System

Abstract

An object of a system according to an embodiment is to realize a payment procedure by voice.SOLUTION: A system includes a voice recognition unit, a natural language processing unit, and a settlement processing unit. The voice recognition unit recognizes a voice of a user. The natural language processing unit analyzes the voice recognized by the voice recognition unit. The settlement processing unit performs a settlement procedure based on the content analyzed by the natural language processing 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] With conventional technology, payments using QR codes were the norm, and there was an issue that voice-based payment procedures were not fully implemented.

[0005] The system according to the embodiment aims to realize a payment procedure using voice. [Means for solving the problem]

[0006] The system according to the embodiment includes a speech recognition unit, a natural language processing unit, and a payment processing unit. The speech recognition unit recognizes a user's speech. The natural language processing unit analyzes the speech recognized by the speech recognition unit. The payment processing unit performs a payment procedure based on the content analyzed by the natural language processing unit. [Effects of the Invention]

[0007] The system according to the embodiment can realize a payment procedure by voice. [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) The voice payment system according to an embodiment of the present invention recognizes the user's voice, analyzes it using a generation AI, and then performs the payment procedure. This enables payments by voice without using a QR code, providing a smooth and convenient payment experience.

[0029] The voice payment system according to the embodiment includes a voice recognition unit, a natural language processing unit, and a payment processing unit. The voice recognition unit recognizes a user's voice. For example, the voice recognition unit converts the voice into text using an acoustic model. The voice recognition unit can also analyze the voice using deep learning technology. The voice recognition unit can also recognize the voice using a language model. For example, the voice recognition unit extracts voice features using the acoustic model and converts them into text. Deep learning technology learns from large amounts of voice data to achieve highly accurate voice recognition. The language model understands the context of the voice and performs accurate text conversion. The natural language processing unit analyzes the voice recognized by the voice recognition unit. For example, the natural language processing unit analyzes the content of the voice using morphological analysis. The natural language processing unit can also analyze the structure of the voice using grammatical analysis. The natural language processing unit can also understand the meaning of the voice using semantic analysis. For example, the natural language processing unit uses morphological analysis to divide and analyze the voice into words. Grammatical analysis analyzes the grammatical structure of the voice to understand the meaning. The semantic analysis understands the context of the voice and performs accurate analysis. The payment processing unit performs payment procedures based on the content analyzed by the natural language processing unit. For example, the payment processing unit performs credit card payments. The payment processing unit can also perform electronic money payments. The payment processing unit can also perform bank transfers. For example, the payment processing unit inputs credit card information and performs payments. Electronic money payments are performed using electronic money account information. Bank transfers are performed using bank account information. This allows the voice payment system according to the embodiment to enable payments by voice without using a QR code, providing a smooth and comfortable payment experience. For example, if a user says, "PayPay, please," the generation AI automatically performs the payment procedures, and if the user says "OK," the payment is completed. In this way, efficient payments are achieved by eliminating the need to exchange QR codes.

[0030] The natural language processing unit can refer to the user's past conversation history and learn individual language patterns. For example, the natural language processing unit refers to the conversation history of when the user previously said, "PayPay, please," and the generation AI learns that pattern. For example, if the user uses the same phrase again, it responds quickly. The natural language processing unit also analyzes the user's past conversation history and learns specific language patterns and phrases. For example, it prioritizes recognizing phrases that the user frequently uses, improving the accuracy of natural language processing. The natural language processing unit also learns individual language patterns based on the user's past conversation history, allowing the generation AI to more accurately understand the user's intentions. For example, if the user prefers a specific phrase, it prioritizes recognizing that pattern. This improves the accuracy of natural language processing based on the user's past conversation history.

[0031] The natural language processing unit can perform multi-stage analysis based on context. For example, when a user says, "Pay with PayPay, please," the generation AI uses speech recognition technology to convert the speech into text and then performs multi-stage analysis taking the context into account. For example, if the user has used the same phrase in the past, the AI ​​can refer to that context. Furthermore, when analyzing a user's utterances, the generation AI performs multi-stage analysis taking the context into account to more accurately understand the user's intent. For example, if a user says, "Pay with PayPay, please," and then responds with "OK," the AI ​​can proceed with the payment process based on that context. Furthermore, the natural language processing unit combines speech recognition and natural language processing to perform multi-stage analysis taking the context into account to more accurately understand the user's intent. For example, if a user says, "Pay with PayPay, please," and then responds with "Cancel," the AI ​​can cancel the payment process based on that context. This multi-stage analysis taking the context into account allows the AI ​​to more accurately understand the user's intent.

[0032] The voice recognition unit can authenticate the user's voiceprint and perform personal authentication. For example, when a user says, "Pay with PayPay, please," the generation AI uses voice recognition technology to authenticate the user's voiceprint and perform personal authentication. For example, the payment process will proceed only if the user's voiceprint is registered. The voice recognition unit will also use voice recognition technology to build a system that authenticates the user's voiceprint and performs personal authentication. For example, when a user says "OK," voiceprint authentication will be performed to enhance security. The voice recognition unit will also develop a system that authenticates the user's voiceprint and performs personal authentication to enhance security. For example, when a user says, "Pay with PayPay, please," voiceprint authentication will be performed to prevent fraudulent payments. In this way, security is enhanced by authenticating the user's voiceprint.

[0033] The natural language processing unit can build an automatic response system to user questions, thereby streamlining customer support. For example, when a user says, "I'd like to pay with PayPay," the generation AI uses natural language processing technology to generate an automatic response to the question. For example, it could reply, "Payment will be XX yen via PayPay. If that's okay, please say OK." The natural language processing unit also uses natural language processing technology to build an automatic response system to user questions, thereby streamlining customer support. For example, when a user says, "How do I pay?", it automatically responds. The natural language processing unit can build an automatic response system to user questions, thereby streamlining customer support. For example, when a user says, "I'd like to cancel," it automatically responds. This can improve the efficiency of customer support.

[0034] The payment processing unit can analyze the user's purchase history and automatically suggest the optimal payment method for the next purchase. For example, the payment processing unit builds a system that analyzes the user's purchase history and automatically suggests the optimal payment method for the next purchase. For example, if the user has used PayPay in the past, PayPay will be suggested next time as well. In addition, to further automate payment procedures, the payment processing unit uses a generation AI to analyze the user's purchase history and suggest the optimal payment method. For example, if the user often uses credit cards, a credit card will be suggested. In addition, the payment processing unit develops a system that automatically suggests the optimal payment method for the next purchase based on the user's purchase history. For example, if the user often uses electronic money, electronic money will be suggested. This makes it possible to suggest the optimal payment method based on the user's purchase history.

[0035] The payment processing unit can process multiple payment methods simultaneously, allowing the user to select the most advantageous payment method. The payment processing unit, for example, processes multiple payment methods simultaneously during payment procedures, building a system that allows the user to select the most advantageous payment method. For example, it processes credit cards, electronic money, bank transfers, etc. simultaneously. The payment processing unit also develops a system that processes multiple payment methods simultaneously so that the user can select the most advantageous payment method. For example, it proposes a payment method that allows the user to earn the most points. The payment processing unit also processes multiple payment methods simultaneously during payment procedures, and the generation AI analyzes multiple payment methods to allow the user to select the most advantageous payment method. For example, it proposes the payment method with the lowest fees. This allows the user to select the most advantageous payment method.

[0036] The payment processing unit uses the technology for automating payment procedures to build an automatic payment system for an online shopping site, thereby improving user convenience. The payment processing unit, for example, uses the technology for automating payment procedures to build an automatic payment system for an online shopping site. For example, after a user adds a product to their cart, the payment processing unit automatically performs the payment procedure. Furthermore, the payment processing unit builds an automatic payment system for an online shopping site, and the generation AI utilizes the technology for automating payment procedures to improve user convenience. For example, the payment procedure is automatically performed when a user says "purchase." Furthermore, the payment processing unit uses the technology for automating payment procedures to build an automatic payment system for an online shopping site, thereby developing a system that improves user convenience. For example, the payment procedure is automatically performed when a user says "OK." As a result, automatic payment on an online shopping site improves user convenience.

[0037] The payment processing unit can use the payment procedure automation technology to automate public transportation fare payments and improve passenger convenience. The payment processing unit, for example, uses the payment procedure automation technology to build a system that automates public transportation fare payments. For example, the fare is automatically paid when a passenger says, "Please pay." The generation AI also utilizes the payment procedure automation technology to automate public transportation fare payments and improve passenger convenience. For example, the fare is automatically paid when a passenger says, "OK." The payment processing unit also uses the payment procedure automation technology to develop a system that automates public transportation fare payments and improve passenger convenience. For example, the fare is automatically paid when a passenger says, "I'd like to pay with PayPay." This improves passenger convenience through the automation of public transportation fare payments.

[0038] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0039] The natural language processing unit can refer to the user's past conversation history and learn individual language patterns. For example, the generation AI can refer to the conversation history of when the user previously said, "PayPay please," and learn that pattern. If the user uses the same phrase again, it can respond quickly. It can also analyze the user's past conversation history and learn specific language patterns and phrases. It prioritizes recognition of phrases frequently used by users, improving the accuracy of natural language processing. Furthermore, based on the user's past conversation history, the generation AI can learn individual language patterns to more accurately understand the user's intentions. If the user prefers a particular phrase, it will prioritize recognition of that pattern. This improves the accuracy of natural language processing based on the user's past conversation history.

[0040] The natural language processing unit can perform multi-stage analysis based on context. For example, when a user says, "Pay with Pay, please," the generation AI uses speech recognition technology to convert the speech into text and then performs multi-stage analysis taking the context into account. If the user has used the same phrase in the past, that context is referenced. Furthermore, when analyzing a user's utterances, the generation AI performs multi-stage analysis taking the context into account to more accurately understand the user's intent. If a user says "Pay with Pay, please" and then "OK," the payment process will proceed based on that context. Furthermore, by combining speech recognition and natural language processing, the generation AI performs multi-stage analysis taking the context into account to more accurately understand the user's intent. If a user says "Cancel" after saying "Pay with Pay, please," the payment process will be canceled based on that context. This multi-stage analysis taking the context into account allows for a more accurate understanding of the user's intent.

[0041] The voice recognition unit can authenticate the user's voiceprint and perform personal authentication. For example, when a user says, "PayPay, please," the generation AI uses voice recognition technology to authenticate the user's voiceprint and perform personal authentication. The payment process will proceed only if the user's voiceprint is registered. We will also build a system that uses voice recognition technology to authenticate the user's voiceprint and perform personal authentication. When the user says "OK," voiceprint authentication will be performed to strengthen security. We will also develop a system that strengthens security by authenticating the user's voiceprint and performing personal authentication. When the user says, "PayPay, please," voiceprint authentication will be performed to prevent fraudulent payments. In this way, security will be strengthened by authenticating the user's voiceprint.

[0042] Natural language processing can build an automatic response system for user questions, making customer support more efficient. For example, when a user says, "I'd like to pay with PayPay," the generation AI uses natural language processing technology to generate an automatic response to that question. It replies, "Payment will be XX yen with PayPay. If that's okay, please say OK." Natural language processing technology can also be used to build an automatic response system for user questions, making customer support more efficient. When a user says, "Please tell me how to pay," it automatically responds. Furthermore, to build an automatic response system for user questions and make customer support more efficient, the generation AI utilizes natural language processing technology. When a user says, "I'd like to cancel," it automatically responds. This can make customer support more efficient.

[0043] The payment processing unit can analyze the user's purchase history and automatically suggest the optimal payment method for the next purchase. For example, we will build a system that analyzes the user's purchase history and automatically suggests the optimal payment method for the next purchase. If the user has used PayPay in the past, PayPay will be suggested for the next purchase as well. In addition, to further automate the payment process, the generation AI will analyze the user's purchase history and suggest the optimal payment method. If the user frequently uses credit cards, a credit card will be suggested. Furthermore, we will develop a system that automatically suggests the optimal payment method for the next purchase based on the user's purchase history. If the user frequently uses electronic money, electronic money will be suggested. This makes it possible to suggest the optimal payment method based on the user's purchase history.

[0044] The payment processing unit can process multiple payment methods simultaneously, allowing the user to select the most advantageous payment method. For example, we will build a system that processes multiple payment methods simultaneously during payment procedures, allowing the user to select the most advantageous payment method. It will process credit cards, electronic money, bank transfers, etc. simultaneously. We will also develop a system that processes multiple payment methods simultaneously so that the user can select the most advantageous payment method. It will propose a payment method that allows the user to earn the most points. Furthermore, in order to process multiple payment methods simultaneously during payment procedures and allow the user to select the most advantageous payment method, the generation AI will analyze multiple payment methods. It will propose the payment method with the lowest fees. This will allow the user to select the most advantageous payment method.

[0045] The processing flow of the first embodiment will be briefly explained below.

[0046] Step 1: The speech recognition unit recognizes the user's speech. For example, the speech recognition unit uses an acoustic model to convert speech to text. It can also analyze and recognize speech using deep learning technology and language models. The acoustic model extracts speech features and converts them into text. Deep learning technology learns from large amounts of speech data to achieve highly accurate speech recognition. The language model understands the context of the speech and performs accurate text conversion. Step 2: The natural language processing unit analyzes the speech recognized by the speech recognition unit. For example, it can use morphological analysis to analyze the content of the speech, and grammatical analysis to analyze the structure of the speech. It can also use semantic analysis to understand the meaning of the speech. Morphological analysis divides and analyzes the words in the speech. Grammatical analysis analyzes the grammatical structure of the speech and understands the meaning. Semantic analysis understands the context of the speech and performs accurate analysis. Step 3: The payment processing unit performs the payment procedure based on the content analyzed by the natural language processing unit. For example, credit card payment, electronic money payment, bank transfer, etc. can be performed. Credit card information is entered and payment is made. For electronic money payment, payment is made using electronic money account information. For bank transfer, payment is made using bank account information. This makes it possible to make payments by voice without using a QR code, providing a smooth and comfortable payment experience.

[0047] (Example 2) The voice payment system according to an embodiment of the present invention recognizes the user's voice, analyzes it using a generation AI, and then performs the payment procedure. This enables payments by voice without using a QR code, providing a smooth and convenient payment experience.

[0048] The voice payment system according to the embodiment includes a voice recognition unit, a natural language processing unit, and a payment processing unit. The voice recognition unit recognizes a user's voice. For example, the voice recognition unit converts the voice into text using an acoustic model. The voice recognition unit can also analyze the voice using deep learning technology. The voice recognition unit can also recognize the voice using a language model. For example, the voice recognition unit extracts voice features using the acoustic model and converts them into text. Deep learning technology learns from large amounts of voice data to achieve highly accurate voice recognition. The language model understands the context of the voice and performs accurate text conversion. The natural language processing unit analyzes the voice recognized by the voice recognition unit. For example, the natural language processing unit analyzes the content of the voice using morphological analysis. The natural language processing unit can also analyze the structure of the voice using grammatical analysis. The natural language processing unit can also understand the meaning of the voice using semantic analysis. For example, the natural language processing unit uses morphological analysis to divide and analyze the voice into words. Grammatical analysis analyzes the grammatical structure of the voice to understand the meaning. The semantic analysis understands the context of the voice and performs accurate analysis. The payment processing unit performs payment procedures based on the content analyzed by the natural language processing unit. For example, the payment processing unit performs credit card payments. The payment processing unit can also perform electronic money payments. The payment processing unit can also perform bank transfers. For example, the payment processing unit inputs credit card information and performs payments. Electronic money payments are performed using electronic money account information. Bank transfers are performed using bank account information. This allows the voice payment system according to the embodiment to enable payments by voice without using a QR code, providing a smooth and comfortable payment experience. For example, if a user says, "PayPay, please," the generation AI automatically performs the payment procedures, and if the user says "OK," the payment is completed. In this way, efficient payments are achieved by eliminating the need to exchange QR codes.

[0049] The speech recognition unit analyzes the tone and speed of the user's voice and uses the emotion estimation function to understand the user's emotional state and generate an appropriate response. For example, when a user says, "Pay with Pay, please," the generation AI not only converts the speech to text using speech recognition technology, but also analyzes the tone and speed of the voice to determine whether the user is in a hurry. For example, if the user is in a hurry, the generation AI generates a quick response. The speech recognition unit also analyzes the tone and speed of the user's voice and uses the emotion estimation function to determine whether the user is stressed. For example, if the user is stressed, the generation AI responds in a calm tone to reassure the user. The speech recognition unit also analyzes the tone and speed of the user's voice and uses the emotion estimation function to determine whether the user is happy. For example, if the user is happy, the generation AI generates a positive response, increasing the user's satisfaction. This enables appropriate responses to be provided according to the user's emotional state.

[0050] The natural language processing unit can refer to the user's past conversation history and learn individual language patterns. For example, the natural language processing unit refers to the conversation history of when the user previously said, "PayPay, please," and the generation AI learns that pattern. For example, if the user uses the same phrase again, it responds quickly. The natural language processing unit also analyzes the user's past conversation history and learns specific language patterns and phrases. For example, it prioritizes recognizing phrases that the user frequently uses, improving the accuracy of natural language processing. The natural language processing unit also learns individual language patterns based on the user's past conversation history, allowing the generation AI to more accurately understand the user's intentions. For example, if the user prefers a specific phrase, it prioritizes recognizing that pattern. This improves the accuracy of natural language processing based on the user's past conversation history.

[0051] The natural language processing unit can perform multi-stage analysis based on context. For example, when a user says, "Pay with PayPay, please," the generation AI uses speech recognition technology to convert the speech into text and then performs multi-stage analysis taking the context into account. For example, if the user has used the same phrase in the past, the AI ​​can refer to that context. Furthermore, when analyzing a user's utterances, the generation AI performs multi-stage analysis taking the context into account to more accurately understand the user's intent. For example, if a user says, "Pay with PayPay, please," and then responds with "OK," the AI ​​can proceed with the payment process based on that context. Furthermore, the natural language processing unit combines speech recognition and natural language processing to perform multi-stage analysis taking the context into account to more accurately understand the user's intent. For example, if a user says, "Pay with PayPay, please," and then responds with "Cancel," the AI ​​can cancel the payment process based on that context. This multi-stage analysis taking the context into account allows the AI ​​to more accurately understand the user's intent.

[0052] The voice recognition unit can authenticate the user's voiceprint and perform personal authentication. For example, when a user says, "Pay with PayPay, please," the generation AI uses voice recognition technology to authenticate the user's voiceprint and perform personal authentication. For example, the payment process will proceed only if the user's voiceprint is registered. The voice recognition unit will also use voice recognition technology to build a system that authenticates the user's voiceprint and performs personal authentication. For example, when a user says "OK," voiceprint authentication will be performed to enhance security. The voice recognition unit will also develop a system that authenticates the user's voiceprint and performs personal authentication to enhance security. For example, when a user says, "Pay with PayPay, please," voiceprint authentication will be performed to prevent fraudulent payments. In this way, security is enhanced by authenticating the user's voiceprint.

[0053] The natural language processing unit can build an automatic response system to user questions, thereby streamlining customer support. For example, when a user says, "I'd like to pay with PayPay," the generation AI uses natural language processing technology to generate an automatic response to the question. For example, it could reply, "Payment will be XX yen via PayPay. If that's okay, please say OK." The natural language processing unit also uses natural language processing technology to build an automatic response system to user questions, thereby streamlining customer support. For example, when a user says, "How do I pay?", it automatically responds. The natural language processing unit can build an automatic response system to user questions, thereby streamlining customer support. For example, when a user says, "I'd like to cancel," it automatically responds. This can improve the efficiency of customer support.

[0054] The natural language processing unit can use the emotion estimation function to develop a system that automatically recommends music and media content according to a user's emotions. For example, when a user says, "Pay with PayPay, please," the generation AI uses the emotion estimation function to analyze the user's emotions and recommends music and media content according to those emotions. For example, if the user is relaxed, relaxing music is recommended. The natural language processing unit also uses the emotion estimation function to develop a system that automatically recommends music and media content according to a user's emotions. For example, if the user is feeling stressed, relaxing music is recommended. The natural language processing unit also uses the emotion estimation function to develop a system that automatically recommends music and media content according to a user's emotions. For example, if the user is happy, positive music is recommended. This makes it possible to recommend music and media content according to a user's emotions.

[0055] The payment processing unit can use the emotion estimation function to understand the user's emotional state and provide an interface for reducing stress. For example, when a user says, "Pay with PayPay, please," the generation AI uses the emotion estimation function to understand the user's emotional state and provide an interface for reducing stress. For example, if the user is feeling stressed, the generation AI responds in a calm tone. The payment processing unit also uses the emotion estimation function to understand the user's emotional state during payment procedures and builds a system that provides an interface for reducing stress. For example, the generation AI plays music that helps the user relax. The payment processing unit also uses the emotion estimation function to understand the user's emotional state and provide an interface for reducing stress. For example, the generation AI displays a message that puts the user at ease. This provides an interface that corresponds to the user's emotional state, thereby reducing stress.

[0056] The payment processing unit can analyze the user's purchase history and automatically suggest the optimal payment method for the next purchase. For example, the payment processing unit builds a system that analyzes the user's purchase history and automatically suggests the optimal payment method for the next purchase. For example, if the user has used PayPay in the past, PayPay will be suggested next time as well. In addition, to further automate payment procedures, the payment processing unit uses a generation AI to analyze the user's purchase history and suggest the optimal payment method. For example, if the user often uses credit cards, a credit card will be suggested. In addition, the payment processing unit develops a system that automatically suggests the optimal payment method for the next purchase based on the user's purchase history. For example, if the user often uses electronic money, electronic money will be suggested. This makes it possible to suggest the optimal payment method based on the user's purchase history.

[0057] The payment processing unit can process multiple payment methods simultaneously, allowing the user to select the most advantageous payment method. The payment processing unit, for example, processes multiple payment methods simultaneously during payment procedures, building a system that allows the user to select the most advantageous payment method. For example, it processes credit cards, electronic money, bank transfers, etc. simultaneously. The payment processing unit also develops a system that processes multiple payment methods simultaneously so that the user can select the most advantageous payment method. For example, it proposes a payment method that allows the user to earn the most points. The payment processing unit also processes multiple payment methods simultaneously during payment procedures, and the generation AI analyzes multiple payment methods to allow the user to select the most advantageous payment method. For example, it proposes the payment method with the lowest fees. This allows the user to select the most advantageous payment method.

[0058] The payment processing unit uses the technology for automating payment procedures to build an automatic payment system for an online shopping site, thereby improving user convenience. The payment processing unit, for example, uses the technology for automating payment procedures to build an automatic payment system for an online shopping site. For example, after a user adds a product to their cart, the payment processing unit automatically performs the payment procedure. Furthermore, the payment processing unit builds an automatic payment system for an online shopping site, and the generation AI utilizes the technology for automating payment procedures to improve user convenience. For example, the payment procedure is automatically performed when a user says "purchase." Furthermore, the payment processing unit uses the technology for automating payment procedures to build an automatic payment system for an online shopping site, thereby developing a system that improves user convenience. For example, the payment procedure is automatically performed when a user says "OK." As a result, automatic payment on an online shopping site improves user convenience.

[0059] The payment processing unit can use the payment procedure automation technology to automate public transportation fare payments and improve passenger convenience. The payment processing unit, for example, uses the payment procedure automation technology to build a system that automates public transportation fare payments. For example, the fare is automatically paid when a passenger says, "Please pay." The generation AI also utilizes the payment procedure automation technology to automate public transportation fare payments and improve passenger convenience. For example, the fare is automatically paid when a passenger says, "OK." The payment processing unit also uses the payment procedure automation technology to develop a system that automates public transportation fare payments and improve passenger convenience. For example, the fare is automatically paid when a passenger says, "I'd like to pay with PayPay." This improves passenger convenience through the automation of public transportation fare payments.

[0060] The payment processing unit can use the emotion estimation function to build a feedback system for providing a payment procedure that will satisfy the user most. The payment processing unit, for example, uses the emotion estimation function to build a feedback system for providing a payment procedure that will satisfy the user most. For example, the payment processing unit analyzes the user's emotional response and suggests an optimal payment procedure. The payment processing unit also develops a feedback system for providing a payment procedure that will satisfy the user most based on the user's emotional response. For example, when the user says "OK," the emotion estimation function is used to evaluate satisfaction. The payment processing unit also uses the emotion estimation function to build a feedback system for providing a payment procedure that will satisfy the user most. The generation AI analyzes the user's emotional response in real time to build a feedback system for providing a payment procedure that will satisfy the user most. For example, when the user says "PayPay, please," the emotion estimation function is used to evaluate satisfaction. This makes it possible to provide a payment procedure that will satisfy the user most.

[0061] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0062] The speech recognition unit analyzes the tone and speed of the user's voice and uses the emotion estimation function to understand the user's emotional state and generate an appropriate response. For example, when a user says, "Pay with Pay, please," the generation AI not only converts the speech to text using speech recognition technology, but also analyzes the tone and speed of the voice to determine whether the user is in a hurry. If the user is in a hurry, it generates a quick response. The generation AI also analyzes the tone and speed of the user's voice and uses the emotion estimation function to determine whether the user is stressed. If the user is stressed, it responds in a calm tone to reassure the user. The generation AI also analyzes the tone and speed of the user's voice and uses the emotion estimation function to determine whether the user is happy. If the user is happy, the generation AI generates a positive response, increasing the user's satisfaction. This enables appropriate responses to be provided according to the user's emotional state.

[0063] The natural language processing unit can refer to the user's past conversation history and learn individual language patterns. For example, the generation AI can refer to the conversation history of when the user previously said, "PayPay please," and learn that pattern. If the user uses the same phrase again, it can respond quickly. It can also analyze the user's past conversation history and learn specific language patterns and phrases. It prioritizes recognition of phrases frequently used by users, improving the accuracy of natural language processing. Furthermore, based on the user's past conversation history, the generation AI can learn individual language patterns to more accurately understand the user's intentions. If the user prefers a particular phrase, it will prioritize recognition of that pattern. This improves the accuracy of natural language processing based on the user's past conversation history.

[0064] The natural language processing unit can perform multi-stage analysis based on context. For example, when a user says, "Pay with Pay, please," the generation AI uses speech recognition technology to convert the speech into text and then performs multi-stage analysis taking the context into account. If the user has used the same phrase in the past, that context is referenced. Furthermore, when analyzing a user's utterances, the generation AI performs multi-stage analysis taking the context into account to more accurately understand the user's intent. If a user says "Pay with Pay, please" and then "OK," the payment process will proceed based on that context. Furthermore, by combining speech recognition and natural language processing, the generation AI performs multi-stage analysis taking the context into account to more accurately understand the user's intent. If a user says "Cancel" after saying "Pay with Pay, please," the payment process will be canceled based on that context. This multi-stage analysis taking the context into account allows for a more accurate understanding of the user's intent.

[0065] The voice recognition unit can authenticate the user's voiceprint and perform personal authentication. For example, when a user says, "PayPay, please," the generation AI uses voice recognition technology to authenticate the user's voiceprint and perform personal authentication. The payment process will proceed only if the user's voiceprint is registered. We will also build a system that uses voice recognition technology to authenticate the user's voiceprint and perform personal authentication. When the user says "OK," voiceprint authentication will be performed to strengthen security. We will also develop a system that strengthens security by authenticating the user's voiceprint and performing personal authentication. When the user says, "PayPay, please," voiceprint authentication will be performed to prevent fraudulent payments. In this way, security will be strengthened by authenticating the user's voiceprint.

[0066] Natural language processing can build an automatic response system for user questions, making customer support more efficient. For example, when a user says, "I'd like to pay with PayPay," the generation AI uses natural language processing technology to generate an automatic response to that question. It replies, "Payment will be XX yen with PayPay. If that's okay, please say OK." Natural language processing technology can also be used to build an automatic response system for user questions, making customer support more efficient. When a user says, "Please tell me how to pay," it automatically responds. Furthermore, to build an automatic response system for user questions and make customer support more efficient, the generation AI utilizes natural language processing technology. When a user says, "I'd like to cancel," it automatically responds. This can make customer support more efficient.

[0067] The natural language processing unit can use the emotion estimation function to develop a system that automatically recommends music and media content according to the user's emotions. For example, when a user says, "PayPay, please," the generative AI uses the emotion estimation function to analyze the user's emotions and recommends music and media content according to those emotions. If the user is relaxed, relaxing music will be recommended. We will also use the emotion estimation function to develop a system that automatically recommends music and media content according to the user's emotions. If the user is feeling stressed, relaxing music will be recommended. Furthermore, to develop a system that automatically recommends music and media content according to the user's emotions, the generative AI will utilize the emotion estimation function. If the user is happy, positive music will be recommended. This makes it possible to recommend music and media content according to the user's emotions.

[0068] The payment processing unit can use the emotion estimation function to grasp the user's emotional state and provide an interface to reduce stress. For example, when a user says, "Pay with PayPay, please," the generation AI uses the emotion estimation function to grasp the user's emotional state and provides an interface to reduce stress. If the user is feeling stressed, the AI ​​responds in a calm tone. Furthermore, a system is constructed that uses the emotion estimation function to grasp the user's emotional state during payment procedures and provides an interface to reduce stress. Music that helps the user relax is played. Furthermore, the generation AI utilizes the emotion estimation function to grasp the user's emotional state and provide an interface to reduce stress. A message that puts the user at ease is displayed. This provides an interface that corresponds to the user's emotional state, thereby reducing stress.

[0069] The payment processing unit can analyze the user's purchase history and automatically suggest the optimal payment method for the next purchase. For example, we will build a system that analyzes the user's purchase history and automatically suggests the optimal payment method for the next purchase. If the user has used PayPay in the past, PayPay will be suggested for the next purchase as well. In addition, to further automate the payment process, the generation AI will analyze the user's purchase history and suggest the optimal payment method. If the user frequently uses credit cards, a credit card will be suggested. Furthermore, we will develop a system that automatically suggests the optimal payment method for the next purchase based on the user's purchase history. If the user frequently uses electronic money, electronic money will be suggested. This makes it possible to suggest the optimal payment method based on the user's purchase history.

[0070] The payment processing unit can process multiple payment methods simultaneously, allowing the user to select the most advantageous payment method. For example, we will build a system that processes multiple payment methods simultaneously during payment procedures, allowing the user to select the most advantageous payment method. It will process credit cards, electronic money, bank transfers, etc. simultaneously. We will also develop a system that processes multiple payment methods simultaneously so that the user can select the most advantageous payment method. It will propose a payment method that allows the user to earn the most points. Furthermore, in order to process multiple payment methods simultaneously during payment procedures and allow the user to select the most advantageous payment method, the generation AI will analyze multiple payment methods. It will propose the payment method with the lowest fees. This will allow the user to select the most advantageous payment method.

[0071] The payment processing unit can use the emotion estimation function to build a feedback system to provide a payment procedure that satisfies the user most. For example, the emotion estimation function is used to build a feedback system to provide a payment procedure that satisfies the user most. The user's emotional response is analyzed and the optimal payment procedure is suggested. Furthermore, a feedback system to provide a payment procedure that satisfies the user most is developed based on the user's emotional response. When the user says "OK," the emotion estimation function is used to evaluate satisfaction. Furthermore, to build a feedback system to provide a payment procedure that satisfies the user most using the emotion estimation function, the generation AI analyzes the user's emotional response in real time. When the user says "PayPay, please," the emotion estimation function is used to evaluate satisfaction. This makes it possible to provide a payment procedure that satisfies the user most.

[0072] The processing flow of the second embodiment will be briefly explained below.

[0073] Step 1: The speech recognition unit recognizes the user's speech. For example, the speech recognition unit uses an acoustic model to convert speech to text. It can also analyze and recognize speech using deep learning technology and language models. The acoustic model extracts speech features and converts them into text. Deep learning technology learns from large amounts of speech data to achieve highly accurate speech recognition. The language model understands the context of the speech and performs accurate text conversion. Step 2: The natural language processing unit analyzes the speech recognized by the speech recognition unit. For example, it can use morphological analysis to analyze the content of the speech, and grammatical analysis to analyze the structure of the speech. It can also use semantic analysis to understand the meaning of the speech. Morphological analysis divides and analyzes the words in the speech. Grammatical analysis analyzes the grammatical structure of the speech and understands the meaning. Semantic analysis understands the context of the speech and performs accurate analysis. Step 3: The payment processing unit performs the payment procedure based on the content analyzed by the natural language processing unit. For example, credit card payment, electronic money payment, bank transfer, etc. can be performed. Credit card information is entered and payment is made. For electronic money payment, payment is made using electronic money account information. For bank transfer, payment is made using bank account information. This makes it possible to make payments by voice without using a QR code, providing a smooth and comfortable payment experience.

[0074] 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.

[0075] 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.

[0076] 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.

[0077] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0078] 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.

[0079] 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.

[0080] 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.

[0081] 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.

[0082] 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).

[0083] 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.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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 AI 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.

[0091] 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.

[0092] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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).

[0098] 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.

[0099] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0100] 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.

[0101] 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.

[0102] In the headset type terminal 314, 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. 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 specific processing unit 290 using these models.

[0103] 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.

[0104] 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.

[0105] 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 AI 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.

[0106] 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.

[0107] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0108] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] In the robot 414, 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 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 processing similar to that of the specific processing unit 290 using these models.

[0119] 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.

[0120] 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.

[0121] 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 AI 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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."

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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, in order to avoid confusion and to 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.

[0140] 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. [Explanation of symbols]

[0141] 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 speech recognition unit that recognizes a user's speech; a natural language processing unit that analyzes the speech recognized by the speech recognition unit; a payment processing unit that performs a payment procedure based on the content analyzed by the natural language processing unit. A system characterized by:

2. The voice recognition unit Analyzes the tone and rate of the user's voice to understand their emotional state and generate appropriate responses 2. The system of claim 1.

3. The natural language processing unit Refer to the user's past conversation history and learn individual language patterns.

2. The system of claim 1.

4. The natural language processing unit Conduct context-based multi-level analysis 2. The system of claim 1.

5. The voice recognition unit Authenticate the user's voiceprint and perform personal authentication 2. The system of claim 1.

6. The natural language processing unit Build an automated response system for user questions to streamline customer support 2. The system of claim 1.

7. The natural language processing unit Develop a system that automatically recommends music and media content based on the user's emotions 2. The system of claim 1.

8. The payment processing unit Provide an interface to understand the user's emotional state and reduce stress 2. The system of claim 1.

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