System

Abstract

An object of the system according to the embodiment is to enable, in particular, elderly people and people with disabilities to easily access public procedures.SOLUTION: A system according to an embodiment includes a reception part, a AI recognition part, a security protection part, a multilingual support part, and a speech guide part. The reception unit receives a voice input. The voice recognition unit analyzes the voice received by the reception unit and extracts necessary information. The security protection unit ensures the safety of the information extracted by the voice recognition unit. The multilingual support unit converts the information protected by the security protection unit into a specified language. The AI guide unit provides a guide to the user based on the information converted by the multilingual support unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has made it difficult for people, especially the elderly and those with disabilities, to access public procedures.

[0005] The system according to the embodiment aims to make public procedures easily accessible, especially for the elderly and people with disabilities. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a voice recognition unit, a security protection unit, a multilingual support unit, and an AI guide unit. The reception unit receives voice input. The voice recognition unit analyzes the voice received by the reception unit and extracts necessary information. The security protection unit ensures the security of the information extracted by the voice recognition unit. The multilingual support unit converts the information protected by the security protection unit into a specified language. The AI ​​guide unit provides guidance to the user based on the information converted by the multilingual support unit. [Effects of the Invention]

[0007] The system according to the embodiment can make public procedures easily accessible, especially for the elderly and people with disabilities. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system based on an embodiment of the present invention utilizes voice recognition and AI to simplify bank and city hall procedures, making them easily accessible, especially for seniors and people with disabilities. The system provides basic functions including a voice input UI, highly accurate voice recognition, security protection, multilingual support, and a friendly AI guide. For example, the system allows a user to initiate a transaction through the voice input UI. Next, a highly accurate voice recognition module analyzes the user's voice and extracts the necessary information. A security module ensures data security, and a multilingual support module converts the information into the appropriate language. Finally, a friendly AI guide module provides guidance to the user. This provides a user interface optimized for seniors, featuring large text, high-contrast colors, intuitive icons, and voice and text communication. This system can reduce digital fragmentation and aim to realize a society in which everyone can use services equally. For example, this could enhance a company's brand value, create new business opportunities, establish a market leader position, and build a long-term competitive advantage.

[0029] A procedure assistance system according to an embodiment includes a reception unit, a voice recognition unit, a security protection unit, a multilingual support unit, and an AI guide unit. The reception unit receives a user's voice input. For example, the user may vocally input, "I would like to open an account." This voice is received by a voice input UI. The voice recognition unit analyzes the voice received by the reception unit and extracts necessary information. For example, the voice recognition unit recognizes the keyword "open an account" and identifies the necessary procedures. Some or all of the above-described processing in the voice recognition unit may be performed using, for example, AI, or without AI. The security protection unit ensures the security of the information extracted by the voice recognition unit. For example, the security protection unit encrypts the user's personal information to protect it from unauthorized access. Some or all of the above-described processing in the security protection unit may be performed using, for example, AI, or without AI. The multilingual support unit converts information protected by the security protection unit into an appropriate language. For example, the security protection unit converts voice input in Japanese into English to accommodate English-speaking users. Some or all of the above-described processing in the multilingual support unit may be performed using, for example, AI, or may be performed without using AI. The AI ​​guide unit provides guidance to the user based on the information converted by the multilingual support unit. For example, specific instructions such as "Next, please submit the necessary documents" are provided in voice and text. Some or all of the above-described processing in the AI ​​guide unit may be performed using, for example, AI, or may be performed without using AI. As a result, the procedure support system according to the embodiment can simplify procedures through voice input, making them easily accessible, especially for the elderly and people with disabilities.

[0030] The reception unit analyzes the user's past voice input history and selects the optimal reception method. The reception unit, for example, prioritizes reception of phrases that the user has frequently used in the past. The reception unit can also predict and accept phrases that will be used in a specific time period from the user's past voice input history. The reception unit can also analyze the user's past voice input history and suggest the most efficient reception method. In this way, by analyzing the user's past voice input history, the optimal reception method can be selected and the accuracy of voice input can be improved. Some or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or without using AI.

[0031] The reception unit filters the user's current environmental sound to remove noise when receiving a voice input. For example, when the user is in a noisy environment, the reception unit uses AI to filter the environmental sound and remove noise before accepting the voice input. Furthermore, when the user is in a quiet environment, the reception unit can also use AI to filter the environmental sound to improve the accuracy of the voice input. Furthermore, when the user is on the move, the reception unit can also use AI to filter the environmental sound and remove noise before accepting the voice input. In this way, the accuracy of the voice input can be improved by filtering the environmental sound and removing noise. The filtering of the environmental sound is achieved using, for example, noise canceling technology or a filtering algorithm. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI.

[0032] When receiving a voice input, the reception unit selects an appropriate reception means according to the user's input method. For example, if the user selects voice input, the reception unit causes the AI ​​to preferentially receive the voice input. Furthermore, if the user selects text input, the reception unit can also cause the AI ​​to preferentially receive the text input. Furthermore, if the user selects gesture input, the reception unit can also cause the AI ​​to preferentially receive the gesture input. This allows the optimum reception means to be selected according to the user's input method, thereby improving the accuracy of receiving the voice input. Some or all of the above-described processing in the reception unit may be performed, for example, using AI, or may be performed without using AI.

[0033] When receiving a voice input, the reception unit prioritizes receiving a highly relevant input by taking into account the user's geographical location information. For example, when the user is in a specific location, the reception unit uses AI to consider the geographical location information and prioritize receiving a voice input related to the location. Furthermore, when the user is traveling, the reception unit can also use AI to consider the geographical location information and prioritize receiving a voice input related to traveling. Furthermore, when the user is at home, the reception unit can also use AI to consider the geographical location information and prioritize receiving a voice input related to the home. In this way, by taking the user's geographical location information into account, highly relevant voice input can be prioritized. The geographical location information can be acquired using, for example, GPS data or a location information service. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI.

[0034] When receiving a voice input, the reception unit analyzes the user's social media activity and receives related input. For example, the reception unit preferentially receives voice input related to places where the user has checked in on social media. The reception unit can also analyze the content of the user's social media posts and preferentially receive related voice input. The reception unit can also refer to the activities of the user's friends on social media and preferentially receive related voice input. In this way, by analyzing the user's social media activity, it is possible to preferentially receive related voice input. Analysis of social media activity is realized using, for example, the content of posts, the number of likes, comments, etc. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI.

[0035] The reception unit customizes the reception method by reflecting the user's past feedback when receiving voice input. The reception unit proposes an optimal voice input reception method, for example, based on feedback provided by the user in the past. The reception unit can also preferentially accept a specific voice input method based on the user's past feedback. The reception unit can also analyze the user's past feedback and propose the most efficient voice input reception method. In this way, the optimal voice input reception method can be proposed by reflecting the user's past feedback. The reflection of past feedback is realized by using, for example, user ratings, comments, survey results, etc. Some or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI.

[0036] The speech recognition unit adjusts the level of detail of speech recognition based on specific keywords during speech recognition. For example, if a user says "important," the AI ​​in the speech recognition unit recognizes the keyword and performs detailed speech recognition. Furthermore, if a user says "urgent," the AI ​​in the speech recognition unit can recognize the keyword and perform quick speech recognition. Furthermore, if a user says "confirm," the AI ​​in the speech recognition unit can recognize the keyword and perform detailed speech recognition for confirmation. Thus, by adjusting the level of detail of recognition based on specific keywords, the accuracy of speech recognition can be improved. Recognition of specific keywords is performed based on, for example, important words or frequently used words. Some or all of the above-described processing in the speech recognition unit may be performed, for example, using AI, or may be performed without using AI.

[0037] The speech recognition unit learns the user's pronunciation habits during speech recognition to improve recognition accuracy. For example, if the user has a particular pronunciation habit, the AI ​​in the speech recognition unit learns the habit and improves speech recognition accuracy. Furthermore, if the user has a different accent, the AI ​​in the speech recognition unit can learn the accent and improve speech recognition accuracy. Furthermore, if the user prefers a particular phrase, the AI ​​in the speech recognition unit can learn the phrase and improve speech recognition accuracy. In this way, by learning the user's pronunciation habits, the speech recognition accuracy can be improved. Learning pronunciation habits is performed, for example, by analyzing speech data and extracting features. Some or all of the above-mentioned processing in the speech recognition unit may be performed, for example, using AI or without AI.

[0038] During speech recognition, the speech recognition unit improves the accuracy of recognition by referring to the user's past recognition results. For example, the speech recognition unit uses AI to improve speech recognition accuracy based on speech data previously recognized by the user. The speech recognition unit can also learn specific patterns from the user's past recognition results to improve speech recognition accuracy. The speech recognition unit can also analyze the user's past recognition results and suggest the most efficient speech recognition method. In this way, the accuracy of speech recognition can be improved by referring to the user's past recognition results. The reference to past recognition results is performed based on, for example, recognition accuracy and a history of corrections to recognition errors. Some or all of the above-mentioned processing in the speech recognition unit may be performed using AI, for example, or without AI.

[0039] During voice recognition, the voice recognition unit analyzes background sounds of input voice to improve the accuracy of recognition. For example, when the user is in a noisy environment, the voice recognition unit uses AI to analyze the background sounds and remove noise to improve the voice recognition accuracy. Furthermore, when the user is in a quiet environment, the voice recognition unit can also analyze the background sounds and remove noise to improve the voice recognition accuracy. Furthermore, when the user is on the move, the voice recognition unit can also analyze the background sounds and remove noise to improve the voice recognition accuracy. Thus, by analyzing the background sounds of input voice, the voice recognition accuracy can be improved. The analysis of background sounds is performed, for example, using noise level measurement or voice filtering technology. Some or all of the above-described processing in the voice recognition unit may be performed, for example, using AI, or may be performed without using AI.

[0040] During speech recognition, the speech recognition unit adjusts the use of technical terms for recognition according to the user's level of expertise. For example, if the user has specialized knowledge, the speech recognition unit may have the AI ​​use technical terms to perform speech recognition. Alternatively, if the user has general knowledge, the speech recognition unit may have the AI ​​use general terms to perform speech recognition. Alternatively, if the user is a beginner, the speech recognition unit may have the AI ​​use simple terms to perform speech recognition. This allows the accuracy of speech recognition to be improved by adjusting the use of technical terms for recognition according to the user's level of expertise. The adjustment of the use of technical terms is performed, for example, based on the user's occupation and past utterances. Some or all of the above-described processing in the speech recognition unit may be performed, for example, using AI, or may be performed without using AI.

[0041] During voice recognition, the voice recognition unit switches the recognition algorithm according to the user's language setting. For example, if the user sets Japanese, the AI ​​in the voice recognition unit uses a Japanese recognition algorithm. Also, if the user sets English, the AI ​​in the voice recognition unit can use an English recognition algorithm. Also, if the user sets multiple languages, the AI ​​in the voice recognition unit can automatically switch the recognition algorithm for the appropriate language. This allows for improved voice recognition accuracy by switching the recognition algorithm according to the user's language setting. The language setting is switched based on, for example, the user's profile setting or the device's language setting. Some or all of the above-mentioned processing in the voice recognition unit may be performed, for example, using AI, or may be performed without using AI.

[0042] During security protection, the security protection unit analyzes the user's past security history to select the optimal protection method. The security protection unit, for example, suggests the optimal protection method based on security settings used by the user in the past. The security protection unit can also predict specific risks from the user's past security history and select an appropriate protection method. The security protection unit can also analyze the user's past security history to suggest the most effective protection method. In this way, by analyzing the user's past security history, the optimal protection method can be selected and the accuracy of security protection can be improved. The analysis of the past security history is performed, for example, based on records of security incidents and history of protection methods. Some or all of the above-mentioned processing in the security protection unit may be performed, for example, using AI, or may be performed without using AI.

[0043] The security protection unit dynamically changes the encryption algorithm of data during security protection. For example, if the user's data is at high risk, the AI ​​in the security protection unit strengthens the encryption algorithm. In addition, if the user's data is at low risk, the AI ​​in the security protection unit can also return the encryption algorithm to a normal level. In addition, if the user's data meets certain conditions, the AI ​​in the security protection unit can dynamically change the encryption algorithm. In this way, by dynamically changing the data encryption algorithm, the accuracy of security protection can be improved. The dynamic change of the encryption algorithm is performed using encryption technologies such as AES and RSA. Some or all of the above-mentioned processing in the security protection unit may be performed using, for example, AI, or may be performed without using AI.

[0044] The security protection unit performs multi-layered verification of user authentication information during security protection. For example, when a user logs in, the security protection unit uses AI to verify the user's authentication using a combination of password and biometric authentication. The security protection unit can also require two-step authentication when the user performs an important operation. The security protection unit can also require additional authentication information when the user accesses from a new device. This multi-layered verification of user authentication information can improve the accuracy of security protection. Multi-layered verification of authentication information is performed using, for example, two-factor authentication, biometric authentication, password, etc. Some or all of the above-mentioned processing in the security protection unit may be performed using, for example, AI, or may be performed without using AI.

[0045] The security protection unit adjusts the protection level during security protection, taking into account the user's geographical location information. For example, when the user is in a high-risk area, the security protection unit uses AI to consider the geographical location information and increase the security level. Furthermore, when the user is in a low-risk area, the security protection unit can also maintain a normal security level by taking into account the geographical location information. Furthermore, when the user is moving, the security protection unit can also set an appropriate security level by taking into account the geographical location information. In this way, appropriate security protection can be provided by taking into account the user's geographical location information. The geographical location information is acquired using, for example, GPS data or a location information service. Some or all of the above-mentioned processing in the security protection unit may be performed using, for example, AI, or may be performed without using AI.

[0046] During security protection, the security protection unit analyzes the user's device information and selects the optimal protection method. For example, if the user is using a new device, the security protection unit uses AI to analyze the device information and suggest additional security measures. Furthermore, if the user is using an older device, the security protection unit can also use AI to analyze the device information and suggest appropriate security measures. Furthermore, if the user is using a shared device, the security protection unit can also analyze the device information and select the optimal protection method. By analyzing the user's device information, the optimal protection method can be selected and the accuracy of security protection can be improved. The device information is analyzed based on, for example, the device type, OS version, and security settings. Some or all of the above-described processing in the security protection unit may be performed using, for example, AI, or without AI.

[0047] The security protection unit customizes the protection method by reflecting the user's past feedback during security protection. The security protection unit proposes an optimal security protection method, for example, based on feedback provided by the user in the past. The security protection unit can also prioritize the implementation of specific security measures based on the user's past feedback. The security protection unit can also analyze the user's past feedback and propose the most effective security protection method. In this way, the optimal security protection method can be proposed by reflecting the user's past feedback. The reflection of past feedback is performed based on, for example, user ratings, comments, and survey results. Some or all of the above-mentioned processing in the security protection unit may be performed using, for example, AI, or may be performed without using AI.

[0048] The multilingual support unit adjusts the level of detail of the translation based on a specific language pair when supporting multiple languages. For example, when a user translates from Japanese to English, the multilingual support unit uses AI to provide a detailed translation. Furthermore, when a user translates from English to Spanish, the multilingual support unit can also use AI to provide a concise translation. Furthermore, when a user translates from French to German, the multilingual support unit can also use AI to provide a translation with an appropriate level of detail. In this way, by adjusting the level of detail of the translation based on a specific language pair, the accuracy of the translation can be improved. The specific language pair is selected based on, for example, English-Japanese or French-German. Some or all of the above-mentioned processing in the multilingual support unit may be performed using AI, for example, or without AI.

[0049] When providing multilingual support, the multilingual support unit improves translation accuracy by referring to the user's past translation history. For example, the multilingual support unit uses AI to improve translation accuracy based on content previously translated by the user. The multilingual support unit can also learn specific terms from the user's past translation history to improve translation accuracy. The multilingual support unit can also analyze the user's past translation history and suggest the most appropriate translation method. In this way, by referring to the user's past translation history, translation accuracy can be improved. The past translation history is referred to based on, for example, translation results and revision history. Some or all of the above-mentioned processing in the multilingual support unit may be performed, for example, using AI, or may be performed without using AI.

[0050] When providing multilingual support, the multilingual support unit adjusts the use of technical terms in translation according to the user's level of expertise. For example, if the user has technical knowledge, the multilingual support unit may have the AI ​​use technical terms to perform translation. Furthermore, if the user has general knowledge, the multilingual support unit may also have the AI ​​use general terms to perform translation. Furthermore, if the user is a beginner, the multilingual support unit may have the AI ​​use simple terms to perform translation. This allows for the use of technical terms in translation to be adjusted according to the user's level of expertise, thereby improving the accuracy of the translation. The adjustment of the use of technical terms is performed, for example, based on the user's occupation and past comments. Some or all of the above-described processing in the multilingual support unit may be performed using AI, for example, or without AI.

[0051] When supporting multiple languages, the multilingual support unit analyzes background sounds of input speech to improve translation accuracy. For example, when the user is in a noisy environment, the multilingual support unit uses AI to analyze the background sounds and remove noise to improve translation accuracy. Furthermore, when the user is in a quiet environment, the multilingual support unit can also analyze background sounds and remove noise to improve translation accuracy. Furthermore, when the user is on the move, the multilingual support unit can also analyze background sounds and remove noise to improve translation accuracy. Thus, analyzing the background sounds of input speech can improve translation accuracy. The analysis of background sounds is performed, for example, using noise level measurement or voice filtering technology. Some or all of the above-described processing in the multilingual support unit may be performed, for example, using AI, or may be performed without using AI.

[0052] When supporting multiple languages, the multilingual support unit switches the translation algorithm according to the user's language settings. For example, if the user sets Japanese as the language of the multilingual support unit, the AI ​​uses a Japanese translation algorithm. Furthermore, if the user sets English as the language of the multilingual support unit, the AI ​​can also use an English translation algorithm. Furthermore, if the user sets multiple languages, the multilingual support unit can automatically switch the translation algorithm of the appropriate language. This allows for improved translation accuracy by switching the translation algorithm according to the user's language settings. The language settings are switched based on, for example, the user's profile settings or the language settings of the device. Some or all of the above-mentioned processing in the multilingual support unit may be performed, for example, using AI, or may be performed without using AI.

[0053] When providing multilingual support, the multilingual support unit determines the priority of translations by taking into account the user's geographical location information. For example, when the user is in a specific area, the AI ​​in the multilingual support unit takes into account the geographical location information and prioritizes translations related to that area. Furthermore, when the user is traveling, the AI ​​in the multilingual support unit can also take into account the geographical location information and prioritize translations related to the user's travel. Furthermore, when the user is at home, the AI ​​in the multilingual support unit can also take into account the geographical location information and prioritize translations related to the user's home. This allows for appropriate translations to be provided by taking into account the user's geographical location information. The geographical location information is acquired using, for example, GPS data or a location information service. Some or all of the above-described processing in the multilingual support unit may be performed using, for example, AI, or may be performed without using AI.

[0054] When providing guidance, the AI ​​guide unit improves the accuracy of the guidance by referring to the user's past guidance history. For example, the AI ​​guide unit improves the accuracy of the guidance based on the content of the guidance the user has received in the past. The AI ​​guide unit can also learn specific patterns from the user's past guidance history and improve the accuracy of the guidance. The AI ​​guide unit can also analyze the user's past guidance history and suggest the most appropriate guidance method. In this way, the accuracy of the guidance can be improved by referring to the user's past guidance history. The past guidance history is referred to based on, for example, the content of the guidance and the revision history. Some or all of the above-mentioned processing in the AI ​​guide unit may be performed, for example, using AI, or may be performed without using AI.

[0055] When providing a guide, the AI ​​guide unit customizes the guide content according to the user's current task. For example, if the user is performing a bank procedure, the AI ​​guide unit can provide guide content according to the task. Also, if the user is performing a city hall procedure, the AI ​​guide unit can provide guide content according to the task. Also, if the user is shopping online, the AI ​​guide unit can provide guide content according to the task. In this way, by customizing the guide content according to the user's current task, it is possible to provide an appropriate guide. The current task is acquired, for example, based on the user's operation history and the progress of the task. Some or all of the above-mentioned processing in the AI ​​guide unit may be performed, for example, using AI, or may be performed without using AI.

[0056] When providing guidance, the AI ​​guide unit reflects user feedback to improve the guidance method. For example, the AI ​​guide unit improves the guidance method based on feedback previously provided by the user. The AI ​​guide unit can also prioritize providing a specific guidance method based on user feedback. The AI ​​guide unit can also analyze user feedback and suggest the most effective guidance method. In this way, the optimal guidance method can be suggested by reflecting user feedback. Feedback is reflected based on, for example, user ratings, comments, and survey results. Some or all of the above-mentioned processing in the AI ​​guide unit may be performed, for example, using AI, or may be performed without using AI.

[0057] When providing guidance, the AI ​​guide unit selects the optimal guidance method by taking into account the user's geographical location information. For example, when the user is in a specific location, the AI ​​guide unit takes into account the geographical location information and provides guidance related to that location. Furthermore, when the user is traveling, the AI ​​guide unit can also take into account the geographical location information and provide guidance related to the travel. Furthermore, when the user is at home, the AI ​​guide unit can also take into account the geographical location information and provide guidance related to the home. In this way, appropriate guidance can be provided by taking into account the user's geographical location information. The geographical location information is acquired using, for example, GPS data or a location information service. Some or all of the above-mentioned processing in the AI ​​guide unit may be performed, for example, using AI, or may be performed without using AI.

[0058] When providing guidance, the AI ​​guide unit analyzes the user's device information and selects the optimal guidance method. For example, if the user is using a smartphone, the AI ​​guide unit analyzes the device information and provides the optimal guidance method. Furthermore, if the user is using a tablet, the AI ​​guide unit can also analyze the device information and provide the optimal guidance method. Furthermore, if the user is using a smartwatch, the AI ​​guide unit can also analyze the device information and provide the optimal guidance method. In this way, the optimal guidance method can be suggested by analyzing the user's device information. The device information is analyzed based on, for example, the device type, OS version, and security settings. Some or all of the above-mentioned processing in the AI ​​guide unit may be performed using, for example, AI, or may be performed without using AI.

[0059] When providing a guide, the AI ​​guide unit makes the guide content multilingual according to the user's language setting. For example, if the user has set Japanese, the AI ​​guide unit provides the guide in Japanese. Furthermore, if the user has set English, the AI ​​guide unit can also provide the guide in English. Furthermore, if the user has set multiple languages, the AI ​​guide unit can also provide the guide in an appropriate language. This makes it possible to provide an appropriate guide by making the guide content multilingual according to the user's language setting. The language setting is acquired based on, for example, the user's profile setting or the language setting of the device. Some or all of the above-mentioned processing in the AI ​​guide unit may be performed, for example, using AI, or may be performed without using AI.

[0060] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0061] When accepting a user's voice input, the acceptance unit can analyze the user's past behavioral history and select the optimal acceptance method. For example, phrases that the user has frequently used in the past can be preferentially accepted. The acceptance unit can also predict and accept phrases that will be used during a specific time period based on the user's past behavioral history. Furthermore, the acceptance unit can analyze the user's past behavioral history and suggest the most efficient acceptance method. In this way, by analyzing the user's past behavioral history, the optimal acceptance method can be selected and the accuracy of the voice input can be improved. Some or all of the above-described processing in the acceptance unit can be performed, for example, using AI or without using AI.

[0062] The reception unit can also filter the user's current environmental sound to remove noise when receiving the voice input. For example, if the user is in a noisy environment, the AI ​​can filter the environmental sound and remove noise before receiving the voice input. Furthermore, if the user is in a quiet environment, the reception unit can also filter the environmental sound and remove noise before receiving the voice input. Furthermore, if the user is on the move, the reception unit can also filter the environmental sound and remove noise before receiving the voice input. This can improve the accuracy of the voice input by filtering the environmental sound and removing noise. The filtering of the environmental sound is achieved, for example, using noise canceling technology or a filtering algorithm. Some or all of the above-described processing in the reception unit can be performed, for example, using AI, or can be performed without using AI.

[0063] When receiving a voice input, the reception unit can select an appropriate reception means according to the user's input method. For example, if the user selects voice input, the AI ​​can prioritize receiving the voice input. Furthermore, if the user selects text input, the reception unit can also prioritize receiving the text input. Furthermore, if the user selects gesture input, the AI ​​can also prioritize receiving the gesture input. This allows for selecting the optimal reception means according to the user's input method, thereby improving the accuracy of receiving voice input. Some or all of the above-described processing in the reception unit may be performed, for example, using AI, or may be performed without using AI.

[0064] The speech recognition unit can also adjust the level of detail of speech recognition based on specific keywords during speech recognition. For example, if a user says "important," the AI ​​recognizes the keyword and performs detailed speech recognition. Furthermore, if a user says "urgent," the speech recognition unit can also recognize the keyword and perform quick speech recognition. Furthermore, if a user says "confirm," the AI ​​can also recognize the keyword and perform detailed speech recognition for confirmation. This allows the accuracy of speech recognition to be improved by adjusting the level of detail of recognition based on specific keywords. Recognition of specific keywords is performed, for example, based on important words or frequently occurring words. Some or all of the above-described processing in the speech recognition unit may be performed, for example, using AI, or may be performed without using AI.

[0065] During speech recognition, the speech recognition unit can also learn the user's pronunciation habits to improve recognition accuracy. For example, if the user has a particular pronunciation habit, the AI ​​can learn that habit and improve speech recognition accuracy. Also, if the user has a different accent, the speech recognition unit can learn that accent and improve speech recognition accuracy. Furthermore, if the user prefers a particular phrase, the AI ​​can learn that phrase and improve speech recognition accuracy. In this way, by learning the user's pronunciation habits, the speech recognition accuracy can be improved. Learning pronunciation habits is performed, for example, using speech data analysis and feature extraction. Some or all of the above-mentioned processing in the speech recognition unit may be performed, for example, using AI, or may be performed without using AI.

[0066] During speech recognition, the speech recognition unit can also improve the accuracy of recognition by referring to the user's past recognition results. For example, AI improves speech recognition accuracy based on speech data that the user has previously recognized. The speech recognition unit can also learn specific patterns from the user's past recognition results to improve speech recognition accuracy. Furthermore, the speech recognition unit can analyze the user's past recognition results and suggest the most efficient speech recognition method. In this way, the accuracy of speech recognition can be improved by referring to the user's past recognition results. The reference to past recognition results is performed based on, for example, recognition accuracy and a history of corrections to recognition errors. Some or all of the above-mentioned processing in the speech recognition unit may be performed, for example, using AI, or may be performed without using AI.

[0067] The processing flow of the first embodiment will be briefly explained below.

[0068] Step 1: The reception unit receives a voice input from the user. For example, the user inputs "I would like to open an account" by voice. This voice is received by the voice input UI. Step 2: The speech recognition unit analyzes the speech received by the reception unit and extracts the necessary information. For example, it recognizes the keyword "open an account" and identifies the necessary procedures. The processing in the speech recognition unit may be performed using AI or without AI. Step 3: The security protection unit ensures the security of the information extracted by the speech recognition unit. For example, it encrypts the user's personal information to protect it from unauthorized access. The processing in the security protection unit may be performed using AI or without AI. Step 4: The multilingual support unit converts the information protected by the security protection unit into an appropriate language. For example, it converts voice input in Japanese into English to accommodate English-speaking users. The processing in the multilingual support unit may be performed using AI or without AI. Step 5: The AI ​​guide unit provides guidance to the user based on the information converted by the multilingual support unit. For example, specific instructions such as "Next, please submit the necessary documents" are provided in voice and text. The processing in the AI ​​guide unit may be performed using AI or without AI.

[0069] (Example 2) A system based on an embodiment of the present invention utilizes voice recognition and AI to simplify bank and city hall procedures, making them easily accessible, especially for seniors and people with disabilities. The system provides basic functions including a voice input UI, highly accurate voice recognition, security protection, multilingual support, and a friendly AI guide. For example, the system allows a user to initiate a transaction through the voice input UI. Next, a highly accurate voice recognition module analyzes the user's voice and extracts the necessary information. A security module ensures data security, and a multilingual support module converts the information into the appropriate language. Finally, a friendly AI guide module provides guidance to the user. This provides a user interface optimized for seniors, featuring large text, high-contrast colors, intuitive icons, and voice and text communication. This system can reduce digital fragmentation and aim to realize a society in which everyone can use services equally. For example, this could enhance a company's brand value, create new business opportunities, establish a market leader position, and build a long-term competitive advantage.

[0070] A procedure assistance system according to an embodiment includes a reception unit, a voice recognition unit, a security protection unit, a multilingual support unit, and an AI guide unit. The reception unit receives a user's voice input. For example, the user may vocally input, "I would like to open an account." This voice is received by a voice input UI. The voice recognition unit analyzes the voice received by the reception unit and extracts necessary information. For example, the voice recognition unit recognizes the keyword "open an account" and identifies the necessary procedures. Some or all of the above-described processing in the voice recognition unit may be performed using, for example, AI, or without AI. The security protection unit ensures the security of the information extracted by the voice recognition unit. For example, the security protection unit encrypts the user's personal information to protect it from unauthorized access. Some or all of the above-described processing in the security protection unit may be performed using, for example, AI, or without AI. The multilingual support unit converts information protected by the security protection unit into an appropriate language. For example, the security protection unit converts voice input in Japanese into English to accommodate English-speaking users. Some or all of the above-described processing in the multilingual support unit may be performed using, for example, AI, or may be performed without using AI. The AI ​​guide unit provides guidance to the user based on the information converted by the multilingual support unit. For example, specific instructions such as "Next, please submit the necessary documents" are provided in voice and text. Some or all of the above-described processing in the AI ​​guide unit may be performed using, for example, AI, or may be performed without using AI. As a result, the procedure support system according to the embodiment can simplify procedures through voice input, making them easily accessible, especially for the elderly and people with disabilities.

[0071] The reception unit estimates the user's emotion and adjusts the timing of receiving voice input based on the estimated user emotion. For example, if the user is nervous, the reception unit uses AI to estimate the emotion and delay receiving voice input until the user relaxes. Furthermore, if the user is in a hurry, the reception unit can also estimate the emotion and immediately accept voice input. Furthermore, if the user is feeling stressed, the reception unit can also estimate the emotion, temporarily suspend receiving voice input, and provide guidance for relaxation. This allows the timing of receiving voice input to be adjusted according to the user's emotion, thereby enabling voice input to be accepted at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0072] The reception unit analyzes the user's past voice input history and selects the optimal reception method. The reception unit, for example, prioritizes reception of phrases that the user has frequently used in the past. The reception unit can also predict and accept phrases that will be used in a specific time period from the user's past voice input history. The reception unit can also analyze the user's past voice input history and suggest the most efficient reception method. In this way, by analyzing the user's past voice input history, the optimal reception method can be selected and the accuracy of voice input can be improved. Some or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or without using AI.

[0073] The reception unit filters the user's current environmental sound to remove noise when receiving a voice input. For example, when the user is in a noisy environment, the reception unit uses AI to filter the environmental sound and remove noise before accepting the voice input. Furthermore, when the user is in a quiet environment, the reception unit can also use AI to filter the environmental sound to improve the accuracy of the voice input. Furthermore, when the user is on the move, the reception unit can also use AI to filter the environmental sound and remove noise before accepting the voice input. In this way, the accuracy of the voice input can be improved by filtering the environmental sound and removing noise. The filtering of the environmental sound is achieved using, for example, noise canceling technology or a filtering algorithm. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI.

[0074] When receiving a voice input, the reception unit selects an appropriate reception means according to the user's input method. For example, if the user selects voice input, the reception unit causes the AI ​​to preferentially receive the voice input. Furthermore, if the user selects text input, the reception unit can also cause the AI ​​to preferentially receive the text input. Furthermore, if the user selects gesture input, the reception unit can also cause the AI ​​to preferentially receive the gesture input. This allows the optimum reception means to be selected according to the user's input method, thereby improving the accuracy of receiving the voice input. Some or all of the above-described processing in the reception unit may be performed, for example, using AI, or may be performed without using AI.

[0075] The reception unit estimates the user's emotion and determines the priority of voice inputs to be received based on the estimated user emotion. For example, when the user is nervous, the reception unit uses AI to estimate the emotion and prioritizes receiving important voice inputs. Furthermore, when the user is relaxed, the reception unit can also use AI to estimate the emotion and prioritize receiving normal voice inputs. Furthermore, when the user is in a hurry, the reception unit can also use AI to estimate the emotion and prioritize receiving urgent voice inputs. Thus, by determining the priority of voice inputs according to the user's emotion, important voice inputs can be prioritized. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or without AI.

[0076] When receiving a voice input, the reception unit prioritizes receiving a highly relevant input by taking into account the user's geographical location information. For example, when the user is in a specific location, the reception unit uses AI to consider the geographical location information and prioritize receiving a voice input related to the location. Furthermore, when the user is traveling, the reception unit can also use AI to consider the geographical location information and prioritize receiving a voice input related to traveling. Furthermore, when the user is at home, the reception unit can also use AI to consider the geographical location information and prioritize receiving a voice input related to the home. In this way, by taking the user's geographical location information into account, highly relevant voice input can be prioritized. The geographical location information can be acquired using, for example, GPS data or a location information service. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI.

[0077] When receiving a voice input, the reception unit analyzes the user's social media activity and receives related input. For example, the reception unit preferentially receives voice input related to places where the user has checked in on social media. The reception unit can also analyze the content of the user's social media posts and preferentially receive related voice input. The reception unit can also refer to the activities of the user's friends on social media and preferentially receive related voice input. In this way, by analyzing the user's social media activity, it is possible to preferentially receive related voice input. Analysis of social media activity is realized using, for example, the content of posts, the number of likes, comments, etc. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI.

[0078] The reception unit customizes the reception method by reflecting the user's past feedback when receiving voice input. The reception unit proposes an optimal voice input reception method, for example, based on feedback provided by the user in the past. The reception unit can also preferentially accept a specific voice input method based on the user's past feedback. The reception unit can also analyze the user's past feedback and propose the most efficient voice input reception method. In this way, the optimal voice input reception method can be proposed by reflecting the user's past feedback. The reflection of past feedback is realized by using, for example, user ratings, comments, survey results, etc. Some or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI.

[0079] The speech recognition unit estimates the user's emotion and adjusts the accuracy of speech recognition based on the estimated user emotion. For example, when the user is nervous, the speech recognition unit uses AI to estimate the emotion and improve speech recognition accuracy. Furthermore, when the user is relaxed, the speech recognition unit can also estimate the emotion and maintain normal speech recognition accuracy. Furthermore, when the user is in a hurry, the speech recognition unit can also estimate the emotion and quickly adjust the speech recognition accuracy. This allows the speech recognition accuracy to be improved by adjusting the speech recognition accuracy according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the speech recognition unit may be performed using, for example, AI, or without AI.

[0080] The speech recognition unit adjusts the level of detail of speech recognition based on specific keywords during speech recognition. For example, if a user says "important," the AI ​​in the speech recognition unit recognizes the keyword and performs detailed speech recognition. Furthermore, if a user says "urgent," the AI ​​in the speech recognition unit can recognize the keyword and perform quick speech recognition. Furthermore, if a user says "confirm," the AI ​​in the speech recognition unit can recognize the keyword and perform detailed speech recognition for confirmation. Thus, by adjusting the level of detail of recognition based on specific keywords, the accuracy of speech recognition can be improved. Recognition of specific keywords is performed based on, for example, important words or frequently used words. Some or all of the above-described processing in the speech recognition unit may be performed, for example, using AI, or may be performed without using AI.

[0081] The speech recognition unit learns the user's pronunciation habits during speech recognition to improve recognition accuracy. For example, if the user has a particular pronunciation habit, the AI ​​in the speech recognition unit learns the habit and improves speech recognition accuracy. Furthermore, if the user has a different accent, the AI ​​in the speech recognition unit can learn the accent and improve speech recognition accuracy. Furthermore, if the user prefers a particular phrase, the AI ​​in the speech recognition unit can learn the phrase and improve speech recognition accuracy. In this way, by learning the user's pronunciation habits, the speech recognition accuracy can be improved. Learning pronunciation habits is performed, for example, by analyzing speech data and extracting features. Some or all of the above-mentioned processing in the speech recognition unit may be performed, for example, using AI or without AI.

[0082] During speech recognition, the speech recognition unit improves the accuracy of recognition by referring to the user's past recognition results. For example, the speech recognition unit uses AI to improve speech recognition accuracy based on speech data previously recognized by the user. The speech recognition unit can also learn specific patterns from the user's past recognition results to improve speech recognition accuracy. The speech recognition unit can also analyze the user's past recognition results and suggest the most efficient speech recognition method. In this way, the accuracy of speech recognition can be improved by referring to the user's past recognition results. The reference to past recognition results is performed based on, for example, recognition accuracy and a history of corrections to recognition errors. Some or all of the above-mentioned processing in the speech recognition unit may be performed using AI, for example, or without AI.

[0083] The speech recognition unit estimates the user's emotion and adjusts the speech recognition speed based on the estimated user emotion. For example, if the user is nervous, the AI ​​in the speech recognition unit estimates the emotion and slows down the speech recognition speed. Alternatively, if the user is relaxed, the AI ​​in the speech recognition unit can estimate the emotion and maintain a normal speech recognition speed. Alternatively, if the user is in a hurry, the AI ​​in the speech recognition unit can estimate the emotion and speed up the speech recognition speed. This allows the speech recognition speed to be optimized by adjusting the speech recognition speed according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the speech recognition unit may be performed using, for example, an AI, or may be performed without using an AI.

[0084] During voice recognition, the voice recognition unit analyzes background sounds of input voice to improve the accuracy of recognition. For example, when the user is in a noisy environment, the voice recognition unit uses AI to analyze the background sounds and remove noise to improve the voice recognition accuracy. Furthermore, when the user is in a quiet environment, the voice recognition unit can also analyze the background sounds and remove noise to improve the voice recognition accuracy. Furthermore, when the user is on the move, the voice recognition unit can also analyze the background sounds and remove noise to improve the voice recognition accuracy. Thus, by analyzing the background sounds of input voice, the voice recognition accuracy can be improved. The analysis of background sounds is performed, for example, using noise level measurement or voice filtering technology. Some or all of the above-described processing in the voice recognition unit may be performed, for example, using AI, or may be performed without using AI.

[0085] During speech recognition, the speech recognition unit adjusts the use of technical terms for recognition according to the user's level of expertise. For example, if the user has specialized knowledge, the speech recognition unit may have the AI ​​use technical terms to perform speech recognition. Alternatively, if the user has general knowledge, the speech recognition unit may have the AI ​​use general terms to perform speech recognition. Alternatively, if the user is a beginner, the speech recognition unit may have the AI ​​use simple terms to perform speech recognition. This allows the accuracy of speech recognition to be improved by adjusting the use of technical terms for recognition according to the user's level of expertise. The adjustment of the use of technical terms is performed, for example, based on the user's occupation and past utterances. Some or all of the above-described processing in the speech recognition unit may be performed, for example, using AI, or may be performed without using AI.

[0086] During voice recognition, the voice recognition unit switches the recognition algorithm according to the user's language setting. For example, if the user sets Japanese, the AI ​​in the voice recognition unit uses a Japanese recognition algorithm. Also, if the user sets English, the AI ​​in the voice recognition unit can use an English recognition algorithm. Also, if the user sets multiple languages, the AI ​​in the voice recognition unit can automatically switch the recognition algorithm for the appropriate language. This allows for improved voice recognition accuracy by switching the recognition algorithm according to the user's language setting. The language setting is switched based on, for example, the user's profile setting or the device's language setting. Some or all of the above-mentioned processing in the voice recognition unit may be performed, for example, using AI, or may be performed without using AI.

[0087] The security protection unit estimates the user's emotions and adjusts the security level based on the estimated user emotions. For example, if the user is nervous, the security protection unit uses AI to estimate the user's emotions and increase the security level. Furthermore, if the user is relaxed, the security protection unit can also estimate the user's emotions and maintain a normal security level. Furthermore, if the user is in a hurry, the security protection unit can also estimate the user's emotions and perform a quick security check. This allows appropriate security protection to be provided by adjusting the security level according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the security protection unit may be performed using, for example, AI, or without AI.

[0088] During security protection, the security protection unit analyzes the user's past security history to select the optimal protection method. The security protection unit, for example, suggests the optimal protection method based on security settings used by the user in the past. The security protection unit can also predict specific risks from the user's past security history and select an appropriate protection method. The security protection unit can also analyze the user's past security history to suggest the most effective protection method. In this way, by analyzing the user's past security history, the optimal protection method can be selected and the accuracy of security protection can be improved. The analysis of the past security history is performed, for example, based on records of security incidents and history of protection methods. Some or all of the above-mentioned processing in the security protection unit may be performed, for example, using AI, or may be performed without using AI.

[0089] The security protection unit dynamically changes the encryption algorithm of data during security protection. For example, if the user's data is at high risk, the AI ​​in the security protection unit strengthens the encryption algorithm. In addition, if the user's data is at low risk, the AI ​​in the security protection unit can also return the encryption algorithm to a normal level. In addition, if the user's data meets certain conditions, the AI ​​in the security protection unit can dynamically change the encryption algorithm. In this way, by dynamically changing the data encryption algorithm, the accuracy of security protection can be improved. The dynamic change of the encryption algorithm is performed using encryption technologies such as AES and RSA. Some or all of the above-mentioned processing in the security protection unit may be performed using, for example, AI, or may be performed without using AI.

[0090] The security protection unit performs multi-layered verification of user authentication information during security protection. For example, when a user logs in, the security protection unit uses AI to verify the user's authentication using a combination of password and biometric authentication. The security protection unit can also require two-step authentication when the user performs an important operation. The security protection unit can also require additional authentication information when the user accesses from a new device. This multi-layered verification of user authentication information can improve the accuracy of security protection. Multi-layered verification of authentication information is performed using, for example, two-factor authentication, biometric authentication, password, etc. Some or all of the above-mentioned processing in the security protection unit may be performed using, for example, AI, or may be performed without using AI.

[0091] The security protection unit estimates the user's emotions and adjusts the frequency of security notifications based on the estimated user emotions. For example, if the user is nervous, the security protection unit uses AI to estimate the user's emotions and reduce the frequency of security notifications. Alternatively, if the user is relaxed, the security protection unit can also use AI to estimate the user's emotions and maintain a normal frequency of security notifications. Alternatively, if the user is in a hurry, the security protection unit can also use AI to estimate the user's emotions and send only important security notifications. This allows appropriate security notifications to be provided by adjusting the frequency of security notifications according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the security protection unit may be performed using, for example, AI, or without AI.

[0092] The security protection unit adjusts the protection level during security protection, taking into account the user's geographical location information. For example, when the user is in a high-risk area, the security protection unit uses AI to consider the geographical location information and increase the security level. Furthermore, when the user is in a low-risk area, the security protection unit can also maintain a normal security level by taking into account the geographical location information. Furthermore, when the user is moving, the security protection unit can also set an appropriate security level by taking into account the geographical location information. In this way, appropriate security protection can be provided by taking into account the user's geographical location information. The geographical location information is acquired using, for example, GPS data or a location information service. Some or all of the above-mentioned processing in the security protection unit may be performed using, for example, AI, or may be performed without using AI.

[0093] During security protection, the security protection unit analyzes the user's device information and selects the optimal protection method. For example, if the user is using a new device, the security protection unit uses AI to analyze the device information and suggest additional security measures. Furthermore, if the user is using an older device, the security protection unit can also use AI to analyze the device information and suggest appropriate security measures. Furthermore, if the user is using a shared device, the security protection unit can also analyze the device information and select the optimal protection method. By analyzing the user's device information, the optimal protection method can be selected and the accuracy of security protection can be improved. The device information is analyzed based on, for example, the device type, OS version, and security settings. Some or all of the above-described processing in the security protection unit may be performed using, for example, AI, or without AI.

[0094] The security protection unit customizes the protection method by reflecting the user's past feedback during security protection. The security protection unit proposes an optimal security protection method, for example, based on feedback provided by the user in the past. The security protection unit can also prioritize the implementation of specific security measures based on the user's past feedback. The security protection unit can also analyze the user's past feedback and propose the most effective security protection method. In this way, the optimal security protection method can be proposed by reflecting the user's past feedback. The reflection of past feedback is performed based on, for example, user ratings, comments, and survey results. Some or all of the above-mentioned processing in the security protection unit may be performed using, for example, AI, or may be performed without using AI.

[0095] The multilingual support unit estimates the user's emotions and adjusts the translation expression method based on the estimated user emotions. For example, if the user is nervous, the multilingual support unit uses AI to estimate the user's emotions and provide a concise and easy-to-understand translation. Furthermore, if the user is relaxed, the multilingual support unit can also estimate the user's emotions and provide a detailed translation. Furthermore, if the user is in a hurry, the multilingual support unit can also estimate the user's emotions and provide a quick translation. This allows for an appropriate translation to be provided by adjusting the translation expression method according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the multilingual support unit may be performed using, for example, AI, or without AI.

[0096] The multilingual support unit adjusts the level of detail of the translation based on a specific language pair when supporting multiple languages. For example, when a user translates from Japanese to English, the multilingual support unit uses AI to provide a detailed translation. Furthermore, when a user translates from English to Spanish, the multilingual support unit can also use AI to provide a concise translation. Furthermore, when a user translates from French to German, the multilingual support unit can also use AI to provide a translation with an appropriate level of detail. In this way, by adjusting the level of detail of the translation based on a specific language pair, the accuracy of the translation can be improved. The specific language pair is selected based on, for example, English-Japanese or French-German. Some or all of the above-mentioned processing in the multilingual support unit may be performed using AI, for example, or without AI.

[0097] When providing multilingual support, the multilingual support unit improves translation accuracy by referring to the user's past translation history. For example, the multilingual support unit uses AI to improve translation accuracy based on content previously translated by the user. The multilingual support unit can also learn specific terms from the user's past translation history to improve translation accuracy. The multilingual support unit can also analyze the user's past translation history and suggest the most appropriate translation method. In this way, by referring to the user's past translation history, translation accuracy can be improved. The past translation history is referred to based on, for example, translation results and revision history. Some or all of the above-mentioned processing in the multilingual support unit may be performed, for example, using AI, or may be performed without using AI.

[0098] When providing multilingual support, the multilingual support unit adjusts the use of technical terms in translation according to the user's level of expertise. For example, if the user has technical knowledge, the multilingual support unit may have the AI ​​use technical terms to perform translation. Furthermore, if the user has general knowledge, the multilingual support unit may also have the AI ​​use general terms to perform translation. Furthermore, if the user is a beginner, the multilingual support unit may have the AI ​​use simple terms to perform translation. This allows for the use of technical terms in translation to be adjusted according to the user's level of expertise, thereby improving the accuracy of the translation. The adjustment of the use of technical terms is performed, for example, based on the user's occupation and past comments. Some or all of the above-described processing in the multilingual support unit may be performed using AI, for example, or without AI.

[0099] The multilingual support unit estimates the user's emotions and adjusts the translation speed based on the estimated user emotions. For example, if the user is nervous, the AI ​​in the multilingual support unit estimates the user's emotions and slows down the translation speed. Alternatively, if the user is relaxed, the AI ​​in the multilingual support unit can estimate the user's emotions and maintain a normal translation speed. Alternatively, if the user is in a hurry, the AI ​​in the multilingual support unit can estimate the user's emotions and speed up the translation speed. This allows for appropriate translation by adjusting the translation speed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the multilingual support unit may be performed using, for example, an AI, or may be performed without using an AI.

[0100] When supporting multiple languages, the multilingual support unit analyzes background sounds of input speech to improve translation accuracy. For example, when the user is in a noisy environment, the multilingual support unit uses AI to analyze the background sounds and remove noise to improve translation accuracy. Furthermore, when the user is in a quiet environment, the multilingual support unit can also analyze background sounds and remove noise to improve translation accuracy. Furthermore, when the user is on the move, the multilingual support unit can also analyze background sounds and remove noise to improve translation accuracy. Thus, analyzing the background sounds of input speech can improve translation accuracy. The analysis of background sounds is performed, for example, using noise level measurement or voice filtering technology. Some or all of the above-described processing in the multilingual support unit may be performed, for example, using AI, or may be performed without using AI.

[0101] When supporting multiple languages, the multilingual support unit switches the translation algorithm according to the user's language settings. For example, if the user sets Japanese as the language of the multilingual support unit, the AI ​​uses a Japanese translation algorithm. Furthermore, if the user sets English as the language of the multilingual support unit, the AI ​​can also use an English translation algorithm. Furthermore, if the user sets multiple languages, the multilingual support unit can automatically switch the translation algorithm of the appropriate language. This allows for improved translation accuracy by switching the translation algorithm according to the user's language settings. The language settings are switched based on, for example, the user's profile settings or the language settings of the device. Some or all of the above-mentioned processing in the multilingual support unit may be performed, for example, using AI, or may be performed without using AI.

[0102] When providing multilingual support, the multilingual support unit determines the priority of translations by taking into account the user's geographical location information. For example, when the user is in a specific area, the AI ​​in the multilingual support unit takes into account the geographical location information and prioritizes translations related to that area. Furthermore, when the user is traveling, the AI ​​in the multilingual support unit can also take into account the geographical location information and prioritize translations related to the user's travel. Furthermore, when the user is at home, the AI ​​in the multilingual support unit can also take into account the geographical location information and prioritize translations related to the user's home. This allows for appropriate translations to be provided by taking into account the user's geographical location information. The geographical location information is acquired using, for example, GPS data or a location information service. Some or all of the above-described processing in the multilingual support unit may be performed using, for example, AI, or may be performed without using AI.

[0103] The AI ​​guide unit estimates the user's emotions and adjusts the way the guidance is expressed based on the estimated user emotions. For example, if the user is nervous, the AI ​​guide unit estimates the user's emotions and provides guidance in a calm manner. Furthermore, if the user is relaxed, the AI ​​guide unit can estimate the user's emotions and provide guidance in a cheerful manner. Furthermore, if the user is in a hurry, the AI ​​guide unit can estimate the user's emotions and provide guidance in a quick and concise manner. This allows appropriate guidance to be provided by adjusting the way the guidance is expressed according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the AI ​​guide unit may be performed using, for example, an AI, or may be performed without using an AI.

[0104] When providing guidance, the AI ​​guide unit improves the accuracy of the guidance by referring to the user's past guidance history. For example, the AI ​​guide unit improves the accuracy of the guidance based on the content of the guidance the user has received in the past. The AI ​​guide unit can also learn specific patterns from the user's past guidance history and improve the accuracy of the guidance. The AI ​​guide unit can also analyze the user's past guidance history and suggest the most appropriate guidance method. In this way, the accuracy of the guidance can be improved by referring to the user's past guidance history. The past guidance history is referred to based on, for example, the content of the guidance and the revision history. Some or all of the above-mentioned processing in the AI ​​guide unit may be performed, for example, using AI, or may be performed without using AI.

[0105] When providing a guide, the AI ​​guide unit customizes the guide content according to the user's current task. For example, if the user is performing a bank procedure, the AI ​​guide unit can provide guide content according to the task. Also, if the user is performing a city hall procedure, the AI ​​guide unit can provide guide content according to the task. Also, if the user is shopping online, the AI ​​guide unit can provide guide content according to the task. In this way, by customizing the guide content according to the user's current task, it is possible to provide an appropriate guide. The current task is acquired, for example, based on the user's operation history and the progress of the task. Some or all of the above-mentioned processing in the AI ​​guide unit may be performed, for example, using AI, or may be performed without using AI.

[0106] When providing guidance, the AI ​​guide unit reflects user feedback to improve the guidance method. For example, the AI ​​guide unit improves the guidance method based on feedback previously provided by the user. The AI ​​guide unit can also prioritize providing a specific guidance method based on user feedback. The AI ​​guide unit can also analyze user feedback and suggest the most effective guidance method. In this way, the optimal guidance method can be suggested by reflecting user feedback. Feedback is reflected based on, for example, user ratings, comments, and survey results. Some or all of the above-mentioned processing in the AI ​​guide unit may be performed, for example, using AI, or may be performed without using AI.

[0107] The AI ​​guide unit estimates the user's emotions and determines the priority of guidance based on the estimated user emotions. For example, if the user is nervous, the AI ​​guide unit estimates the user's emotions and prioritizes providing important guidance. Furthermore, if the user is relaxed, the AI ​​guide unit can estimate the user's emotions and prioritize providing regular guidance. Furthermore, if the user is in a hurry, the AI ​​guide unit can estimate the user's emotions and prioritize providing urgent guidance. This allows important guidance to be prioritized by determining the priority of guidance based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the AI ​​guide unit may be performed using, for example, an AI, or may be performed without using an AI.

[0108] When providing guidance, the AI ​​guide unit selects the optimal guidance method by taking into account the user's geographical location information. For example, when the user is in a specific location, the AI ​​guide unit takes into account the geographical location information and provides guidance related to that location. Furthermore, when the user is traveling, the AI ​​guide unit can also take into account the geographical location information and provide guidance related to the travel. Furthermore, when the user is at home, the AI ​​guide unit can also take into account the geographical location information and provide guidance related to the home. In this way, appropriate guidance can be provided by taking into account the user's geographical location information. The geographical location information is acquired using, for example, GPS data or a location information service. Some or all of the above-mentioned processing in the AI ​​guide unit may be performed, for example, using AI, or may be performed without using AI.

[0109] When providing guidance, the AI ​​guide unit analyzes the user's device information and selects the optimal guidance method. For example, if the user is using a smartphone, the AI ​​guide unit analyzes the device information and provides the optimal guidance method. Furthermore, if the user is using a tablet, the AI ​​guide unit can also analyze the device information and provide the optimal guidance method. Furthermore, if the user is using a smartwatch, the AI ​​guide unit can also analyze the device information and provide the optimal guidance method. In this way, the optimal guidance method can be suggested by analyzing the user's device information. The device information is analyzed based on, for example, the device type, OS version, and security settings. Some or all of the above-mentioned processing in the AI ​​guide unit may be performed using, for example, AI, or may be performed without using AI.

[0110] When providing a guide, the AI ​​guide unit makes the guide content multilingual according to the user's language setting. For example, if the user has set Japanese, the AI ​​guide unit provides the guide in Japanese. Furthermore, if the user has set English, the AI ​​guide unit can also provide the guide in English. Furthermore, if the user has set multiple languages, the AI ​​guide unit can also provide the guide in an appropriate language. This makes it possible to provide an appropriate guide by making the guide content multilingual according to the user's language setting. The language setting is acquired based on, for example, the user's profile setting or the language setting of the device. Some or all of the above-mentioned processing in the AI ​​guide unit may be performed, for example, using AI, or may be performed without using AI. === Hard Collateral 1-1 === Each of the multiple elements, including the reception unit, voice recognition unit, security protection unit, multilingual support unit, and AI guide unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14 and receives voice input from the user. The voice recognition unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the voice and extracts necessary information. The security protection unit is realized by the specific processing unit 290 of the data processing device 12 and ensures data security. The multilingual support unit is realized by the specific processing unit 290 of the data processing device 12 and converts the voice into an appropriate language. The AI ​​guide unit is realized by the control unit 46A of the smart device 14 and provides guidance to the user. === Hard Collateral 1-2 === Each of the multiple elements, including the reception unit, voice recognition unit, security protection unit, multilingual support unit, and AI guide unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and receives voice input from the user. The voice recognition unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the voice and extracts necessary information. The security protection unit is realized by the specific processing unit 290 of the data processing device 12 and ensures data security. The multilingual support unit is realized by the specific processing unit 290 of the data processing device 12 and converts the voice into an appropriate language. The AI ​​guide unit is realized by the control unit 46A of the smart glasses 214 and provides guidance to the user. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, voice recognition unit, security protection unit, multilingual support unit, and AI guide unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset-type terminal 314 and receives voice input from the user. The voice recognition unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the voice and extracts necessary information. The security protection unit is realized by the specific processing unit 290 of the data processing device 12 and ensures data security. The multilingual support unit is realized by the specific processing unit 290 of the data processing device 12 and converts the voice into an appropriate language. The AI ​​guide unit is realized by the control unit 46A of the headset-type terminal 314 and provides guidance to the user. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, voice recognition unit, security protection unit, multilingual support unit, and AI guide unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414 and receives voice input from the user. The voice recognition unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the voice and extracts necessary information. The security protection unit is realized by the specific processing unit 290 of the data processing device 12 and ensures the security of data. The multilingual support unit is realized by the specific processing unit 290 of the data processing device 12 and converts the voice into an appropriate language. The AI ​​guide unit is realized by the control unit 46A of the robot 414 and provides guidance to the user.

[0111] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0112] When accepting a user's voice input, the acceptance unit can analyze the user's past behavioral history and select the optimal acceptance method. For example, phrases that the user has frequently used in the past can be preferentially accepted. The acceptance unit can also predict and accept phrases that will be used during a specific time period based on the user's past behavioral history. Furthermore, the acceptance unit can analyze the user's past behavioral history and suggest the most efficient acceptance method. In this way, by analyzing the user's past behavioral history, the optimal acceptance method can be selected and the accuracy of the voice input can be improved. Some or all of the above-described processing in the acceptance unit can be performed, for example, using AI or without using AI.

[0113] The reception unit can also estimate the user's emotions and adjust the timing of voice input reception based on the estimated user emotions. For example, if the user is nervous, the AI ​​can estimate the user's emotions and delay the reception of voice input until the user relaxes. Furthermore, if the user is in a hurry, the AI ​​can estimate the user's emotions and immediately accept the voice input. Furthermore, if the user is feeling stressed, the AI ​​can estimate the user's emotions, temporarily suspend the reception of voice input, and provide guidance for relaxation. This allows the voice input reception timing to be adjusted according to the user's emotions, thereby enabling the voice input to be received at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0114] The reception unit can also filter the user's current environmental sound to remove noise when receiving the voice input. For example, if the user is in a noisy environment, the AI ​​can filter the environmental sound and remove noise before receiving the voice input. Furthermore, if the user is in a quiet environment, the reception unit can also filter the environmental sound and remove noise before receiving the voice input. Furthermore, if the user is on the move, the reception unit can also filter the environmental sound and remove noise before receiving the voice input. This can improve the accuracy of the voice input by filtering the environmental sound and removing noise. The filtering of the environmental sound is achieved, for example, using noise canceling technology or a filtering algorithm. Some or all of the above-described processing in the reception unit can be performed, for example, using AI, or can be performed without using AI.

[0115] When receiving a voice input, the reception unit can select an appropriate reception means according to the user's input method. For example, if the user selects voice input, the AI ​​can prioritize receiving the voice input. Furthermore, if the user selects text input, the reception unit can also prioritize receiving the text input. Furthermore, if the user selects gesture input, the AI ​​can also prioritize receiving the gesture input. This allows for selecting the optimal reception means according to the user's input method, thereby improving the accuracy of receiving voice input. Some or all of the above-described processing in the reception unit may be performed, for example, using AI, or may be performed without using AI.

[0116] The reception unit can also estimate the user's emotions and determine the priority of voice inputs to be received based on the estimated user emotions. For example, if the user is nervous, the AI ​​can estimate the user's emotions and prioritize important voice inputs. The reception unit can also estimate the user's emotions and prioritize normal voice inputs if the user is relaxed. Furthermore, if the user is in a hurry, the AI ​​can estimate the user's emotions and prioritize urgent voice inputs. This allows the priority of voice inputs to be determined according to the user's emotions, thereby prioritizing important voice inputs. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the reception unit can be performed, for example, using AI, or without AI.

[0117] The speech recognition unit can also estimate the user's emotions and adjust the accuracy of speech recognition based on the estimated user emotions. For example, if the user is nervous, the AI ​​can estimate the user's emotions and improve the accuracy of speech recognition. Furthermore, if the user is relaxed, the AI ​​can estimate the user's emotions and maintain normal speech recognition accuracy. Furthermore, if the user is in a hurry, the AI ​​can estimate the user's emotions and quickly adjust the accuracy of speech recognition. This allows the accuracy of speech recognition to be improved by adjusting the accuracy of speech recognition according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the speech recognition unit can be performed using, for example, an AI, or without an AI.

[0118] The speech recognition unit can also adjust the level of detail of speech recognition based on specific keywords during speech recognition. For example, if a user says "important," the AI ​​recognizes the keyword and performs detailed speech recognition. Furthermore, if a user says "urgent," the speech recognition unit can also recognize the keyword and perform quick speech recognition. Furthermore, if a user says "confirm," the AI ​​can also recognize the keyword and perform detailed speech recognition for confirmation. This allows the accuracy of speech recognition to be improved by adjusting the level of detail of recognition based on specific keywords. Recognition of specific keywords is performed, for example, based on important words or frequently occurring words. Some or all of the above-described processing in the speech recognition unit may be performed, for example, using AI, or may be performed without using AI.

[0119] During speech recognition, the speech recognition unit can also learn the user's pronunciation habits to improve recognition accuracy. For example, if the user has a particular pronunciation habit, the AI ​​can learn that habit and improve speech recognition accuracy. Also, if the user has a different accent, the speech recognition unit can learn that accent and improve speech recognition accuracy. Furthermore, if the user prefers a particular phrase, the AI ​​can learn that phrase and improve speech recognition accuracy. In this way, by learning the user's pronunciation habits, the speech recognition accuracy can be improved. Learning pronunciation habits is performed, for example, using speech data analysis and feature extraction. Some or all of the above-mentioned processing in the speech recognition unit may be performed, for example, using AI, or may be performed without using AI.

[0120] During speech recognition, the speech recognition unit can also improve the accuracy of recognition by referring to the user's past recognition results. For example, AI improves speech recognition accuracy based on speech data that the user has previously recognized. The speech recognition unit can also learn specific patterns from the user's past recognition results to improve speech recognition accuracy. Furthermore, the speech recognition unit can analyze the user's past recognition results and suggest the most efficient speech recognition method. In this way, the accuracy of speech recognition can be improved by referring to the user's past recognition results. The reference to past recognition results is performed based on, for example, recognition accuracy and a history of corrections to recognition errors. Some or all of the above-mentioned processing in the speech recognition unit may be performed, for example, using AI, or may be performed without using AI.

[0121] The speech recognition unit can also estimate the user's emotion and adjust the speech recognition speed based on the estimated user emotion. For example, if the user is nervous, the AI ​​can estimate the emotion and slow down the speech recognition speed. Furthermore, if the user is relaxed, the AI ​​can estimate the emotion and maintain a normal speech recognition speed. Furthermore, if the user is in a hurry, the AI ​​can estimate the emotion and speed up the speech recognition speed. This allows the speech recognition speed to be optimized by adjusting the speech recognition speed according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the speech recognition unit can be performed using, for example, an AI, or without an AI.

[0122] The processing flow of the second embodiment will be briefly explained below.

[0123] Step 1: The reception unit receives a voice input from the user. For example, the user inputs "I would like to open an account" by voice. This voice is received by the voice input UI. Step 2: The speech recognition unit analyzes the speech received by the reception unit and extracts the necessary information. For example, it recognizes the keyword "open an account" and identifies the necessary procedures. The processing in the speech recognition unit may be performed using AI or without AI. Step 3: The security protection unit ensures the security of the information extracted by the speech recognition unit. For example, it encrypts the user's personal information to protect it from unauthorized access. The processing in the security protection unit may be performed using AI or without AI. Step 4: The multilingual support unit converts the information protected by the security protection unit into an appropriate language. For example, it converts voice input in Japanese into English to accommodate English-speaking users. The processing in the multilingual support unit may be performed using AI or without AI. Step 5: The AI ​​guide unit provides guidance to the user based on the information converted by the multilingual support unit. For example, specific instructions such as "Next, please submit the necessary documents" are provided in voice and text. The processing in the AI ​​guide unit may be performed using AI or without AI.

[0124] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0125] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0126] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0127] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0128] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0129] 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.

[0130] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0131] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0132] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0133] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0134] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0135] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0136] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0137] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0138] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0139] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0140] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0141] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0142] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0143] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0144] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0145] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0146] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0147] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0148] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0149] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0150] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0151] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0152] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0153] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0154] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0155] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0156] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0157] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0158] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0159] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0160] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0161] 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.

[0162] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0163] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0164] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0165] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0166] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0167] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0168] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0169] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0170] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0171] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0172] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0173] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0174] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0175] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0176] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0177] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0178] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0179] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0180] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0181] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0182] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0183] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0184] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0185] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0186] 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.

[0187] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0188] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0189] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0190] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0191] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0192] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0193] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0194] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0195] [Explanation of symbols]

[0196] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives voice input; a speech recognition unit that analyzes the speech received by the reception unit and extracts necessary information; a security protection unit that ensures the security of the information extracted by the speech recognition unit; a multilingual support unit that converts the information protected by the security protection unit into a specified language; an AI guide unit that provides a guide to a user based on the information converted by the multilingual support unit; A system characterized by:

2. The reception unit Estimates the user's emotions and adjusts the timing of voice input acceptance based on the estimated user emotions. The system of claim 1 .

3. The reception unit Analyze the user's past voice input history and select the optimal reception method The system of claim 1 .

4. The reception unit When accepting voice input, filters the user's current ambient sounds to remove noise The system of claim 1 .

5. The reception unit When accepting voice input, select the appropriate acceptance method depending on the user's input method. The system of claim 1 .

6. The reception unit Estimate the user's emotions and prioritize the voice inputs to be accepted based on the estimated user emotions. The system of claim 1 .

7. The reception unit When accepting voice input, prioritize relevant input based on the user's geographic location. The system of claim 1 .

8. The reception unit When receiving voice input, analyze the user's social media activity and receive related input. The system of claim 1 .

Images