiple speakers. As a result, the output unit can output the converted voice at a clear and appropriate volume, and support smooth communication between the hearing impaired person and the people around them. Furthermore, the output unit can provide not only voice output, but also other feedback means such as text display and vibration notification. For example, at the same time as the voice output, the converted words are displayed on a text display device to provide visual feedback. As a result, the output unit can provide various communication means between the hearing impaired person and the people around them, and realize smooth information transmission.

[0073] The collection unit has a built-in microphone and can improve the accuracy of the voiced voice. The collection unit has a built-in microphone, for example, and collects the voiced voice with high accuracy. For example, the collection unit can use noise canceling technology to remove ambient noise and improve the accuracy of the voiced voice. The collection unit can also improve the accuracy of the voiced voice by adjusting the sensitivity of the microphone. For example, the collection unit can collect even a small voice by increasing the sensitivity of the microphone. As a result, the voiced voice is collected with high accuracy, and the analysis accuracy is improved. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the voice data collected by the microphone to the generation AI and cause the generation AI to improve the accuracy of the voice data.

[0074] The analysis unit can analyze the collected voice data and identify the spoken words. The analysis unit can analyze the collected voice data using, for example, AI and identify the spoken words. For example, the analysis unit can identify the word "hello" from the collected voice data using a voice recognition algorithm. The analysis unit can also identify the spoken words using a dictionary database. For example, the analysis unit can match the collected voice data with a dictionary database and identify the spoken words. By doing so, the conversion accuracy is improved by identifying the spoken words. A part 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 the collected voice data to a generation AI and cause the generation AI to identify the spoken words.

[0075] The conversion unit can convert the specified words into normal voice. The conversion unit converts the specified words into normal voice, for example, using AI. For example, the conversion unit can convert the word "hello" into normal voice. The conversion unit converts the specified words into normal voice based on voice data previously learned by the AI. For example, the conversion unit can convert the word "hello" into normal voice using voice data learned by the AI. As a result, the specified words are converted into normal voice, and the speech of the hearing impaired person is output as normal voice. A part or all of the above-mentioned processing in the conversion unit may be performed, for example, using AI, or may be performed without using AI. For example, the conversion unit can input the specified words into the generation AI and cause the generation AI to execute the conversion into normal voice.

[0076] The output unit can output the converted voice clearly. The output unit, for example, has a built-in speaker and outputs the converted voice clearly. For example, the output unit can output a normal voice saying "hello" from the speaker. The output unit can also use noise canceling technology to output the converted voice clearly. For example, the output unit can use noise canceling technology to remove ambient noise and output the converted voice clearly. As a result, the converted voice is output clearly, so that the speech of the hearing impaired person is transmitted clearly. A part or all of the above-mentioned processing in the output unit may be performed, for example, using AI, or may be performed without using AI. For example, the output unit can input the converted voice data to a generation AI and cause the generation AI to clarify the voice output.

[0077] The speech conversion system has a bow tie shape, is easy to wear, and has low visibility. The bow tie shape is easy to wear and unnoticeable, making it easy to use in daily life. For example, by wearing it during a meeting or presentation, the words spoken by a hearing-impaired person are converted into normal speech, realizing smooth communication. The bow tie shape can be made less visible by, for example, changing the color, shape, and size. For example, the bow tie can be made less noticeable by making the color of the bow tie the same as the clothing. Also, the bow tie shape can be made less visible by simplifying its shape. As a result, the bow tie shape makes it easy to use in daily life.

[0078] The collection unit can estimate the user's emotion and adjust the collection sensitivity of the voice based on the estimated emotion of the user. For example, when the user is nervous, the collection unit can increase the collection sensitivity to collect even a quiet voice. For example, when the user is nervous, the collection unit can collect even a quiet voice by increasing the collection sensitivity. In addition, when the user is relaxed, the collection unit can return the collection sensitivity to normal and collect a natural voice. For example, when the user is relaxed, the collection unit can return the collection sensitivity to normal and collect a natural voice. In addition, when the user is excited, the collection unit can adjust the collection sensitivity to suppress excessive noise. For example, when the user is excited, the collection unit can adjust the collection sensitivity to suppress excessive noise. As a result, optimal voice collection is possible by adjusting the collection sensitivity according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotional data to the generation AI and cause the generation AI to adjust the collection sensitivity.

[0079] The collection unit can analyze the surrounding environmental sound in real time and collect the voice while performing noise canceling. For example, when the surrounding noise is large, the collection unit strengthens the noise canceling and collects the voice. For example, when the surrounding noise is large, the collection unit strengthens the noise canceling to collect a clear voice. In addition, the collection unit can minimize the noise canceling in a quiet environment to collect a natural voice. For example, in a quiet environment, the collection unit can minimize the noise canceling to collect a natural voice. In addition, when a sudden sound occurs, the collection unit can cancel the sound in real time and collect the voice. For example, when a sudden sound occurs, the collection unit can cancel the sound in real time to collect a clear voice. This makes it possible to collect a clear voice by noise canceling. A part or all of the above-mentioned 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 surrounding environmental sound data to the generation AI and cause the generation AI to adjust the noise canceling.

[0080] The collection unit can learn the user's speech pattern and automatically adjust the collection timing. For example, when the user speaks slowly, the collection unit adjusts the collection timing to match the pace. For example, when the user speaks slowly, the collection unit can adjust the collection timing to match the pace, thereby performing optimal voice collection. In addition, when the user speaks quickly, the collection unit can adjust the collection timing to match the pace. For example, when the user speaks quickly, the collection unit can adjust the collection timing to match the pace, thereby performing optimal voice collection. In addition, when the user speaks with a pause, the collection unit can adjust the collection timing taking into account the pause. For example, when the user speaks with a pause, the collection unit can adjust the collection timing taking into account the pause, thereby performing optimal voice collection. As a result, the accuracy of voice collection is improved by adjusting the collection timing according to the user's speech pattern. A part or all of the above-mentioned 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 speech pattern data to the generation AI and cause the generation AI to adjust the collection timing.

[0081] The collection unit can estimate the user's emotion and determine the priority of the voices to be collected based on the estimated user's emotion. For example, when the user is nervous, the collection unit preferentially collects important voices. For example, when the user is nervous, the collection unit can perform optimal voice collection by preferentially collecting important voices. In addition, the collection unit can also collect all voices evenly when the user is relaxed. For example, when the user is relaxed, the collection unit can perform optimal voice collection by collecting all voices evenly. In addition, the collection unit can also preferentially collect voices with strong emotions when the user is excited. For example, when the user is excited, the collection unit can perform optimal voice collection by preferentially collecting voices with strong emotions. This allows important voices to be preferentially collected by determining the priority of voices according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (for example, LLM) or a multimodal generation AI. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input the user's emotion data to the generation AI and cause the generation AI to determine the priority order of the voices.

[0082] The collection unit can preferentially collect highly relevant voices in consideration of the geographical location information of the user when collecting voices. For example, when the user is in a specific location, the collection unit preferentially collects voices related to the location. For example, when the user is in a specific location, the collection unit can perform optimal voice collection by preferentially collecting voices related to the location. In addition, when the user is moving, the collection unit can also preferentially collect voices related to the destination. For example, when the user is moving, the collection unit can perform optimal voice collection by preferentially collecting voices related to the destination. In addition, when the user is participating in a specific event, the collection unit can also preferentially collect voices related to the event. For example, when the user is participating in a specific event, the collection unit can perform optimal voice collection by preferentially collecting voices related to the event. As a result, highly relevant voices can be preferentially collected by voice collection based on geographical location information. A part or all of the above-mentioned 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 geographic location information data to the generation AI and have the generation AI determine the priority of the audio.

[0083] The collection unit can analyze the user's social media activity and collect related voices when collecting voices. The collection unit, for example, collects voices related to the contents that the user is talking about on social media. For example, the collection unit can perform optimal voice collection by collecting voices related to the contents that the user is talking about on social media. The collection unit can also collect voices related to the statements of people that the user follows on social media. For example, the collection unit can perform optimal voice collection by collecting voices related to the statements of people that the user follows on social media. The collection unit can also collect voices related to the topics of groups in which the user participates on social media. For example, the collection unit can perform optimal voice collection by collecting voices related to the topics of groups in which the user participates on social media. This makes it possible to collect highly relevant voices by collecting voices based on social media activities. Part or all of the above-mentioned 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 social media activity data to the generation AI and cause the generation AI to execute the determination of the priority order of the voices.

[0084] The analysis unit can estimate the user's emotion and adjust the analysis algorithm based on the estimated user's emotion. For example, when the user is nervous, the analysis unit adjusts the analysis algorithm to emphasize accuracy. For example, when the user is nervous, the analysis unit can perform optimal analysis by adjusting the analysis algorithm to emphasize accuracy. In addition, the analysis unit can also adjust the analysis algorithm to emphasize speed when the user is relaxed. For example, when the user is relaxed, the analysis unit can perform optimal analysis by adjusting the analysis algorithm to emphasize speed. In addition, the analysis unit can also adjust the analysis algorithm to emphasize balance when the user is excited. For example, when the user is excited, the analysis unit can perform optimal analysis by adjusting the analysis algorithm to emphasize balance. Thereby, the analysis accuracy is improved by adjusting the analysis algorithm according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input a user's emotional data into the generation AI and have the generation AI adjust the analysis algorithm.

[0085] The analysis unit can remove background noise from the voice data during analysis to improve the accuracy of the spoken words. The analysis unit can, for example, remove surrounding noise during analysis to improve the accuracy of the voice data. For example, the analysis unit can improve the accuracy of the voice data by removing surrounding noise during analysis. The analysis unit can also remove wind sounds during analysis to improve the accuracy of the spoken words. For example, the analysis unit can improve the accuracy of the spoken words by removing wind sounds during analysis. The analysis unit can also remove echoes during analysis to improve the accuracy of the voice data. For example, the analysis unit can improve the accuracy of the voice data by removing echoes during analysis. As a result, the accuracy of the spoken words is improved by removing the background noise. A part 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 background noise from the voice data to the generation AI and cause the generation AI to perform noise removal.

[0086] The analysis unit can improve the analysis accuracy by taking into account the user's speech pattern during analysis. For example, when the user speaks slowly, the analysis unit improves the analysis accuracy according to the pace. For example, when the user speaks slowly, the analysis unit can perform optimal analysis by improving the analysis accuracy according to the pace. In addition, when the user speaks quickly, the analysis unit can improve the analysis accuracy according to the pace. For example, when the user speaks quickly, the analysis unit can perform optimal analysis by improving the analysis accuracy according to the pace. In addition, when the user speaks with a pause, the analysis unit can improve the analysis accuracy by taking into account the pause. For example, when the user speaks with a pause, the analysis unit can perform optimal analysis by improving the analysis accuracy by taking into account the pause. As a result, the analysis accuracy is improved by the analysis based on the user's speech pattern. Part 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 the user's speech pattern data to the generation AI and cause the generation AI to execute improvement of the analysis accuracy.

[0087] The analysis unit can estimate the user's emotion and adjust the display method of the analysis result based on the estimated user's emotion. For example, when the user is nervous, the analysis unit provides a simple and highly visible display method. For example, when the user is nervous, the analysis unit can display the optimal analysis result by providing a simple and highly visible display method. In addition, the analysis unit can also provide a display method including detailed information when the user is relaxed. For example, when the user is relaxed, the analysis unit can display the optimal analysis result by providing a display method including detailed information. In addition, the analysis unit can also provide a visually stimulating display method when the user is excited. For example, when the user is excited, the analysis unit can display the optimal analysis result by providing a visually stimulating display method. This improves visibility by adjusting the display method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part 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 may input the user's emotion data to the generation AI and cause the generation AI to adjust the display method.

[0088] The analysis unit can determine the priority of the analysis based on the collection time of the voice data at the time of analysis. For example, when the collected voice data is new, the analysis unit preferentially analyzes the data. For example, when the collected voice data is new, the analysis unit can perform optimal analysis by analyzing the data preferentially. In addition, when the collected voice data is old, the analysis unit can also analyze the data later. For example, when the collected voice data is old, the analysis unit can perform optimal analysis by analyzing the data later. In addition, the analysis unit can also determine the priority of the analysis based on the importance of the collected voice data. For example, the analysis unit can perform optimal analysis by determining the priority of the analysis based on the importance of the collected voice data. As a result, important data can be analyzed preferentially by determining the priority of the analysis based on the collection time. Part 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 the collection time data of the voice data to the generation AI and cause the generation AI to execute the determination of the priority of the analysis.

[0089] The analysis unit can adjust the order of analysis based on the relevance of the voice data during analysis. For example, when the collected voice data is highly relevant, the analysis unit prioritizes the analysis of the data. For example, when the collected voice data is highly relevant, the analysis unit can perform optimal analysis by analyzing the data preferentially. In addition, when the collected voice data is less relevant, the analysis unit can also analyze the data later. For example, when the collected voice data is less relevant, the analysis unit can perform optimal analysis by analyzing the data later. In addition, the analysis unit can adjust the order of analysis based on the contents of the collected voice data. For example, the analysis unit can perform optimal analysis by adjusting the order of analysis based on the contents of the collected voice data. This enables efficient analysis by adjusting the order of analysis based on the relevance. Part 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 relevance data of the voice data to the generation AI and cause the generation AI to adjust the order of analysis.

[0090] The conversion unit can estimate the user's emotion and adjust the tone and pitch of the voice conversion based on the estimated emotion of the user. For example, when the user is nervous, the conversion unit converts the voice with a calm tone. For example, when the user is nervous, the conversion unit can perform optimal voice conversion by converting the voice with a calm tone. In addition, the conversion unit can also convert the voice with a natural tone when the user is relaxed. For example, when the user is relaxed, the conversion unit can perform optimal voice conversion by converting the voice with a natural tone. In addition, the conversion unit can also convert the voice with a bright tone when the user is excited. For example, when the user is excited, the conversion unit can perform optimal voice conversion by converting the voice with a bright tone. This enables natural voice conversion by adjusting the tone and pitch according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processes in the conversion unit may be performed using AI, or may be performed without using AI. For example, the conversion unit may input the user's emotion data to the generation AI and have the generation AI adjust the tone and pitch.

[0091] The conversion unit can learn the user's speech pattern at the time of conversion and perform optimal speech conversion. For example, when the user speaks slowly, the conversion unit converts the speech to match the pace. For example, when the user speaks slowly, the conversion unit can perform optimal speech conversion by converting the speech to match the pace. In addition, when the user speaks quickly, the conversion unit can also convert the speech to match the pace. For example, when the user speaks quickly, the conversion unit can perform optimal speech conversion by converting the speech to match the pace. In addition, when the user speaks with a pause, the conversion unit can also convert the speech taking into account the pause. For example, when the user speaks with a pause, the conversion unit can perform optimal speech conversion by converting the speech taking into account the pause. As a result, the speech conversion based on the user's speech pattern improves the conversion accuracy. A part or all of the above-mentioned processing in the conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversion unit can input the user's speech pattern data to the generation AI and cause the generation AI to execute the speech conversion.

[0092] The conversion unit can add a multilingual conversion function for handling different languages ​​at the time of conversion. For example, when the user speaks English, the conversion unit converts the voice into Japanese. For example, when the user speaks English, the conversion unit can perform optimal voice conversion by converting the voice into Japanese. In addition, the conversion unit can also convert the voice into English when the user speaks French. For example, when the user speaks French, the conversion unit can perform optimal voice conversion by converting the voice into English. In addition, the conversion unit can also convert the voice into French when the user speaks Spanish. For example, when the user speaks Spanish, the conversion unit can perform optimal voice conversion by converting the voice into French. This enables voice conversion of different languages ​​by the multilingual conversion function. Part or all of the above-mentioned processing in the conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversion unit can input voice data of different languages ​​to the generation AI and cause the generation AI to execute the multilingual conversion.

[0093] The conversion unit can estimate the user's emotion and determine the priority of the converted voice based on the estimated user's emotion. For example, when the user is nervous, the conversion unit converts important voice with priority. For example, when the user is nervous, the conversion unit can perform optimal voice conversion by converting important voice with priority. In addition, the conversion unit can convert all voices evenly when the user is relaxed. For example, when the user is relaxed, the conversion unit can perform optimal voice conversion by converting all voices evenly. In addition, the conversion unit can convert voices with strong emotions with priority when the user is excited. For example, when the user is excited, the conversion unit can perform optimal voice conversion by converting voices with strong emotions with priority. This allows important voices to be converted with priority by determining the priority of voices according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversion unit may input the user's emotion data to the generation AI and cause the generation AI to determine the priority order of the voices.

[0094] The conversion unit can adjust the conversion algorithm based on the collection location of the voice data at the time of conversion. For example, when converting a voice collected by a user outdoors, the conversion unit adjusts the conversion algorithm taking into account the sound of the wind. For example, when converting a voice collected by a user outdoors, the conversion unit can perform optimal voice conversion by adjusting the conversion algorithm taking into account the sound of the wind. In addition, when converting a voice collected by a user indoors, the conversion unit can also adjust the conversion algorithm taking into account the echo. For example, when converting a voice collected by a user indoors, the conversion unit can perform optimal voice conversion by adjusting the conversion algorithm taking into account the echo. In addition, when converting a voice collected by a user inside a car, the conversion unit can also adjust the conversion algorithm taking into account the engine sound. For example, when converting a voice collected by a user inside a car, the conversion unit can perform optimal voice conversion by adjusting the conversion algorithm taking into account the engine sound. As a result, optimal voice conversion is possible by adjusting the conversion algorithm based on the collection location. A part or all of the above-mentioned processing in the conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversion unit can input data on the location where the voice data was collected to the generation AI and cause the generation AI to adjust the conversion algorithm.

[0095] The conversion unit can analyze the user's social media activities and perform relevant voice conversion at the time of conversion. The conversion unit, for example, converts voice related to the content that the user is talking about on social media. For example, the conversion unit can perform optimal voice conversion by converting voice related to the content that the user is talking about on social media. The conversion unit can also convert voice related to the statement of a person that the user follows on social media. For example, the conversion unit can perform optimal voice conversion by converting voice related to the statement of a person that the user follows on social media. The conversion unit can also convert voice related to the topic of a group in which the user participates on social media. For example, the conversion unit can perform optimal voice conversion by converting voice related to the topic of a group in which the user participates on social media. This enables highly relevant voice conversion by voice conversion based on social media activities. Part or all of the above-mentioned processing in the conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversion unit can input the user's social media activity data to the generation AI and cause the generation AI to execute voice conversion.

[0096] The output unit can estimate the user's emotion and adjust the volume and tone of the voice output based on the estimated user's emotion. For example, when the user is nervous, the output unit outputs voice in a calm tone. For example, when the user is nervous, the output unit can perform optimal voice output by outputting voice in a calm tone. In addition, the output unit can also output voice in a natural tone when the user is relaxed. For example, when the user is relaxed, the output unit can perform optimal voice output by outputting voice in a natural tone. In addition, the output unit can also output voice in a bright tone when the user is excited. For example, when the user is excited, the output unit can perform optimal voice output by outputting voice in a bright tone. This makes it possible to perform optimal voice output by adjusting the volume and tone according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the output unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the output unit can input a user's emotional data into the generation AI and have the generation AI adjust the volume and tone.

[0097] The output unit can analyze the surrounding environmental sound in real time at the time of output and adjust the audio output. For example, when the surroundings are noisy, the output unit increases the volume of the audio output. For example, when the surroundings are noisy, the output unit can perform optimal audio output by increasing the volume of the audio output. In addition, when the surroundings are quiet, the output unit can also lower the volume of the audio output. For example, when the surroundings are quiet, the output unit can perform optimal audio output by lowering the volume of the audio output. In addition, when a sudden sound occurs, the output unit can analyze the sound in real time and adjust the audio output. For example, when a sudden sound occurs, the output unit can analyze the sound in real time and perform optimal audio output. This enables clear audio output by audio output based on the surrounding environmental sound. Part or all of the above-mentioned processing in the output unit may be performed using, for example, AI, or may be performed without using AI. For example, the output unit can input the surrounding environmental sound data to the generation AI and cause the generation AI to adjust the audio output.

[0098] The output unit can improve the accuracy of the voice output by considering the user's speech pattern at the time of output. For example, when the user speaks slowly, the output unit adjusts the voice output to match the pace. For example, when the user speaks slowly, the output unit can perform optimal voice output by adjusting the voice output to match the pace. In addition, when the user speaks quickly, the output unit can also adjust the voice output to match the pace. For example, when the user speaks quickly, the output unit can perform optimal voice output by adjusting the voice output to match the pace. In addition, when the user speaks with a pause, the output unit can also adjust the voice output taking into account the pause. For example, when the user speaks with a pause, the output unit can perform optimal voice output by adjusting the voice output taking into account the pause. As a result, the output accuracy is improved by the voice output based on the user's speech pattern. A part or all of the above-mentioned processing in the output unit may be performed using, for example, AI, or may be performed without using AI. For example, the output unit can input the user's speech pattern data to the generation AI and cause the generation AI to execute the adjustment of the voice output.

[0099] The output unit can estimate the user's emotion and determine the priority of the voice output based on the estimated user's emotion. For example, when the user is nervous, the output unit preferentially outputs important voice. For example, when the user is nervous, the output unit can perform optimal voice output by preferentially outputting important voice. In addition, the output unit can output all voices evenly when the user is relaxed. For example, when the user is relaxed, the output unit can perform optimal voice output by outputting all voices evenly. In addition, the output unit can preferentially output voices with strong emotions when the user is excited. For example, when the user is excited, the output unit can perform optimal voice output by preferentially outputting voices with strong emotions. This allows important voices to be preferentially output by determining the priority of voice output according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (for example, LLM) or a multimodal generation AI. A part or all of the above-mentioned processing in the output unit may be performed using, for example, AI, or may be performed without using AI. For example, the output unit may input the user's emotion data to the generation AI and cause the generation AI to determine the priority order of the voice output.

[0100] The output unit can perform optimal audio output in consideration of the geographical location information of the user when outputting audio. For example, when the user is in a specific location, the output unit preferentially outputs audio related to the location. For example, when the user is in a specific location, the output unit can perform optimal audio output by preferentially outputting audio related to the location. In addition, when the user is moving, the output unit can also preferentially output audio related to the destination. For example, when the user is moving, the output unit can perform optimal audio output by preferentially outputting audio related to the destination. In addition, when the user is participating in a specific event, the output unit can also preferentially output audio related to the event. For example, when the user is participating in a specific event, the output unit can perform optimal audio output by preferentially outputting audio related to the event. As a result, audio output based on geographical location information can preferentially output audio with high relevance. Part or all of the above-mentioned processing in the output unit may be performed using, for example, AI, or may be performed without using AI. For example, the output unit can input the geographical location information data of the user to the generation AI and cause the generation AI to execute adjustment of the audio output.

[0101] The output unit can analyze the user's social media activity and perform related audio output at the time of audio output. The output unit, for example, outputs audio related to the content that the user is talking about on social media. For example, the output unit can perform optimal audio output by outputting audio related to the content that the user is talking about on social media. The output unit can also output audio related to the statement of a person that the user follows on social media. For example, the output unit can perform optimal audio output by outputting audio related to the statement of a person that the user follows on social media. The output unit can also output audio related to the topic of a group in which the user participates on social media. For example, the output unit can perform optimal audio output by outputting audio related to the topic of a group in which the user participates on social media. This makes it possible to output highly relevant audio by audio output based on social media activity. Part or all of the above-mentioned processing in the output unit may be performed using, for example, AI, or may be performed without using AI. For example, the output unit can input the user's social media activity data to the generation AI and cause the generation AI to adjust the audio output.

[0102] The bow tie shape can estimate the user's emotion and adjust the bow tie design and color based on the estimated user's emotion. The bow tie shape can provide a calm color design when the user is nervous. For example, the bow tie shape can improve visual comfort by providing a calm color design when the user is nervous. The bow tie shape can also provide a bright color design when the user is relaxed. For example, the bow tie shape can improve visual comfort by providing a bright color design when the user is relaxed. The bow tie shape can also provide a visually stimulating design when the user is excited. For example, the bow tie shape can improve visual comfort by providing a visually stimulating design when the user is excited. This improves visual comfort by adjusting the design and color according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the bow tie shape may be performed using, for example, an AI, or may be performed without using an AI. For example, the shape of a bow tie can be determined by inputting the user's emotional data into a generation AI, which can then adjust the design and color.

[0103] The shape of the bow tie can be improved by changing the material of the bow tie to improve the fit and durability. The shape of the bow tie can be improved by using a soft material, for example. For example, the shape of the bow tie can be improved by using a soft material. The shape of the bow tie can also be made to withstand long-term use by using a highly durable material. For example, the shape of the bow tie can be made to withstand long-term use by using a highly durable material. The shape of the bow tie can also be made to provide a comfortable fit by using a material with good breathability. For example, the shape of the bow tie can be made to provide a comfortable fit by using a material with good breathability. As a result, the fit and durability are improved by changing the material. Part or all of the above-mentioned processing in the shape of the bow tie may be performed, for example, using AI, or may be performed without using AI. For example, the shape of the bow tie can be made by inputting material selection data into the generation AI and causing the generation AI to change the material.

[0104] The bow tie shape can incorporate additional functions in the bow tie, for example, a solar panel for extending the battery life. The bow tie shape can incorporate, for example, a solar panel for extending the battery life. For example, the bow tie shape can incorporate a solar panel to extend the battery life. The bow tie shape can also incorporate a battery pack to enable long-term use. For example, the bow tie shape can incorporate a battery pack to enable long-term use. The bow tie shape can also add a wireless charging function to eliminate the trouble of charging. For example, the bow tie shape can add a wireless charging function to eliminate the trouble of charging. This allows the additional function to extend the battery life and enable long-term use. Some or all of the above-mentioned processing in the bow tie shape may be performed, for example, using AI, or may be performed without using AI. For example, the bow tie shape can input design data of the additional function into the generation AI, and have the generation AI execute the incorporation of the additional function.

[0105] The shape of the bow tie can estimate the user's emotion and adjust the way the bow tie is worn based on the estimated emotion of the user. The shape of the bow tie can provide an easy way to wear it when the user is nervous, for example. For example, the shape of the bow tie can improve the comfort of wearing it by providing an easy way to wear it when the user is nervous. The shape of the bow tie can also provide a variety of ways to wear it when the user is relaxed. For example, the shape of the bow tie can improve the comfort of wearing it when the user is relaxed, by providing a variety of ways to wear it. The shape of the bow tie can also simplify the way to wear it when the user is excited, so that it can be quickly put on. For example, the shape of the bow tie can simplify the way to wear it when the user is excited, so that it can be quickly put on, so that it can be quickly put on. This improves the comfort of wearing it by adjusting the way to wear it according to the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (for example, LLM) or a multimodal generation AI. Some or all of the above-mentioned processes for the shape of the bow tie may be performed using AI, or may be performed without using AI. For example, the shape of the bow tie may be generated by inputting the user's emotional data into a generation AI and having the generation AI adjust the way the bow tie is worn.

[0106] The shape of the bow tie can be applied to other accessories. The shape of the bow tie can be applied to, for example, a brooch or a necklace. For example, the shape of the bow tie can be applied to a brooch to expand the range of wear. The shape of the bow tie can also be applied to a necklace to enhance fashionability. For example, the shape of the bow tie can be applied to a necklace to enhance fashionability. The shape of the bow tie can also be applied to a hair accessory to accommodate a variety of styles. For example, the shape of the bow tie can be applied to a hair accessory to accommodate a variety of styles. This expands the range of wear by applying it to other accessories. Some or all of the above-mentioned processing for the shape of the bow tie may be performed, for example, using AI, or may be performed without using AI. For example, the shape of the bow tie can be applied to other accessories by inputting application data to the generation AI, and the generation AI can execute the application.

[0107] The shape of the bow tie can make the design of the bow tie customizable and allow the user to change it to suit his / her own preferences. The shape of the bow tie can, for example, allow the user to select a design, providing a bow tie tailored to an individual's preferences. For example, the shape of the bow tie can allow the user to select a design, providing a bow tie tailored to an individual's preferences. The shape of the bow tie can also allow the user to select a color, providing a bow tie tailored to an individual's style. For example, the shape of the bow tie can allow the user to select a color, providing a bow tie tailored to an individual's style. The shape of the bow tie can also allow the user to select a material, providing a bow tie tailored to an individual's fit. For example, the shape of the bow tie can allow the user to select a material, providing a bow tie tailored to an individual's fit. This allows the design to be customized to suit the user's preferences. Some or all of the above-mentioned processing in the shape of the bow tie may be performed, for example, using AI, or may be performed without using AI. For example, the shape of the bow tie can be customized by inputting customization data into a generation AI, and causing the generation AI to execute the customization of the design.

[0108] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0109] The voice conversion system can further include a function of analyzing the tone and pitch of the user's voice and estimating the user's emotions. For example, the collection unit can analyze the tone and pitch of the user's voice and estimate whether the user is tense or relaxed. The analysis unit can adjust the analysis algorithm based on the estimated emotions to perform more accurate analysis. The conversion unit can adjust the tone and pitch of the voice conversion based on the estimated emotions to achieve natural voice conversion. The output unit can adjust the volume and tone of the voice output based on the estimated emotions to perform optimal voice output. This enables voice conversion and output according to the user's emotions, thereby achieving more natural communication.

[0110] The voice conversion system can further include a function of learning the user's speech pattern and automatically adjusting the sensitivity of the collection unit. For example, if the user speaks slowly, the collection unit can adjust the sensitivity to match the pace and perform optimal voice collection. Also, if the user speaks quickly, the sensitivity can be adjusted to match the pace. Furthermore, if the user speaks with pauses, the sensitivity can be adjusted to take these pauses into account. This improves the accuracy of voice collection by adjusting the sensitivity according to the user's speech pattern.

[0111] The analysis unit can further include a function of learning the user's speech pattern and automatically adjusting the analysis algorithm. For example, if the user speaks slowly, the analysis unit can adjust the analysis algorithm to match the pace and perform optimal analysis. Also, if the user speaks quickly, the analysis algorithm can be adjusted to match the pace. Furthermore, if the user speaks with pauses, the analysis algorithm can be adjusted to take these pauses into account. This improves the analysis accuracy by adjusting the analysis algorithm according to the user's speech pattern.

[0112] The conversion unit may further include a multi-language conversion function for handling different languages. For example, if the user speaks English, the voice can be converted into Japanese. If the user speaks French, the voice can also be converted into English. If the user speaks Spanish, the voice can also be converted into French. Thus, the multi-language conversion function enables voice conversion between different languages, facilitating international communication.

[0113] The output unit can further include a function for analyzing surrounding environmental sounds in real time and adjusting the audio output. For example, when the surroundings are noisy, the volume of the audio output can be increased. Also, when the surroundings are quiet, the volume of the audio output can be decreased. Furthermore, when a sudden sound occurs, the sound can be analyzed in real time and the audio output can be adjusted. This enables clear audio output based on the surrounding environmental sounds.

[0114] The voice conversion system can further include a function of estimating the user's emotion and adjusting the tone and pitch of the voice conversion based on the estimated emotion. For example, if the user is nervous, the conversion unit can convert the voice to a calm tone. If the user is relaxed, the conversion unit can also convert the voice to a natural tone. Furthermore, if the user is excited, the conversion unit can also convert the voice to a bright tone. This makes it possible to achieve natural voice conversion by adjusting the tone and pitch according to the user's emotion.

[0115] The voice conversion system can further include a function of estimating the user's emotion and adjusting the analysis algorithm based on the estimated emotion. For example, the analysis unit can adjust the analysis algorithm to prioritize accuracy when the user is nervous. Also, the analysis unit can adjust the analysis algorithm to prioritize speed when the user is relaxed. Furthermore, the analysis algorithm can be adjusted to prioritize balance when the user is excited. This improves the analysis accuracy by adjusting the analysis algorithm according to the user's emotion.

[0116] The voice conversion system can further include a function of estimating the user's emotion and adjusting the volume and tone of the voice output based on the estimated emotion. For example, the output unit can output voice in a calm tone when the user is nervous. Also, it can output voice in a natural tone when the user is relaxed. Furthermore, it can output voice in a bright tone when the user is excited. This makes it possible to provide optimal voice output by adjusting the volume and tone according to the user's emotion.

[0117] The voice conversion system can further include a function of estimating the user's emotion and prioritizing the voices based on the estimated emotion. For example, when the user is nervous, the collection unit can collect important voices with priority. When the user is relaxed, the collection unit can collect all voices equally. When the user is excited, the collection unit can collect voices with strong emotions with priority. In this way, important voices can be collected with priority by determining the priority of voices according to the user's emotion.

[0118] The voice conversion system may further include a function for collecting and analyzing voice in consideration of the geographic location information of the user. For example, when the user is in a specific location, the collection unit may preferentially collect voice related to the location. Also, when the collected voice data is related to a specific location, the analysis unit may preferentially analyze the data. Furthermore, the conversion unit may adjust the conversion algorithm based on the collection location. This allows voice collection and analysis based on geographic location information to preferentially process highly relevant voice.

[0119] The process flow of the second embodiment will be briefly described below.

[0120] Step 1: The collection unit collects words uttered by the hearing-impaired person. The collection unit, for example, has a built-in microphone and collects the uttered voice with high accuracy. For example, when the hearing-impaired person utters "hello," the collection unit can collect the voice with the microphone. Step 2: The analysis unit analyzes the voice data collected by the collection unit. The analysis unit, for example, uses AI to analyze the collected voice data and identify the spoken words. For example, the analysis unit can identify the word "hello" from the collected voice data. Step 3: The conversion unit converts the voice data analyzed by the analysis unit into normal voice. The conversion unit converts, for example, words identified using AI into normal voice. For example, the conversion unit can convert the word "hello" into normal voice. Step 4: The output unit outputs the voice converted by the conversion unit. The output unit, for example, has a built-in speaker and outputs the converted voice clearly. For example, the output unit can output a normal voice saying "Hello" from the speaker.

[0121] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0122] 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<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. 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 a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0123] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0124] Each of the multiple elements including the above-mentioned collection unit, analysis unit, conversion unit, and output unit is realized, for example, in at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects words spoken by a hearing-impaired person using a microphone of the smart device 14. The analysis unit analyzes, for example, the voice data collected by the specific processing unit 290 of the data processing device 12 and identifies the spoken words. The conversion unit converts, for example, the voice data analyzed by the specific processing unit 290 of the data processing device 12 into normal voice. The output unit outputs the converted voice using, for example, the speaker of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0125] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0126] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0127] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0128] 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.

[0129] 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 the voice according to instructions from the processor 46.

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0131] 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 performed in a secure state.

[0132] Fig. 4 shows an example of 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.

[0133] 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.

[0134] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0135] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 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 also perform processing similar to that of the specific processing unit 290 using these models.

[0136] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0137] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0138] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0139] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0140] Each of the multiple elements including the above-mentioned collection unit, analysis unit, conversion unit, and output unit is realized, for example, in at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects words spoken by a hearing-impaired person using a microphone of the smart glasses 214. The analysis unit, for example, analyzes the voice data collected by the specific processing unit 290 of the data processing device 12 and identifies the spoken words. The conversion unit, for example, converts the voice data analyzed by the specific processing unit 290 of the data processing device 12 into normal voice. The output unit, for example, outputs the converted voice using the speaker of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0141] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0142] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0143] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0144] 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.

[0145] 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 the voice according to instructions from the processor 46.

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0147] 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 performed in a secure state.

[0148] 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.

[0149] 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.

[0150] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0151] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific 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.

[0152] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0153] 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 voice indicating a user input for 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.

[0154] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0155] 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0156] Each of the multiple elements including the above-mentioned collection unit, analysis unit, conversion unit, and output unit is realized, for example, in at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit collects words spoken by a hearing-impaired person using a microphone of the headset type terminal 314. The analysis unit analyzes, for example, the voice data collected by the specific processing unit 290 of the data processing device 12 and identifies the spoken words. The conversion unit converts, for example, the voice data analyzed by the specific processing unit 290 of the data processing device 12 into normal voice. The output unit outputs the converted voice using, for example, the speaker of the headset type terminal 314. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0157] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0158] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0159] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0160] 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.

[0161] 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 the voice according to instructions from the processor 46.

[0162] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0163] 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 performed in a secure state.

[0164] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0165] 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.

[0166] 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.

[0167] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0168] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0169] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0170] 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 a voice indicating a user input for 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.

[0171] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0172] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information 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.

[0173] Each of the multiple elements including the above-mentioned collection unit, analysis unit, conversion unit, and output unit is realized, for example, in at least one of the robot 414 and the data processing device 12. For example, the collection unit collects words uttered by a hearing-impaired person using a microphone of the robot 414. The analysis unit analyzes, for example, the voice data collected by the specific processing unit 290 of the data processing device 12, and identifies the uttered words. The conversion unit converts, for example, the voice data analyzed by the specific processing unit 290 of the data processing device 12 into normal voice. The output unit outputs the converted voice using, for example, the speaker of the robot 414. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0174] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0175] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0176] These emotions are distributed in the three o'clock direction of emotion map 400 and usually 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.

[0177] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0178] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0179] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0180] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0181] 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.

[0182] 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 Universal Serial Bus (USB) 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.

[0183] In addition, 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 upon request from the data processing device 12.

[0184] 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.

[0185] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0186] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0187] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the 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.

[0188] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0189] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0190] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0191] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0192] (Appendix 1) A collection unit that collects voice; an analysis unit that analyzes the voice data collected by the collection unit; a conversion unit that converts the voice data analyzed by the analysis unit into normal voice; an output unit that outputs the voice converted by the conversion unit; A system characterized by: (Appendix 2) The collecting unit includes: Built-in microphone for improved accuracy of spoken voice 2. The system of claim 1, (Appendix 3) The analysis unit is Analyze collected voice data to identify spoken words 2. The system of claim 1, (Appendix 4) The conversion unit is Convert identified words into normal audio 2. The system of claim 1, (Appendix 5) The output unit is Output converted audio clearly 2. The system of claim 1, (Appendix 6) The device is Bow tie shape, easy to put on and low visibility 2. The system of claim 1, (Appendix 7) The collecting unit includes: A user's emotion is estimated, and the sensitivity of collecting voice is adjusted based on the estimated user's emotion. 2. The system of claim 1, (Appendix 8) The collecting unit includes: Analyzes surrounding environmental sounds in real time and collects audio while canceling noise 2. The system of claim 1, (Appendix 9) The collecting unit includes: Learns the user's speech patterns and automatically adjusts collection timing 2. The system of claim 1, (Appendix 10) The collecting unit includes: Estimate the user's emotions and prioritize the audio to be collected based on the estimated user's emotions. 2. The system of claim 1, (Appendix 11) The collecting unit includes: When collecting audio, the system takes into account the user's geographic location information to prioritize collection of audio that is highly relevant. 2. The system of claim 1, (Appendix 12) The collecting unit includes: When collecting audio, analyze the user's social media activity and collect related audio. 2. The system of claim 1, (Appendix 13) The analysis unit is Inferring user emotions and adjusting analysis algorithms based on the inferred user emotions 2. The system of claim 1, (Appendix 14) The analysis unit is During analysis, background noise in the audio data is removed to improve the accuracy of spoken words 2. The system of claim 1, (Appendix 15) The analysis unit is During analysis, the accuracy of analysis is improved by taking into account the user's speech patterns. 2. The system of claim 1, (Appendix 16) The analysis unit is Infer user emotions and adjust the way analysis results are displayed based on the inferred user emotions 2. The system of claim 1, (Appendix 17) The analysis unit is During analysis, prioritize analysis based on when the audio data was collected 2. The system of claim 1, (Appendix 18) The analysis unit is During analysis, adjust the order of analysis based on the relevance of the audio data 2. The system of claim 1, (Appendix 19) The conversion unit is Estimating a user's emotion and adjusting the tone and pitch of the speech conversion based on the estimated user's emotion 2. The system of claim 1, (Appendix 20) The conversion unit is During conversion, the system learns the user's speech patterns and performs optimal voice conversion. 2. The system of claim 1, (Appendix 21) The conversion unit is Add multilingual conversion function to accommodate different languages ​​during conversion. 2. The system of claim 1, (Appendix 22) The conversion unit is Estimating a user's emotion and prioritizing converted speech based on the estimated user's emotion 2. The system of claim 1, (Appendix 23) The conversion unit is During conversion, the conversion algorithm adjusts based on where the audio data was collected 2. The system of claim 1, (Appendix 24) The conversion unit is During conversion, it analyzes the user's social media activity and provides relevant voice conversions. 2. The system of claim 1, (Appendix 25) The output unit is Estimating a user's emotions and adjusting the volume and tone of the audio output based on the estimated user's emotions 2. The system of claim 1, (Appendix 26) The output unit is During output, the surrounding environmental sound is analyzed in real time and the audio output is adjusted 2. The system of claim 1, (Appendix 27) The output unit is At the time of output, the accuracy of the voice output is improved by taking into account the user's speech pattern. 2. The system of claim 1, (Appendix 28) The output unit is To estimate a user's emotion and determine a priority of speech output based on the estimated user's emotion. 2. The system of claim 1, (Appendix 29) The output unit is When outputting voice, the system takes into account the user's geographic location information to provide optimal voice output. 2. The system of claim 1, (Appendix 30) The output unit is When audio is output, the user's social media activity is analyzed and relevant audio output is provided. 2. The system of claim 1, (Appendix 31) The shape of the bow tie is Inferring a user's emotions and adjusting the design and color of the bow tie based on the estimated user's emotions 2. The system of claim 1, (Appendix 32) The shape of the bow tie is The material of the bow tie has been changed to improve comfort and durability. 2. The system of claim 1, (Appendix 33) The shape of the bow tie is Build additional features into the bow tie, such as a solar panel to extend battery life 2. The system of claim 1, (Appendix 34) The shape of the bow tie is Estimating a user's emotion and adjusting a manner in which the bow tie is worn based on the estimated user's emotion 2. The system of claim 1, (Appendix 35) The shape of the bow tie is Applying the bow tie shape to other accessories 2. The system of claim 1, (Appendix 36) The shape of the bow tie is Make the bow tie design customizable, allowing users to change it to suit their own tastes 2. The system of claim 1, [Explanation of symbols]

[0193] 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 voice; an analysis unit that analyzes the voice data collected by the collection unit; a conversion unit that converts the voice data analyzed by the analysis unit into normal voice; an output unit that outputs the voice converted by the conversion unit; A system characterized by:

2. The collecting unit includes: Built-in microphone for improved accuracy of spoken voice 2. The system of claim 1 .

3. The analysis unit is Analyze collected voice data to identify spoken words 2. The system of claim 1 .

4. Has a bow tie shape 2. The system of claim 1 .

5. The collecting unit includes: A user's emotion is estimated, and the sensitivity of collecting voice is adjusted based on the estimated user's emotion.

2. The system of claim 1 .

6. The collecting unit includes: Analyzes surrounding environmental sounds in real time and collects audio while canceling noise 2. The system of claim 1 .

7. The collecting unit includes: Learns the user's speech patterns and automatically adjusts collection timing 2. The system of claim 1 .

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