system

Abstract

We provide the system. [Solution] An interface for receiving purchase requests from users, A generation means that automatically selects recommended products based on the aforementioned request, A confirmation means that presents information about the recommended products to the user and initiates the ordering process based on the user's approval, A processing method that automatically executes product delivery and payment after an order is placed, 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 persona chatbot control method 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 chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a 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] The expense purchase process in a company requires many procedures and approval flows, imposing a heavy burden on the person in charge. As a result, the business tends to become personal, and there is a problem that it takes time for a new person in charge to understand the process. Also, due to the complexity of the procedures, the problem is that business efficiency decreases.

Means for Solving the Problems

[0005] This invention provides a system that simplifies the purchasing process by receiving purchase requests from users through an interface and automatically selecting recommended products using AI via a generation means. Furthermore, the system automatically initiates the ordering process when the user approves the selected products via a confirmation means, and then automates the delivery and payment of the products using a processing means. In this way, the system significantly simplifies complex purchasing procedures, eliminates reliance on individual expertise, and improves operational efficiency.

[0006] An "interface means" is a communication means for receiving purchase requests from users and transmitting them to the server.

[0007] The "generation method" refers to a function that automatically selects recommended products using AI based on user requests.

[0008] A "verification mechanism" is a function that presents product information generated by a generation mechanism to the user and obtains their approval.

[0009] The "processing means" refers to a function that automatically handles product ordering, delivery, and payment procedures based on user approval. [Brief explanation of the drawing]

[0010] [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, which incorporates an emotion engine. [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]

[0011] 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.

[0012] First, let's explain the terminology used in the following explanation.

[0013] In the following embodiments, the numbered 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.

[0014] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0015] In the following embodiments, the numbered 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.

[0016] In the following embodiments, the numbered 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.

[0017] 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."

[0018] [First Embodiment]

[0019] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0020] 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.

[0021] 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).

[0022] 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.

[0023] 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.

[0024] 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.

[0025] 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.

[0026] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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".

[0031] The system of this invention consists of three main components: a user, a terminal, and a server. The user enters a purchase request into a chat interface, and the terminal sends the contents to the server. The server analyzes the request and uses a generation means to perform AI-powered product recommendations. As a result, the server selects relevant products from an existing catalog and sends that information to the terminal.

[0032] The terminal displays product information received from the server to the user, who then reviews and approves the selected products. Once the user approves, the terminal transmits this information to the server, which then automatically initiates the ordering process using a verification mechanism.

[0033] The server uses processing tools to consistently manage the ordering, delivery, and payment processes for goods. Once delivery is complete, the server automatically executes the payment process, records all processing logs, and then notifies the user of the transaction completion via the terminal.

[0034] As a concrete example, if a user requests to purchase a new projector via chat, the device transmits this request to the server, which uses AI to recommend several projectors. Once the user selects one and approves it through the device, the server immediately proceeds with the order process. After the product's delivery is confirmed, payment is processed automatically, and the entire process is completed seamlessly for the user. In this way, this system significantly streamlines a company's purchasing process.

[0035] The following describes the processing flow.

[0036] Step 1:

[0037] The user enters a purchase request into the chat interface. The terminal receives this input and sends its contents to the server.

[0038] Step 2:

[0039] The server analyzes the received request and extracts keywords. The server uses AI with generation methods to search for related products from the existing catalog.

[0040] Step 3:

[0041] The server sends the selected product information to the terminal. The terminal receives this information and presents it to the user. The user reviews the presented products and makes a selection.

[0042] Step 4:

[0043] Once the user makes a selection and indicates their approval, the terminal transmits this information to the server. The server then uses a confirmation mechanism to initiate the ordering process.

[0044] Step 5:

[0045] The server connects to the catalog purchasing system and registers the order information for the products selected by the user. The server then verifies the order details and sets the delivery schedule.

[0046] Step 6:

[0047] The server manages the delivery status of the goods and confirms that delivery is complete. After confirmation, the server automatically processes the payment.

[0048] Step 7:

[0049] The server logs the entire transaction process and sends a completion notification to the terminal. The terminal receives this notification and informs the user that the transaction is complete.

[0050] (Example 1)

[0051] 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."

[0052] In online shopping and corporate purchasing processes, there is a growing need for rapid and accurate product recommendations based on user needs, followed by automated purchasing procedures. However, existing systems often rely on manual processes for recommending appropriate products based on user requests, as well as managing product ordering, delivery, and payment, resulting in inefficiencies. In particular, when users have diverse needs, selecting the optimal product for each request and completing the purchasing process seamlessly presents a significant challenge.

[0053] 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.

[0054] In this invention, the server includes communication means, knowledge processing means, dialogue confirmation means, resource management means, and reporting means. This enables the analysis of user requests, product recommendations using artificial intelligence, and the automation of the process from ordering to payment, allowing for efficient and accurate processing.

[0055] A "communication method" is a mechanism that has the function of sending purchase requests from users to a server and enables the sending and receiving of data.

[0056] A "knowledge processing tool" is a processing function that analyzes a user's purchase request and automatically selects relevant products using a generative AI model.

[0057] A "dialogue confirmation method" is a system that has an interface and functions to present recommended products to the user and obtain their selection and approval.

[0058] A "resource management system" is a set of processing functions that automatically manages and executes the entire purchasing process, from ordering goods to delivery and payment.

[0059] A "reporting mechanism" is a function that notifies users of the completion of a transaction or information about the purchasing process.

[0060] A "generative AI model" is an artificial intelligence-based system used to suggest the most suitable products in response to user requests.

[0061] A "prompt sentence" is an input sentence that gives instructions or questions to a generative AI model, enabling it to generate appropriate responses or recommendations.

[0062] This invention is a system for streamlining the purchasing process and includes communication means, knowledge processing means, dialogue confirmation means, resource management means, and reporting means. Users input purchasing requests using a chat interface via a terminal. This terminal can be a typical personal computer or smartphone, equipped with internet-connected software (e.g., a chat application or web browser). Examples include messaging services.

[0063] The terminal sends the user's input request to the server via a communication method. The server uses knowledge processing tools to analyze and understand the received request. Here, widely used software libraries for natural language processing (e.g., spaCy and NLTK) are used. Based on the analyzed information, a generative AI model (e.g., GPT-3®) is used to recommend a product suitable for the user. For example, the prompt might be, "I want to buy a new projector. Please recommend one."

[0064] The server uses this generative AI model to select relevant products from an existing product database. The selected product information is presented to the user via the terminal. The user selects a specific product from the list and indicates their intention to purchase it. The dialogue confirmation mechanism supports this selection and approval process. Through this mechanism, the user can smoothly decide on a purchase.

[0065] Furthermore, based on approved information, the server uses resource management tools to automate the product ordering and delivery process. This process involves checking product inventory, adjusting shipping schedules, and utilizing integrated systems as needed. Once the products have been delivered, the server executes the payment process, and the resource management tools monitor the entire process. Upon completion of the transaction, the user is notified through reporting tools. In this way, the system streamlines the entire purchasing process and achieves end-to-end automation.

[0066] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0067] Step 1:

[0068] The user enters a purchase request through a chat interface. This input may include product categories and specific product requests. For example, the user might enter "I want a new projector." This is entered into the terminal. The terminal receives the user input as text data and formats it for transmission to the server.

[0069] Step 2:

[0070] The terminal sends the request data received from the user to the server using a communication method. Specifically, it sends a POST request along with the data to the server using the HTTP protocol. The input is the text of the user request, and the output is the status of the successful transmission to the server.

[0071] Step 3:

[0072] The server uses a natural language processing library to parse the received request data. This parsing extracts keywords and their intent from the request. The input is the user's request text, and the output is the parsed keywords and phrases. Specifically, keywords such as "projector" and "purchase" are parsed.

[0073] Step 4:

[0074] The server uses a generated AI model based on the analysis results to recommend products. It generates a prompt message and inputs it to the AI ​​in the form of "I'm looking for a projector. Please list the best options below." The input is the analyzed keywords, and the output is a list of recommended products.

[0075] Step 5:

[0076] The server generates a product list and sends it to the terminal. The terminal then presents this list to the user. Specifically, it displays product names, prices, features, etc., in a list format on the chat interface. The input is a list of recommended products, and the output is the product information presented to the user.

[0077] Step 6:

[0078] The user makes a selection from the presented product list. They then confirm their selection and indicate their intention to purchase via chat. For example, they might type, "I want to buy product A." This input constitutes the user's selection information, and the device sends this as a confirmation message to the server.

[0079] Step 7:

[0080] The terminal sends user selection information to the server. The server automates the ordering process for the selected products. Input is user approval information, and output is the order processing start status. Specific actions include sending data to the ordering system and checking inventory.

[0081] Step 8:

[0082] The server manages and integrates all processes, including product delivery and payment, using resource management tools. Once product delivery is complete, payment processing is also automated. Input is order information, and output is a record of the completion status. Integration with the logistics system is also included.

[0083] Step 9:

[0084] After all processing is complete, the server sends a transaction completion notification to the user via the terminal using a reporting mechanism. Specifically, this involves sending a notification message to the chat interface. The input is the completion status, and the output is a transaction completion confirmation notification to the user.

[0085] (Application Example 1)

[0086] 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."

[0087] In modern online shopping, there are problems such as the difficulty for users to efficiently search for desired products and receive appropriate recommendations. Furthermore, the process from product selection to payment completion is cumbersome, requiring multiple steps. Additionally, the lack of more intuitive operation, such as voice input, limits the user experience.

[0088] 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.

[0089] In this invention, the server includes an interactive interface means for receiving purchase requests from users, a generation means using an artificial intelligence model to generate multiple products, and a processing means for automatically ordering products, and handling delivery and payment processing after product selection. This allows users to easily input purchase requests using voice input and complete the entire process quickly and seamlessly for the selected products.

[0090] "Interactive interface means" refers to a communication method, such as chat or voice input, that allows users to directly input purchase requests.

[0091] "Generation methods using artificial intelligence models" refer to methods that analyze user input information and automatically select and generate multiple related products.

[0092] A "confirmation and approval mechanism" is a method that presents recommended products to the user, allowing the user to select and approve them to initiate the ordering process.

[0093] A "processing device" is a means of automatically managing the ordering of selected products, monitoring of delivery, and completion of payment after delivery.

[0094] "Voice input" is an input method in which the user communicates purchase requests to a device by speaking.

[0095] "Natural language processing technology" is a technology that analyzes natural language input by users to efficiently understand and process purchase requests.

[0096] "Key words" refer to important words or phrases included in the user's purchase request, and are the words that the AI ​​uses as criteria when selecting related products.

[0097] The system for realizing this invention mainly consists of a user's smart device (e.g., a smartphone), a server, and a generative AI model.

[0098] Users enter their purchase requests through an interactive interface on their smartphones. This interface uses a chat format and also supports voice input. If a user wants to find a product, they can make a voice request such as, "I want a jacket for spring."

[0099] The terminal receives this request and sends it to the server. The server receives this information and analyzes the request using natural language processing technology. A generative AI model (e.g., OpenAI® GPT-4®) uses this analysis to select several relevant products from an existing product catalog and recommend them to the user. For example, the information is processed in the form of a prompt message: "user: 'I'm looking for a spring jacket. Can you recommend one?'"

[0100] Once the user reviews and approves the presented product, the terminal notifies the server. The server automatically places an order based on the user's selection and simultaneously monitors the product's delivery status. Once delivery is confirmed, the server automatically processes the payment.

[0101] This system integrates multiple technologies. It utilizes Socket.IO and React Native for real-time chat functionality, OpenAI APIs for natural language processing, Stripe APIs for payment processing, and AWS Lambda for order process management. This allows for an efficient and intuitive shopping experience for users.

[0102] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0103] Step 1:

[0104] Users enter purchase requests through an interactive interface on their smart devices. They can use either text or voice input, making requests such as "I want a spring jacket." The input is in natural language, and this information is sent to the server via the smart device.

[0105] Step 2:

[0106] The server analyzes the natural language purchase requests it receives. The input is text data sent by the user, and the server uses natural language processing techniques to extract key words. This results in the output of a keyword list for extracting products that belong to a specific category or have certain attributes.

[0107] Step 3:

[0108] The server uses a generative AI model to generate a list of recommended products based on the analyzed keywords. The input is the keyword list output in step 2, and the AI ​​model performs data calculations to select multiple highly relevant products. This product list is then generated as output and prepared to be presented to the user.

[0109] Step 4:

[0110] The terminal displays a list of products sent from the server to the user. The user makes a selection of one or more products from the displayed list. The input is the product list obtained from the server, and the user selects their desired products from this list, resulting in the user's selection being output.

[0111] Step 5:

[0112] The server receives the user's selection and starts the automated ordering process. The input is the selected product information, and AWS Lambda is used to check inventory and process the order. The output records that the order has been completed and the delivery process has begun.

[0113] Step 6:

[0114] The server continuously monitors the delivery status of the product and automatically processes payment once delivery is complete. The input is a delivery completion notification, and data processing is performed using the Stripe API to generate payment information and execute the settlement. At this point, an output indicating the completion of the transaction is generated and notified to the user.

[0115] 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.

[0116] The system of this invention consists of four main components: a user, a terminal, a server, and an emotion engine. The user enters a purchase request into the chat interface, and the terminal receives this information and sends it to the server. The server analyzes the received request and uses AI with generation means to select recommended products from an existing catalog. At this time, the emotion engine analyzes the user's emotions and influences the selection of recommended products based on the results.

[0117] The terminal displays product information returned from the server to the user, and the sentiment engine analyzes the user's response in real time. If the user gives an approval or a negative response, the sentiment engine communicates the result to the server and adjusts the recommendations accordingly.

[0118] For approved products, the terminal sends the information to the server, automating the ordering process using a verification mechanism. The server uses processing tools to automatically manage all processes, including ordering, delivery, and payment. The emotion engine collects user emotion data to improve the user experience and optimize future purchasing processes.

[0119] For example, if a user requests via chat that they "want to buy a new headset," the device transmits this request to the server, which then uses AI to recommend several headsets. The emotion engine understands the user's preferences, and if the user smiles at one of the presented products, this information is transmitted to the server, and that product is highlighted. Once the user approves the selection, the server immediately begins the ordering process, and payment is automatically processed once delivery is complete. The feedback from the emotion engine makes the next purchasing experience more personalized, allowing users to make comfortable choices in less time. In this way, this system significantly streamlines the company's purchasing process while simultaneously improving the user's purchasing experience.

[0120] The following describes the processing flow.

[0121] Step 1:

[0122] The user types "I want to buy a new headset" into the chat interface. The terminal receives this input and sends the request to the server.

[0123] Step 2:

[0124] The server analyzes the received request to identify the key product type. Using a generation method, the server leverages an AI algorithm to select suitable headset candidates from the existing catalog database.

[0125] Step 3:

[0126] The server sends information about the selected candidate products to the terminal. The terminal presents this to the user and prompts them to make a selection. An emotion engine monitors the user's facial expressions and reactions in the background and analyzes positive or negative emotions.

[0127] Step 4:

[0128] The emotion engine detects when a user shows interest in one of the presented products and exhibits a positive reaction, such as a smile, to that product. The device notifies the server of this information, and the server then re-presents the user with special emphasis or additional information about that product.

[0129] Step 5:

[0130] The user makes a final selection and approves the product. The terminal sends this approval information to the server. The server uses a verification mechanism to initiate the ordering process for the selected product.

[0131] Step 6:

[0132] The server registers order information in the catalog purchasing system and sets the delivery schedule for the goods. The server manages the status until delivery is complete, and immediately executes the payment process automatically once delivery is confirmed.

[0133] Step 7:

[0134] The server records a log of the completion of the entire process and sends a completion notification to the terminal. The terminal notifies the user that "Your headset order and payment have been completed." The emotion engine stores the emotion data obtained from this transaction and uses it as a reference for future purchasing processes.

[0135] (Example 2)

[0136] 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".

[0137] Traditional purchasing systems often select products based on user requests without considering the user's emotional state, making it difficult to recommend the most suitable products for individual users. Furthermore, manual delivery and payment processing after purchase hindered the provision of an efficient purchasing experience.

[0138] 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.

[0139] In this invention, the server includes a communication device that receives purchase requests from users and relays the information, an analysis device that analyzes the requests and selects recommended products using a generative artificial intelligence model, and an auxiliary device that analyzes the user's emotional state and reflects it in the selection of recommended products. This enables optimal product recommendations based on the individual emotional state of the user, and by automating delivery and payment processing, an efficient and personalized purchasing experience is provided.

[0140] A "communication device" is a device that receives purchase requests from users and relays that information to a server.

[0141] An "analysis device" is a device that analyzes a user's request and selects the most suitable recommended product using a generated artificial intelligence model.

[0142] An "auxiliary device" is a device that analyzes the user's emotional state and reflects the analysis results in product recommendations.

[0143] An "authentication device" is a device that presents recommended product information to the user and initiates the ordering process based on the user's approval.

[0144] A "management device" is a device that automatically handles the delivery and payment processing of goods after an order has been placed, and manages these processes efficiently.

[0145] A "generative artificial intelligence model" is an artificial intelligence technology used to analyze user purchase requests and select appropriate recommended products.

[0146] "Information resources" refers to a database or catalog used to present product options selected based on user request analysis.

[0147] This invention is an advanced shopping system designed to enhance the user's purchasing experience. The system primarily consists of a user, a terminal, a server, and a secondary emotion engine.

[0148] Users enter their requests for desired products into the interface using free-form language. This interface is presented in a chat format, making it user-friendly and easy to use. As a result, product requests can be made in a more natural, conversational manner.

[0149] The terminal receives information entered by the user in real time and sends it to the server via the appropriate protocol. The terminal uses a common communication protocol (e.g., HTTPS) for this communication to ensure data security.

[0150] The server utilizes a generative artificial intelligence model to analyze incoming requests. This model uses NLP (Natural Language Processing) techniques to understand user requests and select the most suitable products from an existing catalog database. Furthermore, an emotion engine analyzes the user's emotional state in real time, and this information can be used to further improve the accuracy of product selection.

[0151] Based on this data, the server selects the most suitable recommended products for the user. To do this, it uses a pre-configured algorithm and sentiment analysis results to determine product priorities. The selected product information is then transmitted from the server to the terminal and presented to the user.

[0152] To illustrate the system's operation with a concrete example, when a user requests to purchase a new headset, the terminal transmits the request to the server, which then uses a generated AI model to select several headsets. The emotion engine analyzes the user's reactions (e.g., smiles and expressions of interest) and highlights products that the user reacts favorably to. Once the user approves, the server immediately initiates the ordering process for that product, and then automatically completes the delivery and payment.

[0153] Examples of prompts include "I want to buy a new headset" and "I'm looking for the latest smartphone model." This allows the system to quickly and accurately provide products that meet the user's specific needs.

[0154] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0155] Step 1:

[0156] The user enters a purchase request into the chat interface. Once entered, the terminal receives the text data and prepares to send it to the server. In this step, the input is text data from the user, and the output is data that the terminal sends to the server.

[0157] Step 2:

[0158] The terminal uses the appropriate communication protocol to send the received request data to the server. In this process, the input is the user's request received by the terminal, and the output is the request data forwarded to the server. Specifically, the process involves data encryption and transmission according to the protocol.

[0159] Step 3:

[0160] The server parses the received user request. The input is the request data sent from the terminal, and the output is the parsed request content. Natural language processing is performed using a generative AI model to identify the intent of the request and the category of the requested product. In this process, the AI ​​model clarifies ambiguous expressions and extracts specific purchase needs.

[0161] Step 4:

[0162] The server selects relevant products from the existing catalog based on the analysis results. The input for this step is the analyzed request data, and the output is a list of candidate products. Using an emotion engine, past user data is considered to prioritize products that match the user's preferences. The specific operations here involve database searching and selection based on priority.

[0163] Step 5:

[0164] The server sends information about the selected products to the terminal. The input is the product list obtained in the previous step, and the output is the product information sent to the terminal. Specifically, a data packet containing detailed information and prices of the selected products is sent to the terminal.

[0165] Step 6:

[0166] The terminal receives information from the server and presents product information to the user. The input is product information sent from the server, and the output is information presented to the user's visual sense. Specific actions are taken through the user interface to display product images and descriptions and prompt the user to make a selection.

[0167] Step 7:

[0168] This step involves the user's reaction to a presented product. The input is visually presented product information, and the output is the user's reaction, such as behavior or feedback. In this step, the emotion engine analyzes the user's facial expressions and attitudes in real time and prepares to send the resulting emotion data to the server.

[0169] Step 8:

[0170] The server adjusts recommendations based on the analysis results from the emotion engine. The input is the user's emotion data, and the output is adjusted product recommendation information. Specifically, the system prioritizes the display of products that the user has shown favorability towards and adjusts the data to reflect their ratings.

[0171] Step 9:

[0172] The order processing takes place when the user approves the purchase. The inputs are the adjusted product information and the user's approval action, and the output is order data that is transferred to the ordering system. Here, the automatic generation and confirmation of the order, and the ordering instructions issued by the server are specifically performed.

[0173] Step 10:

[0174] The server automatically manages product delivery and payment. This includes issuing instructions to delivery companies based on order information and processing payments in the payment system. Input is user-approved order information, and output is completed delivery and payment data. This allows users to receive their products without any hassle.

[0175] (Application Example 2)

[0176] 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".

[0177] When consumers shop online, the process of selecting products from a vast number of options is complex, and there is a need to improve the user experience. In particular, product recommendations that take into account consumer emotions and preferences are currently time-consuming and have not been automated. Furthermore, the amount of time consumers spend selecting products can decrease their purchase satisfaction.

[0178] 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.

[0179] In this invention, the server includes an information input means for receiving purchase requests from users, a generation means for automatically selecting recommended items based on the requests, and an emotion analysis means for analyzing the user's facial expressions in real time and emphasizing recommended items based on their emotional state. This enables personalized product recommendations that respond to the consumer's emotions, providing an efficient and highly satisfying purchasing experience.

[0180] An "information input device" is a device that provides an interface for receiving purchase requests from consumers.

[0181] A "generation device" is a device that automatically selects the optimal product based on consumer purchasing requests.

[0182] An "emotion analysis device" is a device that analyzes a consumer's facial expressions and determines their emotional state in real time.

[0183] An "approval confirmation device" is a device that accepts approval for a product presented by a consumer and initiates the order processing.

[0184] A "payment method" is a device that automatically handles the delivery of goods and payment after an order has been placed.

[0185] Modes for carrying out the invention

[0186] The system for implementing this invention mainly consists of three elements: a server, a terminal, and a user. Specific details are shown below.

[0187] The server functions as both an information input and generation mechanism, receiving purchase requests from users. The server analyzes this request information and uses a generative AI model to select appropriate products. This AI model can be implemented using platforms such as Amazon Web Services' SageMaker or Google® Cloud AI.

[0188] The device includes emotion analysis capabilities to capture the user's facial expressions in real time. Here, facial data is collected using the smartphone or tablet camera and analyzed using emotion analysis software such as Apple's Vision API or Google's ML Kit. The analyzed emotion data is used to highlight selected products and present related product information.

[0189] The terminal also functions as an approval confirmation mechanism; once the user approves their selection, it sends that information to the server. Upon receiving the approval information, the server uses the payment method to automatically proceed with ordering and payment. Possible payment systems used here include APIs such as Stripe or PayPal.

[0190] As a concrete example, suppose a user attempts to search for a "professional-grade camera" using their smartphone. The server receives this request and uses a generative AI model to select multiple camera options. When the emotion analysis system detects that the user is smiling in the device's camera view, the selected product is highlighted. Finally, once the user selects and approves the camera, the server immediately places the order and completes the payment. This allows the user to have an efficient and emotionally resonant purchasing experience.

[0191] Specific examples of prompt statements for generative AI models:

[0192] "The user typed 'search for professional cameras.' We recommend the MU-101 camera. Are there any other popular cameras? If the user is smiling, which product should we highlight?"

[0193] This system enables flexible and rapid product recommendations based on user emotions, further enhancing the online shopping experience.

[0194] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0195] Step 1:

[0196] The user enters a purchase request using their device. The user opens a smartphone application and enters information about the product and category into the chat interface. This input data is sent to the server by the device. The input data includes the product name and product category.

[0197] Step 2:

[0198] The server analyzes the received purchase request. The server utilizes a generative AI model to select the most suitable recommended product based on the input information. This involves using a keyword-based data search algorithm to list candidate products from the existing catalog. Information about the products selected by the generative AI model is then sent back from the server to the terminal.

[0199] Step 3:

[0200] The device displays information about returned items to the user. At this time, it visually displays a list of recommended products using the smartphone's display and notification functions.

[0201] Step 4:

[0202] The device captures the user's facial expressions in real time. By using the device's camera function and facial recognition technology to capture the user's facial expressions, the emotional state is determined. The emotion analysis means analyzes the user's emotional data (e.g., smile or confused expression) and sends that data to the server.

[0203] Step 5:

[0204] The server emphasizes product recommendations based on the received sentiment data, or makes adjustments to suggest more appropriate products. Based on the feedback corresponding to the sentiment data, it sends a prompt message to the AI ​​model that prioritizes the list of products to display. This prompt message updates the list of products again.

[0205] Step 6:

[0206] The user selects a product from those presented by the server and enters their approval via their terminal. Once the user decides to purchase a product, the approval information is sent from the terminal to the server.

[0207] Step 7:

[0208] The server automatically initiates the order and payment process after receiving the approval information. The order management system on the server starts running, placing orders for the selected products and simultaneously calling the payment API to confirm the payment process applied to the user.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] [Second Embodiment]

[0213] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0214] 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.

[0215] 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).

[0216] 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.

[0217] 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.

[0218] 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).

[0219] 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.

[0220] 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.

[0221] 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.

[0222] 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.

[0223] 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.

[0224] 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".

[0225] The system of this invention consists of three main components: a user, a terminal, and a server. The user enters a purchase request into a chat interface, and the terminal sends the contents to the server. The server analyzes the request and uses a generation means to perform AI-powered product recommendations. As a result, the server selects relevant products from an existing catalog and sends that information to the terminal.

[0226] The terminal displays product information received from the server to the user, who then reviews and approves the selected products. Once the user approves, the terminal transmits this information to the server, which then automatically initiates the ordering process using a verification mechanism.

[0227] The server uses processing tools to consistently manage the ordering, delivery, and payment processes for goods. Once delivery is complete, the server automatically executes the payment process, records all processing logs, and then notifies the user of the transaction completion via the terminal.

[0228] As a concrete example, if a user requests to purchase a new projector via chat, the device transmits this request to the server, which uses AI to recommend several projectors. Once the user selects one and approves it through the device, the server immediately proceeds with the order process. After the product's delivery is confirmed, payment is processed automatically, and the entire process is completed seamlessly for the user. In this way, this system significantly streamlines a company's purchasing process.

[0229] The following describes the processing flow.

[0230] Step 1:

[0231] The user enters a purchase request into the chat interface. The terminal receives this input and sends its contents to the server.

[0232] Step 2:

[0233] The server analyzes the received request and extracts keywords. The server uses AI with generation methods to search for related products from the existing catalog.

[0234] Step 3:

[0235] The server sends the selected product information to the terminal. The terminal receives this information and presents it to the user. The user reviews the presented products and makes a selection.

[0236] Step 4:

[0237] Once the user makes a selection and indicates their approval, the terminal transmits this information to the server. The server then uses a confirmation mechanism to initiate the ordering process.

[0238] Step 5:

[0239] The server connects to the catalog purchasing system and registers the order information for the products selected by the user. The server then verifies the order details and sets the delivery schedule.

[0240] Step 6:

[0241] The server manages the delivery status of the goods and confirms that delivery is complete. After confirmation, the server automatically processes the payment.

[0242] Step 7:

[0243] The server logs the entire transaction process and sends a completion notification to the terminal. The terminal receives this notification and informs the user that the transaction is complete.

[0244] (Example 1)

[0245] 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."

[0246] In online shopping and corporate purchasing processes, there is a growing need for rapid and accurate product recommendations based on user needs, followed by automated purchasing procedures. However, existing systems often rely on manual processes for recommending appropriate products based on user requests, as well as managing product ordering, delivery, and payment, resulting in inefficiencies. In particular, when users have diverse needs, selecting the optimal product for each request and completing the purchasing process seamlessly presents a significant challenge.

[0247] 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.

[0248] In this invention, the server includes communication means, knowledge processing means, dialogue confirmation means, resource management means, and reporting means. This enables the analysis of user requests, product recommendations using artificial intelligence, and the automation of the process from ordering to payment, allowing for efficient and accurate processing.

[0249] A "communication method" is a mechanism that has the function of sending purchase requests from users to a server and enables the sending and receiving of data.

[0250] A "knowledge processing tool" is a processing function that analyzes a user's purchase request and automatically selects relevant products using a generative AI model.

[0251] A "dialogue confirmation method" is a system that has an interface and functions to present recommended products to the user and obtain their selection and approval.

[0252] A "resource management system" is a set of processing functions that automatically manages and executes the entire purchasing process, from ordering goods to delivery and payment.

[0253] A "reporting mechanism" is a function that notifies users of the completion of a transaction or information about the purchasing process.

[0254] A "generative AI model" is an artificial intelligence-based system used to suggest the most suitable products in response to user requests.

[0255] A "prompt sentence" is an input sentence that gives instructions or questions to a generative AI model, enabling it to generate appropriate responses or recommendations.

[0256] This invention is a system for streamlining the purchasing process and includes communication means, knowledge processing means, dialogue confirmation means, resource management means, and reporting means. Users input purchasing requests using a chat interface via a terminal. This terminal can be a typical personal computer or smartphone, equipped with internet-connected software (e.g., a chat application or web browser). Examples include messaging services.

[0257] The terminal sends the user's input request to the server via a communication method. The server uses knowledge processing tools to analyze and understand the received request. Here, widely used software libraries for natural language processing (e.g., spaCy and NLTK) are used. Based on the analyzed information, a generative AI model (e.g., GPT-3) is used to recommend a product suitable for the user. For example, the prompt might be, "I want to buy a new projector. Please tell me your recommendations."

[0258] The server uses this generative AI model to select relevant products from an existing product database. The selected product information is presented to the user via the terminal. The user selects a specific product from the list and indicates their intention to purchase it. The dialogue confirmation mechanism supports this selection and approval process. Through this mechanism, the user can smoothly decide on a purchase.

[0259] Furthermore, based on approved information, the server uses resource management tools to automate the product ordering and delivery process. This process involves checking product inventory, adjusting shipping schedules, and utilizing integrated systems as needed. Once the products have been delivered, the server executes the payment process, and the resource management tools monitor the entire process. Upon completion of the transaction, the user is notified through reporting tools. In this way, the system streamlines the entire purchasing process and achieves end-to-end automation.

[0260] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0261] Step 1:

[0262] The user enters a purchase request through a chat interface. This input may include product categories and specific product requests. For example, the user might enter "I want a new projector." This is entered into the terminal. The terminal receives the user input as text data and formats it for transmission to the server.

[0263] Step 2:

[0264] The terminal sends the request data received from the user to the server using a communication method. Specifically, it sends a POST request along with the data to the server using the HTTP protocol. The input is the text of the user request, and the output is the status of the successful transmission to the server.

[0265] Step 3:

[0266] The server uses a natural language processing library to parse the received request data. This parsing extracts keywords and their intent from the request. The input is the user's request text, and the output is the parsed keywords and phrases. Specifically, keywords such as "projector" and "purchase" are parsed.

[0267] Step 4:

[0268] The server uses a generated AI model based on the analysis results to recommend products. It generates a prompt message and inputs it to the AI ​​in the form of "I'm looking for a projector. Please list the best options below." The input is the analyzed keywords, and the output is a list of recommended products.

[0269] Step 5:

[0270] The server generates a product list and sends it to the terminal. The terminal then presents this list to the user. Specifically, it displays product names, prices, features, etc., in a list format on the chat interface. The input is a list of recommended products, and the output is the product information presented to the user.

[0271] Step 6:

[0272] The user makes a selection from the presented product list. They then confirm their selection and indicate their intention to purchase via chat. For example, they might type, "I want to buy product A." This input constitutes the user's selection information, and the device sends this as a confirmation message to the server.

[0273] Step 7:

[0274] The terminal sends user selection information to the server. The server automates the ordering process for the selected products. Input is user approval information, and output is the order processing start status. Specific actions include sending data to the ordering system and checking inventory.

[0275] Step 8:

[0276] The server manages and integrates all processes, including product delivery and payment, using resource management tools. Once product delivery is complete, payment processing is also automated. Input is order information, and output is a record of the completion status. Integration with the logistics system is also included.

[0277] Step 9:

[0278] After all processing is complete, the server sends a transaction completion notification to the user via the terminal using a reporting mechanism. Specifically, this involves sending a notification message to the chat interface. The input is the completion status, and the output is a transaction completion confirmation notification to the user.

[0279] (Application Example 1)

[0280] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as a "server", and the smart glasses 214 are referred to as a "terminal".

[0281] In modern online shopping, there is a problem that it is difficult for users to efficiently search for desired products and obtain appropriate recommendations. Also, the process from when a user selects a product until payment is completed requires multiple operations and is time-consuming. Furthermore, there is a lack of more intuitive operability using voice input, and the user experience is limited.

[0282] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0283] In this invention, the server includes an interactive interface means for receiving a purchase request from a user, a generation means using an artificial intelligence model for generating a plurality of products, and a processing means for automatically placing an order for a product after selection and performing delivery and payment processing. As a result, the user can easily input a purchase request using voice input, and it becomes possible to complete the entire process quickly and seamlessly for the selected product.

[0284] The "interactive interface means" is a communication means by chat or voice input through which a user can directly input a purchase request.

[0285] The "generation means using an artificial intelligence model" is a means for analyzing input information of a user and automatically selecting and generating a plurality of related products.

[0286] The "confirmation and approval means" is a means for presenting a product recommended to a user and starting an order procedure by the user selecting and approving it.

[0287] The "processing means" is a means for automatically managing the order placement of a selected product, monitoring the delivery, and completing the payment after receipt.

[0288] "Voice input" is an input method in which the user communicates purchase requests to a device by speaking.

[0289] "Natural language processing technology" is a technology that analyzes natural language input by users to efficiently understand and process purchase requests.

[0290] "Key words" refer to important words or phrases included in the user's purchase request, and are the words that the AI ​​uses as criteria when selecting related products.

[0291] The system for realizing this invention mainly consists of a user's smart device (e.g., a smartphone), a server, and a generative AI model.

[0292] Users enter their purchase requests through an interactive interface on their smartphones. This interface uses a chat format and also supports voice input. If a user wants to find a product, they can make a voice request such as, "I want a jacket for spring."

[0293] The terminal receives this request and sends it to the server. The server receives this information and analyzes the request using natural language processing technology. A generative AI model (e.g., OpenAI GPT-4) uses this analysis to select several relevant products from an existing product catalog and recommend them to the user. For example, the information is processed in the form of a prompt message: "user: 'I'm looking for a spring jacket. Can you recommend one?'"

[0294] Once the user reviews and approves the presented product, the terminal notifies the server. The server automatically places an order based on the user's selection and simultaneously monitors the product's delivery status. Once delivery is confirmed, the server automatically processes the payment.

[0295] This system integrates multiple technologies. It utilizes Socket.IO and React Native for real-time chat functionality, OpenAI APIs for natural language processing, Stripe APIs for payment processing, and AWS Lambda for order process management. This allows for an efficient and intuitive shopping experience for users.

[0296] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0297] Step 1:

[0298] Users enter purchase requests through an interactive interface on their smart devices. They can use either text or voice input, making requests such as "I want a spring jacket." The input is in natural language, and this information is sent to the server via the smart device.

[0299] Step 2:

[0300] The server analyzes the natural language purchase requests it receives. The input is text data sent by the user, and the server uses natural language processing techniques to extract key words. This results in the output of a keyword list for extracting products that belong to a specific category or have certain attributes.

[0301] Step 3:

[0302] The server uses a generative AI model to generate a list of recommended products based on the analyzed keywords. The input is the keyword list output in step 2, and the AI ​​model performs data calculations to select multiple highly relevant products. This product list is then generated as output and prepared to be presented to the user.

[0303] Step 4:

[0304] The terminal presents the user with a list of products sent from the server. Through the user's operation, one or more selections are made from the presented multiple products. The input is the product list obtained from the server, and the user can select the desired products from it, and the user's selection result is obtained as the output.

[0305] Step 5:

[0306] The server receives the user's selection result and starts the automatic ordering process. The input is the selected product information, and inventory checking and order instruction processing are performed using AWS Lambda. As the output, the fact that the order is completed and the delivery process starts is recorded.

[0307] Step 6:

[0308] The server continuously monitors the delivery status of the product and automatically performs payment processing when the delivery is completed. The input is the delivery completion notice, and data processing for generating payment information and executing settlement is performed using the Stripe API. At this point, an output as the completion of the transaction is generated and notified to the user.

[0309] Furthermore, an emotion engine for estimating the user's emotion may be combined. That is, the specific processing unit 290 may estimate the user's emotion using the emotion specific model 59 and perform specific processing using the user's emotion.

[0310] The system of the present invention consists of four main components: the user, the terminal, the server, and the emotion engine. The user inputs a purchase request into the chat interface, and the terminal receives this information and sends it to the server. The server analyzes the received request, utilizes AI using the generation means, and selects recommended products from the existing catalog. At this time, the emotion engine analyzes the user's emotion and influences the selection of the recommended products based on the result.

[0311] The terminal displays product information returned from the server to the user, and the sentiment engine analyzes the user's response in real time. If the user gives an approval or a negative response, the sentiment engine communicates the result to the server and adjusts the recommendations accordingly.

[0312] For approved products, the terminal sends the information to the server, automating the ordering process using a verification mechanism. The server uses processing tools to automatically manage all processes, including ordering, delivery, and payment. The emotion engine collects user emotion data to improve the user experience and optimize future purchasing processes.

[0313] For example, if a user requests via chat that they "want to buy a new headset," the device transmits this request to the server, which then uses AI to recommend several headsets. The emotion engine understands the user's preferences, and if the user smiles at one of the presented products, this information is transmitted to the server, and that product is highlighted. Once the user approves the selection, the server immediately begins the ordering process, and payment is automatically processed once delivery is complete. The feedback from the emotion engine makes the next purchasing experience more personalized, allowing users to make comfortable choices in less time. In this way, this system significantly streamlines the company's purchasing process while simultaneously improving the user's purchasing experience.

[0314] The following describes the processing flow.

[0315] Step 1:

[0316] The user types "I want to buy a new headset" into the chat interface. The terminal receives this input and sends the request to the server.

[0317] Step 2:

[0318] The server analyzes the received request to identify the key product type. Using a generation method, the server leverages an AI algorithm to select suitable headset candidates from the existing catalog database.

[0319] Step 3:

[0320] The server sends information about the selected candidate products to the terminal. The terminal presents this to the user and prompts them to make a selection. An emotion engine monitors the user's facial expressions and reactions in the background and analyzes positive or negative emotions.

[0321] Step 4:

[0322] The emotion engine detects when a user shows interest in one of the presented products and exhibits a positive reaction, such as a smile, to that product. The device notifies the server of this information, and the server then re-presents the user with special emphasis or additional information about that product.

[0323] Step 5:

[0324] The user makes a final selection and approves the product. The terminal sends this approval information to the server. The server uses a verification mechanism to initiate the ordering process for the selected product.

[0325] Step 6:

[0326] The server registers order information in the catalog purchasing system and sets the delivery schedule for the goods. The server manages the status until delivery is complete, and immediately executes the payment process automatically once delivery is confirmed.

[0327] Step 7:

[0328] The server records a log of the completion of the entire process and sends a completion notification to the terminal. The terminal notifies the user that "Your headset order and payment have been completed." The emotion engine stores the emotion data obtained from this transaction and uses it as a reference for future purchasing processes.

[0329] (Example 2)

[0330] 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".

[0331] Traditional purchasing systems often select products based on user requests without considering the user's emotional state, making it difficult to recommend the most suitable products for individual users. Furthermore, manual delivery and payment processing after purchase hindered the provision of an efficient purchasing experience.

[0332] 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.

[0333] In this invention, the server includes a communication device that receives purchase requests from users and relays the information, an analysis device that analyzes the requests and selects recommended products using a generative artificial intelligence model, and an auxiliary device that analyzes the user's emotional state and reflects it in the selection of recommended products. This enables optimal product recommendations based on the individual emotional state of the user, and by automating delivery and payment processing, an efficient and personalized purchasing experience is provided.

[0334] A "communication device" is a device that receives purchase requests from users and relays that information to a server.

[0335] An "analysis device" is a device that analyzes a user's request and selects the most suitable recommended product using a generated artificial intelligence model.

[0336] An "auxiliary device" is a device that analyzes the user's emotional state and reflects the analysis results in product recommendations.

[0337] An "authentication device" is a device that presents recommended product information to the user and initiates the ordering process based on the user's approval.

[0338] A "management device" is a device that automatically handles the delivery and payment processing of goods after an order has been placed, and manages these processes efficiently.

[0339] A "generative artificial intelligence model" is an artificial intelligence technology used to analyze user purchase requests and select appropriate recommended products.

[0340] "Information resources" refers to a database or catalog used to present product options selected based on user request analysis.

[0341] This invention is an advanced shopping system designed to enhance the user's purchasing experience. The system primarily consists of a user, a terminal, a server, and a secondary emotion engine.

[0342] Users enter their requests for desired products into the interface using free-form language. This interface is presented in a chat format, making it user-friendly and easy to use. As a result, product requests can be made in a more natural, conversational manner.

[0343] The terminal receives information entered by the user in real time and sends it to the server via the appropriate protocol. The terminal uses a common communication protocol (e.g., HTTPS) for this communication to ensure data security.

[0344] The server utilizes a generative artificial intelligence model to analyze incoming requests. This model uses NLP (Natural Language Processing) techniques to understand user requests and select the most suitable products from an existing catalog database. Furthermore, an emotion engine analyzes the user's emotional state in real time, and this information can be used to further improve the accuracy of product selection.

[0345] Based on this data, the server selects the most suitable recommended products for the user. To do this, it uses a pre-configured algorithm and sentiment analysis results to determine product priorities. The selected product information is then transmitted from the server to the terminal and presented to the user.

[0346] To illustrate the system's operation with a concrete example, when a user requests to purchase a new headset, the terminal transmits the request to the server, which then uses a generated AI model to select several headsets. The emotion engine analyzes the user's reactions (e.g., smiles and expressions of interest) and highlights products that the user reacts favorably to. Once the user approves, the server immediately initiates the ordering process for that product, and then automatically completes the delivery and payment.

[0347] Examples of prompts include "I want to buy a new headset" and "I'm looking for the latest smartphone model." This allows the system to quickly and accurately provide products that meet the user's specific needs.

[0348] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0349] Step 1:

[0350] The user enters a purchase request into the chat interface. Once entered, the terminal receives the text data and prepares to send it to the server. In this step, the input is text data from the user, and the output is data that the terminal sends to the server.

[0351] Step 2:

[0352] The terminal uses the appropriate communication protocol to send the received request data to the server. In this process, the input is the user's request received by the terminal, and the output is the request data forwarded to the server. Specifically, the process involves data encryption and transmission according to the protocol.

[0353] Step 3:

[0354] The server parses the received user request. The input is the request data sent from the terminal, and the output is the parsed request content. Natural language processing is performed using a generative AI model to identify the intent of the request and the category of the requested product. In this process, the AI ​​model clarifies ambiguous expressions and extracts specific purchase needs.

[0355] Step 4:

[0356] The server selects relevant products from the existing catalog based on the analysis results. The input for this step is the analyzed request data, and the output is a list of candidate products. Using an emotion engine, past user data is considered to prioritize products that match the user's preferences. The specific operations here involve database searching and selection based on priority.

[0357] Step 5:

[0358] The server sends information about the selected products to the terminal. The input is the product list obtained in the previous step, and the output is the product information sent to the terminal. Specifically, a data packet containing detailed information and prices of the selected products is sent to the terminal.

[0359] Step 6:

[0360] The terminal receives information from the server and presents product information to the user. The input is product information sent from the server, and the output is information presented to the user's visual sense. Specific actions are taken through the user interface to display product images and descriptions and prompt the user to make a selection.

[0361] Step 7:

[0362] This step involves the user's reaction to a presented product. The input is visually presented product information, and the output is the user's reaction, such as behavior or feedback. In this step, the emotion engine analyzes the user's facial expressions and attitudes in real time and prepares to send the resulting emotion data to the server.

[0363] Step 8:

[0364] The server adjusts recommendations based on the analysis results from the emotion engine. The input is the user's emotion data, and the output is adjusted product recommendation information. Specifically, the system prioritizes the display of products that the user has shown favorability towards and adjusts the data to reflect their ratings.

[0365] Step 9:

[0366] The order processing takes place when the user approves the purchase. The inputs are the adjusted product information and the user's approval action, and the output is order data that is transferred to the ordering system. Here, the automatic generation and confirmation of the order, and the ordering instructions issued by the server are specifically performed.

[0367] Step 10:

[0368] The server automatically manages product delivery and payment. This includes issuing instructions to delivery companies based on order information and processing payments in the payment system. Input is user-approved order information, and output is completed delivery and payment data. This allows users to receive their products without any hassle.

[0369] (Application Example 2)

[0370] 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."

[0371] When consumers shop online, the process of selecting products from a vast number of options is complex, and there is a need to improve the user experience. In particular, product recommendations that take into account consumer emotions and preferences are currently time-consuming and have not been automated. Furthermore, the amount of time consumers spend selecting products can decrease their purchase satisfaction.

[0372] 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.

[0373] In this invention, the server includes an information input means for receiving purchase requests from users, a generation means for automatically selecting recommended items based on the requests, and an emotion analysis means for analyzing the user's facial expressions in real time and emphasizing recommended items based on their emotional state. This enables personalized product recommendations that respond to the consumer's emotions, providing an efficient and highly satisfying purchasing experience.

[0374] An "information input device" is a device that provides an interface for receiving purchase requests from consumers.

[0375] A "generation device" is a device that automatically selects the optimal product based on consumer purchasing requests.

[0376] An "emotion analysis device" is a device that analyzes a consumer's facial expressions and determines their emotional state in real time.

[0377] An "approval confirmation device" is a device that accepts approval for a product presented by a consumer and initiates the order processing.

[0378] A "payment method" is a device that automatically handles the delivery of goods and payment after an order has been placed.

[0379] Modes for carrying out the invention

[0380] The system for implementing this invention mainly consists of three elements: a server, a terminal, and a user. Specific details are shown below.

[0381] The server functions as both an information input and generation mechanism, receiving purchase requests from users. The server analyzes this request information and uses a generative AI model to select appropriate products. This AI model can be implemented using platforms such as Amazon Web Services' SageMaker or Google Cloud AI.

[0382] The device includes emotion analysis capabilities to capture the user's facial expressions in real time. Here, facial data is collected using the smartphone or tablet camera and analyzed using emotion analysis software such as Apple's Vision API or Google's ML Kit. The analyzed emotion data is used to highlight selected products and present related product information.

[0383] The terminal also functions as an approval confirmation mechanism; once the user approves their selection, it sends that information to the server. Upon receiving the approval information, the server uses the payment method to automatically proceed with ordering and payment. Possible payment systems used here include APIs such as Stripe or PayPal.

[0384] As a concrete example, suppose a user attempts to search for a "professional-grade camera" using their smartphone. The server receives this request and uses a generative AI model to select multiple camera options. When the emotion analysis system detects that the user is smiling in the device's camera view, the selected product is highlighted. Finally, once the user selects and approves the camera, the server immediately places the order and completes the payment. This allows the user to have an efficient and emotionally resonant purchasing experience.

[0385] Specific examples of prompt statements for generative AI models:

[0386] "The user typed 'search for professional cameras.' We recommend the MU-101 camera. Are there any other popular cameras? If the user is smiling, which product should we highlight?"

[0387] This system enables flexible and rapid product recommendations based on user emotions, further enhancing the online shopping experience.

[0388] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0389] Step 1:

[0390] The user enters a purchase request using their device. The user opens a smartphone application and enters information about the product and category into the chat interface. This input data is sent to the server by the device. The input data includes the product name and product category.

[0391] Step 2:

[0392] The server analyzes the received purchase request. The server utilizes a generative AI model to select the most suitable recommended product based on the input information. This involves using a keyword-based data search algorithm to list candidate products from the existing catalog. Information about the products selected by the generative AI model is then sent back from the server to the terminal.

[0393] Step 3:

[0394] The device displays information about returned items to the user. At this time, it visually displays a list of recommended products using the smartphone's display and notification functions.

[0395] Step 4:

[0396] The device captures the user's facial expressions in real time. By using the device's camera function and facial recognition technology to capture the user's facial expressions, the emotional state is determined. The emotion analysis means analyzes the user's emotional data (e.g., smile or confused expression) and sends that data to the server.

[0397] Step 5:

[0398] The server emphasizes product recommendations based on the received sentiment data, or makes adjustments to suggest more appropriate products. Based on the feedback corresponding to the sentiment data, it sends a prompt message to the AI ​​model that prioritizes the list of products to display. This prompt message updates the list of products again.

[0399] Step 6:

[0400] The user selects a product from those presented by the server and enters their approval via their terminal. Once the user decides to purchase a product, the approval information is sent from the terminal to the server.

[0401] Step 7:

[0402] The server automatically initiates the order and payment process after receiving the approval information. The order management system on the server starts running, placing orders for the selected products and simultaneously calling the payment API to confirm the payment process applied to the user.

[0403] 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.

[0404] 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.

[0405] 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.

[0406] [Third Embodiment]

[0407] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0408] 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.

[0409] 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).

[0410] 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.

[0411] 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.

[0412] 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).

[0413] 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.

[0414] 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.

[0415] 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.

[0416] 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.

[0417] 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.

[0418] 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".

[0419] The system of this invention consists of three main components: a user, a terminal, and a server. The user enters a purchase request into a chat interface, and the terminal sends the contents to the server. The server analyzes the request and uses a generation means to perform AI-powered product recommendations. As a result, the server selects relevant products from an existing catalog and sends that information to the terminal.

[0420] The terminal displays product information received from the server to the user, who then reviews and approves the selected products. Once the user approves, the terminal transmits this information to the server, which then automatically initiates the ordering process using a verification mechanism.

[0421] The server uses processing tools to consistently manage the ordering, delivery, and payment processes for goods. Once delivery is complete, the server automatically executes the payment process, records all processing logs, and then notifies the user of the transaction completion via the terminal.

[0422] As a concrete example, if a user requests to purchase a new projector via chat, the device transmits this request to the server, which uses AI to recommend several projectors. Once the user selects one and approves it through the device, the server immediately proceeds with the order process. After the product's delivery is confirmed, payment is processed automatically, and the entire process is completed seamlessly for the user. In this way, this system significantly streamlines a company's purchasing process.

[0423] The following describes the processing flow.

[0424] Step 1:

[0425] The user enters a purchase request into the chat interface. The terminal receives this input and sends its contents to the server.

[0426] Step 2:

[0427] The server analyzes the received request and extracts keywords. The server uses AI with generation methods to search for related products from the existing catalog.

[0428] Step 3:

[0429] The server sends the selected product information to the terminal. The terminal receives this information and presents it to the user. The user reviews the presented products and makes a selection.

[0430] Step 4:

[0431] Once the user makes a selection and indicates their approval, the terminal transmits this information to the server. The server then uses a confirmation mechanism to initiate the ordering process.

[0432] Step 5:

[0433] The server connects to the catalog purchasing system and registers the order information for the products selected by the user. The server then verifies the order details and sets the delivery schedule.

[0434] Step 6:

[0435] The server manages the delivery status of the goods and confirms that delivery is complete. After confirmation, the server automatically processes the payment.

[0436] Step 7:

[0437] The server logs the entire transaction process and sends a completion notification to the terminal. The terminal receives this notification and informs the user that the transaction is complete.

[0438] (Example 1)

[0439] 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."

[0440] In online shopping and corporate purchasing processes, there is a growing need for rapid and accurate product recommendations based on user needs, followed by automated purchasing procedures. However, existing systems often rely on manual processes for recommending appropriate products based on user requests, as well as managing product ordering, delivery, and payment, resulting in inefficiencies. In particular, when users have diverse needs, selecting the optimal product for each request and completing the purchasing process seamlessly presents a significant challenge.

[0441] 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.

[0442] In this invention, the server includes communication means, knowledge processing means, dialogue confirmation means, resource management means, and reporting means. This enables the analysis of user requests, product recommendations using artificial intelligence, and the automation of the process from ordering to payment, allowing for efficient and accurate processing.

[0443] A "communication method" is a mechanism that has the function of sending purchase requests from users to a server and enables the sending and receiving of data.

[0444] A "knowledge processing tool" is a processing function that analyzes a user's purchase request and automatically selects relevant products using a generative AI model.

[0445] A "dialogue confirmation method" is a system that has an interface and functions to present recommended products to the user and obtain their selection and approval.

[0446] A "resource management system" is a set of processing functions that automatically manages and executes the entire purchasing process, from ordering goods to delivery and payment.

[0447] A "reporting mechanism" is a function that notifies users of the completion of a transaction or information about the purchasing process.

[0448] A "generative AI model" is an artificial intelligence-based system used to suggest the most suitable products in response to user requests.

[0449] A "prompt sentence" is an input sentence that gives instructions or questions to a generative AI model, enabling it to generate appropriate responses or recommendations.

[0450] This invention is a system for streamlining the purchasing process and includes communication means, knowledge processing means, dialogue confirmation means, resource management means, and reporting means. Users input purchasing requests using a chat interface via a terminal. This terminal can be a typical personal computer or smartphone, equipped with internet-connected software (e.g., a chat application or web browser). Examples include messaging services.

[0451] The terminal sends the user's input request to the server via a communication method. The server uses knowledge processing tools to analyze and understand the received request. Here, widely used software libraries for natural language processing (e.g., spaCy and NLTK) are used. Based on the analyzed information, a generative AI model (e.g., GPT-3) is used to recommend a product suitable for the user. For example, the prompt might be, "I want to buy a new projector. Please tell me your recommendations."

[0452] The server uses this generative AI model to select relevant products from an existing product database. The selected product information is presented to the user via the terminal. The user selects a specific product from the list and indicates their intention to purchase it. The dialogue confirmation mechanism supports this selection and approval process. Through this mechanism, the user can smoothly decide on a purchase.

[0453] Furthermore, based on approved information, the server uses resource management tools to automate the product ordering and delivery process. This process involves checking product inventory, adjusting shipping schedules, and utilizing integrated systems as needed. Once the products have been delivered, the server executes the payment process, and the resource management tools monitor the entire process. Upon completion of the transaction, the user is notified through reporting tools. In this way, the system streamlines the entire purchasing process and achieves end-to-end automation.

[0454] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0455] Step 1:

[0456] The user enters a purchase request through a chat interface. This input may include product categories and specific product requests. For example, the user might enter "I want a new projector." This is entered into the terminal. The terminal receives the user input as text data and formats it for transmission to the server.

[0457] Step 2:

[0458] The terminal sends the request data received from the user to the server using a communication method. Specifically, it sends a POST request along with the data to the server using the HTTP protocol. The input is the text of the user request, and the output is the status of the successful transmission to the server.

[0459] Step 3:

[0460] The server uses a natural language processing library to parse the received request data. This parsing extracts keywords and their intent from the request. The input is the user's request text, and the output is the parsed keywords and phrases. Specifically, keywords such as "projector" and "purchase" are parsed.

[0461] Step 4:

[0462] The server uses a generated AI model based on the analysis results to recommend products. It generates a prompt message and inputs it to the AI ​​in the form of "I'm looking for a projector. Please list the best options below." The input is the analyzed keywords, and the output is a list of recommended products.

[0463] Step 5:

[0464] The server generates a product list and sends it to the terminal. The terminal then presents this list to the user. Specifically, it displays product names, prices, features, etc., in a list format on the chat interface. The input is a list of recommended products, and the output is the product information presented to the user.

[0465] Step 6:

[0466] The user makes a selection from the presented product list. They then confirm their selection and indicate their intention to purchase via chat. For example, they might type, "I want to buy product A." This input constitutes the user's selection information, and the device sends this as a confirmation message to the server.

[0467] Step 7:

[0468] The terminal sends user selection information to the server. The server automates the ordering process for the selected products. Input is user approval information, and output is the order processing start status. Specific actions include sending data to the ordering system and checking inventory.

[0469] Step 8:

[0470] The server manages and integrates all processes, including product delivery and payment, using resource management tools. Once product delivery is complete, payment processing is also automated. Input is order information, and output is a record of the completion status. Integration with the logistics system is also included.

[0471] Step 9:

[0472] After all processing is complete, the server sends a transaction completion notification to the user via the terminal using a reporting mechanism. Specifically, this involves sending a notification message to the chat interface. The input is the completion status, and the output is a transaction completion confirmation notification to the user.

[0473] (Application Example 1)

[0474] 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."

[0475] In modern online shopping, there are problems such as the difficulty for users to efficiently search for desired products and receive appropriate recommendations. Furthermore, the process from product selection to payment completion is cumbersome, requiring multiple steps. Additionally, the lack of more intuitive operation, such as voice input, limits the user experience.

[0476] 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.

[0477] In this invention, the server includes an interactive interface means for receiving purchase requests from users, a generation means using an artificial intelligence model to generate multiple products, and a processing means for automatically ordering products, and handling delivery and payment processing after product selection. This allows users to easily input purchase requests using voice input and complete the entire process quickly and seamlessly for the selected products.

[0478] "Interactive interface means" refers to a communication method, such as chat or voice input, that allows users to directly input purchase requests.

[0479] "Generation methods using artificial intelligence models" refer to methods that analyze user input information and automatically select and generate multiple related products.

[0480] A "confirmation and approval mechanism" is a method that presents recommended products to the user, allowing the user to select and approve them to initiate the ordering process.

[0481] A "processing device" is a means of automatically managing the ordering of selected products, monitoring of delivery, and completion of payment after delivery.

[0482] "Voice input" is an input method in which the user communicates purchase requests to a device by speaking.

[0483] "Natural language processing technology" is a technology that analyzes natural language input by users to efficiently understand and process purchase requests.

[0484] "Key words" refer to important words or phrases included in the user's purchase request, and are the words that the AI ​​uses as criteria when selecting related products.

[0485] The system for realizing this invention mainly consists of a user's smart device (e.g., a smartphone), a server, and a generative AI model.

[0486] Users enter their purchase requests through an interactive interface on their smartphones. This interface uses a chat format and also supports voice input. If a user wants to find a product, they can make a voice request such as, "I want a jacket for spring."

[0487] The terminal receives this request and sends it to the server. The server receives this information and analyzes the request using natural language processing technology. A generative AI model (e.g., OpenAI GPT-4) uses this analysis to select several relevant products from an existing product catalog and recommend them to the user. For example, the information is processed in the form of a prompt message: "user: 'I'm looking for a spring jacket. Can you recommend one?'"

[0488] Once the user reviews and approves the presented product, the terminal notifies the server. The server automatically places an order based on the user's selection and simultaneously monitors the product's delivery status. Once delivery is confirmed, the server automatically processes the payment.

[0489] This system integrates multiple technologies. It utilizes Socket.IO and React Native for real-time chat functionality, OpenAI APIs for natural language processing, Stripe APIs for payment processing, and AWS Lambda for order process management. This allows for an efficient and intuitive shopping experience for users.

[0490] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0491] Step 1:

[0492] Users enter purchase requests through an interactive interface on their smart devices. They can use either text or voice input, making requests such as "I want a spring jacket." The input is in natural language, and this information is sent to the server via the smart device.

[0493] Step 2:

[0494] The server analyzes the natural language purchase requests it receives. The input is text data sent by the user, and the server uses natural language processing techniques to extract key words. This results in the output of a keyword list for extracting products that belong to a specific category or have certain attributes.

[0495] Step 3:

[0496] The server uses a generative AI model to generate a list of recommended products based on the analyzed keywords. The input is the keyword list output in step 2, and the AI ​​model performs data calculations to select multiple highly relevant products. This product list is then generated as output and prepared to be presented to the user.

[0497] Step 4:

[0498] The terminal displays a list of products sent from the server to the user. The user makes a selection of one or more products from the displayed list. The input is the product list obtained from the server, and the user selects their desired products from this list, resulting in the user's selection being output.

[0499] Step 5:

[0500] The server receives the user's selection and starts the automated ordering process. The input is the selected product information, and AWS Lambda is used to check inventory and process the order. The output records that the order has been completed and the delivery process has begun.

[0501] Step 6:

[0502] The server continuously monitors the delivery status of the product and automatically processes payment once delivery is complete. The input is a delivery completion notification, and data processing is performed using the Stripe API to generate payment information and execute the settlement. At this point, an output indicating the completion of the transaction is generated and notified to the user.

[0503] 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.

[0504] The system of this invention consists of four main components: a user, a terminal, a server, and an emotion engine. The user enters a purchase request into the chat interface, and the terminal receives this information and sends it to the server. The server analyzes the received request and uses AI with generation means to select recommended products from an existing catalog. At this time, the emotion engine analyzes the user's emotions and influences the selection of recommended products based on the results.

[0505] The terminal displays product information returned from the server to the user, and the sentiment engine analyzes the user's response in real time. If the user gives an approval or a negative response, the sentiment engine communicates the result to the server and adjusts the recommendations accordingly.

[0506] For approved products, the terminal sends the information to the server, automating the ordering process using a verification mechanism. The server uses processing tools to automatically manage all processes, including ordering, delivery, and payment. The emotion engine collects user emotion data to improve the user experience and optimize future purchasing processes.

[0507] For example, if a user requests via chat that they "want to buy a new headset," the device transmits this request to the server, which then uses AI to recommend several headsets. The emotion engine understands the user's preferences, and if the user smiles at one of the presented products, this information is transmitted to the server, and that product is highlighted. Once the user approves the selection, the server immediately begins the ordering process, and payment is automatically processed once delivery is complete. The feedback from the emotion engine makes the next purchasing experience more personalized, allowing users to make comfortable choices in less time. In this way, this system significantly streamlines the company's purchasing process while simultaneously improving the user's purchasing experience.

[0508] The following describes the processing flow.

[0509] Step 1:

[0510] The user types "I want to buy a new headset" into the chat interface. The terminal receives this input and sends the request to the server.

[0511] Step 2:

[0512] The server analyzes the received request to identify the key product type. Using a generation method, the server leverages an AI algorithm to select suitable headset candidates from the existing catalog database.

[0513] Step 3:

[0514] The server sends information about the selected candidate products to the terminal. The terminal presents this to the user and prompts them to make a selection. An emotion engine monitors the user's facial expressions and reactions in the background and analyzes positive or negative emotions.

[0515] Step 4:

[0516] The emotion engine detects when a user shows interest in one of the presented products and exhibits a positive reaction, such as a smile, to that product. The device notifies the server of this information, and the server then re-presents the user with special emphasis or additional information about that product.

[0517] Step 5:

[0518] The user makes a final selection and approves the product. The terminal sends this approval information to the server. The server uses a verification mechanism to initiate the ordering process for the selected product.

[0519] Step 6:

[0520] The server registers order information in the catalog purchasing system and sets the delivery schedule for the goods. The server manages the status until delivery is complete, and immediately executes the payment process automatically once delivery is confirmed.

[0521] Step 7:

[0522] The server records a log of the completion of the entire process and sends a completion notification to the terminal. The terminal notifies the user that "Your headset order and payment have been completed." The emotion engine stores the emotion data obtained from this transaction and uses it as a reference for future purchasing processes.

[0523] (Example 2)

[0524] 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."

[0525] Traditional purchasing systems often select products based on user requests without considering the user's emotional state, making it difficult to recommend the most suitable products for individual users. Furthermore, manual delivery and payment processing after purchase hindered the provision of an efficient purchasing experience.

[0526] 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.

[0527] In this invention, the server includes a communication device that receives purchase requests from users and relays the information, an analysis device that analyzes the requests and selects recommended products using a generative artificial intelligence model, and an auxiliary device that analyzes the user's emotional state and reflects it in the selection of recommended products. This enables optimal product recommendations based on the individual emotional state of the user, and by automating delivery and payment processing, an efficient and personalized purchasing experience is provided.

[0528] A "communication device" is a device that receives purchase requests from users and relays that information to a server.

[0529] An "analysis device" is a device that analyzes a user's request and selects the most suitable recommended product using a generated artificial intelligence model.

[0530] An "auxiliary device" is a device that analyzes the user's emotional state and reflects the analysis results in product recommendations.

[0531] An "authentication device" is a device that presents recommended product information to the user and initiates the ordering process based on the user's approval.

[0532] A "management device" is a device that automatically handles the delivery and payment processing of goods after an order has been placed, and manages these processes efficiently.

[0533] A "generative artificial intelligence model" is an artificial intelligence technology used to analyze user purchase requests and select appropriate recommended products.

[0534] "Information resources" refers to a database or catalog used to present product options selected based on user request analysis.

[0535] This invention is an advanced shopping system designed to enhance the user's purchasing experience. The system primarily consists of a user, a terminal, a server, and a secondary emotion engine.

[0536] Users enter their requests for desired products into the interface using free-form language. This interface is presented in a chat format, making it user-friendly and easy to use. As a result, product requests can be made in a more natural, conversational manner.

[0537] The terminal receives information entered by the user in real time and sends it to the server via the appropriate protocol. The terminal uses a common communication protocol (e.g., HTTPS) for this communication to ensure data security.

[0538] The server utilizes a generative artificial intelligence model to analyze incoming requests. This model uses NLP (Natural Language Processing) techniques to understand user requests and select the most suitable products from an existing catalog database. Furthermore, an emotion engine analyzes the user's emotional state in real time, and this information can be used to further improve the accuracy of product selection.

[0539] Based on this data, the server selects the most suitable recommended products for the user. To do this, it uses a pre-configured algorithm and sentiment analysis results to determine product priorities. The selected product information is then transmitted from the server to the terminal and presented to the user.

[0540] To illustrate the system's operation with a concrete example, when a user requests to purchase a new headset, the terminal transmits the request to the server, which then uses a generated AI model to select several headsets. The emotion engine analyzes the user's reactions (e.g., smiles and expressions of interest) and highlights products that the user reacts favorably to. Once the user approves, the server immediately initiates the ordering process for that product, and then automatically completes the delivery and payment.

[0541] Examples of prompts include "I want to buy a new headset" and "I'm looking for the latest smartphone model." This allows the system to quickly and accurately provide products that meet the user's specific needs.

[0542] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0543] Step 1:

[0544] The user enters a purchase request into the chat interface. Once entered, the terminal receives the text data and prepares to send it to the server. In this step, the input is text data from the user, and the output is data that the terminal sends to the server.

[0545] Step 2:

[0546] The terminal uses the appropriate communication protocol to send the received request data to the server. In this process, the input is the user's request received by the terminal, and the output is the request data forwarded to the server. Specifically, the process involves data encryption and transmission according to the protocol.

[0547] Step 3:

[0548] The server parses the received user request. The input is the request data sent from the terminal, and the output is the parsed request content. Natural language processing is performed using a generative AI model to identify the intent of the request and the category of the requested product. In this process, the AI ​​model clarifies ambiguous expressions and extracts specific purchase needs.

[0549] Step 4:

[0550] The server selects relevant products from the existing catalog based on the analysis results. The input for this step is the analyzed request data, and the output is a list of candidate products. Using an emotion engine, past user data is considered to prioritize products that match the user's preferences. The specific operations here involve database searching and selection based on priority.

[0551] Step 5:

[0552] The server sends information about the selected products to the terminal. The input is the product list obtained in the previous step, and the output is the product information sent to the terminal. Specifically, a data packet containing detailed information and prices of the selected products is sent to the terminal.

[0553] Step 6:

[0554] The terminal receives information from the server and presents product information to the user. The input is product information sent from the server, and the output is information presented to the user's visual sense. Specific actions are taken through the user interface to display product images and descriptions and prompt the user to make a selection.

[0555] Step 7:

[0556] This step involves the user's reaction to a presented product. The input is visually presented product information, and the output is the user's reaction, such as behavior or feedback. In this step, the emotion engine analyzes the user's facial expressions and attitudes in real time and prepares to send the resulting emotion data to the server.

[0557] Step 8:

[0558] The server adjusts recommendations based on the analysis results from the emotion engine. The input is the user's emotion data, and the output is adjusted product recommendation information. Specifically, the system prioritizes the display of products that the user has shown favorability towards and adjusts the data to reflect their ratings.

[0559] Step 9:

[0560] The order processing takes place when the user approves the purchase. The inputs are the adjusted product information and the user's approval action, and the output is order data that is transferred to the ordering system. Here, the automatic generation and confirmation of the order, and the ordering instructions issued by the server are specifically performed.

[0561] Step 10:

[0562] The server automatically manages product delivery and payment. This includes issuing instructions to delivery companies based on order information and processing payments in the payment system. Input is user-approved order information, and output is completed delivery and payment data. This allows users to receive their products without any hassle.

[0563] (Application Example 2)

[0564] 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."

[0565] When consumers shop online, the process of selecting products from a vast number of options is complex, and there is a need to improve the user experience. In particular, product recommendations that take into account consumer emotions and preferences are currently time-consuming and have not been automated. Furthermore, the amount of time consumers spend selecting products can decrease their purchase satisfaction.

[0566] 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.

[0567] In this invention, the server includes an information input means for receiving purchase requests from users, a generation means for automatically selecting recommended items based on the requests, and an emotion analysis means for analyzing the user's facial expressions in real time and emphasizing recommended items based on their emotional state. This enables personalized product recommendations that respond to the consumer's emotions, providing an efficient and highly satisfying purchasing experience.

[0568] An "information input device" is a device that provides an interface for receiving purchase requests from consumers.

[0569] A "generation device" is a device that automatically selects the optimal product based on consumer purchasing requests.

[0570] An "emotion analysis device" is a device that analyzes a consumer's facial expressions and determines their emotional state in real time.

[0571] An "approval confirmation device" is a device that accepts approval for a product presented by a consumer and initiates the order processing.

[0572] A "payment method" is a device that automatically handles the delivery of goods and payment after an order has been placed.

[0573] Modes for carrying out the invention

[0574] The system for implementing this invention mainly consists of three elements: a server, a terminal, and a user. Specific details are shown below.

[0575] The server functions as both an information input and generation mechanism, receiving purchase requests from users. The server analyzes this request information and uses a generative AI model to select appropriate products. This AI model can be implemented using platforms such as Amazon Web Services' SageMaker or Google Cloud AI.

[0576] The device includes emotion analysis capabilities to capture the user's facial expressions in real time. Here, facial data is collected using the smartphone or tablet camera and analyzed using emotion analysis software such as Apple's Vision API or Google's ML Kit. The analyzed emotion data is used to highlight selected products and present related product information.

[0577] The terminal also functions as an approval confirmation mechanism; once the user approves their selection, it sends that information to the server. Upon receiving the approval information, the server uses the payment method to automatically proceed with ordering and payment. Possible payment systems used here include APIs such as Stripe or PayPal.

[0578] As a concrete example, suppose a user attempts to search for a "professional-grade camera" using their smartphone. The server receives this request and uses a generative AI model to select multiple camera options. When the emotion analysis system detects that the user is smiling in the device's camera view, the selected product is highlighted. Finally, once the user selects and approves the camera, the server immediately places the order and completes the payment. This allows the user to have an efficient and emotionally resonant purchasing experience.

[0579] Specific examples of prompt statements for generative AI models:

[0580] "The user typed 'search for professional cameras.' We recommend the MU-101 camera. Are there any other popular cameras? If the user is smiling, which product should we highlight?"

[0581] This system enables flexible and rapid product recommendations based on user emotions, further enhancing the online shopping experience.

[0582] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0583] Step 1:

[0584] The user enters a purchase request using their device. The user opens a smartphone application and enters information about the product and category into the chat interface. This input data is sent to the server by the device. The input data includes the product name and product category.

[0585] Step 2:

[0586] The server analyzes the received purchase request. The server utilizes a generative AI model to select the most suitable recommended product based on the input information. This involves using a keyword-based data search algorithm to list candidate products from the existing catalog. Information about the products selected by the generative AI model is then sent back from the server to the terminal.

[0587] Step 3:

[0588] The device displays information about returned items to the user. At this time, it visually displays a list of recommended products using the smartphone's display and notification functions.

[0589] Step 4:

[0590] The device captures the user's facial expressions in real time. By using the device's camera function and facial recognition technology to capture the user's facial expressions, the emotional state is determined. The emotion analysis means analyzes the user's emotional data (e.g., smile or confused expression) and sends that data to the server.

[0591] Step 5:

[0592] The server emphasizes product recommendations based on the received sentiment data, or makes adjustments to suggest more appropriate products. Based on the feedback corresponding to the sentiment data, it sends a prompt message to the AI ​​model that prioritizes the list of products to display. This prompt message updates the list of products again.

[0593] Step 6:

[0594] The user selects a product from those presented by the server and enters their approval via their terminal. Once the user decides to purchase a product, the approval information is sent from the terminal to the server.

[0595] Step 7:

[0596] The server automatically initiates the order and payment process after receiving the approval information. The order management system on the server starts running, placing orders for the selected products and simultaneously calling the payment API to confirm the payment process applied to the user.

[0597] 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.

[0598] 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.

[0599] 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.

[0600] [Fourth Embodiment]

[0601] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0602] 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.

[0603] 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).

[0604] 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.

[0605] 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.

[0606] 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).

[0607] 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.

[0608] 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.

[0609] 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.

[0610] 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.

[0611] 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.

[0612] 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.

[0613] 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".

[0614] The system of this invention consists of three main components: a user, a terminal, and a server. The user enters a purchase request into a chat interface, and the terminal sends the contents to the server. The server analyzes the request and uses a generation means to perform AI-powered product recommendations. As a result, the server selects relevant products from an existing catalog and sends that information to the terminal.

[0615] The terminal displays product information received from the server to the user, who then reviews and approves the selected products. Once the user approves, the terminal transmits this information to the server, which then automatically initiates the ordering process using a verification mechanism.

[0616] The server uses processing tools to consistently manage the ordering, delivery, and payment processes for goods. Once delivery is complete, the server automatically executes the payment process, records all processing logs, and then notifies the user of the transaction completion via the terminal.

[0617] As a concrete example, if a user requests to purchase a new projector via chat, the device transmits this request to the server, which uses AI to recommend several projectors. Once the user selects one and approves it through the device, the server immediately proceeds with the order process. After the product's delivery is confirmed, payment is processed automatically, and the entire process is completed seamlessly for the user. In this way, this system significantly streamlines a company's purchasing process.

[0618] The following describes the processing flow.

[0619] Step 1:

[0620] The user enters a purchase request into the chat interface. The terminal receives this input and sends its contents to the server.

[0621] Step 2:

[0622] The server analyzes the received request and extracts keywords. The server uses AI with generation methods to search for related products from the existing catalog.

[0623] Step 3:

[0624] The server sends the selected product information to the terminal. The terminal receives this information and presents it to the user. The user reviews the presented products and makes a selection.

[0625] Step 4:

[0626] Once the user makes a selection and indicates their approval, the terminal transmits this information to the server. The server then uses a confirmation mechanism to initiate the ordering process.

[0627] Step 5:

[0628] The server connects to the catalog purchasing system and registers the order information for the products selected by the user. The server then verifies the order details and sets the delivery schedule.

[0629] Step 6:

[0630] The server manages the delivery status of the goods and confirms that delivery is complete. After confirmation, the server automatically processes the payment.

[0631] Step 7:

[0632] The server logs the entire transaction process and sends a completion notification to the terminal. The terminal receives this notification and informs the user that the transaction is complete.

[0633] (Example 1)

[0634] 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".

[0635] In online shopping and corporate purchasing processes, there is a growing need for rapid and accurate product recommendations based on user needs, followed by automated purchasing procedures. However, existing systems often rely on manual processes for recommending appropriate products based on user requests, as well as managing product ordering, delivery, and payment, resulting in inefficiencies. In particular, when users have diverse needs, selecting the optimal product for each request and completing the purchasing process seamlessly presents a significant challenge.

[0636] 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.

[0637] In this invention, the server includes communication means, knowledge processing means, dialogue confirmation means, resource management means, and reporting means. This enables the analysis of user requests, product recommendations using artificial intelligence, and the automation of the process from ordering to payment, allowing for efficient and accurate processing.

[0638] A "communication method" is a mechanism that has the function of sending purchase requests from users to a server and enables the sending and receiving of data.

[0639] A "knowledge processing tool" is a processing function that analyzes a user's purchase request and automatically selects relevant products using a generative AI model.

[0640] A "dialogue confirmation method" is a system that has an interface and functions to present recommended products to the user and obtain their selection and approval.

[0641] A "resource management system" is a set of processing functions that automatically manages and executes the entire purchasing process, from ordering goods to delivery and payment.

[0642] A "reporting mechanism" is a function that notifies users of the completion of a transaction or information about the purchasing process.

[0643] A "generative AI model" is an artificial intelligence-based system used to suggest the most suitable products in response to user requests.

[0644] A "prompt sentence" is an input sentence that gives instructions or questions to a generative AI model, enabling it to generate appropriate responses or recommendations.

[0645] This invention is a system for streamlining the purchasing process and includes communication means, knowledge processing means, dialogue confirmation means, resource management means, and reporting means. Users input purchasing requests using a chat interface via a terminal. This terminal can be a typical personal computer or smartphone, equipped with internet-connected software (e.g., a chat application or web browser). Examples include messaging services.

[0646] The terminal sends the user's input request to the server via a communication method. The server uses knowledge processing tools to analyze and understand the received request. Here, widely used software libraries for natural language processing (e.g., spaCy and NLTK) are used. Based on the analyzed information, a generative AI model (e.g., GPT-3) is used to recommend a product suitable for the user. For example, the prompt might be, "I want to buy a new projector. Please tell me your recommendations."

[0647] The server uses this generative AI model to select relevant products from an existing product database. The selected product information is presented to the user via the terminal. The user selects a specific product from the list and indicates their intention to purchase it. The dialogue confirmation mechanism supports this selection and approval process. Through this mechanism, the user can smoothly decide on a purchase.

[0648] Furthermore, based on approved information, the server uses resource management tools to automate the product ordering and delivery process. This process involves checking product inventory, adjusting shipping schedules, and utilizing integrated systems as needed. Once the products have been delivered, the server executes the payment process, and the resource management tools monitor the entire process. Upon completion of the transaction, the user is notified through reporting tools. In this way, the system streamlines the entire purchasing process and achieves end-to-end automation.

[0649] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0650] Step 1:

[0651] The user enters a purchase request through a chat interface. This input may include product categories and specific product requests. For example, the user might enter "I want a new projector." This is entered into the terminal. The terminal receives the user input as text data and formats it for transmission to the server.

[0652] Step 2:

[0653] The terminal sends the request data received from the user to the server using a communication method. Specifically, it sends a POST request along with the data to the server using the HTTP protocol. The input is the text of the user request, and the output is the status of the successful transmission to the server.

[0654] Step 3:

[0655] The server uses a natural language processing library to parse the received request data. This parsing extracts keywords and their intent from the request. The input is the user's request text, and the output is the parsed keywords and phrases. Specifically, keywords such as "projector" and "purchase" are parsed.

[0656] Step 4:

[0657] The server uses a generated AI model based on the analysis results to recommend products. It generates a prompt message and inputs it to the AI ​​in the form of "I'm looking for a projector. Please list the best options below." The input is the analyzed keywords, and the output is a list of recommended products.

[0658] Step 5:

[0659] The server generates a product list and sends it to the terminal. The terminal then presents this list to the user. Specifically, it displays product names, prices, features, etc., in a list format on the chat interface. The input is a list of recommended products, and the output is the product information presented to the user.

[0660] Step 6:

[0661] The user makes a selection from the presented product list. They then confirm their selection and indicate their intention to purchase via chat. For example, they might type, "I want to buy product A." This input constitutes the user's selection information, and the device sends this as a confirmation message to the server.

[0662] Step 7:

[0663] The terminal sends user selection information to the server. The server automates the ordering process for the selected products. Input is user approval information, and output is the order processing start status. Specific actions include sending data to the ordering system and checking inventory.

[0664] Step 8:

[0665] The server manages and integrates all processes, including product delivery and payment, using resource management tools. Once product delivery is complete, payment processing is also automated. Input is order information, and output is a record of the completion status. Integration with the logistics system is also included.

[0666] Step 9:

[0667] After all processing is complete, the server sends a transaction completion notification to the user via the terminal using a reporting mechanism. Specifically, this involves sending a notification message to the chat interface. The input is the completion status, and the output is a transaction completion confirmation notification to the user.

[0668] (Application Example 1)

[0669] 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".

[0670] In modern online shopping, there are problems such as the difficulty for users to efficiently search for desired products and receive appropriate recommendations. Furthermore, the process from product selection to payment completion is cumbersome, requiring multiple steps. Additionally, the lack of more intuitive operation, such as voice input, limits the user experience.

[0671] 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.

[0672] In this invention, the server includes an interactive interface means for receiving purchase requests from users, a generation means using an artificial intelligence model to generate multiple products, and a processing means for automatically ordering products, and handling delivery and payment processing after product selection. This allows users to easily input purchase requests using voice input and complete the entire process quickly and seamlessly for the selected products.

[0673] "Interactive interface means" refers to a communication method, such as chat or voice input, that allows users to directly input purchase requests.

[0674] "Generation methods using artificial intelligence models" refer to methods that analyze user input information and automatically select and generate multiple related products.

[0675] A "confirmation and approval mechanism" is a method that presents recommended products to the user, allowing the user to select and approve them to initiate the ordering process.

[0676] A "processing device" is a means of automatically managing the ordering of selected products, monitoring of delivery, and completion of payment after delivery.

[0677] "Voice input" is an input method in which the user communicates purchase requests to a device by speaking.

[0678] "Natural language processing technology" is a technology that analyzes natural language input by users to efficiently understand and process purchase requests.

[0679] "Key words" refer to important words or phrases included in the user's purchase request, and are the words that the AI ​​uses as criteria when selecting related products.

[0680] The system for realizing this invention mainly consists of a user's smart device (e.g., a smartphone), a server, and a generative AI model.

[0681] Users enter their purchase requests through an interactive interface on their smartphones. This interface uses a chat format and also supports voice input. If a user wants to find a product, they can make a voice request such as, "I want a jacket for spring."

[0682] The terminal receives this request and sends it to the server. The server receives this information and analyzes the request using natural language processing technology. A generative AI model (e.g., OpenAI GPT-4) uses this analysis to select several relevant products from an existing product catalog and recommend them to the user. For example, the information is processed in the form of a prompt message: "user: 'I'm looking for a spring jacket. Can you recommend one?'"

[0683] Once the user reviews and approves the presented product, the terminal notifies the server. The server automatically places an order based on the user's selection and simultaneously monitors the product's delivery status. Once delivery is confirmed, the server automatically processes the payment.

[0684] This system integrates multiple technologies. It utilizes Socket.IO and React Native for real-time chat functionality, OpenAI APIs for natural language processing, Stripe APIs for payment processing, and AWS Lambda for order process management. This allows for an efficient and intuitive shopping experience for users.

[0685] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0686] Step 1:

[0687] Users enter purchase requests through an interactive interface on their smart devices. They can use either text or voice input, making requests such as "I want a spring jacket." The input is in natural language, and this information is sent to the server via the smart device.

[0688] Step 2:

[0689] The server analyzes the natural language purchase requests it receives. The input is text data sent by the user, and the server uses natural language processing techniques to extract key words. This results in the output of a keyword list for extracting products that belong to a specific category or have certain attributes.

[0690] Step 3:

[0691] The server uses a generative AI model to generate a list of recommended products based on the analyzed keywords. The input is the keyword list output in step 2, and the AI ​​model performs data calculations to select multiple highly relevant products. This product list is then generated as output and prepared to be presented to the user.

[0692] Step 4:

[0693] The terminal displays a list of products sent from the server to the user. The user makes a selection of one or more products from the displayed list. The input is the product list obtained from the server, and the user selects their desired products from this list, resulting in the user's selection being output.

[0694] Step 5:

[0695] The server receives the user's selection and starts the automated ordering process. The input is the selected product information, and AWS Lambda is used to check inventory and process the order. The output records that the order has been completed and the delivery process has begun.

[0696] Step 6:

[0697] The server continuously monitors the delivery status of the product and automatically processes payment once delivery is complete. The input is a delivery completion notification, and data processing is performed using the Stripe API to generate payment information and execute the settlement. At this point, an output indicating the completion of the transaction is generated and notified to the user.

[0698] 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.

[0699] The system of this invention consists of four main components: a user, a terminal, a server, and an emotion engine. The user enters a purchase request into the chat interface, and the terminal receives this information and sends it to the server. The server analyzes the received request and uses AI with generation means to select recommended products from an existing catalog. At this time, the emotion engine analyzes the user's emotions and influences the selection of recommended products based on the results.

[0700] The terminal displays product information returned from the server to the user, and the sentiment engine analyzes the user's response in real time. If the user gives an approval or a negative response, the sentiment engine communicates the result to the server and adjusts the recommendations accordingly.

[0701] For approved products, the terminal sends the information to the server, automating the ordering process using a verification mechanism. The server uses processing tools to automatically manage all processes, including ordering, delivery, and payment. The emotion engine collects user emotion data to improve the user experience and optimize future purchasing processes.

[0702] For example, if a user requests via chat that they "want to buy a new headset," the device transmits this request to the server, which then uses AI to recommend several headsets. The emotion engine understands the user's preferences, and if the user smiles at one of the presented products, this information is transmitted to the server, and that product is highlighted. Once the user approves the selection, the server immediately begins the ordering process, and payment is automatically processed once delivery is complete. The feedback from the emotion engine makes the next purchasing experience more personalized, allowing users to make comfortable choices in less time. In this way, this system significantly streamlines the company's purchasing process while simultaneously improving the user's purchasing experience.

[0703] The following describes the processing flow.

[0704] Step 1:

[0705] The user types "I want to buy a new headset" into the chat interface. The terminal receives this input and sends the request to the server.

[0706] Step 2:

[0707] The server analyzes the received request to identify the key product type. Using a generation method, the server leverages an AI algorithm to select suitable headset candidates from the existing catalog database.

[0708] Step 3:

[0709] The server sends information about the selected candidate products to the terminal. The terminal presents this to the user and prompts them to make a selection. An emotion engine monitors the user's facial expressions and reactions in the background and analyzes positive or negative emotions.

[0710] Step 4:

[0711] The emotion engine detects when a user shows interest in one of the presented products and exhibits a positive reaction, such as a smile, to that product. The device notifies the server of this information, and the server then re-presents the user with special emphasis or additional information about that product.

[0712] Step 5:

[0713] The user makes a final selection and approves the product. The terminal sends this approval information to the server. The server uses a verification mechanism to initiate the ordering process for the selected product.

[0714] Step 6:

[0715] The server registers order information in the catalog purchasing system and sets the delivery schedule for the goods. The server manages the status until delivery is complete, and immediately executes the payment process automatically once delivery is confirmed.

[0716] Step 7:

[0717] The server records a log of the completion of the entire process and sends a completion notification to the terminal. The terminal notifies the user that "Your headset order and payment have been completed." The emotion engine stores the emotion data obtained from this transaction and uses it as a reference for future purchasing processes.

[0718] (Example 2)

[0719] 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".

[0720] Traditional purchasing systems often select products based on user requests without considering the user's emotional state, making it difficult to recommend the most suitable products for individual users. Furthermore, manual delivery and payment processing after purchase hindered the provision of an efficient purchasing experience.

[0721] 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.

[0722] In this invention, the server includes a communication device that receives purchase requests from users and relays the information, an analysis device that analyzes the requests and selects recommended products using a generative artificial intelligence model, and an auxiliary device that analyzes the user's emotional state and reflects it in the selection of recommended products. This enables optimal product recommendations based on the individual emotional state of the user, and by automating delivery and payment processing, an efficient and personalized purchasing experience is provided.

[0723] A "communication device" is a device that receives purchase requests from users and relays that information to a server.

[0724] An "analysis device" is a device that analyzes a user's request and selects the most suitable recommended product using a generated artificial intelligence model.

[0725] An "auxiliary device" is a device that analyzes the user's emotional state and reflects the analysis results in product recommendations.

[0726] An "authentication device" is a device that presents recommended product information to the user and initiates the ordering process based on the user's approval.

[0727] A "management device" is a device that automatically handles the delivery and payment processing of goods after an order has been placed, and manages these processes efficiently.

[0728] A "generative artificial intelligence model" is an artificial intelligence technology used to analyze user purchase requests and select appropriate recommended products.

[0729] "Information resources" refers to a database or catalog used to present product options selected based on user request analysis.

[0730] This invention is an advanced shopping system designed to enhance the user's purchasing experience. The system primarily consists of a user, a terminal, a server, and a secondary emotion engine.

[0731] Users enter their requests for desired products into the interface using free-form language. This interface is presented in a chat format, making it user-friendly and easy to use. As a result, product requests can be made in a more natural, conversational manner.

[0732] The terminal receives information entered by the user in real time and sends it to the server via the appropriate protocol. The terminal uses a common communication protocol (e.g., HTTPS) for this communication to ensure data security.

[0733] The server utilizes a generative artificial intelligence model to analyze incoming requests. This model uses NLP (Natural Language Processing) techniques to understand user requests and select the most suitable products from an existing catalog database. Furthermore, an emotion engine analyzes the user's emotional state in real time, and this information can be used to further improve the accuracy of product selection.

[0734] Based on this data, the server selects the most suitable recommended products for the user. To do this, it uses a pre-configured algorithm and sentiment analysis results to determine product priorities. The selected product information is then transmitted from the server to the terminal and presented to the user.

[0735] To illustrate the system's operation with a concrete example, when a user requests to purchase a new headset, the terminal transmits the request to the server, which then uses a generated AI model to select several headsets. The emotion engine analyzes the user's reactions (e.g., smiles and expressions of interest) and highlights products that the user reacts favorably to. Once the user approves, the server immediately initiates the ordering process for that product, and then automatically completes the delivery and payment.

[0736] Examples of prompts include "I want to buy a new headset" and "I'm looking for the latest smartphone model." This allows the system to quickly and accurately provide products that meet the user's specific needs.

[0737] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0738] Step 1:

[0739] The user enters a purchase request into the chat interface. Once entered, the terminal receives the text data and prepares to send it to the server. In this step, the input is text data from the user, and the output is data that the terminal sends to the server.

[0740] Step 2:

[0741] The terminal uses the appropriate communication protocol to send the received request data to the server. In this process, the input is the user's request received by the terminal, and the output is the request data forwarded to the server. Specifically, the process involves data encryption and transmission according to the protocol.

[0742] Step 3:

[0743] The server parses the received user request. The input is the request data sent from the terminal, and the output is the parsed request content. Natural language processing is performed using a generative AI model to identify the intent of the request and the category of the requested product. In this process, the AI ​​model clarifies ambiguous expressions and extracts specific purchase needs.

[0744] Step 4:

[0745] The server selects relevant products from the existing catalog based on the analysis results. The input for this step is the analyzed request data, and the output is a list of candidate products. Using an emotion engine, past user data is considered to prioritize products that match the user's preferences. The specific operations here involve database searching and selection based on priority.

[0746] Step 5:

[0747] The server sends information about the selected products to the terminal. The input is the product list obtained in the previous step, and the output is the product information sent to the terminal. Specifically, a data packet containing detailed information and prices of the selected products is sent to the terminal.

[0748] Step 6:

[0749] The terminal receives information from the server and presents product information to the user. The input is product information sent from the server, and the output is information presented to the user's visual sense. Specific actions are taken through the user interface to display product images and descriptions and prompt the user to make a selection.

[0750] Step 7:

[0751] This step involves the user's reaction to a presented product. The input is visually presented product information, and the output is the user's reaction, such as behavior or feedback. In this step, the emotion engine analyzes the user's facial expressions and attitudes in real time and prepares to send the resulting emotion data to the server.

[0752] Step 8:

[0753] The server adjusts recommendations based on the analysis results from the emotion engine. The input is the user's emotion data, and the output is adjusted product recommendation information. Specifically, the system prioritizes the display of products that the user has shown favorability towards and adjusts the data to reflect their ratings.

[0754] Step 9:

[0755] The order processing takes place when the user approves the purchase. The inputs are the adjusted product information and the user's approval action, and the output is order data that is transferred to the ordering system. Here, the automatic generation and confirmation of the order, and the ordering instructions issued by the server are specifically performed.

[0756] Step 10:

[0757] The server automatically manages product delivery and payment. This includes issuing instructions to delivery companies based on order information and processing payments in the payment system. Input is user-approved order information, and output is completed delivery and payment data. This allows users to receive their products without any hassle.

[0758] (Application Example 2)

[0759] 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".

[0760] When consumers shop online, the process of selecting products from a vast number of options is complex, and there is a need to improve the user experience. In particular, product recommendations that take into account consumer emotions and preferences are currently time-consuming and have not been automated. Furthermore, the amount of time consumers spend selecting products can decrease their purchase satisfaction.

[0761] 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.

[0762] In this invention, the server includes an information input means for receiving purchase requests from users, a generation means for automatically selecting recommended items based on the requests, and an emotion analysis means for analyzing the user's facial expressions in real time and emphasizing recommended items based on their emotional state. This enables personalized product recommendations that respond to the consumer's emotions, providing an efficient and highly satisfying purchasing experience.

[0763] An "information input device" is a device that provides an interface for receiving purchase requests from consumers.

[0764] A "generation device" is a device that automatically selects the optimal product based on consumer purchasing requests.

[0765] An "emotion analysis device" is a device that analyzes a consumer's facial expressions and determines their emotional state in real time.

[0766] An "approval confirmation device" is a device that accepts approval for a product presented by a consumer and initiates the order processing.

[0767] A "payment method" is a device that automatically handles the delivery of goods and payment after an order has been placed.

[0768] Modes for carrying out the invention

[0769] The system for implementing this invention mainly consists of three elements: a server, a terminal, and a user. Specific details are shown below.

[0770] The server functions as both an information input and generation mechanism, receiving purchase requests from users. The server analyzes this request information and uses a generative AI model to select appropriate products. This AI model can be implemented using platforms such as Amazon Web Services' SageMaker or Google Cloud AI.

[0771] The device includes emotion analysis capabilities to capture the user's facial expressions in real time. Here, facial data is collected using the smartphone or tablet camera and analyzed using emotion analysis software such as Apple's Vision API or Google's ML Kit. The analyzed emotion data is used to highlight selected products and present related product information.

[0772] The terminal also functions as an approval confirmation mechanism; once the user approves their selection, it sends that information to the server. Upon receiving the approval information, the server uses the payment method to automatically proceed with ordering and payment. Possible payment systems used here include APIs such as Stripe or PayPal.

[0773] As a concrete example, suppose a user attempts to search for a "professional-grade camera" using their smartphone. The server receives this request and uses a generative AI model to select multiple camera options. When the emotion analysis system detects that the user is smiling in the device's camera view, the selected product is highlighted. Finally, once the user selects and approves the camera, the server immediately places the order and completes the payment. This allows the user to have an efficient and emotionally resonant purchasing experience.

[0774] Specific examples of prompt statements for generative AI models:

[0775] "The user typed 'search for professional cameras.' We recommend the MU-101 camera. Are there any other popular cameras? If the user is smiling, which product should we highlight?"

[0776] This system enables flexible and rapid product recommendations based on user emotions, further enhancing the online shopping experience.

[0777] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0778] Step 1:

[0779] The user enters a purchase request using their device. The user opens a smartphone application and enters information about the product and category into the chat interface. This input data is sent to the server by the device. The input data includes the product name and product category.

[0780] Step 2:

[0781] The server analyzes the received purchase request. The server utilizes a generative AI model to select the most suitable recommended product based on the input information. This involves using a keyword-based data search algorithm to list candidate products from the existing catalog. Information about the products selected by the generative AI model is then sent back from the server to the terminal.

[0782] Step 3:

[0783] The device displays information about returned items to the user. At this time, it visually displays a list of recommended products using the smartphone's display and notification functions.

[0784] Step 4:

[0785] The device captures the user's facial expressions in real time. By using the device's camera function and facial recognition technology to capture the user's facial expressions, the emotional state is determined. The emotion analysis means analyzes the user's emotional data (e.g., smile or confused expression) and sends that data to the server.

[0786] Step 5:

[0787] The server emphasizes product recommendations based on the received sentiment data, or makes adjustments to suggest more appropriate products. Based on the feedback corresponding to the sentiment data, it sends a prompt message to the AI ​​model that prioritizes the list of products to display. This prompt message updates the list of products again.

[0788] Step 6:

[0789] The user selects a product from those presented by the server and enters their approval via their terminal. Once the user decides to purchase a product, the approval information is sent from the terminal to the server.

[0790] Step 7:

[0791] The server automatically initiates the order and payment process after receiving the approval information. The order management system on the server starts running, placing orders for the selected products and simultaneously calling the payment API to confirm the payment process applied to the user.

[0792] 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.

[0793] 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.

[0794] In the above embodiment, an example was given in which the 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.

[0795] 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.

[0796] 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.

[0797] 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.

[0798] 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.

[0799] 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.

[0800] 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."

[0801] 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.

[0802] 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.

[0803] 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.

[0804] 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.

[0805] 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.

[0806] 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.

[0807] 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.

[0808] 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.

[0809] 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.

[0810] 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.

[0811] 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.

[0812] 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.

[0813] The following is further disclosed regarding the embodiments described above.

[0814] (Claim 1)

[0815] An interface for receiving purchase requests from users,

[0816] A generation means that automatically selects recommended products based on the aforementioned request,

[0817] A confirmation means that presents information about the recommended products to the user and initiates the ordering process based on the user's approval,

[0818] A processing method that automatically executes product delivery and payment after an order is placed,

[0819] A system that includes this.

[0820] (Claim 2)

[0821] The system according to claim 1, wherein the processing means tracks the delivery status of the goods and automatically executes payment after delivery is completed.

[0822] (Claim 3)

[0823] The system according to claim 1, wherein the generation means analyzes keywords related to the user's purchase request and selects multiple products from an existing catalog.

[0824] "Example 1"

[0825] (Claim 1)

[0826] A means of communication for receiving purchase requests from users,

[0827] A knowledge processing means that analyzes the intent of the request and automatically generates recommended products based on the aforementioned request,

[0828] A dialogue confirmation mechanism that displays information on generated recommended products to the user and initiates the ordering process based on the user's selection and approval.

[0829] A resource management system that automatically handles product delivery and payment after an order is placed, and records the entire process.

[0830] A reporting method to send a transaction completion notification to the user,

[0831] A system that includes this.

[0832] (Claim 2)

[0833] The system according to claim 1, wherein the resource management means tracks the delivery status of goods and executes payment processing after delivery is completed.

[0834] (Claim 3)

[0835] The system according to claim 1, wherein the knowledge processing means utilizes a generative AI model to analyze the user's purchasing intent and selects a number of relevant products from an existing product database.

[0836] "Application Example 1"

[0837] (Claim 1)

[0838] An interactive interface means for receiving purchase requests from users,

[0839] A generation means using an artificial intelligence model to generate multiple recommended products based on the aforementioned request,

[0840] A confirmation and approval mechanism that presents the user with the recommended product information and initiates the ordering process based on the user's selection,

[0841] A system that automatically places an order after product selection, monitors the delivery status, and completes payment after delivery.

[0842] A system that includes this.

[0843] (Claim 2)

[0844] The system according to claim 1, wherein the processing means receives a user's purchase request using voice input.

[0845] (Claim 3)

[0846] The system according to claim 1, wherein the generation means analyzes key words related to the user's purchase request using natural language processing technology and selects multiple appropriate products from an existing product catalog.

[0847] "Example 2 of combining an emotion engine"

[0848] (Claim 1)

[0849] A communication device that receives purchase requests from users and relays the information,

[0850] An analysis device that analyzes the aforementioned request and selects recommended products using a generative artificial intelligence model,

[0851] An auxiliary device that analyzes the user's emotional state and reflects it in the selection of recommended products,

[0852] An authentication device that presents information on the recommended products to the user and initiates the ordering process based on the user's approval,

[0853] A management device that automatically handles product delivery and payment processing after an order is placed,

[0854] A system that includes this.

[0855] (Claim 2)

[0856] The system according to claim 1, wherein the management device monitors the delivery status of the goods and automatically makes payments after delivery is completed.

[0857] (Claim 3)

[0858] The system according to claim 1, wherein the analysis device analyzes indicators related to the user's purchase request and presents multiple options from existing information resources.

[0859] "Application example 2 when combining with an emotional engine"

[0860] (Claim 1)

[0861] A means of inputting information to receive purchase requests from users,

[0862] A generation means that automatically selects recommended items based on the aforementioned request,

[0863] An emotion analysis method that analyzes the user's facial expressions in real time and emphasizes recommended items based on their emotional state,

[0864] An approval confirmation means that presents information on the recommended items to the user and initiates the order process based on the user's approval,

[0865] A payment method that automatically handles the delivery of goods and payment after an order is placed,

[0866] A system that includes this.

[0867] (Claim 2)

[0868] The system according to claim 1, wherein the payment means tracks the delivery status of goods and automatically executes payment after delivery is completed.

[0869] (Claim 3)

[0870] The system according to claim 1, wherein the generation means analyzes keywords related to a user's purchase request and selects multiple items from an existing database. [Explanation of Symbols]

[0871] 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. An interface for receiving purchase requests from users, A generation means that automatically selects recommended products based on the aforementioned request, A confirmation means that presents information about the recommended products to the user and initiates the ordering process based on the user's approval, A processing method that automatically executes product delivery and payment after an order is placed, A system that includes this.

2. The system according to claim 1, wherein the processing means tracks the delivery status of the goods and automatically executes payment after delivery is completed.

3. The system according to claim 1, wherein the generation means analyzes keywords related to the user's purchase request and selects multiple products from an existing catalog.

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