system

Abstract

The system according to the embodiment aims to convert voice data into text data accurately and quickly. [Solution] A system according to an embodiment includes a receiving unit, an analyzing unit, a converting unit, and a providing unit. The receiving unit inputs voice data. The analyzing unit analyzes the voice data input by the receiving unit. The converting unit converts the voice data analyzed by the analyzing unit into text data. The providing unit provides the text data converted by the converting unit.

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have had the problem of difficulty in converting voice data into text data accurately and quickly.

[0005] The system according to the embodiment aims to convert voice data into text data accurately and quickly. [Means for solving the problem]

[0006] The system according to the embodiment includes a receiving unit, an analyzing unit, a converting unit, and a providing unit. The receiving unit inputs voice data. The analyzing unit analyzes the voice data input by the receiving unit. The converting unit converts the voice data analyzed by the analyzing unit into text data. The providing unit provides the text data converted by the converting unit. [Effects of the Invention]

[0007] The system according to the embodiment can convert voice data into text data accurately and quickly. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A speech-to-text system according to an embodiment of the present invention automatically converts voice data input by a user into text. In this speech-to-text system, a user records voice data and inputs it into the system. The system then analyzes the voice data using advanced voice recognition technology and converts it into text data. This converted text data is provided to the user. For example, the system can be used for a variety of purposes, such as creating meeting minutes and transcribing interviews. The advanced analytical capabilities of voice recognition technology enable accurate and rapid conversion of voice into text. For example, a user records voice data and inputs it into the system. The user may record voice data using a device such as a smartphone or a PC and upload it to the system. For example, voice data recorded on a smartphone during a meeting can be uploaded to the system. The system then analyzes the voice data using advanced voice recognition technology. The voice recognition technology analyzes the waveform of the voice data and converts the voice content into text data. For example, specific words or phrases may be extracted from the voice data and output as text data. The analyzed text data is provided to the user. The user can download the text data provided by the system and use it for creating meeting minutes, transcribing interviews, and other purposes. For example, when creating minutes of a meeting, minutes can be created efficiently by using the text data provided by the system. This mechanism makes it possible to convert voice data into text accurately and quickly. Users can easily convert voice data into text data without performing complicated operations. For example, when transcribing an interview, using the system can significantly reduce the time required compared to manual transcription. This allows the voice-to-text system to allow users to easily convert voice data into text data.

[0029] The speech-to-text system according to the embodiment includes a receiving unit, an analyzing unit, a converting unit, and a providing unit. The receiving unit is a unit through which a user inputs speech data. For example, a user can record speech data using a device such as a smartphone or a PC and upload it to the receiving unit. When receiving speech data, the receiving unit checks the format and type of the data and accepts it in an appropriate format. For example, the receiving unit can accept audio file formats such as WAV and MP3. The receiving unit can also check whether the content of the speech data is conversation, music, or the like. The analyzing unit analyzes the speech data input by the receiving unit. The analyzing unit uses advanced speech recognition technology to analyze the waveform of the speech data and convert the speech content into text data. For example, the analyzing unit extracts specific words or phrases from the speech data and outputs it as text data. The analyzing unit can also remove noise from the speech data using noise reduction technology. For example, the analyzing unit removes noise from the speech data using filtering technology. The converting unit converts the speech data analyzed by the analyzing unit into text data. The conversion unit converts the voice data into text data using a voice-to-text conversion algorithm. For example, the conversion unit converts the voice data into text data using a voice recognition algorithm. The conversion unit can also adjust the format of the text data. For example, the conversion unit adjusts the text layout, font size, line spacing, etc. The providing unit provides the text data converted by the conversion unit to a user. The providing unit provides the text data in a downloadable format. For example, the providing unit provides the text data in PDF format, TXT format, DOCX format, etc. The providing unit allows the user to easily download the text data. In this way, the voice-to-text system according to the embodiment allows the user to easily convert voice data into text data and download it.

[0030] The analysis unit may include a noise removal unit that removes noise from the audio data. The noise removal unit is a component for removing noise from the audio data. The noise removal unit removes noise from the audio data using filtering technology. For example, the noise removal unit can remove noise in a specific frequency band from the audio data. The noise removal unit can also detect a specific noise pattern from the audio data and remove noise from that portion in detail. For example, the noise removal unit can remove specific background sounds from the audio data. This allows the noise removal unit to improve the analysis accuracy of the audio data. Some or all of the above-described processing in the noise removal unit may be performed using, for example, AI, or may be performed without using AI. For example, the noise removal unit may input audio data to a generation AI and have the generation AI perform noise removal.

[0031] The conversion unit may include a format adjustment unit that adjusts the format of the text data. The format adjustment unit is a component for adjusting the format of the text data. The format adjustment unit adjusts the text layout, font size, line spacing, etc. For example, the format adjustment unit may provide a format that emphasizes specific keywords in the text data. Furthermore, if a specific phrase is repeated in the text data, the format adjustment unit may provide a format that emphasizes that portion. For example, the format adjustment unit may detect a specific pattern in the text data and provide a format that emphasizes that portion. This allows the format adjustment unit to provide text data that is easy for the user to read. Some or all of the above-described processing in the format adjustment unit may be performed, for example, using AI or without AI. For example, the format adjustment unit may input text data to a generation AI and cause the generation AI to perform format adjustment.

[0032] The reception unit can receive voice data from at least one device, such as a smartphone or a personal computer. The reception unit is a component through which a user inputs voice data. For example, a user can record voice data using a device such as a smartphone or a personal computer and upload it to the reception unit. When receiving voice data, the reception unit checks the format and type of the data and accepts it in an appropriate format. For example, the reception unit can accept audio file formats such as WAV and MP3. The reception unit can also check whether the content of the voice data is conversation, music, or the like. This allows the reception unit to receive voice data from a variety of devices. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit can input voice data to a generation AI and have the generation AI check the format and type of the data.

[0033] The providing unit can provide the text data to the user in a downloadable format. The providing unit is a component that provides the text data converted by the conversion unit to the user. The providing unit provides the text data in a downloadable format. For example, the providing unit provides the text data in PDF format, TXT format, DOCX format, or the like. The providing unit enables the user to easily download the text data. This allows the providing unit to easily download the text data. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit may input the text data to a generation AI and cause the generation AI to provide the text data in a downloadable format.

[0034] The reception unit can analyze the user's past voice data history and select an appropriate reception method when receiving voice data. The reception unit is a component that analyzes the user's past voice data history and selects an appropriate reception method when receiving voice data. For example, the reception unit can prioritize reception of voice data formats previously used by the user. The reception unit can also suggest the most appropriate reception method for voice data based on the user's past voice data history. Furthermore, the reception unit can prioritize reception of devices previously used by the user. This allows the reception unit to provide the optimal reception method based on the user's past history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past voice data history into a generation AI and cause the generation AI to select the optimal reception method.

[0035] The reception unit can perform filtering based on the user's current environmental sound when receiving the voice data. The reception unit is a component for performing filtering taking the user's current environmental sound into consideration when receiving the voice data. For example, when the user is in a noisy environment, the reception unit can apply noise canceling and receive the voice data. Furthermore, when the user is in a quiet environment, the reception unit can also receive the voice data with minimal filtering. Furthermore, when the user is outdoors, the reception unit can remove wind noise and receive the voice data. This allows the reception unit to perform appropriate filtering according to the environmental sound. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's current environmental sound data to a generation AI and have the generation AI perform filtering.

[0036] The reception unit, when receiving voice data, can prioritize receiving highly relevant voice data in consideration of the user's geographical location information. The reception unit is a component for, when receiving voice data, prioritize receiving highly relevant voice data in consideration of the user's geographical location information. For example, when the user is in a specific area, the reception unit can prioritize receiving voice data related to that area. Furthermore, when the user is traveling, the reception unit can prioritize receiving voice data related to the travel destination. Furthermore, when the user is at home, the reception unit can prioritize receiving voice data related to the user's home. This allows the reception unit to prioritize receiving highly relevant voice data based on the geographical location information. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's geographical location information to the generation AI and cause the generation AI to select highly relevant voice data.

[0037] The reception unit can analyze the user's social media activity when receiving the voice data and receive related voice data. The reception unit is a component for analyzing the user's social media activity when receiving the voice data and receiving related voice data. For example, the reception unit can preferentially receive voice data related to topics the user is talking about on social media. The reception unit can also preferentially receive voice data related to accounts the user follows. Furthermore, the reception unit can preferentially receive voice data related to groups the user participates in. This allows the reception unit to receive related voice data based on the social media activity. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media activity data into a generation AI and cause the generation AI to select related voice data.

[0038] The analysis unit can detect specific patterns in the voice data during analysis and adjust the level of analysis detail. The analysis unit is a component for detecting specific patterns in the voice data during analysis and adjusting the level of analysis detail. For example, if the voice data contains a specific keyword, the analysis unit can analyze that portion in detail. Furthermore, if the voice data contains a specific phrase, the analysis unit can analyze that portion in detail. Furthermore, if the voice data contains a specific voice pattern, the analysis unit can analyze that portion in detail. This enables the analysis unit to perform a detailed analysis based on the specific pattern. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the voice data to a generation AI and cause the generation AI to detect specific patterns and adjust the level of analysis detail.

[0039] The analysis unit can apply different analysis algorithms depending on the category of the audio data during analysis. The analysis unit is a component for applying different analysis algorithms depending on the category of the audio data during analysis. For example, the analysis unit can apply an analysis algorithm dedicated to meetings to audio data of meetings. The analysis unit can also apply an analysis algorithm dedicated to interviews to audio data of interviews. Furthermore, the analysis unit can apply an analysis algorithm dedicated to lectures to audio data of lectures. This enables the analysis unit to perform appropriate analysis depending on the category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input audio data to a generation AI and cause the generation AI to apply an analysis algorithm depending on the category.

[0040] The analysis unit can determine the priority of analysis based on the submission time of the voice data during analysis. The analysis unit is a component for determining the priority of analysis based on the submission time of the voice data during analysis. The analysis unit can, for example, prioritize analysis of recently submitted voice data. The analysis unit can also prioritize analysis of voice data with high urgency. Furthermore, the analysis unit can prioritize analysis of voice data with an approaching submission deadline. This allows the analysis unit to perform analysis in priority order based on the submission time. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the submission time of the voice data to the generation AI and have the generation AI determine the priority order.

[0041] The analysis unit can adjust the order of analysis based on the relevance of the audio data during analysis. The analysis unit is a component for adjusting the order of analysis based on the relevance of the audio data during analysis. The analysis unit can, for example, prioritize analysis of highly relevant audio data. The analysis unit can also postpone analysis of less relevant audio data. Furthermore, the analysis unit can group and analyze relevant audio data. This allows the analysis unit to perform analysis in an appropriate order based on the relevance. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the audio data to the generation AI and cause the generation AI to adjust the order of analysis.

[0042] The conversion unit can adjust the level of detail of the conversion based on the importance of the audio data during conversion. The conversion unit is a component for adjusting the level of detail of the conversion based on the importance of the audio data during conversion. The conversion unit can, for example, perform detailed conversion on important audio data. The conversion unit can also perform standard conversion on normal audio data. Furthermore, the conversion unit can perform quick conversion on audio data with high urgency. This enables the conversion unit to perform appropriate conversion according to the importance of the audio data. Some 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 importance of the audio data to the generation AI and cause the generation AI to adjust the level of detail of the conversion.

[0043] The conversion unit can apply different conversion algorithms depending on the category of the audio data during conversion. The conversion unit is a component for applying different conversion algorithms depending on the category of the audio data during conversion. For example, the conversion unit can apply a conversion algorithm dedicated to conferences to audio data of a conference. The conversion unit can also apply a conversion algorithm dedicated to interviews to audio data of an interview. Furthermore, the conversion unit can apply a conversion algorithm dedicated to lectures to audio data of a lecture. This enables the conversion unit to perform appropriate conversion depending on the category. Some 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 category of the audio data to the generation AI and cause the generation AI to apply a conversion algorithm depending on the category.

[0044] The conversion unit can determine the priority of conversion based on the submission time of the voice data during conversion. The conversion unit is a component for determining the priority of conversion based on the submission time of the voice data during conversion. For example, the conversion unit can prioritize conversion of recently submitted voice data. The conversion unit can also prioritize conversion of voice data with high urgency. Furthermore, the conversion unit can prioritize conversion of voice data with an upcoming submission deadline. This allows the conversion unit to perform conversion in order of priority based on the submission time. Some or all of the above-described 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 submission time of the voice data to the generation AI and have the generation AI determine the priority.

[0045] The conversion unit can adjust the order of conversion based on the relevance of the audio data during conversion. The conversion unit is a component for adjusting the order of conversion based on the relevance of the audio data during conversion. For example, the conversion unit can prioritize conversion of highly relevant audio data. The conversion unit can also postpone conversion of less relevant audio data. Furthermore, the conversion unit can group and convert relevant audio data. This allows the conversion unit to perform conversion in an appropriate order based on the relevance. Some 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 relevance of the audio data to a generation AI and cause the generation AI to adjust the order of conversion.

[0046] The providing unit can select the optimal delivery method by referring to the user's past usage history at the time of provision. The providing unit is a component for selecting the optimal delivery method by referring to the user's past usage history at the time of provision. The providing unit can, for example, provide the text data in a format previously used by the user. The providing unit can also suggest the optimal delivery method based on the user's past usage history. Furthermore, the providing unit can provide the text data in a format optimal for the device previously used by the user. This allows the providing unit to provide the optimal delivery method based on the past usage history. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past usage history into the generation AI and cause the generation AI to select the optimal delivery method.

[0047] The providing unit can customize the means of provision based on the user's current device information at the time of provision. The providing unit is a component for customizing the means of provision based on the user's current device information at the time of provision. For example, if the user is using a smartphone, the providing unit can provide the text data in a format optimal for the smartphone. Furthermore, if the user is using a personal computer, the providing unit can provide the text data in a format optimal for the personal computer. Furthermore, if the user is using a tablet, the providing unit can provide the text data in a format optimal for the tablet. This allows the providing unit to provide the text data in an optimal format based on the current device information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's current device information to the generation AI and cause the generation AI to customize the means of provision.

[0048] The providing unit can select the optimal delivery method by taking into consideration the user's geographical location information when providing the data. The providing unit is a component for selecting the optimal delivery method by taking into consideration the user's geographical location information when providing the data. For example, when the user is in a specific area, the providing unit can prioritize providing text data related to the area. Furthermore, when the user is traveling, the providing unit can prioritize providing text data related to the travel destination. Furthermore, when the user is at home, the providing unit can prioritize providing text data related to the user's home. This allows the providing unit to provide the optimal delivery method based on the geographical location information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's geographical location information to the generation AI and cause the generation AI to select the optimal delivery method.

[0049] The providing unit can analyze the user's social media activity and suggest a means of provision at the time of provision. The providing unit is a component for analyzing the user's social media activity and suggesting a means of provision at the time of provision. For example, the providing unit can prioritize providing text data related to topics the user is talking about on social media. The providing unit can also prioritize providing text data related to accounts the user follows. Furthermore, the providing unit can prioritize providing text data related to groups the user is participating in. This allows the providing unit to suggest an optimal means of provision based on the social media activity. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's social media activity data into a generation AI and cause the generation AI to suggest a means of provision.

[0050] The noise removal unit can detect a specific pattern in the audio data during noise removal and adjust the level of detail of the noise removal. The noise removal unit is a component that detects a specific pattern in the audio data and adjusts the level of detail of the noise removal during noise removal. For example, if the audio data contains a specific noise pattern, the noise removal unit can remove that part in detail. Furthermore, if the audio data contains noise in a specific frequency band, the noise removal unit can remove that part in detail. Furthermore, if the audio data contains a specific background sound, the noise removal unit can remove that part in detail. This enables the noise removal unit to perform detailed noise removal based on a specific pattern. Some or all of the above-described processing in the noise removal unit may be performed using, for example, AI, or may be performed without using AI. For example, the noise removal unit can input audio data to a generation AI and cause the generation AI to detect a specific pattern and adjust the level of detail of the noise removal.

[0051] The noise removal unit can determine the priority of noise removal based on the submission date of the audio data when removing noise. The noise removal unit is a component for determining the priority of noise removal based on the submission date of the audio data when removing noise. The noise removal unit can, for example, prioritize noise removal from recently submitted audio data. The noise removal unit can also prioritize noise removal from audio data with a high urgency. Furthermore, the noise removal unit can prioritize noise removal from audio data with an upcoming submission deadline. This allows the noise removal unit to perform noise removal in accordance with the priority based on the submission date. Some or all of the above-described processing in the noise removal unit may be performed using, for example, AI, or may be performed without using AI. For example, the noise removal unit can input the submission date of the audio data to the generation AI and have the generation AI determine the priority.

[0052] The format adjustment unit can detect specific patterns in text data and adjust the level of detail of the format during format adjustment. The format adjustment unit is a component that detects specific patterns in text data and adjusts the level of detail of the format during format adjustment. For example, if the text data contains a specific keyword, the format adjustment unit can provide a format that emphasizes that portion. Furthermore, if the text data contains a specific phrase, the format adjustment unit can provide a format that emphasizes that portion. Furthermore, if the text data contains a specific pattern, the format adjustment unit can provide a format that emphasizes that portion. This enables the format adjustment unit to perform detailed format adjustment based on the specific pattern. Some or all of the above-described processing in the format adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the format adjustment unit can input text data to a generation AI and cause the generation AI to detect the specific pattern and adjust the level of detail of the format.

[0053] The format adjustment unit can determine the priority of formats based on the submission date of the text data during format adjustment. The format adjustment unit is a component for determining the priority of formats based on the submission date of the text data during format adjustment. For example, the format adjustment unit can prioritize adjusting the format of recently submitted text data. The format adjustment unit can also prioritize adjusting the format of text data with a high urgency. Furthermore, the format adjustment unit can prioritize adjusting the format of text data with an upcoming submission deadline. This allows the format adjustment unit to perform format adjustment based on the priority of the submission date. Some or all of the above-described processing in the format adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the format adjustment unit can input the submission date of the text data to the generation AI and cause the generation AI to determine the priority.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] When accepting a user's voice data, the acceptance unit can analyze the user's past voice data usage patterns and automatically select the optimal voice data format. For example, if the user has used a lot of MP3 format voice data in the past, the acceptance unit can automatically recommend the MP3 format. Also, if the user has previously uploaded voice data using a specific device, the acceptance unit can select the optimal format for that device. Furthermore, if the user has previously uploaded voice data during a specific time period, the acceptance unit can suggest the optimal acceptance method for that time period. This allows the acceptance unit to provide the optimal voice data acceptance method based on the user's past usage patterns.

[0056] When analyzing voice data, the analysis unit can automatically adjust the accuracy of the analysis by referring to the user's past analysis results. For example, if the user has frequently used specific words or phrases in the past, the analysis unit can prioritize analyzing those words or phrases. Also, if the user has provided a lot of voice data containing a specific noise pattern in the past, the noise pattern can be automatically removed. Furthermore, if the user has requested a specific analysis accuracy in the past, the analysis unit can perform the analysis based on that accuracy. This allows the analysis unit to provide the optimal analysis method based on the user's past analysis results.

[0057] When converting voice data into text data, the conversion unit can automatically select the optimal conversion algorithm by referring to the user's past conversion history. For example, if the user has frequently used a specific conversion algorithm in the past, that algorithm can be used preferentially. Also, if the user has provided text data in a specific format in the past, conversion can be performed based on that format. Furthermore, if the user has previously requested a specific conversion accuracy, conversion can be performed based on that accuracy. This allows the conversion unit to provide the optimal conversion method based on the user's past conversion history.

[0058] When providing text data to a user, the providing unit can automatically select the optimal providing method by referring to the user's past usage history. For example, if the user has downloaded a lot of text data in PDF format in the past, the providing unit can automatically recommend the PDF format. Also, if the user has downloaded text data using a specific device in the past, the providing unit can select the optimal format for that device. Furthermore, if the user has downloaded text data during a specific time period in the past, the providing unit can suggest the optimal providing method for that time period. This allows the providing unit to provide the optimal text data providing method based on the user's past usage history.

[0059] When removing noise from audio data, the noise removal unit can automatically select the optimal noise removal method by referring to the user's past noise removal history. For example, if the user has removed a specific noise pattern frequently in the past, the noise removal unit can prioritize removal of that noise pattern. Also, if the user has requested a specific noise removal accuracy in the past, the noise removal unit can perform noise removal based on that accuracy. Furthermore, if the user has used a specific noise removal algorithm in the past, the algorithm can be used preferentially. This allows the noise removal unit to provide the optimal noise removal method based on the user's past noise removal history.

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

[0061] Step 1: The reception unit is the part where the user inputs voice data. The user can record voice data using a device such as a smartphone or PC and upload it to the reception unit. When receiving voice data, the reception unit checks the format and type of the data and accepts it in the appropriate format. For example, it can accept audio file formats such as WAV and MP3. It can also check whether the content of the voice data is conversation, music, etc. Step 2: The analysis unit is the part that analyzes the voice data input by the reception unit. The analysis unit uses advanced voice recognition technology to analyze the waveform of the voice data and convert the voice content into text data. For example, the analysis unit extracts specific words or phrases from the voice data and outputs them as text data. Furthermore, noise removal technology can be used to remove noise from the voice data. For example, filtering technology can be used to remove noise from the voice data. Step 3: The conversion unit converts the voice data analyzed by the analysis unit into text data. The conversion unit converts the voice data into text data using a voice-to-text conversion algorithm. For example, the conversion unit converts the voice data into text data using a voice recognition algorithm. Furthermore, the conversion unit can also adjust the format of the text data. For example, the conversion unit can adjust the text layout, font size, line spacing, etc. Step 4: The providing unit provides the user with the text data converted by the converting unit. The providing unit provides the text data in a downloadable format. For example, the providing unit provides the text data in PDF format, TXT format, DOCX format, etc. The providing unit allows the user to easily download the text data.

[0062] (Example 2) A speech-to-text system according to an embodiment of the present invention automatically converts voice data input by a user into text. In this speech-to-text system, a user records voice data and inputs it into the system. The system then analyzes the voice data using advanced voice recognition technology and converts it into text data. This converted text data is provided to the user. For example, the system can be used for a variety of purposes, such as creating meeting minutes and transcribing interviews. The advanced analytical capabilities of voice recognition technology enable accurate and rapid conversion of voice into text. For example, a user records voice data and inputs it into the system. The user may record voice data using a device such as a smartphone or a PC and upload it to the system. For example, voice data recorded on a smartphone during a meeting can be uploaded to the system. The system then analyzes the voice data using advanced voice recognition technology. The voice recognition technology analyzes the waveform of the voice data and converts the voice content into text data. For example, specific words or phrases may be extracted from the voice data and output as text data. The analyzed text data is provided to the user. The user can download the text data provided by the system and use it for creating meeting minutes, transcribing interviews, and other purposes. For example, when creating minutes of a meeting, minutes can be created efficiently by using the text data provided by the system. This mechanism makes it possible to convert voice data into text accurately and quickly. Users can easily convert voice data into text data without performing complicated operations. For example, when transcribing an interview, using the system can significantly reduce the time required compared to manual transcription. This allows the voice-to-text system to allow users to easily convert voice data into text data.

[0063] The speech-to-text system according to the embodiment includes a receiving unit, an analyzing unit, a converting unit, and a providing unit. The receiving unit is a unit through which a user inputs speech data. For example, a user can record speech data using a device such as a smartphone or a PC and upload it to the receiving unit. When receiving speech data, the receiving unit checks the format and type of the data and accepts it in an appropriate format. For example, the receiving unit can accept audio file formats such as WAV and MP3. The receiving unit can also check whether the content of the speech data is conversation, music, or the like. The analyzing unit analyzes the speech data input by the receiving unit. The analyzing unit uses advanced speech recognition technology to analyze the waveform of the speech data and convert the speech content into text data. For example, the analyzing unit extracts specific words or phrases from the speech data and outputs it as text data. The analyzing unit can also remove noise from the speech data using noise reduction technology. For example, the analyzing unit removes noise from the speech data using filtering technology. The converting unit converts the speech data analyzed by the analyzing unit into text data. The conversion unit converts the voice data into text data using a voice-to-text conversion algorithm. For example, the conversion unit converts the voice data into text data using a voice recognition algorithm. The conversion unit can also adjust the format of the text data. For example, the conversion unit adjusts the text layout, font size, line spacing, etc. The providing unit provides the text data converted by the conversion unit to a user. The providing unit provides the text data in a downloadable format. For example, the providing unit provides the text data in PDF format, TXT format, DOCX format, etc. The providing unit allows the user to easily download the text data. In this way, the voice-to-text system according to the embodiment allows the user to easily convert voice data into text data and download it.

[0064] The analysis unit may include a noise removal unit that removes noise from the audio data. The noise removal unit is a component for removing noise from the audio data. The noise removal unit removes noise from the audio data using filtering technology. For example, the noise removal unit can remove noise in a specific frequency band from the audio data. The noise removal unit can also detect a specific noise pattern from the audio data and remove noise from that portion in detail. For example, the noise removal unit can remove specific background sounds from the audio data. This allows the noise removal unit to improve the analysis accuracy of the audio data. Some or all of the above-described processing in the noise removal unit may be performed using, for example, AI, or may be performed without using AI. For example, the noise removal unit may input audio data to a generation AI and have the generation AI perform noise removal.

[0065] The conversion unit may include a format adjustment unit that adjusts the format of the text data. The format adjustment unit is a component for adjusting the format of the text data. The format adjustment unit adjusts the text layout, font size, line spacing, etc. For example, the format adjustment unit may provide a format that emphasizes specific keywords in the text data. Furthermore, if a specific phrase is repeated in the text data, the format adjustment unit may provide a format that emphasizes that portion. For example, the format adjustment unit may detect a specific pattern in the text data and provide a format that emphasizes that portion. This allows the format adjustment unit to provide text data that is easy for the user to read. Some or all of the above-described processing in the format adjustment unit may be performed, for example, using AI or without AI. For example, the format adjustment unit may input text data to a generation AI and cause the generation AI to perform format adjustment.

[0066] The reception unit can receive voice data from at least one device, such as a smartphone or a personal computer. The reception unit is a component through which a user inputs voice data. For example, a user can record voice data using a device such as a smartphone or a personal computer and upload it to the reception unit. When receiving voice data, the reception unit checks the format and type of the data and accepts it in an appropriate format. For example, the reception unit can accept audio file formats such as WAV and MP3. The reception unit can also check whether the content of the voice data is conversation, music, or the like. This allows the reception unit to receive voice data from a variety of devices. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit can input voice data to a generation AI and have the generation AI check the format and type of the data.

[0067] The providing unit can provide the text data to the user in a downloadable format. The providing unit is a component that provides the text data converted by the conversion unit to the user. The providing unit provides the text data in a downloadable format. For example, the providing unit provides the text data in PDF format, TXT format, DOCX format, or the like. The providing unit enables the user to easily download the text data. This allows the providing unit to easily download the text data. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit may input the text data to a generation AI and cause the generation AI to provide the text data in a downloadable format.

[0068] The reception unit can estimate the user's emotion and adjust the timing of receiving voice data based on the estimated user emotion. The reception unit is a component for estimating the user's emotion and adjusting the timing of receiving voice data based on the estimated user emotion. For example, if the user is nervous, the reception unit can delay the timing of receiving voice data so that the user can relax. Furthermore, if the user is in a hurry, the reception unit can advance the timing of receiving voice data to quickly receive voice data. Furthermore, if the user is relaxed, the reception unit can receive voice data at a natural timing. This allows the reception unit to receive voice data at an appropriate timing according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's emotion data to the generation AI and cause the generation AI to estimate the emotion and adjust the timing of receiving the voice data.

[0069] The reception unit can analyze the user's past voice data history and select an appropriate reception method when receiving voice data. The reception unit is a component that analyzes the user's past voice data history and selects an appropriate reception method when receiving voice data. For example, the reception unit can prioritize reception of voice data formats previously used by the user. The reception unit can also suggest the most appropriate reception method for voice data based on the user's past voice data history. Furthermore, the reception unit can prioritize reception of devices previously used by the user. This allows the reception unit to provide the optimal reception method based on the user's past history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past voice data history into a generation AI and cause the generation AI to select the optimal reception method.

[0070] The reception unit can perform filtering based on the user's current environmental sound when receiving the voice data. The reception unit is a component for performing filtering taking the user's current environmental sound into consideration when receiving the voice data. For example, when the user is in a noisy environment, the reception unit can apply noise canceling and receive the voice data. Furthermore, when the user is in a quiet environment, the reception unit can also receive the voice data with minimal filtering. Furthermore, when the user is outdoors, the reception unit can remove wind noise and receive the voice data. This allows the reception unit to perform appropriate filtering according to the environmental sound. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's current environmental sound data to a generation AI and have the generation AI perform filtering.

[0071] The reception unit can estimate the user's emotion and determine the priority of the voice data to be received based on the estimated user's emotion. The reception unit is a component for estimating the user's emotion and determining the priority of the voice data to be received based on the estimated user's emotion. For example, when the user is nervous, the reception unit can prioritize receiving important voice data. Furthermore, when the user is relaxed, the reception unit can prioritize receiving normal voice data. Furthermore, when the user is in a hurry, the reception unit can prioritize receiving urgent voice data. This allows the reception unit to receive voice data in a priority order according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's emotion data to the generation AI and cause the generation AI to estimate the emotion and determine the priority.

[0072] The reception unit, when receiving voice data, can prioritize receiving highly relevant voice data in consideration of the user's geographical location information. The reception unit is a component for, when receiving voice data, prioritize receiving highly relevant voice data in consideration of the user's geographical location information. For example, when the user is in a specific area, the reception unit can prioritize receiving voice data related to that area. Furthermore, when the user is traveling, the reception unit can prioritize receiving voice data related to the travel destination. Furthermore, when the user is at home, the reception unit can prioritize receiving voice data related to the user's home. This allows the reception unit to prioritize receiving highly relevant voice data based on the geographical location information. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's geographical location information to the generation AI and cause the generation AI to select highly relevant voice data.

[0073] The reception unit can analyze the user's social media activity when receiving the voice data and receive related voice data. The reception unit is a component for analyzing the user's social media activity when receiving the voice data and receiving related voice data. For example, the reception unit can preferentially receive voice data related to topics the user is talking about on social media. The reception unit can also preferentially receive voice data related to accounts the user follows. Furthermore, the reception unit can preferentially receive voice data related to groups the user participates in. This allows the reception unit to receive related voice data based on the social media activity. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media activity data into a generation AI and cause the generation AI to select related voice data.

[0074] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated user emotions. The analysis unit is a component that estimates the user's emotions and adjusts the accuracy of the analysis based on the estimated user emotions. For example, when the user is nervous, the analysis unit can increase the accuracy of the analysis and provide accurate text data. Furthermore, when the user is relaxed, the analysis unit can provide text data with normal analysis accuracy. Furthermore, when the user is in a hurry, the analysis unit can quickly perform analysis and provide text data. This enables the analysis unit to adjust the analysis accuracy according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some 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 the user's emotion data into the generation AI and cause the generation AI to estimate emotions and adjust the analysis accuracy.

[0075] The analysis unit can detect specific patterns in the voice data during analysis and adjust the level of analysis detail. The analysis unit is a component for detecting specific patterns in the voice data during analysis and adjusting the level of analysis detail. For example, if the voice data contains a specific keyword, the analysis unit can analyze that portion in detail. Furthermore, if the voice data contains a specific phrase, the analysis unit can analyze that portion in detail. Furthermore, if the voice data contains a specific voice pattern, the analysis unit can analyze that portion in detail. This enables the analysis unit to perform a detailed analysis based on the specific pattern. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the voice data to a generation AI and cause the generation AI to detect specific patterns and adjust the level of analysis detail.

[0076] The analysis unit can apply different analysis algorithms depending on the category of the audio data during analysis. The analysis unit is a component for applying different analysis algorithms depending on the category of the audio data during analysis. For example, the analysis unit can apply an analysis algorithm dedicated to meetings to audio data of meetings. The analysis unit can also apply an analysis algorithm dedicated to interviews to audio data of interviews. Furthermore, the analysis unit can apply an analysis algorithm dedicated to lectures to audio data of lectures. This enables the analysis unit to perform appropriate analysis depending on the category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input audio data to a generation AI and cause the generation AI to apply an analysis algorithm depending on the category.

[0077] The analysis unit can estimate the user's emotions and determine the analysis priority based on the estimated user emotions. The analysis unit is a component for estimating the user's emotions and determining the analysis priority based on the estimated user emotions. For example, if the user is nervous, the analysis unit can prioritize analyzing important voice data. Furthermore, if the user is relaxed, the analysis unit can prioritize analyzing normal voice data. Furthermore, if the user is in a hurry, the analysis unit can prioritize analyzing urgent voice data. This allows the analysis unit to perform analysis in accordance with the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-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 the user's emotion data into the generation AI and have the generation AI perform emotion estimation and analysis priority determination.

[0078] The analysis unit can determine the priority of analysis based on the submission time of the voice data during analysis. The analysis unit is a component for determining the priority of analysis based on the submission time of the voice data during analysis. The analysis unit can, for example, prioritize analysis of recently submitted voice data. The analysis unit can also prioritize analysis of voice data with high urgency. Furthermore, the analysis unit can prioritize analysis of voice data with an approaching submission deadline. This allows the analysis unit to perform analysis in priority order based on the submission time. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the submission time of the voice data to the generation AI and have the generation AI determine the priority order.

[0079] The analysis unit can adjust the order of analysis based on the relevance of the audio data during analysis. The analysis unit is a component for adjusting the order of analysis based on the relevance of the audio data during analysis. The analysis unit can, for example, prioritize analysis of highly relevant audio data. The analysis unit can also postpone analysis of less relevant audio data. Furthermore, the analysis unit can group and analyze relevant audio data. This allows the analysis unit to perform analysis in an appropriate order based on the relevance. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the audio data to the generation AI and cause the generation AI to adjust the order of analysis.

[0080] The conversion unit can estimate the user's emotion and adjust the expression method of the conversion based on the estimated user's emotion. The conversion unit is a component that estimates the user's emotion and adjusts the expression method of the conversion based on the estimated user's emotion. For example, if the user is nervous, the conversion unit can use a simple and easy-to-understand expression method. Furthermore, if the user is relaxed, the conversion unit can use a detailed expression method. Furthermore, if the user is in a hurry, the conversion unit can use an expression method that can be quickly understood. This allows the conversion unit to perform conversion using an appropriate expression method according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the conversion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the conversion unit can input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion and adjust the expression method.

[0081] The conversion unit can adjust the level of detail of the conversion based on the importance of the audio data during conversion. The conversion unit is a component for adjusting the level of detail of the conversion based on the importance of the audio data during conversion. The conversion unit can, for example, perform detailed conversion on important audio data. The conversion unit can also perform standard conversion on normal audio data. Furthermore, the conversion unit can perform quick conversion on audio data with high urgency. This enables the conversion unit to perform appropriate conversion according to the importance of the audio data. Some 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 importance of the audio data to the generation AI and cause the generation AI to adjust the level of detail of the conversion.

[0082] The conversion unit can apply different conversion algorithms depending on the category of the audio data during conversion. The conversion unit is a component for applying different conversion algorithms depending on the category of the audio data during conversion. For example, the conversion unit can apply a conversion algorithm dedicated to conferences to audio data of a conference. The conversion unit can also apply a conversion algorithm dedicated to interviews to audio data of an interview. Furthermore, the conversion unit can apply a conversion algorithm dedicated to lectures to audio data of a lecture. This enables the conversion unit to perform appropriate conversion depending on the category. Some 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 category of the audio data to the generation AI and cause the generation AI to apply a conversion algorithm depending on the category.

[0083] The conversion unit can estimate the user's emotion and adjust the length of the conversion based on the estimated user emotion. The conversion unit is a component that estimates the user's emotion and adjusts the length of the conversion based on the estimated user emotion. For example, if the user is nervous, the conversion unit can perform a short, concise conversion. Furthermore, if the user is relaxed, the conversion unit can perform a longer conversion that includes detailed explanations. Furthermore, if the user is in a hurry, the conversion unit can perform a short, quickly understandable conversion. This allows the conversion unit to perform a conversion of an appropriate length according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the conversion unit may be performed using, for example, an AI. For example, the conversion unit can input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion and adjust the length of the conversion.

[0084] The conversion unit can determine the priority of conversion based on the submission time of the voice data during conversion. The conversion unit is a component for determining the priority of conversion based on the submission time of the voice data during conversion. For example, the conversion unit can prioritize conversion of recently submitted voice data. The conversion unit can also prioritize conversion of voice data with high urgency. Furthermore, the conversion unit can prioritize conversion of voice data with an upcoming submission deadline. This allows the conversion unit to perform conversion in order of priority based on the submission time. Some or all of the above-described 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 submission time of the voice data to the generation AI and have the generation AI determine the priority.

[0085] The conversion unit can adjust the order of conversion based on the relevance of the audio data during conversion. The conversion unit is a component for adjusting the order of conversion based on the relevance of the audio data during conversion. For example, the conversion unit can prioritize conversion of highly relevant audio data. The conversion unit can also postpone conversion of less relevant audio data. Furthermore, the conversion unit can group and convert relevant audio data. This allows the conversion unit to perform conversion in an appropriate order based on the relevance. Some 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 relevance of the audio data to a generation AI and cause the generation AI to adjust the order of conversion.

[0086] The providing unit can estimate the user's emotion and adjust the format of the text data to be provided based on the estimated user's emotion. The providing unit is a component for estimating the user's emotion and adjusting the format of the text data to be provided based on the estimated user's emotion. For example, if the user is nervous, the providing unit can provide the text data in a simple and easy-to-understand format. Furthermore, if the user is relaxed, the providing unit can provide the text data in a detailed format. Furthermore, if the user is in a hurry, the providing unit can provide the text data in a format that can be quickly understood. This allows the providing unit to provide the text data in an appropriate format according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data to the generation AI and cause the generation AI to estimate the emotion and adjust the format of the text data.

[0087] The providing unit can select the optimal delivery method by referring to the user's past usage history at the time of provision. The providing unit is a component for selecting the optimal delivery method by referring to the user's past usage history at the time of provision. The providing unit can, for example, provide the text data in a format previously used by the user. The providing unit can also suggest the optimal delivery method based on the user's past usage history. Furthermore, the providing unit can provide the text data in a format optimal for the device previously used by the user. This allows the providing unit to provide the optimal delivery method based on the past usage history. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past usage history into the generation AI and cause the generation AI to select the optimal delivery method.

[0088] The providing unit can customize the means of provision based on the user's current device information at the time of provision. The providing unit is a component for customizing the means of provision based on the user's current device information at the time of provision. For example, if the user is using a smartphone, the providing unit can provide the text data in a format optimal for the smartphone. Furthermore, if the user is using a personal computer, the providing unit can provide the text data in a format optimal for the personal computer. Furthermore, if the user is using a tablet, the providing unit can provide the text data in a format optimal for the tablet. This allows the providing unit to provide the text data in an optimal format based on the current device information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's current device information to the generation AI and cause the generation AI to customize the means of provision.

[0089] The providing unit can estimate the user's emotions and determine the priority of text data to be provided based on the estimated user's emotions. The providing unit is a component for estimating the user's emotions and determining the priority of text data to be provided based on the estimated user's emotions. For example, when the user is nervous, the providing unit can prioritize providing important text data. Furthermore, when the user is relaxed, the providing unit can prioritize providing normal text data. Furthermore, when the user is in a hurry, the providing unit can prioritize providing urgent text data. This allows the providing unit to provide text data in a priority order according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data to the generation AI and cause the generation AI to estimate emotions and determine the priority of text data.

[0090] The providing unit can select the optimal delivery method by taking into consideration the user's geographical location information when providing the data. The providing unit is a component for selecting the optimal delivery method by taking into consideration the user's geographical location information when providing the data. For example, when the user is in a specific area, the providing unit can prioritize providing text data related to the area. Furthermore, when the user is traveling, the providing unit can prioritize providing text data related to the travel destination. Furthermore, when the user is at home, the providing unit can prioritize providing text data related to the user's home. This allows the providing unit to provide the optimal delivery method based on the geographical location information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's geographical location information to the generation AI and cause the generation AI to select the optimal delivery method.

[0091] The providing unit can analyze the user's social media activity and suggest a means of provision at the time of provision. The providing unit is a component for analyzing the user's social media activity and suggesting a means of provision at the time of provision. For example, the providing unit can prioritize providing text data related to topics the user is talking about on social media. The providing unit can also prioritize providing text data related to accounts the user follows. Furthermore, the providing unit can prioritize providing text data related to groups the user is participating in. This allows the providing unit to suggest an optimal means of provision based on the social media activity. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's social media activity data into a generation AI and cause the generation AI to suggest a means of provision.

[0092] The noise removal unit can estimate the user's emotion and adjust the accuracy of noise removal based on the estimated user's emotion. The noise removal unit is a component that estimates the user's emotion and adjusts the accuracy of noise removal based on the estimated user's emotion. For example, when the user is nervous, the noise removal unit can increase the accuracy of noise removal to provide clear voice data. Furthermore, when the user is relaxed, the noise removal unit can also provide voice data with normal noise removal accuracy. Furthermore, when the user is in a hurry, the noise removal unit can quickly remove noise and provide voice data. This enables the noise removal unit to perform appropriate noise removal according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the noise removal unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the noise removal unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation and adjust the accuracy of noise removal.

[0093] The noise removal unit can detect a specific pattern in the audio data during noise removal and adjust the level of detail of the noise removal. The noise removal unit is a component that detects a specific pattern in the audio data and adjusts the level of detail of the noise removal during noise removal. For example, if the audio data contains a specific noise pattern, the noise removal unit can remove that part in detail. Furthermore, if the audio data contains noise in a specific frequency band, the noise removal unit can remove that part in detail. Furthermore, if the audio data contains a specific background sound, the noise removal unit can remove that part in detail. This enables the noise removal unit to perform detailed noise removal based on a specific pattern. Some or all of the above-described processing in the noise removal unit may be performed using, for example, AI, or may be performed without using AI. For example, the noise removal unit can input audio data to a generation AI and cause the generation AI to detect a specific pattern and adjust the level of detail of the noise removal.

[0094] The noise removal unit can estimate the user's emotions and determine the priority of noise removal based on the estimated user's emotions. The noise removal unit is a component for estimating the user's emotions and determining the priority of noise removal based on the estimated user's emotions. For example, when the user is nervous, the noise removal unit can prioritize noise removal from important voice data. Furthermore, when the user is relaxed, the noise removal unit can prioritize noise removal from normal voice data. Furthermore, when the user is in a hurry, the noise removal unit can prioritize noise removal from urgent voice data. This allows the noise removal unit to perform noise removal in accordance with the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the noise removal unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the noise removal unit can input user emotion data into the generation AI and have the generation AI estimate the emotion and determine the priority of noise removal.

[0095] The noise removal unit can determine the priority of noise removal based on the submission date of the audio data when removing noise. The noise removal unit is a component for determining the priority of noise removal based on the submission date of the audio data when removing noise. The noise removal unit can, for example, prioritize noise removal from recently submitted audio data. The noise removal unit can also prioritize noise removal from audio data with a high urgency. Furthermore, the noise removal unit can prioritize noise removal from audio data with an upcoming submission deadline. This allows the noise removal unit to perform noise removal in accordance with the priority based on the submission date. Some or all of the above-described processing in the noise removal unit may be performed using, for example, AI, or may be performed without using AI. For example, the noise removal unit can input the submission date of the audio data to the generation AI and have the generation AI determine the priority.

[0096] The format adjustment unit can estimate a user's emotion and determine a format adjustment method based on the estimated user's emotion. The format adjustment unit is a component that estimates a user's emotion and determines a format adjustment method based on the estimated user's emotion. For example, if the user is nervous, the format adjustment unit can provide a simple and easy-to-understand format. Furthermore, if the user is relaxed, the format adjustment unit can provide a detailed format. Furthermore, if the user is in a hurry, the format adjustment unit can provide a format that can be quickly understood. This enables the format adjustment unit to appropriately adjust the format according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the format adjustment unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the format adjustment unit can input user emotion data into the generation AI and cause the generation AI to estimate the emotion and determine a format adjustment method.

[0097] The format adjustment unit can detect specific patterns in text data and adjust the level of detail of the format during format adjustment. The format adjustment unit is a component that detects specific patterns in text data and adjusts the level of detail of the format during format adjustment. For example, if the text data contains a specific keyword, the format adjustment unit can provide a format that emphasizes that portion. Furthermore, if the text data contains a specific phrase, the format adjustment unit can provide a format that emphasizes that portion. Furthermore, if the text data contains a specific pattern, the format adjustment unit can provide a format that emphasizes that portion. This enables the format adjustment unit to perform detailed format adjustment based on the specific pattern. Some or all of the above-described processing in the format adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the format adjustment unit can input text data to a generation AI and cause the generation AI to detect the specific pattern and adjust the level of detail of the format.

[0098] The format adjustment unit can estimate a user's emotions and determine format priorities based on the estimated user emotions. The format adjustment unit is a component for estimating a user's emotions and determining format priorities based on the estimated user emotions. For example, if the user is nervous, the format adjustment unit can prioritize adjusting the format of important text data. Furthermore, if the user is relaxed, the format adjustment unit can prioritize adjusting the format of normal text data. Furthermore, if the user is in a hurry, the format adjustment unit can prioritize adjusting the format of urgent text data. This allows the format adjustment unit to perform format adjustments based on the priorities of the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the format adjustment unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the format adjustment unit can input user emotion data to the generation AI and cause the generation AI to estimate emotions and determine format priorities.

[0099] The format adjustment unit can determine the priority of formats based on the submission date of the text data during format adjustment. The format adjustment unit is a component for determining the priority of formats based on the submission date of the text data during format adjustment. For example, the format adjustment unit can prioritize adjusting the format of recently submitted text data. The format adjustment unit can also prioritize adjusting the format of text data with a high urgency. Furthermore, the format adjustment unit can prioritize adjusting the format of text data with an upcoming submission deadline. This allows the format adjustment unit to perform format adjustment based on the priority of the submission date. Some or all of the above-described processing in the format adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the format adjustment unit can input the submission date of the text data to the generation AI and cause the generation AI to determine the priority. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, conversion unit, and provision unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14, and allows a user to record voice data and upload it to the system. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the waveform of the voice data and converts the voice content into text data. The conversion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and converts the voice data into text data. The provision unit is realized, for example, by the control unit 46A of the smart device 14, and provides the converted text data to the user. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, conversion unit, and provision unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214, and allows a user to record voice data and upload it to the system. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the waveform of the voice data and converts the voice content into text data. The conversion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and converts the voice data into text data. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214, and provides the converted text data to the user. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, conversion unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset type terminal 314, and allows the user to record voice data and upload it to the system. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the waveform of the voice data and converts the voice content into text data. The conversion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and converts the voice data into text data. The provision unit is realized, for example, by the control unit 46A of the headset type terminal 314, and provides the converted text data to the user. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, conversion unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414, and allows the user to record voice data and upload it to the system. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the waveform of the voice data and converts the voice content into text data. The conversion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and converts the voice data into text data. The provision unit is realized, for example, by the control unit 46A of the robot 414, and provides the converted text data to the user.

[0100] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0101] When accepting a user's voice data, the acceptance unit can analyze the user's past voice data usage patterns and automatically select the optimal voice data format. For example, if the user has used a lot of MP3 format voice data in the past, the acceptance unit can automatically recommend the MP3 format. Also, if the user has previously uploaded voice data using a specific device, the acceptance unit can select the optimal format for that device. Furthermore, if the user has previously uploaded voice data during a specific time period, the acceptance unit can suggest the optimal acceptance method for that time period. This allows the acceptance unit to provide the optimal voice data acceptance method based on the user's past usage patterns.

[0102] When analyzing voice data, the analysis unit can automatically adjust the accuracy of the analysis by referring to the user's past analysis results. For example, if the user has frequently used specific words or phrases in the past, the analysis unit can prioritize analyzing those words or phrases. Also, if the user has provided a lot of voice data containing a specific noise pattern in the past, the noise pattern can be automatically removed. Furthermore, if the user has requested a specific analysis accuracy in the past, the analysis unit can perform the analysis based on that accuracy. This allows the analysis unit to provide the optimal analysis method based on the user's past analysis results.

[0103] When converting voice data into text data, the conversion unit can automatically select the optimal conversion algorithm by referring to the user's past conversion history. For example, if the user has frequently used a specific conversion algorithm in the past, that algorithm can be used preferentially. Also, if the user has provided text data in a specific format in the past, conversion can be performed based on that format. Furthermore, if the user has previously requested a specific conversion accuracy, conversion can be performed based on that accuracy. This allows the conversion unit to provide the optimal conversion method based on the user's past conversion history.

[0104] When providing text data to a user, the providing unit can automatically select the optimal providing method by referring to the user's past usage history. For example, if the user has downloaded a lot of text data in PDF format in the past, the providing unit can automatically recommend the PDF format. Also, if the user has downloaded text data using a specific device in the past, the providing unit can select the optimal format for that device. Furthermore, if the user has downloaded text data during a specific time period in the past, the providing unit can suggest the optimal providing method for that time period. This allows the providing unit to provide the optimal text data providing method based on the user's past usage history.

[0105] When removing noise from audio data, the noise removal unit can automatically select the optimal noise removal method by referring to the user's past noise removal history. For example, if the user has removed a specific noise pattern frequently in the past, the noise removal unit can prioritize removal of that noise pattern. Also, if the user has requested a specific noise removal accuracy in the past, the noise removal unit can perform noise removal based on that accuracy. Furthermore, if the user has used a specific noise removal algorithm in the past, the algorithm can be used preferentially. This allows the noise removal unit to provide the optimal noise removal method based on the user's past noise removal history.

[0106] The reception unit can estimate the user's emotions and adjust the method of receiving voice data based on the estimated user's emotions. For example, if the user is nervous, the reception unit can change the method of receiving voice data so that the user can relax. Also, if the user is relaxed, the normal reception method can be used. Furthermore, if the user is in a hurry, the reception method can be adjusted to quickly receive voice data. In this way, the reception unit can provide an appropriate method of receiving voice data according to the user's emotions.

[0107] The analysis unit can estimate the user's emotions and adjust the analysis priority based on the estimated user's emotions. For example, if the user is nervous, important voice data can be analyzed with priority. If the user is relaxed, normal voice data can be analyzed with priority. Furthermore, if the user is in a hurry, urgent voice data can be analyzed with priority. This allows the analysis unit to analyze voice data with priority according to the user's emotions.

[0108] The conversion unit can estimate the user's emotion and adjust the expression method of the conversion based on the estimated user's emotion. For example, if the user is nervous, a simple and easy-to-understand expression method can be used. If the user is relaxed, a detailed expression method can be used. Furthermore, if the user is in a hurry, an expression method that can be quickly understood can be used. In this way, the conversion unit can convert voice data into text data using an appropriate expression method according to the user's emotion.

[0109] The providing unit can estimate the user's emotion and adjust the format of the text data to be provided based on the estimated user's emotion. For example, if the user is nervous, the text data can be provided in a simple and easy-to-understand format. If the user is relaxed, the text data can be provided in a detailed format. Furthermore, if the user is in a hurry, the text data can be provided in a format that can be quickly understood. This allows the providing unit to provide the text data in an appropriate format according to the user's emotion.

[0110] The noise removal unit can estimate the user's emotions and adjust the accuracy of noise removal based on the estimated user's emotions. For example, if the user is nervous, the noise removal accuracy can be increased to provide clear voice data. Alternatively, if the user is relaxed, the voice data can be provided with normal noise removal accuracy. Furthermore, if the user is in a hurry, the noise removal unit can quickly perform noise removal and provide voice data. This enables the noise removal unit to perform appropriate noise removal according to the user's emotions.

[0111] The processing flow of the second embodiment will be briefly explained below.

[0112] Step 1: The reception unit is the part where the user inputs voice data. The user can record voice data using a device such as a smartphone or PC and upload it to the reception unit. When receiving voice data, the reception unit checks the format and type of the data and accepts it in the appropriate format. For example, it can accept audio file formats such as WAV and MP3. It can also check whether the content of the voice data is conversation, music, etc. Step 2: The analysis unit is the part that analyzes the voice data input by the reception unit. The analysis unit uses advanced voice recognition technology to analyze the waveform of the voice data and convert the voice content into text data. For example, the analysis unit extracts specific words or phrases from the voice data and outputs them as text data. Furthermore, noise removal technology can be used to remove noise from the voice data. For example, filtering technology can be used to remove noise from the voice data. Step 3: The conversion unit converts the voice data analyzed by the analysis unit into text data. The conversion unit converts the voice data into text data using a voice-to-text conversion algorithm. For example, the conversion unit converts the voice data into text data using a voice recognition algorithm. Furthermore, the conversion unit can also adjust the format of the text data. For example, the conversion unit can adjust the text layout, font size, line spacing, etc. Step 4: The providing unit provides the user with the text data converted by the converting unit. The providing unit provides the text data in a downloadable format. For example, the providing unit provides the text data in PDF format, TXT format, DOCX format, etc. The providing unit allows the user to easily download the text data.

[0113] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0114] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0115] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0116] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0117] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0118] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0119] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0120] 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.

[0121] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0123] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0124] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0125] 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.

[0126] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0127] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0128] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0129] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0130] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0131] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0132] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0133] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0134] 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.

[0135] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0136] 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.

[0137] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0139] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0140] 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.

[0141] 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.

[0142] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0143] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0144] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0145] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0146] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0147] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0148] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0149] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0150] 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.

[0151] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0152] 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.

[0153] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0154] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0155] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0156] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0157] 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.

[0158] 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.

[0159] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0160] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0161] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0162] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0163] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0164] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0165] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0166] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0167] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0168] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0169] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0170] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0171] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0172] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0173] 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.

[0174] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0175] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0176] 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.

[0177] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0178] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0179] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0180] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0181] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0182] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0183] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0184] [Explanation of symbols]

[0185] 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 reception unit for inputting voice data; an analysis unit that analyzes the voice data input by the reception unit; a conversion unit that converts the voice data analyzed by the analysis unit into text data; a providing unit that provides the text data converted by the converting unit. A system characterized by:

2. The analysis unit Equipped with a noise reduction unit that removes noise from audio data 2. The system of claim 1.

3. The conversion unit A format adjustment unit is provided to adjust the format of text data.

2. The system of claim 1.

4. The reception unit Accepts voice data from at least one device, either a smartphone or a computer.

2. The system of claim 1.

5. The providing unit Providing users with text data in a downloadable format 2. The system of claim 1.

6. The reception unit The user's emotions are estimated, and the timing of receiving voice data is adjusted based on the estimated user's emotions.

2. The system of claim 1.

7. The reception unit When receiving voice data, analyze the user's past voice data history and select the appropriate reception method.

2. The system of claim 1.

8. The reception unit When receiving audio data, filtering is performed based on the user's current ambient sound.

2. The system of claim 1.

9. The reception unit Estimate the user's emotions and determine the priority of the voice data to be received based on the estimated user emotions.

2. The system of claim 1.

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