system

Abstract

We provide the system. [Solution] A detection means for detecting the remaining amount of consumables, A selection means for selecting the optimal purchase candidate by collecting product information from multiple e-commerce platforms based on the out-of-stock information obtained by the aforementioned detection means, A user interface means for presenting the purchase candidates selected by the selection means to the user and accepting orders, An analytical tool for accumulating user purchase history and analyzing consumption patterns, A system including an advice-providing means for providing savings advice based on the analysis results obtained by the aforementioned analysis means.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Inventory management of consumables routinely used in the home can cause problems of out-of-stock or overstock, and as a result, consumers often incur unnecessary labor and costs. Also, information collection and comparison of EC sites for purchasing these consumables are complicated and laborious tasks for users. Furthermore, since there is a lack of understanding of consumption patterns and proposals for saving methods, it is difficult for consumers to optimize their purchasing behavior. The present invention aims to comprehensively solve these problems and improve convenience for consumers.

Means for Solving the Problems

[0005] According to the present invention, a detection means is provided for continuously monitoring the remaining amount of consumables and detecting shortages as needed. Based on the information obtained by the detection means, product information is collected from multiple e-commerce platforms, and the optimal purchase candidates are selected by the selection means based on criteria such as price, delivery conditions, and seller ratings. Furthermore, the selected purchase candidates are presented to the user using a user interface means, allowing for easy order completion. In addition, the user's purchase history is accumulated by the analysis means, and consumption patterns are analyzed to provide savings advice that leads to more efficient consumption. Moreover, custom order buttons are generated for products that the user regularly purchases, making reordering easy. By using this series of means, the management of household consumables is automated, and consumer convenience is greatly improved.

[0006] "Detection means" refers to a device or process that has the function of measuring the remaining amount of consumables and generating shortage information when it falls below a set threshold.

[0007] A "selection method" is a device or system that analyzes product information collected from multiple e-commerce platforms and has the function of selecting the most suitable purchase candidate based on criteria such as price, delivery conditions, and seller ratings.

[0008] "User interface means" refers to a device or software that provides an interface for visually presenting product selection results to the user and accepting orders.

[0009] "Analysis means" refers to a device or algorithm that has the ability to formally store a user's purchase history and analyze consumption patterns based on that data.

[0010] An "advice-providing device" is a device or program that has the function of providing users with advice on saving money and efficient consumption behavior based on the results of consumption pattern analysis obtained by the analysis device.

[0011] An "e-commerce platform" is a website or service that provides information about buying and selling goods and services over the internet. [Brief explanation of the drawing]

[0012] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when combined with an emotion engine.

Embodiments for Carrying Out the Invention

[0013] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0014] First, the terms used in the following description will be explained.

[0015] In the following embodiments, a 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 CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), etc.

[0016] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0017] In the following embodiments, a 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 disk (e.g., hard disk), or magnetic tape, etc.

[0018] 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).

[0019] 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."

[0020] [First Embodiment]

[0021] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0022] 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.

[0023] 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).

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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.

[0028] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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".

[0033] This invention provides a system for efficiently managing, purchasing, and saving on consumables within the home. Specific embodiments are described below.

[0034] Detection and notification

[0035] The core of this system lies in a detection mechanism that monitors the remaining amount of consumables. Sensors physically measure the remaining amount of consumables, and when it falls below a certain threshold, a notification is sent to the terminal. Users can also manually report shortages to the terminal. This detection information is sent to a server and used as data for the next step.

[0036] Product information acquisition and proposal

[0037] Based on the received out-of-stock information, the server uses AI to collect product information from multiple e-commerce platforms. This process collects and analyzes data such as price, delivery time, and seller ratings. The server then selects the most suitable products based on these criteria and generates recommendations for purchasing consumables.

[0038] The terminal displays suggestions generated by the server on the screen, offering the user choices. These suggestions include detailed product information as well as the benefits of purchasing. The user can easily select the desired product from the displayed options and proceed with the purchase.

[0039] Purchase history analysis and advice

[0040] The system stores a history of purchased items in a database. The server analyzes the user's consumption patterns based on this data. At the same time, it obtains general consumption trends by comparing this data with that of other users.

[0041] The server uses the analysis results to generate advice on optimizing consumption. This money-saving advice is provided to the user through their terminal. For example, if someone is overconsuming, it will offer specific suggestions such as "you can reduce costs by buying in bulk."

[0042] Generating an order button

[0043] For consumables that users frequently purchase, the device provides a custom order button for easy reordering. Using this button, users can complete the purchase process with a single click, without having to conduct further market research or price comparisons.

[0044] As described above, the system of the present invention provides consumers with efficient and convenient management and purchase of consumables, contributing to savings and optimization of lifestyle.

[0045] The following describes the processing flow.

[0046] Step 1:

[0047] The sensor monitors the remaining levels of household consumables in real time. When a set threshold is broken, it sends detection information to the terminal in real time. Users can also manually enter depletion information into the terminal for consumables that do not have sensors.

[0048] Step 2:

[0049] The device sends the received out-of-stock information to the server. The device also provides user profile information to the server as part of the request, enabling more personalized suggestions.

[0050] Step 3:

[0051] Based on the received out-of-stock information, the server uses an AI agent to collect information on relevant products from multiple e-commerce platforms. It retrieves data such as price, availability, shipping options, and seller ratings.

[0052] Step 4:

[0053] The server analyzes the collected product information and selects the best purchase candidates. It compares products based on criteria such as price, delivery speed, and seller reliability to narrow down the options to the most suitable product for the user.

[0054] Step 5:

[0055] The server sends the selected purchase candidates to the terminal and creates a purchase proposal for the user. This proposal includes selection criteria and detailed product information.

[0056] Step 6:

[0057] The terminal displays purchase suggestions received from the server, presenting the user with available options. The user can compare the displayed options and select the desired product.

[0058] Step 7:

[0059] The user clicks the order button for the selected product to confirm the purchase.

[0060] Step 8:

[0061] The terminal sends the user's order decision to the server and provides the necessary information to initiate the purchase process.

[0062] Step 9:

[0063] Based on the received order information, the server sends a purchase request to the e-commerce platform and completes the transaction. This process includes sending a confirmation email or notification.

[0064] Step 10:

[0065] Once an order is completed, the server saves the purchase history to a database. This historical data is used to analyze future consumption patterns.

[0066] Step 11:

[0067] The server analyzes accumulated purchase history and generates reports on consumption patterns, including comparisons with other user data.

[0068] Step 12:

[0069] The server generates savings advice based on the analysis results and provides it to the user via the terminal. This advice includes suggestions for optimizing purchase frequency and bulk buying.

[0070] Step 13:

[0071] The device generates custom order buttons for frequently purchased consumables to enhance user convenience. These buttons provide users with the ability to easily reorder.

[0072] (Example 1)

[0073] 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."

[0074] Managing inventory of consumables is time-consuming, and it's difficult to get advice on efficient purchasing and cost savings. Furthermore, users need to research and compare a large amount of information themselves to make the best purchase choices, which adds to the complexity.

[0075] 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.

[0076] In this invention, the server includes means for automatically detecting the inventory of consumables, means for suggesting the optimal purchase option using a generative AI model, and means for analyzing the user's behavior history and providing advice on saving money. As a result, inventory management of consumables is automated, and users can purchase necessary consumables efficiently and economically without having to collect complex information.

[0077] "Consumable goods" are items that are used on a daily basis and consumed over time.

[0078] A "detection means" is a device or method that has the function of sensing a specific physical quantity or state, and is used to understand the inventory status of consumables, etc.

[0079] "Communication means" refers to technologies and devices used to transmit information from one location to another.

[0080] A "generative AI model" is a collection of programs or algorithms built to perform specific tasks using artificial intelligence technology.

[0081] A "selection means" is a device or method for selecting the most appropriate option from a given set of choices.

[0082] "Display means" refers to devices or methods for visually presenting information to a user.

[0083] "Analysis methods" refer to techniques and methods for processing data and evaluating its content to derive specific conclusions or results.

[0084] "Means of providing advice" refers to technologies or methods for providing users with useful suggestions or guidance based on analyzed information.

[0085] This invention is a system that automates the management of household consumables and optimizes efficient purchasing and consumption. Specific embodiments for carrying out the invention are described below.

[0086] The system primarily consists of three elements: terminals, servers, and users. First, the terminals acquire data from sensors attached to various household consumables. These sensors include ultrasonic distance sensors and load sensors, which can physically measure the remaining amount of consumables. For example, they can detect when the amount of detergent falls below a certain level.

[0087] Next, the device transmits this detection data to a server via Wi-Fi or Bluetooth. The server processes the information received through the communication and uses a generative AI model to collect optimal purchase options from multiple data platforms. In this process, product price, delivery time, and seller ratings are important analytical factors. For example, it may suggest information such as "a specific brand of detergent can be delivered as early as the next day."

[0088] Furthermore, the server accumulates purchase history and analyzes consumption patterns to provide users with advice on saving money. This analysis considers not only past purchase data but also the general consumption trends of other users. Based on this analysis, users can receive specific advice such as, "The best time to make your next purchase is in three weeks."

[0089] Furthermore, the terminal has a feature that allows users to place custom buttons for frequently purchased consumables, enabling one-click reordering. This button is particularly useful for frequently used consumables. For example, a reorder button for regularly purchased pet food would be convenient.

[0090] An example of a prompt is, "Create a program that uses sensors to detect the inventory of household consumables and optimizes online purchases." Using such prompts enables efficient consumable management by the model.

[0091] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0092] Step 1:

[0093] The terminal acquires physical data from sensors attached to consumables. These sensors, for example, ultrasonic distance sensors, measure the remaining amount of consumables in their containers. This input data is raw data from the sensors. The terminal's built-in processor analyzes this data to determine whether the remaining amount of consumables falls below a set threshold. The analysis results generate information about the inventory status.

[0094] Step 2:

[0095] Based on the analysis results, the device generates an alert notification if it detects that the remaining amount of consumables has fallen below a threshold. This notification informs the user through a message displayed on the screen or an audio alert. This alert indicates that the user needs to replenish the consumables.

[0096] Step 3:

[0097] The user uses the terminal interface to check for detected stock shortages and select the consumables that need to be replenished. This selection information is entered, and the terminal prepares to send this data to the server.

[0098] Step 4:

[0099] The device transmits information about insufficient stock to the server via Wi-Fi or Bluetooth communication. The input is user selection information, and the output is communication data to the server.

[0100] Step 5:

[0101] The server receives the submitted information about insufficient stock. The server then uses a generative AI model to begin collecting optimal purchase options from multiple data platforms. The input is the user's information about insufficient stock, and the output is a list of product information including price, shipping conditions, and seller ratings.

[0102] Step 6:

[0103] The server analyzes the collected product information and compares products based on price, shipping conditions, and seller ratings. It determines the optimal choice and generates a purchase suggestion. The input is a list of product information, and the output is the optimal purchase choice.

[0104] Step 7:

[0105] The server sends the optimal purchase suggestion to the device. This suggestion includes specific product information and benefits. The device displays this suggestion information to the user. The user reviews the received suggestion and makes a purchase decision.

[0106] Step 8:

[0107] The user selects the necessary consumables from the options presented on the device and proceeds with the purchase. This selection is entered into the device, and once the purchase process is complete, confirmation information is sent from the device to the server.

[0108] Step 9:

[0109] The server stores the user's purchase history in a database. This allows the server to analyze consumption patterns and prepare to generate advice on future purchase timings and savings through bulk buying. The input is purchase confirmation information, and the output is updated purchase history data.

[0110] (Application Example 1)

[0111] 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."

[0112] Managing consumables is a cumbersome task for most households, and misjudging remaining quantities or the timing of repurchases often leads to inconvenience. Furthermore, gathering information and comparing prices to purchase the right products at the optimal price requires considerable time and effort. In addition, effective analysis of consumption patterns and savings advice are necessary to avoid wasteful spending and unnecessary expenses on consumables.

[0113] 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.

[0114] This invention includes a server that automatically checks the remaining amount of consumables using sensors, a means for selecting the optimal product from an e-commerce platform based on the consumable shortage information, and a user interface means for providing consumers with purchase information for the selected product. This streamlines the management of consumables in the home and reduces the time and effort required for purchasing. Furthermore, by utilizing purchase history, consumption patterns can be analyzed and practical advice for saving on living expenses can be provided.

[0115] "Consumable goods" are products that gradually decrease in quantity through use and require repurchase.

[0116] "Detection means" refers to a device or system used to check the remaining amount of consumables.

[0117] "Out of stock information" refers to notifications or data issued when consumables fall below a certain standard.

[0118] "Selection means" refers to a device or method for selecting the most suitable product from among several products.

[0119] An "e-commerce platform" is an online environment for buying and selling goods and services via the internet.

[0120] "User interface means" refers to a system or device for a user to receive information and perform operations.

[0121] A "monitoring system" is a configuration for periodically checking the status of a system or device and notifying information as needed.

[0122] "Analysis methods" refer to methods for deriving results based on collected data, following certain rules or algorithms.

[0123] "Advice provision means" refers to a method or system for providing useful advice to users based on data analysis results.

[0124] The system for implementing this invention is designed to streamline the management of household consumables and optimize the purchasing process. At the heart of this system is a monitoring device composed of sensors and an information processing terminal that works in conjunction with it.

[0125] First, the monitoring system automatically measures the remaining amount of consumables. For example, a weighing scale sensor installed in the kitchen or bathroom will issue a warning when it falls below a certain threshold. These sensors connect to the network using hardware such as a Raspberry Pi and send the collected data to a server.

[0126] The server automatically collects information on relevant products from e-commerce platforms based on the received data. This process uses Python to collect and analyze product information and is built as a web application using Flask. Product information is collected using web scraping technology, and an AI algorithm selects the best purchase candidates, including price, shipping conditions, and seller ratings.

[0127] The information processing terminal provides users with selected product information. At this stage, the user interface is built using React Native or other mobile application frameworks to allow consumers to easily complete their purchase.

[0128] Furthermore, the server stores users' purchase history in a database and analyzes consumption patterns using a Python library. Based on this analysis, a recommendation system is designed via a generating AI model to provide user-friendly money-saving advice.

[0129] For example, when the amount of tempura oil remaining is low, a sensor detects this and the server sends a notification saying, "Your tempura oil stock is low. We have tempura oil on sale; would you like to purchase it?" At this time, the AI ​​model is prompted with the message, "I am considering purchasing new tempura oil. Please suggest the best purchase plan based on the current stock status and sales information." This allows consumers to quickly make the best purchasing decision.

[0130] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0131] Step 1:

[0132] The monitoring device detects the remaining amount of consumables. A weight sensor attached to the consumable measures the remaining amount, and this data is sent to the server via a Raspberry Pi. The input is the numerical data measured by the sensor, and the output is the remaining amount information of the consumable sent to the server.

[0133] Step 2:

[0134] The server extracts information on the shortage of necessary products based on consumable information. The server analyzes the received remaining quantity information and generates a shortage notification if it falls below a certain threshold. The input is the remaining quantity information of consumables, and the output is a notification of the shortage.

[0135] Step 3:

[0136] The server collects product information from e-commerce platforms based on out-of-stock information. It uses Python to perform web scraping, retrieving product data based on price, shipping conditions, and seller ratings. The input is out-of-stock information, and the output is a list of the best product candidates.

[0137] Step 4:

[0138] The information processing terminal notifies the user of product candidates received from the server. An application using React Native displays detailed product information and provides a user interface to confirm the user's purchase intention. The input is a list of product candidates, and the output is the presentation of product information to the user.

[0139] Step 5:

[0140] The user makes a purchase decision based on the presented product options and enters it into the terminal. This is sent to the server as a purchase order and passed on to the next process. The input is the product selection, and the output is the purchase order.

[0141] Step 6:

[0142] The server accumulates user purchase history and analyzes consumption patterns. Past purchase data is stored in a database, and consumption trends are analyzed using a Python library. The input is the purchase order, and the output is the analysis result of the consumption pattern.

[0143] Step 7:

[0144] The server generates savings advice using a generative AI model based on the analysis results. It utilizes the "generative AI model and prompt text" to generate specific advice for the user and send it to the terminal. The input is the analysis results of consumption patterns, and the output is savings advice.

[0145] 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.

[0146] This invention combines a system for streamlining the management and purchase of household consumables with an emotion engine that recognizes user emotions. This makes it possible to provide suggestions and advice that take into account the consumer's psychological state.

[0147] Utilizing the Emotion Engine

[0148] The device collects emotional data through interaction with the user. This emotional data includes the user's voice tone, selection speed, and facial expression data (if a camera is used). The device sends this data to a server.

[0149] The server uses an emotion engine to analyze the received emotion data and identify the user's emotional state. For example, it can determine if the user is feeling stressed about shopping or hesitant to purchase a particular product. Based on this, the server adjusts the way it provides information and makes purchase suggestions according to the user's emotions.

[0150] Advice and user interface adjustments

[0151] The server generates personalized advice based on the emotional state analyzed by the emotion engine. For example, if a user is feeling anxious about a purchase, the server will offer suggestions that include additional information and reassurance. It can also add words of encouragement regarding saving money.

[0152] Furthermore, the device adjusts the interface to reflect the user's emotional state. This may include changing the interface's color scheme to calmer colors, slowing down the pace, or simplifying it by reducing the number of options.

[0153] Specific example

[0154] This explains how a user might feel stressed while considering purchasing consumables. The device collects emotional data based on the user's tone of voice and the time spent on each action, and immediately analyzes it using an emotion engine. If the server detects emotions indicating fear or anxiety, it provides the user with suggestions including detailed product reviews, safety assurances, and past successful purchase examples. It also changes the interface background color to stress-reducing colors such as blue or green to provide a sense of security.

[0155] In this way, the present invention enables flexible responses to consumers' psychological states, and can evolve the management and purchase of consumables into a more personal and comfortable experience.

[0156] The following describes the processing flow.

[0157] Step 1:

[0158] The device monitors user actions and collects emotion-related data such as voice input, touch speed, and facial expression data. Each time data is collected, the device sends it to the server.

[0159] Step 2:

[0160] The server receives emotion-related data sent from the terminal and analyzes the data using an emotion engine. It identifies the user's emotional state and evaluates how that state affects the consumable purchase process.

[0161] Step 3:

[0162] The server adjusts purchase suggestions for the user based on the emotional state identified by the emotion engine. For example, if the server determines that the user is stressed, the purchase suggestions will include detailed product information, reviews, and reassuring elements.

[0163] Step 4:

[0164] The server also adjusts the user interface display based on the results of the emotion engine. For example, it might change to a calming color scheme or switch to a simpler interface.

[0165] Step 5:

[0166] The server sends the adjusted purchase proposal and interface information to the terminal. The terminal then presents the user with a purchase proposal optimized for them.

[0167] Step 6:

[0168] Users review the presented purchase information, comfortably select products in an environment that takes their emotional state into consideration, and proceed with the purchase.

[0169] Step 7:

[0170] After a user completes a purchase, the device sends the results to a server, where they are stored in a purchase history database. This data is used for future sentiment trend analysis.

[0171] Step 8:

[0172] The server analyzes long-term consumption patterns and emotional trends based on user sentiment data and purchase history, and uses this information to improve the quality of future suggestions and advice.

[0173] (Example 2)

[0174] 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".

[0175] Traditional consumables management systems could detect user consumption trends and remaining consumable levels and make purchase suggestions, but they could not provide advice that took into account the individual user's emotional state. This poses a challenge in reducing the stress and anxiety users feel when shopping and providing a more comfortable purchasing experience. Therefore, there is a need for a system that can detect user emotions in real time and provide personalized suggestions and advice accordingly.

[0176] 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.

[0177] In this invention, the server includes detection means for detecting the remaining amount of consumables, emotion recognition means for recognizing the user's emotions, and advice provision means for providing optimized advice to the user based on the emotion data. This enables an interactive and personalized purchasing experience that responds to the user's emotions.

[0178] A "detection means" is a mechanism for determining the remaining quantity and condition of consumables in real time and acquiring that information.

[0179] "Selection methods" refer to the process of comparing multiple transaction methods and product information to select the most suitable purchase option for the user.

[0180] A "display mechanism" is an interface that presents selected purchase options and advice to the user, enabling interactive communication.

[0181] "Emotion recognition means" refers to technology that analyzes a user's emotions from their tone of voice, facial expressions, and how they operate the device, and uses that information to understand their emotional state.

[0182] A "proposal provision method" is a system that provides users with precise and personalized advice based on data obtained through emotion recognition methods.

[0183] "Adjustment mechanisms" refer to functions that change the interface's color tone and display speed according to the user's emotional state, providing a comfortable user environment.

[0184] "Analysis methods" refer to the process of analyzing users' purchase history and behavioral patterns, and using the results to clarify their consumption trends.

[0185] A "proposal provision system" is a system that, based on information obtained through analysis, makes product purchase suggestions to users that take into account cost savings and convenience.

[0186] This invention is a system for individually optimizing the management and purchase of consumables. This system is implemented through interaction between terminals, servers, and users. The specific method is described below.

[0187] The device is initially installed in the home and is equipped with a microphone to collect the user's voice, a touch sensor to detect operation speed, and a camera to analyze facial expressions. Through this hardware, the device acquires the user's emotional data in real time. The device also has the function to transmit this emotional data to a server.

[0188] The server receives emotional data transmitted from the terminal and uses a generative AI model that functions as an emotion recognition tool. This AI model analyzes the user's voice tone, operation speed, and facial expression data to identify the user's emotional state. Based on the results, it uses an advisory tool to provide personalized advice tailored to the user's emotions. This advice may include reassuring information and past success stories to give the user a sense of security and support rational purchasing decisions.

[0189] Furthermore, the terminal adjusts the user interface based on feedback from the server. Specifically, it uses information obtained from emotion recognition to change the interface's color scheme to calming colors such as blue or green, and adjust the pace of information presentation. In this way, it provides an environment in which users can operate the system with reduced stress.

[0190] A concrete example is when a user is about to purchase a new detergent. If the user shows anxiety, the device collects their tone of voice and facial expression and sends it to the server, which then sends a prompt to the AI ​​model. A possible prompt might be: "Create advice to alleviate the user's anxiety about purchasing a new detergent. Include detailed product reviews and past success stories to provide reassurance." The server then sends the resulting advice back to the device, presenting the user with the most relevant information.

[0191] This system enables the management and purchase support of consumables while taking user emotions into consideration, providing more personalized convenience.

[0192] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0193] Step 1:

[0194] The device senses the user's voice, operation speed, and facial expressions in real time. This is achieved using a microphone, touch sensor, and camera. Inputs include the user's voice tone, touch panel usage frequency, and facial expression data captured by the camera. Based on this input data, the device generates emotion data and sends it to the server as output.

[0195] Step 2:

[0196] The server receives emotional data from the terminal as input. Using emotion recognition tools, the server analyzes the emotional data with a generative AI model to identify the user's emotional state. This process involves analyzing voice tone and speed, as well as facial expression detection results, and performing data calculations to determine the emotional state. The user's emotional state, as a result of the analysis, is then output.

[0197] Step 3:

[0198] The server generates optimized advice based on the emotional state. The input is the analyzed emotional state, and the AI ​​model generates advice by inputting prompt sentences. The prompt sentence used here is: "Create advice to alleviate the user's anxiety when purchasing a new detergent. Include detailed product reviews and past success stories to provide reassurance." Personalized advice is then generated as output.

[0199] Step 4:

[0200] The terminal receives advice and emotional state information sent from the server as input. At this point, the user interface is adjusted. Specifically, the interface's color scheme is changed to calming colors such as blue or green, the speed of information presentation is increased, and the operation menus are simplified, resulting in a comfortable operating environment for the user.

[0201] Step 5:

[0202] Users make product selection and purchase decisions based on a tailored interface and advice. Inputs are visual and advisory information, and the output is a rational and emotionally conscious purchasing decision based on this information.

[0203] (Application Example 2)

[0204] 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 device 14 will be referred to as the "terminal."

[0205] In the purchasing process for consumable goods, mechanical suggestions are often made without considering the user's emotional state, which can result in stress and anxiety. This disregard for psychological factors in consumer behavior creates a problem where the consumption process becomes uncomfortable.

[0206] 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.

[0207] In this invention, the server includes emotion recognition means, interface adjustment means, and suggestion personalization means. This enables personalized suggestions and interface adjustments based on the user's emotional state.

[0208] "Detection means" refers to devices or methods for detecting the remaining amount of consumables using sensors or data analysis.

[0209] "Selection method" refers to a process or system for selecting the most suitable purchase candidate based on product information collected from multiple e-commerce platforms.

[0210] "User interface means" refers to the operating interface or software functions used to present information to the user and accept orders.

[0211] "Analysis methods" refer to methods and algorithms for collecting and accumulating users' past purchase history and analyzing their consumption patterns.

[0212] "Advice provision means" refers to a function or method for providing users with advice on saving money or making purchases based on analysis results.

[0213] "Emotion recognition means" refers to technologies and systems that collect emotional data from the user's voice tone, facial expressions, operation speed, etc., and determine their emotional state.

[0214] "Interface adjustment means" refers to a method for providing a more comfortable user experience by adjusting the visual and operational elements of the user interface according to the user's emotional state.

[0215] "Personalized proposal methods" refer to processes and algorithms that generate personalized purchase proposals based on the user's emotional state.

[0216] The system of this invention optimizes the management and purchasing process of consumables by having a home robot and a server work together. The robot is equipped with data collection devices such as a camera and a microphone, which are used to acquire user emotional data. When the terminal is a robot, it checks the inventory of consumables through patrolling the home and interacting with the user. It also collects emotional data from the user's voice tone, facial expressions, and operation speed, and transmits it to the server.

[0217] Based on this data, the server uses an emotion recognition engine to analyze the user's psychological state. Based on the analysis results, it sends instructions back to the robot to adjust the user interface. These adjustments include changing the color scheme according to the user's psychological state and simplifying the operating procedures. The server also personalizes purchase suggestions based on the user's emotions and presents appropriate product information through the robot.

[0218] For example, if a user realizes they are running low on bottled water during a busy time before going out, the robot will offer a suggestion such as, "You can purchase your favorite water at your leisure. When you're busy, why not try one of these value sets?" To reduce user stress, the robot's display is set to calming blue and green tones.

[0219] An example of a prompt for the generating AI model is: "Generate a calm, simple, and reassuring suggestion to encourage users to easily purchase items with low stock when they are feeling stressed."

[0220] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0221] Step 1:

[0222] The device uses its built-in camera and microphone to sense the user's voice tone, facial expressions, and operation speed, and collects this data. The input is the user's voice and video, and through data processing, it extracts feature values ​​that indicate the user's emotions and outputs them as emotion data.

[0223] Step 2:

[0224] The device sends collected emotional data to the server. The server receives this emotional data and analyzes it using an emotion recognition engine. The input is emotional data, and the server outputs the estimated emotional state of the user through the analysis process.

[0225] Step 3:

[0226] The server determines how to display the user interface based on the analyzed emotional state of the user. For example, if the result indicates stress, it generates instructions to change the interface color to blue or green. The input is the emotional state, and the output is an interface adjustment instruction.

[0227] Step 4:

[0228] The server uses selection criteria to choose the most suitable product information to generate purchase suggestions tailored to the user's emotional state. This process also considers the user's purchase history and market information. The input consists of emotional state and product data, while the output is personalized product suggestions.

[0229] Step 5:

[0230] The generated product suggestions and interface adjustment instructions are sent to the terminal, which then displays them to the user. The user can review the displayed content and accept or reject the suggestions. The input is the suggestions and instructions from the server, and the output is the content presented to the user.

[0231] Step 6:

[0232] If the user approves the proposed purchase, the terminal returns the order information to the server. Based on the received information, the server processes the user's order through the e-commerce platform. The input is the user's order information, and the output is a notification that the order processing is complete.

[0233] By using a generative AI model and prompt text, the adjustment of each suggested text and interface is automated, resulting in a comfortable and less stressful consumable purchase experience for users.

[0234] 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.

[0235] 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.

[0236] 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.

[0237] [Second Embodiment]

[0238] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0239] 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.

[0240] 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).

[0241] 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.

[0242] 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.

[0243] 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).

[0244] 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.

[0245] 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.

[0246] 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.

[0247] 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.

[0248] 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.

[0249] 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".

[0250] This invention provides a system for efficiently managing, purchasing, and saving on consumables within the home. Specific embodiments are described below.

[0251] Detection and notification

[0252] The core of this system lies in a detection mechanism that monitors the remaining amount of consumables. Sensors physically measure the remaining amount of consumables, and when it falls below a certain threshold, a notification is sent to the terminal. Users can also manually report shortages to the terminal. This detection information is sent to a server and used as data for the next step.

[0253] Product information acquisition and proposal

[0254] Based on the received out-of-stock information, the server uses AI to collect product information from multiple e-commerce platforms. This process collects and analyzes data such as price, delivery time, and seller ratings. The server then selects the most suitable products based on these criteria and generates recommendations for purchasing consumables.

[0255] The terminal displays suggestions generated by the server on the screen, offering the user choices. These suggestions include detailed product information as well as the benefits of purchasing. The user can easily select the desired product from the displayed options and proceed with the purchase.

[0256] Purchase history analysis and advice

[0257] The system stores a history of purchased items in a database. The server analyzes the user's consumption patterns based on this data. At the same time, it obtains general consumption trends by comparing this data with that of other users.

[0258] The server uses the analysis results to generate advice on optimizing consumption. This money-saving advice is provided to the user through their terminal. For example, if someone is overconsuming, it will offer specific suggestions such as "you can reduce costs by buying in bulk."

[0259] Generating an order button

[0260] For consumables that users frequently purchase, the device provides a custom order button for easy reordering. Using this button, users can complete the purchase process with a single click, without having to conduct further market research or price comparisons.

[0261] As described above, the system of the present invention provides consumers with efficient and convenient management and purchase of consumables, contributing to savings and optimization of lifestyle.

[0262] The following describes the processing flow.

[0263] Step 1:

[0264] The sensor monitors the remaining levels of household consumables in real time. When a set threshold is broken, it sends detection information to the terminal in real time. Users can also manually enter depletion information into the terminal for consumables that do not have sensors.

[0265] Step 2:

[0266] The device sends the received out-of-stock information to the server. The device also provides user profile information to the server as part of the request, enabling more personalized suggestions.

[0267] Step 3:

[0268] Based on the received out-of-stock information, the server uses an AI agent to collect information on relevant products from multiple e-commerce platforms. It retrieves data such as price, availability, shipping options, and seller ratings.

[0269] Step 4:

[0270] The server analyzes the collected product information and selects the best purchase candidates. It compares products based on criteria such as price, delivery speed, and seller reliability to narrow down the options to the most suitable product for the user.

[0271] Step 5:

[0272] The server sends the selected purchase candidates to the terminal and creates a purchase proposal for the user. This proposal includes selection criteria and detailed product information.

[0273] Step 6:

[0274] The terminal displays the purchase proposals received from the server and presents the possible options to the user. The user can compare the displayed options and select the desired products.

[0275] Step 7:

[0276] The user clicks the order button for the selected product and decides to purchase.

[0277] Step 8:

[0278] The terminal sends the user's order decision to the server and provides the necessary information to start the purchase process.

[0279] Step 9:

[0280] Based on the received order information, the server sends a purchase request to the e-commerce platform to complete the transaction. This process includes sending a confirmation email or notification.

[0281] Step 10:

[0282] When the order is completed, the server saves the purchase history in the database. The history data is used for analyzing future consumption patterns.

[0283] Step 11:

[0284] The server analyzes the accumulated purchase history and creates a report on consumption patterns. This also includes comparison with other user data.

[0285] Step 12:

[0286] Based on the analysis results, the server creates savings advice and provides it to the user through the terminal. This advice includes optimizing purchase frequency and suggesting bulk purchases.

[0287] Step 13:

[0288] The device generates custom order buttons for frequently purchased consumables to enhance user convenience. These buttons provide users with the ability to easily reorder.

[0289] (Example 1)

[0290] 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."

[0291] Managing inventory of consumables is time-consuming, and it's difficult to get advice on efficient purchasing and cost savings. Furthermore, users need to research and compare a large amount of information themselves to make the best purchase choices, which adds to the complexity.

[0292] 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.

[0293] In this invention, the server includes means for automatically detecting the inventory of consumables, means for suggesting the optimal purchase option using a generative AI model, and means for analyzing the user's behavior history and providing advice on saving money. As a result, inventory management of consumables is automated, and users can purchase necessary consumables efficiently and economically without having to collect complex information.

[0294] "Consumable goods" are items that are used on a daily basis and consumed over time.

[0295] A "detection means" is a device or method that has the function of sensing a specific physical quantity or state, and is used to understand the inventory status of consumables, etc.

[0296] "Communication means" refers to technologies and devices used to transmit information from one location to another.

[0297] A "generative AI model" is a collection of programs or algorithms built to perform specific tasks using artificial intelligence technology.

[0298] A "selection means" is a device or method for selecting the most appropriate option from a given set of choices.

[0299] "Display means" refers to devices or methods for visually presenting information to a user.

[0300] "Analysis methods" refer to techniques and methods for processing data and evaluating its content to derive specific conclusions or results.

[0301] "Means of providing advice" refers to technologies or methods for providing users with useful suggestions or guidance based on analyzed information.

[0302] This invention is a system that automates the management of household consumables and optimizes efficient purchasing and consumption. Specific embodiments for carrying out the invention are described below.

[0303] The system primarily consists of three elements: terminals, servers, and users. First, the terminals acquire data from sensors attached to various household consumables. These sensors include ultrasonic distance sensors and load sensors, which can physically measure the remaining amount of consumables. For example, they can detect when the amount of detergent falls below a certain level.

[0304] Next, the device transmits this detection data to a server via Wi-Fi or Bluetooth. The server processes the information received through the communication and uses a generative AI model to collect optimal purchase options from multiple data platforms. In this process, product price, delivery time, and seller ratings are important analytical factors. For example, it may suggest information such as "a specific brand of detergent can be delivered as early as the next day."

[0305] Furthermore, the server accumulates purchase histories and analyzes consumption patterns to provide advice on savings to users. In analyzing consumption patterns, in addition to past purchase data, general consumption trends of other users are also considered. Based on this analysis, users can receive specific advice such as "The optimal time for the next purchase is in three weeks."

[0306] Also, the terminal has a function to arrange a custom operation button that enables one-click reordering for consumables that the user frequently purchases. This button is especially useful for frequently used consumables. For example, it is convenient to have a reorder button for pet food that is purchased regularly.

[0307] An example of a prompt sentence is "Create a program that detects the inventory of household consumables with a sensor and optimizes online purchases." By using such a prompt, efficient management of consumables becomes possible with the model.

[0308] The flow of the specific process in Example 1 will be described using FIG. 11.

[0309] Step 1:

[0310] The terminal acquires physical data from the sensor attached to the consumable. The sensor uses, for example, an ultrasonic distance sensor to measure the remaining amount in the container of the consumable. This input data is raw data from the sensor. The built-in processor of the terminal analyzes this data to determine whether the remaining amount of the consumable is below the set threshold. As an analysis result, information about the inventory status is generated.

[0311] Step 2:

[0312] [[ID=二十]] When the terminal detects that the remaining amount of the consumable has fallen below the threshold based on the analysis result, it generates an alert notification. This notification informs the user, such as a message displayed on the display or an audio alert. This alert indicates that the user needs to replenish the consumable.

[0313] Step 3:

[0314] The user uses the terminal interface to check for detected stock shortages and select the consumables that need to be replenished. This selection information is entered, and the terminal prepares to send this data to the server.

[0315] Step 4:

[0316] The device transmits information about insufficient stock to the server via Wi-Fi or Bluetooth communication. The input is user selection information, and the output is communication data to the server.

[0317] Step 5:

[0318] The server receives the submitted information about insufficient stock. The server then uses a generative AI model to begin collecting optimal purchase options from multiple data platforms. The input is the user's information about insufficient stock, and the output is a list of product information including price, shipping conditions, and seller ratings.

[0319] Step 6:

[0320] The server analyzes the collected product information and compares products based on price, shipping conditions, and seller ratings. It determines the optimal choice and generates a purchase suggestion. The input is a list of product information, and the output is the optimal purchase choice.

[0321] Step 7:

[0322] The server sends the optimal purchase suggestion to the device. This suggestion includes specific product information and benefits. The device displays this suggestion information to the user. The user reviews the received suggestion and makes a purchase decision.

[0323] Step 8:

[0324] The user selects the necessary consumables from the options presented on the device and proceeds with the purchase. This selection is entered into the device, and once the purchase process is complete, confirmation information is sent from the device to the server.

[0325] Step 9:

[0326] The server stores the user's purchase history in a database. This allows the server to analyze consumption patterns and prepare to generate advice on future purchase timings and savings through bulk buying. The input is purchase confirmation information, and the output is updated purchase history data.

[0327] (Application Example 1)

[0328] 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."

[0329] Managing consumables is a cumbersome task for most households, and misjudging remaining quantities or the timing of repurchases often leads to inconvenience. Furthermore, gathering information and comparing prices to purchase the right products at the optimal price requires considerable time and effort. In addition, effective analysis of consumption patterns and savings advice are necessary to avoid wasteful spending and unnecessary expenses on consumables.

[0330] 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.

[0331] This invention includes a server that automatically checks the remaining amount of consumables using sensors, a means for selecting the optimal product from an e-commerce platform based on the consumable shortage information, and a user interface means for providing consumers with purchase information for the selected product. This streamlines the management of consumables in the home and reduces the time and effort required for purchasing. Furthermore, by utilizing purchase history, consumption patterns can be analyzed and practical advice for saving on living expenses can be provided.

[0332] "Consumable goods" are products that gradually decrease in quantity through use and require repurchase.

[0333] "Detection means" refers to a device or system used to check the remaining amount of consumables.

[0334] "Out of stock information" refers to notifications or data issued when consumables fall below a certain standard.

[0335] "Selection means" refers to a device or method for selecting the most suitable product from among several products.

[0336] An "e-commerce platform" is an online environment for buying and selling goods and services via the internet.

[0337] "User interface means" refers to a system or device for a user to receive information and perform operations.

[0338] A "monitoring system" is a configuration for periodically checking the status of a system or device and notifying information as needed.

[0339] "Analysis methods" refer to methods for deriving results based on collected data, following certain rules or algorithms.

[0340] "Advice provision means" refers to a method or system for providing useful advice to users based on data analysis results.

[0341] The system for implementing this invention is designed to streamline the management of household consumables and optimize the purchasing process. At the heart of this system is a monitoring device composed of sensors and an information processing terminal that works in conjunction with it.

[0342] First, the monitoring system automatically measures the remaining amount of consumables. For example, a weighing scale sensor installed in the kitchen or bathroom will issue a warning when it falls below a certain threshold. These sensors connect to the network using hardware such as a Raspberry Pi and send the collected data to a server.

[0343] The server automatically collects information on relevant products from e-commerce platforms based on the received data. This process uses Python to collect and analyze product information and is built as a web application using Flask. Product information is collected using web scraping technology, and an AI algorithm selects the best purchase candidates, including price, shipping conditions, and seller ratings.

[0344] The information processing terminal provides users with selected product information. At this stage, the user interface is built using React Native or other mobile application frameworks to allow consumers to easily complete their purchase.

[0345] Furthermore, the server stores users' purchase history in a database and analyzes consumption patterns using a Python library. Based on this analysis, a recommendation system is designed via a generating AI model to provide user-friendly money-saving advice.

[0346] For example, when the amount of tempura oil remaining is low, a sensor detects this and the server sends a notification saying, "Your tempura oil stock is low. We have tempura oil on sale; would you like to purchase it?" At this time, the AI ​​model is prompted with the message, "I am considering purchasing new tempura oil. Please suggest the best purchase plan based on the current stock status and sales information." This allows consumers to quickly make the best purchasing decision.

[0347] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0348] Step 1:

[0349] The monitoring device detects the remaining amount of consumables. A weight sensor attached to the consumable measures the remaining amount, and this data is sent to the server via a Raspberry Pi. The input is the numerical data measured by the sensor, and the output is the remaining amount information of the consumable sent to the server.

[0350] Step 2:

[0351] The server extracts information on the shortage of necessary products based on consumable information. The server analyzes the received remaining quantity information and generates a shortage notification if it falls below a certain threshold. The input is the remaining quantity information of consumables, and the output is a notification of the shortage.

[0352] Step 3:

[0353] The server collects product information from e-commerce platforms based on out-of-stock information. It uses Python to perform web scraping, retrieving product data based on price, shipping conditions, and seller ratings. The input is out-of-stock information, and the output is a list of the best product candidates.

[0354] Step 4:

[0355] The information processing terminal notifies the user of product candidates received from the server. An application using React Native displays detailed product information and provides a user interface to confirm the user's purchase intention. The input is a list of product candidates, and the output is the presentation of product information to the user.

[0356] Step 5:

[0357] The user makes a purchase decision based on the presented product options and enters it into the terminal. This is sent to the server as a purchase order and passed on to the next process. The input is the product selection, and the output is the purchase order.

[0358] Step 6:

[0359] The server accumulates user purchase history and analyzes consumption patterns. Past purchase data is stored in a database, and consumption trends are analyzed using a Python library. The input is the purchase order, and the output is the analysis result of the consumption pattern.

[0360] Step 7:

[0361] The server generates savings advice using a generative AI model based on the analysis results. It utilizes the "generative AI model and prompt text" to generate specific advice for the user and send it to the terminal. The input is the analysis results of consumption patterns, and the output is savings advice.

[0362] 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.

[0363] This invention combines a system for streamlining the management and purchase of household consumables with an emotion engine that recognizes user emotions. This makes it possible to provide suggestions and advice that take into account the consumer's psychological state.

[0364] Utilizing the Emotion Engine

[0365] The device collects emotional data through interaction with the user. This emotional data includes the user's voice tone, selection speed, and facial expression data (if a camera is used). The device sends this data to a server.

[0366] The server uses an emotion engine to analyze the received emotion data and identify the user's emotional state. For example, it can determine if the user is feeling stressed about shopping or hesitant to purchase a particular product. Based on this, the server adjusts the way it provides information and makes purchase suggestions according to the user's emotions.

[0367] Advice and user interface adjustments

[0368] The server generates personalized advice based on the emotional state analyzed by the emotion engine. For example, if a user is feeling anxious about a purchase, the server will offer suggestions that include additional information and reassurance. It can also add words of encouragement regarding saving money.

[0369] Furthermore, the device adjusts the interface to reflect the user's emotional state. This may include changing the interface's color scheme to calmer colors, slowing down the pace, or simplifying it by reducing the number of options.

[0370] Specific example

[0371] This explains how a user might feel stressed while considering purchasing consumables. The device collects emotional data based on the user's tone of voice and the time spent on each action, and immediately analyzes it using an emotion engine. If the server detects emotions indicating fear or anxiety, it provides the user with suggestions including detailed product reviews, safety assurances, and past successful purchase examples. It also changes the interface background color to stress-reducing colors such as blue or green to provide a sense of security.

[0372] In this way, the present invention enables flexible responses to consumers' psychological states, and can evolve the management and purchase of consumables into a more personal and comfortable experience.

[0373] The following describes the processing flow.

[0374] Step 1:

[0375] The device monitors user actions and collects emotion-related data such as voice input, touch speed, and facial expression data. Each time data is collected, the device sends it to the server.

[0376] Step 2:

[0377] The server receives emotion-related data sent from the terminal and analyzes the data using an emotion engine. It identifies the user's emotional state and evaluates how that state affects the consumable purchase process.

[0378] Step 3:

[0379] The server adjusts purchase suggestions for the user based on the emotional state identified by the emotion engine. For example, if the server determines that the user is stressed, the purchase suggestions will include detailed product information, reviews, and reassuring elements.

[0380] Step 4:

[0381] The server also adjusts the user interface display based on the results of the emotion engine. For example, it might change to a calming color scheme or switch to a simpler interface.

[0382] Step 5:

[0383] The server sends the adjusted purchase proposal and interface information to the terminal. The terminal then presents the user with a purchase proposal optimized for them.

[0384] Step 6:

[0385] Users review the presented purchase information, comfortably select products in an environment that takes their emotional state into consideration, and proceed with the purchase.

[0386] Step 7:

[0387] After a user completes a purchase, the device sends the results to a server, where they are stored in a purchase history database. This data is used for future sentiment trend analysis.

[0388] Step 8:

[0389] The server analyzes long-term consumption patterns and emotional trends based on user sentiment data and purchase history, and uses this information to improve the quality of future suggestions and advice.

[0390] (Example 2)

[0391] 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".

[0392] Traditional consumables management systems could detect user consumption trends and remaining consumable levels and make purchase suggestions, but they could not provide advice that took into account the individual user's emotional state. This poses a challenge in reducing the stress and anxiety users feel when shopping and providing a more comfortable purchasing experience. Therefore, there is a need for a system that can detect user emotions in real time and provide personalized suggestions and advice accordingly.

[0393] 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.

[0394] In this invention, the server includes detection means for detecting the remaining amount of consumables, emotion recognition means for recognizing the user's emotions, and advice provision means for providing optimized advice to the user based on the emotion data. This enables an interactive and personalized purchasing experience that responds to the user's emotions.

[0395] A "detection means" is a mechanism for determining the remaining quantity and condition of consumables in real time and acquiring that information.

[0396] "Selection methods" refer to the process of comparing multiple transaction methods and product information to select the most suitable purchase option for the user.

[0397] A "display mechanism" is an interface that presents selected purchase options and advice to the user, enabling interactive communication.

[0398] "Emotion recognition means" refers to technology that analyzes a user's emotions from their tone of voice, facial expressions, and how they operate the device, and uses that information to understand their emotional state.

[0399] A "proposal provision method" is a system that provides users with precise and personalized advice based on data obtained through emotion recognition methods.

[0400] "Adjustment mechanisms" refer to functions that change the interface's color tone and display speed according to the user's emotional state, providing a comfortable user environment.

[0401] "Analysis methods" refer to the process of analyzing users' purchase history and behavioral patterns, and using the results to clarify their consumption trends.

[0402] A "proposal provision system" is a system that, based on information obtained through analysis, makes product purchase suggestions to users that take into account cost savings and convenience.

[0403] This invention is a system for individually optimizing the management and purchase of consumables. This system is implemented through interaction between terminals, servers, and users. The specific method is described below.

[0404] The device is initially installed in the home and is equipped with a microphone to collect the user's voice, a touch sensor to detect operation speed, and a camera to analyze facial expressions. Through this hardware, the device acquires the user's emotional data in real time. The device also has the function to transmit this emotional data to a server.

[0405] The server receives emotional data transmitted from the terminal and uses a generative AI model that functions as an emotion recognition tool. This AI model analyzes the user's voice tone, operation speed, and facial expression data to identify the user's emotional state. Based on the results, it uses an advisory tool to provide personalized advice tailored to the user's emotions. This advice may include reassuring information and past success stories to give the user a sense of security and support rational purchasing decisions.

[0406] Furthermore, the terminal adjusts the user interface based on feedback from the server. Specifically, it uses information obtained from emotion recognition to change the interface's color scheme to calming colors such as blue or green, and adjust the pace of information presentation. In this way, it provides an environment in which users can operate the system with reduced stress.

[0407] A concrete example is when a user is about to purchase a new detergent. If the user shows anxiety, the device collects their tone of voice and facial expression and sends it to the server, which then sends a prompt to the AI ​​model. A possible prompt might be: "Create advice to alleviate the user's anxiety about purchasing a new detergent. Include detailed product reviews and past success stories to provide reassurance." The server then sends the resulting advice back to the device, presenting the user with the most relevant information.

[0408] This system enables the management and purchase support of consumables while taking user emotions into consideration, providing more personalized convenience.

[0409] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0410] Step 1:

[0411] The device senses the user's voice, operation speed, and facial expressions in real time. This is achieved using a microphone, touch sensor, and camera. Inputs include the user's voice tone, touch panel usage frequency, and facial expression data captured by the camera. Based on this input data, the device generates emotion data and sends it to the server as output.

[0412] Step 2:

[0413] The server receives emotional data from the terminal as input. Using emotion recognition tools, the server analyzes the emotional data with a generative AI model to identify the user's emotional state. This process involves analyzing voice tone and speed, as well as facial expression detection results, and performing data calculations to determine the emotional state. The user's emotional state, as a result of the analysis, is then output.

[0414] Step 3:

[0415] The server generates optimized advice based on the emotional state. The input is the analyzed emotional state, and the AI ​​model generates advice by inputting prompt sentences. The prompt sentence used here is: "Create advice to alleviate the user's anxiety when purchasing a new detergent. Include detailed product reviews and past success stories to provide reassurance." Personalized advice is then generated as output.

[0416] Step 4:

[0417] The terminal receives advice and emotional state information sent from the server as input. At this point, the user interface is adjusted. Specifically, the interface's color scheme is changed to calming colors such as blue or green, the speed of information presentation is increased, and the operation menus are simplified, resulting in a comfortable operating environment for the user.

[0418] Step 5:

[0419] Users make product selection and purchase decisions based on a tailored interface and advice. Inputs are visual and advisory information, and the output is a rational and emotionally conscious purchasing decision based on this information.

[0420] (Application Example 2)

[0421] 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."

[0422] In the purchasing process for consumable goods, mechanical suggestions are often made without considering the user's emotional state, which can result in stress and anxiety. This disregard for psychological factors in consumer behavior creates a problem where the consumption process becomes uncomfortable.

[0423] 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.

[0424] In this invention, the server includes emotion recognition means, interface adjustment means, and suggestion personalization means. This enables personalized suggestions and interface adjustments based on the user's emotional state.

[0425] "Detection means" refers to devices or methods for detecting the remaining amount of consumables using sensors or data analysis.

[0426] "Selection method" refers to a process or system for selecting the most suitable purchase candidate based on product information collected from multiple e-commerce platforms.

[0427] "User interface means" refers to the operating interface or software functions used to present information to the user and accept orders.

[0428] "Analysis methods" refer to methods and algorithms for collecting and accumulating users' past purchase history and analyzing their consumption patterns.

[0429] "Advice provision means" refers to a function or method for providing users with advice on saving money or making purchases based on analysis results.

[0430] "Emotion recognition means" refers to technologies and systems that collect emotional data from the user's voice tone, facial expressions, operation speed, etc., and determine their emotional state.

[0431] "Interface adjustment means" refers to a method for providing a more comfortable user experience by adjusting the visual and operational elements of the user interface according to the user's emotional state.

[0432] "Personalized proposal methods" refer to processes and algorithms that generate personalized purchase proposals based on the user's emotional state.

[0433] The system of this invention optimizes the management and purchasing process of consumables by having a home robot and a server work together. The robot is equipped with data collection devices such as a camera and a microphone, which are used to acquire user emotional data. When the terminal is a robot, it checks the inventory of consumables through patrolling the home and interacting with the user. It also collects emotional data from the user's voice tone, facial expressions, and operation speed, and transmits it to the server.

[0434] Based on this data, the server uses an emotion recognition engine to analyze the user's psychological state. Based on the analysis results, it sends instructions back to the robot to adjust the user interface. These adjustments include changing the color scheme according to the user's psychological state and simplifying the operating procedures. The server also personalizes purchase suggestions based on the user's emotions and presents appropriate product information through the robot.

[0435] For example, if a user realizes they are running low on bottled water during a busy time before going out, the robot will offer a suggestion such as, "You can purchase your favorite water at your leisure. When you're busy, why not try one of these value sets?" To reduce user stress, the robot's display is set to calming blue and green tones.

[0436] An example of a prompt for the generating AI model is: "Generate a calm, simple, and reassuring suggestion to encourage users to easily purchase items with low stock when they are feeling stressed."

[0437] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0438] Step 1:

[0439] The device uses its built-in camera and microphone to sense the user's voice tone, facial expressions, and operation speed, and collects this data. The input is the user's voice and video, and through data processing, it extracts feature values ​​that indicate the user's emotions and outputs them as emotion data.

[0440] Step 2:

[0441] The device sends collected emotional data to the server. The server receives this emotional data and analyzes it using an emotion recognition engine. The input is emotional data, and the server outputs the estimated emotional state of the user through the analysis process.

[0442] Step 3:

[0443] The server determines how to display the user interface based on the analyzed emotional state of the user. For example, if the result indicates stress, it generates instructions to change the interface color to blue or green. The input is the emotional state, and the output is an interface adjustment instruction.

[0444] Step 4:

[0445] The server uses selection criteria to choose the most suitable product information to generate purchase suggestions tailored to the user's emotional state. This process also considers the user's purchase history and market information. The input consists of emotional state and product data, while the output is personalized product suggestions.

[0446] Step 5:

[0447] The generated product suggestions and interface adjustment instructions are sent to the terminal, which then displays them to the user. The user can review the displayed content and accept or reject the suggestions. The input is the suggestions and instructions from the server, and the output is the content presented to the user.

[0448] Step 6:

[0449] If the user approves the proposed purchase, the terminal returns the order information to the server. Based on the received information, the server processes the user's order through the e-commerce platform. The input is the user's order information, and the output is a notification that the order processing is complete.

[0450] By using a generative AI model and prompt text, the adjustment of each suggested text and interface is automated, resulting in a comfortable and less stressful consumable purchase experience for users.

[0451] 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.

[0452] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An 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.

[0453] 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.

[0454] [Third Embodiment]

[0455] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0456] 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.

[0457] 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).

[0458] 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.

[0459] 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.

[0460] 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).

[0461] 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.

[0462] 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.

[0463] 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.

[0464] 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.

[0465] 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.

[0466] 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".

[0467] This invention provides a system for efficiently managing, purchasing, and saving on consumables within the home. Specific embodiments are described below.

[0468] Detection and notification

[0469] The core of this system lies in a detection mechanism that monitors the remaining amount of consumables. Sensors physically measure the remaining amount of consumables, and when it falls below a certain threshold, a notification is sent to the terminal. Users can also manually report shortages to the terminal. This detection information is sent to a server and used as data for the next step.

[0470] Product information acquisition and proposal

[0471] Based on the received out-of-stock information, the server uses AI to collect product information from multiple e-commerce platforms. This process collects and analyzes data such as price, delivery time, and seller ratings. The server then selects the most suitable products based on these criteria and generates recommendations for purchasing consumables.

[0472] The terminal displays suggestions generated by the server on the screen, offering the user choices. These suggestions include detailed product information as well as the benefits of purchasing. The user can easily select the desired product from the displayed options and proceed with the purchase.

[0473] Purchase history analysis and advice

[0474] The system stores a history of purchased items in a database. The server analyzes the user's consumption patterns based on this data. At the same time, it obtains general consumption trends by comparing this data with that of other users.

[0475] The server uses the analysis results to generate advice on optimizing consumption. This money-saving advice is provided to the user through their terminal. For example, if someone is overconsuming, it will offer specific suggestions such as "you can reduce costs by buying in bulk."

[0476] Generating an order button

[0477] For consumables that users frequently purchase, the device provides a custom order button for easy reordering. Using this button, users can complete the purchase process with a single click, without having to conduct further market research or price comparisons.

[0478] As described above, the system of the present invention provides consumers with efficient and convenient management and purchase of consumables, contributing to savings and optimization of lifestyle.

[0479] The following describes the processing flow.

[0480] Step 1:

[0481] The sensor monitors the remaining levels of household consumables in real time. When a set threshold is broken, it sends detection information to the terminal in real time. Users can also manually enter depletion information into the terminal for consumables that do not have sensors.

[0482] Step 2:

[0483] The device sends the received out-of-stock information to the server. The device also provides user profile information to the server as part of the request, enabling more personalized suggestions.

[0484] Step 3:

[0485] Based on the received out-of-stock information, the server uses an AI agent to collect information on relevant products from multiple e-commerce platforms. It retrieves data such as price, availability, shipping options, and seller ratings.

[0486] Step 4:

[0487] The server analyzes the collected product information and selects the best purchase candidates. It compares products based on criteria such as price, delivery speed, and seller reliability to narrow down the options to the most suitable product for the user.

[0488] Step 5:

[0489] The server sends the selected purchase candidates to the terminal and creates a purchase proposal for the user. This proposal includes selection criteria and detailed product information.

[0490] Step 6:

[0491] The terminal displays purchase suggestions received from the server, presenting the user with available options. The user can compare the displayed options and select the desired product.

[0492] Step 7:

[0493] The user clicks the order button for the selected product to confirm the purchase.

[0494] Step 8:

[0495] The terminal sends the user's order decision to the server and provides the necessary information to initiate the purchase process.

[0496] Step 9:

[0497] Based on the received order information, the server sends a purchase request to the e-commerce platform and completes the transaction. This process includes sending a confirmation email or notification.

[0498] Step 10:

[0499] Once an order is completed, the server saves the purchase history to a database. This historical data is used to analyze future consumption patterns.

[0500] Step 11:

[0501] The server analyzes accumulated purchase history and generates reports on consumption patterns, including comparisons with other user data.

[0502] Step 12:

[0503] The server generates savings advice based on the analysis results and provides it to the user via the terminal. This advice includes suggestions for optimizing purchase frequency and bulk buying.

[0504] Step 13:

[0505] The device generates custom order buttons for frequently purchased consumables to enhance user convenience. These buttons provide users with the ability to easily reorder.

[0506] (Example 1)

[0507] 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."

[0508] Managing inventory of consumables is time-consuming, and it's difficult to get advice on efficient purchasing and cost savings. Furthermore, users need to research and compare a large amount of information themselves to make the best purchase choices, which adds to the complexity.

[0509] 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.

[0510] In this invention, the server includes means for automatically detecting the inventory of consumables, means for suggesting the optimal purchase option using a generative AI model, and means for analyzing the user's behavior history and providing advice on saving money. As a result, inventory management of consumables is automated, and users can purchase necessary consumables efficiently and economically without having to collect complex information.

[0511] "Consumable goods" are items that are used on a daily basis and consumed over time.

[0512] A "detection means" is a device or method that has the function of sensing a specific physical quantity or state, and is used to understand the inventory status of consumables, etc.

[0513] "Communication means" refers to technologies and devices used to transmit information from one location to another.

[0514] A "generative AI model" is a collection of programs or algorithms built to perform specific tasks using artificial intelligence technology.

[0515] A "selection means" is a device or method for selecting the most appropriate option from a given set of choices.

[0516] "Display means" refers to devices or methods for visually presenting information to a user.

[0517] "Analysis methods" refer to techniques and methods for processing data and evaluating its content to derive specific conclusions or results.

[0518] "Means of providing advice" refers to technologies or methods for providing users with useful suggestions or guidance based on analyzed information.

[0519] This invention is a system that automates the management of household consumables and optimizes efficient purchasing and consumption. Specific embodiments for carrying out the invention are described below.

[0520] The system primarily consists of three elements: terminals, servers, and users. First, the terminals acquire data from sensors attached to various household consumables. These sensors include ultrasonic distance sensors and load sensors, which can physically measure the remaining amount of consumables. For example, they can detect when the amount of detergent falls below a certain level.

[0521] Next, the device transmits this detection data to a server via Wi-Fi or Bluetooth. The server processes the information received through the communication and uses a generative AI model to collect optimal purchase options from multiple data platforms. In this process, product price, delivery time, and seller ratings are important analytical factors. For example, it may suggest information such as "a specific brand of detergent can be delivered as early as the next day."

[0522] Furthermore, the server accumulates purchase history and analyzes consumption patterns to provide users with advice on saving money. This analysis considers not only past purchase data but also the general consumption trends of other users. Based on this analysis, users can receive specific advice such as, "The best time to make your next purchase is in three weeks."

[0523] Furthermore, the terminal has a feature that allows users to place custom buttons for frequently purchased consumables, enabling one-click reordering. This button is particularly useful for frequently used consumables. For example, a reorder button for regularly purchased pet food would be convenient.

[0524] An example of a prompt is, "Create a program that uses sensors to detect the inventory of household consumables and optimizes online purchases." Using such prompts enables efficient consumable management by the model.

[0525] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0526] Step 1:

[0527] The terminal acquires physical data from sensors attached to consumables. These sensors, for example, ultrasonic distance sensors, measure the remaining amount of consumables in their containers. This input data is raw data from the sensors. The terminal's built-in processor analyzes this data to determine whether the remaining amount of consumables falls below a set threshold. The analysis results generate information about the inventory status.

[0528] Step 2:

[0529] Based on the analysis results, the device generates an alert notification if it detects that the remaining amount of consumables has fallen below a threshold. This notification informs the user through a message displayed on the screen or an audio alert. This alert indicates that the user needs to replenish the consumables.

[0530] Step 3:

[0531] The user uses the terminal interface to check for detected stock shortages and select the consumables that need to be replenished. This selection information is entered, and the terminal prepares to send this data to the server.

[0532] Step 4:

[0533] The device transmits information about insufficient stock to the server via Wi-Fi or Bluetooth communication. The input is user selection information, and the output is communication data to the server.

[0534] Step 5:

[0535] The server receives the submitted information about insufficient stock. The server then uses a generative AI model to begin collecting optimal purchase options from multiple data platforms. The input is the user's information about insufficient stock, and the output is a list of product information including price, shipping conditions, and seller ratings.

[0536] Step 6:

[0537] The server analyzes the collected product information and compares products based on price, shipping conditions, and seller ratings. It determines the optimal choice and generates a purchase suggestion. The input is a list of product information, and the output is the optimal purchase choice.

[0538] Step 7:

[0539] The server sends the optimal purchase suggestion to the device. This suggestion includes specific product information and benefits. The device displays this suggestion information to the user. The user reviews the received suggestion and makes a purchase decision.

[0540] Step 8:

[0541] The user selects the necessary consumables from the options presented on the device and proceeds with the purchase. This selection is entered into the device, and once the purchase process is complete, confirmation information is sent from the device to the server.

[0542] Step 9:

[0543] The server stores the user's purchase history in a database. This allows the server to analyze consumption patterns and prepare to generate advice on future purchase timings and savings through bulk buying. The input is purchase confirmation information, and the output is updated purchase history data.

[0544] (Application Example 1)

[0545] 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."

[0546] Managing consumables is a cumbersome task for most households, and misjudging remaining quantities or the timing of repurchases often leads to inconvenience. Furthermore, gathering information and comparing prices to purchase the right products at the optimal price requires considerable time and effort. In addition, effective analysis of consumption patterns and savings advice are necessary to avoid wasteful spending and unnecessary expenses on consumables.

[0547] 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.

[0548] This invention includes a server that automatically checks the remaining amount of consumables using sensors, a means for selecting the optimal product from an e-commerce platform based on the consumable shortage information, and a user interface means for providing consumers with purchase information for the selected product. This streamlines the management of consumables in the home and reduces the time and effort required for purchasing. Furthermore, by utilizing purchase history, consumption patterns can be analyzed and practical advice for saving on living expenses can be provided.

[0549] "Consumable goods" are products that gradually decrease in quantity through use and require repurchase.

[0550] "Detection means" refers to a device or system used to check the remaining amount of consumables.

[0551] "Out of stock information" refers to notifications or data issued when consumables fall below a certain standard.

[0552] "Selection means" refers to a device or method for selecting the most suitable product from among several products.

[0553] An "e-commerce platform" is an online environment for buying and selling goods and services via the internet.

[0554] "User interface means" refers to a system or device for a user to receive information and perform operations.

[0555] A "monitoring system" is a configuration for periodically checking the status of a system or device and notifying information as needed.

[0556] "Analysis methods" refer to methods for deriving results based on collected data, following certain rules or algorithms.

[0557] "Advice provision means" refers to a method or system for providing useful advice to users based on data analysis results.

[0558] The system for implementing this invention is designed to streamline the management of household consumables and optimize the purchasing process. At the heart of this system is a monitoring device composed of sensors and an information processing terminal that works in conjunction with it.

[0559] First, the monitoring system automatically measures the remaining amount of consumables. For example, a weighing scale sensor installed in the kitchen or bathroom will issue a warning when it falls below a certain threshold. These sensors connect to the network using hardware such as a Raspberry Pi and send the collected data to a server.

[0560] The server automatically collects information on relevant products from e-commerce platforms based on the received data. This process uses Python to collect and analyze product information and is built as a web application using Flask. Product information is collected using web scraping technology, and an AI algorithm selects the best purchase candidates, including price, shipping conditions, and seller ratings.

[0561] The information processing terminal provides users with selected product information. At this stage, the user interface is built using React Native or other mobile application frameworks to allow consumers to easily complete their purchase.

[0562] Furthermore, the server stores users' purchase history in a database and analyzes consumption patterns using a Python library. Based on this analysis, a recommendation system is designed via a generating AI model to provide user-friendly money-saving advice.

[0563] For example, when the amount of tempura oil remaining is low, a sensor detects this and the server sends a notification saying, "Your tempura oil stock is low. We have tempura oil on sale; would you like to purchase it?" At this time, the AI ​​model is prompted with the message, "I am considering purchasing new tempura oil. Please suggest the best purchase plan based on the current stock status and sales information." This allows consumers to quickly make the best purchasing decision.

[0564] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0565] Step 1:

[0566] The monitoring device detects the remaining amount of consumables. A weight sensor attached to the consumable measures the remaining amount, and this data is sent to the server via a Raspberry Pi. The input is the numerical data measured by the sensor, and the output is the remaining amount information of the consumable sent to the server.

[0567] Step 2:

[0568] The server extracts information on the shortage of necessary products based on consumable information. The server analyzes the received remaining quantity information and generates a shortage notification if it falls below a certain threshold. The input is the remaining quantity information of consumables, and the output is a notification of the shortage.

[0569] Step 3:

[0570] The server collects product information from e-commerce platforms based on out-of-stock information. It uses Python to perform web scraping, retrieving product data based on price, shipping conditions, and seller ratings. The input is out-of-stock information, and the output is a list of the best product candidates.

[0571] Step 4:

[0572] The information processing terminal notifies the user of product candidates received from the server. An application using React Native displays detailed product information and provides a user interface to confirm the user's purchase intention. The input is a list of product candidates, and the output is the presentation of product information to the user.

[0573] Step 5:

[0574] The user makes a purchase decision based on the presented product options and enters it into the terminal. This is sent to the server as a purchase order and passed on to the next process. The input is the product selection, and the output is the purchase order.

[0575] Step 6:

[0576] The server accumulates user purchase history and analyzes consumption patterns. Past purchase data is stored in a database, and consumption trends are analyzed using a Python library. The input is the purchase order, and the output is the analysis result of the consumption pattern.

[0577] Step 7:

[0578] The server generates savings advice using a generative AI model based on the analysis results. It utilizes the "generative AI model and prompt text" to generate specific advice for the user and send it to the terminal. The input is the analysis results of consumption patterns, and the output is savings advice.

[0579] 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.

[0580] This invention combines a system for streamlining the management and purchase of household consumables with an emotion engine that recognizes user emotions. This makes it possible to provide suggestions and advice that take into account the consumer's psychological state.

[0581] Utilizing the Emotion Engine

[0582] The device collects emotional data through interaction with the user. This emotional data includes the user's voice tone, selection speed, and facial expression data (if a camera is used). The device sends this data to a server.

[0583] The server uses an emotion engine to analyze the received emotion data and identify the user's emotional state. For example, it can determine if the user is feeling stressed about shopping or hesitant to purchase a particular product. Based on this, the server adjusts the way it provides information and makes purchase suggestions according to the user's emotions.

[0584] Advice and user interface adjustments

[0585] The server generates personalized advice based on the emotional state analyzed by the emotion engine. For example, if a user is feeling anxious about a purchase, the server will offer suggestions that include additional information and reassurance. It can also add words of encouragement regarding saving money.

[0586] Furthermore, the device adjusts the interface to reflect the user's emotional state. This may include changing the interface's color scheme to calmer colors, slowing down the pace, or simplifying it by reducing the number of options.

[0587] Specific example

[0588] This explains how a user might feel stressed while considering purchasing consumables. The device collects emotional data based on the user's tone of voice and the time spent on each action, and immediately analyzes it using an emotion engine. If the server detects emotions indicating fear or anxiety, it provides the user with suggestions including detailed product reviews, safety assurances, and past successful purchase examples. It also changes the interface background color to stress-reducing colors such as blue or green to provide a sense of security.

[0589] In this way, the present invention enables flexible responses to consumers' psychological states, and can evolve the management and purchase of consumables into a more personal and comfortable experience.

[0590] The following describes the processing flow.

[0591] Step 1:

[0592] The device monitors user actions and collects emotion-related data such as voice input, touch speed, and facial expression data. Each time data is collected, the device sends it to the server.

[0593] Step 2:

[0594] The server receives emotion-related data sent from the terminal and analyzes the data using an emotion engine. It identifies the user's emotional state and evaluates how that state affects the consumable purchase process.

[0595] Step 3:

[0596] The server adjusts purchase suggestions for the user based on the emotional state identified by the emotion engine. For example, if the server determines that the user is stressed, the purchase suggestions will include detailed product information, reviews, and reassuring elements.

[0597] Step 4:

[0598] The server also adjusts the user interface display based on the results of the emotion engine. For example, it might change to a calming color scheme or switch to a simpler interface.

[0599] Step 5:

[0600] The server sends the adjusted purchase proposal and interface information to the terminal. The terminal then presents the user with a purchase proposal optimized for them.

[0601] Step 6:

[0602] Users review the presented purchase information, comfortably select products in an environment that takes their emotional state into consideration, and proceed with the purchase.

[0603] Step 7:

[0604] After a user completes a purchase, the device sends the results to a server, where they are stored in a purchase history database. This data is used for future sentiment trend analysis.

[0605] Step 8:

[0606] The server analyzes long-term consumption patterns and emotional trends based on user sentiment data and purchase history, and uses this information to improve the quality of future suggestions and advice.

[0607] (Example 2)

[0608] 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."

[0609] Traditional consumables management systems could detect user consumption trends and remaining consumable levels and make purchase suggestions, but they could not provide advice that took into account the individual user's emotional state. This poses a challenge in reducing the stress and anxiety users feel when shopping and providing a more comfortable purchasing experience. Therefore, there is a need for a system that can detect user emotions in real time and provide personalized suggestions and advice accordingly.

[0610] 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.

[0611] In this invention, the server includes detection means for detecting the remaining amount of consumables, emotion recognition means for recognizing the user's emotions, and advice provision means for providing optimized advice to the user based on the emotion data. This enables an interactive and personalized purchasing experience that responds to the user's emotions.

[0612] A "detection means" is a mechanism for determining the remaining quantity and condition of consumables in real time and acquiring that information.

[0613] "Selection methods" refer to the process of comparing multiple transaction methods and product information to select the most suitable purchase option for the user.

[0614] A "display mechanism" is an interface that presents selected purchase options and advice to the user, enabling interactive communication.

[0615] "Emotion recognition means" refers to technology that analyzes a user's emotions from their tone of voice, facial expressions, and how they operate the device, and uses that information to understand their emotional state.

[0616] A "proposal provision method" is a system that provides users with precise and personalized advice based on data obtained through emotion recognition methods.

[0617] "Adjustment mechanisms" refer to functions that change the interface's color tone and display speed according to the user's emotional state, providing a comfortable user environment.

[0618] "Analysis methods" refer to the process of analyzing users' purchase history and behavioral patterns, and using the results to clarify their consumption trends.

[0619] A "proposal provision system" is a system that, based on information obtained through analysis, makes product purchase suggestions to users that take into account cost savings and convenience.

[0620] This invention is a system for individually optimizing the management and purchase of consumables. This system is implemented through interaction between terminals, servers, and users. The specific method is described below.

[0621] The device is initially installed in the home and is equipped with a microphone to collect the user's voice, a touch sensor to detect operation speed, and a camera to analyze facial expressions. Through this hardware, the device acquires the user's emotional data in real time. The device also has the function to transmit this emotional data to a server.

[0622] The server receives emotional data transmitted from the terminal and uses a generative AI model that functions as an emotion recognition tool. This AI model analyzes the user's voice tone, operation speed, and facial expression data to identify the user's emotional state. Based on the results, it uses an advisory tool to provide personalized advice tailored to the user's emotions. This advice may include reassuring information and past success stories to give the user a sense of security and support rational purchasing decisions.

[0623] Furthermore, the terminal adjusts the user interface based on feedback from the server. Specifically, it uses information obtained from emotion recognition to change the interface's color scheme to calming colors such as blue or green, and adjust the pace of information presentation. In this way, it provides an environment in which users can operate the system with reduced stress.

[0624] A concrete example is when a user is about to purchase a new detergent. If the user shows anxiety, the device collects their tone of voice and facial expression and sends it to the server, which then sends a prompt to the AI ​​model. A possible prompt might be: "Create advice to alleviate the user's anxiety about purchasing a new detergent. Include detailed product reviews and past success stories to provide reassurance." The server then sends the resulting advice back to the device, presenting the user with the most relevant information.

[0625] This system enables the management and purchase support of consumables while taking user emotions into consideration, providing more personalized convenience.

[0626] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0627] Step 1:

[0628] The device senses the user's voice, operation speed, and facial expressions in real time. This is achieved using a microphone, touch sensor, and camera. Inputs include the user's voice tone, touch panel usage frequency, and facial expression data captured by the camera. Based on this input data, the device generates emotion data and sends it to the server as output.

[0629] Step 2:

[0630] The server receives emotional data from the terminal as input. Using emotion recognition tools, the server analyzes the emotional data with a generative AI model to identify the user's emotional state. This process involves analyzing voice tone and speed, as well as facial expression detection results, and performing data calculations to determine the emotional state. The user's emotional state, as a result of the analysis, is then output.

[0631] Step 3:

[0632] The server generates optimized advice based on the emotional state. The input is the analyzed emotional state, and the AI ​​model generates advice by inputting prompt sentences. The prompt sentence used here is: "Create advice to alleviate the user's anxiety when purchasing a new detergent. Include detailed product reviews and past success stories to provide reassurance." Personalized advice is then generated as output.

[0633] Step 4:

[0634] The terminal receives advice and emotional state information sent from the server as input. At this point, the user interface is adjusted. Specifically, the interface's color scheme is changed to calming colors such as blue or green, the speed of information presentation is increased, and the operation menus are simplified, resulting in a comfortable operating environment for the user.

[0635] Step 5:

[0636] Users make product selection and purchase decisions based on a tailored interface and advice. Inputs are visual and advisory information, and the output is a rational and emotionally conscious purchasing decision based on this information.

[0637] (Application Example 2)

[0638] 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."

[0639] In the purchasing process for consumable goods, mechanical suggestions are often made without considering the user's emotional state, which can result in stress and anxiety. This disregard for psychological factors in consumer behavior creates a problem where the consumption process becomes uncomfortable.

[0640] 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.

[0641] In this invention, the server includes emotion recognition means, interface adjustment means, and suggestion personalization means. This enables personalized suggestions and interface adjustments based on the user's emotional state.

[0642] "Detection means" refers to devices or methods for detecting the remaining amount of consumables using sensors or data analysis.

[0643] "Selection method" refers to a process or system for selecting the most suitable purchase candidate based on product information collected from multiple e-commerce platforms.

[0644] "User interface means" refers to the operating interface or software functions used to present information to the user and accept orders.

[0645] "Analysis methods" refer to methods and algorithms for collecting and accumulating users' past purchase history and analyzing their consumption patterns.

[0646] "Advice provision means" refers to a function or method for providing users with advice on saving money or making purchases based on analysis results.

[0647] "Emotion recognition means" refers to technologies and systems that collect emotional data from the user's voice tone, facial expressions, operation speed, etc., and determine their emotional state.

[0648] "Interface adjustment means" refers to a method for providing a more comfortable user experience by adjusting the visual and operational elements of the user interface according to the user's emotional state.

[0649] "Personalized proposal methods" refer to processes and algorithms that generate personalized purchase proposals based on the user's emotional state.

[0650] The system of this invention optimizes the management and purchasing process of consumables by having a home robot and a server work together. The robot is equipped with data collection devices such as a camera and a microphone, which are used to acquire user emotional data. When the terminal is a robot, it checks the inventory of consumables through patrolling the home and interacting with the user. It also collects emotional data from the user's voice tone, facial expressions, and operation speed, and transmits it to the server.

[0651] Based on this data, the server uses an emotion recognition engine to analyze the user's psychological state. Based on the analysis results, it sends instructions back to the robot to adjust the user interface. These adjustments include changing the color scheme according to the user's psychological state and simplifying the operating procedures. The server also personalizes purchase suggestions based on the user's emotions and presents appropriate product information through the robot.

[0652] For example, if a user realizes they are running low on bottled water during a busy time before going out, the robot will offer a suggestion such as, "You can purchase your favorite water at your leisure. When you're busy, why not try one of these value sets?" To reduce user stress, the robot's display is set to calming blue and green tones.

[0653] An example of a prompt for the generating AI model is: "Generate a calm, simple, and reassuring suggestion to encourage users to easily purchase items with low stock when they are feeling stressed."

[0654] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0655] Step 1:

[0656] The device uses its built-in camera and microphone to sense the user's voice tone, facial expressions, and operation speed, and collects this data. The input is the user's voice and video, and through data processing, it extracts feature values ​​that indicate the user's emotions and outputs them as emotion data.

[0657] Step 2:

[0658] The device sends collected emotional data to the server. The server receives this emotional data and analyzes it using an emotion recognition engine. The input is emotional data, and the server outputs the estimated emotional state of the user through the analysis process.

[0659] Step 3:

[0660] The server determines how to display the user interface based on the analyzed emotional state of the user. For example, if the result indicates stress, it generates instructions to change the interface color to blue or green. The input is the emotional state, and the output is an interface adjustment instruction.

[0661] Step 4:

[0662] The server uses selection criteria to choose the most suitable product information to generate purchase suggestions tailored to the user's emotional state. This process also considers the user's purchase history and market information. The input consists of emotional state and product data, while the output is personalized product suggestions.

[0663] Step 5:

[0664] The generated product suggestions and interface adjustment instructions are sent to the terminal, which then displays them to the user. The user can review the displayed content and accept or reject the suggestions. The input is the suggestions and instructions from the server, and the output is the content presented to the user.

[0665] Step 6:

[0666] If the user approves the proposed purchase, the terminal returns the order information to the server. Based on the received information, the server processes the user's order through the e-commerce platform. The input is the user's order information, and the output is a notification that the order processing is complete.

[0667] By using a generative AI model and prompt text, the adjustment of each suggested text and interface is automated, resulting in a comfortable and less stressful consumable purchase experience for users.

[0668] 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.

[0669] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An 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.

[0670] 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.

[0671] [Fourth Embodiment]

[0672] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0673] 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.

[0674] 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).

[0675] 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.

[0676] 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.

[0677] 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).

[0678] 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.

[0679] 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.

[0680] 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.

[0681] 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.

[0682] 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.

[0683] 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.

[0684] 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".

[0685] This invention provides a system for efficiently managing, purchasing, and saving on consumables within the home. Specific embodiments are described below.

[0686] Detection and notification

[0687] The core of this system lies in a detection mechanism that monitors the remaining amount of consumables. Sensors physically measure the remaining amount of consumables, and when it falls below a certain threshold, a notification is sent to the terminal. Users can also manually report shortages to the terminal. This detection information is sent to a server and used as data for the next step.

[0688] Product information acquisition and proposal

[0689] Based on the received out-of-stock information, the server uses AI to collect product information from multiple e-commerce platforms. This process collects and analyzes data such as price, delivery time, and seller ratings. The server then selects the most suitable products based on these criteria and generates recommendations for purchasing consumables.

[0690] The terminal displays suggestions generated by the server on the screen, offering the user choices. These suggestions include detailed product information as well as the benefits of purchasing. The user can easily select the desired product from the displayed options and proceed with the purchase.

[0691] Purchase history analysis and advice

[0692] The system stores a history of purchased items in a database. The server analyzes the user's consumption patterns based on this data. At the same time, it obtains general consumption trends by comparing this data with that of other users.

[0693] The server uses the analysis results to generate advice on optimizing consumption. This money-saving advice is provided to the user through their terminal. For example, if someone is overconsuming, it will offer specific suggestions such as "you can reduce costs by buying in bulk."

[0694] Generating an order button

[0695] For consumables that users frequently purchase, the device provides a custom order button for easy reordering. Using this button, users can complete the purchase process with a single click, without having to conduct further market research or price comparisons.

[0696] As described above, the system of the present invention provides consumers with efficient and convenient management and purchase of consumables, contributing to savings and optimization of lifestyle.

[0697] The following describes the processing flow.

[0698] Step 1:

[0699] The sensor monitors the remaining levels of household consumables in real time. When a set threshold is broken, it sends detection information to the terminal in real time. Users can also manually enter depletion information into the terminal for consumables that do not have sensors.

[0700] Step 2:

[0701] The device sends the received out-of-stock information to the server. The device also provides user profile information to the server as part of the request, enabling more personalized suggestions.

[0702] Step 3:

[0703] Based on the received out-of-stock information, the server uses an AI agent to collect information on relevant products from multiple e-commerce platforms. It retrieves data such as price, availability, shipping options, and seller ratings.

[0704] Step 4:

[0705] The server analyzes the collected product information and selects the best purchase candidates. It compares products based on criteria such as price, delivery speed, and seller reliability to narrow down the options to the most suitable product for the user.

[0706] Step 5:

[0707] The server sends the selected purchase candidates to the terminal and creates a purchase proposal for the user. This proposal includes selection criteria and detailed product information.

[0708] Step 6:

[0709] The terminal displays purchase suggestions received from the server, presenting the user with available options. The user can compare the displayed options and select the desired product.

[0710] Step 7:

[0711] The user clicks the order button for the selected product to confirm the purchase.

[0712] Step 8:

[0713] The terminal sends the user's order decision to the server and provides the necessary information to initiate the purchase process.

[0714] Step 9:

[0715] Based on the received order information, the server sends a purchase request to the e-commerce platform and completes the transaction. This process includes sending a confirmation email or notification.

[0716] Step 10:

[0717] Once an order is completed, the server saves the purchase history to a database. This historical data is used to analyze future consumption patterns.

[0718] Step 11:

[0719] The server analyzes accumulated purchase history and generates reports on consumption patterns, including comparisons with other user data.

[0720] Step 12:

[0721] The server generates savings advice based on the analysis results and provides it to the user via the terminal. This advice includes suggestions for optimizing purchase frequency and bulk buying.

[0722] Step 13:

[0723] The device generates custom order buttons for frequently purchased consumables to enhance user convenience. These buttons provide users with the ability to easily reorder.

[0724] (Example 1)

[0725] 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".

[0726] Managing inventory of consumables is time-consuming, and it's difficult to get advice on efficient purchasing and cost savings. Furthermore, users need to research and compare a large amount of information themselves to make the best purchase choices, which adds to the complexity.

[0727] 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.

[0728] In this invention, the server includes means for automatically detecting the inventory of consumables, means for suggesting the optimal purchase option using a generative AI model, and means for analyzing the user's behavior history and providing advice on saving money. As a result, inventory management of consumables is automated, and users can purchase necessary consumables efficiently and economically without having to collect complex information.

[0729] "Consumable goods" are items that are used on a daily basis and consumed over time.

[0730] A "detection means" is a device or method that has the function of sensing a specific physical quantity or state, and is used to understand the inventory status of consumables, etc.

[0731] "Communication means" refers to technologies and devices used to transmit information from one location to another.

[0732] A "generative AI model" is a collection of programs or algorithms built to perform specific tasks using artificial intelligence technology.

[0733] A "selection means" is a device or method for selecting the most appropriate option from a given set of choices.

[0734] "Display means" refers to devices or methods for visually presenting information to a user.

[0735] "Analysis methods" refer to techniques and methods for processing data and evaluating its content to derive specific conclusions or results.

[0736] "Means of providing advice" refers to technologies or methods for providing users with useful suggestions or guidance based on analyzed information.

[0737] This invention is a system that automates the management of household consumables and optimizes efficient purchasing and consumption. Specific embodiments for carrying out the invention are described below.

[0738] The system primarily consists of three elements: terminals, servers, and users. First, the terminals acquire data from sensors attached to various household consumables. These sensors include ultrasonic distance sensors and load sensors, which can physically measure the remaining amount of consumables. For example, they can detect when the amount of detergent falls below a certain level.

[0739] Next, the device transmits this detection data to a server via Wi-Fi or Bluetooth. The server processes the information received through the communication and uses a generative AI model to collect optimal purchase options from multiple data platforms. In this process, product price, delivery time, and seller ratings are important analytical factors. For example, it may suggest information such as "a specific brand of detergent can be delivered as early as the next day."

[0740] Furthermore, the server accumulates purchase history and analyzes consumption patterns to provide users with advice on saving money. This analysis considers not only past purchase data but also the general consumption trends of other users. Based on this analysis, users can receive specific advice such as, "The best time to make your next purchase is in three weeks."

[0741] Furthermore, the terminal has a feature that allows users to place custom buttons for frequently purchased consumables, enabling one-click reordering. This button is particularly useful for frequently used consumables. For example, a reorder button for regularly purchased pet food would be convenient.

[0742] An example of a prompt is, "Create a program that uses sensors to detect the inventory of household consumables and optimizes online purchases." Using such prompts enables efficient consumable management by the model.

[0743] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0744] Step 1:

[0745] The terminal acquires physical data from sensors attached to consumables. These sensors, for example, ultrasonic distance sensors, measure the remaining amount of consumables in their containers. This input data is raw data from the sensors. The terminal's built-in processor analyzes this data to determine whether the remaining amount of consumables falls below a set threshold. The analysis results generate information about the inventory status.

[0746] Step 2:

[0747] Based on the analysis results, the device generates an alert notification if it detects that the remaining amount of consumables has fallen below a threshold. This notification informs the user through a message displayed on the screen or an audio alert. This alert indicates that the user needs to replenish the consumables.

[0748] Step 3:

[0749] The user uses the terminal interface to check for detected stock shortages and select the consumables that need to be replenished. This selection information is entered, and the terminal prepares to send this data to the server.

[0750] Step 4:

[0751] The device transmits information about insufficient stock to the server via Wi-Fi or Bluetooth communication. The input is user selection information, and the output is communication data to the server.

[0752] Step 5:

[0753] The server receives the submitted information about insufficient stock. The server then uses a generative AI model to begin collecting optimal purchase options from multiple data platforms. The input is the user's information about insufficient stock, and the output is a list of product information including price, shipping conditions, and seller ratings.

[0754] Step 6:

[0755] The server analyzes the collected product information and compares products based on price, shipping conditions, and seller ratings. It determines the optimal choice and generates a purchase suggestion. The input is a list of product information, and the output is the optimal purchase choice.

[0756] Step 7:

[0757] The server sends the optimal purchase suggestion to the device. This suggestion includes specific product information and benefits. The device displays this suggestion information to the user. The user reviews the received suggestion and makes a purchase decision.

[0758] Step 8:

[0759] The user selects the necessary consumables from the options presented on the device and proceeds with the purchase. This selection is entered into the device, and once the purchase process is complete, confirmation information is sent from the device to the server.

[0760] Step 9:

[0761] The server stores the user's purchase history in a database. This allows the server to analyze consumption patterns and prepare to generate advice on future purchase timings and savings through bulk buying. The input is purchase confirmation information, and the output is updated purchase history data.

[0762] (Application Example 1)

[0763] 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".

[0764] Managing consumables is a cumbersome task for most households, and misjudging remaining quantities or the timing of repurchases often leads to inconvenience. Furthermore, gathering information and comparing prices to purchase the right products at the optimal price requires considerable time and effort. In addition, effective analysis of consumption patterns and savings advice are necessary to avoid wasteful spending and unnecessary expenses on consumables.

[0765] 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.

[0766] This invention includes a server that automatically checks the remaining amount of consumables using sensors, a means for selecting the optimal product from an e-commerce platform based on the consumable shortage information, and a user interface means for providing consumers with purchase information for the selected product. This streamlines the management of consumables in the home and reduces the time and effort required for purchasing. Furthermore, by utilizing purchase history, consumption patterns can be analyzed and practical advice for saving on living expenses can be provided.

[0767] "Consumable goods" are products that gradually decrease in quantity through use and require repurchase.

[0768] "Detection means" refers to a device or system used to check the remaining amount of consumables.

[0769] "Out of stock information" refers to notifications or data issued when consumables fall below a certain standard.

[0770] "Selection means" refers to a device or method for selecting the most suitable product from among several products.

[0771] An "e-commerce platform" is an online environment for buying and selling goods and services via the internet.

[0772] "User interface means" refers to a system or device for a user to receive information and perform operations.

[0773] A "monitoring system" is a configuration for periodically checking the status of a system or device and notifying information as needed.

[0774] "Analysis methods" refer to methods for deriving results based on collected data, following certain rules or algorithms.

[0775] "Advice provision means" refers to a method or system for providing useful advice to users based on data analysis results.

[0776] The system for implementing this invention is designed to streamline the management of household consumables and optimize the purchasing process. At the heart of this system is a monitoring device composed of sensors and an information processing terminal that works in conjunction with it.

[0777] First, the monitoring system automatically measures the remaining amount of consumables. For example, a weighing scale sensor installed in the kitchen or bathroom will issue a warning when it falls below a certain threshold. These sensors connect to the network using hardware such as a Raspberry Pi and send the collected data to a server.

[0778] The server automatically collects information on relevant products from e-commerce platforms based on the received data. This process uses Python to collect and analyze product information and is built as a web application using Flask. Product information is collected using web scraping technology, and an AI algorithm selects the best purchase candidates, including price, shipping conditions, and seller ratings.

[0779] The information processing terminal provides users with selected product information. At this stage, the user interface is built using React Native or other mobile application frameworks to allow consumers to easily complete their purchase.

[0780] Furthermore, the server stores users' purchase history in a database and analyzes consumption patterns using a Python library. Based on this analysis, a recommendation system is designed via a generating AI model to provide user-friendly money-saving advice.

[0781] For example, when the amount of tempura oil remaining is low, a sensor detects this and the server sends a notification saying, "Your tempura oil stock is low. We have tempura oil on sale; would you like to purchase it?" At this time, the AI ​​model is prompted with the message, "I am considering purchasing new tempura oil. Please suggest the best purchase plan based on the current stock status and sales information." This allows consumers to quickly make the best purchasing decision.

[0782] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0783] Step 1:

[0784] The monitoring device detects the remaining amount of consumables. A weight sensor attached to the consumable measures the remaining amount, and this data is sent to the server via a Raspberry Pi. The input is the numerical data measured by the sensor, and the output is the remaining amount information of the consumable sent to the server.

[0785] Step 2:

[0786] The server extracts information on the shortage of necessary products based on consumable information. The server analyzes the received remaining quantity information and generates a shortage notification if it falls below a certain threshold. The input is the remaining quantity information of consumables, and the output is a notification of the shortage.

[0787] Step 3:

[0788] The server collects product information from e-commerce platforms based on out-of-stock information. It uses Python to perform web scraping, retrieving product data based on price, shipping conditions, and seller ratings. The input is out-of-stock information, and the output is a list of the best product candidates.

[0789] Step 4:

[0790] The information processing terminal notifies the user of product candidates received from the server. An application using React Native displays detailed product information and provides a user interface to confirm the user's purchase intention. The input is a list of product candidates, and the output is the presentation of product information to the user.

[0791] Step 5:

[0792] The user makes a purchase decision based on the presented product options and enters it into the terminal. This is sent to the server as a purchase order and passed on to the next process. The input is the product selection, and the output is the purchase order.

[0793] Step 6:

[0794] The server accumulates user purchase history and analyzes consumption patterns. Past purchase data is stored in a database, and consumption trends are analyzed using a Python library. The input is the purchase order, and the output is the analysis result of the consumption pattern.

[0795] Step 7:

[0796] The server generates savings advice using a generative AI model based on the analysis results. It utilizes the "generative AI model and prompt text" to generate specific advice for the user and send it to the terminal. The input is the analysis results of consumption patterns, and the output is savings advice.

[0797] 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.

[0798] This invention combines a system for streamlining the management and purchase of household consumables with an emotion engine that recognizes user emotions. This makes it possible to provide suggestions and advice that take into account the consumer's psychological state.

[0799] Utilizing the Emotion Engine

[0800] The device collects emotional data through interaction with the user. This emotional data includes the user's voice tone, selection speed, and facial expression data (if a camera is used). The device sends this data to a server.

[0801] The server uses an emotion engine to analyze the received emotion data and identify the user's emotional state. For example, it can determine if the user is feeling stressed about shopping or hesitant to purchase a particular product. Based on this, the server adjusts the way it provides information and makes purchase suggestions according to the user's emotions.

[0802] Advice and user interface adjustments

[0803] The server generates personalized advice based on the emotional state analyzed by the emotion engine. For example, if a user is feeling anxious about a purchase, the server will offer suggestions that include additional information and reassurance. It can also add words of encouragement regarding saving money.

[0804] Furthermore, the device adjusts the interface to reflect the user's emotional state. This may include changing the interface's color scheme to calmer colors, slowing down the pace, or simplifying it by reducing the number of options.

[0805] Specific example

[0806] This explains how a user might feel stressed while considering purchasing consumables. The device collects emotional data based on the user's tone of voice and the time spent on each action, and immediately analyzes it using an emotion engine. If the server detects emotions indicating fear or anxiety, it provides the user with suggestions including detailed product reviews, safety assurances, and past successful purchase examples. It also changes the interface background color to stress-reducing colors such as blue or green to provide a sense of security.

[0807] In this way, the present invention enables flexible responses to consumers' psychological states, and can evolve the management and purchase of consumables into a more personal and comfortable experience.

[0808] The following describes the processing flow.

[0809] Step 1:

[0810] The device monitors user actions and collects emotion-related data such as voice input, touch speed, and facial expression data. Each time data is collected, the device sends it to the server.

[0811] Step 2:

[0812] The server receives emotion-related data sent from the terminal and analyzes the data using an emotion engine. It identifies the user's emotional state and evaluates how that state affects the consumable purchase process.

[0813] Step 3:

[0814] The server adjusts purchase suggestions for the user based on the emotional state identified by the emotion engine. For example, if the server determines that the user is stressed, the purchase suggestions will include detailed product information, reviews, and reassuring elements.

[0815] Step 4:

[0816] The server also adjusts the user interface display based on the results of the emotion engine. For example, it might change to a calming color scheme or switch to a simpler interface.

[0817] Step 5:

[0818] The server sends the adjusted purchase proposal and interface information to the terminal. The terminal then presents the user with a purchase proposal optimized for them.

[0819] Step 6:

[0820] Users review the presented purchase information, comfortably select products in an environment that takes their emotional state into consideration, and proceed with the purchase.

[0821] Step 7:

[0822] After a user completes a purchase, the device sends the results to a server, where they are stored in a purchase history database. This data is used for future sentiment trend analysis.

[0823] Step 8:

[0824] The server analyzes long-term consumption patterns and emotional trends based on user sentiment data and purchase history, and uses this information to improve the quality of future suggestions and advice.

[0825] (Example 2)

[0826] 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".

[0827] Traditional consumables management systems could detect user consumption trends and remaining consumable levels and make purchase suggestions, but they could not provide advice that took into account the individual user's emotional state. This poses a challenge in reducing the stress and anxiety users feel when shopping and providing a more comfortable purchasing experience. Therefore, there is a need for a system that can detect user emotions in real time and provide personalized suggestions and advice accordingly.

[0828] 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.

[0829] In this invention, the server includes detection means for detecting the remaining amount of consumables, emotion recognition means for recognizing the user's emotions, and advice provision means for providing optimized advice to the user based on the emotion data. This enables an interactive and personalized purchasing experience that responds to the user's emotions.

[0830] A "detection means" is a mechanism for determining the remaining quantity and condition of consumables in real time and acquiring that information.

[0831] "Selection methods" refer to the process of comparing multiple transaction methods and product information to select the most suitable purchase option for the user.

[0832] A "display mechanism" is an interface that presents selected purchase options and advice to the user, enabling interactive communication.

[0833] "Emotion recognition means" refers to technology that analyzes a user's emotions from their tone of voice, facial expressions, and how they operate the device, and uses that information to understand their emotional state.

[0834] A "proposal provision method" is a system that provides users with precise and personalized advice based on data obtained through emotion recognition methods.

[0835] "Adjustment mechanisms" refer to functions that change the interface's color tone and display speed according to the user's emotional state, providing a comfortable user environment.

[0836] "Analysis methods" refer to the process of analyzing users' purchase history and behavioral patterns, and using the results to clarify their consumption trends.

[0837] A "proposal provision system" is a system that, based on information obtained through analysis, makes product purchase suggestions to users that take into account cost savings and convenience.

[0838] This invention is a system for individually optimizing the management and purchase of consumables. This system is implemented through interaction between terminals, servers, and users. The specific method is described below.

[0839] The device is initially installed in the home and is equipped with a microphone to collect the user's voice, a touch sensor to detect operation speed, and a camera to analyze facial expressions. Through this hardware, the device acquires the user's emotional data in real time. The device also has the function to transmit this emotional data to a server.

[0840] The server receives emotional data transmitted from the terminal and uses a generative AI model that functions as an emotion recognition tool. This AI model analyzes the user's voice tone, operation speed, and facial expression data to identify the user's emotional state. Based on the results, it uses an advisory tool to provide personalized advice tailored to the user's emotions. This advice may include reassuring information and past success stories to give the user a sense of security and support rational purchasing decisions.

[0841] Furthermore, the terminal adjusts the user interface based on feedback from the server. Specifically, it uses information obtained from emotion recognition to change the interface's color scheme to calming colors such as blue or green, and adjust the pace of information presentation. In this way, it provides an environment in which users can operate the system with reduced stress.

[0842] A concrete example is when a user is about to purchase a new detergent. If the user shows anxiety, the device collects their tone of voice and facial expression and sends it to the server, which then sends a prompt to the AI ​​model. A possible prompt might be: "Create advice to alleviate the user's anxiety about purchasing a new detergent. Include detailed product reviews and past success stories to provide reassurance." The server then sends the resulting advice back to the device, presenting the user with the most relevant information.

[0843] This system enables the management and purchase support of consumables while taking user emotions into consideration, providing more personalized convenience.

[0844] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0845] Step 1:

[0846] The device senses the user's voice, operation speed, and facial expressions in real time. This is achieved using a microphone, touch sensor, and camera. Inputs include the user's voice tone, touch panel usage frequency, and facial expression data captured by the camera. Based on this input data, the device generates emotion data and sends it to the server as output.

[0847] Step 2:

[0848] The server receives emotional data from the terminal as input. Using emotion recognition tools, the server analyzes the emotional data with a generative AI model to identify the user's emotional state. This process involves analyzing voice tone and speed, as well as facial expression detection results, and performing data calculations to determine the emotional state. The user's emotional state, as a result of the analysis, is then output.

[0849] Step 3:

[0850] The server generates optimized advice based on the emotional state. The input is the analyzed emotional state, and the AI ​​model generates advice by inputting prompt sentences. The prompt sentence used here is: "Create advice to alleviate the user's anxiety when purchasing a new detergent. Include detailed product reviews and past success stories to provide reassurance." Personalized advice is then generated as output.

[0851] Step 4:

[0852] The terminal receives advice and emotional state information sent from the server as input. At this point, the user interface is adjusted. Specifically, the interface's color scheme is changed to calming colors such as blue or green, the speed of information presentation is increased, and the operation menus are simplified, resulting in a comfortable operating environment for the user.

[0853] Step 5:

[0854] Users make product selection and purchase decisions based on a tailored interface and advice. Inputs are visual and advisory information, and the output is a rational and emotionally conscious purchasing decision based on this information.

[0855] (Application Example 2)

[0856] 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".

[0857] In the purchasing process for consumable goods, mechanical suggestions are often made without considering the user's emotional state, which can result in stress and anxiety. This disregard for psychological factors in consumer behavior creates a problem where the consumption process becomes uncomfortable.

[0858] 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.

[0859] In this invention, the server includes emotion recognition means, interface adjustment means, and suggestion personalization means. This enables personalized suggestions and interface adjustments based on the user's emotional state.

[0860] "Detection means" refers to devices or methods for detecting the remaining amount of consumables using sensors or data analysis.

[0861] "Selection method" refers to a process or system for selecting the most suitable purchase candidate based on product information collected from multiple e-commerce platforms.

[0862] "User interface means" refers to the operating interface or software functions used to present information to the user and accept orders.

[0863] "Analysis methods" refer to methods and algorithms for collecting and accumulating users' past purchase history and analyzing their consumption patterns.

[0864] "Advice provision means" refers to a function or method for providing users with advice on saving money or making purchases based on analysis results.

[0865] "Emotion recognition means" refers to technologies and systems that collect emotional data from the user's voice tone, facial expressions, operation speed, etc., and determine their emotional state.

[0866] "Interface adjustment means" refers to a method for providing a more comfortable user experience by adjusting the visual and operational elements of the user interface according to the user's emotional state.

[0867] "Personalized proposal methods" refer to processes and algorithms that generate personalized purchase proposals based on the user's emotional state.

[0868] The system of this invention optimizes the management and purchasing process of consumables by having a home robot and a server work together. The robot is equipped with data collection devices such as a camera and a microphone, which are used to acquire user emotional data. When the terminal is a robot, it checks the inventory of consumables through patrolling the home and interacting with the user. It also collects emotional data from the user's voice tone, facial expressions, and operation speed, and transmits it to the server.

[0869] Based on this data, the server uses an emotion recognition engine to analyze the user's psychological state. Based on the analysis results, it sends instructions back to the robot to adjust the user interface. These adjustments include changing the color scheme according to the user's psychological state and simplifying the operating procedures. The server also personalizes purchase suggestions based on the user's emotions and presents appropriate product information through the robot.

[0870] For example, if a user realizes they are running low on bottled water during a busy time before going out, the robot will offer a suggestion such as, "You can purchase your favorite water at your leisure. When you're busy, why not try one of these value sets?" To reduce user stress, the robot's display is set to calming blue and green tones.

[0871] An example of a prompt for the generating AI model is: "Generate a calm, simple, and reassuring suggestion to encourage users to easily purchase items with low stock when they are feeling stressed."

[0872] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0873] Step 1:

[0874] The device uses its built-in camera and microphone to sense the user's voice tone, facial expressions, and operation speed, and collects this data. The input is the user's voice and video, and through data processing, it extracts feature values ​​that indicate the user's emotions and outputs them as emotion data.

[0875] Step 2:

[0876] The device sends collected emotional data to the server. The server receives this emotional data and analyzes it using an emotion recognition engine. The input is emotional data, and the server outputs the estimated emotional state of the user through the analysis process.

[0877] Step 3:

[0878] The server determines how to display the user interface based on the analyzed emotional state of the user. For example, if the result indicates stress, it generates instructions to change the interface color to blue or green. The input is the emotional state, and the output is an interface adjustment instruction.

[0879] Step 4:

[0880] The server uses selection criteria to choose the most suitable product information to generate purchase suggestions tailored to the user's emotional state. This process also considers the user's purchase history and market information. The input consists of emotional state and product data, while the output is personalized product suggestions.

[0881] Step 5:

[0882] The generated product suggestions and interface adjustment instructions are sent to the terminal, which then displays them to the user. The user can review the displayed content and accept or reject the suggestions. The input is the suggestions and instructions from the server, and the output is the content presented to the user.

[0883] Step 6:

[0884] If the user approves the proposed purchase, the terminal returns the order information to the server. Based on the received information, the server processes the user's order through the e-commerce platform. The input is the user's order information, and the output is a notification that the order processing is complete.

[0885] By using a generative AI model and prompt text, the adjustment of each suggested text and interface is automated, resulting in a comfortable and less stressful consumable purchase experience for users.

[0886] 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.

[0887] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An 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.

[0888] 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.

[0889] 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.

[0890] 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.

[0891] 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.

[0892] 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.

[0893] 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.

[0894] 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."

[0895] 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.

[0896] 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.

[0897] 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.

[0898] 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.

[0899] 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.

[0900] 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.

[0901] 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.

[0902] 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.

[0903] 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.

[0904] 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.

[0905] 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.

[0906] 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.

[0907] The following is further disclosed regarding the embodiments described above.

[0908] (Claim 1)

[0909] A detection means for detecting the remaining amount of consumables,

[0910] A selection means for selecting the optimal purchase candidate by collecting product information from multiple e-commerce platforms based on the out-of-stock information obtained by the aforementioned detection means,

[0911] A user interface means for presenting the purchase candidates selected by the selection means to the user and accepting orders,

[0912] An analytical tool for accumulating user purchase history and analyzing consumption patterns,

[0913] A system including an advice-providing means for providing savings advice based on the analysis results obtained by the aforementioned analysis means.

[0914] (Claim 2)

[0915] The system according to claim 1, wherein the user interface means further includes means for generating custom order buttons for products that the user regularly purchases.

[0916] (Claim 3)

[0917] The system according to claim 1, wherein the selection means includes means for comparing and examining product information based on price, delivery conditions, and seller ratings.

[0918] "Example 1"

[0919] (Claim 1)

[0920] A detection means for detecting the inventory of consumables,

[0921] A communication means for transmitting data acquired by the detection means to a server,

[0922] A server-generated AI model is used to collect data from multiple data platforms and select the optimal option.

[0923] A display means for displaying the options proposed by the selection means to the user and accepting their selection,

[0924] An analytical method for accumulating user behavior history and analyzing trends,

[0925] A system including an advisory means that provides advice on saving money based on the analysis results obtained by the aforementioned analysis means.

[0926] (Claim 2)

[0927] The system according to claim 1, further comprising a function of the display means for generating custom operation buttons for items that the user repeatedly needs.

[0928] (Claim 3)

[0929] The system according to claim 1, wherein the selection means includes means for comparing data based on cost, delivery time, and provider evaluation.

[0930] "Application Example 1"

[0931] (Claim 1)

[0932] A detection means for detecting the remaining amount of consumables,

[0933] A selection means for selecting the optimal purchase candidate by collecting product information from multiple e-commerce platforms based on the out-of-stock information obtained by the aforementioned detection means,

[0934] A user interface means for presenting the purchase candidates selected by the selection means to an information processing device and accepting orders,

[0935] A machine operated by an information processing device has monitoring means for periodically measuring the remaining amount of consumables and sending notifications,

[0936] Analytical means for accumulating consumer purchase history and analyzing consumption patterns,

[0937] A system including an advisory means for providing advice on saving money based on the analysis results obtained by the aforementioned analysis means.

[0938] (Claim 2)

[0939] The system according to claim 1, further comprising means for generating custom purchase buttons for products that the user frequently purchases.

[0940] (Claim 3)

[0941] The system according to claim 1, wherein the selection means includes means for comparing and examining product information based on price, delivery conditions, and seller ratings.

[0942] "Example 2 of combining an emotion engine"

[0943] (Claim 1)

[0944] A detection means for detecting the remaining amount of consumables,

[0945] A selection means for collecting product information from multiple transaction methods based on the out-of-stock information obtained by the aforementioned detection means and selecting the optimal purchase candidate,

[0946] A display means for presenting the purchase candidates selected by the selection means to the user and accepting orders,

[0947] A means of recognizing user emotions,

[0948] An advice provision means for providing optimized advice to the user based on the emotional data acquired by the aforementioned emotion recognition means,

[0949] An adjustment means for adjusting the user interface based on the aforementioned emotional data,

[0950] An analytical means for accumulating users' purchase history and analyzing consumption trends,

[0951] A system including a proposal-providing means for providing cost-saving suggestions based on the analysis results obtained by the aforementioned analysis means.

[0952] (Claim 2)

[0953] The system according to claim 1, wherein the display means further includes means for generating a custom order function for products that a user regularly purchases.

[0954] (Claim 3)

[0955] The system according to claim 1, wherein the selection means includes means for comparing and examining product information based on price, delivery conditions, and provider evaluation.

[0956] "Application example 2 when combining with an emotional engine"

[0957] (Claim 1)

[0958] A detection means for detecting the remaining amount of consumables,

[0959] A selection means for selecting the optimal purchase candidate by collecting product information from multiple e-commerce platforms based on the out-of-stock information obtained by the aforementioned detection means,

[0960] A user interface means for presenting the purchase candidates selected by the selection means to the user and accepting orders,

[0961] An analytical tool for accumulating user purchase history and analyzing consumption patterns,

[0962] An advice-providing means for providing savings advice based on the analysis results obtained by the aforementioned analysis means,

[0963] A means of recognizing an emotion to recognize the emotional state of a user,

[0964] An interface adjustment means for analyzing data obtained by the emotion recognition means and adjusting the interface according to the user's emotional state,

[0965] A means for personalizing suggestions to users based on their emotional state,

[0966] A system that includes this.

[0967] (Claim 2)

[0968] The system according to claim 1, wherein the user interface means further includes means for generating custom order buttons for products that the user regularly purchases, and means for changing the visual elements of the interface based on the user's emotional state.

[0969] (Claim 3)

[0970] The system according to claim 1, wherein the selection means includes means for comparing and examining product information based on price, logistics conditions, and seller evaluation, and optimizing the selection based on feedback from the user's emotional state. [Explanation of Symbols]

[0971] 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 detection means for detecting the remaining amount of consumables, A selection means for selecting the optimal purchase candidate by collecting product information from multiple e-commerce platforms based on the out-of-stock information obtained by the aforementioned detection means, A user interface means for presenting the purchase candidates selected by the selection means to the user and accepting orders, An analytical tool for accumulating user purchase history and analyzing consumption patterns, A system including an advice-providing means for providing savings advice based on the analysis results obtained by the aforementioned analysis means.

2. The system according to claim 1, wherein the user interface means further includes means for generating custom order buttons for products that the user regularly purchases.

3. The system according to claim 1, wherein the selection means includes means for comparing and examining product information based on price, delivery conditions, and seller evaluation.

Images