System

Abstract

An object of a system according to an embodiment is to provide an efficient payment experience by releasing a user from a time-consuming payment procedure.SOLUTION: A system according to an embodiment includes a voice recognition unit, a generation unit, and a transaction execution unit. The voice recognition unit recognizes a voice command of a user. The generation unit analyzes the voice command recognized by the voice recognition unit. The transaction execution unit executes a transaction based on the voice command analyzed by the generation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the drawback of making payment procedures cumbersome and making it difficult to provide an efficient payment experience.

[0005] The system according to the embodiment aims to provide users with an efficient payment experience by freeing them from time-consuming payment procedures. [Means for solving the problem]

[0006] A system according to an embodiment includes a voice recognition unit, a generation unit, and a transaction execution unit. The voice recognition unit recognizes a voice command from a user. The generation unit analyzes the voice command recognized by the voice recognition unit. The transaction execution unit executes a transaction based on the voice command analyzed by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide a user with an efficient payment experience by freeing them from time-consuming payment procedures. [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 nonvolatile storage devices that store various programs, various parameters, etc. Examples of nonvolatile 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the 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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 payment system according to an embodiment of the present invention automatically recognizes a user's voice commands, and a generation AI processes the voice data to execute a safe and fast transaction, thereby freeing users from the tedious payment process and providing an efficient payment experience through the use of voice commands.

[0029] A payment system according to an embodiment includes a voice recognition unit, a generation unit, and a transaction execution unit. The voice recognition unit recognizes a user's voice command. For example, when a user issues a voice command such as "I'll pay 1,000 yen," the voice recognition unit converts the command into text data. The voice recognition unit also supports multiple languages ​​and can accommodate international users. The generation unit analyzes the voice command recognized by the voice recognition unit. For example, the generation unit uses a generation AI (e.g., a text generation AI or a multimodal generation AI) to analyze the voice command and process it as a payment instruction. The generation unit can also analyze the user's voice data, learn payment history and patterns, and predict the next payment. The transaction execution unit executes a transaction based on the voice command analyzed by the generation unit. For example, the transaction execution unit confirms the payee information and amount and executes the transaction. The transaction execution unit can also perform voiceprint authentication to achieve more advanced identity verification. This enables the payment system according to an embodiment to execute payments safely and quickly based on the user's voice command. For example, users can complete payments using voice commands without having to take out cash or a card at the register, and the generative AI quickly executes payment transactions, reducing waiting times.

[0030] The voice recognition unit can analyze the tone and speed of the user's voice to determine the urgency and importance of the payment. For example, when a user makes a payment, the voice recognition unit uses voice recognition technology to analyze the tone and speed of the voice to determine the urgency of the payment. For example, if the user is in a hurry, the payment procedure can be expedited. The voice recognition unit also analyzes the tone and speed of the voice to determine the importance of the payment. For example, if the payment is important, additional confirmation procedures can be performed. This makes it possible to take appropriate action depending on the urgency and importance of the payment.

[0031] The voice recognition unit can learn the characteristics of a user's voice and generate voice commands optimized for each individual user. The voice recognition unit, for example, uses voice recognition technology to learn the characteristics of a user's voice and generate voice commands optimized for each individual user. For example, it provides commands that match the user's pronunciation and accent. The voice recognition unit also learns the characteristics of a user's voice and improves the accuracy of voice recognition. For example, it optimizes the voice recognition algorithm based on the user's usage history. This generates voice commands optimized for each user, improving recognition accuracy.

[0032] The speech recognition unit can support multiple languages ​​and build a payment system that can accommodate international users. The speech recognition unit, for example, uses speech recognition technology to build a payment system that can accommodate multiple languages. For example, it can support multiple languages ​​such as English, French, and Chinese. The speech recognition unit also builds a payment system that can accommodate international users. For example, it provides appropriate voice commands to users with different cultural backgrounds. This makes it possible to provide a payment system that can accommodate international users by supporting multiple languages.

[0033] The voice recognition unit can translate a user's voice commands in real time, enabling payments between different languages. The voice recognition unit, for example, uses voice recognition technology to translate a user's voice commands in real time, enabling payments between different languages. For example, a Japanese voice command is translated into English to execute a payment. The voice recognition unit also facilitates communication between different languages ​​by translating in real time. For example, users who speak different languages ​​can use the same payment system. This allows payments between different languages, facilitating international transactions.

[0034] The generation unit can analyze the user's voice data, learn the payment history and patterns, and predict the next payment. The generation unit, for example, uses a generation AI to analyze the user's voice data and learn the payment history and patterns. For example, it predicts the next payment based on past payment data. The generation unit also understands the user's payment tendencies by learning the payment history and patterns. For example, it predicts the timing and amount of regular payments. In this way, by learning the payment history and patterns, it can predict the next payment, improving user convenience.

[0035] The generation unit can analyze the user's voice data and automatically provide additional information required at the time of payment. The generation unit, for example, uses a generation AI to analyze the user's voice data and automatically provide additional information required at the time of payment. For example, available coupons and discount information may be presented. The generation unit also improves user convenience by providing additional information required at the time of payment. For example, it may provide promotional information related to payment. This improves user convenience by automatically providing additional information required at the time of payment.

[0036] The generation unit analyzes the user's voice data and can simultaneously process tasks other than payment. The generation unit, for example, uses a generation AI to analyze the user's voice data and simultaneously process tasks other than payment. For example, making a restaurant reservation or making an inquiry at the same time. The generation unit also improves user convenience by simultaneously processing tasks other than payment. For example, checking the delivery status of a product at the same time as making a payment. This improves user convenience by simultaneously processing tasks other than payment.

[0037] The generation unit can analyze the user's voice data and automatically generate documents and certificates required for payment. The generation unit, for example, uses generation AI to analyze the user's voice data and automatically generate documents and certificates required for payment. For example, it automatically creates receipts and invoices. The generation unit also improves user convenience by automatically generating documents and certificates required for payment. For example, it automatically creates contracts and certificates. In this way, the automatic generation of documents and certificates required for payment improves user convenience.

[0038] The transaction execution unit can simultaneously perform multiple security checks during a transaction. The transaction execution unit can, for example, use generation AI to simultaneously perform multiple security checks during a transaction. For example, it can analyze location information and device information to prevent fraudulent transactions. The transaction execution unit also improves the security of transactions by simultaneously performing multiple security checks. For example, it can introduce two-factor authentication to strengthen security. This improves the security of transactions by simultaneously performing multiple security checks.

[0039] The transaction execution unit can utilize blockchain technology during transactions to improve the transparency and reliability of transactions. The transaction execution unit utilizes blockchain technology during transactions, for example, using generative AI to improve the transparency and reliability of transactions. For example, transaction history is recorded on the blockchain. The transaction execution unit also utilizes blockchain technology to prevent transaction tampering. For example, smart contracts are used to ensure the automation and transparency of transactions. In this way, the utilization of blockchain technology improves the transparency and reliability of transactions.

[0040] The transaction execution unit can perform risk assessment in real time at the time of a transaction and automatically block high-risk transactions. The transaction execution unit, for example, uses generative AI to perform risk assessment in real time at the time of a transaction and automatically block high-risk transactions. For example, it detects and blocks fraudulent transactions. Furthermore, by performing risk assessment in real time, the transaction execution unit is able to respond quickly. For example, it detects abnormal transaction patterns and immediately blocks them. This strengthens security by performing risk assessment in real time and automatically blocking high-risk transactions.

[0041] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0042] The payment system may further include a location information acquisition unit that acquires the user's location information. The location information acquisition unit may use, for example, GPS technology to identify the user's current location and utilize the location information when making a payment. For example, the location information acquisition unit may automatically recognize payments at specific stores, simplifying the payment process. The location information acquisition unit may also suggest nearby stores and services based on the user's location information. This improves the convenience of the payment process by utilizing the user's location information.

[0043] The payment system may further include a purchase history analysis unit that analyzes the user's purchase history. The purchase history analysis unit, for example, analyzes past payment data to understand the user's purchasing trends. For example, it analyzes the purchase frequency of specific products or services and predicts the next purchase. The purchase history analysis unit can also suggest related products or services based on the user's purchase history. This allows the user's purchase history to be utilized to make personalized suggestions and improve user convenience.

[0044] The payment system may further include a coupon providing unit that provides personalized coupons based on the user's purchase history. The coupon providing unit may, for example, analyze the user's past purchase history and provide coupons for products or services that the user frequently purchases. For example, the coupon providing unit may automatically generate discount coupons for specific stores. The coupon providing unit may also suggest coupons for related products or services based on the user's purchase history. This allows the user's purchase history to be utilized to provide personalized coupons and improve user convenience.

[0045] The payment system may further include a document generation unit that analyzes the user's voice data and automatically generates documents and certificates required for payment. The document generation unit may, for example, use generation AI to analyze the user's voice data and automatically generate documents and certificates required for payment. For example, it may automatically create receipts and invoices. The document generation unit may also improve user convenience by automatically generating documents and certificates required for payment. For example, it may automatically create contracts and certificates. In this way, the automatic generation of documents and certificates required for payment improves user convenience.

[0046] The payment system can further include a task processing unit that analyzes user voice data and simultaneously processes tasks other than payment. The task processing unit, for example, uses a generative AI to analyze user voice data and simultaneously processes tasks other than payment. For example, making restaurant reservations and inquiries at the same time. The task processing unit also improves user convenience by simultaneously processing tasks other than payment. For example, checking the delivery status of a product at the same time as making a payment. This improves user convenience by simultaneously processing tasks other than payment.

[0047] The payment system may further include an information providing unit that analyzes the user's voice data and automatically provides additional information required for payment. The information providing unit may, for example, use a generation AI to analyze the user's voice data and automatically provide additional information required for payment. For example, it may present available coupons or discount information. The information providing unit may also improve user convenience by providing additional information required for payment. For example, it may provide promotional information related to payment. This improves user convenience by automatically providing additional information required for payment.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The voice recognition unit recognizes the user's voice command. For example, if the user issues a voice command such as "I will pay 1,000 yen," the command is converted into text data. The voice recognition unit also supports multiple languages, making it suitable for international users. Step 2: The generation unit analyzes the voice command recognized by the voice recognition unit. For example, the generation unit uses a generation AI (e.g., a text generation AI or a multimodal generation AI) to analyze the voice command and process it as a payment instruction. The generation unit can also analyze the user's voice data, learn payment history and patterns, and predict the next payment. Step 3: The transaction execution unit executes the transaction based on the voice command analyzed by the generation unit. For example, the transaction execution unit confirms the payee information and amount and executes the transaction. The transaction execution unit can also perform voiceprint authentication to achieve more advanced identity verification.

[0050] (Example 2) The payment system according to an embodiment of the present invention automatically recognizes a user's voice commands, and a generation AI processes the voice data to execute a safe and fast transaction, thereby freeing users from the tedious payment process and providing an efficient payment experience through the use of voice commands.

[0051] A payment system according to an embodiment includes a voice recognition unit, a generation unit, and a transaction execution unit. The voice recognition unit recognizes a user's voice command. For example, when a user issues a voice command such as "I'll pay 1,000 yen," the voice recognition unit converts the command into text data. The voice recognition unit also supports multiple languages ​​and can accommodate international users. The generation unit analyzes the voice command recognized by the voice recognition unit. For example, the generation unit uses a generation AI (e.g., a text generation AI or a multimodal generation AI) to analyze the voice command and process it as a payment instruction. The generation unit can also analyze the user's voice data, learn payment history and patterns, and predict the next payment. The transaction execution unit executes a transaction based on the voice command analyzed by the generation unit. For example, the transaction execution unit confirms the payee information and amount and executes the transaction. The transaction execution unit can also perform voiceprint authentication to achieve more advanced identity verification. This enables the payment system according to an embodiment to execute payments safely and quickly based on the user's voice command. For example, users can complete payments using voice commands without having to take out cash or a card at the register, and the generative AI quickly executes payment transactions, reducing waiting times.

[0052] The voice recognition unit can analyze the tone and speed of the user's voice to determine the urgency and importance of the payment. For example, when a user makes a payment, the voice recognition unit uses voice recognition technology to analyze the tone and speed of the voice to determine the urgency of the payment. For example, if the user is in a hurry, the payment procedure can be expedited. The voice recognition unit also analyzes the tone and speed of the voice to determine the importance of the payment. For example, if the payment is important, additional confirmation procedures can be performed. This makes it possible to take appropriate action depending on the urgency and importance of the payment.

[0053] The voice recognition unit can learn the characteristics of a user's voice and generate voice commands optimized for each individual user. The voice recognition unit, for example, uses voice recognition technology to learn the characteristics of a user's voice and generate voice commands optimized for each individual user. For example, it provides commands that match the user's pronunciation and accent. The voice recognition unit also learns the characteristics of a user's voice and improves the accuracy of voice recognition. For example, it optimizes the voice recognition algorithm based on the user's usage history. This generates voice commands optimized for each user, improving recognition accuracy.

[0054] The voice recognition unit can use the emotion estimation function to estimate emotions from the user's voice, and make suggestions to simplify the payment procedure if the user is feeling stressed or anxious. The voice recognition unit, for example, uses the emotion estimation function to estimate emotions from the user's voice, and makes suggestions to simplify the payment procedure if the user is feeling stressed or anxious. For example, it presents an easy payment method. The voice recognition unit also uses the emotion estimation function to estimate emotions from the user's voice, and makes suggestions to automate the payment procedure if the user is feeling stressed or anxious. For example, it automates regular payments. In this way, the burden on the user is reduced by simplifying the payment procedure according to the user's emotions.

[0055] The speech recognition unit can support multiple languages ​​and build a payment system that can accommodate international users. The speech recognition unit, for example, uses speech recognition technology to build a payment system that can accommodate multiple languages. For example, it can support multiple languages ​​such as English, French, and Chinese. The speech recognition unit also builds a payment system that can accommodate international users. For example, it provides appropriate voice commands to users with different cultural backgrounds. This makes it possible to provide a payment system that can accommodate international users by supporting multiple languages.

[0056] The voice recognition unit can translate a user's voice commands in real time, enabling payments between different languages. The voice recognition unit, for example, uses voice recognition technology to translate a user's voice commands in real time, enabling payments between different languages. For example, a Japanese voice command is translated into English to execute a payment. The voice recognition unit also facilitates communication between different languages ​​by translating in real time. For example, users who speak different languages ​​can use the same payment system. This allows payments between different languages, facilitating international transactions.

[0057] The voice recognition unit can use the emotion estimation function to estimate emotions from the user's voice and provide voice feedback to elicit positive emotions. The voice recognition unit, for example, uses the emotion estimation function to estimate emotions from the user's voice and provide voice feedback to elicit positive emotions. For example, it returns encouraging words or positive messages. The voice recognition unit also uses the emotion estimation function to estimate emotions from the user's voice and provide voice guidance to elicit positive emotions. For example, it provides voice guidance that helps the user relax. This elicits positive emotions and improves user satisfaction.

[0058] The generation unit can analyze the user's voice data, learn the payment history and patterns, and predict the next payment. The generation unit, for example, uses a generation AI to analyze the user's voice data and learn the payment history and patterns. For example, it predicts the next payment based on past payment data. The generation unit also understands the user's payment tendencies by learning the payment history and patterns. For example, it predicts the timing and amount of regular payments. In this way, by learning the payment history and patterns, it can predict the next payment, improving user convenience.

[0059] The generation unit can analyze the user's voice data and automatically provide additional information required at the time of payment. The generation unit, for example, uses a generation AI to analyze the user's voice data and automatically provide additional information required at the time of payment. For example, available coupons and discount information may be presented. The generation unit also improves user convenience by providing additional information required at the time of payment. For example, it may provide promotional information related to payment. This improves user convenience by automatically providing additional information required at the time of payment.

[0060] The generation unit can use the emotion estimation function to estimate emotions from the user's voice and provide a payment method that corresponds to the emotion. For example, the generation unit uses the emotion estimation function to estimate emotions from the user's voice and provide a payment method that corresponds to the emotion. For example, if the user is feeling stressed, the generation unit can suggest installment payments. The generation unit can also use the emotion estimation function to estimate emotions from the user's voice and provide a payment plan that corresponds to the emotion. For example, the generation unit can present a deferred payment option. This reduces the burden on the user by providing a payment method that corresponds to the user's emotion.

[0061] The generation unit analyzes the user's voice data and can simultaneously process tasks other than payment. The generation unit, for example, uses a generation AI to analyze the user's voice data and simultaneously process tasks other than payment. For example, making a restaurant reservation or making an inquiry at the same time. The generation unit also improves user convenience by simultaneously processing tasks other than payment. For example, checking the delivery status of a product at the same time as making a payment. This improves user convenience by simultaneously processing tasks other than payment.

[0062] The generation unit can analyze the user's voice data and automatically generate documents and certificates required for payment. The generation unit, for example, uses generation AI to analyze the user's voice data and automatically generate documents and certificates required for payment. For example, it automatically creates receipts and invoices. The generation unit also improves user convenience by automatically generating documents and certificates required for payment. For example, it automatically creates contracts and certificates. In this way, the automatic generation of documents and certificates required for payment improves user convenience.

[0063] The generation unit can use the emotion estimation function to estimate emotions from the user's voice and provide customer support that corresponds to the emotions. For example, the generation unit uses the emotion estimation function to estimate emotions from the user's voice and provide customer support that corresponds to the emotions. For example, if the user is feeling stressed, a quick response is provided. The generation unit also uses the emotion estimation function to estimate emotions from the user's voice and provide a support plan that corresponds to the emotions. For example, specialized support is provided for a specific problem. In this way, by providing customer support that corresponds to the user's emotions, user satisfaction is improved.

[0064] The transaction execution unit can simultaneously perform multiple security checks during a transaction. The transaction execution unit can, for example, use generation AI to simultaneously perform multiple security checks during a transaction. For example, it can analyze location information and device information to prevent fraudulent transactions. The transaction execution unit also improves the security of transactions by simultaneously performing multiple security checks. For example, it can introduce two-factor authentication to strengthen security. This improves the security of transactions by simultaneously performing multiple security checks.

[0065] The transaction execution unit can use the emotion estimation function to estimate emotions from the user's voice and request additional confirmation procedures if a fraudulent transaction is suspected. For example, the transaction execution unit uses the emotion estimation function to estimate emotions from the user's voice and request additional confirmation procedures if a fraudulent transaction is suspected. For example, additional confirmation is performed if the user is feeling stressed or anxious. The transaction execution unit also uses the emotion estimation function to estimate emotions from the user's voice and introduces an additional authentication step if a fraudulent transaction is suspected. For example, additional identity verification is performed. This enhances security by requiring additional confirmation procedures if a fraudulent transaction is suspected.

[0066] The transaction execution unit can utilize blockchain technology during transactions to improve the transparency and reliability of transactions. The transaction execution unit utilizes blockchain technology during transactions, for example, using generative AI to improve the transparency and reliability of transactions. For example, transaction history is recorded on the blockchain. The transaction execution unit also utilizes blockchain technology to prevent transaction tampering. For example, smart contracts are used to ensure the automation and transparency of transactions. In this way, the utilization of blockchain technology improves the transparency and reliability of transactions.

[0067] The transaction execution unit can perform risk assessment in real time at the time of a transaction and automatically block high-risk transactions. The transaction execution unit, for example, uses generative AI to perform risk assessment in real time at the time of a transaction and automatically block high-risk transactions. For example, it detects and blocks fraudulent transactions. Furthermore, by performing risk assessment in real time, the transaction execution unit is able to respond quickly. For example, it detects abnormal transaction patterns and immediately blocks them. This strengthens security by performing risk assessment in real time and automatically blocking high-risk transactions.

[0068] The transaction execution unit can use the emotion estimation function to estimate emotions from the user's voice and dynamically adjust the security level according to the emotion. The transaction execution unit, for example, uses the emotion estimation function to estimate emotions from the user's voice and dynamically adjust the security level according to the emotion. For example, if the user is feeling stressed, the security level is increased. The transaction execution unit also uses the emotion estimation function to estimate emotions from the user's voice and change the security policy according to the emotion. For example, if the user is relaxed, the security level is lowered. In this way, security is strengthened by dynamically adjusting the security level according to the emotion.

[0069] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0070] The payment system may further include a location information acquisition unit that acquires the user's location information. The location information acquisition unit may use, for example, GPS technology to identify the user's current location and utilize the location information when making a payment. For example, the location information acquisition unit may automatically recognize payments at specific stores, simplifying the payment process. The location information acquisition unit may also suggest nearby stores and services based on the user's location information. This improves the convenience of the payment process by utilizing the user's location information.

[0071] The payment system may further include a purchase history analysis unit that analyzes the user's purchase history. The purchase history analysis unit, for example, analyzes past payment data to understand the user's purchasing trends. For example, it analyzes the purchase frequency of specific products or services and predicts the next purchase. The purchase history analysis unit can also suggest related products or services based on the user's purchase history. This allows the user's purchase history to be utilized to make personalized suggestions and improve user convenience.

[0072] The payment system may further include a health monitoring unit that monitors the user's health condition. The health monitoring unit may, for example, use a wearable device to measure the user's heart rate and stress level and reflect the results in the payment procedure. For example, if the user is feeling stressed, the health monitoring unit may suggest a simplified payment procedure. The health monitoring unit may also suggest an appropriate payment method depending on the user's health condition. This reduces the burden on the user by providing a payment procedure that takes the user's health condition into consideration.

[0073] The payment system may further include an advertisement providing unit that estimates emotions from the user's voice and provides advertisements that correspond to the emotions. The advertisement providing unit, for example, uses an emotion estimation function to estimate emotions from the user's voice and provides advertisements that elicit positive emotions. For example, if the user is relaxed, an advertisement for a product that has a relaxing effect is displayed. The advertisement providing unit may also adjust the content and timing of the advertisements according to the user's emotions. In this way, advertisements that correspond to the user's emotions are provided, thereby maximizing the effectiveness of the advertisements.

[0074] The payment system may further include a music providing unit that estimates emotions from the user's voice and provides music corresponding to the emotions. The music providing unit, for example, uses an emotion estimation function to estimate emotions from the user's voice and provides music with a relaxing effect. For example, if the user is feeling stressed, relaxing music is played. The music providing unit may also adjust the genre and tempo of the music according to the user's emotions. In this way, providing music corresponding to the user's emotions enhances the user's relaxation effect.

[0075] The payment system may further include a feedback providing unit that estimates emotions from the user's voice and provides feedback according to the emotions. The feedback providing unit, for example, estimates emotions from the user's voice using an emotion estimation function and provides positive feedback. For example, it may return encouraging words when the user successfully makes a payment. The feedback providing unit may also adjust the content and timing of the feedback according to the user's emotions. In this way, providing feedback according to the user's emotions improves user satisfaction.

[0076] The payment system may further include a coupon providing unit that provides personalized coupons based on the user's purchase history. The coupon providing unit may, for example, analyze the user's past purchase history and provide coupons for products or services that the user frequently purchases. For example, the coupon providing unit may automatically generate discount coupons for specific stores. The coupon providing unit may also suggest coupons for related products or services based on the user's purchase history. This allows the user's purchase history to be utilized to provide personalized coupons and improve user convenience.

[0077] The payment system may further include a document generation unit that analyzes the user's voice data and automatically generates documents and certificates required for payment. The document generation unit may, for example, use generation AI to analyze the user's voice data and automatically generate documents and certificates required for payment. For example, it may automatically create receipts and invoices. The document generation unit may also improve user convenience by automatically generating documents and certificates required for payment. For example, it may automatically create contracts and certificates. In this way, the automatic generation of documents and certificates required for payment improves user convenience.

[0078] The payment system can further include a task processing unit that analyzes user voice data and simultaneously processes tasks other than payment. The task processing unit, for example, uses a generative AI to analyze user voice data and simultaneously processes tasks other than payment. For example, making restaurant reservations and inquiries at the same time. The task processing unit also improves user convenience by simultaneously processing tasks other than payment. For example, checking the delivery status of a product at the same time as making a payment. This improves user convenience by simultaneously processing tasks other than payment.

[0079] The payment system may further include an information providing unit that analyzes the user's voice data and automatically provides additional information required for payment. The information providing unit may, for example, use a generation AI to analyze the user's voice data and automatically provide additional information required for payment. For example, it may present available coupons or discount information. The information providing unit may also improve user convenience by providing additional information required for payment. For example, it may provide promotional information related to payment. This improves user convenience by automatically providing additional information required for payment.

[0080] The processing flow of the second embodiment will be briefly explained below.

[0081] Step 1: The voice recognition unit recognizes the user's voice command. For example, if the user issues a voice command such as "I will pay 1,000 yen," the command is converted into text data. The voice recognition unit also supports multiple languages, making it suitable for international users. Step 2: The generation unit analyzes the voice command recognized by the voice recognition unit. For example, the generation unit uses a generation AI (e.g., a text generation AI or a multimodal generation AI) to analyze the voice command and process it as a payment instruction. The generation unit can also analyze the user's voice data, learn payment history and patterns, and predict the next payment. Step 3: The transaction execution unit executes the transaction based on the voice command analyzed by the generation unit. For example, the transaction execution unit confirms the payee information and amount and executes the transaction. The transaction execution unit can also perform voiceprint authentication to achieve more advanced identity verification.

[0082] 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.

[0083] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<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.

[0084] 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.

[0085] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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).

[0091] 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.

[0092] 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.

[0093] 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.

[0094] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0095] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0096] 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.

[0097] 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.

[0098] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0099] 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.

[0100] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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).

[0106] 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.

[0107] 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.

[0108] 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.

[0109] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0110] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0111] 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.

[0112] 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.

[0113] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0114] 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.

[0115] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0116] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0126] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0127] 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.

[0128] 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.

[0129] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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."

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0148] 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]

[0149] 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 voice recognition unit that recognizes voice commands from a user; a generation unit that analyzes the voice command recognized by the voice recognition unit; a transaction execution unit that executes a transaction based on the voice command analyzed by the generation unit. A system characterized by:

2. The voice recognition unit Learns the characteristics of the user's voice and generates voice commands optimized for each individual user 2. The system of claim 1.

3. The voice recognition unit Build a payment system that supports multiple languages ​​and caters to international users 2. The system of claim 1.

4. The generation unit Analyze the user's voice data, learn payment history and patterns, and predict the next payment.

2. The system of claim 1.

5. The transaction execution unit By analyzing the user's voice data and performing voiceprint authentication, more advanced identity verification is achieved.

2. The system of claim 1.

6. The voice recognition unit Using an emotion estimation function, the system estimates the user's emotions from their voice, and if the user feels stressed or anxious, it makes suggestions to simplify the payment process.

2. The system of claim 1.

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