system

Abstract

We provide the system. [Solution] A means of receiving requests from users in natural language and analyzing the relevant information, A means for retrieving relevant product information from a database based on the analyzed request and generating a list of suggested products, A means of forming and displaying a dialogue that suggests products to the user based on the generated product list, A means of receiving product selections from users and transmitting the selected product information to the order processing system, A system that includes means for notifying users of the results of order processing.

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 in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] For elderly people and users unaccustomed to online shopping, existing e-commerce platforms are complex and difficult to understand, which is a factor hindering a smooth shopping experience. Especially in the product selection and order process, many steps have to be taken, which causes stress and misorders. An object of the present invention is to provide a means for such users to simply and intuitively select and order products through natural dialogue.

Means for Solving the Problems

[0005] This invention provides a natural language processing means that receives and analyzes requests from users in natural language, thereby accurately understanding the user's intent. Furthermore, it includes means for retrieving relevant product information from a database based on the analysis results and generating a list of suggested products. Based on this product list, it provides suggestions to the user in an interactive format and supports product selection through a user interface. In addition, it automatically transmits the selected product information to the order processing system and notifies the user of the result after the order is completed, thereby realizing a simple and efficient overall process. In this way, it provides a means to reduce the burden on users, especially the elderly and those unfamiliar with the system, and to improve ease of use.

[0006] A "user" refers to an individual who operates the system and inputs requests using natural language.

[0007] "Natural language" refers to the forms of language that humans use on a daily basis, including spoken language and written communication.

[0008] A "request" refers to the wishes or objectives that a user communicates to the system, and includes specific actions or purchase intentions.

[0009] "Analysis" refers to the process of converting requests written in natural language into a format that a computer can understand and interpreting their intent.

[0010] "Product information" refers to detailed data about the products being sold, including name, price, and description.

[0011] A "database" refers to an information management system that systematically stores product information and allows it to be searched and retrieved.

[0012] A "product list" refers to a set of products suggested to the user, generated based on the analyzed requests.

[0013] "Dialogue format" refers to a continuous and natural form of communication between the user and the system.

[0014] "Order processing" refers to a series of operations and procedures for purchasing the products selected by the user.

[0015] "Notification" refers to the act of the system conveying information to the user, including the progress and results of the order.

Brief Explanation of Drawings

[0016] [Figure 1] It is a conceptual diagram showing an example of the configuration of the data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of the data processing device and the smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of the data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of the data processing device and the smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of the data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of the data processing device and the headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of the data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of the data processing device and the robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13]It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when a sentiment engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when a sentiment engine is combined.

Embodiment for Implementing the Invention

[0017] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0018] First, the terms used in the following description will be explained.

[0019] 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), etc.

[0020] 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.

[0021] 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, etc.

[0022] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0023] 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."

[0024] [First Embodiment]

[0025] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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".

[0037] This invention is an online shopping support system that suggests and places orders for products based on requests entered by the user in natural language. This system is designed to be easy and intuitive to use, especially for the elderly and those unfamiliar with online shopping.

[0038] The user launches the system's application using a device such as a smartphone or computer. The application accepts user requests in the form of voice input or text input. For example, if the user says, "I want to make hot pot on Saturday night," the device sends that request to the server.

[0039] The server uses advanced natural language processing technology to analyze the user's request. Through this analysis, the server recognizes the need for "ingredients for a hot pot dish" and retrieves the necessary product information from the database. Based on the retrieved information, the server creates a list of products to suggest to the user.

[0040] The terminal receives the suggested product list and displays it on the user's screen. The user can review the list and select products as needed, or search for and add new products.

[0041] After the user selects products, the terminal sends the selected product data back to the server, which then compiles the final order information. Based on the received information, the server processes the order and confirms the user's order in conjunction with the seller's system.

[0042] Once an order is confirmed, the server notifies the terminal of this information, informing the user that the order is complete and providing the estimated delivery date. In this way, the system of the present invention provides an environment in which users can easily shop online through a natural language-based interactive interface.

[0043] This invention places particular emphasis on supporting elderly people who are unfamiliar with technology, and is designed to complete orders in as few steps as possible to reduce the complexity of operation. This allows users to enjoy online shopping with peace of mind.

[0044] The following describes the processing flow.

[0045] Step 1:

[0046] The user launches the application on their device and enters their request via voice or text input. For example, if the user enters "I want to make hot pot on Saturday night," the device converts that request into text data.

[0047] Step 2:

[0048] The terminal sends the converted text data to the server. Based on the received data, the server uses a natural language processing engine to analyze the request and understand the user's intent.

[0049] Step 3:

[0050] Based on the analysis results, the server retrieves product information from the database that matches the user's request. It identifies the ingredients and related products needed for hot pot dishes and generates a list.

[0051] Step 4:

[0052] The server sends the generated product list back to the terminal. The terminal displays the list on the user's screen and prompts them to select a product. The list includes detailed information such as product name, price, and descriptive image.

[0053] Step 5:

[0054] The user views a list on their device and selects the items they wish to purchase. They can also search for and add additional items to the list as needed.

[0055] Step 6:

[0056] The terminal sends information about the selected products to the server, and the server compiles the final order information based on the user's decision.

[0057] Step 7:

[0058] The server processes the order and confirms it officially in conjunction with the seller's system. It then sends a notification to the device indicating that processing is complete and including the estimated delivery date or shipping information.

[0059] Step 8:

[0060] The user checks the notification on their device and learns that the order has been successfully completed. This marks the end of the ordering process, and the user completes their online shopping.

[0061] (Example 1)

[0062] 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."

[0063] In online shopping, there is a challenge in that it is difficult for elderly people and tech-savvy users to intuitively search for and purchase products. Traditional systems require complex operations, and the process from selecting a product to completing an order is not easy to understand.

[0064] 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.

[0065] In this invention, the server includes means for receiving requests from users in natural language, converting the requests into prompt format and analyzing them, means for obtaining relevant product information from an information set based on the analyzed requests and generating a list of suggested products, and means for transmitting the generated product list to an information processing device and presenting it visually to the user. This makes it possible for users to intuitively find products based on their requests and easily complete the purchase procedure.

[0066] "User" refers to a person who uses the system to search for and purchase products.

[0067] "Natural language" refers to the forms of language that people use on a daily basis, and includes requests made in either spoken or written form.

[0068] A "request" refers to the content of a user's wishes or commands to the system.

[0069] "Prompt format" refers to a document format that has been modified to make natural language requests easier to parse.

[0070] "Means of analysis" refers to methods that use generative AI models to understand user requests and translate those requests into specific needs.

[0071] "Product information" refers to detailed data about the products offered, including price, specifications, and stock information.

[0072] An "information collection" refers to a centralized storage location for product information accessed by a server.

[0073] A "product list" refers to a list of related products selected for presentation to users.

[0074] "Information processing device" refers to a terminal held by a user, specifically a device that has the function of displaying a product list.

[0075] "Human-machine interface" refers to the user interface used to adjust or add products to a product list.

[0076] This invention is a system designed to make online shopping easier, especially for elderly and technologically unfamiliar users. The system assists users in efficiently searching for and ordering products using natural language. The entire process is primarily handled through the cooperation of a server and a terminal.

[0077] Users launch system applications using devices such as smartphones and personal computers. These devices accept natural language requests from users via voice or text input. For example, if a user inputs "I want to make hot pot on Saturday night," the device converts this request into a prompt format. An example of such a prompt might be: "User request: I want to make hot pot on Saturday night. Please suggest related products."

[0078] The server analyzes the prompt using a generative AI model (e.g., an AI engine with advanced natural language processing technology). Through this analysis, it recognizes specific needs from the user's request and retrieves product information corresponding to these needs from an information collection (e.g., a database). Based on the retrieved product information, the server generates a list of products to suggest.

[0079] The product list is sent to the terminal, which visually displays this information on the user's screen. The user can review the presented product list and select the necessary items. Furthermore, the user can add or adjust items in the product list through the human-machine interface.

[0080] The selected product information is sent from the terminal to the server, which then processes the order based on that information. Once the final order is confirmed, the server notifies the terminal of the order result and estimated delivery date for the user to confirm. In this way, users can enjoy stress-free online shopping with minimal effort.

[0081] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0082] Step 1:

[0083] The user launches the application on a device such as a smartphone or PC and inputs a request in natural language via voice or text. For example, they might input something like, "I want to make hot pot on Saturday night." Because the input request is difficult to parse directly, the device converts the request into a prompt format. It generates a prompt message in the format of "User request: I want to make hot pot on Saturday night. Please suggest related products," and sends it to the server.

[0084] Step 2:

[0085] The server analyzes the received prompt message using a generation AI model. Specifically, it interprets the user's request using natural language processing technology and extracts specific needs, such as "ingredients for making hot pot." As a result of this analysis, relevant search keywords are generated. Based on these needs, the server sends queries to an information collection (e.g., a database) to retrieve relevant product information.

[0086] Step 3:

[0087] The server creates a list of products to suggest to the user based on the acquired product information. Specifically, it organizes detailed information about products that meet the extracted needs (e.g., name, price, reviews, availability) and compiles it into a list format. This list information is then formatted into a data format by the server and sent to the terminal.

[0088] Step 4:

[0089] The terminal visually displays a list of products received from the server on the user's screen. Detailed information, including images, prices, and ratings, is also presented to facilitate product selection. The user can scroll through the displayed product list and select the desired item.

[0090] Step 5:

[0091] When a user selects a product from the product list, the terminal compiles the selected product information and sends it back to the server. The input here is the specific product information selected by the user, and the output is a request for the order details sent to the server.

[0092] Step 6:

[0093] The server processes the order based on the data of the selected items received. Order processing includes data calculations such as inventory checks, initiating payment procedures, and setting delivery schedules. The server then collaborates with the seller's system to finalize the order. The confirmed order information is sent to the user's terminal for notification.

[0094] Step 7:

[0095] The terminal notifies the user of order confirmation information from the server. Specifically, it displays an order completion message on the screen, along with the estimated delivery date and payment completion notification. This allows the user to confirm that their order has been successfully completed.

[0096] (Application Example 1)

[0097] 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."

[0098] Online shopping can be complex and difficult to use for the elderly and users unfamiliar with technology. Furthermore, challenges exist in operating the terminal and effectively communicating requests. Therefore, there is a need for a system that supports easy and intuitive product searching and purchase.

[0099] 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.

[0100] In this invention, the server includes means for receiving requests in natural language from users and analyzing the relevant information; means for obtaining relevant product information from a database based on the analyzed requests and generating a list of suggested products; means for forming and displaying a dialogue to propose to the user based on the generated product list; means for recognizing speech and converting the user's requests into text data; means for outputting the generated product suggestions as speech using speech synthesis technology; and means for notifying the user of the results of the order processing. This makes it possible for users to easily search for, select, and complete orders for products through voice.

[0101] "Natural language" refers to the language that humans use on a daily basis, and is a form of communication through words and sentences.

[0102] "Analysis" is the process of taking given information and unraveling it in order to understand its meaning.

[0103] "Product information" refers to detailed data about a product, including its name, price, description, and availability.

[0104] A "database" is a system that organizes and stores large amounts of data, enabling efficient searching and retrieval.

[0105] A "product list" is a compilation of information about suggested or selectable products.

[0106] "Speech recognition" is a technology that converts human speech into digital signals and then analyzes them.

[0107] "Text data" refers to a form of information that is represented as a string of characters.

[0108] "Speech synthesis technology" is a technology that generates sounds that resemble human speech based on text data.

[0109] "Order processing" refers to a series of operations that complete the purchase process after selecting products.

[0110] To implement this invention, the user first launches an application on a device such as a smartphone or personal computer. The device uses speech recognition software to convert the user's voice into text data. For example, the device recognizes a request such as "I want to make hot pot on Saturday night" and sends that information to the server.

[0111] The server utilizes advanced natural language processing technology to analyze user requests. Based on the analysis, the server retrieves relevant product information from the database and creates a product list. During this process, a generative AI model is used to suggest products and select highly relevant items.

[0112] The terminal uses speech synthesis technology to communicate product suggestions to the user via voice, based on a product list received from the server. The user selects products via voice or screen, and the selected product information is sent to the server for order processing.

[0113] The server notifies the terminal of the order processing result, informing the user of the estimated delivery date and order details via voice or text. This allows users to complete online shopping easily and efficiently through an intuitive voice interface.

[0114] For example, if a user says, "I want to have hot pot with my family this weekend," the system will automatically suggest the necessary ingredients and recommended meal sets via voice.

[0115] An example of a prompt for a generative AI model is: "Based on the user's natural language request, retrieve relevant products from the server and provide product information via voice."

[0116] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0117] Step 1:

[0118] The user launches the application on the device and inputs a request by voice. The device uses speech recognition software to convert the input voice into text data. Here, the input is the user's voice, and the output is text data. The voice data is captured in digital format, analyzed using an acoustic model, and converted into a corresponding string.

[0119] Step 2:

[0120] The terminal sends the generated text data to the server. The server uses a natural language processing engine to analyze this text data. The input for analysis is text data, and the output is the analyzed request content. The server performs grammatical and semantic analysis to identify what the user is requesting.

[0121] Step 3:

[0122] Based on the analysis results, the server retrieves relevant product information from the database. The input at this stage is the analyzed request, and the output is product information. The server uses a generative AI model to select the product best suited to the user's request and builds a product list.

[0123] Step 4:

[0124] The server sends a product list to the terminal. The terminal uses speech synthesis technology to present the contents of this product list to the user in audio. The input here is the product list, and the output is audio information. The speech synthesis engine generates natural-sounding speech from the text and delivers it to the user.

[0125] Step 5:

[0126] The user selects products based on information presented via voice or on-screen. The terminal receives the user's selection and sends it to the server. The input is the user's product selection, and the output is data of the selected products sent to the server. The user's selection is captured and digitized through the interface.

[0127] Step 6:

[0128] The server processes orders based on the received selection data. The input is the selected product data, and the output is order confirmation information. The server works in conjunction with the sales system to check inventory and complete payment procedures, thus finalizing the order.

[0129] Step 7:

[0130] The server notifies the terminal of order confirmation information, and the terminal conveys this information to the user via voice or text. The input here is order confirmation information, and the output is the notification content. The terminal uses a speech synthesis or text message generation engine to provide the information to the user.

[0131] 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.

[0132] This invention provides a system that allows users to input both natural language and emotions during online shopping, analyzes that information, and makes product recommendations optimized for the user. In particular, by combining it with an emotion engine, the system aims to understand the user's emotional state and propose appropriate products and services.

[0133] First, the user launches the application from a device such as a smartphone or tablet. Using voice input or text input, the user enters their request for the desired product or service. At this time, the application also has a function that uses an emotion sensor to detect the user's emotions from the tone of their voice and context.

[0134] The terminal sends the input natural language and emotion data to the server. The server uses a natural language processing engine to analyze the user's request and an emotion engine to identify the user's emotional state. Based on these results, highly relevant product information is retrieved from the database.

[0135] For example, if a user enters "I'm tired from work lately, so I want some relaxing tea," the server doesn't simply use "tea" as a search key. Instead, it adds keywords related to emotions such as "tired" and "relaxed" to the analysis results and selects an effective relaxing tea.

[0136] The selected products are listed according to the user's emotional state and prioritized. This list is displayed on the device, making it easier for the user to select products. After the user selects products, the selected information is sent back to the server, the order process is initiated, and a confirmation notification is sent to the device.

[0137] In this way, the present invention takes into account the user's emotions and provides a more personalized shopping experience. By employing an emotion engine, it becomes possible to suggest the most appropriate products according to the user's mental state. This makes online shopping more comfortable and effective.

[0138] The following describes the processing flow.

[0139] Step 1:

[0140] The user launches a shopping application on their device and enters their request via voice or text. For example, they might type, "I'm stressed, I want to relax." The device then retrieves the input via voice recognition or as text.

[0141] Step 2:

[0142] When the terminal sends input natural language data and voice data to the server, it also includes data that infers the user's emotional state from their voice tone and speaking speed.

[0143] Step 3:

[0144] The server analyzes the received data. The natural language processing engine analyzes the requests and extracts emotions and needs such as "stress" and "relaxation." The emotion engine determines specific emotional states from the audio data and adds supplementary information to the analysis results.

[0145] Step 4:

[0146] The server combines the results of natural language processing and sentiment analysis to retrieve the most suitable product information from the database. For example, it selects products such as "relaxing tea" or "aromatherapy goods."

[0147] Step 5:

[0148] Based on the acquired product information, the server constructs a product list with priority levels according to the user's emotional state and sends it back to the terminal.

[0149] Step 6:

[0150] The terminal displays a list of products sent from the server in its user interface. The user scrolls through the list and selects products of interest.

[0151] Step 7:

[0152] The user's selection is sent from the terminal to the server, which processes the order and confirms it in coordination with the seller.

[0153] Step 8:

[0154] The server confirms that the order has been successfully completed and sends a notification to the terminal. The user then checks the screen for information such as order completion and delivery schedule.

[0155] (Example 2)

[0156] 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".

[0157] Traditional online shopping systems typically suggest products without considering the user's emotions, and product selection was not optimized for the user's psychological state. This resulted in the challenge of finding products that matched the user's needs and emotional state at the time they wanted them.

[0158] 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.

[0159] In this invention, the server includes means for receiving and analyzing requests and emotional states from the user in natural language; means for obtaining relevant product information from information sources based on the analyzed requests and emotional states and generating a product list optimized for the user; and means for forming and displaying a dialogue that reflects the user's emotions based on the generated product list. This makes it possible to take the user's emotions into consideration and provide more personalized product suggestions.

[0160] A "user" refers to an individual who uses the system and is responsible for inputting requests and emotional states into the system using natural language.

[0161] "Natural language" refers to the linguistic forms that people use on a daily basis, and is the form in which a system expresses a request that it analyzes.

[0162] "Emotional state" refers to the user's psychological and physiological reactions and circumstances, and is a factor that the system considers during analysis.

[0163] "Analysis" refers to the process of understanding the requests and emotional states obtained from users and interpreting their meaning and intent.

[0164] "Information source" refers to a collection of databases or external data that a system accesses to obtain relevant product information.

[0165] An "optimized product list" refers to a group of products selected based on analysis results to best match the user's emotional state and needs.

[0166] "Dialogue" refers to the interaction between a system and its user, specifically the conversational format used to present information.

[0167] An "order processing device" refers to a hardware or software system that executes the purchase process based on the product information selected by the user.

[0168] This invention is a system that enables users to conduct online shopping using natural language and emotions. This allows for product recommendations optimized for each individual user.

[0169] Users launch a dedicated application from a device such as a smartphone or tablet. Here, they can input their desired products and services in natural language using voice or text input. An emotion sensor is incorporated to analyze the user's voice tone and context, detecting their emotional state and supplementing the information accordingly.

[0170] The terminal sends the input natural language request and detected emotion data to the server. The server analyzes the user's request using a natural language processing engine (e.g., general-purpose natural language processing software). It also uses an emotion engine (e.g., emotion analysis software) to identify the user's emotional state. Based on these analysis results, highly relevant product information is retrieved from the database. This allows the selection of the product best suited to the user's environment and psychological state.

[0171] For example, if a user enters "I'm tired from work lately, so I want some relaxing tea," the server will not just search for "tea," but will also consider keywords related to emotions such as "tired" and "relaxing," and suggest herbal teas that have a high relaxing effect.

[0172] Examples of specific prompts include, "What tea would you recommend for a 30-year-old woman who wants to improve her fatigue?" and "Please suggest products that enhance relaxation."

[0173] Thus, the present invention realizes personalized product recommendations based on each user's emotions, thereby improving the online shopping experience.

[0174] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0175] Step 1:

[0176] The user launches an application on their smartphone or tablet. Here, they input their request for a desired product or service using natural language via voice or text input. During input, an emotion sensor acquires emotional data from the user's voice tone and context. With both the natural language request and emotional data obtained, the device prepares to send the data to the server.

[0177] Step 2:

[0178] The terminal sends natural language request data from the user and acquired sentiment data to the server. The transmitted data is converted into a format such as XML or JSON and securely sent to the server using a communication protocol. The input in this process is the user's request and sentiment data, and the output is the transmission of data to the server.

[0179] Step 3:

[0180] The server analyzes the received data. It uses a natural language processing engine to analyze the request, splitting the text and extracting important keywords and phrases. Simultaneously, it uses an emotion engine to analyze the user's emotional state. As a result of the analysis, data representing the user's intentions and emotional state is output.

[0181] Step 4:

[0182] The server retrieves relevant product information from the database based on the analysis results. Here, the product information is filtered based on the obtained keywords and sentiment information to generate a list of products best suited to the user. The input in this step is the analyzed data, and the output is a product list.

[0183] Step 5:

[0184] The selected product list is sent to the terminal and displayed to the user. The user reviews the displayed list and selects the desired products. Here, the product list is input, and the user's selections are output.

[0185] Step 6:

[0186] When a user selects an item, that information is resent from the terminal to the server, and the order process begins. The server verifies the order and processes it through the payment system. Once processing is complete, an order confirmation notification is sent to the terminal. In this step, the input is the user's selected item information, and the output is the order confirmation notification.

[0187] (Application Example 2)

[0188] 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".

[0189] Traditional online shopping systems failed to consider the user's emotional state when suggesting products, simply displaying items based on search keywords. As a result, it was difficult for users to find products or services that suited their current emotional state. Consequently, users wasted time and struggled to find the products they truly wanted. Solving these problems is crucial.

[0190] 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.

[0191] In this invention, the server includes means for receiving natural language and emotional input from the user and analyzing the relevant information and emotional data; means for retrieving relevant product information from a database based on the analyzed request and emotional data and generating a product list corresponding to the emotional state; and means for forming and displaying a dialogue to propose to the user based on the generated product list corresponding to the emotional state. This enables personalized product suggestions that correspond to the user's mental state.

[0192] "Natural language" refers to the language that humans use on a daily basis, and it is the language that computer systems analyze in order to understand user input.

[0193] "Emotional input" refers to information that indicates the user's emotional state, and is derived from voice tone and text context.

[0194] "Emotional data" refers to data about the emotional state of users, which the system uses to optimize product recommendations.

[0195] "Product information" refers to detailed information about individual products stored in the database, which the system references when generating a list of suggestions.

[0196] A "product list" is a list containing multiple products suggested to the user, generated based on analyzed request and sentiment data.

[0197] "Dialogue" refers to the exchange of information between the system and the user, and is displayed to facilitate the user's choices.

[0198] "Speech recognition" is a technology that converts voice input into text data, and is a pre-processing step that allows a system to treat it as text information.

[0199] "Emotion recognition means" refers to methods for detecting emotional states from a user's voice or text and using that information to provide appropriate product recommendations.

[0200] An "interface" refers to the technical means that serves as a window for users to interact with the system, and provides functions that allow for easy adjustment and addition of product lists.

[0201] To realize this invention, a user-owned terminal, a server, and a network connecting them are utilized. The terminal could be a smartphone or tablet. First, the user launches a dedicated application on the terminal and inputs a request via voice or text. This input includes emotions as well as normal natural language. A terminal equipped with a voice recognition system converts this voice element into text data.

[0202] The server analyzes the received natural language and sentiment data using a natural language processing engine and sentiment recognition tools. For natural language processing, technologies such as Google® Natural Language API and IBM Watson® can be used. For sentiment recognition, tools like IBM Watson Tone Analyzer, which identifies emotional states from speech tone and context, are employed. During this analysis process, keywords based on the user's request and current emotional state are extracted.

[0203] Next, the server retrieves product information from the database that is suitable for the user's emotional state. For example, if a user enters "I'm tired today, so I want to listen to some relaxing music," the server analyzes keywords such as "tired" and "relaxing" and generates a list of music products with relaxing effects. As a result, the user can more easily find music that is soothing. The generated product list is sent to the terminal with priority and displayed to the user.

[0204] Once a user selects an item, the selected information is sent back to the server to proceed with the order process. The result of the order processing is promptly notified to the user. This allows users to enjoy an emotionally responsive product purchase experience through a smooth and intuitive UI.

[0205] As a concrete example, consider a scenario where a user enters the request, "I'm feeling down today, so I'd like to find a book that will cheer me up." In this case, the server recognizes emotional keywords such as "down" and "cheer up" and suggests self-help books or books with uplifting stories related to those emotions. An example of a prompt message used to facilitate this process would be, "Collect emotional keywords from the user's input and generate a suggestion list of appropriate product information."

[0206] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0207] Step 1:

[0208] The user launches the application on their device and provides natural language and emotional input via voice or text. The input information is converted from speech to text data by a speech recognition system. This process ensures that the user's requests are presented as written information.

[0209] Step 2:

[0210] The device sends the converted text data to the server. The server passes the received data to a natural language processing engine, which extracts important keywords and intent from the text. Furthermore, it uses an emotion recognition engine to analyze emotional data from tone of voice and text context. This process identifies the user's request and emotional state.

[0211] Step 3:

[0212] The server retrieves relevant product information from the database based on the analysis results. Here, it searches for the most relevant products based on the extracted keywords and sentiment data, and generates a product list. Each item in the generated product list is assigned a priority corresponding to the sentiment state.

[0213] Step 4:

[0214] The server sends the generated list of emotion-responsive products to the terminal. The terminal displays the list on the user interface so that the user can easily compare and consider the products. During this display process, products are displayed based on priority, allowing the user to quickly make the best choice.

[0215] Step 5:

[0216] When a user selects their desired products on their device, the selection information is sent back to the server. The server confirms the order using its order processing system and processes the results. Order confirmation and status are promptly notified to the user's device, and the purchase process is completed.

[0217] Generative AI models:

[0218] To assist with specific parts of this process, a generative AI model is applied to perform natural language generation and sentiment analysis based on prompt text with high accuracy. A possible prompt might be, "Collect sentiment-related keywords from the user's input and generate a suggested list of appropriate product information."

[0219] 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.

[0220] 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.

[0221] 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.

[0222] [Second Embodiment]

[0223] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0224] 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.

[0225] 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).

[0226] 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.

[0227] 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.

[0228] 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).

[0229] 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.

[0230] 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.

[0231] 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.

[0232] 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.

[0233] 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.

[0234] 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".

[0235] This invention is an online shopping support system that suggests and places orders for products based on requests entered by the user in natural language. This system is designed to be easy and intuitive to use, especially for the elderly and those unfamiliar with online shopping.

[0236] The user launches the system's application using a device such as a smartphone or computer. The application accepts user requests in the form of voice input or text input. For example, if the user says, "I want to make hot pot on Saturday night," the device sends that request to the server.

[0237] The server uses advanced natural language processing technology to analyze the user's request. Through this analysis, the server recognizes the need for "ingredients for a hot pot dish" and retrieves the necessary product information from the database. Based on the retrieved information, the server creates a list of products to suggest to the user.

[0238] The terminal receives the suggested product list and displays it on the user's screen. The user can review the list and select products as needed, or search for and add new products.

[0239] After the user selects products, the terminal sends the selected product data back to the server, which then compiles the final order information. Based on the received information, the server processes the order and confirms the user's order in conjunction with the seller's system.

[0240] Once an order is confirmed, the server notifies the terminal of this information, informing the user that the order is complete and providing the estimated delivery date. In this way, the system of the present invention provides an environment in which users can easily shop online through a natural language-based interactive interface.

[0241] This invention places particular emphasis on supporting elderly people who are unfamiliar with technology, and is designed to complete orders in as few steps as possible to reduce the complexity of operation. This allows users to enjoy online shopping with peace of mind.

[0242] The following describes the processing flow.

[0243] Step 1:

[0244] The user launches the application on their device and enters their request via voice or text input. For example, if the user enters "I want to make hot pot on Saturday night," the device converts that request into text data.

[0245] Step 2:

[0246] The terminal sends the converted text data to the server. Based on the received data, the server uses a natural language processing engine to analyze the request and understand the user's intent.

[0247] Step 3:

[0248] Based on the analysis results, the server retrieves product information from the database that matches the user's request. It identifies the ingredients and related products needed for hot pot dishes and generates a list.

[0249] Step 4:

[0250] The server sends the generated product list back to the terminal. The terminal displays the list on the user's screen and prompts them to select a product. The list includes detailed information such as product name, price, and descriptive image.

[0251] Step 5:

[0252] The user views a list on their device and selects the items they wish to purchase. They can also search for and add additional items to the list as needed.

[0253] Step 6:

[0254] The terminal sends information about the selected products to the server, and the server compiles the final order information based on the user's decision.

[0255] Step 7:

[0256] The server processes the order and confirms it officially in conjunction with the seller's system. It then sends a notification to the device indicating that processing is complete and including the estimated delivery date or shipping information.

[0257] Step 8:

[0258] The user checks the notification on their device and learns that the order has been successfully completed. This marks the end of the ordering process, and the user completes their online shopping.

[0259] (Example 1)

[0260] 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."

[0261] In online shopping, there is a challenge in that it is difficult for elderly people and tech-savvy users to intuitively search for and purchase products. Traditional systems require complex operations, and the process from selecting a product to completing an order is not easy to understand.

[0262] 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.

[0263] In this invention, the server includes means for receiving requests from users in natural language, converting the requests into prompt format and analyzing them, means for obtaining relevant product information from an information set based on the analyzed requests and generating a list of suggested products, and means for transmitting the generated product list to an information processing device and presenting it visually to the user. This makes it possible for users to intuitively find products based on their requests and easily complete the purchase procedure.

[0264] "User" refers to a person who uses the system to search for and purchase products.

[0265] "Natural language" refers to the forms of language that people use on a daily basis, and includes requests made in either spoken or written form.

[0266] A "request" refers to the content of a user's wishes or commands to the system.

[0267] "Prompt format" refers to a document format that has been modified to make natural language requests easier to parse.

[0268] "Means of analysis" refers to methods that use generative AI models to understand user requests and translate those requests into specific needs.

[0269] "Product information" refers to detailed data about the products offered, including price, specifications, and stock information.

[0270] An "information collection" refers to a centralized storage location for product information accessed by a server.

[0271] A "product list" refers to a list of related products selected for presentation to users.

[0272] "Information processing device" refers to a terminal held by a user, specifically a device that has the function of displaying a product list.

[0273] "Human-machine interface" refers to the user interface used to adjust or add products to a product list.

[0274] This invention is a system designed to make online shopping easier, especially for elderly and technologically unfamiliar users. The system assists users in efficiently searching for and ordering products using natural language. The entire process is primarily handled through the cooperation of a server and a terminal.

[0275] Users launch system applications using devices such as smartphones and personal computers. These devices accept natural language requests from users via voice or text input. For example, if a user inputs "I want to make hot pot on Saturday night," the device converts this request into a prompt format. An example of such a prompt might be: "User request: I want to make hot pot on Saturday night. Please suggest related products."

[0276] The server analyzes the prompt using a generative AI model (e.g., an AI engine with advanced natural language processing technology). Through this analysis, it recognizes specific needs from the user's request and retrieves product information corresponding to these needs from an information collection (e.g., a database). Based on the retrieved product information, the server generates a list of products to suggest.

[0277] The product list is sent to the terminal, which visually displays this information on the user's screen. The user can review the presented product list and select the necessary items. Furthermore, the user can add or adjust items in the product list through the human-machine interface.

[0278] The selected product information is sent from the terminal to the server, which then processes the order based on that information. Once the final order is confirmed, the server notifies the terminal of the order result and estimated delivery date for the user to confirm. In this way, users can enjoy stress-free online shopping with minimal effort.

[0279] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0280] Step 1:

[0281] The user launches the application on a device such as a smartphone or PC and inputs a request in natural language via voice or text. For example, they might input something like, "I want to make hot pot on Saturday night." Because the input request is difficult to parse directly, the device converts the request into a prompt format. It generates a prompt message in the format of "User request: I want to make hot pot on Saturday night. Please suggest related products," and sends it to the server.

[0282] Step 2:

[0283] The server analyzes the received prompt text using a generative AI model. Specifically, it interprets the user's request with natural language processing technology and extracts specific needs such as "ingredients for cooking in a pot". As a result of this analysis process, relevant search keywords are generated. Based on these needs, the server sends a query to an information aggregate (e.g., a database) to obtain relevant product information.

[0284] Step 3:

[0285] Based on the acquired product information, the server creates a list of products to propose to the user. Specifically, it organizes the detailed information of the products corresponding to the extracted needs (e.g., name, price, review, inventory status) and summarizes it in a list format. This list information is formatted into a data form by the server and sent to the terminal.

[0286] Step 4:

[0287] The terminal visually displays the list of products received from the server on the user's screen. Here, detailed information including images, prices, evaluations, etc. is presented together so that the user can easily select a product. The user can scroll through the displayed list of products and select the necessary products.

[0288] Step 5:

[0289] When the user selects a product from the list of products, the terminal collates the selected product information and returns it to the server. The input here is the specific product information selected by the user, and the output is in the form of a request for the order content to the server.

[0290] Step 6:

[0291] The server performs order processing based on the received data of the selected product. Order processing includes data operations such as inventory confirmation, initiation of payment procedures, setting of delivery schedules, etc. The server cooperates with the seller's system to finalize the order. The finalized order information is sent to the terminal for notification to the user.

[0292] Step 7:

[0293] The terminal notifies the user of order confirmation information from the server. Specifically, it displays an order completion message on the screen, along with the estimated delivery date and payment completion notification. This allows the user to confirm that their order has been successfully completed.

[0294] (Application Example 1)

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

[0296] Online shopping can be complex and difficult to use for the elderly and users unfamiliar with technology. Furthermore, challenges exist in operating the terminal and effectively communicating requests. Therefore, there is a need for a system that supports easy and intuitive product searching and purchase.

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

[0298] In this invention, the server includes means for receiving requests in natural language from users and analyzing the relevant information; means for obtaining relevant product information from a database based on the analyzed requests and generating a list of suggested products; means for forming and displaying a dialogue to propose to the user based on the generated product list; means for recognizing speech and converting the user's requests into text data; means for outputting the generated product suggestions as speech using speech synthesis technology; and means for notifying the user of the results of the order processing. This makes it possible for users to easily search for, select, and complete orders for products through voice.

[0299] "Natural language" refers to the language that humans use on a daily basis, and is a form of communication through words and sentences.

[0300] "Analysis" is the process of unpacking and understanding the meaning by analyzing the given information.

[0301] "Product information" refers to detailed data about a product, including its name, price, description, inventory status, etc.

[0302] "Database" is a system for organizing and storing a large amount of data, enabling efficient search and retrieval.

[0303] "Product list" is a list that gathers information about products that can be proposed or selected.

[0304] "Speech recognition" is a technology that converts human speech into digital signals and analyzes them.

[0305] "Text data" is a form of information represented as a string of characters.

[0306] "Speech synthesis technology" is a technology that generates sounds similar to human speech based on text data.

[0307] "Order processing" is a series of operations to complete the purchase procedure after product selection.

[0308] To implement this invention, the user first launches an application on a terminal such as a smartphone or a personal computer. The terminal uses speech recognition software to convert the user's speech into text data. For example, the terminal recognizes a request such as "I want to make a hot pot on Saturday night" and sends the information to the server.

[0309] The server utilizes advanced natural language processing technology to analyze the user's request. Based on the analysis, the server retrieves relevant product information from the database and creates a product list. At this time, a generative AI model is used to select products for proposal and highly relevant products.

[0310] The terminal uses speech synthesis technology to communicate product suggestions to the user via voice, based on a product list received from the server. The user selects products via voice or screen, and the selected product information is sent to the server for order processing.

[0311] The server notifies the terminal of the order processing result, informing the user of the estimated delivery date and order details via voice or text. This allows users to complete online shopping easily and efficiently through an intuitive voice interface.

[0312] For example, if a user says, "I want to have hot pot with my family this weekend," the system will automatically suggest the necessary ingredients and recommended meal sets via voice.

[0313] An example of a prompt for a generative AI model is: "Based on the user's natural language request, retrieve relevant products from the server and provide product information via voice."

[0314] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0315] Step 1:

[0316] The user launches the application on the device and inputs a request by voice. The device uses speech recognition software to convert the input voice into text data. Here, the input is the user's voice, and the output is text data. The voice data is captured in digital format, analyzed using an acoustic model, and converted into a corresponding string.

[0317] Step 2:

[0318] The terminal sends the generated text data to the server. The server uses a natural language processing engine to analyze this text data. The input for analysis is text data, and the output is the analyzed request content. The server performs grammatical and semantic analysis to identify what the user is requesting.

[0319] Step 3:

[0320] Based on the analysis results, the server retrieves relevant product information from the database. The input at this stage is the analyzed request, and the output is product information. The server uses a generative AI model to select the product best suited to the user's request and builds a product list.

[0321] Step 4:

[0322] The server sends a product list to the terminal. The terminal uses speech synthesis technology to present the contents of this product list to the user in audio. The input here is the product list, and the output is audio information. The speech synthesis engine generates natural-sounding speech from the text and delivers it to the user.

[0323] Step 5:

[0324] The user selects products based on information presented via voice or on-screen. The terminal receives the user's selection and sends it to the server. The input is the user's product selection, and the output is data of the selected products sent to the server. The user's selection is captured and digitized through the interface.

[0325] Step 6:

[0326] The server processes orders based on the received selection data. The input is the selected product data, and the output is order confirmation information. The server works in conjunction with the sales system to check inventory and complete payment procedures, thus finalizing the order.

[0327] Step 7:

[0328] The server notifies the terminal of order confirmation information, and the terminal conveys this information to the user via voice or text. The input here is order confirmation information, and the output is the notification content. The terminal uses a speech synthesis or text message generation engine to provide the information to the user.

[0329] 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.

[0330] This invention provides a system that allows users to input both natural language and emotions during online shopping, analyzes that information, and makes product recommendations optimized for the user. In particular, by combining it with an emotion engine, the system aims to understand the user's emotional state and propose appropriate products and services.

[0331] First, the user launches the application from a device such as a smartphone or tablet. Using voice input or text input, the user enters their request for the desired product or service. At this time, the application also has a function that uses an emotion sensor to detect the user's emotions from the tone of their voice and context.

[0332] The terminal sends the input natural language and emotion data to the server. The server uses a natural language processing engine to analyze the user's request and an emotion engine to identify the user's emotional state. Based on these results, highly relevant product information is retrieved from the database.

[0333] For example, if a user enters "I'm tired from work lately, so I want some relaxing tea," the server doesn't simply use "tea" as a search key. Instead, it adds keywords related to emotions such as "tired" and "relaxed" to the analysis results and selects an effective relaxing tea.

[0334] The selected products are listed according to the user's emotional state and prioritized. This list is displayed on the device, making it easier for the user to select products. After the user selects products, the selected information is sent back to the server, the order process is initiated, and a confirmation notification is sent to the device.

[0335] In this way, the present invention takes into account the user's emotions and provides a more personalized shopping experience. By employing an emotion engine, it becomes possible to suggest the most appropriate products according to the user's mental state. This makes online shopping more comfortable and effective.

[0336] The following describes the processing flow.

[0337] Step 1:

[0338] The user launches a shopping application on their device and enters their request via voice or text. For example, they might type, "I'm stressed, I want to relax." The device then retrieves the input via voice recognition or as text.

[0339] Step 2:

[0340] When the terminal sends input natural language data and voice data to the server, it also includes data that infers the user's emotional state from their voice tone and speaking speed.

[0341] Step 3:

[0342] The server analyzes the received data. The natural language processing engine analyzes the requests and extracts emotions and needs such as "stress" and "relaxation." The emotion engine determines specific emotional states from the audio data and adds supplementary information to the analysis results.

[0343] Step 4:

[0344] The server combines the results of natural language processing and sentiment analysis to retrieve the most suitable product information from the database. For example, it selects products such as "relaxing tea" or "aromatherapy goods."

[0345] Step 5:

[0346] Based on the acquired product information, the server constructs a product list with priority levels according to the user's emotional state and sends it back to the terminal.

[0347] Step 6:

[0348] The terminal displays a list of products sent from the server in its user interface. The user scrolls through the list and selects products of interest.

[0349] Step 7:

[0350] The user's selection is sent from the terminal to the server, which processes the order and confirms it in coordination with the seller.

[0351] Step 8:

[0352] The server confirms that the order has been successfully completed and sends a notification to the terminal. The user then checks the screen for information such as order completion and delivery schedule.

[0353] (Example 2)

[0354] 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".

[0355] Traditional online shopping systems typically suggest products without considering the user's emotions, and product selection was not optimized for the user's psychological state. This resulted in the challenge of finding products that matched the user's needs and emotional state at the time they wanted them.

[0356] 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.

[0357] In this invention, the server includes means for receiving and analyzing requests and emotional states from the user in natural language; means for obtaining relevant product information from information sources based on the analyzed requests and emotional states and generating a product list optimized for the user; and means for forming and displaying a dialogue that reflects the user's emotions based on the generated product list. This makes it possible to take the user's emotions into consideration and provide more personalized product suggestions.

[0358] A "user" refers to an individual who uses the system and is responsible for inputting requests and emotional states into the system using natural language.

[0359] "Natural language" refers to the linguistic forms that people use on a daily basis, and is the form in which a system expresses a request that it analyzes.

[0360] "Emotional state" refers to the user's psychological and physiological reactions and circumstances, and is a factor that the system considers during analysis.

[0361] "Analysis" refers to the process of understanding the requests and emotional states obtained from users and interpreting their meaning and intent.

[0362] "Information source" refers to a collection of databases or external data that a system accesses to obtain relevant product information.

[0363] An "optimized product list" refers to a group of products selected based on analysis results to best match the user's emotional state and needs.

[0364] "Dialogue" refers to the interaction between a system and its user, specifically the conversational format used to present information.

[0365] An "order processing device" refers to a hardware or software system that executes the purchase process based on the product information selected by the user.

[0366] This invention is a system that enables users to conduct online shopping using natural language and emotions. This allows for product recommendations optimized for each individual user.

[0367] Users launch a dedicated application from a device such as a smartphone or tablet. Here, they can input their desired products and services in natural language using voice or text input. An emotion sensor is incorporated to analyze the user's voice tone and context, detecting their emotional state and supplementing the information accordingly.

[0368] The terminal sends the input natural language request and detected emotion data to the server. The server analyzes the user's request using a natural language processing engine (e.g., general-purpose natural language processing software). It also uses an emotion engine (e.g., emotion analysis software) to identify the user's emotional state. Based on these analysis results, highly relevant product information is retrieved from the database. This allows the selection of the product best suited to the user's environment and psychological state.

[0369] For example, if a user enters "I'm tired from work lately, so I want some relaxing tea," the server will not just search for "tea," but will also consider keywords related to emotions such as "tired" and "relaxing," and suggest herbal teas that have a high relaxing effect.

[0370] Examples of specific prompts include, "What tea would you recommend for a 30-year-old woman who wants to improve her fatigue?" and "Please suggest products that enhance relaxation."

[0371] Thus, the present invention realizes personalized product recommendations based on each user's emotions, thereby improving the online shopping experience.

[0372] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0373] Step 1:

[0374] The user launches an application on their smartphone or tablet. Here, they input their request for a desired product or service using natural language via voice or text input. During input, an emotion sensor acquires emotional data from the user's voice tone and context. With both the natural language request and emotional data obtained, the device prepares to send the data to the server.

[0375] Step 2:

[0376] The terminal sends natural language request data from the user and acquired sentiment data to the server. The transmitted data is converted into a format such as XML or JSON and securely sent to the server using a communication protocol. The input in this process is the user's request and sentiment data, and the output is the transmission of data to the server.

[0377] Step 3:

[0378] The server analyzes the received data. It uses a natural language processing engine to analyze the request, splitting the text and extracting important keywords and phrases. Simultaneously, it uses an emotion engine to analyze the user's emotional state. As a result of the analysis, data representing the user's intentions and emotional state is output.

[0379] Step 4:

[0380] The server retrieves relevant product information from the database based on the analysis results. Here, the product information is filtered based on the obtained keywords and sentiment information to generate a list of products best suited to the user. The input in this step is the analyzed data, and the output is a product list.

[0381] Step 5:

[0382] The selected product list is sent to the terminal and displayed to the user. The user reviews the displayed list and selects the desired products. Here, the product list is input, and the user's selections are output.

[0383] Step 6:

[0384] When a user selects an item, that information is resent from the terminal to the server, and the order process begins. The server verifies the order and processes it through the payment system. Once processing is complete, an order confirmation notification is sent to the terminal. In this step, the input is the user's selected item information, and the output is the order confirmation notification.

[0385] (Application Example 2)

[0386] 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."

[0387] Traditional online shopping systems failed to consider the user's emotional state when suggesting products, simply displaying items based on search keywords. As a result, it was difficult for users to find products or services that suited their current emotional state. Consequently, users wasted time and struggled to find the products they truly wanted. Solving these problems is crucial.

[0388] 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.

[0389] In this invention, the server includes means for receiving natural language and emotional input from the user and analyzing the relevant information and emotional data; means for retrieving relevant product information from a database based on the analyzed request and emotional data and generating a product list corresponding to the emotional state; and means for forming and displaying a dialogue to propose to the user based on the generated product list corresponding to the emotional state. This enables personalized product suggestions that correspond to the user's mental state.

[0390] "Natural language" refers to the language that humans use on a daily basis, and it is the language that computer systems analyze in order to understand user input.

[0391] "Emotional input" refers to information that indicates the user's emotional state, and is derived from voice tone and text context.

[0392] "Emotional data" refers to data about the emotional state of users, which the system uses to optimize product recommendations.

[0393] "Product information" refers to detailed information about individual products stored in the database, which the system references when generating a list of suggestions.

[0394] A "product list" is a list containing multiple products suggested to the user, generated based on analyzed request and sentiment data.

[0395] "Dialogue" refers to the exchange of information between the system and the user, and is displayed to facilitate the user's choices.

[0396] "Speech recognition" is a technology that converts voice input into text data, and is a pre-processing step that allows a system to treat it as text information.

[0397] "Emotion recognition means" refers to methods for detecting emotional states from a user's voice or text and using that information to provide appropriate product recommendations.

[0398] An "interface" refers to the technical means that serves as a window for users to interact with the system, and provides functions that allow for easy adjustment and addition of product lists.

[0399] To realize this invention, a user-owned terminal, a server, and a network connecting them are utilized. The terminal could be a smartphone or tablet. First, the user launches a dedicated application on the terminal and inputs a request via voice or text. This input includes emotions as well as normal natural language. A terminal equipped with a voice recognition system converts this voice element into text data.

[0400] The server analyzes the received natural language and sentiment data using a natural language processing engine and sentiment recognition tools. For natural language processing, technologies such as Google Natural Language API and IBM Watson can be used. For sentiment recognition, tools like IBM Watson Tone Analyzer, which identifies emotional states from speech tone and context, are employed. During this analysis process, keywords based on the user's request and current emotional state are extracted.

[0401] Next, the server retrieves product information from the database that is suitable for the user's emotional state. For example, if a user enters "I'm tired today, so I want to listen to some relaxing music," the server analyzes keywords such as "tired" and "relaxing" and generates a list of music products with relaxing effects. As a result, the user can more easily find music that is soothing. The generated product list is sent to the terminal with priority and displayed to the user.

[0402] Once a user selects an item, the selected information is sent back to the server to proceed with the order process. The result of the order processing is promptly notified to the user. This allows users to enjoy an emotionally responsive product purchase experience through a smooth and intuitive UI.

[0403] As a concrete example, consider a scenario where a user enters the request, "I'm feeling down today, so I'd like to find a book that will cheer me up." In this case, the server recognizes emotional keywords such as "down" and "cheer up" and suggests self-help books or books with uplifting stories related to those emotions. An example of a prompt message used to facilitate this process would be, "Collect emotional keywords from the user's input and generate a suggestion list of appropriate product information."

[0404] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0405] Step 1:

[0406] The user launches the application on their device and provides natural language and emotional input via voice or text. The input information is converted from speech to text data by a speech recognition system. This process ensures that the user's requests are presented as written information.

[0407] Step 2:

[0408] The device sends the converted text data to the server. The server passes the received data to a natural language processing engine, which extracts important keywords and intent from the text. Furthermore, it uses an emotion recognition engine to analyze emotional data from tone of voice and text context. This process identifies the user's request and emotional state.

[0409] Step 3:

[0410] The server retrieves relevant product information from the database based on the analysis results. Here, it searches for the most relevant products based on the extracted keywords and sentiment data, and generates a product list. Each item in the generated product list is assigned a priority corresponding to the sentiment state.

[0411] Step 4:

[0412] The server sends the generated list of emotion-responsive products to the terminal. The terminal displays the list on the user interface so that the user can easily compare and consider the products. During this display process, products are displayed based on priority, allowing the user to quickly make the best choice.

[0413] Step 5:

[0414] When a user selects their desired products on their device, the selection information is sent back to the server. The server confirms the order using its order processing system and processes the results. Order confirmation and status are promptly notified to the user's device, and the purchase process is completed.

[0415] Generative AI models:

[0416] To assist with specific parts of this process, a generative AI model is applied to perform natural language generation and sentiment analysis based on prompt text with high accuracy. A possible prompt might be, "Collect sentiment-related keywords from the user's input and generate a suggested list of appropriate product information."

[0417] 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.

[0418] 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.

[0419] 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.

[0420] [Third Embodiment]

[0421] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0422] 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.

[0423] 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).

[0424] 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.

[0425] 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.

[0426] 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).

[0427] 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.

[0428] 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.

[0429] 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.

[0430] 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.

[0431] 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.

[0432] 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".

[0433] This invention is an online shopping support system that suggests and places orders for products based on requests entered by the user in natural language. This system is designed to be easy and intuitive to use, especially for the elderly and those unfamiliar with online shopping.

[0434] The user launches the system's application using a device such as a smartphone or computer. The application accepts user requests in the form of voice input or text input. For example, if the user says, "I want to make hot pot on Saturday night," the device sends that request to the server.

[0435] The server uses advanced natural language processing technology to analyze the user's request. Through this analysis, the server recognizes the need for "ingredients for a hot pot dish" and retrieves the necessary product information from the database. Based on the retrieved information, the server creates a list of products to suggest to the user.

[0436] The terminal receives the suggested product list and displays it on the user's screen. The user can review the list and select products as needed, or search for and add new products.

[0437] After the user selects products, the terminal sends the selected product data back to the server, which then compiles the final order information. Based on the received information, the server processes the order and confirms the user's order in conjunction with the seller's system.

[0438] Once an order is confirmed, the server notifies the terminal of this information, informing the user that the order is complete and providing the estimated delivery date. In this way, the system of the present invention provides an environment in which users can easily shop online through a natural language-based interactive interface.

[0439] This invention places particular emphasis on supporting elderly people who are unfamiliar with technology, and is designed to complete orders in as few steps as possible to reduce the complexity of operation. This allows users to enjoy online shopping with peace of mind.

[0440] The following describes the processing flow.

[0441] Step 1:

[0442] The user launches the application on their device and enters their request via voice or text input. For example, if the user enters "I want to make hot pot on Saturday night," the device converts that request into text data.

[0443] Step 2:

[0444] The terminal sends the converted text data to the server. Based on the received data, the server uses a natural language processing engine to analyze the request and understand the user's intent.

[0445] Step 3:

[0446] Based on the analysis results, the server retrieves product information from the database that matches the user's request. It identifies the ingredients and related products needed for hot pot dishes and generates a list.

[0447] Step 4:

[0448] The server sends the generated product list back to the terminal. The terminal displays the list on the user's screen and prompts them to select a product. The list includes detailed information such as product name, price, and descriptive image.

[0449] Step 5:

[0450] The user views a list on their device and selects the items they wish to purchase. They can also search for and add additional items to the list as needed.

[0451] Step 6:

[0452] The terminal sends information about the selected products to the server, and the server compiles the final order information based on the user's decision.

[0453] Step 7:

[0454] The server processes the order and confirms it officially in conjunction with the seller's system. It then sends a notification to the device indicating that processing is complete and including the estimated delivery date or shipping information.

[0455] Step 8:

[0456] The user checks the notification on their device and learns that the order has been successfully completed. This marks the end of the ordering process, and the user completes their online shopping.

[0457] (Example 1)

[0458] 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."

[0459] In online shopping, there is a challenge in that it is difficult for elderly people and tech-savvy users to intuitively search for and purchase products. Traditional systems require complex operations, and the process from selecting a product to completing an order is not easy to understand.

[0460] 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.

[0461] In this invention, the server includes means for receiving requests from users in natural language, converting the requests into prompt format and analyzing them, means for obtaining relevant product information from an information set based on the analyzed requests and generating a list of suggested products, and means for transmitting the generated product list to an information processing device and presenting it visually to the user. This makes it possible for users to intuitively find products based on their requests and easily complete the purchase procedure.

[0462] "User" refers to a person who uses the system to search for and purchase products.

[0463] "Natural language" refers to the forms of language that people use on a daily basis, and includes requests made in either spoken or written form.

[0464] A "request" refers to the content of a user's wishes or commands to the system.

[0465] "Prompt format" refers to a document format that has been modified to make natural language requests easier to parse.

[0466] "Means of analysis" refers to methods that use generative AI models to understand user requests and translate those requests into specific needs.

[0467] "Product information" refers to detailed data about the products offered, including price, specifications, and stock information.

[0468] An "information collection" refers to a centralized storage location for product information accessed by a server.

[0469] A "product list" refers to a list of related products selected for presentation to users.

[0470] "Information processing device" refers to a terminal held by a user, specifically a device that has the function of displaying a product list.

[0471] "Human-machine interface" refers to the user interface used to adjust or add products to a product list.

[0472] This invention is a system designed to make online shopping easier, especially for elderly and technologically unfamiliar users. The system assists users in efficiently searching for and ordering products using natural language. The entire process is primarily handled through the cooperation of a server and a terminal.

[0473] Users launch system applications using devices such as smartphones and personal computers. These devices accept natural language requests from users via voice or text input. For example, if a user inputs "I want to make hot pot on Saturday night," the device converts this request into a prompt format. An example of such a prompt might be: "User request: I want to make hot pot on Saturday night. Please suggest related products."

[0474] The server analyzes the prompt using a generative AI model (e.g., an AI engine with advanced natural language processing technology). Through this analysis, it recognizes specific needs from the user's request and retrieves product information corresponding to these needs from an information collection (e.g., a database). Based on the retrieved product information, the server generates a list of products to suggest.

[0475] The product list is sent to the terminal, which visually displays this information on the user's screen. The user can review the presented product list and select the necessary items. Furthermore, the user can add or adjust items in the product list through the human-machine interface.

[0476] The selected product information is sent from the terminal to the server, which then processes the order based on that information. Once the final order is confirmed, the server notifies the terminal of the order result and estimated delivery date for the user to confirm. In this way, users can enjoy stress-free online shopping with minimal effort.

[0477] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0478] Step 1:

[0479] The user launches the application on a device such as a smartphone or PC and inputs a request in natural language via voice or text. For example, they might input something like, "I want to make hot pot on Saturday night." Because the input request is difficult to parse directly, the device converts the request into a prompt format. It generates a prompt message in the format of "User request: I want to make hot pot on Saturday night. Please suggest related products," and sends it to the server.

[0480] Step 2:

[0481] The server analyzes the received prompt message using a generation AI model. Specifically, it interprets the user's request using natural language processing technology and extracts specific needs, such as "ingredients for making hot pot." As a result of this analysis, relevant search keywords are generated. Based on these needs, the server sends queries to an information collection (e.g., a database) to retrieve relevant product information.

[0482] Step 3:

[0483] The server creates a list of products to suggest to the user based on the acquired product information. Specifically, it organizes detailed information about products that meet the extracted needs (e.g., name, price, reviews, availability) and compiles it into a list format. This list information is then formatted into a data format by the server and sent to the terminal.

[0484] Step 4:

[0485] The terminal visually displays a list of products received from the server on the user's screen. Detailed information, including images, prices, and ratings, is also presented to facilitate product selection. The user can scroll through the displayed product list and select the desired item.

[0486] Step 5:

[0487] When a user selects a product from the product list, the terminal compiles the selected product information and sends it back to the server. The input here is the specific product information selected by the user, and the output is a request for the order details sent to the server.

[0488] Step 6:

[0489] The server processes the order based on the data of the selected items received. Order processing includes data calculations such as inventory checks, initiating payment procedures, and setting delivery schedules. The server then collaborates with the seller's system to finalize the order. The confirmed order information is sent to the user's terminal for notification.

[0490] Step 7:

[0491] The terminal notifies the user of order confirmation information from the server. Specifically, it displays an order completion message on the screen, along with the estimated delivery date and payment completion notification. This allows the user to confirm that their order has been successfully completed.

[0492] (Application Example 1)

[0493] 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."

[0494] Online shopping can be complex and difficult to use for the elderly and users unfamiliar with technology. Furthermore, challenges exist in operating the terminal and effectively communicating requests. Therefore, there is a need for a system that supports easy and intuitive product searching and purchase.

[0495] 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.

[0496] In this invention, the server includes means for receiving requests in natural language from users and analyzing the relevant information; means for obtaining relevant product information from a database based on the analyzed requests and generating a list of suggested products; means for forming and displaying a dialogue to propose to the user based on the generated product list; means for recognizing speech and converting the user's requests into text data; means for outputting the generated product suggestions as speech using speech synthesis technology; and means for notifying the user of the results of the order processing. This makes it possible for users to easily search for, select, and complete orders for products through voice.

[0497] "Natural language" refers to the language that humans use on a daily basis, and is a form of communication through words and sentences.

[0498] "Analysis" is the process of taking given information and unraveling it in order to understand its meaning.

[0499] "Product information" refers to detailed data about a product, including its name, price, description, and availability.

[0500] A "database" is a system that organizes and stores large amounts of data, enabling efficient searching and retrieval.

[0501] A "product list" is a compilation of information about suggested or selectable products.

[0502] "Speech recognition" is a technology that converts human speech into digital signals and then analyzes them.

[0503] "Text data" refers to a form of information that is represented as a string of characters.

[0504] "Speech synthesis technology" is a technology that generates sounds that resemble human speech based on text data.

[0505] "Order processing" refers to a series of operations that complete the purchase process after selecting products.

[0506] To implement this invention, the user first launches an application on a device such as a smartphone or personal computer. The device uses speech recognition software to convert the user's voice into text data. For example, the device recognizes a request such as "I want to make hot pot on Saturday night" and sends that information to the server.

[0507] The server utilizes advanced natural language processing technology to analyze user requests. Based on the analysis, the server retrieves relevant product information from the database and creates a product list. During this process, a generative AI model is used to suggest products and select highly relevant items.

[0508] The terminal uses speech synthesis technology to communicate product suggestions to the user via voice, based on a product list received from the server. The user selects products via voice or screen, and the selected product information is sent to the server for order processing.

[0509] The server notifies the terminal of the order processing result, informing the user of the estimated delivery date and order details via voice or text. This allows users to complete online shopping easily and efficiently through an intuitive voice interface.

[0510] For example, if a user says, "I want to have hot pot with my family this weekend," the system will automatically suggest the necessary ingredients and recommended meal sets via voice.

[0511] An example of a prompt for a generative AI model is: "Based on the user's natural language request, retrieve relevant products from the server and provide product information via voice."

[0512] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0513] Step 1:

[0514] The user launches the application on the device and inputs a request by voice. The device uses speech recognition software to convert the input voice into text data. Here, the input is the user's voice, and the output is text data. The voice data is captured in digital format, analyzed using an acoustic model, and converted into a corresponding string.

[0515] Step 2:

[0516] The terminal sends the generated text data to the server. The server uses a natural language processing engine to analyze this text data. The input for analysis is text data, and the output is the analyzed request content. The server performs grammatical and semantic analysis to identify what the user is requesting.

[0517] Step 3:

[0518] Based on the analysis results, the server retrieves relevant product information from the database. The input at this stage is the analyzed request, and the output is product information. The server uses a generative AI model to select the product best suited to the user's request and builds a product list.

[0519] Step 4:

[0520] The server sends a product list to the terminal. The terminal uses speech synthesis technology to present the contents of this product list to the user in audio. The input here is the product list, and the output is audio information. The speech synthesis engine generates natural-sounding speech from the text and delivers it to the user.

[0521] Step 5:

[0522] The user selects products based on information presented via voice or on-screen. The terminal receives the user's selection and sends it to the server. The input is the user's product selection, and the output is data of the selected products sent to the server. The user's selection is captured and digitized through the interface.

[0523] Step 6:

[0524] The server processes orders based on the received selection data. The input is the selected product data, and the output is order confirmation information. The server works in conjunction with the sales system to check inventory and complete payment procedures, thus finalizing the order.

[0525] Step 7:

[0526] The server notifies the terminal of order confirmation information, and the terminal conveys this information to the user via voice or text. The input here is order confirmation information, and the output is the notification content. The terminal uses a speech synthesis or text message generation engine to provide the information to the user.

[0527] 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.

[0528] This invention provides a system that allows users to input both natural language and emotions during online shopping, analyzes that information, and makes product recommendations optimized for the user. In particular, by combining it with an emotion engine, the system aims to understand the user's emotional state and propose appropriate products and services.

[0529] First, the user launches the application from a device such as a smartphone or tablet. Using voice input or text input, the user enters their request for the desired product or service. At this time, the application also has a function that uses an emotion sensor to detect the user's emotions from the tone of their voice and context.

[0530] The terminal sends the input natural language and emotion data to the server. The server uses a natural language processing engine to analyze the user's request and an emotion engine to identify the user's emotional state. Based on these results, highly relevant product information is retrieved from the database.

[0531] For example, if a user enters "I'm tired from work lately, so I want some relaxing tea," the server doesn't simply use "tea" as a search key. Instead, it adds keywords related to emotions such as "tired" and "relaxed" to the analysis results and selects an effective relaxing tea.

[0532] The selected products are listed according to the user's emotional state and prioritized. This list is displayed on the device, making it easier for the user to select products. After the user selects products, the selected information is sent back to the server, the order process is initiated, and a confirmation notification is sent to the device.

[0533] In this way, the present invention takes into account the user's emotions and provides a more personalized shopping experience. By employing an emotion engine, it becomes possible to suggest the most appropriate products according to the user's mental state. This makes online shopping more comfortable and effective.

[0534] The following describes the processing flow.

[0535] Step 1:

[0536] The user launches a shopping application on their device and enters their request via voice or text. For example, they might type, "I'm stressed, I want to relax." The device then retrieves the input via voice recognition or as text.

[0537] Step 2:

[0538] When the terminal sends input natural language data and voice data to the server, it also includes data that infers the user's emotional state from their voice tone and speaking speed.

[0539] Step 3:

[0540] The server analyzes the received data. The natural language processing engine analyzes the requests and extracts emotions and needs such as "stress" and "relaxation." The emotion engine determines specific emotional states from the audio data and adds supplementary information to the analysis results.

[0541] Step 4:

[0542] The server combines the results of natural language processing and sentiment analysis to retrieve the most suitable product information from the database. For example, it selects products such as "relaxing tea" or "aromatherapy goods."

[0543] Step 5:

[0544] Based on the acquired product information, the server constructs a product list with priority levels according to the user's emotional state and sends it back to the terminal.

[0545] Step 6:

[0546] The terminal displays a list of products sent from the server in its user interface. The user scrolls through the list and selects products of interest.

[0547] Step 7:

[0548] The user's selection is sent from the terminal to the server, which processes the order and confirms it in coordination with the seller.

[0549] Step 8:

[0550] The server confirms that the order has been successfully completed and sends a notification to the terminal. The user then checks the screen for information such as order completion and delivery schedule.

[0551] (Example 2)

[0552] 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."

[0553] Traditional online shopping systems typically suggest products without considering the user's emotions, and product selection was not optimized for the user's psychological state. This resulted in the challenge of finding products that matched the user's needs and emotional state at the time they wanted them.

[0554] 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.

[0555] In this invention, the server includes means for receiving and analyzing requests and emotional states from the user in natural language; means for obtaining relevant product information from information sources based on the analyzed requests and emotional states and generating a product list optimized for the user; and means for forming and displaying a dialogue that reflects the user's emotions based on the generated product list. This makes it possible to take the user's emotions into consideration and provide more personalized product suggestions.

[0556] A "user" refers to an individual who uses the system and is responsible for inputting requests and emotional states into the system using natural language.

[0557] "Natural language" refers to the linguistic forms that people use on a daily basis, and is the form in which a system expresses a request that it analyzes.

[0558] "Emotional state" refers to the user's psychological and physiological reactions and circumstances, and is a factor that the system considers during analysis.

[0559] "Analysis" refers to the process of understanding the requests and emotional states obtained from users and interpreting their meaning and intent.

[0560] "Information source" refers to a collection of databases or external data that a system accesses to obtain relevant product information.

[0561] An "optimized product list" refers to a group of products selected based on analysis results to best match the user's emotional state and needs.

[0562] "Dialogue" refers to the interaction between a system and its user, specifically the conversational format used to present information.

[0563] An "order processing device" refers to a hardware or software system that executes the purchase process based on the product information selected by the user.

[0564] This invention is a system that enables users to conduct online shopping using natural language and emotions. This allows for product recommendations optimized for each individual user.

[0565] Users launch a dedicated application from a device such as a smartphone or tablet. Here, they can input their desired products and services in natural language using voice or text input. An emotion sensor is incorporated to analyze the user's voice tone and context, detecting their emotional state and supplementing the information accordingly.

[0566] The terminal sends the input natural language request and detected emotion data to the server. The server analyzes the user's request using a natural language processing engine (e.g., general-purpose natural language processing software). It also uses an emotion engine (e.g., emotion analysis software) to identify the user's emotional state. Based on these analysis results, highly relevant product information is retrieved from the database. This allows the selection of the product best suited to the user's environment and psychological state.

[0567] For example, if a user enters "I'm tired from work lately, so I want some relaxing tea," the server will not just search for "tea," but will also consider keywords related to emotions such as "tired" and "relaxing," and suggest herbal teas that have a high relaxing effect.

[0568] Examples of specific prompts include, "What tea would you recommend for a 30-year-old woman who wants to improve her fatigue?" and "Please suggest products that enhance relaxation."

[0569] Thus, the present invention realizes personalized product recommendations based on each user's emotions, thereby improving the online shopping experience.

[0570] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0571] Step 1:

[0572] The user launches an application on their smartphone or tablet. Here, they input their request for a desired product or service using natural language via voice or text input. During input, an emotion sensor acquires emotional data from the user's voice tone and context. With both the natural language request and emotional data obtained, the device prepares to send the data to the server.

[0573] Step 2:

[0574] The terminal sends natural language request data from the user and acquired sentiment data to the server. The transmitted data is converted into a format such as XML or JSON and securely sent to the server using a communication protocol. The input in this process is the user's request and sentiment data, and the output is the transmission of data to the server.

[0575] Step 3:

[0576] The server analyzes the received data. It uses a natural language processing engine to analyze the request, splitting the text and extracting important keywords and phrases. Simultaneously, it uses an emotion engine to analyze the user's emotional state. As a result of the analysis, data representing the user's intentions and emotional state is output.

[0577] Step 4:

[0578] The server retrieves relevant product information from the database based on the analysis results. Here, the product information is filtered based on the obtained keywords and sentiment information to generate a list of products best suited to the user. The input in this step is the analyzed data, and the output is a product list.

[0579] Step 5:

[0580] The selected product list is sent to the terminal and displayed to the user. The user reviews the displayed list and selects the desired products. Here, the product list is input, and the user's selections are output.

[0581] Step 6:

[0582] When a user selects an item, that information is resent from the terminal to the server, and the order process begins. The server verifies the order and processes it through the payment system. Once processing is complete, an order confirmation notification is sent to the terminal. In this step, the input is the user's selected item information, and the output is the order confirmation notification.

[0583] (Application Example 2)

[0584] 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."

[0585] Traditional online shopping systems failed to consider the user's emotional state when suggesting products, simply displaying items based on search keywords. As a result, it was difficult for users to find products or services that suited their current emotional state. Consequently, users wasted time and struggled to find the products they truly wanted. Solving these problems is crucial.

[0586] 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.

[0587] In this invention, the server includes means for receiving natural language and emotional input from the user and analyzing the relevant information and emotional data; means for retrieving relevant product information from a database based on the analyzed request and emotional data and generating a product list corresponding to the emotional state; and means for forming and displaying a dialogue to propose to the user based on the generated product list corresponding to the emotional state. This enables personalized product suggestions that correspond to the user's mental state.

[0588] "Natural language" refers to the language that humans use on a daily basis, and it is the language that computer systems analyze in order to understand user input.

[0589] "Emotional input" refers to information that indicates the user's emotional state, and is derived from voice tone and text context.

[0590] "Emotional data" refers to data about the emotional state of users, which the system uses to optimize product recommendations.

[0591] "Product information" refers to detailed information about individual products stored in the database, which the system references when generating a list of suggestions.

[0592] A "product list" is a list containing multiple products suggested to the user, generated based on analyzed request and sentiment data.

[0593] "Dialogue" refers to the exchange of information between the system and the user, and is displayed to facilitate the user's choices.

[0594] "Speech recognition" is a technology that converts voice input into text data, and is a pre-processing step that allows a system to treat it as text information.

[0595] "Emotion recognition means" refers to methods for detecting emotional states from a user's voice or text and using that information to provide appropriate product recommendations.

[0596] An "interface" refers to the technical means that serves as a window for users to interact with the system, and provides functions that allow for easy adjustment and addition of product lists.

[0597] To realize this invention, a user-owned terminal, a server, and a network connecting them are utilized. The terminal could be a smartphone or tablet. First, the user launches a dedicated application on the terminal and inputs a request via voice or text. This input includes emotions as well as normal natural language. A terminal equipped with a voice recognition system converts this voice element into text data.

[0598] The server analyzes the received natural language and sentiment data using a natural language processing engine and sentiment recognition tools. For natural language processing, technologies such as Google Natural Language API and IBM Watson can be used. For sentiment recognition, tools like IBM Watson Tone Analyzer, which identifies emotional states from speech tone and context, are employed. During this analysis process, keywords based on the user's request and current emotional state are extracted.

[0599] Next, the server retrieves product information from the database that is suitable for the user's emotional state. For example, if a user enters "I'm tired today, so I want to listen to some relaxing music," the server analyzes keywords such as "tired" and "relaxing" and generates a list of music products with relaxing effects. As a result, the user can more easily find music that is soothing. The generated product list is sent to the terminal with priority and displayed to the user.

[0600] Once a user selects an item, the selected information is sent back to the server to proceed with the order process. The result of the order processing is promptly notified to the user. This allows users to enjoy an emotionally responsive product purchase experience through a smooth and intuitive UI.

[0601] As a concrete example, consider a scenario where a user enters the request, "I'm feeling down today, so I'd like to find a book that will cheer me up." In this case, the server recognizes emotional keywords such as "down" and "cheer up" and suggests self-help books or books with uplifting stories related to those emotions. An example of a prompt message used to facilitate this process would be, "Collect emotional keywords from the user's input and generate a suggestion list of appropriate product information."

[0602] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0603] Step 1:

[0604] The user launches the application on their device and provides natural language and emotional input via voice or text. The input information is converted from speech to text data by a speech recognition system. This process ensures that the user's requests are presented as written information.

[0605] Step 2:

[0606] The device sends the converted text data to the server. The server passes the received data to a natural language processing engine, which extracts important keywords and intent from the text. Furthermore, it uses an emotion recognition engine to analyze emotional data from tone of voice and text context. This process identifies the user's request and emotional state.

[0607] Step 3:

[0608] The server retrieves relevant product information from the database based on the analysis results. Here, it searches for the most relevant products based on the extracted keywords and sentiment data, and generates a product list. Each item in the generated product list is assigned a priority corresponding to the sentiment state.

[0609] Step 4:

[0610] The server sends the generated list of emotion-responsive products to the terminal. The terminal displays the list on the user interface so that the user can easily compare and consider the products. During this display process, products are displayed based on priority, allowing the user to quickly make the best choice.

[0611] Step 5:

[0612] When a user selects their desired products on their device, the selection information is sent back to the server. The server confirms the order using its order processing system and processes the results. Order confirmation and status are promptly notified to the user's device, and the purchase process is completed.

[0613] Generative AI models:

[0614] To assist with specific parts of this process, a generative AI model is applied to perform natural language generation and sentiment analysis based on prompt text with high accuracy. A possible prompt might be, "Collect sentiment-related keywords from the user's input and generate a suggested list of appropriate product information."

[0615] 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.

[0616] 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.

[0617] 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.

[0618] [Fourth Embodiment]

[0619] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0620] 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.

[0621] 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).

[0622] 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.

[0623] 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.

[0624] 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).

[0625] 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.

[0626] 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.

[0627] 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.

[0628] 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.

[0629] 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.

[0630] 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.

[0631] 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".

[0632] This invention is an online shopping support system that suggests and places orders for products based on requests entered by the user in natural language. This system is designed to be easy and intuitive to use, especially for the elderly and those unfamiliar with online shopping.

[0633] The user launches the system's application using a device such as a smartphone or computer. The application accepts user requests in the form of voice input or text input. For example, if the user says, "I want to make hot pot on Saturday night," the device sends that request to the server.

[0634] The server uses advanced natural language processing technology to analyze the user's request. Through this analysis, the server recognizes the need for "ingredients for a hot pot dish" and retrieves the necessary product information from the database. Based on the retrieved information, the server creates a list of products to suggest to the user.

[0635] The terminal receives the suggested product list and displays it on the user's screen. The user can review the list and select products as needed, or search for and add new products.

[0636] After the user selects products, the terminal sends the selected product data back to the server, which then compiles the final order information. Based on the received information, the server processes the order and confirms the user's order in conjunction with the seller's system.

[0637] Once an order is confirmed, the server notifies the terminal of this information, informing the user that the order is complete and providing the estimated delivery date. In this way, the system of the present invention provides an environment in which users can easily shop online through a natural language-based interactive interface.

[0638] This invention places particular emphasis on supporting elderly people who are unfamiliar with technology, and is designed to complete orders in as few steps as possible to reduce the complexity of operation. This allows users to enjoy online shopping with peace of mind.

[0639] The following describes the processing flow.

[0640] Step 1:

[0641] The user launches the application on their device and enters their request via voice or text input. For example, if the user enters "I want to make hot pot on Saturday night," the device converts that request into text data.

[0642] Step 2:

[0643] The terminal sends the converted text data to the server. Based on the received data, the server uses a natural language processing engine to analyze the request and understand the user's intent.

[0644] Step 3:

[0645] Based on the analysis results, the server retrieves product information from the database that matches the user's request. It identifies the ingredients and related products needed for hot pot dishes and generates a list.

[0646] Step 4:

[0647] The server sends the generated product list back to the terminal. The terminal displays the list on the user's screen and prompts them to select a product. The list includes detailed information such as product name, price, and descriptive image.

[0648] Step 5:

[0649] The user views a list on their device and selects the items they wish to purchase. They can also search for and add additional items to the list as needed.

[0650] Step 6:

[0651] The terminal sends information about the selected products to the server, and the server compiles the final order information based on the user's decision.

[0652] Step 7:

[0653] The server processes the order and confirms it officially in conjunction with the seller's system. It then sends a notification to the device indicating that processing is complete and including the estimated delivery date or shipping information.

[0654] Step 8:

[0655] The user checks the notification on their device and learns that the order has been successfully completed. This marks the end of the ordering process, and the user completes their online shopping.

[0656] (Example 1)

[0657] 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".

[0658] In online shopping, there is a challenge in that it is difficult for elderly people and tech-savvy users to intuitively search for and purchase products. Traditional systems require complex operations, and the process from selecting a product to completing an order is not easy to understand.

[0659] 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.

[0660] In this invention, the server includes means for receiving requests from users in natural language, converting the requests into prompt format and analyzing them, means for obtaining relevant product information from an information set based on the analyzed requests and generating a list of suggested products, and means for transmitting the generated product list to an information processing device and presenting it visually to the user. This makes it possible for users to intuitively find products based on their requests and easily complete the purchase procedure.

[0661] "User" refers to a person who uses the system to search for and purchase products.

[0662] "Natural language" refers to the forms of language that people use on a daily basis, and includes requests made in either spoken or written form.

[0663] A "request" refers to the content of a user's wishes or commands to the system.

[0664] "Prompt format" refers to a document format that has been modified to make natural language requests easier to parse.

[0665] "Means of analysis" refers to methods that use generative AI models to understand user requests and translate those requests into specific needs.

[0666] "Product information" refers to detailed data about the products offered, including price, specifications, and stock information.

[0667] An "information collection" refers to a centralized storage location for product information accessed by a server.

[0668] A "product list" refers to a list of related products selected for presentation to users.

[0669] "Information processing device" refers to a terminal held by a user, specifically a device that has the function of displaying a product list.

[0670] "Human-machine interface" refers to the user interface used to adjust or add products to a product list.

[0671] This invention is a system designed to make online shopping easier, especially for elderly and technologically unfamiliar users. The system assists users in efficiently searching for and ordering products using natural language. The entire process is primarily handled through the cooperation of a server and a terminal.

[0672] Users launch system applications using devices such as smartphones and personal computers. These devices accept natural language requests from users via voice or text input. For example, if a user inputs "I want to make hot pot on Saturday night," the device converts this request into a prompt format. An example of such a prompt might be: "User request: I want to make hot pot on Saturday night. Please suggest related products."

[0673] The server analyzes the prompt using a generative AI model (e.g., an AI engine with advanced natural language processing technology). Through this analysis, it recognizes specific needs from the user's request and retrieves product information corresponding to these needs from an information collection (e.g., a database). Based on the retrieved product information, the server generates a list of products to suggest.

[0674] The product list is sent to the terminal, which visually displays this information on the user's screen. The user can review the presented product list and select the necessary items. Furthermore, the user can add or adjust items in the product list through the human-machine interface.

[0675] The selected product information is sent from the terminal to the server, which then processes the order based on that information. Once the final order is confirmed, the server notifies the terminal of the order result and estimated delivery date for the user to confirm. In this way, users can enjoy stress-free online shopping with minimal effort.

[0676] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0677] Step 1:

[0678] The user launches the application on a device such as a smartphone or PC and inputs a request in natural language via voice or text. For example, they might input something like, "I want to make hot pot on Saturday night." Because the input request is difficult to parse directly, the device converts the request into a prompt format. It generates a prompt message in the format of "User request: I want to make hot pot on Saturday night. Please suggest related products," and sends it to the server.

[0679] Step 2:

[0680] The server analyzes the received prompt message using a generation AI model. Specifically, it interprets the user's request using natural language processing technology and extracts specific needs, such as "ingredients for making hot pot." As a result of this analysis, relevant search keywords are generated. Based on these needs, the server sends queries to an information collection (e.g., a database) to retrieve relevant product information.

[0681] Step 3:

[0682] The server creates a list of products to suggest to the user based on the acquired product information. Specifically, it organizes detailed information about products that meet the extracted needs (e.g., name, price, reviews, availability) and compiles it into a list format. This list information is then formatted into a data format by the server and sent to the terminal.

[0683] Step 4:

[0684] The terminal visually displays a list of products received from the server on the user's screen. Detailed information, including images, prices, and ratings, is also presented to facilitate product selection. The user can scroll through the displayed product list and select the desired item.

[0685] Step 5:

[0686] When a user selects a product from the product list, the terminal compiles the selected product information and sends it back to the server. The input here is the specific product information selected by the user, and the output is a request for the order details sent to the server.

[0687] Step 6:

[0688] The server processes the order based on the data of the selected items received. Order processing includes data calculations such as inventory checks, initiating payment procedures, and setting delivery schedules. The server then collaborates with the seller's system to finalize the order. The confirmed order information is sent to the user's terminal for notification.

[0689] Step 7:

[0690] The terminal notifies the user of order confirmation information from the server. Specifically, it displays an order completion message on the screen, along with the estimated delivery date and payment completion notification. This allows the user to confirm that their order has been successfully completed.

[0691] (Application Example 1)

[0692] 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".

[0693] Online shopping can be complex and difficult to use for the elderly and users unfamiliar with technology. Furthermore, challenges exist in operating the terminal and effectively communicating requests. Therefore, there is a need for a system that supports easy and intuitive product searching and purchase.

[0694] 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.

[0695] In this invention, the server includes means for receiving requests in natural language from users and analyzing the relevant information; means for obtaining relevant product information from a database based on the analyzed requests and generating a list of suggested products; means for forming and displaying a dialogue to propose to the user based on the generated product list; means for recognizing speech and converting the user's requests into text data; means for outputting the generated product suggestions as speech using speech synthesis technology; and means for notifying the user of the results of the order processing. This makes it possible for users to easily search for, select, and complete orders for products through voice.

[0696] "Natural language" refers to the language that humans use on a daily basis, and is a form of communication through words and sentences.

[0697] "Analysis" is the process of taking given information and unraveling it in order to understand its meaning.

[0698] "Product information" refers to detailed data about a product, including its name, price, description, and availability.

[0699] A "database" is a system that organizes and stores large amounts of data, enabling efficient searching and retrieval.

[0700] A "product list" is a compilation of information about suggested or selectable products.

[0701] "Speech recognition" is a technology that converts human speech into digital signals and then analyzes them.

[0702] "Text data" refers to a form of information that is represented as a string of characters.

[0703] "Speech synthesis technology" is a technology that generates sounds that resemble human speech based on text data.

[0704] "Order processing" refers to a series of operations that complete the purchase process after selecting products.

[0705] To implement this invention, the user first launches an application on a device such as a smartphone or personal computer. The device uses speech recognition software to convert the user's voice into text data. For example, the device recognizes a request such as "I want to make hot pot on Saturday night" and sends that information to the server.

[0706] The server utilizes advanced natural language processing technology to analyze user requests. Based on the analysis, the server retrieves relevant product information from the database and creates a product list. During this process, a generative AI model is used to suggest products and select highly relevant items.

[0707] The terminal uses speech synthesis technology to communicate product suggestions to the user via voice, based on a product list received from the server. The user selects products via voice or screen, and the selected product information is sent to the server for order processing.

[0708] The server notifies the terminal of the order processing result, informing the user of the estimated delivery date and order details via voice or text. This allows users to complete online shopping easily and efficiently through an intuitive voice interface.

[0709] For example, if a user says, "I want to have hot pot with my family this weekend," the system will automatically suggest the necessary ingredients and recommended meal sets via voice.

[0710] An example of a prompt for a generative AI model is: "Based on the user's natural language request, retrieve relevant products from the server and provide product information via voice."

[0711] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0712] Step 1:

[0713] The user launches the application on the device and inputs a request by voice. The device uses speech recognition software to convert the input voice into text data. Here, the input is the user's voice, and the output is text data. The voice data is captured in digital format, analyzed using an acoustic model, and converted into a corresponding string.

[0714] Step 2:

[0715] The terminal sends the generated text data to the server. The server uses a natural language processing engine to analyze this text data. The input for analysis is text data, and the output is the analyzed request content. The server performs grammatical and semantic analysis to identify what the user is requesting.

[0716] Step 3:

[0717] Based on the analysis results, the server retrieves relevant product information from the database. The input at this stage is the analyzed request, and the output is product information. The server uses a generative AI model to select the product best suited to the user's request and builds a product list.

[0718] Step 4:

[0719] The server sends a product list to the terminal. The terminal uses speech synthesis technology to present the contents of this product list to the user in audio. The input here is the product list, and the output is audio information. The speech synthesis engine generates natural-sounding speech from the text and delivers it to the user.

[0720] Step 5:

[0721] The user selects products based on information presented via voice or on-screen. The terminal receives the user's selection and sends it to the server. The input is the user's product selection, and the output is data of the selected products sent to the server. The user's selection is captured and digitized through the interface.

[0722] Step 6:

[0723] The server processes orders based on the received selection data. The input is the selected product data, and the output is order confirmation information. The server works in conjunction with the sales system to check inventory and complete payment procedures, thus finalizing the order.

[0724] Step 7:

[0725] The server notifies the terminal of order confirmation information, and the terminal conveys this information to the user via voice or text. The input here is order confirmation information, and the output is the notification content. The terminal uses a speech synthesis or text message generation engine to provide the information to the user.

[0726] 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.

[0727] This invention provides a system that allows users to input both natural language and emotions during online shopping, analyzes that information, and makes product recommendations optimized for the user. In particular, by combining it with an emotion engine, the system aims to understand the user's emotional state and propose appropriate products and services.

[0728] First, the user launches the application from a device such as a smartphone or tablet. Using voice input or text input, the user enters their request for the desired product or service. At this time, the application also has a function that uses an emotion sensor to detect the user's emotions from the tone of their voice and context.

[0729] The terminal sends the input natural language and emotion data to the server. The server uses a natural language processing engine to analyze the user's request and an emotion engine to identify the user's emotional state. Based on these results, highly relevant product information is retrieved from the database.

[0730] For example, if a user enters "I'm tired from work lately, so I want some relaxing tea," the server doesn't simply use "tea" as a search key. Instead, it adds keywords related to emotions such as "tired" and "relaxed" to the analysis results and selects an effective relaxing tea.

[0731] The selected products are listed according to the user's emotional state and prioritized. This list is displayed on the device, making it easier for the user to select products. After the user selects products, the selected information is sent back to the server, the order process is initiated, and a confirmation notification is sent to the device.

[0732] In this way, the present invention takes into account the user's emotions and provides a more personalized shopping experience. By employing an emotion engine, it becomes possible to suggest the most appropriate products according to the user's mental state. This makes online shopping more comfortable and effective.

[0733] The following describes the processing flow.

[0734] Step 1:

[0735] The user launches a shopping application on their device and enters their request via voice or text. For example, they might type, "I'm stressed, I want to relax." The device then retrieves the input via voice recognition or as text.

[0736] Step 2:

[0737] When the terminal sends input natural language data and voice data to the server, it also includes data that infers the user's emotional state from their voice tone and speaking speed.

[0738] Step 3:

[0739] The server analyzes the received data. The natural language processing engine analyzes the requests and extracts emotions and needs such as "stress" and "relaxation." The emotion engine determines specific emotional states from the audio data and adds supplementary information to the analysis results.

[0740] Step 4:

[0741] The server combines the results of natural language processing and sentiment analysis to retrieve the most suitable product information from the database. For example, it selects products such as "relaxing tea" or "aromatherapy goods."

[0742] Step 5:

[0743] Based on the acquired product information, the server constructs a product list with priority levels according to the user's emotional state and sends it back to the terminal.

[0744] Step 6:

[0745] The terminal displays a list of products sent from the server in its user interface. The user scrolls through the list and selects products of interest.

[0746] Step 7:

[0747] The user's selection is sent from the terminal to the server, which processes the order and confirms it in coordination with the seller.

[0748] Step 8:

[0749] The server confirms that the order has been successfully completed and sends a notification to the terminal. The user then checks the screen for information such as order completion and delivery schedule.

[0750] (Example 2)

[0751] 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".

[0752] Traditional online shopping systems typically suggest products without considering the user's emotions, and product selection was not optimized for the user's psychological state. This resulted in the challenge of finding products that matched the user's needs and emotional state at the time they wanted them.

[0753] 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.

[0754] In this invention, the server includes means for receiving and analyzing requests and emotional states from the user in natural language; means for obtaining relevant product information from information sources based on the analyzed requests and emotional states and generating a product list optimized for the user; and means for forming and displaying a dialogue that reflects the user's emotions based on the generated product list. This makes it possible to take the user's emotions into consideration and provide more personalized product suggestions.

[0755] A "user" refers to an individual who uses the system and is responsible for inputting requests and emotional states into the system using natural language.

[0756] "Natural language" refers to the linguistic forms that people use on a daily basis, and is the form in which a system expresses a request that it analyzes.

[0757] "Emotional state" refers to the user's psychological and physiological reactions and circumstances, and is a factor that the system considers during analysis.

[0758] "Analysis" refers to the process of understanding the requests and emotional states obtained from users and interpreting their meaning and intent.

[0759] "Information source" refers to a collection of databases or external data that a system accesses to obtain relevant product information.

[0760] An "optimized product list" refers to a group of products selected based on analysis results to best match the user's emotional state and needs.

[0761] "Dialogue" refers to the interaction between a system and its user, specifically the conversational format used to present information.

[0762] An "order processing device" refers to a hardware or software system that executes the purchase process based on the product information selected by the user.

[0763] This invention is a system that enables users to conduct online shopping using natural language and emotions. This allows for product recommendations optimized for each individual user.

[0764] Users launch a dedicated application from a device such as a smartphone or tablet. Here, they can input their desired products and services in natural language using voice or text input. An emotion sensor is incorporated to analyze the user's voice tone and context, detecting their emotional state and supplementing the information accordingly.

[0765] The terminal sends the input natural language request and detected emotion data to the server. The server analyzes the user's request using a natural language processing engine (e.g., general-purpose natural language processing software). It also uses an emotion engine (e.g., emotion analysis software) to identify the user's emotional state. Based on these analysis results, highly relevant product information is retrieved from the database. This allows the selection of the product best suited to the user's environment and psychological state.

[0766] For example, if a user enters "I'm tired from work lately, so I want some relaxing tea," the server will not just search for "tea," but will also consider keywords related to emotions such as "tired" and "relaxing," and suggest herbal teas that have a high relaxing effect.

[0767] Examples of specific prompts include, "What tea would you recommend for a 30-year-old woman who wants to improve her fatigue?" and "Please suggest products that enhance relaxation."

[0768] Thus, the present invention realizes personalized product recommendations based on each user's emotions, thereby improving the online shopping experience.

[0769] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0770] Step 1:

[0771] The user launches an application on their smartphone or tablet. Here, they input their request for a desired product or service using natural language via voice or text input. During input, an emotion sensor acquires emotional data from the user's voice tone and context. With both the natural language request and emotional data obtained, the device prepares to send the data to the server.

[0772] Step 2:

[0773] The terminal sends natural language request data from the user and acquired sentiment data to the server. The transmitted data is converted into a format such as XML or JSON and securely sent to the server using a communication protocol. The input in this process is the user's request and sentiment data, and the output is the transmission of data to the server.

[0774] Step 3:

[0775] The server analyzes the received data. It uses a natural language processing engine to analyze the request, splitting the text and extracting important keywords and phrases. Simultaneously, it uses an emotion engine to analyze the user's emotional state. As a result of the analysis, data representing the user's intentions and emotional state is output.

[0776] Step 4:

[0777] The server retrieves relevant product information from the database based on the analysis results. Here, the product information is filtered based on the obtained keywords and sentiment information to generate a list of products best suited to the user. The input in this step is the analyzed data, and the output is a product list.

[0778] Step 5:

[0779] The selected product list is sent to the terminal and displayed to the user. The user reviews the displayed list and selects the desired products. Here, the product list is input, and the user's selections are output.

[0780] Step 6:

[0781] When a user selects an item, that information is resent from the terminal to the server, and the order process begins. The server verifies the order and processes it through the payment system. Once processing is complete, an order confirmation notification is sent to the terminal. In this step, the input is the user's selected item information, and the output is the order confirmation notification.

[0782] (Application Example 2)

[0783] 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".

[0784] Traditional online shopping systems failed to consider the user's emotional state when suggesting products, simply displaying items based on search keywords. As a result, it was difficult for users to find products or services that suited their current emotional state. Consequently, users wasted time and struggled to find the products they truly wanted. Solving these problems is crucial.

[0785] 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.

[0786] In this invention, the server includes means for receiving natural language and emotional input from the user and analyzing the relevant information and emotional data; means for retrieving relevant product information from a database based on the analyzed request and emotional data and generating a product list corresponding to the emotional state; and means for forming and displaying a dialogue to propose to the user based on the generated product list corresponding to the emotional state. This enables personalized product suggestions that correspond to the user's mental state.

[0787] "Natural language" refers to the language that humans use on a daily basis, and it is the language that computer systems analyze in order to understand user input.

[0788] "Emotional input" refers to information that indicates the user's emotional state, and is derived from voice tone and text context.

[0789] "Emotional data" refers to data about the emotional state of users, which the system uses to optimize product recommendations.

[0790] "Product information" refers to detailed information about individual products stored in the database, which the system references when generating a list of suggestions.

[0791] A "product list" is a list containing multiple products suggested to the user, generated based on analyzed request and sentiment data.

[0792] "Dialogue" refers to the exchange of information between the system and the user, and is displayed to facilitate the user's choices.

[0793] "Speech recognition" is a technology that converts voice input into text data, and is a pre-processing step that allows a system to treat it as text information.

[0794] "Emotion recognition means" refers to methods for detecting emotional states from a user's voice or text and using that information to provide appropriate product recommendations.

[0795] An "interface" refers to the technical means that serves as a window for users to interact with the system, and provides functions that allow for easy adjustment and addition of product lists.

[0796] To realize this invention, a user-owned terminal, a server, and a network connecting them are utilized. The terminal could be a smartphone or tablet. First, the user launches a dedicated application on the terminal and inputs a request via voice or text. This input includes emotions as well as normal natural language. A terminal equipped with a voice recognition system converts this voice element into text data.

[0797] The server analyzes the received natural language and sentiment data using a natural language processing engine and sentiment recognition tools. For natural language processing, technologies such as Google Natural Language API and IBM Watson can be used. For sentiment recognition, tools like IBM Watson Tone Analyzer, which identifies emotional states from speech tone and context, are employed. During this analysis process, keywords based on the user's request and current emotional state are extracted.

[0798] Next, the server retrieves product information from the database that is suitable for the user's emotional state. For example, if a user enters "I'm tired today, so I want to listen to some relaxing music," the server analyzes keywords such as "tired" and "relaxing" and generates a list of music products with relaxing effects. As a result, the user can more easily find music that is soothing. The generated product list is sent to the terminal with priority and displayed to the user.

[0799] Once a user selects an item, the selected information is sent back to the server to proceed with the order process. The result of the order processing is promptly notified to the user. This allows users to enjoy an emotionally responsive product purchase experience through a smooth and intuitive UI.

[0800] As a concrete example, consider a scenario where a user enters the request, "I'm feeling down today, so I'd like to find a book that will cheer me up." In this case, the server recognizes emotional keywords such as "down" and "cheer up" and suggests self-help books or books with uplifting stories related to those emotions. An example of a prompt message used to facilitate this process would be, "Collect emotional keywords from the user's input and generate a suggestion list of appropriate product information."

[0801] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0802] Step 1:

[0803] The user launches the application on their device and provides natural language and emotional input via voice or text. The input information is converted from speech to text data by a speech recognition system. This process ensures that the user's requests are presented as written information.

[0804] Step 2:

[0805] The device sends the converted text data to the server. The server passes the received data to a natural language processing engine, which extracts important keywords and intent from the text. Furthermore, it uses an emotion recognition engine to analyze emotional data from tone of voice and text context. This process identifies the user's request and emotional state.

[0806] Step 3:

[0807] The server retrieves relevant product information from the database based on the analysis results. Here, it searches for the most relevant products based on the extracted keywords and sentiment data, and generates a product list. Each item in the generated product list is assigned a priority corresponding to the sentiment state.

[0808] Step 4:

[0809] The server sends the generated list of emotion-responsive products to the terminal. The terminal displays the list on the user interface so that the user can easily compare and consider the products. During this display process, products are displayed based on priority, allowing the user to quickly make the best choice.

[0810] Step 5:

[0811] When a user selects their desired products on their device, the selection information is sent back to the server. The server confirms the order using its order processing system and processes the results. Order confirmation and status are promptly notified to the user's device, and the purchase process is completed.

[0812] Generative AI models:

[0813] To assist with specific parts of this process, a generative AI model is applied to perform natural language generation and sentiment analysis based on prompt text with high accuracy. A possible prompt might be, "Collect sentiment-related keywords from the user's input and generate a suggested list of appropriate product information."

[0814] 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.

[0815] 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.

[0816] 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.

[0817] 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.

[0818] 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.

[0819] 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.

[0820] 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.

[0821] 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.

[0822] 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."

[0823] 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.

[0824] 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.

[0825] 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.

[0826] 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.

[0827] 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.

[0828] 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.

[0829] 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.

[0830] 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.

[0831] 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.

[0832] 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.

[0833] 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.

[0834] 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.

[0835] The following is further disclosed regarding the embodiments described above.

[0836] (Claim 1)

[0837] A means of receiving requests from users in natural language and analyzing the relevant information,

[0838] A means for retrieving relevant product information from a database based on the analyzed request and generating a list of suggested products,

[0839] A means of forming and displaying a dialogue that suggests products to the user based on the generated product list,

[0840] A means of receiving product selections from users and transmitting the selected product information to the order processing system,

[0841] A system that includes means for notifying users of the results of order processing.

[0842] (Claim 2)

[0843] The system according to claim 1, comprising a speech recognition means for converting a user's voice input into text data.

[0844] (Claim 3)

[0845] The system according to claim 1, comprising means for providing an interface that allows a user to adjust or add to a product list.

[0846] "Example 1"

[0847] (Claim 1)

[0848] A means for receiving requests from users in natural language, converting the requests into prompt format, and analyzing them,

[0849] A means for obtaining relevant product information from an information set based on the analyzed requirements and generating a proposed list of products,

[0850] A means of transmitting the generated product list to an information processing device and presenting it visually to the user,

[0851] A means for receiving product selections from users and transmitting the selected product information to an order processing device,

[0852] A system that includes means for notifying users of the order processing results and the estimated delivery date.

[0853] (Claim 2)

[0854] The system according to claim 1, comprising a speech recognition means for converting a user's voice input into text data.

[0855] (Claim 3)

[0856] The system according to claim 1, comprising means for providing a human-machine interface that allows a user to adjust or add to a product list.

[0857] "Application Example 1"

[0858] (Claim 1)

[0859] A means of receiving requests from users in natural language and analyzing the relevant information,

[0860] A means for retrieving relevant product information from a database based on the analyzed request and generating a list of suggested products,

[0861] A means of forming and displaying a dialogue that suggests products to the user based on the generated product list,

[0862] A means of receiving product selections from users and transmitting the selected product information to the order processing system,

[0863] A means of recognizing speech and converting user requests into text data,

[0864] A means of outputting product suggestions generated using speech synthesis technology as audio,

[0865] A system that includes means for notifying users of the results of order processing.

[0866] (Claim 2)

[0867] The system according to claim 1, which uses speech recognition means and speech synthesis technology to convert a user's voice input into text data.

[0868] (Claim 3)

[0869] The system according to claim 1, comprising means for providing an interface that allows a user to adjust or add to a product list by voice.

[0870] "Example 2 of combining an emotion engine"

[0871] (Claim 1)

[0872] A means for receiving and analyzing requests and emotional states from users in natural language,

[0873] A means for obtaining relevant product information from information sources based on analyzed requests and emotional states, and for generating a product list optimized for the user,

[0874] A means of forming and displaying a dialogue that reflects user sentiment based on the generated product list,

[0875] A system that includes means for receiving product selections from a user, transmitting the selected product information to an order processing device, and notifying the user of the order processing result.

[0876] (Claim 2)

[0877] The system according to claim 1, comprising a speech recognition means for converting a user's voice input into text data and an emotion sensor.

[0878] (Claim 3)

[0879] The system according to claim 1, comprising means for providing an interactive device that allows a user to adjust or add to a product list.

[0880] "Application example 2 when combining with an emotional engine"

[0881] (Claim 1)

[0882] A means for receiving natural language and emotional input from users and analyzing the corresponding information and emotional data,

[0883] A means for retrieving relevant product information from a database based on analyzed request and sentiment data, and generating a product list corresponding to the sentiment state,

[0884] A means of forming and displaying a dialogue that suggests products to the user based on a product list corresponding to the generated emotional state,

[0885] An emotion recognition means for analyzing the emotions of a user based on their voice and text input,

[0886] A means of receiving product selections from users and transmitting the selected product information to the order processing system,

[0887] A system that includes means for notifying users of the results of order processing.

[0888] (Claim 2)

[0889] The system according to claim 1, which extracts emotional data using speech recognition and natural language processing and makes product suggestions that correspond to the user's mental state.

[0890] (Claim 3)

[0891] The system according to claim 1, which provides an interface that allows users to adjust or add to a product list and is optimized to reflect their emotional state. [Explanation of Symbols]

[0892] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of receiving requests from users in natural language and analyzing the relevant information, A means for retrieving relevant product information from a database based on the analyzed request and generating a list of suggested products, A means of forming and displaying a dialogue that suggests products to the user based on the generated product list, A means of receiving product selections from users and transmitting the selected product information to the order processing system, A system that includes means for notifying users of the results of order processing.

2. The system according to claim 1, comprising a speech recognition means for converting a user's voice input into text data.

3. The system according to claim 1, comprising means for providing an interface that allows a user to adjust or add to a product list.

Images