system

Abstract

The system according to this embodiment aims to accurately recognize voice commands even in places with a lot of ambient noise. [Solution] The system according to the embodiment comprises a reception unit, a removal unit, a conversion unit, and an output unit. The reception unit receives voice commands. The removal unit removes noise from the voice commands received by the reception unit. The conversion unit converts the voice commands from which noise has been removed by the removal unit into text. The output unit outputs the text converted by the conversion unit to the user interface.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that the recognition accuracy of voice commands in a place with a lot of ambient noise is low, and accurate operation is difficult.

[0005] The system according to the embodiment aims to accurately recognize voice commands even in a place with a lot of ambient noise.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a removal unit, a conversion unit, and an output unit. The reception unit receives voice commands. The removal unit removes noise from the voice commands received by the reception unit. The conversion unit converts the voice commands, from which noise has been removed by the removal unit, into text. The output unit outputs the text converted by the conversion unit to the user interface. [Effects of the Invention]

[0007] The system according to this embodiment can accurately recognize voice commands even in places with a lot of ambient noise. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) An embodiment of the present invention provides a smartphone voice recognition support tool AI assistant, which is a system that converts a user's voice commands into text. This system receives voice commands, removes noise, converts them into text in a specified language or dialect, and outputs them to a user interface. For example, there is a reception unit that receives voice commands, and when a user issues a voice command, this reception unit receives the voice. Next, the received voice command is sent to a noise reduction unit to remove ambient noise, wind noise, etc. The noise-removed voice command is sent to a conversion unit and converted into text in a specified language or dialect. The converted text is output to a user interface, for example, displayed on the smartphone screen. This system supports various languages ​​and dialects and has noise reduction and filtering functions, enabling accurate recognition even amidst ambient noise. Furthermore, the conversion unit includes a language identification unit that identifies the language of the noise-removed voice command and converts it into text corresponding to that language. The conversion unit also includes a translation unit that translates the converted text into text in the specified language. Furthermore, the translation unit includes a dialect correction unit that corrects any dialects in the translated text to standard Japanese. The reception unit includes an authentication unit that authenticates the user's voiceprint before beginning to receive voice commands, thereby enhancing security. The reception unit can also accept user requests for specific noises, and a noise removal unit can remove these specified noises. For example, specific background noises can be removed. Finally, the conversion unit includes a grammar check unit that performs grammatical checks on the converted text and corrects errors, resulting in more accurate text. This allows smartphone voice recognition assistance tools and AI assistants to accurately convert user voice commands into text and output it to the user interface.

[0029] The AI ​​assistant voice recognition support tool for smartphones according to this embodiment comprises a reception unit, a removal unit, a conversion unit, and an output unit. The reception unit receives voice commands from the user. For example, when a user utters a voice command such as "Play music," the reception unit receives this voice. The reception unit includes an authentication unit that performs voiceprint authentication of the user, and can authenticate the user before starting to receive voice commands. This improves security. The removal unit removes noise from the voice command received by the reception unit. For example, it removes ambient noise and wind noise. The removal unit can accept the user's specification of specific noise and remove the specified specific noise. For example, it can remove specific background noise. The conversion unit converts the voice command from which the noise has been removed by the removal unit into text. For example, the voice command "Play music" is converted into the text "Play music." The conversion unit supports specified languages ​​and dialects and can convert into text in various languages ​​and dialects. For example, it supports languages ​​such as English, French, and Spanish, as well as dialects such as Kansai dialect and Tohoku dialect. The conversion unit includes a language identification unit that identifies the language of the noise-removed voice command and converts it into text corresponding to that language. Furthermore, the conversion unit includes a translation unit that translates the converted text into text in the specified language. The translation unit includes a dialect correction unit that corrects any dialects in the translated text to standard Japanese. The output unit outputs the text converted by the conversion unit to the user interface. For example, the text "Play music" is displayed on the smartphone screen. As a result, the AI ​​assistant voice recognition support tool for smartphones according to this embodiment can accurately convert the user's voice commands into text and output it to the user interface.

[0030] The reception unit receives voice commands from users. For example, if a user issues a voice command such as "Play music," the reception unit receives this voice. The reception unit is equipped with an authentication unit that performs voiceprint authentication, allowing it to authenticate the user before beginning to receive voice commands. This enhances security. Specifically, the reception unit is equipped with a high-sensitivity microphone to clearly capture the user's voice. The voice data is first preprocessed with noise filtering to remove unwanted components such as background noise and echoes. Next, a speech recognition engine analyzes the voice data and converts the voice command into text data. In this process, the speech recognition engine uses a deep learning model to achieve highly accurate speech recognition. Furthermore, the authentication unit compares the user's voiceprint with a pre-registered database to confirm that the user is legitimate. Voiceprint authentication extracts features from the voice and uses them to individually identify the user. This prevents others from issuing voice commands fraudulently, thereby strengthening security. The reception unit is designed to quickly receive and authenticate voice commands, allowing users to operate it without stress.

[0031] The noise reduction unit removes noise from voice commands received by the reception unit. For example, it removes ambient noise and wind noise. The noise reduction unit can also accept user specifications for specific noises and remove those specified noises. For example, it can remove specific background noises. Specifically, the noise reduction unit uses advanced noise cancellation algorithms to remove unwanted noise components from the voice signal in real time. This involves spectral subtraction and adaptive filtering techniques. Spectral subtraction analyzes the frequency spectrum of the voice signal to identify and remove noise components. Adaptive filtering techniques dynamically cancel noise components by utilizing the correlation between the voice signal and the noise signal. Furthermore, the noise reduction unit has a function that allows the user to specify specific noises, for example, removing noise in a specific frequency band. This allows the user to customize the noise reduction settings to suit their environment. The noise reduction unit is designed to perform noise reduction processing at high speed and minimize delays in voice commands. This allows the user to input clear voice commands into the system, improving the accuracy of voice recognition.

[0032] The conversion unit converts the voice command, from which noise has been removed by the noise removal unit, into text. For example, the voice command "Play music" is converted into the text "Play music". The conversion unit supports specified languages ​​and dialects and can convert into text in various languages ​​and dialects. For example, it supports languages ​​such as English, French, and Spanish, as well as dialects such as Kansai dialect and Tohoku dialect. The conversion unit includes a language identification unit that identifies the language of the noise-removed voice command and converts it into text corresponding to that language. Furthermore, the conversion unit includes a translation unit that translates the converted text into text in the specified language. The translation unit includes a dialect correction unit that corrects any dialects in the translated text to standard Japanese. Specifically, the conversion unit analyzes the voice signal using a speech recognition engine and recognizes phonemes and words. The speech recognition engine uses a deep learning model trained on a large amount of voice data to achieve highly accurate speech recognition. The language identification unit analyzes the features of the voice signal to identify which language it is. This enables text conversion in the appropriate language even when the user issues voice commands in different languages. The translation unit has the function to translate the converted text into other languages; for example, it can translate English voice commands into Japanese text. The dialect correction unit corrects dialects contained in the translated text to standard Japanese, converting it into text that is easier to understand. As a result, the conversion unit achieves multilingual support and high-precision speech recognition, improving user convenience.

[0033] The output unit displays the text converted by the conversion unit to the user interface. For example, the text "Play music" might be displayed on the smartphone screen. In addition to displaying text, the output unit can also output text as voice using speech synthesis technology. Specifically, the output unit has an interface for displaying text on the smartphone's display, allowing the user to visually confirm it. Furthermore, it can play the converted text in a natural-sounding voice using a speech synthesis engine. The speech synthesis engine converts the text data into a speech waveform and outputs it through the speaker. This allows the user to confirm the results of voice commands both visually and aurally. The output unit also has a function to customize the design and layout of the user interface, allowing for display tailored to the user's preferences. For example, the font size, color, and display position can be changed. The output unit also has a function to accept user feedback, allowing for continuous improvement of the display content and the quality of the voice output. This enables the output unit to provide intuitive and easy-to-understand information to the user, improving the usability of the speech recognition support tool.

[0034] The conversion unit can convert voice commands, from which noise has been removed by the removal unit, into text in a specified language. For example, the conversion unit converts English voice commands into English text. It can also convert French voice commands into French text. Furthermore, it can convert Spanish voice commands into Spanish text. This enables text conversion corresponding to a specified language. Some or all of the above processing in the conversion unit may be performed using, for example, a generating AI, or without a generating AI. For example, the conversion unit can input a voice command into a generating AI, which can then convert it into text in a specified language.

[0035] The conversion unit can convert voice commands, from which noise has been removed by the removal unit, into text in a specified dialect or standard Japanese. For example, the conversion unit can convert voice commands in Kansai dialect into text in Kansai dialect. It can also convert voice commands in Tohoku dialect into text in Tohoku dialect. Furthermore, the conversion unit can convert voice commands in standard Japanese into text in standard Japanese. This enables text conversion corresponding to specified dialects or standard Japanese. Some or all of the above processing in the conversion unit may be performed using, for example, a generating AI, or without a generating AI. For example, the conversion unit can input a voice command into a generating AI, which can then convert it into text in a specified dialect or standard Japanese.

[0036] The conversion unit comprises a language identification unit that identifies the language of the voice command from which noise has been removed by the removal unit, a text conversion unit that converts the voice command from which noise has been removed by the removal unit into text in the specific language identified by the language identification unit, and a translation unit that translates the text converted by the text conversion unit into text in a specified language. For example, the language identification unit identifies whether the voice command is in English or French. The text conversion unit converts the voice command into text based on the identified language. The translation unit translates the converted text into the specified language. This makes it possible to identify the language of a voice command and translate it into a specified language. Some or all of the above-described processes in the conversion unit may be performed using, for example, a generating AI, or without a generating AI. For example, the conversion unit can input a voice command to a generating AI, which can perform language identification, text conversion, and translation.

[0037] The translation unit includes a dialect correction unit that corrects dialects in the translated text to standard Japanese if the dialect is included. For example, the translation unit corrects Kansai dialect text to standard Japanese. The translation unit can also correct Tohoku dialect text to standard Japanese. This allows for the provision of more standard text by correcting dialects to standard Japanese. Some or all of the above processing in the translation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the translation unit can input a text containing dialects into a generative AI, which can then correct it to standard Japanese.

[0038] The reception unit includes an authentication unit that performs voiceprint authentication of the user before it begins receiving voice commands. For example, the reception unit authenticates the user's voiceprint before the user issues a voice command. This enhances security. Some or all of the above-described processing in the authentication unit may be performed using, for example, a generating AI, or without a generating AI. For example, the authentication unit can input the user's voiceprint data into a generating AI, which can then perform voiceprint authentication.

[0039] The reception unit receives the user's specified noise, and the removal unit can remove the specified noise that has been received by the reception unit. For example, the reception unit can receive the user's specification of a specific background sound. The removal unit removes the specified background sound. This allows for clearer voice commands by removing the specific noise specified by the user. Some or all of the above processing in the reception unit and removal unit may be performed using, for example, a generating AI, or without a generating AI. For example, the reception unit can input the user's specification to the generating AI, which can receive the noise specification. The removal unit can input the specified noise to the generating AI, which can remove the noise.

[0040] The conversion unit includes a grammar checking unit that performs grammatical checks on the converted text and corrects errors. For example, the conversion unit checks the grammar of the converted text and detects grammatical errors. The conversion unit can also correct the detected grammatical errors. This allows for the provision of more accurate text through grammatical checking. Some or all of the above-described processes in the conversion unit may be performed using, for example, a generation AI, or without a generation AI. For example, the conversion unit can input the converted text into a generation AI, which can then perform grammatical checks and corrections.

[0041] The reception unit can analyze the user's past voice command history and select the optimal receiving method. For example, the reception unit prioritizes receiving voice commands that the user has frequently used in the past. The reception unit can also analyze patterns in the user's past voice command usage and suggest the optimal receiving method. Furthermore, the reception unit can predict commands to be used during specific time periods based on the user's past voice command history and adjust the receiving method accordingly. This allows the reception unit to select the optimal receiving method by analyzing the past voice command history. Some or all of the above processing in the reception unit may be performed using, for example, a generative AI, or without a generative AI. For example, the reception unit can input the past voice command history into a generative AI, which can then analyze the history and select the optimal receiving method.

[0042] The reception unit can filter voice commands based on the user's current activity and environment. For example, if the user is driving, the reception unit can receive only important voice commands and filter out others. The reception unit can also temporarily stop receiving voice commands if the user is in a meeting. Furthermore, if the user is in a quiet environment, the reception unit can receive all voice commands without detailed filtering. This allows for the reception of more appropriate voice commands by filtering them based on the user's activity and environment. Some or all of the above processing in the reception unit may be performed using, for example, a generative AI, or not. For example, the reception unit can input user activity and environment data into a generative AI, which can then perform the filtering.

[0043] The reception unit can prioritize receiving highly relevant commands by considering the user's geographical location when receiving voice commands. For example, if the user is in a specific location, the reception unit will prioritize receiving voice commands related to that location. The reception unit can also prioritize receiving voice commands related to movement if the user is on the move. Furthermore, if the user is at home, the reception unit can prioritize receiving voice commands related to operations within the home. This allows for the priority reception of highly relevant voice commands by considering the user's geographical location. Some or all of the above processing in the reception unit may be performed using, for example, a generative AI, or without a generative AI. For example, the reception unit can input the user's geographical location information into a generative AI, which can then prioritize receiving highly relevant commands.

[0044] The reception unit can analyze the user's social media activity upon receiving a voice command and receive relevant commands. For example, if the reception unit is discussing a specific topic on social media, it will prioritize receiving voice commands related to that topic. The reception unit can also enhance the reception of voice commands during times when the user is most active on social media. Furthermore, the reception unit can analyze the content of the user's social media posts and suggest relevant voice commands. This allows the reception unit to receive relevant voice commands by analyzing the user's social media activity. Some or all of the above processing in the reception unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the reception unit can input the user's social media activity data into a generative AI, which can then receive relevant commands.

[0045] The noise reduction unit can prioritize the removal of specific noises based on the content of the voice command during noise reduction. For example, if the voice command contains important instructions, the noise reduction unit will prioritize the removal of background noise. Furthermore, if the voice command is long, the noise reduction unit can prioritize the removal of specific noises such as wind or car noises. Additionally, if the voice command is short, the noise reduction unit can perform overall noise reduction to provide clear audio. This allows for the provision of clear voice commands by prioritizing the removal of specific noises based on the content of the voice command. Some or all of the above processing in the noise reduction unit may be performed using, for example, a generating AI, or without a generating AI. For example, the noise reduction unit can input the content of the voice command into a generating AI, which can then prioritize the removal of specific noises.

[0046] The noise reduction unit can learn ambient sound patterns and apply the optimal noise reduction algorithm during noise reduction. For example, the unit can learn the sound patterns of environments the user frequently inhabits and apply the most suitable noise reduction algorithm for that environment. Furthermore, if the user is in a new environment, the unit can quickly learn the sound patterns of that environment and perform noise reduction. Additionally, if the user is moving, the unit can learn the ambient sounds of their destination in real time and optimize noise reduction. This allows the unit to apply the optimal noise reduction algorithm by learning ambient sound patterns. Some or all of the above processing in the noise reduction unit may be performed using, for example, a generative AI, or without one. For example, the noise reduction unit can input ambient sound data into a generative AI, which can then learn sound patterns and apply a noise reduction algorithm.

[0047] The noise reduction unit can remove region-specific noise by considering the user's geographical location information during noise reduction. For example, if the user is in an urban area, the noise reduction unit will prioritize removing traffic noise. It can also prioritize removing natural sounds if the user is in a suburban area. Furthermore, if the user is in a specific region, the noise reduction unit can learn and remove region-specific noise. This allows for the removal of region-specific noise by considering the user's geographical location information. Some or all of the above processing in the noise reduction unit may be performed using, for example, a generative AI, or without a generative AI. For example, the noise reduction unit can input the user's geographical location information into a generative AI, which can then remove region-specific noise.

[0048] The noise reduction unit can analyze the user's social media activity and remove relevant noise during noise reduction. For example, if the user is talking about a specific topic on social media, the noise reduction unit will prioritize removing noise related to that topic. The noise reduction unit can also enhance noise reduction during times when the user is most active on social media. Furthermore, the noise reduction unit can analyze the content of the user's social media posts and remove relevant noise. This allows for the removal of relevant noise by analyzing the user's social media activity. Some or all of the above processing in the noise reduction unit may be performed using, for example, a generative AI, or without a generative AI. For example, the noise reduction unit can input the user's social media activity data into a generative AI, which can then remove the relevant noise.

[0049] The conversion unit can adjust the level of detail in the conversion based on the importance of the voice command during text conversion. For example, the conversion unit performs detailed text conversion for important voice commands. It can also perform standard text conversion for common voice commands. Furthermore, it can perform concise text conversion for simple voice commands. This allows for appropriate text conversion by adjusting the level of detail based on the importance of the voice command. Some or all of the above processing in the conversion unit may be performed using, for example, a generating AI, or without a generating AI. For example, the conversion unit can input voice command importance data into a generating AI, which can then adjust the level of detail.

[0050] The conversion unit can apply different conversion algorithms depending on the category of the voice command during text conversion. For example, in the case of music-related voice commands, the conversion unit can apply a conversion algorithm specialized for music. Similarly, in the case of navigation-related voice commands, the conversion unit can apply a conversion algorithm specialized for navigation. Furthermore, in the case of message-related voice commands, the conversion unit can apply a conversion algorithm specialized for messages. This allows for appropriate text conversion by applying the optimal conversion algorithm according to the category of the voice command. Some or all of the above processing in the conversion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the conversion unit can input the category data of the voice command into a generative AI, which can then apply a conversion algorithm.

[0051] The conversion unit can determine the conversion priority based on when the voice commands were submitted during text conversion. For example, the conversion unit can prioritize the conversion of the most recently submitted voice commands. It can also prioritize the conversion of voice commands submitted within a specific time period. Furthermore, it can prioritize the conversion of voice commands submitted within a time period specified by the user. This allows for appropriate text conversion by determining the conversion priority based on when the voice commands were submitted. Some or all of the above processing in the conversion unit may be performed using, for example, a generating AI, or without a generating AI. For example, the conversion unit can input voice command submission time data into a generating AI, which can then determine the conversion priority.

[0052] The conversion unit can adjust the order of conversion based on the relevance of the voice commands during text conversion. For example, the conversion unit prioritizes converting important voice commands to text. It can also convert common voice commands to text in a standard manner. Furthermore, it can postpone the conversion of simple voice commands. This allows for appropriate text conversion by adjusting the order of conversion based on the relevance of the voice commands. Some or all of the above processing in the conversion unit may be performed using, for example, a generating AI, or without a generating AI. For example, the conversion unit can input voice command relevance data into a generating AI, which can then adjust the order of conversion.

[0053] The output unit can select the optimal display method by referring to the user's past operation history when outputting. For example, the output unit can prioritize providing display methods that the user has used in the past. The output unit can also suggest the optimal display method based on the user's past operation history. Furthermore, the output unit can predict and provide display methods that the user will use during a specific time period. This allows the optimal display method to be selected by referring to the user's past operation history. Some or all of the above processing in the output unit may be performed using, for example, a generation AI, or without a generation AI. For example, the output unit can input past operation history data into a generation AI, which can then select the optimal display method.

[0054] The output unit can customize the output content based on the user's current activity status at the time of output. For example, the output unit can provide a highly visible display method when the user is driving. It can also provide a quiet notification method when the user is in a meeting. Furthermore, it can provide a display method that includes detailed information when the user is relaxing. This allows for appropriate display by customizing the output content based on the user's current activity status. Some or all of the above processing in the output unit may be performed using, for example, a generating AI, or without a generating AI. For example, the output unit can input user activity data into a generating AI, which can then customize the output content.

[0055] The output unit can select the optimal display method when outputting, taking into account the user's device information. For example, if the user is using a smartphone, the output unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the output unit can provide a display method optimized for a larger screen. Additionally, if the user is using a smartwatch, the output unit can provide a concise and highly visible display method. This allows for the selection of the optimal display method by considering the user's device information. Some or all of the above processing in the output unit may be performed using, for example, a generation AI, or without a generation AI. For example, the output unit can input the user's device information into a generation AI, which can then select the optimal display method.

[0056] The output unit can analyze the user's social media activity and provide relevant output content at the time of output. For example, if the user is talking about a specific topic on social media, the output unit will prioritize displaying output content related to that topic. The output unit can also enhance relevant output content during times when the user is most active on social media. Furthermore, the output unit can analyze the user's social media posts and provide relevant output content. In this way, relevant output content can be provided by analyzing the user's social media activity. Some or all of the above processing in the output unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the output unit can input the user's social media activity data into a generative AI, which can then provide relevant output content.

[0057] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0058] The reception unit can analyze the user's past voice command history and select the optimal receiving method. For example, the reception unit prioritizes receiving voice commands that the user has frequently used in the past. The reception unit can also analyze patterns in the user's past voice command usage and suggest the optimal receiving method. Furthermore, the reception unit can predict commands to be used during specific time periods based on the user's past voice command history and adjust the receiving method accordingly. This allows the reception unit to select the optimal receiving method by analyzing the past voice command history. Some or all of the above processing in the reception unit may be performed using, for example, a generative AI, or without a generative AI. For example, the reception unit can input the past voice command history into a generative AI, which can then analyze the history and select the optimal receiving method.

[0059] The noise reduction unit can prioritize the removal of specific noises based on the content of the voice command during noise reduction. For example, if the voice command contains important instructions, the noise reduction unit will prioritize the removal of background noise. Furthermore, if the voice command is long, the noise reduction unit can prioritize the removal of specific noises such as wind or car noises. Additionally, if the voice command is short, the noise reduction unit can perform overall noise reduction to provide clear audio. This allows for the provision of clear voice commands by prioritizing the removal of specific noises based on the content of the voice command. Some or all of the above processing in the noise reduction unit may be performed using, for example, a generating AI, or without a generating AI. For example, the noise reduction unit can input the content of the voice command into a generating AI, which can then prioritize the removal of specific noises.

[0060] The conversion unit can adjust the level of detail in the conversion based on the importance of the voice command during text conversion. For example, the conversion unit performs detailed text conversion for important voice commands. It can also perform standard text conversion for common voice commands. Furthermore, it can perform concise text conversion for simple voice commands. This allows for appropriate text conversion by adjusting the level of detail based on the importance of the voice command. Some or all of the above processing in the conversion unit may be performed using, for example, a generating AI, or without a generating AI. For example, the conversion unit can input voice command importance data into a generating AI, which can then adjust the level of detail.

[0061] The output unit can select the optimal display method by referring to the user's past operation history when outputting. For example, the output unit can prioritize providing display methods that the user has used in the past. The output unit can also suggest the optimal display method based on the user's past operation history. Furthermore, the output unit can predict and provide display methods that the user will use during a specific time period. This allows the optimal display method to be selected by referring to the user's past operation history. Some or all of the above processing in the output unit may be performed using, for example, a generation AI, or without a generation AI. For example, the output unit can input past operation history data into a generation AI, which can then select the optimal display method.

[0062] The reception unit can prioritize receiving highly relevant commands by considering the user's geographical location when receiving voice commands. For example, if the user is in a specific location, the reception unit will prioritize receiving voice commands related to that location. The reception unit can also prioritize receiving voice commands related to movement if the user is on the move. Furthermore, if the user is at home, the reception unit can prioritize receiving voice commands related to operations within the home. This allows for the priority reception of highly relevant voice commands by considering the user's geographical location. Some or all of the above processing in the reception unit may be performed using, for example, a generative AI, or without a generative AI. For example, the reception unit can input the user's geographical location information into a generative AI, which can then prioritize receiving highly relevant commands.

[0063] The following briefly describes the processing flow for example form 1.

[0064] Step 1: The reception unit receives the user's voice command. For example, if the user utters a voice command such as "Play music," the reception unit receives this voice. The reception unit is equipped with an authentication unit that performs voiceprint authentication of the user, allowing it to authenticate the user before starting to receive voice commands. This enhances security. Step 2: The noise removal unit removes noise from the voice command received by the reception unit. For example, it removes ambient noise and wind noise. The noise removal unit can also accept user specifications for specific noises and remove those specified noises. For example, it can remove specific background noises. Step 3: The conversion unit converts the voice command, from which noise has been removed by the removal unit, into text. For example, the voice command "Play music" is converted into the text "Play music". The conversion unit supports specified languages ​​and dialects and can convert to text in various languages ​​and dialects. For example, it supports languages ​​such as English, French, and Spanish, as well as dialects such as Kansai dialect and Tohoku dialect. The conversion unit includes a language identification unit that identifies the language of the voice command from which noise has been removed and converts it into text corresponding to that language. Furthermore, the conversion unit includes a translation unit that translates the converted text into text in the specified language. The translation unit includes a dialect correction unit that corrects any dialects in the translated text to standard Japanese. Step 4: The output unit outputs the text converted by the conversion unit to the user interface. For example, the text "Play music" is displayed on the smartphone screen. This allows the AI ​​assistant voice recognition support tool for smartphones according to the embodiment to accurately convert the user's voice commands into text and output them to the user interface.

[0065] (Example of form 2) An embodiment of the present invention provides a smartphone voice recognition support tool AI assistant, which is a system that converts a user's voice commands into text. This system receives voice commands, removes noise, converts them into text in a specified language or dialect, and outputs them to a user interface. For example, there is a reception unit that receives voice commands, and when a user issues a voice command, this reception unit receives the voice. Next, the received voice command is sent to a noise reduction unit to remove ambient noise, wind noise, etc. The noise-removed voice command is sent to a conversion unit and converted into text in a specified language or dialect. The converted text is output to a user interface, for example, displayed on the smartphone screen. This system supports various languages ​​and dialects and has noise reduction and filtering functions, enabling accurate recognition even amidst ambient noise. Furthermore, the conversion unit includes a language identification unit that identifies the language of the noise-removed voice command and converts it into text corresponding to that language. The conversion unit also includes a translation unit that translates the converted text into text in the specified language. Furthermore, the translation unit includes a dialect correction unit that corrects any dialects in the translated text to standard Japanese. The reception unit includes an authentication unit that authenticates the user's voiceprint before beginning to receive voice commands, thereby enhancing security. The reception unit can also accept user requests for specific noises, and a noise removal unit can remove these specified noises. For example, specific background noises can be removed. Finally, the conversion unit includes a grammar check unit that performs grammatical checks on the converted text and corrects errors, resulting in more accurate text. This allows smartphone voice recognition assistance tools and AI assistants to accurately convert user voice commands into text and output it to the user interface.

[0066] The AI ​​assistant voice recognition support tool for smartphones according to this embodiment comprises a reception unit, a removal unit, a conversion unit, and an output unit. The reception unit receives voice commands from the user. For example, when a user utters a voice command such as "Play music," the reception unit receives this voice. The reception unit includes an authentication unit that performs voiceprint authentication of the user, and can authenticate the user before starting to receive voice commands. This improves security. The removal unit removes noise from the voice command received by the reception unit. For example, it removes ambient noise and wind noise. The removal unit can accept the user's specification of specific noise and remove the specified specific noise. For example, it can remove specific background noise. The conversion unit converts the voice command from which the noise has been removed by the removal unit into text. For example, the voice command "Play music" is converted into the text "Play music." The conversion unit supports specified languages ​​and dialects and can convert into text in various languages ​​and dialects. For example, it supports languages ​​such as English, French, and Spanish, as well as dialects such as Kansai dialect and Tohoku dialect. The conversion unit includes a language identification unit that identifies the language of the noise-removed voice command and converts it into text corresponding to that language. Furthermore, the conversion unit includes a translation unit that translates the converted text into text in the specified language. The translation unit includes a dialect correction unit that corrects any dialects in the translated text to standard Japanese. The output unit outputs the text converted by the conversion unit to the user interface. For example, the text "Play music" is displayed on the smartphone screen. As a result, the AI ​​assistant voice recognition support tool for smartphones according to this embodiment can accurately convert the user's voice commands into text and output it to the user interface.

[0067] The reception unit receives voice commands from users. For example, if a user issues a voice command such as "Play music," the reception unit receives this voice. The reception unit is equipped with an authentication unit that performs voiceprint authentication, allowing it to authenticate the user before beginning to receive voice commands. This enhances security. Specifically, the reception unit is equipped with a high-sensitivity microphone to clearly capture the user's voice. The voice data is first preprocessed with noise filtering to remove unwanted components such as background noise and echoes. Next, a speech recognition engine analyzes the voice data and converts the voice command into text data. In this process, the speech recognition engine uses a deep learning model to achieve highly accurate speech recognition. Furthermore, the authentication unit compares the user's voiceprint with a pre-registered database to confirm that the user is legitimate. Voiceprint authentication extracts features from the voice and uses them to individually identify the user. This prevents others from issuing voice commands fraudulently, thereby strengthening security. The reception unit is designed to quickly receive and authenticate voice commands, allowing users to operate it without stress.

[0068] The noise reduction unit removes noise from voice commands received by the reception unit. For example, it removes ambient noise and wind noise. The noise reduction unit can also accept user specifications for specific noises and remove those specified noises. For example, it can remove specific background noises. Specifically, the noise reduction unit uses advanced noise cancellation algorithms to remove unwanted noise components from the voice signal in real time. This involves spectral subtraction and adaptive filtering techniques. Spectral subtraction analyzes the frequency spectrum of the voice signal to identify and remove noise components. Adaptive filtering techniques dynamically cancel noise components by utilizing the correlation between the voice signal and the noise signal. Furthermore, the noise reduction unit has a function that allows the user to specify specific noises, for example, removing noise in a specific frequency band. This allows the user to customize the noise reduction settings to suit their environment. The noise reduction unit is designed to perform noise reduction processing at high speed and minimize delays in voice commands. This allows the user to input clear voice commands into the system, improving the accuracy of voice recognition.

[0069] The conversion unit converts the voice command, from which noise has been removed by the noise removal unit, into text. For example, the voice command "Play music" is converted into the text "Play music". The conversion unit supports specified languages ​​and dialects and can convert into text in various languages ​​and dialects. For example, it supports languages ​​such as English, French, and Spanish, as well as dialects such as Kansai dialect and Tohoku dialect. The conversion unit includes a language identification unit that identifies the language of the noise-removed voice command and converts it into text corresponding to that language. Furthermore, the conversion unit includes a translation unit that translates the converted text into text in the specified language. The translation unit includes a dialect correction unit that corrects any dialects in the translated text to standard Japanese. Specifically, the conversion unit analyzes the voice signal using a speech recognition engine and recognizes phonemes and words. The speech recognition engine uses a deep learning model trained on a large amount of voice data to achieve highly accurate speech recognition. The language identification unit analyzes the features of the voice signal to identify which language it is. This enables text conversion in the appropriate language even when the user issues voice commands in different languages. The translation unit has the function to translate the converted text into other languages; for example, it can translate English voice commands into Japanese text. The dialect correction unit corrects dialects contained in the translated text to standard Japanese, converting it into text that is easier to understand. As a result, the conversion unit achieves multilingual support and high-precision speech recognition, improving user convenience.

[0070] The output unit displays the text converted by the conversion unit to the user interface. For example, the text "Play music" might be displayed on the smartphone screen. In addition to displaying text, the output unit can also output text as voice using speech synthesis technology. Specifically, the output unit has an interface for displaying text on the smartphone's display, allowing the user to visually confirm it. Furthermore, it can play the converted text in a natural-sounding voice using a speech synthesis engine. The speech synthesis engine converts the text data into a speech waveform and outputs it through the speaker. This allows the user to confirm the results of voice commands both visually and aurally. The output unit also has a function to customize the design and layout of the user interface, allowing for display tailored to the user's preferences. For example, the font size, color, and display position can be changed. The output unit also has a function to accept user feedback, allowing for continuous improvement of the display content and the quality of the voice output. This enables the output unit to provide intuitive and easy-to-understand information to the user, improving the usability of the speech recognition support tool.

[0071] The conversion unit can convert voice commands, from which noise has been removed by the removal unit, into text in a specified language. For example, the conversion unit converts English voice commands into English text. It can also convert French voice commands into French text. Furthermore, it can convert Spanish voice commands into Spanish text. This enables text conversion corresponding to a specified language. Some or all of the above processing in the conversion unit may be performed using, for example, a generating AI, or without a generating AI. For example, the conversion unit can input a voice command into a generating AI, which can then convert it into text in a specified language.

[0072] The conversion unit can convert voice commands, from which noise has been removed by the removal unit, into text in a specified dialect or standard Japanese. For example, the conversion unit can convert voice commands in Kansai dialect into text in Kansai dialect. It can also convert voice commands in Tohoku dialect into text in Tohoku dialect. Furthermore, the conversion unit can convert voice commands in standard Japanese into text in standard Japanese. This enables text conversion corresponding to specified dialects or standard Japanese. Some or all of the above processing in the conversion unit may be performed using, for example, a generating AI, or without a generating AI. For example, the conversion unit can input a voice command into a generating AI, which can then convert it into text in a specified dialect or standard Japanese.

[0073] The conversion unit comprises a language identification unit that identifies the language of the voice command from which noise has been removed by the removal unit, a text conversion unit that converts the voice command from which noise has been removed by the removal unit into text in the specific language identified by the language identification unit, and a translation unit that translates the text converted by the text conversion unit into text in a specified language. For example, the language identification unit identifies whether the voice command is in English or French. The text conversion unit converts the voice command into text based on the identified language. The translation unit translates the converted text into the specified language. This makes it possible to identify the language of a voice command and translate it into a specified language. Some or all of the above-described processes in the conversion unit may be performed using, for example, a generating AI, or without a generating AI. For example, the conversion unit can input a voice command to a generating AI, which can perform language identification, text conversion, and translation.

[0074] The translation unit includes a dialect correction unit that corrects dialects in the translated text to standard Japanese if the dialect is included. For example, the translation unit corrects Kansai dialect text to standard Japanese. The translation unit can also correct Tohoku dialect text to standard Japanese. This allows for the provision of more standard text by correcting dialects to standard Japanese. Some or all of the above processing in the translation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the translation unit can input a text containing dialects into a generative AI, which can then correct it to standard Japanese.

[0075] The reception unit includes an authentication unit that performs voiceprint authentication of the user before it begins receiving voice commands. For example, the reception unit authenticates the user's voiceprint before the user issues a voice command. This enhances security. Some or all of the above-described processing in the authentication unit may be performed using, for example, a generating AI, or without a generating AI. For example, the authentication unit can input the user's voiceprint data into a generating AI, which can then perform voiceprint authentication.

[0076] The reception unit receives the user's specified noise, and the removal unit can remove the specified noise that has been received by the reception unit. For example, the reception unit can receive the user's specification of a specific background sound. The removal unit removes the specified background sound. This allows for clearer voice commands by removing the specific noise specified by the user. Some or all of the above processing in the reception unit and removal unit may be performed using, for example, a generating AI, or without a generating AI. For example, the reception unit can input the user's specification to the generating AI, which can receive the noise specification. The removal unit can input the specified noise to the generating AI, which can remove the noise.

[0077] The conversion unit includes a grammar checking unit that performs grammatical checks on the converted text and corrects errors. For example, the conversion unit checks the grammar of the converted text and detects grammatical errors. The conversion unit can also correct the detected grammatical errors. This allows for the provision of more accurate text through grammatical checking. Some or all of the above-described processes in the conversion unit may be performed using, for example, a generation AI, or without a generation AI. For example, the conversion unit can input the converted text into a generation AI, which can then perform grammatical checks and corrections.

[0078] The reception unit can estimate the user's emotions and adjust the timing of voice command reception based on the estimated emotions. For example, if the user is stressed, the reception unit can delay receiving voice commands and wait until the user is relaxed. If the user is relaxed, the reception unit can also receive voice commands immediately and begin processing quickly. Furthermore, if the user is in a hurry, the reception unit can prioritize receiving voice commands and temporarily suspend other processing. This allows for more appropriate timing of voice command reception by adjusting the timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using or without a generative AI. For example, the reception unit can input user emotion data into a generative AI, which can then estimate emotions and adjust reception timing.

[0079] The reception unit can analyze the user's past voice command history and select the optimal receiving method. For example, the reception unit prioritizes receiving voice commands that the user has frequently used in the past. The reception unit can also analyze patterns in the user's past voice command usage and suggest the optimal receiving method. Furthermore, the reception unit can predict commands to be used during specific time periods based on the user's past voice command history and adjust the receiving method accordingly. This allows the reception unit to select the optimal receiving method by analyzing the past voice command history. Some or all of the above processing in the reception unit may be performed using, for example, a generative AI, or without a generative AI. For example, the reception unit can input the past voice command history into a generative AI, which can then analyze the history and select the optimal receiving method.

[0080] The reception unit can filter voice commands based on the user's current activity and environment. For example, if the user is driving, the reception unit can receive only important voice commands and filter out others. The reception unit can also temporarily stop receiving voice commands if the user is in a meeting. Furthermore, if the user is in a quiet environment, the reception unit can receive all voice commands without detailed filtering. This allows for the reception of more appropriate voice commands by filtering them based on the user's activity and environment. Some or all of the above processing in the reception unit may be performed using, for example, a generative AI, or not. For example, the reception unit can input user activity and environment data into a generative AI, which can then perform the filtering.

[0081] The reception unit can estimate the user's emotions and, based on the estimated emotions, specifically determine the priority of incoming voice commands. For example, if the user is stressed, the reception unit will prioritize receiving important voice commands. Conversely, if the user is relaxed, the reception unit can also receive all voice commands equally. Furthermore, if the user is in a hurry, the reception unit can prioritize receiving urgent voice commands. This ensures that important voice commands are received preferentially by determining the priority of voice commands according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the reception unit can input user emotion data into a generative AI, which can then estimate emotions and determine priorities.

[0082] The reception unit can prioritize receiving highly relevant commands by considering the user's geographical location when receiving voice commands. For example, if the user is in a specific location, the reception unit will prioritize receiving voice commands related to that location. The reception unit can also prioritize receiving voice commands related to movement if the user is on the move. Furthermore, if the user is at home, the reception unit can prioritize receiving voice commands related to operations within the home. This allows for the priority reception of highly relevant voice commands by considering the user's geographical location. Some or all of the above processing in the reception unit may be performed using, for example, a generative AI, or without a generative AI. For example, the reception unit can input the user's geographical location information into a generative AI, which can then prioritize receiving highly relevant commands.

[0083] The reception unit can analyze the user's social media activity upon receiving a voice command and receive relevant commands. For example, if the reception unit is discussing a specific topic on social media, it will prioritize receiving voice commands related to that topic. The reception unit can also enhance the reception of voice commands during times when the user is most active on social media. Furthermore, the reception unit can analyze the content of the user's social media posts and suggest relevant voice commands. This allows the reception unit to receive relevant voice commands by analyzing the user's social media activity. Some or all of the above processing in the reception unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the reception unit can input the user's social media activity data into a generative AI, which can then receive relevant commands.

[0084] The noise reduction unit can estimate the user's emotions and adjust the noise reduction intensity based on the estimated emotions. For example, if the user is stressed, the noise reduction unit can increase the noise reduction intensity to provide clear voice commands. The noise reduction unit can also moderately adjust the noise reduction intensity if the user is relaxed. Furthermore, if the user is in a hurry, the noise reduction unit can optimize the noise reduction intensity to process voice commands quickly. This allows for the provision of clear voice commands by adjusting the noise reduction intensity according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the noise reduction unit may be performed using, for example, a generative AI, or not. For example, the noise reduction unit can input user emotion data into a generative AI, which can then estimate emotions and adjust the noise reduction intensity.

[0085] The noise reduction unit can prioritize the removal of specific noises based on the content of the voice command during noise reduction. For example, if the voice command contains important instructions, the noise reduction unit will prioritize the removal of background noise. Furthermore, if the voice command is long, the noise reduction unit can prioritize the removal of specific noises such as wind or car noises. Additionally, if the voice command is short, the noise reduction unit can perform overall noise reduction to provide clear audio. This allows for the provision of clear voice commands by prioritizing the removal of specific noises based on the content of the voice command. Some or all of the above processing in the noise reduction unit may be performed using, for example, a generating AI, or without a generating AI. For example, the noise reduction unit can input the content of the voice command into a generating AI, which can then prioritize the removal of specific noises.

[0086] The noise reduction unit can learn ambient sound patterns and apply the optimal noise reduction algorithm during noise reduction. For example, the unit can learn the sound patterns of environments the user frequently inhabits and apply the most suitable noise reduction algorithm for that environment. Furthermore, if the user is in a new environment, the unit can quickly learn the sound patterns of that environment and perform noise reduction. Additionally, if the user is moving, the unit can learn the ambient sounds of their destination in real time and optimize noise reduction. This allows the unit to apply the optimal noise reduction algorithm by learning ambient sound patterns. Some or all of the above processing in the noise reduction unit may be performed using, for example, a generative AI, or without one. For example, the noise reduction unit can input ambient sound data into a generative AI, which can then learn sound patterns and apply a noise reduction algorithm.

[0087] The noise reduction unit can estimate the user's emotions and specifically adjust the noise reduction order based on the estimated emotions. For example, if the user is stressed, the noise reduction unit will prioritize removing the most bothersome noise. If the user is relaxed, the noise reduction unit can perform overall noise reduction to provide a balanced voice. Furthermore, if the user is in a hurry, the noise reduction unit can quickly remove important noise and process voice commands rapidly. This allows for clear voice commands by adjusting the noise reduction order according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the noise reduction unit may be performed using a generative AI, or not. For example, the noise reduction unit can input user emotion data into a generative AI, which can then estimate emotions and adjust the noise reduction order.

[0088] The noise reduction unit can remove region-specific noise by considering the user's geographical location information during noise reduction. For example, if the user is in an urban area, the noise reduction unit will prioritize removing traffic noise. It can also prioritize removing natural sounds if the user is in a suburban area. Furthermore, if the user is in a specific region, the noise reduction unit can learn and remove region-specific noise. This allows for the removal of region-specific noise by considering the user's geographical location information. Some or all of the above processing in the noise reduction unit may be performed using, for example, a generative AI, or without a generative AI. For example, the noise reduction unit can input the user's geographical location information into a generative AI, which can then remove region-specific noise.

[0089] The noise reduction unit can analyze the user's social media activity and remove relevant noise during noise reduction. For example, if the user is talking about a specific topic on social media, the noise reduction unit will prioritize removing noise related to that topic. The noise reduction unit can also enhance noise reduction during times when the user is most active on social media. Furthermore, the noise reduction unit can analyze the content of the user's social media posts and remove relevant noise. This allows for the removal of relevant noise by analyzing the user's social media activity. Some or all of the above processing in the noise reduction unit may be performed using, for example, a generative AI, or without a generative AI. For example, the noise reduction unit can input the user's social media activity data into a generative AI, which can then remove the relevant noise.

[0090] The conversion unit can estimate the user's emotions and adjust the expression of the text conversion based on the estimated emotions. For example, if the user is stressed, the conversion unit can use a simple and easy-to-understand expression. If the user is relaxed, the conversion unit can also use an expression that includes detailed information. Furthermore, if the user is in a hurry, the conversion unit can use a concise expression that gets straight to the point. By adjusting the expression of the text conversion according to the user's emotions, more appropriate text can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the conversion unit may be performed using a generative AI, or not using a generative AI. For example, the conversion unit can input user emotion data into a generative AI, which can then estimate the emotions and adjust the expression.

[0091] The conversion unit can adjust the level of detail in the conversion based on the importance of the voice command during text conversion. For example, the conversion unit performs detailed text conversion for important voice commands. It can also perform standard text conversion for common voice commands. Furthermore, it can perform concise text conversion for simple voice commands. This allows for appropriate text conversion by adjusting the level of detail based on the importance of the voice command. Some or all of the above processing in the conversion unit may be performed using, for example, a generating AI, or without a generating AI. For example, the conversion unit can input voice command importance data into a generating AI, which can then adjust the level of detail.

[0092] The conversion unit can apply different conversion algorithms depending on the category of the voice command during text conversion. For example, in the case of music-related voice commands, the conversion unit can apply a conversion algorithm specialized for music. Similarly, in the case of navigation-related voice commands, the conversion unit can apply a conversion algorithm specialized for navigation. Furthermore, in the case of message-related voice commands, the conversion unit can apply a conversion algorithm specialized for messages. This allows for appropriate text conversion by applying the optimal conversion algorithm according to the category of the voice command. Some or all of the above processing in the conversion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the conversion unit can input the category data of the voice command into a generative AI, which can then apply a conversion algorithm.

[0093] The translation unit can estimate the user's emotions and adjust the length of the text translation based on the estimated emotions. For example, if the user is stressed, the translation unit can produce a short, concise text translation. If the user is relaxed, the translation unit can produce a longer text translation containing more detailed information. Furthermore, if the user is in a hurry, the translation unit can produce a short text translation that can be processed quickly. By adjusting the length of the text translation according to the user's emotions, appropriate text translation becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the translation unit may be performed using a generative AI, or not. For example, the translation unit can input user emotion data into a generative AI, which can then estimate the emotions and adjust the length of the text translation.

[0094] The conversion unit can determine the conversion priority based on when the voice commands were submitted during text conversion. For example, the conversion unit can prioritize the conversion of the most recently submitted voice commands. It can also prioritize the conversion of voice commands submitted within a specific time period. Furthermore, it can prioritize the conversion of voice commands submitted within a time period specified by the user. This allows for appropriate text conversion by determining the conversion priority based on when the voice commands were submitted. Some or all of the above processing in the conversion unit may be performed using, for example, a generating AI, or without a generating AI. For example, the conversion unit can input voice command submission time data into a generating AI, which can then determine the conversion priority.

[0095] The conversion unit can adjust the order of conversion based on the relevance of the voice commands during text conversion. For example, the conversion unit prioritizes converting important voice commands to text. It can also convert common voice commands to text in a standard manner. Furthermore, it can postpone the conversion of simple voice commands. This allows for appropriate text conversion by adjusting the order of conversion based on the relevance of the voice commands. Some or all of the above processing in the conversion unit may be performed using, for example, a generating AI, or without a generating AI. For example, the conversion unit can input voice command relevance data into a generating AI, which can then adjust the order of conversion.

[0096] The output unit can estimate the user's emotions and adjust the display method of the output based on the estimated emotions. For example, if the user is stressed, the output unit can provide a simple and highly visible display method. If the user is relaxed, the output unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the output unit can provide a concise display method. This allows for appropriate display by adjusting the display method of the output according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the output unit may be performed using a generative AI, or not. For example, the output unit can input user emotion data into a generative AI, which can then estimate the emotions and adjust the display method.

[0097] The output unit can select the optimal display method by referring to the user's past operation history when outputting. For example, the output unit can prioritize providing display methods that the user has used in the past. The output unit can also suggest the optimal display method based on the user's past operation history. Furthermore, the output unit can predict and provide display methods that the user will use during a specific time period. This allows the optimal display method to be selected by referring to the user's past operation history. Some or all of the above processing in the output unit may be performed using, for example, a generation AI, or without a generation AI. For example, the output unit can input past operation history data into a generation AI, which can then select the optimal display method.

[0098] The output unit can customize the output content based on the user's current activity status at the time of output. For example, the output unit can provide a highly visible display method when the user is driving. It can also provide a quiet notification method when the user is in a meeting. Furthermore, it can provide a display method that includes detailed information when the user is relaxing. This allows for appropriate display by customizing the output content based on the user's current activity status. Some or all of the above processing in the output unit may be performed using, for example, a generating AI, or without a generating AI. For example, the output unit can input user activity data into a generating AI, which can then customize the output content.

[0099] The output unit can estimate the user's emotions and specifically determine the priority of the output based on the estimated emotions. For example, if the user is stressed, the output unit can prioritize displaying important output content. If the user is relaxed, the output unit can also display all output content equally. Furthermore, if the user is in a hurry, the output unit can prioritize displaying highly urgent output content. This allows for the prioritization of important output content by determining the priority of the output according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the output unit may be performed using, for example, a generative AI, or not. For example, the output unit can input user emotion data into a generative AI, which can then estimate the emotions and determine the priorities.

[0100] The output unit can select the optimal display method when outputting, taking into account the user's device information. For example, if the user is using a smartphone, the output unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the output unit can provide a display method optimized for a larger screen. Additionally, if the user is using a smartwatch, the output unit can provide a concise and highly visible display method. This allows for the selection of the optimal display method by considering the user's device information. Some or all of the above processing in the output unit may be performed using, for example, a generation AI, or without a generation AI. For example, the output unit can input the user's device information into a generation AI, which can then select the optimal display method.

[0101] The output unit can analyze the user's social media activity and provide relevant output content at the time of output. For example, if the user is talking about a specific topic on social media, the output unit will prioritize displaying output content related to that topic. The output unit can also enhance relevant output content during times when the user is most active on social media. Furthermore, the output unit can analyze the user's social media posts and provide relevant output content. In this way, relevant output content can be provided by analyzing the user's social media activity. Some or all of the above processing in the output unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the output unit can input the user's social media activity data into a generative AI, which can then provide relevant output content.

[0102] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0103] The reception unit can estimate the user's emotions and adjust the timing of voice command reception based on the estimated emotions. For example, if the user is stressed, the reception unit can delay receiving voice commands and wait until the user is relaxed. If the user is relaxed, the reception unit can also receive voice commands immediately and begin processing quickly. Furthermore, if the user is in a hurry, the reception unit can prioritize receiving voice commands and temporarily suspend other processing. This allows for more appropriate timing of voice command reception by adjusting the timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using or without a generative AI. For example, the reception unit can input user emotion data into a generative AI, which can then estimate emotions and adjust reception timing.

[0104] The conversion unit can estimate the user's emotions and adjust the expression of the text conversion based on the estimated emotions. For example, if the user is stressed, the conversion unit can use a simple and easy-to-understand expression. If the user is relaxed, the conversion unit can also use an expression that includes detailed information. Furthermore, if the user is in a hurry, the conversion unit can use a concise expression that gets straight to the point. By adjusting the expression of the text conversion according to the user's emotions, more appropriate text can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the conversion unit may be performed using a generative AI, or not using a generative AI. For example, the conversion unit can input user emotion data into a generative AI, which can then estimate the emotions and adjust the expression.

[0105] The output unit can estimate the user's emotions and adjust the display method of the output based on the estimated emotions. For example, if the user is stressed, the output unit can provide a simple and highly visible display method. If the user is relaxed, the output unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the output unit can provide a concise display method. This allows for appropriate display by adjusting the display method of the output according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the output unit may be performed using a generative AI, or not. For example, the output unit can input user emotion data into a generative AI, which can then estimate the emotions and adjust the display method.

[0106] The noise reduction unit can estimate the user's emotions and adjust the noise reduction intensity based on the estimated emotions. For example, if the user is stressed, the noise reduction unit can increase the noise reduction intensity to provide clear voice commands. The noise reduction unit can also moderately adjust the noise reduction intensity if the user is relaxed. Furthermore, if the user is in a hurry, the noise reduction unit can optimize the noise reduction intensity to process voice commands quickly. This allows for the provision of clear voice commands by adjusting the noise reduction intensity according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the noise reduction unit may be performed using, for example, a generative AI, or not. For example, the noise reduction unit can input user emotion data into a generative AI, which can then estimate emotions and adjust the noise reduction intensity.

[0107] The translation unit can estimate the user's emotions and adjust the length of the text translation based on the estimated emotions. For example, if the user is stressed, the translation unit can produce a short, concise text translation. If the user is relaxed, the translation unit can produce a longer text translation containing more detailed information. Furthermore, if the user is in a hurry, the translation unit can produce a short text translation that can be processed quickly. By adjusting the length of the text translation according to the user's emotions, appropriate text translation becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the translation unit may be performed using a generative AI, or not. For example, the translation unit can input user emotion data into a generative AI, which can then estimate the emotions and adjust the length of the text translation.

[0108] The reception unit can analyze the user's past voice command history and select the optimal receiving method. For example, the reception unit prioritizes receiving voice commands that the user has frequently used in the past. The reception unit can also analyze patterns in the user's past voice command usage and suggest the optimal receiving method. Furthermore, the reception unit can predict commands to be used during specific time periods based on the user's past voice command history and adjust the receiving method accordingly. This allows the reception unit to select the optimal receiving method by analyzing the past voice command history. Some or all of the above processing in the reception unit may be performed using, for example, a generative AI, or without a generative AI. For example, the reception unit can input the past voice command history into a generative AI, which can then analyze the history and select the optimal receiving method.

[0109] The noise reduction unit can prioritize the removal of specific noises based on the content of the voice command during noise reduction. For example, if the voice command contains important instructions, the noise reduction unit will prioritize the removal of background noise. Furthermore, if the voice command is long, the noise reduction unit can prioritize the removal of specific noises such as wind or car noises. Additionally, if the voice command is short, the noise reduction unit can perform overall noise reduction to provide clear audio. This allows for the provision of clear voice commands by prioritizing the removal of specific noises based on the content of the voice command. Some or all of the above processing in the noise reduction unit may be performed using, for example, a generating AI, or without a generating AI. For example, the noise reduction unit can input the content of the voice command into a generating AI, which can then prioritize the removal of specific noises.

[0110] The conversion unit can adjust the level of detail in the conversion based on the importance of the voice command during text conversion. For example, the conversion unit performs detailed text conversion for important voice commands. It can also perform standard text conversion for common voice commands. Furthermore, it can perform concise text conversion for simple voice commands. This allows for appropriate text conversion by adjusting the level of detail based on the importance of the voice command. Some or all of the above processing in the conversion unit may be performed using, for example, a generating AI, or without a generating AI. For example, the conversion unit can input voice command importance data into a generating AI, which can then adjust the level of detail.

[0111] The output unit can select the optimal display method by referring to the user's past operation history when outputting. For example, the output unit can prioritize providing display methods that the user has used in the past. The output unit can also suggest the optimal display method based on the user's past operation history. Furthermore, the output unit can predict and provide display methods that the user will use during a specific time period. This allows the optimal display method to be selected by referring to the user's past operation history. Some or all of the above processing in the output unit may be performed using, for example, a generation AI, or without a generation AI. For example, the output unit can input past operation history data into a generation AI, which can then select the optimal display method.

[0112] The reception unit can prioritize receiving highly relevant commands by considering the user's geographical location when receiving voice commands. For example, if the user is in a specific location, the reception unit will prioritize receiving voice commands related to that location. The reception unit can also prioritize receiving voice commands related to movement if the user is on the move. Furthermore, if the user is at home, the reception unit can prioritize receiving voice commands related to operations within the home. This allows for the priority reception of highly relevant voice commands by considering the user's geographical location. Some or all of the above processing in the reception unit may be performed using, for example, a generative AI, or without a generative AI. For example, the reception unit can input the user's geographical location information into a generative AI, which can then prioritize receiving highly relevant commands.

[0113] The following briefly describes the processing flow for example form 2.

[0114] Step 1: The reception unit receives the user's voice command. For example, if the user utters a voice command such as "Play music," the reception unit receives this voice. The reception unit is equipped with an authentication unit that performs voiceprint authentication of the user, allowing it to authenticate the user before starting to receive voice commands. This enhances security. Step 2: The noise removal unit removes noise from the voice command received by the reception unit. For example, it removes ambient noise and wind noise. The noise removal unit can also accept user specifications for specific noises and remove those specified noises. For example, it can remove specific background noises. Step 3: The conversion unit converts the voice command, from which noise has been removed by the removal unit, into text. For example, the voice command "Play music" is converted into the text "Play music". The conversion unit supports specified languages ​​and dialects and can convert to text in various languages ​​and dialects. For example, it supports languages ​​such as English, French, and Spanish, as well as dialects such as Kansai dialect and Tohoku dialect. The conversion unit includes a language identification unit that identifies the language of the voice command from which noise has been removed and converts it into text corresponding to that language. Furthermore, the conversion unit includes a translation unit that translates the converted text into text in the specified language. The translation unit includes a dialect correction unit that corrects any dialects in the translated text to standard Japanese. Step 4: The output unit outputs the text converted by the conversion unit to the user interface. For example, the text "Play music" is displayed on the smartphone screen. This allows the AI ​​assistant voice recognition support tool for smartphones according to the embodiment to accurately convert the user's voice commands into text and output them to the user interface.

[0115] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0116] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0117] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0118] For example, the reception unit is implemented by the reception device 38 of the smart device 14. For example, the microphone 38B of the reception device 38 receives the user's voice command. The removal unit is implemented by the specific processing unit 290 of the data processing device 12 and performs noise reduction processing. The conversion unit is implemented by the specific processing unit 290 of the data processing device 12 and converts the voice command into text. The output unit is implemented by the output device 40 of the smart device 14 and displays the converted text on the smartphone screen. For example, the display 40A of the smart device 14 displays the converted text. The correspondence between each unit and the devices and control units is not limited to the example described above and can be changed in various ways.

[0119] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0120] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0121] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0122] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0123] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0124] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0125] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0126] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0127] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0128] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0129] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0130] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0131] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0132] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0133] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0134] For example, the reception unit is implemented by the microphone 238 of the smart glasses 214. For example, the microphone 238 receives the user's voice command. The removal unit is implemented by the specific processing unit 290 of the data processing device 12 and performs noise reduction processing. The conversion unit is implemented by the specific processing unit 290 of the data processing device 12 and converts the voice command into text. The output unit is implemented by the speaker 240 of the smart glasses 214 and can also output the converted text as voice. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0135] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0136] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0137] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0138] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0139] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0141] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0142] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0143] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0144] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0145] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0146] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0147] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0148] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0149] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0150] For example, the reception unit is implemented by the microphone 238 of the headset terminal 314. For example, the microphone 238 receives the user's voice command. The removal unit is implemented by the specific processing unit 290 of the data processing device 12 and performs noise reduction processing. The conversion unit is implemented by the specific processing unit 290 of the data processing device 12 and converts the voice command into text. The output unit is implemented by the display 343 of the headset terminal 314 and displays the converted text. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0151] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0152] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0153] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0154] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0155] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0156] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0157] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0158] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0159] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0160] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0161] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0162] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0163] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0164] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0165] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0166] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0167] For example, the reception unit is implemented by the microphone 238 of the robot 414. For example, the microphone 238 receives the user's voice command. The removal unit is implemented by the specific processing unit 290 of the data processing device 12 and performs noise reduction processing. The conversion unit is implemented by the specific processing unit 290 of the data processing device 12 and converts the voice command into text. The output unit is implemented by the speaker 240 of the robot 414 and can also output the converted text as voice. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0168] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0169] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0170] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0171] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0172] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0173] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0174] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0175] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0176] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0177] 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.

[0178] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0179] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0180] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0181] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0182] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0183] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0184] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0185] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0186] (Note 1) A reception area that accepts voice commands, A noise reduction unit that removes noise from the voice command received by the reception unit, A conversion unit that converts the voice command, from which noise has been removed by the aforementioned removal unit, into text, The system includes an output unit that outputs the text converted by the conversion unit to a user interface. A system characterized by the following features. (Note 2) The conversion unit is The voice command, from which noise has been removed by the noise removal unit, is converted into text in the specified language. The system described in Appendix 1, characterized by the features described herein. (Note 3) The conversion unit is The voice command, from which noise has been removed by the aforementioned removal unit, is converted into text in a specified dialect or standard language. The system described in Appendix 1, characterized by the features described herein. (Note 4) The conversion unit is A language identification unit that identifies the language of the voice command from which noise has been removed by the noise removal unit, A text conversion unit converts the voice command, from which noise has been removed by the noise removal unit, into text in a specific language identified by the language identification unit. The system includes a translation unit that translates the text converted by the text conversion unit into text in a specified language. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned translation department, If the translated text contains dialect, it includes a dialect correction unit that corrects the dialect to standard Japanese. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is The system includes an authentication unit that performs user voiceprint authentication before starting to receive the aforementioned voice command. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is Accepts the user's specified noise, The removal section is, The specified noise, which has been received by the reception unit, will be removed. The system described in Appendix 1, characterized by the features described herein. (Note 8) The conversion unit is It includes a grammar checking unit that performs grammatical checks on the converted text and corrects errors. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of voice command reception based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is The system analyzes the user's past voice command history and selects the optimal receiving method. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When voice commands are received, filtering is performed based on the user's current activity status and environment. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is It estimates the user's emotions and, based on those estimated emotions, specifically determines the priority of incoming voice commands. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned reception unit is When receiving voice commands, the system prioritizes receiving commands that are more relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned reception unit is When a voice command is received, the system analyzes the user's social media activity and receives relevant commands. The system described in Appendix 1, characterized by the features described herein. (Note 15) The removal section is, It estimates the user's emotions and adjusts the intensity of noise reduction based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The removal section is, During noise reduction, specific noises are prioritized for removal based on the content of the voice command. The system described in Appendix 1, characterized by the features described herein. (Note 17) The removal section is, During noise reduction, the system learns the patterns of ambient noise and applies the optimal noise reduction algorithm. The system described in Appendix 1, characterized by the features described herein. (Note 18) The removal section is, It estimates the user's emotions and then specifically adjusts the noise reduction order based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The removal section is, During noise reduction, the system takes the user's geographical location information into account to remove noise specific to a particular region. The system described in Appendix 1, characterized by the features described herein. (Note 20) The removal section is, During noise reduction, the system analyzes the user's social media activity and removes relevant noise. The system described in Appendix 1, characterized by the features described herein. (Note 21) The conversion unit is It estimates the user's emotions and adjusts the text conversion method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The conversion unit is When converting to text, adjust the level of detail of the conversion based on the importance of the voice command. The system described in Appendix 1, characterized by the features described herein. (Note 23) The conversion unit is When converting to text, different conversion algorithms are applied depending on the category of the voice command. The system described in Appendix 1, characterized by the features described herein. (Note 24) The conversion unit is It estimates the user's emotions and adjusts the length of the text conversion based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The conversion unit is During text conversion, the conversion priority is determined based on when the voice command was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 26) The conversion unit is When converting to text, the order of conversion is adjusted based on the relevance of the voice commands. The system described in Appendix 1, characterized by the features described herein. (Note 27) The output unit is, It estimates the user's emotions and adjusts the display method of the output specifically based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The output unit is, When outputting data, the system selects the optimal display method by referring to the user's past operation history. The system described in Appendix 1, characterized by the features described herein. (Note 29) The output unit is, When outputting, customize the output content based on the user's current activity status. The system described in Appendix 1, characterized by the features described herein. (Note 30) The output unit is, It estimates the user's emotions and then specifically determines the output priority based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The output unit is, When outputting, the system selects the optimal display method considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 32) The output unit is, When outputting data, the system analyzes the user's social media activity and provides relevant output content. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0187] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception area that accepts voice commands, A noise reduction unit that removes noise from the voice command received by the reception unit, A conversion unit that converts the voice command, from which noise has been removed by the aforementioned removal unit, into text, The system includes an output unit that outputs the text converted by the conversion unit to a user interface. A system characterized by the following features.

2. The conversion unit is The voice command, from which noise has been removed by the noise removal unit, is converted into text in the specified language. The system according to feature 1.

3. The conversion unit is The voice command, from which noise has been removed by the aforementioned removal unit, is converted into text in a specified dialect or standard language. The system according to feature 1.

4. The conversion unit is A language identification unit that identifies the language of the voice command from which noise has been removed by the noise removal unit, A text conversion unit converts the voice command, from which noise has been removed by the noise removal unit, into text in a specific language identified by the language identification unit. The system includes a translation unit that translates the text converted by the text conversion unit into text in a specified language. The system according to feature 1.

5. The aforementioned translation department, If the translated text contains dialect, it includes a dialect correction unit that corrects the dialect to standard Japanese. The system according to feature 4.

6. The aforementioned reception unit is The system includes an authentication unit that performs user voiceprint authentication before starting to receive the aforementioned voice command. The system according to feature 1.

7. The aforementioned reception unit is Accepts the user's specified noise, The removal section is, The specified noise, which has been received by the reception unit, will be removed. The system according to feature 1.

8. The conversion unit is It includes a grammar checking unit that performs grammatical checks on the converted text and corrects errors. The system according to feature 1.

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