system

Abstract

We provide the system. [Solution] A speech recognition means that receives voice input and converts the voice data into text data, A product analysis means that analyzes the aforementioned text data and identifies potential purchasable products, A payment processing means that executes an electronic payment procedure based on the product selected from the aforementioned candidate products, A delivery instruction means for instructing the delivery arrangements for the selected product, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In conventional online shopping, users need to manually operate from placing an order to settlement, which is particularly difficult for visually impaired people and the elderly. Also, with the increase in telecommuting and the growing demand for contactless payment, there is a need for a more efficient and waste-free shopping experience, so there is a demand for a system that allows users to easily purchase products through natural conversation.

Means for Solving the Problems

[0005] This invention provides a system that allows users to complete their shopping through natural dialogue without using their hands, by converting voice input into text data using voice recognition means, analyzing the user's intent with product analysis means to identify potential purchase items, and then performing electronic payment for the selected items with payment processing means and instructing delivery of the selected items with delivery instruction means.

[0006] "Speech recognition means" refers to a technology or device for receiving speech input and converting it into text data.

[0007] "Product analysis means" refers to a technology or device for identifying potential purchasable products by analyzing text data converted from speech.

[0008] "Payment processing means" refers to technology or equipment for executing electronic payment procedures related to the purchase of selected goods.

[0009] "Delivery instruction means" refers to technology or equipment for instructing and managing the delivery arrangements for purchased goods.

[0010] A "system" refers to something that integrates multiple technologies and methods to provide specific services or functions to users. [Brief explanation of the drawing]

[0011] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0012] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0013] First, let's explain the terminology used in the following explanation.

[0014] In the following embodiments, the labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units 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), and the like.

[0015] In the following embodiments, the labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0016] In the following embodiments, the labeled storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0017] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor and 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), or Bluetooth (registered trademark), and the like.

[0018] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0019] [First Embodiment]

[0020] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0021] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0022] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0023] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0024] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0025] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0026] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0027] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0028] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0029] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0030] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0031] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0032] This invention provides a system for users to order products using voice commands and to make electronic payments easily and securely. The following describes the configurations for implementing this system.

[0033] First, the user speaks to a device such as a smart speaker to specify the product they want to purchase. The device receives this voice and converts it into text data using speech recognition. This text data is sent to a server, where product analysis tools analyze the user's intent. For example, if the user says, "I want to buy milk," the server generates a list of suggested products that include "milk."

[0034] The server sends the generated list of product candidates to the device as personalized product suggestions based on the user's past purchase history and preferences. The device notifies the user of this information using speech synthesis technology and prompts them to select a product. Once the user selects a specific product, that information is sent back to the server.

[0035] Next, the server uses a payment processing device to perform electronic payment for the selected items. During this process, it works in conjunction with the payment system to ensure secure transactions. Once payment is complete, the server uses a delivery instruction device to instruct the partner store to deliver the items and notifies the terminal that the delivery arrangements are complete.

[0036] For example, even if a user adds an orange juice to their order, the system repeats the same procedure, efficiently processing multiple items ordered by the user. This system allows users to complete their orders by voice, providing a convenient shopping experience that does not rely on visual cues.

[0037] The following describes the processing flow.

[0038] Step 1:

[0039] The user gives a voice command to the smart speaker saying, "I want to buy milk." The device receives this voice input and saves it as digital data.

[0040] Step 2:

[0041] The device sends the stored audio data to the server. During this process, the audio data is encrypted over the network and securely transmitted to the server.

[0042] Step 3:

[0043] The server converts the received audio data into text data using speech recognition technology. This text data is further processed to represent the user's intent.

[0044] Step 4:

[0045] The server analyzes the text data using a product analysis tool and lists potential "milk" products that are available for purchase. During this process, the server queries the product database to retrieve relevant product information.

[0046] Step 5:

[0047] The server lists potential products, personalizes them based on the user's past purchase history and preferences, and sends them to the device as optimized product suggestions.

[0048] Step 6:

[0049] Based on the product information received from the server, the terminal uses speech synthesis technology to ask the user, "A liter of milk from company A costs 500 yen, and a liter of milk from company B costs 480 yen. Which would you like?"

[0050] Step 7:

[0051] The user responds by voice saying, "I select company B's milk," and the terminal sends that voice input back to the server. The server confirms this selection.

[0052] Step 8:

[0053] The server uses payment processing methods for the items it has confirmed, and executes the payment process in conjunction with electronic payment systems such as PayPay. Once the payment is confirmed to be successful, the server notifies the terminal of the result.

[0054] Step 9:

[0055] Based on the payment completion information, the server uses a delivery instruction system to instruct a partner delivery company or store to ship the product. Once the delivery status is confirmed, the server sends that information to the terminal and notifies the user.

[0056] (Example 1)

[0057] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0058] In modern society, there is a growing need for systems that allow for easy and secure product purchases and electronic payments via voice. However, conventional systems lack personalization based on purchase history, limiting the user experience. Furthermore, there is a need for technology that efficiently manages the entire process from voice input to payment and delivery, providing users with a seamless purchasing experience.

[0059] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0060] In this invention, the server includes voice recognition means, information analysis means, and transaction processing means. This enables a consistent process from voice input to product suggestions, electronic payment, and delivery instructions, resulting in a convenient and personalized purchasing experience for the user.

[0061] "Voice recognition means" refers to technology that receives voice input from a user and converts it into text data.

[0062] "Information analysis means" refers to analytical techniques used to identify purchasable products based on converted text data.

[0063] "Transaction processing means" refers to technology for securely executing electronic payment procedures for selected goods.

[0064] "Delivery guidance means" refers to technology used to instruct partner organizations to arrange the delivery of selected products.

[0065] "Suggestion methods" refer to technologies that take into account a user's past transaction history to provide personalized product suggestions.

[0066] "Notification method" refers to a technology that uses speech synthesis technology to inform users of proposals and the completion of transactions.

[0067] This invention is a system that allows users to order products using voice commands and make secure electronic payments. The system includes voice recognition, information analysis, transaction processing, delivery guidance, suggestion, and notification means. The following describes how to implement this system in detail.

[0068] First, the user speaks to a device such as a smart speaker and gives voice commands for the product they wish to purchase. The device has standard voice recognition software installed, and in this example, it uses Google Assistant or a general voice recognition API to convert the voice data into text data.

[0069] Once this audio data is converted to text data, the terminal sends the data to the server. On the server, information analysis tools operate, for example using natural language processing technology to identify potential purchasable products. In this process, product attributes and similar products are analyzed to create a list of candidates.

[0070] The server then uses suggestion tools to generate personalized product suggestions based on the user's past transaction history and preferences. This information is sent back to the terminal and communicated to the user using speech synthesis technology (e.g., Google Text-to-Speech). This allows the user to proceed with the purchasing process using only voice, without needing to visually confirm the information.

[0071] Once the user selects the desired product and that information is sent to the server, the transaction processing mechanism is activated. This mechanism includes payment APIs such as PayPal and Stripe, and performs secure electronic payment for the selected product.

[0072] Once payment is complete, the delivery guidance system is activated and instructs an external service to arrange delivery of the specified items. Delivery information is sent to the partner, and the server notifies the terminal that processing is complete. The terminal then reports this information to the user, informing them that the order is complete.

[0073] As a concrete example, when a user says to the speaker, "I want to buy orange juice," the voice is converted into text, the server lists orange juice as a purchase option, makes suggestions considering past purchase history, and simultaneously completes the payment and arranges delivery, creating a seamless process.

[0074] An example of an input prompt for a generative AI model is, "Please tell me how a user orders a product by voice to a smart speaker." This allows the generative AI to explain in detail the process from voice ordering to electronic payment and delivery instructions.

[0075] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0076] Step 1:

[0077] Users give voice commands to a device such as a smart speaker to select products. When a user says, "I want to buy milk," that voice data is entered into the device.

[0078] Step 2:

[0079] The terminal converts audio data into text data using speech recognition. This process utilizes a speech recognition API to analyze the audio waveform and convert it into the string "I want to buy milk." The converted text data is then output from the terminal to the server.

[0080] Step 3:

[0081] The server passes the received text data to an information analysis tool to analyze the user's request. Here, natural language processing technology is used to generate a list of available products based on the keyword "milk". This list is output as the analysis result.

[0082] Step 4:

[0083] Based on the product analysis results, the server uses suggestion tools to create personalized product recommendations that take into account the user's past purchase history. For example, if a user has frequently purchased a particular brand of milk in the past, that brand will be prioritized in the recommendations. The generated recommendations are then output to the terminal.

[0084] Step 5:

[0085] The device uses speech synthesis technology to notify the user of its recommendations. Specifically, it selects the cheapest or most popular items from the suggested products and says, "I recommend the cheapest brand A milk." This voice information is then provided to the user.

[0086] Step 6:

[0087] The user selects a suggested product. For example, they might voice-instruct the device to make a specific selection, such as "I want to buy milk from brand A." This selection is then recorded as input data in the device.

[0088] Step 7:

[0089] The terminal sends the selection to the server, and the server's transaction processing mechanism is activated. Based on the selected items, electronic payment is carried out in conjunction with the payment system. At this time, the user's payment information is used as input information, and a payment completion status is output.

[0090] Step 8:

[0091] After payment is completed, the server uses a delivery guidance system to instruct partner stores to ship the goods. This delivery instruction includes the shipping address and estimated delivery date and time. This information is forwarded to the delivery company, and the user is notified via their device that the delivery arrangements are complete.

[0092] (Application Example 1)

[0093] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0094] There is a need to provide an electronic transaction method that allows users to intuitively search for products and complete the purchase process without relying on visual cues. Furthermore, it is necessary to accurately offer diverse product suggestions to users to improve their purchasing experience. Additionally, there is a need to develop a system that ensures transactions are conducted safely and quickly.

[0095] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0096] In this invention, the server includes a speech recognition device that receives voice data and converts the voice into text information, a product analysis device that analyzes the text information and determines candidate products that can be acquired, and an information providing device that guides the user through the purchasing process based on the voice input without visual assistance. This makes it possible for the user to efficiently and safely search for and purchase products using only their voice.

[0097] A "speech recognition device" is a device that processes speech data to convert it into text information.

[0098] A "product analysis device" is a device that analyzes textual information to identify potential products that can be acquired.

[0099] A "transaction management device" is a processing device that executes electronic transaction processing based on specified goods.

[0100] A "delivery instruction device" is a device used to perform operations to instruct the delivery of specified goods.

[0101] An "information provision device" is a device that guides users through the purchasing process based on voice input, without providing visual assistance.

[0102] This invention provides a system that allows users to search for and purchase products using voice commands, and mainly consists of a voice recognition device, a product analysis device, a transaction management device, a delivery instruction device, and an information provision device. This system operates using terminals such as smartphones and personal computers, and servers connected to them.

[0103] The user inputs the product they wish to purchase into the device using their voice. The device uses the Google Speech-to-Text API to instantly convert the voice data into text. Subsequently, a server receives this text information via AWS (registered trademark) and uses a product analysis device to identify product candidates based on the user's needs.

[0104] Next, the server uses a transaction management device based on the analysis results to conduct secure electronic transactions using the Stripe API. After this process, the server instructs the logistics system to ship the goods via a delivery instruction device and notifies the user of the delivery schedule using Google Cloud Messaging.

[0105] This system provides information to users without requiring visual assistance, offering suggestions and guidance on a variety of products. For example, if a user says, "I want to buy tea," the system can suggest the most suitable tea product from among several options based on the user's past preferences. An example of a prompt would be, "Show the voice recognition procedure when the user says, 'Order new earphones'." A generative AI model based on this prompt enables accurate recognition and processing of voice commands.

[0106] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0107] Step 1:

[0108] The user uses their smartphone's voice input function to speak the product they wish to purchase. The voice data is captured by the device's microphone and converted into text using the Google Speech-to-Text API. The input is voice data, and the output is text.

[0109] Step 2:

[0110] The terminal sends the converted text information to the server. The server receives this text information and uses a product analysis device on AWS to analyze the user's intent. The data processing performed here is the analysis of the text information, and based on the analyzed information, product candidates are identified. The input is text information, and the output is a list of product candidates.

[0111] Step 3:

[0112] The server uses an information provider to verbally repeat a list of product candidates to the user to inform them of the analysis results. It then makes optimal product recommendations. This step utilizes speech synthesis technology to convert text data back into speech data. The input is a list of product candidates, and the output is speech information.

[0113] Step 4:

[0114] When a user makes a selection from the suggested products, that selection information is sent back to the server. The server processes the electronic transaction using the Stripe API via a transaction management device. Secure settlement is performed through the electronic transaction, and the results are processed. The input is the selected product information, and the output is information regarding the completion of the transaction.

[0115] Step 5:

[0116] Upon receiving payment confirmation, the server instructs the logistics system to ship the goods via a delivery instruction device. This instruction is sent through Google Cloud Messaging. To notify the user that the delivery arrangements are complete, delivery completion information is also provided via voice. The input is transaction completion information, and the output is delivery arrangement completion information.

[0117] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0118] The present invention is a system that improves the user experience by combining a user voice-based product ordering and electronic payment with an emotion engine. This system includes voice recognition means, product analysis means, payment processing means, delivery instruction means, and an emotion engine.

[0119] The user voice-instructs a device, such as a smart speaker, to purchase the desired items. The device receives the user's voice in real time and converts it into text data using speech recognition. This text data is sent to a server, where product analysis tools analyze the user's intent to generate a list of potential purchase items.

[0120] In parallel, the emotion engine analyzes the voice data to estimate the user's emotional state. For example, if a user says, "I'm in a bit of a hurry right now and need milk immediately," the emotion engine detects the emotion of urgency and, based on that, suggests products with faster delivery as a priority. Furthermore, if the user is relaxed when placing an order, the engine can suggest additional related products, enabling flexible suggestions tailored to the user's emotions.

[0121] The server sends personalized product suggestions to the device based on analyzed sentiment information and purchase history. The device then uses speech synthesis technology to present this information to the user and prompt them to make a selection. Once the user selects a product, the information is returned to the server, and electronic payment is processed via the payment processing system.

[0122] After payment is completed, the server uses a delivery instruction system to arrange for the delivery of the selected items. The user will be notified of the delivery status as needed, and additional information and offers will be provided based on the emotions detected by the emotion engine, if necessary.

[0123] In this way, the system can utilize the user's voice and emotional information to provide a more natural and personalized shopping experience. For example, when a user is feeling stressed, the server can suggest products that promote relaxation.

[0124] The following describes the processing flow.

[0125] Step 1:

[0126] The user gives a voice command to the smart speaker saying, "I want to buy milk." The device receives this voice command and converts it into digital data.

[0127] Step 2:

[0128] The device sends the converted audio data to the server. The audio data is encrypted and securely transmitted to the server.

[0129] Step 3:

[0130] The server converts the received audio data into text data using speech recognition technology. Simultaneously, an emotion engine analyzes the tone and speed of the speech to determine the user's emotional state.

[0131] Step 4:

[0132] The server processes text data using product analysis tools and lists potential purchasable products. It then adjusts the content and order of product suggestions, reflecting the emotional state detected by the emotion engine.

[0133] Step 5:

[0134] The server generates personalized product suggestions and sends them to the device in real time. The product list also includes special offers tailored to the user's mood.

[0135] Step 6:

[0136] The terminal receives product information from the server and uses speech synthesis technology to notify the user, asking which product they wish to purchase.

[0137] Step 7:

[0138] The user responds by voice, "I'll choose company B's milk." The device then sends this selection back to the server.

[0139] Step 8:

[0140] The server performs electronic payment for the selected product using the payment processing method. It then verifies that the transaction was conducted securely and that payment was completed.

[0141] Step 9:

[0142] After the server completes the payment, it instructs the partner store to ship the goods using the shipping instruction method. The user is then notified of the shipping status via their terminal.

[0143] Step 10:

[0144] The emotion engine detects the user's current emotional state, and the server uses this information to make additional suggestions and follow up. For example, if the user is feeling stressed, it might suggest relaxation products to improve the user experience.

[0145] (Example 2)

[0146] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0147] Conventional voice-based product ordering systems fail to consider user emotions, relying solely on simple voice recognition and data analysis for product recommendations. This prevents them from providing the personalized experience users desire. Furthermore, these emotionally detached recommendations fail to adequately meet user needs, highlighting the need for improved user experience.

[0148] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0149] In this invention, the server includes voice processing means for receiving voice information and converting the voice into text information, data analysis means for analyzing the text information and identifying candidates for purchasable items, and emotion analysis means for analyzing the user's emotional state and making suggestions based on those emotions. This makes it possible to provide personalized suggestions that take the user's emotions into consideration and improve the user experience.

[0150] "Speech processing means" refers to technology that has the function of receiving speech information and converting that speech into text information.

[0151] "Data analysis means" refers to techniques for analyzing textual information and identifying potential items that can be purchased.

[0152] "Emotional analysis means" refers to technology that analyzes a user's emotional state and makes suggestions based on those emotions.

[0153] A "payment method" is a technology that executes electronic payments based on selected goods.

[0154] "Delivery method" refers to the technology that directs the delivery of selected goods.

[0155] This invention is a system for ordering products via voice input and providing users with a personalized experience. The system includes voice processing means, data analysis means, sentiment analysis means, payment means, and delivery means.

[0156] Audio processing means:

[0157] Users request products by voice through a device such as a smart speaker. The device uses voice recognition software, such as Google Assistant or Amazon Alexa, to convert the voice into text.

[0158] Data analysis methods:

[0159] The server receives text information sent from the terminal. The server uses natural language processing libraries such as NLTK and spaCy to analyze the text information and extract potential purchase items based on the user's intent.

[0160] Emotion analysis means:

[0161] The server uses emotion analysis tools, including IBM Watson®, to estimate the user's emotional state from voice data. Based on this emotion analysis, it makes product recommendations tailored to the user's state.

[0162] For example, when a user places an order in "express" mode, items with shorter delivery times will be prioritized and suggested.

[0163] Payment and shipping methods:

[0164] When a user selects a suggested product, the server processes the payment electronically through a payment method. Payment platforms such as PayPal and Stripe are used. Once the payment is complete, the server arranges for the delivery of the selected items using a shipping method. The user is notified of the delivery status in real time.

[0165] Example of a prompt:

[0166] "A user is feeling stressed and has given voice instructions for the product they want to purchase. What kind of relaxation products would you suggest?"

[0167] "What products should be prioritized when a user is in a hurry?"

[0168] In this way, the system provides advanced personalization features that take into account the user's voice and emotional state.

[0169] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0170] Step 1:

[0171] The user speaks to the voice input device, indicating the product they wish to purchase. The device receives this voice information as input and converts it into text. Specifically, the device applies a voice recognition algorithm and outputs the converted text data.

[0172] Step 2:

[0173] The server receives text information obtained from speech recognition as input. Here, a natural language processing engine is used to analyze the text information, understand the user's intent, and list potential purchase items. This process involves keyword extraction and contextual understanding, and a list of product candidates is output.

[0174] Step 3:

[0175] The server simultaneously receives audio information as input for sentiment analysis of the voice. Using a sentiment analysis engine, it estimates the user's emotional state from the tone and tempo of the voice and outputs sentiment state data. For example, emotions such as "hurried" or "relaxed" may be detected.

[0176] Step 4:

[0177] The server receives a list of potential products and emotional state data as input, combines them to generate personalized product suggestions for the user, and applies specific rules, such as prioritizing products with faster delivery based on the user's emotions, to output a list of products to suggest.

[0178] Step 5:

[0179] The terminal receives product suggestions sent from the server. Using speech synthesis technology, it prompts the user to make a selection by delivering product suggestions to the user via voice. After the voice suggestion is output, the user proceeds to the next selection step.

[0180] Step 6:

[0181] The user selects the product they wish to purchase from the suggested items using voice or an app. The selected product information is then sent back to the server as input from the device.

[0182] Step 7:

[0183] The server receives the product information selected by the user as input and processes the payment using an electronic payment system. This includes processes such as authentication and amount calculation, and a confirmation notification is output after the payment is completed.

[0184] Step 8:

[0185] Upon receiving payment confirmation, the server activates the shipping arrangement system and arranges for the delivery of the selected items. The shipping status is notified to the user in real time, and additional information and offers as needed are included in the output.

[0186] (Application Example 2)

[0187] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0188] In recent years, users in e-commerce have been demanding faster and more personalized product recommendations and purchasing experiences. However, current systems often fail to take into account the user's emotional state, resulting in a non-personalized experience. In particular, the lack of flexible product recommendations tailored to the user's situation makes it difficult to maximize purchasing intent. This invention aims to achieve more effective product recommendations and faster payment procedures by combining the user's voice and emotional state.

[0189] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0190] In this invention, the server includes a speech recognition means that receives voice input and converts the voice data into text data, a product analysis means that analyzes the text data and identifies candidate products that can be purchased, and an emotion analysis means that analyzes the voice data and estimates the user's emotional state. This enables personalized product suggestions tailored to the user's situation, thereby improving the user experience in e-commerce.

[0191] "Speech recognition means" refers to a device or mechanism for receiving speech input in real time and converting speech data into text data.

[0192] A "product analysis means" is a device or mechanism for analyzing text data obtained from a user to identify potential products that can be purchased.

[0193] "Payment processing means" refers to a device or mechanism for executing electronic payment procedures based on selected goods.

[0194] A "delivery instruction means" is a device or mechanism for instructing the delivery arrangements for selected goods and managing the delivery process.

[0195] "Emotional analysis means" refers to a device or mechanism for analyzing voice data to estimate the user's emotional state.

[0196] "Suggestion means" refers to a device or mechanism for making personalized product suggestions based on the user's emotional state and past purchase history information.

[0197] The system based on this invention includes voice recognition means, product analysis means, payment processing means, delivery instruction means, sentiment analysis means, and suggestion means. These elements work together to provide users with a smooth and personalized shopping experience controlled by voice.

[0198] The server receives voice input from the user as a speech recognition tool and converts it into text data in real time using the Google Cloud Speech-to-Text API. This text data is then analyzed by a product analysis tool to identify potential purchase items. The Azure Cognitive Services sentiment analysis API is used to analyze the voice data and estimate the user's emotional state. This process allows the system to understand the user's emotions, such as whether they are "in a hurry" or "relaxed."

[0199] Based on this information, the server uses a suggestion system to recommend products that match the user's emotional state. For example, if the server determines that the user is in a hurry, products that can be delivered quickly will be prioritized in the list. For selected products, the payment processing system uses the Stripe API to execute the payment process.

[0200] After an item is selected, the shipping instructions system arranges for its delivery. The user is notified of the delivery status in real time using Firebase Cloud Messaging.

[0201] For example, if a user voice-inputs, "My child's snacks are almost gone, so I need them delivered quickly," the input is converted to text, and the user's sense of urgency is estimated using sentiment analysis. Based on this, products that can be delivered quickly are suggested.

[0202] Example of a prompt message: "Please tell me the name of the item you would like to order and whether you are in a hurry."

[0203] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0204] Step 1:

[0205] The user makes a voice input to the device. The device receives the input voice data, enabling processing by voice recognition.

[0206] Step 2:

[0207] The device uses the Google Cloud Speech-to-Text API to convert received audio data into text data. In this process, the audio waveform is converted into phonemes, and the corresponding text is generated. As a result, the audio input is converted into text data, which becomes the input for the next analysis process.

[0208] Step 3:

[0209] The server analyzes text data using a product analysis tool. This analysis generates a list of potential products based on the user's specified purchase request, taking into account past purchase history and inventory information. This step involves matching specific strings and performing database searches. As a result, a list of potential products is output.

[0210] Step 4:

[0211] The server sends the audio data to the Azure Cognitive Services sentiment analysis API to estimate the emotional state. Features such as intonation and speed are used in the analysis. The analysis results in an output indicating an emotional state, such as "hurried" or "calm."

[0212] Step 5:

[0213] The server creates personalized product suggestions based on the sentiment analysis results and the list of potential products. If the user's sentiment is estimated to be "urgent," a logic is activated that prioritizes suggesting only products that can be delivered quickly. The generated list of product suggestions is then sent to the device.

[0214] Step 6:

[0215] The user selects a product from the suggested options on their device, and the selection data is sent to the server. The server uses the Stripe API to process the electronic payment based on the product selection data. The payment information is retrieved, and a completion status is output.

[0216] Step 7:

[0217] The server prepares the selected items for delivery based on the delivery instructions. Based on the delivery information, it sends data to the logistics system, and the delivery status is notified to the user via Firebase Cloud Messaging. This allows the user to track the delivery status in real time.

[0218] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0219] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0220] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0221] [Second Embodiment]

[0222] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0223] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0224] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0225] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0226] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0227] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0228] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0229] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0230] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0231] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0232] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0233] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0234] This invention provides a system for users to order products using voice commands and to make electronic payments easily and securely. The following describes the configurations for implementing this system.

[0235] First, the user speaks to a device such as a smart speaker to specify the product they want to purchase. The device receives this voice and converts it into text data using speech recognition. This text data is sent to a server, where product analysis tools analyze the user's intent. For example, if the user says, "I want to buy milk," the server generates a list of suggested products that include "milk."

[0236] The server sends the generated list of product candidates to the device as personalized product suggestions based on the user's past purchase history and preferences. The device notifies the user of this information using speech synthesis technology and prompts them to select a product. Once the user selects a specific product, that information is sent back to the server.

[0237] Next, the server uses a payment processing device to perform electronic payment for the selected items. During this process, it works in conjunction with the payment system to ensure secure transactions. Once payment is complete, the server uses a delivery instruction device to instruct the partner store to deliver the items and notifies the terminal that the delivery arrangements are complete.

[0238] For example, even if a user adds an orange juice to their order, the system repeats the same procedure, efficiently processing multiple items ordered by the user. This system allows users to complete their orders by voice, providing a convenient shopping experience that does not rely on visual cues.

[0239] The following describes the processing flow.

[0240] Step 1:

[0241] The user gives a voice command to the smart speaker saying, "I want to buy milk." The device receives this voice input and saves it as digital data.

[0242] Step 2:

[0243] The device sends the stored audio data to the server. During this process, the audio data is encrypted over the network and securely transmitted to the server.

[0244] Step 3:

[0245] The server converts the received audio data into text data using speech recognition technology. This text data is further processed to represent the user's intent.

[0246] Step 4:

[0247] The server analyzes the text data using a product analysis tool and lists potential "milk" products that are available for purchase. During this process, the server queries the product database to retrieve relevant product information.

[0248] Step 5:

[0249] The server lists potential products, personalizes them based on the user's past purchase history and preferences, and sends them to the device as optimized product suggestions.

[0250] Step 6:

[0251] Based on the product information received from the server, the terminal uses speech synthesis technology to ask the user, "A liter of milk from company A costs 500 yen, and a liter of milk from company B costs 480 yen. Which would you like?"

[0252] Step 7:

[0253] The user responds by voice saying, "I select company B's milk," and the terminal sends that voice input back to the server. The server confirms this selection.

[0254] Step 8:

[0255] The server uses payment processing methods for the items it has confirmed, and executes the payment process in conjunction with electronic payment systems such as PayPay. Once the payment is confirmed to be successful, the server notifies the terminal of the result.

[0256] Step 9:

[0257] Based on the payment completion information, the server uses a delivery instruction system to instruct partner delivery companies or stores to ship the goods. Once the delivery status is confirmed, the server sends that information to the terminal and notifies the user.

[0258] (Example 1)

[0259] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0260] In modern society, there is a growing need for systems that allow for easy and secure product purchases and electronic payments via voice. However, conventional systems lack personalization based on purchase history, limiting the user experience. Furthermore, there is a demand for technology that efficiently manages the entire process from voice input to payment and delivery, providing users with a seamless purchasing experience.

[0261] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0262] In this invention, the server includes voice recognition means, information analysis means, and transaction processing means. This enables a consistent process from voice input to product suggestions, electronic payment, and delivery instructions, resulting in a convenient and personalized purchasing experience for the user.

[0263] "Voice recognition means" refers to technology that receives voice input from a user and converts it into text data.

[0264] "Information analysis means" refers to analytical techniques used to identify purchasable products based on converted text data.

[0265] "Transaction processing means" refers to technology for securely executing electronic payment procedures for selected goods.

[0266] "Delivery guidance means" refers to technology used to instruct partner organizations to arrange the delivery of selected products.

[0267] "Suggestion methods" refer to technologies that take into account a user's past transaction history to provide personalized product suggestions.

[0268] "Notification method" refers to a technology that uses speech synthesis technology to inform users of proposals and the completion of transactions.

[0269] This invention is a system that allows users to order products using voice commands and make secure electronic payments. The system includes voice recognition, information analysis, transaction processing, delivery guidance, suggestion, and notification means. The following describes how to implement this system in detail.

[0270] First, the user speaks to a device such as a smart speaker and gives voice commands for the product they wish to purchase. The device has standard voice recognition software installed, and in this example, it uses Google Assistant or a common voice recognition API to convert the voice data into text data.

[0271] Once this audio data is converted to text data, the terminal sends the data to the server. On the server, information analysis tools operate, for example using natural language processing technology to identify potential purchasable products. In this process, product attributes and similar products are analyzed to create a list of candidates.

[0272] The server then uses suggestion tools to generate personalized product suggestions based on the user's past transaction history and preferences. This information is sent back to the terminal and communicated to the user using speech synthesis technology (e.g., Google Text-to-Speech). This allows the user to proceed with the purchasing process using only voice, without needing to visually confirm the information.

[0273] Once the user selects the desired product and that information is sent to the server, the transaction processing mechanism is activated. This mechanism includes payment APIs such as PayPal and Stripe, and performs secure electronic payment for the selected product.

[0274] Once payment is complete, the delivery guidance system is activated and instructs an external service to arrange delivery of the specified items. Delivery information is sent to the partner, and the server notifies the terminal that processing is complete. The terminal then reports this information to the user, informing them that the order is complete.

[0275] As a concrete example, when a user says to the speaker, "I want to buy orange juice," the voice is converted into text, the server lists orange juice as a purchase option, makes suggestions considering past purchase history, and simultaneously completes the payment and arranges delivery, creating a seamless process.

[0276] An example of an input prompt for a generative AI model is, "Please tell me how a user orders a product by voice to a smart speaker." This allows the generative AI to explain in detail the process from voice ordering to electronic payment and delivery instructions.

[0277] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0278] Step 1:

[0279] Users give voice commands to a device such as a smart speaker to select products. When a user says, "I want to buy milk," that voice data is entered into the device.

[0280] Step 2:

[0281] The terminal converts audio data into text data using speech recognition. This process utilizes a speech recognition API to analyze the audio waveform and convert it into the string "I want to buy milk." The converted text data is then output from the terminal to the server.

[0282] Step 3:

[0283] The server passes the received text data to the information analysis means to analyze the user's requests. Here, using natural language processing technology, a list of available products is generated based on the keyword "milk". This list is output as the analysis result.

[0284] Step 4:

[0285] Based on the results of the product analysis, the server uses the proposal means to create a personalized product proposal considering the user's past purchase history. For example, if a specific brand of milk has been frequently purchased in the past, that brand is proposed with priority. The generated proposal is output to the terminal.

[0286] Step 5:

[0287] The terminal uses voice synthesis technology to notify the user of the proposal content. Specifically, among the proposed products, the ones with low price or high popularity are selected and conveyed to the user by voice as "The cheapest Brand A milk is recommended". This voice information is provided to the user.

[0288] Step 6:

[0289] The user selects the proposed product. For example, a specific selection such as "Purchase Brand A milk" is conveyed to the terminal by voice. This selection is taken into the terminal as input data.

[0290] Step 7:

[0291] The terminal sends the selection content to the server, and the transaction processing means of the server operates. Based on the selected product, electronic payment is carried out in cooperation with the payment system. At this time, the user's payment information is used as input information, and the status of payment completion is output.

[0292] Step 8:

[0293] After payment is completed, the server uses a delivery guidance system to instruct partner stores to ship the goods. This delivery instruction includes the delivery address and estimated delivery date and time. This information is forwarded to the delivery company, and the user is notified via their device that the delivery arrangements are complete.

[0294] (Application Example 1)

[0295] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0296] There is a need to provide an electronic transaction method that allows users to intuitively search for products and complete the purchase process without relying on visual cues. Furthermore, it is necessary to accurately offer diverse product suggestions to users to improve their purchasing experience. Additionally, there is a need to develop a system that ensures transactions are conducted safely and quickly.

[0297] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0298] In this invention, the server includes a speech recognition device that receives voice data and converts the voice into text information, a product analysis device that analyzes the text information and determines candidate products that can be acquired, and an information providing device that guides the user through the purchasing process based on the voice input without visual assistance. This makes it possible for the user to efficiently and safely search for and purchase products using only their voice.

[0299] A "speech recognition device" is a device that processes speech data to convert it into text information.

[0300] A "product analysis device" is a device that analyzes textual information to identify potential products that can be acquired.

[0301] A "transaction management device" is a processing device that executes electronic transaction processing based on specified goods.

[0302] A "delivery instruction device" is a device used to perform operations to instruct the delivery of specified goods.

[0303] An "information provision device" is a device that guides users through the purchasing process based on voice input, without providing visual assistance.

[0304] This invention provides a system that allows users to search for and purchase products using voice commands, and mainly consists of a voice recognition device, a product analysis device, a transaction management device, a delivery instruction device, and an information provision device. This system operates using terminals such as smartphones and personal computers, and servers connected to them.

[0305] The user inputs the product they wish to purchase into the device using their voice. The device uses the Google Speech-to-Text API to instantly convert the voice data into text. Subsequently, a server receives this text information via AWS, and a product analysis device identifies product candidates based on the user's needs.

[0306] Next, the server uses a transaction management device based on the analysis results to conduct secure electronic transactions using the Stripe API. After this process, the server instructs the logistics system to ship the goods via a delivery instruction device and notifies the user of the delivery schedule using Google Cloud Messaging.

[0307] In this system, an information providing device is added in a form that does not require visual assistance for the user, and various products are proposed and guided. For example, when the user instructs "want to buy black tea", the system can propose the optimal product based on the user's past preferences from among multiple black tea products. An example of the prompt text at that time is "Show the voice recognition procedure when the user instructs 'order a new earphone' by voice." Based on the generation AI model based on this prompt text, accurate recognition and processing of voice instructions become possible.

[0308] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0309] Step 1:

[0310] The user uses the voice input function of the smartphone to speak the product to be purchased. The voice data is captured by the microphone of the terminal and converted into character information using the Google Speech-to-Text API. The input is voice data, and the output is character information.

[0311] Step 2:

[0312] The terminal transmits the converted character information to the server. The server receives this character information and analyzes the user's intention using a product analysis device on AWS. The data processing performed here is the analysis of character information, and product candidates are specified based on the analyzed information. The input is character information, and the output is a list of product candidates.

[0313] Step 3:

[0314] The server uses the information providing device to repeat the list of product candidates to the user by voice in order to notify the user of the analysis result. And an optimal product proposal is made. In this step, since the text data is converted back into voice data, voice synthesis technology is utilized. The input is a list of product candidates, and the output is voice information.

[0315] Step 4:

[0316] When a user makes a selection from the suggested products, that selection information is sent back to the server. The server processes the electronic transaction using the Stripe API via a transaction management device. Secure settlement is performed through the electronic transaction, and the results are processed. The input is the selected product information, and the output is information regarding the completion of the transaction.

[0317] Step 5:

[0318] Upon receiving payment confirmation, the server instructs the logistics system to ship the goods via a delivery instruction device. This instruction is sent through Google Cloud Messaging. To notify the user that the delivery arrangements are complete, delivery completion information is also provided via voice. The input is transaction completion information, and the output is delivery arrangement completion information.

[0319] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0320] The present invention is a system that improves the user experience by combining a user voice-based product ordering and electronic payment with an emotion engine. This system includes voice recognition means, product analysis means, payment processing means, delivery instruction means, and an emotion engine.

[0321] The user voice-instructs a device, such as a smart speaker, to purchase the desired items. The device receives the user's voice in real time and converts it into text data using speech recognition. This text data is sent to a server, where product analysis tools analyze the user's intent to generate a list of potential purchase items.

[0322] In parallel, the emotion engine analyzes the voice data to estimate the user's emotional state. For example, if a user says, "I'm in a bit of a hurry right now and need milk immediately," the emotion engine detects the emotion of urgency and, based on that, suggests products with faster delivery as a priority. Furthermore, if the user is relaxed when placing an order, the engine can suggest additional related products, enabling flexible suggestions tailored to the user's emotions.

[0323] The server sends personalized product suggestions to the device based on analyzed sentiment information and purchase history. The device then uses speech synthesis technology to present this information to the user and prompt them to make a selection. Once the user selects a product, the information is returned to the server, and electronic payment is processed via the payment processing system.

[0324] After payment is completed, the server uses a delivery instruction system to arrange for the delivery of the selected items. The user will be notified of the delivery status as needed, and additional information and offers will be provided based on the emotions detected by the emotion engine, if necessary.

[0325] In this way, the system can utilize the user's voice and emotional information to provide a more natural and personalized shopping experience. For example, when a user is feeling stressed, the server can suggest products that promote relaxation.

[0326] The following describes the processing flow.

[0327] Step 1:

[0328] The user gives a voice command to the smart speaker saying, "I want to buy milk." The device receives this voice command and converts it into digital data.

[0329] Step 2:

[0330] The device sends the converted audio data to the server. The audio data is encrypted and securely transmitted to the server.

[0331] Step 3:

[0332] The server converts the received audio data into text data using speech recognition technology. Simultaneously, an emotion engine analyzes the tone and speed of the speech to determine the user's emotional state.

[0333] Step 4:

[0334] The server processes text data using product analysis tools and lists potential purchasable products. It then adjusts the content and order of product suggestions, reflecting the emotional state detected by the emotion engine.

[0335] Step 5:

[0336] The server generates personalized product suggestions and sends them to the device in real time. The product list also includes special offers tailored to the user's mood.

[0337] Step 6:

[0338] The terminal receives product information from the server and uses speech synthesis technology to notify the user, asking which product they wish to purchase.

[0339] Step 7:

[0340] The user responds by voice, "I'll choose company B's milk." The device then sends this selection back to the server.

[0341] Step 8:

[0342] The server performs electronic payment for the selected product using the payment processing method. It then verifies that the transaction was conducted securely and that payment was completed.

[0343] Step 9:

[0344] After the server completes the payment, it instructs the partner store to ship the goods using the shipping instruction method. The user is then notified of the shipping status via their terminal.

[0345] Step 10:

[0346] The emotion engine detects the user's current emotional state, and the server uses this information to make additional suggestions and follow up. For example, if the user is feeling stressed, it might suggest relaxation products to improve the user experience.

[0347] (Example 2)

[0348] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0349] Conventional voice-based product ordering systems fail to consider user emotions, relying solely on simple voice recognition and data analysis for product recommendations. This prevents them from providing the personalized experience users desire. Furthermore, these emotionally detached recommendations fail to adequately meet user needs, highlighting the need for improved user experience.

[0350] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0351] In this invention, the server includes voice processing means for receiving voice information and converting the voice into text information, data analysis means for analyzing the text information and identifying candidates for purchasable items, and emotion analysis means for analyzing the user's emotional state and making suggestions based on those emotions. This makes it possible to provide personalized suggestions that take the user's emotions into consideration and improve the user experience.

[0352] "Speech processing means" refers to technology that has the function of receiving speech information and converting that speech into text information.

[0353] "Data analysis means" refers to techniques for analyzing textual information and identifying potential items that can be purchased.

[0354] "Emotional analysis means" refers to technology that analyzes a user's emotional state and makes suggestions based on those emotions.

[0355] A "payment method" is a technology that executes electronic payments based on selected goods.

[0356] "Delivery method" refers to the technology that directs the delivery of selected goods.

[0357] This invention is a system for ordering products via voice input and providing users with a personalized experience. The system includes voice processing means, data analysis means, sentiment analysis means, payment means, and delivery means.

[0358] Audio processing means:

[0359] Users request products by voice through a device such as a smart speaker. The device uses voice recognition software, such as Google Assistant or Amazon Alexa, to convert the voice into text.

[0360] Data analysis methods:

[0361] The server receives text information sent from the terminal. The server uses natural language processing libraries such as NLTK and spaCy to analyze the text information and extract potential purchase items based on the user's intent.

[0362] Emotion analysis means:

[0363] The server uses emotion analysis tools, including IBM Watson, to estimate the user's emotional state from voice data. Based on this emotion analysis, it makes product recommendations tailored to the user's state.

[0364] For example, when a user places an order in "express" mode, items with shorter delivery times will be prioritized and suggested.

[0365] Payment methods and shipping methods:

[0366] When a user selects a suggested product, the server processes the payment electronically through a payment method. Payment platforms such as PayPal and Stripe are used. Once the payment is complete, the server arranges for the delivery of the selected items using a shipping method. The user is notified of the delivery status in real time.

[0367] Example of a prompt:

[0368] "A user is feeling stressed and has given voice instructions for the product they want to purchase. What kind of relaxation products would you suggest?"

[0369] "What products should be prioritized when a user is in a hurry?"

[0370] In this way, the system provides advanced personalization features that take into account the user's voice and emotional state.

[0371] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0372] Step 1:

[0373] The user speaks to the voice input device, indicating the product they wish to purchase. The device receives this voice information as input and converts it into text. Specifically, the device applies a voice recognition algorithm and outputs the converted text data.

[0374] Step 2:

[0375] The server receives text information obtained from speech recognition as input. Here, a natural language processing engine is used to analyze the text information, understand the user's intent, and list potential purchase options. This process involves keyword extraction and contextual understanding, and the list of product candidates is output.

[0376] Step 3:

[0377] The server simultaneously receives audio information as input for sentiment analysis of the voice. Using a sentiment analysis engine, it estimates the user's emotional state from the tone and tempo of the voice and outputs sentiment state data. For example, emotions such as "hurried" or "relaxed" may be detected.

[0378] Step 4:

[0379] The server receives a list of potential products and emotional state data as input, combines them to generate personalized product suggestions for the user, and applies specific rules, such as prioritizing products with faster delivery based on the user's emotions, to output a list of products to suggest.

[0380] Step 5:

[0381] The terminal receives product suggestions sent from the server. Using speech synthesis technology, it prompts the user to make a selection by delivering product suggestions to the user via voice. After the voice suggestion is output, the user proceeds to the next selection step.

[0382] Step 6:

[0383] The user selects the product they wish to purchase from the suggested items using voice or an app. The selected product information is then sent back to the server as input from the device.

[0384] Step 7:

[0385] The server receives the product information selected by the user as input and processes the payment using an electronic payment system. This includes processes such as authentication and amount calculation, and a confirmation notification is output after the payment is completed.

[0386] Step 8:

[0387] Upon receiving payment confirmation, the server activates the shipping arrangement system and arranges for the delivery of the selected items. The shipping status is notified to the user in real time, and additional information and offers as needed are included in the output.

[0388] (Application Example 2)

[0389] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0390] In recent years, users in e-commerce have been demanding faster and more personalized product recommendations and purchasing experiences. However, current systems often fail to take into account the user's emotional state, resulting in a non-personalized experience. In particular, the lack of flexible product recommendations tailored to the user's situation makes it difficult to maximize purchasing intent. This invention aims to achieve more effective product recommendations and faster payment procedures by combining the user's voice and emotional state.

[0391] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0392] In this invention, the server includes a speech recognition means that receives voice input and converts the voice data into text data, a product analysis means that analyzes the text data and identifies candidate products that can be purchased, and an emotion analysis means that analyzes the voice data and estimates the user's emotional state. This enables personalized product suggestions tailored to the user's situation, thereby improving the user experience in e-commerce.

[0393] "Speech recognition means" refers to a device or mechanism for receiving speech input in real time and converting speech data into text data.

[0394] A "product analysis means" is a device or mechanism for analyzing text data obtained from a user to identify potential products that can be purchased.

[0395] "Payment processing means" refers to a device or mechanism for executing electronic payment procedures based on selected goods.

[0396] A "delivery instruction means" is a device or mechanism for instructing the delivery arrangements for selected goods and managing the delivery process.

[0397] "Emotional analysis means" refers to a device or mechanism for analyzing voice data to estimate the user's emotional state.

[0398] "Suggestion means" refers to a device or mechanism for making personalized product suggestions based on the user's emotional state and past purchase history information.

[0399] The system based on this invention includes voice recognition means, product analysis means, payment processing means, delivery instruction means, sentiment analysis means, and suggestion means. These elements work together to provide users with a smooth and personalized shopping experience controlled by voice.

[0400] The server receives voice input from the user as a speech recognition tool and converts it into text data in real time using the Google Cloud Speech-to-Text API. This text data is then analyzed by a product analysis tool to identify potential purchase items. The Azure Cognitive Services sentiment analysis API is used to analyze the voice data and estimate the user's emotional state. This process allows the system to understand the user's emotions, such as whether they are "in a hurry" or "relaxed."

[0401] Based on this information, the server uses a suggestion system to recommend products that match the user's emotional state. For example, if the server determines that the user is in a hurry, products that can be delivered quickly will be prioritized in the list. For selected products, the payment processing system uses the Stripe API to execute the payment process.

[0402] After an item is selected, the shipping instructions system arranges for its delivery. The user is notified of the delivery status in real time using Firebase Cloud Messaging.

[0403] For example, if a user voice-inputs, "My child's snacks are almost gone, so I need them delivered quickly," the input is converted to text, and the user's sense of urgency is estimated using sentiment analysis. Based on this, products that can be delivered quickly are suggested.

[0404] Example of a prompt message: "Please tell me the name of the item you would like to order and whether you are in a hurry."

[0405] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0406] Step 1:

[0407] The user makes a voice input to the device. The device receives the input voice data, enabling processing by voice recognition.

[0408] Step 2:

[0409] The device uses the Google Cloud Speech-to-Text API to convert received audio data into text data. In this process, the audio waveform is converted into phonemes, and the corresponding text is generated. As a result, the audio input is converted into text data, which becomes the input for the next analysis process.

[0410] Step 3:

[0411] The server analyzes text data using a product analysis tool. This analysis generates a list of potential products based on the user's specified purchase request, taking into account past purchase history and inventory information. This step involves matching specific strings and performing database searches. As a result, a list of potential products is output.

[0412] Step 4:

[0413] The server sends the audio data to the Azure Cognitive Services sentiment analysis API to estimate the emotional state. The analysis uses features such as intonation and speed. The results of this analysis output an emotional state such as "hurried" or "calm."

[0414] Step 5:

[0415] The server creates personalized product suggestions using a suggestion system based on the sentiment analysis results and the list of potential products. If the user's sentiment is estimated to be "urgent," a logic is activated that prioritizes suggesting only products that can be delivered quickly. The generated list of product suggestions is then sent to the device.

[0416] Step 6:

[0417] The user selects a product from the suggested options on their device, and the selection data is sent to the server. The server uses the Stripe API to process the electronic payment based on the product selection data. The payment information is retrieved, and a completion status is output.

[0418] Step 7:

[0419] The server prepares the selected items for delivery based on the delivery instructions. Based on the delivery information, it sends data to the logistics system, and the delivery status is notified to the user via Firebase Cloud Messaging. This allows the user to track the delivery status in real time.

[0420] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0421] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0422] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0423] [Third Embodiment]

[0424] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0425] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0426] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0427] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0428] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0429] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0430] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0431] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0432] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0433] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0434] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0435] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0436] This invention provides a system for users to order products using voice commands and to make electronic payments easily and securely. The following describes the configurations for implementing this system.

[0437] First, the user speaks to a device such as a smart speaker to specify the product they want to purchase. The device receives this voice and converts it into text data using speech recognition. This text data is sent to a server, where product analysis tools analyze the user's intent. For example, if the user says, "I want to buy milk," the server generates a list of suggested products that include "milk."

[0438] The server sends the generated list of product candidates to the device as personalized product suggestions based on the user's past purchase history and preferences. The device notifies the user of this information using speech synthesis technology and prompts them to select a product. Once the user selects a specific product, that information is sent back to the server.

[0439] Next, the server uses a payment processing device to perform electronic payment for the selected items. During this process, it works in conjunction with the payment system to ensure secure transactions. Once payment is complete, the server uses a delivery instruction device to instruct the partner store to deliver the items and notifies the terminal that the delivery arrangements are complete.

[0440] For example, even if a user adds an orange juice to their order, the system repeats the same procedure, efficiently processing multiple items ordered by the user. This system allows users to complete their orders by voice, providing a convenient shopping experience that does not rely on visual cues.

[0441] The following describes the processing flow.

[0442] Step 1:

[0443] The user gives a voice command to the smart speaker saying, "I want to buy milk." The device receives this voice input and saves it as digital data.

[0444] Step 2:

[0445] The device sends the stored audio data to the server. During this process, the audio data is encrypted over the network and securely transmitted to the server.

[0446] Step 3:

[0447] The server converts the received audio data into text data using speech recognition technology. This text data is further processed to represent the user's intent.

[0448] Step 4:

[0449] The server analyzes the text data using a product analysis tool and lists potential "milk" products that are available for purchase. During this process, the server queries the product database to retrieve relevant product information.

[0450] Step 5:

[0451] The server lists potential products, personalizes them based on the user's past purchase history and preferences, and sends them to the device as optimized product suggestions.

[0452] Step 6:

[0453] Based on the product information received from the server, the terminal uses speech synthesis technology to ask the user, "A liter of milk from company A costs 500 yen, and a liter of milk from company B costs 480 yen. Which would you like?"

[0454] Step 7:

[0455] The user responds by voice saying, "I select company B's milk," and the terminal sends that voice input back to the server. The server confirms this selection.

[0456] Step 8:

[0457] The server uses payment processing methods for the items it has confirmed, and executes the payment process in conjunction with electronic payment systems such as PayPay. Once the payment is confirmed to be successful, the server notifies the terminal of the result.

[0458] Step 9:

[0459] Based on the payment completion information, the server uses a delivery instruction system to instruct partner delivery companies or stores to ship the goods. Once the delivery status is confirmed, the server sends that information to the terminal and notifies the user.

[0460] (Example 1)

[0461] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0462] In modern society, there is a growing need for systems that allow for easy and secure product purchases and electronic payments via voice. However, conventional systems lack personalization based on purchase history, limiting the user experience. Furthermore, there is a demand for technology that efficiently manages the entire process from voice input to payment and delivery, providing users with a seamless purchasing experience.

[0463] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0464] In this invention, the server includes voice recognition means, information analysis means, and transaction processing means. This enables a consistent process from voice input to product suggestions, electronic payment, and delivery instructions, resulting in a convenient and personalized purchasing experience for the user.

[0465] "Voice recognition means" refers to technology that receives voice input from a user and converts it into text data.

[0466] "Information analysis means" refers to analytical techniques used to identify purchasable products based on converted text data.

[0467] "Transaction processing means" refers to technology for securely executing electronic payment procedures for selected goods.

[0468] "Delivery guidance means" refers to technology used to instruct partner organizations to arrange the delivery of selected products.

[0469] "Suggestion methods" refer to technologies that take into account a user's past transaction history to provide personalized product suggestions.

[0470] "Notification method" refers to a technology that uses speech synthesis technology to inform users of proposals and the completion of transactions.

[0471] This invention is a system that allows users to order products using voice commands and make secure electronic payments. The system includes voice recognition, information analysis, transaction processing, delivery guidance, suggestion, and notification means. The following describes how to implement this system in detail.

[0472] First, the user speaks to a device such as a smart speaker and gives voice commands for the product they wish to purchase. The device has standard voice recognition software installed, and in this example, it uses Google Assistant or a common voice recognition API to convert the voice data into text data.

[0473] Once this audio data is converted to text data, the terminal sends the data to the server. On the server, information analysis tools operate, for example using natural language processing technology to identify potential purchasable products. In this process, product attributes and similar products are analyzed to create a list of candidates.

[0474] The server then uses suggestion tools to generate personalized product suggestions based on the user's past transaction history and preferences. This information is sent back to the terminal and communicated to the user using speech synthesis technology (e.g., Google Text-to-Speech). This allows the user to proceed with the purchasing process using only voice, without needing to visually confirm the information.

[0475] Once the user selects the desired product and that information is sent to the server, the transaction processing mechanism is activated. This mechanism includes payment APIs such as PayPal and Stripe, and performs secure electronic payment for the selected product.

[0476] Once payment is complete, the delivery guidance system is activated and instructs an external service to arrange delivery of the specified items. Delivery information is sent to the partner, and the server notifies the terminal that processing is complete. The terminal then reports this information to the user, informing them that the order is complete.

[0477] As a concrete example, when a user says to the speaker, "I want to buy orange juice," the voice is converted into text, the server lists orange juice as a purchase option, makes suggestions considering past purchase history, and simultaneously completes the payment and arranges delivery, creating a seamless process.

[0478] An example of an input prompt for a generative AI model is, "Please tell me how a user orders a product by voice to a smart speaker." This allows the generative AI to explain in detail the process from voice ordering to electronic payment and delivery instructions.

[0479] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0480] Step 1:

[0481] Users give voice commands to a device such as a smart speaker to select products. When a user says, "I want to buy milk," that voice data is entered into the device.

[0482] Step 2:

[0483] The terminal converts audio data into text data using speech recognition. This process utilizes a speech recognition API to analyze the audio waveform and convert it into the string "I want to buy milk." The converted text data is then output from the terminal to the server.

[0484] Step 3:

[0485] The server passes the received text data to an information analysis tool to analyze the user's request. Here, natural language processing technology is used to generate a list of available products based on the keyword "milk". This list is output as the analysis result.

[0486] Step 4:

[0487] Based on the product analysis results, the server uses suggestion tools to create personalized product recommendations that take into account the user's past purchase history. For example, if a user has frequently purchased a particular brand of milk in the past, that brand will be prioritized in the recommendations. The generated recommendations are then output to the terminal.

[0488] Step 5:

[0489] The device uses speech synthesis technology to notify the user of its recommendations. Specifically, it selects the cheapest or most popular items from the suggested products and says, "I recommend the cheapest brand A milk." This voice information is then provided to the user.

[0490] Step 6:

[0491] The user selects a suggested product. For example, they might voice-instruct the device to make a specific selection, such as "I want to buy milk from brand A." This selection is then recorded as input data in the device.

[0492] Step 7:

[0493] The terminal sends the selection to the server, and the server's transaction processing mechanism is activated. Based on the selected items, electronic payment is carried out in conjunction with the payment system. At this time, the user's payment information is used as input information, and a payment completion status is output.

[0494] Step 8:

[0495] After payment is completed, the server uses a delivery guidance system to instruct partner stores to ship the goods. This delivery instruction includes the delivery address and estimated delivery date and time. This information is forwarded to the delivery company, and the user is notified via their device that the delivery arrangements are complete.

[0496] (Application Example 1)

[0497] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0498] There is a need to provide an electronic transaction method that allows users to intuitively search for products and complete the purchase process without relying on visual cues. Furthermore, it is necessary to accurately offer diverse product suggestions to users to improve their purchasing experience. Additionally, there is a need to develop a system that ensures transactions are conducted safely and quickly.

[0499] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0500] In this invention, the server includes a speech recognition device that receives voice data and converts the voice into text information, a product analysis device that analyzes the text information and determines candidate products that can be acquired, and an information providing device that guides the user through the purchasing process based on the voice input without visual assistance. This makes it possible for the user to efficiently and safely search for and purchase products using only their voice.

[0501] A "speech recognition device" is a device that processes speech data to convert it into text information.

[0502] A "product analysis device" is a device that analyzes textual information to identify potential products that can be acquired.

[0503] A "transaction management device" is a processing device that executes electronic transaction processing based on specified goods.

[0504] A "delivery instruction device" is a device used to perform operations to instruct the delivery of specified goods.

[0505] An "information provision device" is a device that guides users through the purchasing process based on voice input, without providing visual assistance.

[0506] This invention provides a system that allows users to search for and purchase products using voice commands, and mainly consists of a voice recognition device, a product analysis device, a transaction management device, a delivery instruction device, and an information provision device. This system operates using terminals such as smartphones and personal computers, and servers connected to them.

[0507] The user inputs the product they wish to purchase into the device using their voice. The device uses the Google Speech-to-Text API to instantly convert the voice data into text. Subsequently, a server receives this text information via AWS, and a product analysis device identifies product candidates based on the user's needs.

[0508] Next, the server uses a transaction management device based on the analysis results to conduct secure electronic transactions using the Stripe API. After this process, the server instructs the logistics system to ship the goods via a delivery instruction device and notifies the user of the delivery schedule using Google Cloud Messaging.

[0509] This system provides information to users without requiring visual assistance, offering suggestions and guidance on a variety of products. For example, if a user says, "I want to buy tea," the system can suggest the most suitable tea product from among several options based on the user's past preferences. An example of a prompt would be, "Show the voice recognition procedure when the user says, 'Order new earphones'." A generative AI model based on this prompt enables accurate recognition and processing of voice commands.

[0510] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0511] Step 1:

[0512] The user uses their smartphone's voice input function to speak the product they wish to purchase. The voice data is captured by the device's microphone and converted into text using the Google Speech-to-Text API. The input is voice data, and the output is text.

[0513] Step 2:

[0514] The terminal sends the converted text information to the server. The server receives this text information and uses a product analysis device on AWS to analyze the user's intent. The data processing performed here is the analysis of the text information, and based on the analyzed information, product candidates are identified. The input is text information, and the output is a list of product candidates.

[0515] Step 3:

[0516] The server uses an information provider to verbally repeat a list of product candidates to the user to inform them of the analysis results. It then makes optimal product recommendations. This step utilizes speech synthesis technology to convert text data back into speech data. The input is a list of product candidates, and the output is speech information.

[0517] Step 4:

[0518] When a user makes a selection from the suggested products, that selection information is sent back to the server. The server processes the electronic transaction using the Stripe API via a transaction management device. Secure settlement is performed through the electronic transaction, and the results are processed. The input is the selected product information, and the output is information regarding the completion of the transaction.

[0519] Step 5:

[0520] Upon receiving payment confirmation, the server instructs the logistics system to ship the goods via a delivery instruction device. This instruction is sent through Google Cloud Messaging. To notify the user that the delivery arrangements are complete, delivery completion information is also provided via voice. The input is transaction completion information, and the output is delivery arrangement completion information.

[0521] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0522] The present invention is a system that improves the user experience by combining a user voice-based product ordering and electronic payment with an emotion engine. This system includes voice recognition means, product analysis means, payment processing means, delivery instruction means, and an emotion engine.

[0523] The user voice-instructs a device, such as a smart speaker, to purchase the desired items. The device receives the user's voice in real time and converts it into text data using speech recognition. This text data is sent to a server, where product analysis tools analyze the user's intent to generate a list of potential purchase items.

[0524] In parallel, the emotion engine analyzes the voice data to estimate the user's emotional state. For example, if a user says, "I'm in a bit of a hurry right now and need milk immediately," the emotion engine detects the emotion of urgency and, based on that, suggests products with faster delivery as a priority. Furthermore, if the user is relaxed when placing an order, the engine can suggest additional related products, enabling flexible suggestions tailored to the user's emotions.

[0525] The server sends personalized product suggestions to the device based on analyzed sentiment information and purchase history. The device then uses speech synthesis technology to present this information to the user and prompt them to make a selection. Once the user selects a product, the information is returned to the server, and electronic payment is processed via the payment processing system.

[0526] After payment is completed, the server uses a delivery instruction system to arrange for the delivery of the selected items. The user will be notified of the delivery status as needed, and additional information and offers will be provided based on the emotions detected by the emotion engine, if necessary.

[0527] In this way, the system can utilize the user's voice and emotional information to provide a more natural and personalized shopping experience. For example, when a user is feeling stressed, the server can suggest products that promote relaxation.

[0528] The following describes the processing flow.

[0529] Step 1:

[0530] The user gives a voice command to the smart speaker saying, "I want to buy milk." The device receives this voice command and converts it into digital data.

[0531] Step 2:

[0532] The device sends the converted audio data to the server. The audio data is encrypted and securely transmitted to the server.

[0533] Step 3:

[0534] The server converts the received audio data into text data using speech recognition technology. Simultaneously, an emotion engine analyzes the tone and speed of the speech to determine the user's emotional state.

[0535] Step 4:

[0536] The server processes text data using product analysis tools and lists potential purchasable products. It then adjusts the content and order of product suggestions, reflecting the emotional state detected by the emotion engine.

[0537] Step 5:

[0538] The server generates personalized product suggestions and sends them to the device in real time. The product list also includes special offers tailored to the user's mood.

[0539] Step 6:

[0540] The terminal receives product information from the server and uses speech synthesis technology to notify the user, asking which product they wish to purchase.

[0541] Step 7:

[0542] The user responds by voice, "I'll choose company B's milk." The device then sends this selection back to the server.

[0543] Step 8:

[0544] The server performs electronic payment for the selected product using the payment processing method. It then verifies that the transaction was conducted securely and that payment was completed.

[0545] Step 9:

[0546] After the server completes the payment, it instructs the partner store to ship the goods using the shipping instruction method. The user is then notified of the shipping status via their terminal.

[0547] Step 10:

[0548] The emotion engine detects the user's current emotional state, and the server uses this information to make additional suggestions and follow up. For example, if the user is feeling stressed, it might suggest relaxation products to improve the user experience.

[0549] (Example 2)

[0550] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0551] Conventional voice-based product ordering systems fail to consider user emotions, relying solely on simple voice recognition and data analysis for product recommendations. This prevents them from providing the personalized experience users desire. Furthermore, these emotionally detached recommendations fail to adequately meet user needs, highlighting the need for improved user experience.

[0552] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0553] In this invention, the server includes voice processing means for receiving voice information and converting the voice into text information, data analysis means for analyzing the text information and identifying candidates for purchasable items, and emotion analysis means for analyzing the user's emotional state and making suggestions based on those emotions. This makes it possible to provide personalized suggestions that take the user's emotions into consideration and improve the user experience.

[0554] "Speech processing means" refers to technology that has the function of receiving speech information and converting that speech into text information.

[0555] "Data analysis means" refers to techniques for analyzing textual information and identifying potential items that can be purchased.

[0556] "Emotional analysis means" refers to technology that analyzes a user's emotional state and makes suggestions based on those emotions.

[0557] A "payment method" is a technology that executes electronic payments based on selected goods.

[0558] "Delivery method" refers to the technology that directs the delivery of selected goods.

[0559] This invention is a system for ordering products via voice input and providing users with a personalized experience. The system includes voice processing means, data analysis means, sentiment analysis means, payment means, and delivery means.

[0560] Audio processing means:

[0561] Users request products by voice through a device such as a smart speaker. The device uses voice recognition software, such as Google Assistant or Amazon Alexa, to convert the voice into text.

[0562] Data analysis methods:

[0563] The server receives text information sent from the terminal. The server uses natural language processing libraries such as NLTK and spaCy to analyze the text information and extract potential purchase items based on the user's intent.

[0564] Emotion analysis means:

[0565] The server uses emotion analysis tools, including IBM Watson, to estimate the user's emotional state from voice data. Based on this emotion analysis, it makes product recommendations tailored to the user's state.

[0566] For example, when a user places an order in "express" mode, items with shorter delivery times will be prioritized and suggested.

[0567] Payment methods and shipping methods:

[0568] When a user selects a suggested product, the server processes the payment electronically through a payment method. Payment platforms such as PayPal and Stripe are used. Once the payment is complete, the server arranges for the delivery of the selected items using a shipping method. The user is notified of the delivery status in real time.

[0569] Example of a prompt:

[0570] "A user is feeling stressed and has given voice instructions for the product they want to purchase. What kind of relaxation products would you suggest?"

[0571] "What products should be prioritized when a user is in a hurry?"

[0572] In this way, the system provides advanced personalization features that take into account the user's voice and emotional state.

[0573] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0574] Step 1:

[0575] The user speaks to the voice input device, indicating the product they wish to purchase. The device receives this voice information as input and converts it into text. Specifically, the device applies a voice recognition algorithm and outputs the converted text data.

[0576] Step 2:

[0577] The server receives text information obtained from speech recognition as input. Here, a natural language processing engine is used to analyze the text information, understand the user's intent, and list potential purchase options. This process involves keyword extraction and contextual understanding, and the list of product candidates is output.

[0578] Step 3:

[0579] The server simultaneously receives audio information as input for sentiment analysis of the voice. Using a sentiment analysis engine, it estimates the user's emotional state from the tone and tempo of the voice and outputs sentiment state data. For example, emotions such as "hurried" or "relaxed" may be detected.

[0580] Step 4:

[0581] The server receives a list of potential products and emotional state data as input, combines them to generate personalized product suggestions for the user, and applies specific rules, such as prioritizing products with faster delivery based on the user's emotions, to output a list of products to suggest.

[0582] Step 5:

[0583] The terminal receives product suggestions sent from the server. Using speech synthesis technology, it prompts the user to make a selection by delivering product suggestions to the user via voice. After the voice suggestion is output, the user proceeds to the next selection step.

[0584] Step 6:

[0585] The user selects the product they wish to purchase from the suggested items using voice or an app. The selected product information is then sent back to the server as input from the device.

[0586] Step 7:

[0587] The server receives the product information selected by the user as input and processes the payment using an electronic payment system. This includes processes such as authentication and amount calculation, and a confirmation notification is output after the payment is completed.

[0588] Step 8:

[0589] Upon receiving payment confirmation, the server activates the shipping arrangement system and arranges for the delivery of the selected items. The shipping status is notified to the user in real time, and additional information and offers as needed are included in the output.

[0590] (Application Example 2)

[0591] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0592] In recent years, users in e-commerce have been demanding faster and more personalized product recommendations and purchasing experiences. However, current systems often fail to take into account the user's emotional state, resulting in a non-personalized experience. In particular, the lack of flexible product recommendations tailored to the user's situation makes it difficult to maximize purchasing intent. This invention aims to achieve more effective product recommendations and faster payment procedures by combining the user's voice and emotional state.

[0593] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0594] In this invention, the server includes a speech recognition means that receives voice input and converts the voice data into text data, a product analysis means that analyzes the text data and identifies candidate products that can be purchased, and an emotion analysis means that analyzes the voice data and estimates the user's emotional state. This enables personalized product suggestions tailored to the user's situation, thereby improving the user experience in e-commerce.

[0595] "Speech recognition means" refers to a device or mechanism for receiving speech input in real time and converting speech data into text data.

[0596] A "product analysis means" is a device or mechanism for analyzing text data obtained from a user to identify potential products that can be purchased.

[0597] "Payment processing means" refers to a device or mechanism for executing electronic payment procedures based on selected goods.

[0598] A "delivery instruction means" is a device or mechanism for instructing the delivery arrangements for selected goods and managing the delivery process.

[0599] "Emotional analysis means" refers to a device or mechanism for analyzing voice data to estimate the user's emotional state.

[0600] "Suggestion means" refers to a device or mechanism for making personalized product suggestions based on the user's emotional state and past purchase history information.

[0601] The system based on this invention includes voice recognition means, product analysis means, payment processing means, delivery instruction means, sentiment analysis means, and suggestion means. These elements work together to provide users with a smooth and personalized shopping experience controlled by voice.

[0602] The server receives voice input from the user as a speech recognition tool and converts it into text data in real time using the Google Cloud Speech-to-Text API. This text data is then analyzed by a product analysis tool to identify potential purchase items. The Azure Cognitive Services sentiment analysis API is used to analyze the voice data and estimate the user's emotional state. This process allows the system to understand the user's emotions, such as whether they are "in a hurry" or "relaxed."

[0603] Based on this information, the server uses a suggestion system to recommend products that match the user's emotional state. For example, if the server determines that the user is in a hurry, products that can be delivered quickly will be prioritized in the list. For selected products, the payment processing system uses the Stripe API to execute the payment process.

[0604] After an item is selected, the shipping instructions system arranges for its delivery. The user is notified of the delivery status in real time using Firebase Cloud Messaging.

[0605] For example, if a user voice-inputs, "My child's snacks are almost gone, so I need them delivered quickly," the input is converted to text, and the user's sense of urgency is estimated using sentiment analysis. Based on this, products that can be delivered quickly are suggested.

[0606] Example of a prompt message: "Please tell me the name of the item you would like to order and whether you are in a hurry."

[0607] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0608] Step 1:

[0609] The user makes a voice input to the device. The device receives the input voice data, enabling processing by voice recognition.

[0610] Step 2:

[0611] The device uses the Google Cloud Speech-to-Text API to convert received audio data into text data. In this process, the audio waveform is converted into phonemes, and the corresponding text is generated. As a result, the audio input is converted into text data, which becomes the input for the next analysis process.

[0612] Step 3:

[0613] The server analyzes text data using a product analysis tool. This analysis generates a list of potential products based on the user's specified purchase request, taking into account past purchase history and inventory information. This step involves matching specific strings and performing database searches. As a result, a list of potential products is output.

[0614] Step 4:

[0615] The server sends the audio data to the Azure Cognitive Services sentiment analysis API to estimate the emotional state. The analysis uses features such as intonation and speed. The results of this analysis output an emotional state such as "hurried" or "calm."

[0616] Step 5:

[0617] The server creates personalized product suggestions using a suggestion system based on the sentiment analysis results and the list of potential products. If the user's sentiment is estimated to be "urgent," a logic is activated that prioritizes suggesting only products that can be delivered quickly. The generated list of product suggestions is then sent to the device.

[0618] Step 6:

[0619] The user selects a product from the suggested options on their device, and the selection data is sent to the server. The server uses the Stripe API to process the electronic payment based on the product selection data. The payment information is retrieved, and a completion status is output.

[0620] Step 7:

[0621] The server prepares the selected items for delivery based on the delivery instructions. Based on the delivery information, it sends data to the logistics system, and the delivery status is notified to the user via Firebase Cloud Messaging. This allows the user to track the delivery status in real time.

[0622] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0623] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0624] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0625] [Fourth Embodiment]

[0626] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0627] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0628] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0629] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0630] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0631] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0632] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0633] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0634] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0635] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0636] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0637] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0638] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0639] This invention provides a system for users to order products using voice commands and to make electronic payments easily and securely. The following describes the configurations for implementing this system.

[0640] First, the user speaks to a device such as a smart speaker to specify the product they want to purchase. The device receives this voice and converts it into text data using speech recognition. This text data is sent to a server, where product analysis tools analyze the user's intent. For example, if the user says, "I want to buy milk," the server generates a list of suggested products that include "milk."

[0641] The server sends the generated list of product candidates to the device as personalized product suggestions based on the user's past purchase history and preferences. The device notifies the user of this information using speech synthesis technology and prompts them to select a product. Once the user selects a specific product, that information is sent back to the server.

[0642] Next, the server uses a payment processing device to perform electronic payment for the selected items. During this process, it works in conjunction with the payment system to ensure secure transactions. Once payment is complete, the server uses a delivery instruction device to instruct the partner store to deliver the items and notifies the terminal that the delivery arrangements are complete.

[0643] For example, even if a user adds an orange juice to their order, the system repeats the same procedure, efficiently processing multiple items ordered by the user. This system allows users to complete their orders by voice, providing a convenient shopping experience that does not rely on visual cues.

[0644] The following describes the processing flow.

[0645] Step 1:

[0646] The user gives a voice command to the smart speaker saying, "I want to buy milk." The device receives this voice input and saves it as digital data.

[0647] Step 2:

[0648] The device sends the stored audio data to the server. During this process, the audio data is encrypted over the network and securely transmitted to the server.

[0649] Step 3:

[0650] The server converts the received audio data into text data using speech recognition technology. This text data is further processed to represent the user's intent.

[0651] Step 4:

[0652] The server analyzes the text data using a product analysis tool and lists potential "milk" products that are available for purchase. During this process, the server queries the product database to retrieve relevant product information.

[0653] Step 5:

[0654] The server lists potential products, personalizes them based on the user's past purchase history and preferences, and sends them to the device as optimized product suggestions.

[0655] Step 6:

[0656] Based on the product information received from the server, the terminal uses speech synthesis technology to ask the user, "A liter of milk from company A costs 500 yen, and a liter of milk from company B costs 480 yen. Which would you like?"

[0657] Step 7:

[0658] The user responds by voice saying, "I select company B's milk," and the terminal sends that voice input back to the server. The server confirms this selection.

[0659] Step 8:

[0660] The server uses payment processing methods for the items it has confirmed, and executes the payment process in conjunction with electronic payment systems such as PayPay. Once the payment is confirmed to be successful, the server notifies the terminal of the result.

[0661] Step 9:

[0662] Based on the payment completion information, the server uses a delivery instruction system to instruct partner delivery companies or stores to ship the goods. Once the delivery status is confirmed, the server sends that information to the terminal and notifies the user.

[0663] (Example 1)

[0664] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0665] In modern society, there is a growing need for systems that allow for easy and secure product purchases and electronic payments via voice. However, conventional systems lack personalization based on purchase history, limiting the user experience. Furthermore, there is a demand for technology that efficiently manages the entire process from voice input to payment and delivery, providing users with a seamless purchasing experience.

[0666] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0667] In this invention, the server includes voice recognition means, information analysis means, and transaction processing means. This enables a consistent process from voice input to product suggestions, electronic payment, and delivery instructions, resulting in a convenient and personalized purchasing experience for the user.

[0668] "Voice recognition means" refers to technology that receives voice input from a user and converts it into text data.

[0669] "Information analysis means" refers to analytical techniques used to identify purchasable products based on converted text data.

[0670] "Transaction processing means" refers to technology for securely executing electronic payment procedures for selected goods.

[0671] "Delivery guidance means" refers to technology used to instruct partner organizations to arrange the delivery of selected products.

[0672] "Suggestion methods" refer to technologies that take into account a user's past transaction history to provide personalized product suggestions.

[0673] "Notification method" refers to a technology that uses speech synthesis technology to inform users of proposals and the completion of transactions.

[0674] This invention is a system that allows users to order products using voice commands and make secure electronic payments. The system includes voice recognition, information analysis, transaction processing, delivery guidance, suggestion, and notification means. The following describes how to implement this system in detail.

[0675] First, the user speaks to a device such as a smart speaker and gives voice commands for the product they wish to purchase. The device has standard voice recognition software installed, and in this example, it uses Google Assistant or a common voice recognition API to convert the voice data into text data.

[0676] Once this audio data is converted to text data, the terminal sends the data to the server. On the server, information analysis tools operate, for example using natural language processing technology to identify potential purchasable products. In this process, product attributes and similar products are analyzed to create a list of candidates.

[0677] The server then uses suggestion tools to generate personalized product suggestions based on the user's past transaction history and preferences. This information is sent back to the terminal and communicated to the user using speech synthesis technology (e.g., Google Text-to-Speech). This allows the user to proceed with the purchasing process using only voice, without needing to visually confirm the information.

[0678] Once the user selects the desired product and that information is sent to the server, the transaction processing mechanism is activated. This mechanism includes payment APIs such as PayPal and Stripe, and performs secure electronic payment for the selected product.

[0679] Once payment is complete, the delivery guidance system is activated and instructs an external service to arrange delivery of the specified items. Delivery information is sent to the partner, and the server notifies the terminal that processing is complete. The terminal then reports this information to the user, informing them that the order is complete.

[0680] As a concrete example, when a user says to the speaker, "I want to buy orange juice," the voice is converted into text, the server lists orange juice as a purchase option, makes suggestions considering past purchase history, and simultaneously completes the payment and arranges delivery, creating a seamless process.

[0681] An example of an input prompt for a generative AI model is, "Please tell me how a user orders a product by voice to a smart speaker." This allows the generative AI to explain in detail the process from voice ordering to electronic payment and delivery instructions.

[0682] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0683] Step 1:

[0684] Users give voice commands to a device such as a smart speaker to select products. When a user says, "I want to buy milk," that voice data is entered into the device.

[0685] Step 2:

[0686] The terminal converts audio data into text data using speech recognition. This process utilizes a speech recognition API to analyze the audio waveform and convert it into the string "I want to buy milk." The converted text data is then output from the terminal to the server.

[0687] Step 3:

[0688] The server passes the received text data to an information analysis tool to analyze the user's request. Here, natural language processing technology is used to generate a list of available products based on the keyword "milk". This list is output as the analysis result.

[0689] Step 4:

[0690] Based on the product analysis results, the server uses suggestion tools to create personalized product recommendations that take into account the user's past purchase history. For example, if a user has frequently purchased a particular brand of milk in the past, that brand will be prioritized in the recommendations. The generated recommendations are then output to the terminal.

[0691] Step 5:

[0692] The device uses speech synthesis technology to notify the user of its recommendations. Specifically, it selects the cheapest or most popular items from the suggested products and says, "I recommend the cheapest brand A milk." This voice information is then provided to the user.

[0693] Step 6:

[0694] The user selects a suggested product. For example, they might voice-instruct the device to make a specific selection, such as "I want to buy milk from brand A." This selection is then recorded as input data in the device.

[0695] Step 7:

[0696] The terminal sends the selection to the server, and the server's transaction processing mechanism is activated. Based on the selected items, electronic payment is carried out in conjunction with the payment system. At this time, the user's payment information is used as input information, and a payment completion status is output.

[0697] Step 8:

[0698] After payment is completed, the server uses a delivery guidance system to instruct partner stores to ship the goods. This delivery instruction includes the delivery address and estimated delivery date and time. This information is forwarded to the delivery company, and the user is notified via their device that the delivery arrangements are complete.

[0699] (Application Example 1)

[0700] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0701] There is a need to provide an electronic transaction method that allows users to intuitively search for products and complete the purchase process without relying on visual cues. Furthermore, it is necessary to accurately offer diverse product suggestions to users to improve their purchasing experience. Additionally, there is a need to develop a system that ensures transactions are conducted safely and quickly.

[0702] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0703] In this invention, the server includes a speech recognition device that receives voice data and converts the voice into text information, a product analysis device that analyzes the text information and determines candidate products that can be acquired, and an information providing device that guides the user through the purchasing process based on the voice input without visual assistance. This makes it possible for the user to efficiently and safely search for and purchase products using only their voice.

[0704] A "speech recognition device" is a device that processes speech data to convert it into text information.

[0705] A "product analysis device" is a device that analyzes textual information to identify potential products that can be acquired.

[0706] A "transaction management device" is a processing device that executes electronic transaction processing based on specified goods.

[0707] A "delivery instruction device" is a device used to perform operations to instruct the delivery of specified goods.

[0708] An "information provision device" is a device that guides users through the purchasing process based on voice input, without providing visual assistance.

[0709] This invention provides a system that allows users to search for and purchase products using voice commands, and mainly consists of a voice recognition device, a product analysis device, a transaction management device, a delivery instruction device, and an information provision device. This system operates using terminals such as smartphones and personal computers, and servers connected to them.

[0710] The user inputs the product they wish to purchase into the device using their voice. The device uses the Google Speech-to-Text API to instantly convert the voice data into text. Subsequently, a server receives this text information via AWS, and a product analysis device identifies product candidates based on the user's needs.

[0711] Next, the server uses a transaction management device based on the analysis results to conduct secure electronic transactions using the Stripe API. After this process, the server instructs the logistics system to ship the goods via a delivery instruction device and notifies the user of the delivery schedule using Google Cloud Messaging.

[0712] This system provides information to users without requiring visual assistance, offering suggestions and guidance on a variety of products. For example, if a user says, "I want to buy tea," the system can suggest the most suitable tea product from among several options based on the user's past preferences. An example of a prompt would be, "Show the voice recognition procedure when the user says, 'Order new earphones'." A generative AI model based on this prompt enables accurate recognition and processing of voice commands.

[0713] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0714] Step 1:

[0715] The user uses their smartphone's voice input function to speak the product they wish to purchase. The voice data is captured by the device's microphone and converted into text using the Google Speech-to-Text API. The input is voice data, and the output is text.

[0716] Step 2:

[0717] The terminal sends the converted text information to the server. The server receives this text information and uses a product analysis device on AWS to analyze the user's intent. The data processing performed here is the analysis of the text information, and based on the analyzed information, product candidates are identified. The input is text information, and the output is a list of product candidates.

[0718] Step 3:

[0719] The server uses an information provider to verbally repeat a list of product candidates to the user to inform them of the analysis results. It then makes optimal product recommendations. This step utilizes speech synthesis technology to convert text data back into speech data. The input is a list of product candidates, and the output is speech information.

[0720] Step 4:

[0721] When a user makes a selection from the suggested products, that selection information is sent back to the server. The server processes the electronic transaction using the Stripe API via a transaction management device. Secure settlement is performed through the electronic transaction, and the results are processed. The input is the selected product information, and the output is information regarding the completion of the transaction.

[0722] Step 5:

[0723] Upon receiving payment confirmation, the server instructs the logistics system to ship the goods via a delivery instruction device. This instruction is sent through Google Cloud Messaging. To notify the user that the delivery arrangements are complete, delivery completion information is also provided via voice. The input is transaction completion information, and the output is delivery arrangement completion information.

[0724] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0725] The present invention is a system that improves the user experience by combining a user voice-based product ordering and electronic payment with an emotion engine. This system includes voice recognition means, product analysis means, payment processing means, delivery instruction means, and an emotion engine.

[0726] The user voice-instructs a device, such as a smart speaker, to purchase the desired items. The device receives the user's voice in real time and converts it into text data using speech recognition. This text data is sent to a server, where product analysis tools analyze the user's intent to generate a list of potential purchase items.

[0727] In parallel, the emotion engine analyzes the voice data to estimate the user's emotional state. For example, if a user says, "I'm in a bit of a hurry right now and need milk immediately," the emotion engine detects the emotion of urgency and, based on that, suggests products with faster delivery as a priority. Furthermore, if the user is relaxed when placing an order, the engine can suggest additional related products, enabling flexible suggestions tailored to the user's emotions.

[0728] The server sends personalized product suggestions to the device based on analyzed sentiment information and purchase history. The device then uses speech synthesis technology to present this information to the user and prompt them to make a selection. Once the user selects a product, the information is returned to the server, and electronic payment is processed via the payment processing system.

[0729] After payment is completed, the server uses a delivery instruction system to arrange for the delivery of the selected items. The user will be notified of the delivery status as needed, and additional information and offers will be provided based on the emotions detected by the emotion engine, if necessary.

[0730] In this way, the system can utilize the user's voice and emotional information to provide a more natural and personalized shopping experience. For example, when a user is feeling stressed, the server can suggest products that promote relaxation.

[0731] The following describes the processing flow.

[0732] Step 1:

[0733] The user gives a voice command to the smart speaker saying, "I want to buy milk." The device receives this voice command and converts it into digital data.

[0734] Step 2:

[0735] The device sends the converted audio data to the server. The audio data is encrypted and securely transmitted to the server.

[0736] Step 3:

[0737] The server converts the received audio data into text data using speech recognition technology. Simultaneously, an emotion engine analyzes the tone and speed of the speech to determine the user's emotional state.

[0738] Step 4:

[0739] The server processes text data using product analysis tools and lists potential purchasable products. It then adjusts the content and order of product suggestions, reflecting the emotional state detected by the emotion engine.

[0740] Step 5:

[0741] The server generates personalized product suggestions and sends them to the device in real time. The product list also includes special offers tailored to the user's mood.

[0742] Step 6:

[0743] The terminal receives product information from the server and uses speech synthesis technology to notify the user, asking which product they wish to purchase.

[0744] Step 7:

[0745] The user responds by voice, "I'll choose company B's milk." The device then sends this selection back to the server.

[0746] Step 8:

[0747] The server performs electronic payment for the selected product using the payment processing method. It then verifies that the transaction was conducted securely and that payment was completed.

[0748] Step 9:

[0749] After the server completes the payment, it instructs the partner store to ship the goods using the shipping instruction method. The user is then notified of the shipping status via their terminal.

[0750] Step 10:

[0751] The emotion engine detects the user's current emotional state, and the server uses this information to make additional suggestions and follow up. For example, if the user is feeling stressed, it might suggest relaxation products to improve the user experience.

[0752] (Example 2)

[0753] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0754] Conventional voice-based product ordering systems fail to consider user emotions, relying solely on simple voice recognition and data analysis for product recommendations. This prevents them from providing the personalized experience users desire. Furthermore, these emotionally detached recommendations fail to adequately meet user needs, highlighting the need for improved user experience.

[0755] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0756] In this invention, the server includes voice processing means for receiving voice information and converting the voice into text information, data analysis means for analyzing the text information and identifying candidates for purchasable items, and emotion analysis means for analyzing the user's emotional state and making suggestions based on those emotions. This makes it possible to provide personalized suggestions that take the user's emotions into consideration and improve the user experience.

[0757] "Speech processing means" refers to technology that has the function of receiving speech information and converting that speech into text information.

[0758] "Data analysis means" refers to techniques for analyzing textual information and identifying potential items that can be purchased.

[0759] "Emotional analysis means" refers to technology that analyzes a user's emotional state and makes suggestions based on those emotions.

[0760] A "payment method" is a technology that executes electronic payments based on selected goods.

[0761] "Delivery method" refers to the technology that directs the delivery of selected goods.

[0762] This invention is a system for ordering products via voice input and providing users with a personalized experience. The system includes voice processing means, data analysis means, sentiment analysis means, payment means, and delivery means.

[0763] Audio processing means:

[0764] Users request products by voice through a device such as a smart speaker. The device uses voice recognition software, such as Google Assistant or Amazon Alexa, to convert the voice into text.

[0765] Data analysis methods:

[0766] The server receives text information sent from the terminal. The server uses natural language processing libraries such as NLTK and spaCy to analyze the text information and extract potential purchase items based on the user's intent.

[0767] Emotion analysis means:

[0768] The server uses emotion analysis tools, including IBM Watson, to estimate the user's emotional state from voice data. Based on this emotion analysis, it makes product recommendations tailored to the user's state.

[0769] For example, when a user places an order in "express" mode, items with shorter delivery times will be prioritized and suggested.

[0770] Payment methods and shipping methods:

[0771] When a user selects a suggested product, the server processes the payment electronically through a payment method. Payment platforms such as PayPal and Stripe are used. Once the payment is complete, the server arranges for the delivery of the selected items using a shipping method. The user is notified of the delivery status in real time.

[0772] Example of a prompt:

[0773] "A user is feeling stressed and has given voice instructions for the product they want to purchase. What kind of relaxation products would you suggest?"

[0774] "What products should be prioritized when a user is in a hurry?"

[0775] In this way, the system provides advanced personalization features that take into account the user's voice and emotional state.

[0776] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0777] Step 1:

[0778] The user speaks to the voice input device, indicating the product they wish to purchase. The device receives this voice information as input and converts it into text. Specifically, the device applies a voice recognition algorithm and outputs the converted text data.

[0779] Step 2:

[0780] The server receives text information obtained from speech recognition as input. Here, a natural language processing engine is used to analyze the text information, understand the user's intent, and list potential purchase options. This process involves keyword extraction and contextual understanding, and the list of product candidates is output.

[0781] Step 3:

[0782] The server simultaneously receives audio information as input for sentiment analysis of the voice. Using a sentiment analysis engine, it estimates the user's emotional state from the tone and tempo of the voice and outputs sentiment state data. For example, emotions such as "hurried" or "relaxed" may be detected.

[0783] Step 4:

[0784] The server receives a list of potential products and emotional state data as input, combines them to generate personalized product suggestions for the user, and applies specific rules, such as prioritizing products with faster delivery based on the user's emotions, to output a list of products to suggest.

[0785] Step 5:

[0786] The terminal receives product suggestions sent from the server. Using speech synthesis technology, it prompts the user to make a selection by delivering product suggestions to the user via voice. After the voice suggestion is output, the user proceeds to the next selection step.

[0787] Step 6:

[0788] The user selects the product they wish to purchase from the suggested items using voice or an app. The selected product information is then sent back to the server as input from the device.

[0789] Step 7:

[0790] The server receives the product information selected by the user as input and processes the payment using an electronic payment system. This includes processes such as authentication and amount calculation, and a confirmation notification is output after the payment is completed.

[0791] Step 8:

[0792] Upon receiving payment confirmation, the server activates the shipping arrangement system and arranges for the delivery of the selected items. The shipping status is notified to the user in real time, and additional information and offers as needed are included in the output.

[0793] (Application Example 2)

[0794] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0795] In recent years, users in e-commerce have been demanding faster and more personalized product recommendations and purchasing experiences. However, current systems often fail to take into account the user's emotional state, resulting in a non-personalized experience. In particular, the lack of flexible product recommendations tailored to the user's situation makes it difficult to maximize purchasing intent. This invention aims to achieve more effective product recommendations and faster payment procedures by combining the user's voice and emotional state.

[0796] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0797] In this invention, the server includes a speech recognition means that receives voice input and converts the voice data into text data, a product analysis means that analyzes the text data and identifies candidate products that can be purchased, and an emotion analysis means that analyzes the voice data and estimates the user's emotional state. This enables personalized product suggestions tailored to the user's situation, thereby improving the user experience in e-commerce.

[0798] "Speech recognition means" refers to a device or mechanism for receiving speech input in real time and converting speech data into text data.

[0799] A "product analysis means" is a device or mechanism for analyzing text data obtained from a user to identify potential products that can be purchased.

[0800] "Payment processing means" refers to a device or mechanism for executing electronic payment procedures based on selected goods.

[0801] A "delivery instruction means" is a device or mechanism for instructing the delivery arrangements for selected goods and managing the delivery process.

[0802] "Emotional analysis means" refers to a device or mechanism for analyzing voice data to estimate the user's emotional state.

[0803] "Suggestion means" refers to a device or mechanism for making personalized product suggestions based on the user's emotional state and past purchase history information.

[0804] The system based on this invention includes voice recognition means, product analysis means, payment processing means, delivery instruction means, sentiment analysis means, and suggestion means. These elements work together to provide users with a smooth and personalized shopping experience controlled by voice.

[0805] The server receives voice input from the user as a speech recognition tool and converts it into text data in real time using the Google Cloud Speech-to-Text API. This text data is then analyzed by a product analysis tool to identify potential purchase items. The Azure Cognitive Services sentiment analysis API is used to analyze the voice data and estimate the user's emotional state. This process allows the system to understand the user's emotions, such as whether they are "in a hurry" or "relaxed."

[0806] Based on this information, the server uses a suggestion system to recommend products that match the user's emotional state. For example, if the server determines that the user is in a hurry, products that can be delivered quickly will be prioritized in the list. For selected products, the payment processing system uses the Stripe API to execute the payment process.

[0807] After an item is selected, the shipping instructions system arranges for its delivery. The user is notified of the delivery status in real time using Firebase Cloud Messaging.

[0808] For example, if a user voice-inputs, "My child's snacks are almost gone, so I need them delivered quickly," the input is converted to text, and the user's sense of urgency is estimated using sentiment analysis. Based on this, products that can be delivered quickly are suggested.

[0809] Example of a prompt message: "Please tell me the name of the item you would like to order and whether you are in a hurry."

[0810] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0811] Step 1:

[0812] The user makes a voice input to the device. The device receives the input voice data, enabling processing by voice recognition.

[0813] Step 2:

[0814] The device uses the Google Cloud Speech-to-Text API to convert received audio data into text data. In this process, the audio waveform is converted into phonemes, and the corresponding text is generated. As a result, the audio input is converted into text data, which becomes the input for the next analysis process.

[0815] Step 3:

[0816] The server analyzes text data using a product analysis tool. This analysis generates a list of potential products based on the user's specified purchase request, taking into account past purchase history and inventory information. This step involves matching specific strings and performing database searches. As a result, a list of potential products is output.

[0817] Step 4:

[0818] The server sends the audio data to the Azure Cognitive Services sentiment analysis API to estimate the emotional state. The analysis uses features such as intonation and speed. The results of this analysis output an emotional state such as "hurried" or "calm."

[0819] Step 5:

[0820] The server creates personalized product suggestions using a suggestion system based on the sentiment analysis results and the list of potential products. If the user's sentiment is estimated to be "urgent," a logic is activated that prioritizes suggesting only products that can be delivered quickly. The generated list of product suggestions is then sent to the device.

[0821] Step 6:

[0822] The user selects a product from the suggested options on their device, and the selection data is sent to the server. The server uses the Stripe API to process the electronic payment based on the product selection data. The payment information is retrieved, and a completion status is output.

[0823] Step 7:

[0824] The server prepares the selected items for delivery based on the delivery instructions. Based on the delivery information, it sends data to the logistics system, and the delivery status is notified to the user via Firebase Cloud Messaging. This allows the user to track the delivery status in real time.

[0825] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0826] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0827] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0828] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0829] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0830] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0831] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0832] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0833] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0834] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0835] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0836] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0837] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0838] 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.

[0839] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0840] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0841] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0842] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0843] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0844] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0845] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0846] The following is further disclosed regarding the embodiments described above.

[0847] (Claim 1)

[0848] A speech recognition means that receives voice input and converts the voice data into text data,

[0849] A product analysis means that analyzes the aforementioned text data and identifies potential purchasable products,

[0850] A payment processing means that executes an electronic payment procedure based on the product selected from the aforementioned candidate products,

[0851] A delivery instruction means for instructing the delivery arrangements for the selected product,

[0852] A system that includes this.

[0853] (Claim 2)

[0854] The system according to claim 1, wherein the speech recognition means is configured to process the user's voice in real time.

[0855] (Claim 3)

[0856] The system according to claim 1, wherein the product analysis means is configured to make personalized product suggestions that take into account the user's past purchase history information.

[0857] "Example 1"

[0858] (Claim 1)

[0859] A speech recognition means that receives voice input and converts the voice data into text data,

[0860] Information analysis means for analyzing the aforementioned text data and identifying potential purchasable products,

[0861] A transaction processing means that executes an electronic payment procedure based on the product selected from the aforementioned candidate products,

[0862] A delivery guidance means for instructing the delivery arrangements for the selected goods,

[0863] A suggestion method that provides personalized product suggestions taking into account the user's past transaction history,

[0864] A notification method that uses speech synthesis technology to convey suggestions to the user,

[0865] A system that includes this.

[0866] (Claim 2)

[0867] The system according to claim 1, wherein the speech recognition means is configured to dynamically process the user's voice.

[0868] (Claim 3)

[0869] The system according to claim 1, wherein the transaction processing means is configured to perform transactions in cooperation with a secure settlement system.

[0870] "Application Example 1"

[0871] (Claim 1)

[0872] A speech recognition device that receives audio data and converts the audio into text information,

[0873] A product analysis device that analyzes the aforementioned textual information and determines candidate products that can be obtained,

[0874] A transaction management device that performs electronic transaction processing based on a product specified from the aforementioned candidate products,

[0875] A delivery instruction device that instructs the delivery of the specified product,

[0876] An information provision device that guides the user through the purchasing process without visual assistance based on the aforementioned voice input,

[0877] A system that includes this.

[0878] (Claim 2)

[0879] The system according to claim 1, wherein the voice recognition device processes the user's voice in real time and makes suggestions based on its content.

[0880] (Claim 3)

[0881] The system according to claim 1, wherein the product analysis device is configured to make personalized product suggestions based on the user's past purchase history information.

[0882] "Example 2 of combining an emotion engine"

[0883] (Claim 1)

[0884] A voice processing means that receives voice information and converts the voice into text information,

[0885] A data analysis means for analyzing the aforementioned textual information and identifying potential items that can be purchased,

[0886] An emotion analysis means that analyzes the user's emotional state and makes suggestions based on that emotion,

[0887] A payment means that performs electronic payment based on an item selected from the aforementioned candidate items,

[0888] A delivery means that instructs the delivery of the selected items,

[0889] A system that includes this.

[0890] (Claim 2)

[0891] The system according to claim 1, wherein the voice processing means is configured to process the user's voice in real time, and the emotion analysis means is configured to estimate emotions from the voice.

[0892] (Claim 3)

[0893] The system according to claim 1, wherein the data analysis means is configured to make personalized suggestions that take into account the user's past purchase history and emotional information.

[0894] "Application example 2 when combining with an emotional engine"

[0895] (Claim 1)

[0896] A speech recognition means that receives voice input and converts the voice data into text data,

[0897] A product analysis means that analyzes the aforementioned text data and identifies potential purchasable products,

[0898] A payment processing means that executes an electronic payment procedure based on the product selected from the aforementioned candidate products,

[0899] A delivery instruction means for instructing the delivery arrangements for the selected product,

[0900] A sentiment analysis method that analyzes voice data and estimates the user's emotional state,

[0901] A proposal means that makes personalized product suggestions based on the aforementioned emotional state,

[0902] A system that includes this.

[0903] (Claim 2)

[0904] The system according to claim 1, wherein the speech recognition means is configured to process the user's voice in real time, and the emotion analysis means is configured to estimate the user's emotional state in real time.

[0905] (Claim 3)

[0906] The system according to claim 1, wherein the suggestion means is configured to suggest only products that can be delivered quickly, based on the user's past purchase history information and current emotional state. [Explanation of Symbols]

[0907] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A speech recognition means that receives voice input and converts the voice data into text data, A product analysis means that analyzes the aforementioned text data and identifies potential purchasable products, A payment processing means that executes an electronic payment procedure based on the product selected from the aforementioned candidate products, A delivery instruction means for instructing the delivery arrangements for the selected product, A system that includes this.

2. The system according to claim 1, wherein the speech recognition means is configured to process the user's voice in real time.

3. The system according to claim 1, wherein the product analysis means is configured to make personalized product suggestions that take into account the user's past purchase history information.

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