system

Abstract

The system according to the embodiment aims to enable purchase of goods from an EC site through conversation alone. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, a provision unit, a procedure unit, and a notification unit. The reception unit receives voice input. The analysis unit analyzes the voice received by the reception unit and identifies an item. The provision unit provides information about the item identified by the analysis unit. The procedure unit performs a purchase procedure based on the information provided by the provision unit. The notification unit notifies the progress of the purchase procedure performed by the procedure unit.

Description

[Technical Field]

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

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

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

[0004] With conventional technology, elderly people and people with disabilities who are unfamiliar with using PCs and smartphones had difficulty purchasing items from e-commerce sites.

[0005] The system according to the embodiment aims to enable purchase of goods from an EC site through conversation alone. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a provision unit, a procedure unit, and a notification unit. The reception unit receives voice input. The analysis unit analyzes the voice received by the reception unit and identifies an item. The provision unit provides information about the item identified by the analysis unit. The procedure unit performs a purchase procedure based on the information provided by the provision unit. The notification unit notifies the progress of the purchase procedure performed by the procedure unit. [Effects of the Invention]

[0007] The system according to the embodiment allows a user to purchase an item from an EC site through conversation alone. [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) An AI support system according to an embodiment of the present invention enables elderly people who are unfamiliar with using PCs or smartphones, or people with physical disabilities such as the blind, to purchase items from e-commerce sites through conversation alone (or through conversation alone with the aid of a screen). In this AI support system, the user inputs information about the item they wish to purchase via voice, and the AI ​​analyzes the voice to identify the desired item. The AI ​​then provides information about the identified item via voice, and the user responds again via voice to select the item. The AI ​​then provides detailed information about the selected item, such as its price and availability, via voice. Once the user confirms the purchase, the AI ​​proceeds with the purchase. Furthermore, the AI ​​notifies the user via voice of the progress of the purchase process. This system allows elderly people who are unfamiliar with using PCs or smartphones, or people with physical disabilities such as the blind, to easily purchase items from e-commerce sites through conversation alone, thereby enriching their lives and improving convenience. For example, if a user voice-inputs, "I want to buy a TV," the AI ​​analyzes the voice and provides information about the TV. If the user asks, "What is the price of this TV?", the AI ​​provides price information via voice. When a user verbally instructs, "I want to buy this TV," the AI ​​will proceed with the purchase process and provide voice notifications about the progress. This allows the AI ​​support system to allow even elderly people who are not accustomed to using PCs or smartphones, or people with physical disabilities such as the blind, to easily purchase items from e-commerce sites using only conversation, enriching the lives of those involved and improving convenience.

[0029] An AI support system according to an embodiment includes a reception unit, an analysis unit, a provision unit, a procedure unit, and a notification unit. The reception unit receives information about an item the user wants to purchase via voice. For example, when the user voice-inputs, "I want to buy a television," the reception unit receives the voice. The analysis unit analyzes the voice received by the reception unit and identifies the item. For example, the analysis unit converts the voice into text using a speech recognition algorithm and identifies the item using natural language processing technology. The provision unit provides information about the item identified by the analysis unit via voice. For example, the provision unit provides price information via voice to a user who asks, "How much does this television cost?" The procedure unit performs a purchase procedure based on the information provided by the provision unit. For example, when a user voice-instructs, "I want to purchase this television," the procedure unit proceeds with the purchase procedure. The notification unit provides a voice notification of the progress of the purchase procedure performed by the procedure unit. For example, the notification unit notifies the user via voice, "The purchase procedure has been completed." As a result, the AI ​​support system according to the embodiment allows even elderly people who are not accustomed to using PCs or smartphones, or people with physical disabilities such as the blind, to easily purchase items from e-commerce sites through conversation alone. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit uses a microphone to accept voice input and converts the voice into text using voice recognition technology. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit converts voice into text using a voice recognition algorithm and identifies the item using natural language processing technology. Some or all of the above-described processing in the provision unit may be performed using AI, for example, or without AI. For example, the provision unit uses voice synthesis technology to provide information about the identified item by voice. Some or all of the above-described processing in the transaction unit may be performed using AI, for example, or without AI. For example, the transaction unit accepts voice instructions from the user to proceed with the purchase process and automates the purchase process.Some or all of the above-described processing by the notification unit may be performed using, for example, AI, or may be performed without AI. For example, the notification unit may use voice synthesis technology to notify the progress of the purchase procedure by voice.

[0030] The reception unit can accept voice input. For example, when a user voice-inputs "I want to buy a TV," the reception unit accepts the voice. The reception unit can use a microphone to accept the voice input. For example, the reception unit can accept the voice input using a voice recognition function of a smartphone. The reception unit can also use a dedicated voice recognition device to accept the voice input. For example, the reception unit can recognize the user's voice with high accuracy using the voice recognition device. This allows the reception unit to accept the voice input. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can perform voice recognition using AI to accept the voice input.

[0031] The analysis unit can analyze the voice and identify the item. For example, the analysis unit converts the voice into text using a voice recognition algorithm and identifies the item using natural language processing technology. For example, when a user vocally inputs "I want to buy a television," the analysis unit converts the voice into text and identifies information about the television. The analysis unit can also analyze the user's voice with high accuracy using a voice recognition algorithm. For example, the analysis unit can analyze the user's voice in real time using a voice recognition algorithm and identify the item. The analysis unit can also identify the item from the user's voice using natural language processing technology. For example, the analysis unit can extract the category and characteristics of the item from the user's voice using natural language processing technology and identify the item. This allows the analysis unit to analyze the voice and identify the item. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can use AI to convert the voice into text using a voice recognition algorithm and identify the item using natural language processing technology.

[0032] The providing unit can provide information about the identified item by voice. For example, when a user asks, "What is the price of this TV?", the providing unit provides price information by voice. The providing unit can provide information about the identified item by voice using voice synthesis technology. For example, the providing unit can provide price information about the identified item by voice using voice synthesis technology. The providing unit can also provide detailed information about the identified item by voice. For example, the providing unit can provide stock status and delivery information about the identified item by voice. The providing unit can also customize the information to provide information about the identified item by voice in response to a user's voice instruction. For example, when a user asks, "What are the features of this TV?", the providing unit can provide information about the features of the TV by voice. This allows the providing unit to provide information about the identified item by voice. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can use AI to provide information about the identified item by voice using voice synthesis technology.

[0033] The transaction unit can perform the purchase procedure based on the information provided by the providing unit. For example, when a user gives a voice instruction such as "I want to buy this TV," the transaction unit proceeds with the purchase procedure. The transaction unit can accept a user's voice instruction to automate the purchase procedure. For example, the transaction unit proceeds with the purchase procedure based on the user's voice instruction. The transaction unit can also collect necessary information in accordance with the user's voice instruction to proceed with the purchase procedure. For example, when a user gives an instruction such as "I want to buy this TV with a credit card," the transaction unit can collect credit card information and proceed with the purchase procedure. The transaction unit can also collect delivery information based on the user's voice instruction to proceed with the purchase procedure. For example, when a user gives an instruction such as "Deliver this TV to my home," the transaction unit can collect delivery address information and proceed with the purchase procedure. This allows the transaction unit to perform the purchase procedure based on the information provided by the providing unit. Some or all of the above-described processing in the transaction unit may be performed using, or without, AI. For example, the transaction unit may use AI to proceed with the purchase procedure based on the user's voice instruction.

[0034] The notification unit can provide audio notification of the progress of the purchase procedure performed by the processing unit. For example, the notification unit can provide audio notification of "The purchase procedure has been completed." The notification unit can provide audio notification of the progress of the purchase procedure using voice synthesis technology. For example, the notification unit can provide audio notification of the progress of the purchase procedure in real time. The notification unit can also customize the content of the notification in accordance with a user's voice instruction to provide audio notification of the progress of the purchase procedure. For example, when the user asks, "Tell me the progress of the purchase procedure," the notification unit can provide audio notification of the current progress. The notification unit can also adjust the timing of the notification based on the user's voice instruction to provide audio notification of the progress of the purchase procedure. For example, when the user instructs, "Notify me immediately when the purchase procedure is completed," the notification unit can provide an immediate notification when the purchase procedure is completed. This allows the notification unit to provide audio notification of the progress of the purchase procedure performed by the processing unit. Some or all of the above-described processing by the notification unit may be performed using, for example, AI, or may be performed without AI. For example, the notification unit can use AI to provide voice notification of the progress of the purchase procedure using voice synthesis technology.

[0035] The reception unit can analyze the user's past voice input history and select a voice input method. The reception unit, for example, prioritizes recognition of phrases frequently used by the user in the past, thereby simplifying input. For example, the reception unit can learn the voice input patterns used by the user in the past and suggest an optimal input method. The reception unit can also select an input method suitable for a specific time period from the user's past voice input history. For example, the reception unit can prioritize recognition of phrases frequently used by the user in a specific time period, thereby simplifying input. This allows the reception unit to analyze the user's past voice input history and select a voice input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can use AI to analyze the user's past voice input history and select a voice input method.

[0036] The reception unit can filter the user's current environmental sounds to remove noise when receiving a voice input. For example, when the user is in a noisy environment, the reception unit filters the environmental sounds to improve the accuracy of the voice input. For example, when the user is in a quiet environment, the reception unit can remove even subtle noises to achieve clear voice input. Furthermore, when the user performs voice input while traveling, the reception unit can also filter wind noise and traffic noise to remove noise. For example, when the user performs voice input in a car, the reception unit can filter the in-car environmental sounds to improve the accuracy of the voice input. This allows the reception unit to filter the user's current environmental sounds to remove noise when receiving a voice input. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can use AI to filter the user's current environmental sounds to remove noise when receiving a voice input.

[0037] The reception unit can prioritize acquiring highly relevant information based on the user's geographical location information when receiving a voice input. For example, when the user is in a specific area, the reception unit prioritizes acquiring information related to that area. For example, when the user is traveling, the reception unit can prioritize acquiring information related to the travel destination. Furthermore, when the user is at home, the reception unit can prioritize acquiring information about the area around the user's home. For example, when the user performs voice input at home, the reception unit can prioritize acquiring store information and service information about the area around the user's home. This allows the reception unit to prioritize acquiring highly relevant information based on the user's geographical location information when receiving a voice input. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can use AI to prioritize acquiring highly relevant information based on the user's geographical location information when receiving a voice input.

[0038] The reception unit can analyze the user's social media activities and acquire related information when receiving the voice input. The reception unit, for example, prioritizes acquisition of information related to items frequently mentioned by the user on social media. For example, the reception unit can analyze the content of the user's social media posts and acquire information based on the user's interests. The reception unit can also acquire information related to items mentioned by the user's social media followers and friends. For example, the reception unit can prioritize acquisition of information related to items frequently mentioned by the user's followers and friends. This allows the reception unit to analyze the user's social media activities and acquire related information when receiving the voice input. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can use AI to analyze the user's social media activities and acquire related information when receiving the voice input.

[0039] During voice analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past purchase history. The analysis unit improves the accuracy of the voice analysis, for example, based on information about items purchased by the user in the past. For example, the analysis unit can prioritize analysis of voices related to a specific brand or category from the user's past purchase history. The analysis unit can also analyze the user's past purchase history and accurately analyze voice input about similar items. For example, the analysis unit can improve the accuracy of the voice analysis based on information about items purchased by the user in the past. This allows the analysis unit to improve the accuracy of the analysis by referring to the user's past purchase history during voice analysis. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can use AI to improve the accuracy of the analysis by referring to the user's past purchase history during voice analysis.

[0040] The analysis unit can learn the user's pronunciation characteristics and adjust the analysis algorithm during voice analysis. For example, the analysis unit can learn the user's pronunciation characteristics and apply an individually optimized voice analysis algorithm. For example, if the user has a specific accent or dialect, the analysis unit can adjust the analysis algorithm taking those characteristics into account. The analysis unit can also learn changes in the user's pronunciation in real time and dynamically optimize the analysis algorithm. For example, the analysis unit can learn the user's pronunciation characteristics and apply an individually optimized voice analysis algorithm. This allows the analysis unit to learn the user's pronunciation characteristics and adjust the analysis algorithm during voice analysis. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can use AI to learn the user's pronunciation characteristics and adjust the analysis algorithm during voice analysis.

[0041] The analysis unit can improve the accuracy of analysis by taking into account the user's geographical background during voice analysis. For example, when the user is in a specific region, the analysis unit can improve the accuracy of analysis by taking into account the dialect and accent of that region. For example, when the user is traveling, the analysis unit can improve the accuracy of analysis by taking into account the language and accent of the destination. Furthermore, when the user is at home, the analysis unit can improve the accuracy of analysis by taking into account the geographical background around the user's home. For example, when the user performs voice input at home, the analysis unit can improve the accuracy of analysis by taking into account the geographical background around the user's home. This allows the analysis unit to improve the accuracy of analysis by taking into account the user's geographical background during voice analysis. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can use AI to improve the accuracy of analysis by taking into account the user's geographical background during voice analysis.

[0042] The analysis unit can improve the accuracy of the analysis by referring to literature related to the user during speech analysis. The analysis unit can improve the accuracy of the speech analysis, for example, based on literature previously referenced by the user. For example, the analysis unit can prioritize analysis of specific technical terms or phrases from literature previously referenced by the user. The analysis unit can also analyze literature previously referenced by the user and accurately analyze speech input with similar content. For example, the analysis unit can improve the accuracy of the speech analysis based on literature previously referenced by the user. This allows the analysis unit to improve the accuracy of the analysis by referring to literature previously referenced by the user during speech analysis. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can use AI to improve the accuracy of the analysis by referring to literature previously referenced by the user during speech analysis.

[0043] The providing unit can adjust the level of detail of the information to be provided based on the importance of the item when providing information. For example, the providing unit can provide detailed information about important items to allow the user to fully understand. For example, the providing unit can provide concise information about general items to allow the user to make a quick decision. The providing unit can also provide detailed information about items in which the user is particularly interested to increase user satisfaction. For example, the providing unit can provide detailed information about items in which the user is particularly interested. This allows the providing unit to adjust the level of detail of the information to be provided based on the importance of the item when providing information. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can use AI to adjust the level of detail of the information to be provided based on the importance of the item when providing information.

[0044] The providing unit can apply different information provision algorithms depending on the category of the item when providing information. For example, the providing unit can apply an algorithm including technical details to information about electronic devices. For example, the providing unit can apply an algorithm including details about design and materials to information about clothing. The providing unit can also apply an algorithm including details about ingredients and nutritional value to information about food. For example, the providing unit can provide detailed information about food. This allows the providing unit to apply different information provision algorithms depending on the category of the item when providing information. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can use AI to apply different information provision algorithms depending on the category of the item when providing information.

[0045] The providing unit can determine the priority of information provision based on the time of submission of the item when providing information. For example, the providing unit can provide information about urgently needed items with a higher priority. For example, the providing unit can provide information about general items with a normal priority. The providing unit can also provide information about items in which the user is particularly interested with a higher priority. For example, the providing unit can provide information about items in which the user is particularly interested with a higher priority. This allows the providing unit to determine the priority of information provision based on the time of submission of the item when providing information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can use AI to determine the priority of information provision based on the time of submission of the item when providing information.

[0046] The providing unit can adjust the order of information provision based on the relevance of the items when providing information. For example, the providing unit can prioritize providing information related to items previously purchased by the user. For example, the providing unit can prioritize providing information related to items in which the user is currently interested. The providing unit can also prioritize providing information about highly relevant items based on the user's purchase history. For example, the providing unit can prioritize providing information related to items previously purchased by the user. This allows the providing unit to adjust the order of information provision based on the relevance of the items when providing information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can use AI to adjust the order of information provision based on the relevance of the items when providing information.

[0047] During the purchase process, the procedure unit can select a procedure method based on the user's past purchase history. For example, the procedure unit can prioritize suggesting payment methods that the user has used in the past. For example, the procedure unit can prioritize suggesting a specific delivery method based on the user's past purchase history. The procedure unit can also analyze the user's past purchase history and suggest the most efficient procedure method. For example, the procedure unit can prioritize suggesting payment methods that the user has used in the past. This allows the procedure unit to select a procedure method based on the user's past purchase history during the purchase process. Some or all of the above-described processing in the procedure unit may be performed using, for example, AI, or may be performed without using AI. For example, the procedure unit can use AI to select a procedure method based on the user's past purchase history during the purchase process.

[0048] The procedure unit can customize the procedure means based on the user's current living situation during the purchase process. For example, if the user is busy, the procedure unit can suggest a quick procedure. For example, if the user is relaxed, the procedure unit can suggest a procedure with detailed instructions. The procedure unit can also suggest the procedure optimal for a specific living situation when the user is in that situation. For example, if the user is in a specific living situation, the procedure unit can suggest the procedure optimal for that situation. This allows the procedure unit to customize the procedure means based on the user's current living situation during the purchase process. Some or all of the above-described processing in the procedure unit may be performed using, or without, AI. For example, the procedure unit can use AI to customize the procedure means based on the user's current living situation during the purchase process.

[0049] The procedure unit can select a procedure method based on the user's geographical location information during the purchase process. For example, if the user is in a specific area, the procedure unit can suggest a delivery method appropriate for that area. For example, if the user is traveling, the procedure unit can suggest a procedure method appropriate for the user's travel destination. Furthermore, if the user is at home, the procedure unit can also suggest a delivery method near the user's home. For example, if the user is completing the purchase process at home, the procedure unit can suggest a delivery method near the user's home. This allows the procedure unit to select a procedure method based on the user's geographical location information during the purchase process. Some or all of the above-described processing in the procedure unit may be performed using, for example, AI, or may be performed without using AI. For example, the procedure unit can use AI to select a procedure method based on the user's geographical location information during the purchase process.

[0050] The procedure unit can analyze the user's social media activity to suggest a procedure during the purchase process. The procedure unit can, for example, suggest a payment method frequently mentioned by the user on social media. For example, the procedure unit can analyze the content of the user's social media posts to suggest a procedure based on their interests. The procedure unit can also suggest a procedure used by the user's social media followers or friends. For example, the procedure unit can suggest a procedure frequently used by the user's followers or friends. In this way, the procedure unit can analyze the user's social media activity to suggest a procedure during the purchase process. Some or all of the above-described processing in the procedure unit may be performed using, or without, AI. For example, the procedure unit can use AI to analyze the user's social media activity to suggest a procedure during the purchase process.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] When receiving a user's voice input, the reception unit can automatically present related product candidates by referring to the user's past purchase history. For example, if the user has a history of purchasing televisions in the past, the reception unit can prioritize presenting brands and models of televisions previously purchased when the user voice-inputs, "I want to buy a television." The reception unit can also suggest related accessories and options based on the categories of products the user has previously purchased. For example, if the user has previously purchased a smartphone, the reception unit can suggest cases and chargers suitable for that smartphone. Furthermore, the reception unit can learn the user's preferences for specific brands and categories from the user's past purchase history and suggest optimal products. This allows the reception unit to utilize the user's past purchase history to make more personalized product suggestions.

[0053] When providing information about the identified item, the providing unit can refer to the user's past search history and provide related information preferentially. For example, if the user has frequently searched for television prices in the past, the providing unit can provide price information preferentially when asked, "What is the price of this television?" Also, if the user has been interested in a particular brand or model in the past, detailed information about that brand or model can be provided preferentially. Furthermore, the providing unit can also provide information related to a particular category or feature from the user's past search history preferentially. In this way, the providing unit can utilize the user's past search history to provide more relevant information.

[0054] When notifying the user of the progress of the purchase procedure, the notification unit can select the most appropriate notification method by referring to the user's past notification history. For example, if the user has previously preferred voice notification, the notification unit can prioritize voice notification. Also, if the user has previously preferred text notification, the notification unit can prioritize text notification. Furthermore, if the user has previously preferred to receive notifications during a specific time period, the notification unit can also send notifications during that time period. This allows the notification unit to utilize the user's past notification history to provide a more appropriate notification method.

[0055] During voice analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past pronunciation data. For example, if the user has had a particular accent or dialect in the past, the analysis algorithm can be adjusted taking those characteristics into account. It is also possible to learn changes in the user's pronunciation in real time and dynamically optimize the analysis algorithm. Furthermore, it is also possible to learn the user's pronunciation characteristics and apply an individually optimized voice analysis algorithm. This allows the analysis unit to learn the user's pronunciation characteristics and adjust the analysis algorithm during voice analysis, thereby providing more accurate analysis results.

[0056] The procedure unit can select a procedure method based on the user's past purchase history during the purchase process. For example, it can prioritize the payment method that the user has used in the past. It can also prioritize a specific delivery method based on the user's past purchase history. It can also analyze the user's past purchase history and suggest the most efficient procedure method. This allows the procedure unit to select a procedure method based on the user's past purchase history during the purchase process, thereby providing a smoother purchase process.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The reception unit receives information about the item the user wants to purchase by voice. For example, if the user says, "I want to buy a television," the reception unit receives the voice. The reception unit uses a microphone to receive the voice input and converts the voice into text using voice recognition technology. Step 2: The analysis unit analyzes the voice received by the reception unit and identifies the item. For example, the analysis unit converts the voice into text using a voice recognition algorithm and identifies the item using natural language processing technology. Step 3: The providing unit provides information about the item identified by the analyzing unit by voice. For example, the providing unit uses voice synthesis technology to provide price information by voice to a user who asks, "What is the price of this TV?" Step 4: The procedure unit performs the purchase procedure based on the information provided by the provision unit. For example, when the user gives a voice instruction such as "I want to purchase this TV," the procedure unit accepts the user's voice instruction to proceed with the purchase procedure and automates the purchase procedure. Step 5: The notification unit notifies the user by voice of the progress of the purchase procedure performed by the procedure unit. For example, the notification unit uses voice synthesis technology to notify the user by voice that "the purchase procedure has been completed."

[0059] (Example 2) An AI support system according to an embodiment of the present invention enables elderly people who are unfamiliar with using PCs or smartphones, or people with physical disabilities such as the blind, to purchase items from e-commerce sites through conversation alone (or through conversation alone with the aid of a screen). In this AI support system, the user inputs information about the item they wish to purchase via voice, and the AI ​​analyzes the voice to identify the desired item. The AI ​​then provides information about the identified item via voice, and the user responds again via voice to select the item. The AI ​​then provides detailed information about the selected item, such as its price and availability, via voice. Once the user confirms the purchase, the AI ​​proceeds with the purchase. Furthermore, the AI ​​notifies the user via voice of the progress of the purchase process. This system allows elderly people who are unfamiliar with using PCs or smartphones, or people with physical disabilities such as the blind, to easily purchase items from e-commerce sites through conversation alone, thereby enriching their lives and improving convenience. For example, if a user voice-inputs, "I want to buy a TV," the AI ​​analyzes the voice and provides information about the TV. If the user asks, "What is the price of this TV?", the AI ​​provides price information via voice. When a user verbally instructs, "I want to buy this TV," the AI ​​will proceed with the purchase process and provide voice notifications about the progress. This allows the AI ​​support system to allow even elderly people who are not accustomed to using PCs or smartphones, or people with physical disabilities such as the blind, to easily purchase items from e-commerce sites using only conversation, enriching the lives of those involved and improving convenience.

[0060] An AI support system according to an embodiment includes a reception unit, an analysis unit, a provision unit, a procedure unit, and a notification unit. The reception unit receives information about an item the user wants to purchase via voice. For example, when the user voice-inputs, "I want to buy a television," the reception unit receives the voice. The analysis unit analyzes the voice received by the reception unit and identifies the item. For example, the analysis unit converts the voice into text using a speech recognition algorithm and identifies the item using natural language processing technology. The provision unit provides information about the item identified by the analysis unit via voice. For example, the provision unit provides price information via voice to a user who asks, "How much does this television cost?" The procedure unit performs a purchase procedure based on the information provided by the provision unit. For example, when a user voice-instructs, "I want to purchase this television," the procedure unit proceeds with the purchase procedure. The notification unit provides a voice notification of the progress of the purchase procedure performed by the procedure unit. For example, the notification unit notifies the user via voice, "The purchase procedure has been completed." As a result, the AI ​​support system according to the embodiment allows even elderly people who are not accustomed to using PCs or smartphones, or people with physical disabilities such as the blind, to easily purchase items from e-commerce sites through conversation alone. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit uses a microphone to accept voice input and converts the voice into text using voice recognition technology. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit converts voice into text using a voice recognition algorithm and identifies the item using natural language processing technology. Some or all of the above-described processing in the provision unit may be performed using AI, for example, or without AI. For example, the provision unit uses voice synthesis technology to provide information about the identified item by voice. Some or all of the above-described processing in the transaction unit may be performed using AI, for example, or without AI. For example, the transaction unit accepts voice instructions from the user to proceed with the purchase process and automates the purchase process.Some or all of the above-described processing by the notification unit may be performed using, for example, AI, or may be performed without AI. For example, the notification unit may use voice synthesis technology to notify the progress of the purchase procedure by voice.

[0061] The reception unit can accept voice input. For example, when a user voice-inputs "I want to buy a TV," the reception unit accepts the voice. The reception unit can use a microphone to accept the voice input. For example, the reception unit can accept the voice input using a voice recognition function of a smartphone. The reception unit can also use a dedicated voice recognition device to accept the voice input. For example, the reception unit can recognize the user's voice with high accuracy using the voice recognition device. This allows the reception unit to accept the voice input. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can perform voice recognition using AI to accept the voice input.

[0062] The analysis unit can analyze the voice and identify the item. For example, the analysis unit converts the voice into text using a voice recognition algorithm and identifies the item using natural language processing technology. For example, when a user vocally inputs "I want to buy a television," the analysis unit converts the voice into text and identifies information about the television. The analysis unit can also analyze the user's voice with high accuracy using a voice recognition algorithm. For example, the analysis unit can analyze the user's voice in real time using a voice recognition algorithm and identify the item. The analysis unit can also identify the item from the user's voice using natural language processing technology. For example, the analysis unit can extract the category and characteristics of the item from the user's voice using natural language processing technology and identify the item. This allows the analysis unit to analyze the voice and identify the item. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can use AI to convert the voice into text using a voice recognition algorithm and identify the item using natural language processing technology.

[0063] The providing unit can provide information about the identified item by voice. For example, when a user asks, "What is the price of this TV?", the providing unit provides price information by voice. The providing unit can provide information about the identified item by voice using voice synthesis technology. For example, the providing unit can provide price information about the identified item by voice using voice synthesis technology. The providing unit can also provide detailed information about the identified item by voice. For example, the providing unit can provide stock status and delivery information about the identified item by voice. The providing unit can also customize the information to provide information about the identified item by voice in response to a user's voice instruction. For example, when a user asks, "What are the features of this TV?", the providing unit can provide information about the features of the TV by voice. This allows the providing unit to provide information about the identified item by voice. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can use AI to provide information about the identified item by voice using voice synthesis technology.

[0064] The transaction unit can perform the purchase procedure based on the information provided by the providing unit. For example, when a user gives a voice instruction such as "I want to buy this TV," the transaction unit proceeds with the purchase procedure. The transaction unit can accept a user's voice instruction to automate the purchase procedure. For example, the transaction unit proceeds with the purchase procedure based on the user's voice instruction. The transaction unit can also collect necessary information in accordance with the user's voice instruction to proceed with the purchase procedure. For example, when a user gives an instruction such as "I want to buy this TV with a credit card," the transaction unit can collect credit card information and proceed with the purchase procedure. The transaction unit can also collect delivery information based on the user's voice instruction to proceed with the purchase procedure. For example, when a user gives an instruction such as "Deliver this TV to my home," the transaction unit can collect delivery address information and proceed with the purchase procedure. This allows the transaction unit to perform the purchase procedure based on the information provided by the providing unit. Some or all of the above-described processing in the transaction unit may be performed using, or without, AI. For example, the transaction unit may use AI to proceed with the purchase procedure based on the user's voice instruction.

[0065] The notification unit can provide audio notification of the progress of the purchase procedure performed by the processing unit. For example, the notification unit can provide audio notification of "The purchase procedure has been completed." The notification unit can provide audio notification of the progress of the purchase procedure using voice synthesis technology. For example, the notification unit can provide audio notification of the progress of the purchase procedure in real time. The notification unit can also customize the content of the notification in accordance with a user's voice instruction to provide audio notification of the progress of the purchase procedure. For example, when the user asks, "Tell me the progress of the purchase procedure," the notification unit can provide audio notification of the current progress. The notification unit can also adjust the timing of the notification based on the user's voice instruction to provide audio notification of the progress of the purchase procedure. For example, when the user instructs, "Notify me immediately when the purchase procedure is completed," the notification unit can provide an immediate notification when the purchase procedure is completed. This allows the notification unit to provide audio notification of the progress of the purchase procedure performed by the processing unit. Some or all of the above-described processing by the notification unit may be performed using, for example, AI, or may be performed without AI. For example, the notification unit can use AI to provide voice notification of the progress of the purchase procedure using voice synthesis technology.

[0066] The reception unit can estimate the user's emotion and adjust the timing of receiving the voice input based on the estimated user emotion. For example, if the user is nervous, the reception unit delays the timing of receiving the voice input to provide the user with time to relax. For example, if the user is relaxed, the reception unit can accelerate the timing of receiving the voice input to promote smooth dialogue. Furthermore, if the user is in a hurry, the reception unit can immediately receive the voice input and provide a prompt response. This allows the reception unit to adjust the timing of receiving the voice input according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can use AI to estimate the user's emotion and adjust the timing of receiving the voice input based on the estimated user emotion.

[0067] The reception unit can analyze the user's past voice input history and select a voice input method. The reception unit, for example, prioritizes recognition of phrases frequently used by the user in the past, thereby simplifying input. For example, the reception unit can learn the voice input patterns used by the user in the past and suggest an optimal input method. The reception unit can also select an input method suitable for a specific time period from the user's past voice input history. For example, the reception unit can prioritize recognition of phrases frequently used by the user in a specific time period, thereby simplifying input. This allows the reception unit to analyze the user's past voice input history and select a voice input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can use AI to analyze the user's past voice input history and select a voice input method.

[0068] The reception unit can filter the user's current environmental sounds to remove noise when receiving a voice input. For example, when the user is in a noisy environment, the reception unit filters the environmental sounds to improve the accuracy of the voice input. For example, when the user is in a quiet environment, the reception unit can remove even subtle noises to achieve clear voice input. Furthermore, when the user performs voice input while traveling, the reception unit can also filter wind noise and traffic noise to remove noise. For example, when the user performs voice input in a car, the reception unit can filter the in-car environmental sounds to improve the accuracy of the voice input. This allows the reception unit to filter the user's current environmental sounds to remove noise when receiving a voice input. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can use AI to filter the user's current environmental sounds to remove noise when receiving a voice input.

[0069] The reception unit can estimate the user's emotions and prioritize voice inputs based on the estimated user emotions. For example, when the user is stressed, the reception unit prioritizes important voice inputs and responds promptly. For example, when the user is relaxed, the reception unit can process voice inputs with normal priority. Furthermore, when the user is in a hurry, the reception unit can prioritize urgent voice inputs. For example, when the user issues an urgent instruction, the reception unit can process the instruction with the highest priority. This allows the reception unit to prioritize voice inputs according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can use AI to estimate the user's emotions and prioritize voice inputs based on the estimated user emotions.

[0070] The reception unit can prioritize acquiring highly relevant information based on the user's geographical location information when receiving a voice input. For example, when the user is in a specific area, the reception unit prioritizes acquiring information related to that area. For example, when the user is traveling, the reception unit can prioritize acquiring information related to the travel destination. Furthermore, when the user is at home, the reception unit can prioritize acquiring information about the area around the user's home. For example, when the user performs voice input at home, the reception unit can prioritize acquiring store information and service information about the area around the user's home. This allows the reception unit to prioritize acquiring highly relevant information based on the user's geographical location information when receiving a voice input. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can use AI to prioritize acquiring highly relevant information based on the user's geographical location information when receiving a voice input.

[0071] The reception unit can analyze the user's social media activities and acquire related information when receiving the voice input. The reception unit, for example, prioritizes acquisition of information related to items frequently mentioned by the user on social media. For example, the reception unit can analyze the content of the user's social media posts and acquire information based on the user's interests. The reception unit can also acquire information related to items mentioned by the user's social media followers and friends. For example, the reception unit can prioritize acquisition of information related to items frequently mentioned by the user's followers and friends. This allows the reception unit to analyze the user's social media activities and acquire related information when receiving the voice input. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can use AI to analyze the user's social media activities and acquire related information when receiving the voice input.

[0072] The analysis unit can estimate the user's emotions and adjust the accuracy of the voice analysis based on the estimated user emotions. For example, when the user is nervous, the analysis unit increases the accuracy of the voice analysis to prevent erroneous recognition. For example, when the user is relaxed, the analysis unit can perform voice analysis with normal accuracy. The analysis unit can also adjust the accuracy to perform a quick analysis when the user is in a hurry. For example, when the user is in a hurry, the analysis unit can adjust the accuracy of the voice analysis to perform a quick analysis. This allows the analysis unit to adjust the accuracy of the voice analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can use AI to estimate the user's emotions and adjust the accuracy of the voice analysis based on the estimated user emotions.

[0073] During voice analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past purchase history. The analysis unit improves the accuracy of the voice analysis, for example, based on information about items purchased by the user in the past. For example, the analysis unit can prioritize analysis of voices related to a specific brand or category from the user's past purchase history. The analysis unit can also analyze the user's past purchase history and accurately analyze voice input about similar items. For example, the analysis unit can improve the accuracy of the voice analysis based on information about items purchased by the user in the past. This allows the analysis unit to improve the accuracy of the analysis by referring to the user's past purchase history during voice analysis. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can use AI to improve the accuracy of the analysis by referring to the user's past purchase history during voice analysis.

[0074] The analysis unit can learn the user's pronunciation characteristics and adjust the analysis algorithm during voice analysis. For example, the analysis unit can learn the user's pronunciation characteristics and apply an individually optimized voice analysis algorithm. For example, if the user has a specific accent or dialect, the analysis unit can adjust the analysis algorithm taking those characteristics into account. The analysis unit can also learn changes in the user's pronunciation in real time and dynamically optimize the analysis algorithm. For example, the analysis unit can learn the user's pronunciation characteristics and apply an individually optimized voice analysis algorithm. This allows the analysis unit to learn the user's pronunciation characteristics and adjust the analysis algorithm during voice analysis. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can use AI to learn the user's pronunciation characteristics and adjust the analysis algorithm during voice analysis.

[0075] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit provides a simple, highly visible display method. For example, if the user is relaxed, the analysis unit can provide a display method including detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. For example, if the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. This allows the analysis unit to adjust the display method of the analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can use AI to estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions.

[0076] The analysis unit can improve the accuracy of analysis by taking into account the user's geographical background during voice analysis. For example, when the user is in a specific region, the analysis unit can improve the accuracy of analysis by taking into account the dialect and accent of that region. For example, when the user is traveling, the analysis unit can improve the accuracy of analysis by taking into account the language and accent of the destination. Furthermore, when the user is at home, the analysis unit can improve the accuracy of analysis by taking into account the geographical background around the user's home. For example, when the user performs voice input at home, the analysis unit can improve the accuracy of analysis by taking into account the geographical background around the user's home. This allows the analysis unit to improve the accuracy of analysis by taking into account the user's geographical background during voice analysis. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can use AI to improve the accuracy of analysis by taking into account the user's geographical background during voice analysis.

[0077] The analysis unit can improve the accuracy of the analysis by referring to literature related to the user during speech analysis. The analysis unit can improve the accuracy of the speech analysis, for example, based on literature previously referenced by the user. For example, the analysis unit can prioritize analysis of specific technical terms or phrases from literature previously referenced by the user. The analysis unit can also analyze literature previously referenced by the user and accurately analyze speech input with similar content. For example, the analysis unit can improve the accuracy of the speech analysis based on literature previously referenced by the user. This allows the analysis unit to improve the accuracy of the analysis by referring to literature previously referenced by the user during speech analysis. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can use AI to improve the accuracy of the analysis by referring to literature previously referenced by the user during speech analysis.

[0078] The providing unit can estimate the user's emotions and adjust the way information is presented based on the estimated user's emotions. For example, if the user is nervous, the providing unit can provide a simple, highly visible display method. For example, if the user is relaxed, the providing unit can provide a display method including detailed information. Furthermore, if the user is in a hurry, the providing unit can also provide a display method that focuses on the main points. For example, if the user is in a hurry, the providing unit can provide a display method that focuses on the main points. This allows the providing unit to adjust the way information is presented based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit may be performed using AI, or may be performed without AI. For example, the providing unit can use AI to estimate the user's emotions and adjust the way information is presented based on the estimated user's emotions.

[0079] The providing unit can adjust the level of detail of the information to be provided based on the importance of the item when providing information. For example, the providing unit can provide detailed information about important items to allow the user to fully understand. For example, the providing unit can provide concise information about general items to allow the user to make a quick decision. The providing unit can also provide detailed information about items in which the user is particularly interested to increase user satisfaction. For example, the providing unit can provide detailed information about items in which the user is particularly interested. This allows the providing unit to adjust the level of detail of the information to be provided based on the importance of the item when providing information. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can use AI to adjust the level of detail of the information to be provided based on the importance of the item when providing information.

[0080] The providing unit can apply different information provision algorithms depending on the category of the item when providing information. For example, the providing unit can apply an algorithm including technical details to information about electronic devices. For example, the providing unit can apply an algorithm including details about design and materials to information about clothing. The providing unit can also apply an algorithm including details about ingredients and nutritional value to information about food. For example, the providing unit can provide detailed information about food. This allows the providing unit to apply different information provision algorithms depending on the category of the item when providing information. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can use AI to apply different information provision algorithms depending on the category of the item when providing information.

[0081] The providing unit can estimate the user's emotions and adjust the length of information provided based on the estimated user's emotions. For example, if the user is nervous, the providing unit can provide short, to-the-point information. For example, if the user is relaxed, the providing unit can provide longer information including detailed explanations. The providing unit can also provide quick, concise information if the user is in a hurry. For example, if the user is in a hurry, the providing unit can provide quick, concise information. This allows the providing unit to adjust the length of information provided according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can use AI to estimate the user's emotions and adjust the length of information provided based on the estimated user's emotions.

[0082] The providing unit can determine the priority of information provision based on the time of submission of the item when providing information. For example, the providing unit can provide information about urgently needed items with a higher priority. For example, the providing unit can provide information about general items with a normal priority. The providing unit can also provide information about items in which the user is particularly interested with a higher priority. For example, the providing unit can provide information about items in which the user is particularly interested with a higher priority. This allows the providing unit to determine the priority of information provision based on the time of submission of the item when providing information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can use AI to determine the priority of information provision based on the time of submission of the item when providing information.

[0083] The providing unit can adjust the order of information provision based on the relevance of the items when providing information. For example, the providing unit can prioritize providing information related to items previously purchased by the user. For example, the providing unit can prioritize providing information related to items in which the user is currently interested. The providing unit can also prioritize providing information about highly relevant items based on the user's purchase history. For example, the providing unit can prioritize providing information related to items previously purchased by the user. This allows the providing unit to adjust the order of information provision based on the relevance of the items when providing information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can use AI to adjust the order of information provision based on the relevance of the items when providing information.

[0084] The procedure unit can estimate the user's emotions and adjust the purchase checkout method based on the estimated user emotions. For example, if the user is nervous, the procedure unit can provide a simple, easy-to-understand procedure. For example, if the user is relaxed, the procedure unit can provide a procedure that includes detailed instructions. Furthermore, if the user is in a hurry, the procedure unit can also provide a quick and concise procedure. For example, if the user is in a hurry, the procedure unit can provide a quick and concise procedure. This allows the procedure unit to adjust the purchase checkout method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the procedure unit may be performed using AI, for example, or without AI. For example, the procedure unit can use AI to estimate the user's emotions and adjust the purchase checkout method based on the estimated user emotions.

[0085] During the purchase process, the procedure unit can select a procedure method based on the user's past purchase history. For example, the procedure unit can prioritize suggesting payment methods that the user has used in the past. For example, the procedure unit can prioritize suggesting a specific delivery method based on the user's past purchase history. The procedure unit can also analyze the user's past purchase history and suggest the most efficient procedure method. For example, the procedure unit can prioritize suggesting payment methods that the user has used in the past. This allows the procedure unit to select a procedure method based on the user's past purchase history during the purchase process. Some or all of the above-described processing in the procedure unit may be performed using, for example, AI, or may be performed without using AI. For example, the procedure unit can use AI to select a procedure method based on the user's past purchase history during the purchase process.

[0086] The procedure unit can customize the procedure means based on the user's current living situation during the purchase process. For example, if the user is busy, the procedure unit can suggest a quick procedure. For example, if the user is relaxed, the procedure unit can suggest a procedure with detailed instructions. The procedure unit can also suggest the procedure optimal for a specific living situation when the user is in that situation. For example, if the user is in a specific living situation, the procedure unit can suggest the procedure optimal for that situation. This allows the procedure unit to customize the procedure means based on the user's current living situation during the purchase process. Some or all of the above-described processing in the procedure unit may be performed using, or without, AI. For example, the procedure unit can use AI to customize the procedure means based on the user's current living situation during the purchase process.

[0087] The procedure unit can estimate the user's emotions and determine the priority of the purchase procedure based on the estimated user emotions. For example, if the user is nervous, the procedure unit prioritizes important procedures. For example, if the user is relaxed, the procedure unit can process procedures with normal priority. Furthermore, if the user is in a hurry, the procedure unit can also prioritize urgent procedures. For example, if the user is in a hurry, the procedure unit can prioritize urgent procedures. This allows the procedure unit to determine the priority of the purchase procedure based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the procedure unit may be performed using AI, for example, or without AI. For example, the procedure unit can use AI to estimate the user's emotions and determine the priority of the purchase procedure based on the estimated user emotions.

[0088] The procedure unit can select a procedure method based on the user's geographical location information during the purchase process. For example, if the user is in a specific area, the procedure unit can suggest a delivery method appropriate for that area. For example, if the user is traveling, the procedure unit can suggest a procedure method appropriate for the user's travel destination. Furthermore, if the user is at home, the procedure unit can also suggest a delivery method near the user's home. For example, if the user is completing the purchase process at home, the procedure unit can suggest a delivery method near the user's home. This allows the procedure unit to select a procedure method based on the user's geographical location information during the purchase process. Some or all of the above-described processing in the procedure unit may be performed using, for example, AI, or may be performed without using AI. For example, the procedure unit can use AI to select a procedure method based on the user's geographical location information during the purchase process.

[0089] The procedure unit can analyze the user's social media activity to suggest a procedure during the purchase process. The procedure unit can, for example, suggest a payment method frequently mentioned by the user on social media. For example, the procedure unit can analyze the content of the user's social media posts to suggest a procedure based on their interests. The procedure unit can also suggest a procedure used by the user's social media followers or friends. For example, the procedure unit can suggest a procedure frequently used by the user's followers or friends. In this way, the procedure unit can analyze the user's social media activity to suggest a procedure during the purchase process. Some or all of the above-described processing in the procedure unit may be performed using, or without, AI. For example, the procedure unit can use AI to analyze the user's social media activity to suggest a procedure during the purchase process.

[0090] The notification unit can estimate the user's emotions and adjust the notification method based on the estimated user's emotions. For example, if the user is nervous, the notification unit can provide a notification in a calm voice. For example, if the user is relaxed, the notification unit can provide a notification in a cheerful voice. The notification unit can also provide a quick and concise notification if the user is in a hurry. For example, if the user is in a hurry, the notification unit can provide a quick and concise notification. This allows the notification unit to adjust the notification method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can use AI to estimate the user's emotions and adjust the notification method based on the estimated user's emotions. === Hard Collateral 1-1 === Each of the multiple elements, including the reception unit, analysis unit, provision unit, procedure unit, and notification unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit receives voice input using the microphone 38B of the smart device 14 and converts the voice into text using the control unit 46A. The analysis unit analyzes the voice using the identification processing unit 290 of the data processing device 12 to identify an item. The provision unit provides information about the identified item by voice using the speaker 40B of the smart device 14. The procedure unit proceeds with the purchase procedure using the identification processing unit 290 of the data processing device 12. The notification unit notifies the progress of the purchase procedure by voice using the speaker 40B of the smart device 14. The reception unit can use an emotion engine or a generation AI to estimate the user's emotion and adjust the timing of receiving the voice input based on the estimated user's emotion. === Hard Collateral 1-2 === Each of the multiple elements, including the reception unit, analysis unit, provision unit, procedure unit, and notification unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit receives voice input using the microphone 238 of the smart glasses 214 and converts the voice into text using the control unit 46A. The analysis unit analyzes the voice and identifies an item using the identification processing unit 290 of the data processing device 12. The provision unit provides information about the identified item by voice using the speaker 240 of the smart glasses 214. The procedure unit proceeds with the purchase procedure using the identification processing unit 290 of the data processing device 12. The notification unit notifies the progress of the purchase procedure by voice using the speaker 240 of the smart glasses 214. The reception unit can use an emotion engine or a generative AI to estimate the user's emotion and adjust the timing of receiving voice input based on the estimated user's emotion. === Hard Collateral 1-3 === Each of the multiple elements including the above-described reception unit, analysis unit, provision unit, procedure unit, and notification unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit receives voice input using the microphone 238 of the headset-type terminal 314 and converts the voice into text using the control unit 46A. The analysis unit analyzes the voice using the identification processing unit 290 of the data processing device 12 to identify an item. The provision unit provides information about the identified item by voice using the speaker 240 of the headset-type terminal 314. The procedure unit proceeds with the purchase procedure using the identification processing unit 290 of the data processing device 12. The notification unit notifies the progress of the purchase procedure by voice using the speaker 240 of the headset-type terminal 314. The reception unit can use an emotion engine or a generation AI to estimate the user's emotion and adjust the timing of receiving voice input based on the estimated user's emotion. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, provision unit, procedure unit, and notification unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit receives voice input using the microphone 238 of the robot 414 and converts the voice into text using the control unit 46A. The analysis unit analyzes the voice using the identification processing unit 290 of the data processing device 12 to identify an item. The provision unit provides information about the identified item by voice using the speaker 240 of the robot 414. The procedure unit proceeds with the purchase procedure using the identification processing unit 290 of the data processing device 12. The notification unit notifies the progress of the purchase procedure by voice using the speaker 240 of the robot 414. The reception unit can use an emotion engine or a generative AI to estimate the user's emotion and adjust the timing of receiving voice input based on the estimated user's emotion.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] When receiving a user's voice input, the reception unit can automatically present related product candidates by referring to the user's past purchase history. For example, if the user has a history of purchasing televisions in the past, the reception unit can prioritize presenting brands and models of televisions previously purchased when the user voice-inputs, "I want to buy a television." The reception unit can also suggest related accessories and options based on the categories of products the user has previously purchased. For example, if the user has previously purchased a smartphone, the reception unit can suggest cases and chargers suitable for that smartphone. Furthermore, the reception unit can learn the user's preferences for specific brands and categories from the user's past purchase history and suggest optimal products. This allows the reception unit to utilize the user's past purchase history to make more personalized product suggestions.

[0093] During voice analysis, the analysis unit can estimate the user's current emotional state and dynamically adjust the accuracy of the analysis based on the estimated emotion. For example, if the user is nervous, the analysis unit can increase the accuracy of voice recognition and perform analysis more carefully to prevent misrecognition. Furthermore, if the user is relaxed, voice analysis can be performed with normal accuracy, enabling smooth dialogue. Furthermore, if the user is in a hurry, the analysis unit can adjust the accuracy to perform quick analysis and respond quickly. This allows the analysis unit to dynamically adjust the accuracy of voice analysis according to the user's emotional state and provide more appropriate analysis results.

[0094] When providing information about the identified item, the providing unit can refer to the user's past search history and provide related information preferentially. For example, if the user has frequently searched for television prices in the past, the providing unit can provide price information preferentially when asked, "What is the price of this television?" Also, if the user has been interested in a particular brand or model in the past, detailed information about that brand or model can be provided preferentially. Furthermore, the providing unit can also provide information related to a particular category or feature from the user's past search history preferentially. In this way, the providing unit can utilize the user's past search history to provide more relevant information.

[0095] When the user is making a purchase, the procedure unit can estimate the user's current emotional state and adjust the speed of the procedure based on the estimated emotion. For example, if the user is nervous, the procedure unit can proceed slowly to allow the user to complete the procedure with ease. If the user is relaxed, the procedure unit can proceed at a normal speed to ensure smooth progress. Furthermore, if the user is in a hurry, the procedure unit can proceed quickly to respond promptly to the user's request. This allows the procedure unit to adjust the speed of the procedure according to the user's emotional state and provide a more appropriate purchase procedure.

[0096] When notifying the user of the progress of the purchase procedure, the notification unit can select the most appropriate notification method by referring to the user's past notification history. For example, if the user has previously preferred voice notification, the notification unit can prioritize voice notification. Also, if the user has previously preferred text notification, the notification unit can prioritize text notification. Furthermore, if the user has previously preferred to receive notifications during a specific time period, the notification unit can also send notifications during that time period. This allows the notification unit to utilize the user's past notification history to provide a more appropriate notification method.

[0097] The reception unit can estimate the user's emotions and adjust the timing of receiving the voice input based on the estimated emotions. For example, if the user is nervous, the timing of receiving the voice input can be delayed to provide the user with time to relax. Also, if the user is relaxed, the timing of receiving the voice input can be advanced to promote smooth dialogue. Furthermore, if the user is in a hurry, the timing of receiving the voice input can be made immediate to enable a prompt response. This allows the reception unit to adjust the timing of receiving the voice input according to the user's emotions.

[0098] During voice analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past pronunciation data. For example, if the user has had a particular accent or dialect in the past, the analysis algorithm can be adjusted taking those characteristics into account. It is also possible to learn changes in the user's pronunciation in real time and dynamically optimize the analysis algorithm. Furthermore, it is also possible to learn the user's pronunciation characteristics and apply an individually optimized voice analysis algorithm. This allows the analysis unit to learn the user's pronunciation characteristics and adjust the analysis algorithm during voice analysis, thereby providing more accurate analysis results.

[0099] The providing unit can estimate the user's emotions and adjust the way information is presented based on the estimated emotions. For example, if the user is nervous, a simple, highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points can be provided. This allows the providing unit to adjust the way information is presented in accordance with the user's emotions, thereby providing more appropriate information.

[0100] The procedure unit can select a procedure method based on the user's past purchase history during the purchase process. For example, it can prioritize the payment method that the user has used in the past. It can also prioritize a specific delivery method based on the user's past purchase history. It can also analyze the user's past purchase history and suggest the most efficient procedure method. This allows the procedure unit to select a procedure method based on the user's past purchase history during the purchase process, thereby providing a smoother purchase process.

[0101] The notification unit can estimate the user's emotions and adjust the notification method based on the estimated emotions. For example, if the user is nervous, the notification can be made in a calm voice. If the user is relaxed, the notification can be made in a cheerful voice. Furthermore, if the user is in a hurry, the notification can be made quickly and concisely. This allows the notification unit to adjust the notification method according to the user's emotions and provide more appropriate notifications.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The reception unit receives information about the item the user wants to purchase by voice. For example, if the user says, "I want to buy a television," the reception unit receives the voice. The reception unit uses a microphone to receive the voice input and converts the voice into text using voice recognition technology. Step 2: The analysis unit analyzes the voice received by the reception unit and identifies the item. For example, the analysis unit converts the voice into text using a voice recognition algorithm and identifies the item using natural language processing technology. Step 3: The providing unit provides information about the item identified by the analyzing unit by voice. For example, the providing unit uses voice synthesis technology to provide price information by voice to a user who asks, "What is the price of this TV?" Step 4: The procedure unit performs the purchase procedure based on the information provided by the provision unit. For example, when the user gives a voice instruction such as "I want to purchase this TV," the procedure unit accepts the user's voice instruction to proceed with the purchase procedure and automates the purchase procedure. Step 5: The notification unit notifies the user by voice of the progress of the purchase procedure performed by the procedure unit. For example, the notification unit uses voice synthesis technology to notify the user by voice that "the purchase procedure has been completed."

[0104] 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.

[0105] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0106] 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.

[0107] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0108] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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).

[0114] 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.

[0115] 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.

[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 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.

[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 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.

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

[0122] 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.

[0123] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0124] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0125] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0132] 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.

[0133] 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.

[0134] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0135] 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.

[0136] 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.

[0137] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0138] 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.

[0139] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0140] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0152] 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.

[0153] 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.

[0154] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0155] 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.

[0156] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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."

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] [Explanation of symbols]

[0176] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives voice input; an analysis unit that analyzes the voice received by the reception unit and identifies an item; a providing unit that provides information about the item identified by the analyzing unit; a procedure unit that performs a purchase procedure based on the information provided by the provision unit; a notification unit that notifies the progress of the purchase procedure performed by the procedure unit. A system characterized by:

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

2. The system of claim 1.

3. The reception unit Analyze the user's past voice input history and select a voice input method 2. The system of claim 1.

4. The reception unit When accepting voice input, filters the user's current ambient sounds to remove noise 2. The system of claim 1.

5. The reception unit Estimate the user's emotions and prioritize voice inputs based on the estimated user emotions.

2. The system of claim 1.

6. The reception unit When receiving voice input, prioritize relevant information based on the user's geographic location.

2. The system of claim 1.

7. The reception unit Upon receiving voice input, analyze the user's social media activity and retrieve relevant information 2. The system of claim 1.

8. The analysis unit Estimate the user's emotions and adjust the accuracy of voice analysis based on the estimated user emotions.

2. The system of claim 1.

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