System

Abstract

A system is provided.SOLUTION: An input unit configured to input data of a use and a use frequency when a user purchases a product, a server configured to receive and store the input data, a prediction unit configured to predict a future state of the product using an aging prediction model based on the stored data and product characteristic data, an image generation unit configured to generate an image visually indicating the predicted future state, and a transmission unit configured to transmit the prediction data and the generated image to a user terminal; The user terminal including display means for displaying the prediction data and the generated image.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In conventional e-commerce systems, when users purchase a product, they often lack sufficient information about the product's condition and market value after purchase, making it difficult for them to make a purchasing decision. In particular, because it is not possible to predict the product's aging or future market value, it is difficult to determine the collectible value of the product or the appropriate time to replace it. This can lead to a decrease in users' purchasing motivation, resulting in losses for sellers. A new system that solves these issues is needed. [Means for solving the problem]

[0005] To solve the above problems, the present invention provides a system that allows a user to input data on the purpose and frequency of use when purchasing a product, and predicts and visually displays the product's aging state and future market value based on that data. Specifically, the system includes input means for inputting the purpose and frequency of use data when the user purchases a product, server means for receiving and storing the data, prediction means for predicting the future product state using an aging prediction model based on the stored data and product characteristic data, image generation means for generating an image visually showing the predicted future state, price trend prediction means for predicting price trends based on past market data, transmission means for transmitting the predicted data and the generated image to a user terminal, and display means for displaying the predicted data and the generated image. This system allows users to understand the future state and value of the product at the time of purchase, enhancing their purchasing motivation and enabling them to make appropriate purchasing decisions.

[0006] "User" refers to an individual or corporation that purchases products using the system.

[0007] "Product" means any goods or services sold on the System.

[0008] "Use" refers to information about how a user uses a product.

[0009] "Frequency of use" refers to information about how often a user uses a product.

[0010] "Input means" refers to a device or interface that allows a user to input information such as purpose and frequency of use.

[0011] "Terminal" refers to a device (e.g., a PC or smartphone) that a user uses to access and operate the system.

[0012] "Server" refers to a computer system that receives, processes, and stores data sent by users.

[0013] "Database" refers to a storage device or system for a server to store and manage data.

[0014] "Aging prediction model" refers to an algorithm or computational model for predicting how a product will deteriorate over time.

[0015] "Prediction means" refers to a function or module for predicting the future state of a product using an aging prediction model.

[0016] "Image generation means" refers to a function or module that generates an image to visually show the predicted future state of a product.

[0017] "Price trend prediction means" refers to a function or module that predicts future product prices based on past market data.

[0018] "Transmission means" refers to a function or module for transmitting prediction data and generated images to a user terminal.

[0019] "Display means" refers to a function or interface for the user terminal to display predicted data and generated images. [Brief explanation of the drawings]

[0020] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0021] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0022] First, the terms used in the following description will be explained.

[0023] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0024] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0025] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0026] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0027] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0028] [First embodiment]

[0029] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0030] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0031] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0032] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0033] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0034] 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 of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0035] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0036] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0037] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0038] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0039] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0040] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0041] The present invention relates to an electronic commerce system that predicts and visually displays the future condition and market value of a product based on data on the purpose and frequency of use entered by a user when purchasing the product. This system includes a user terminal, a server, and a database. The processing of the system's program is explained below in natural language.

[0042] User data entry

[0043] A user selects a product on an e-commerce site and accesses the product's details page. On this details page, the user enters information about the purpose and frequency of use. For example, when purchasing a luxury watch, the user enters information such as "daily use" and "five days a week."

[0044] Data transmission and storage

[0045] The terminal verifies the data entered by the user regarding purpose and frequency of use to ensure there are no errors. Data that passes verification is sent to the server. The server receives this data and stores it in a database. The stored data includes the product ID, purpose, frequency of use, user ID, etc.

[0046] Product condition prediction

[0047] The server uses an aging prediction model to predict the future condition of a product based on data on usage and frequency of use stored in the database. For example, the aging prediction for a luxury watch used daily five days a week predicts that after one year, small scratches and a decrease in luster will appear, and after five years, there will be obvious wear and tear and peeling of the plating.

[0048] Price trend forecast

[0049] The server predicts price trends based on past market data for a product. For example, a luxury watch currently priced at 1 million yen is predicted to be priced at 900,000 yen one year from now, and 700,000 yen five years from now. This prediction is made using time series analysis and machine learning models.

[0050] Future State Visualization

[0051] The server uses the predicted data to generate a visual image of the product's future state. For example, it might generate an image of a watch's appearance in one year, showing slight scratches and a loss of luster, and an image of a watch in five years, showing obvious wear and peeling plating. To do this, the server uses an image processing library.

[0052] Sending and displaying forecast data and images

[0053] The server sends the predicted data and generated images to the user's terminal, which receives the data and images and displays them to the user. The displayed information includes text information about the product's aging condition and market value one and five years from now, as well as generated images of the product's future appearance.

[0054] Specific examples

[0055] For example, when a user purchases a luxury watch on an e-commerce site, they enter "daily use" as the intended use and "five days a week" as the frequency of use. The device sends this data to the server, which stores it in a database. The server then uses the entered data to predict aging and price trends. It predicts that after one year, small scratches will appear and the luster will decrease, and after five years, there will be clear wear and peeling of the plating. The server predicts that the price will be 900,000 yen after one year and 700,000 yen after five years. Based on this predicted data, the server generates an image of the product's future appearance, which the device displays to the user. Using this information, the user can visually understand the product's future after purchase and make an appropriate decision.

[0056] In this way, the present invention provides detailed information about the future condition and value of a product when the user is considering purchasing it, thereby enhancing purchasing motivation and supporting appropriate purchasing decisions.

[0057] The processing flow will be explained below.

[0058] Step 1:

[0059] A user accesses an e-commerce site and selects a product. They are taken to the details page of the selected product and enter information such as the purpose and frequency of use.

[0060] Step 2:

[0061] The device validates the purpose and frequency of use data entered by the user, for example by checking that the data is complete and in the correct format.

[0062] Step 3:

[0063] The terminal sends the data that passes the verification to the server, including the product ID, purpose, frequency of use, and user ID.

[0064] Step 4:

[0065] The server stores the received data in a database, where information on the purpose and frequency of use of each product is organized and stored.

[0066] Step 5:

[0067] The server calls the condition prediction module based on the stored data and uses an aging prediction model to predict the future condition of the product. For example, it calculates the deterioration state based on daily use, 5 days a week.

[0068] Step 6:

[0069] The server calls the price trend prediction module based on past market data, and uses time series analysis and machine learning models to predict the market price of the product one or five years from now.

[0070] Step 7:

[0071] The server uses the predicted data to generate images of the future state of the product, using image processing libraries to visualize what the product will look like in one year or five years.

[0072] Step 8:

[0073] The server transmits the forecast data and the generated image to the terminal, including the aging state, price transition, and the generated image.

[0074] Step 9:

[0075] The terminal displays the received data and images on a user interface, including the aging condition and market price one and five years from now, as well as an image of the product's future appearance.

[0076] Step 10:

[0077] Users can check the future condition and value of the product based on the displayed information, which can be used as reference when making purchasing decisions or collecting items.

[0078] Through the above steps, the user can get a detailed understanding of the future condition and market value of the product at the time of purchase, enabling them to make an appropriate selection.

[0079] Example 1

[0080] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0081] In conventional e-commerce systems, it is difficult for users to fully understand the future condition and market value of a product when purchasing it. This makes it difficult for users to make appropriate decisions regarding future use, which can affect their post-purchase satisfaction. The present invention aims to support users in making appropriate purchasing decisions and increase user satisfaction by predicting and visually displaying the future condition and market value of a product when they are considering purchasing it.

[0082] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0083] In this invention, the server includes means for receiving and storing the transmitted data, means for predicting the future state of the product using an aging prediction model based on the stored data, means for predicting price trends based on past market data, means for generating an image visually showing the predicted future state, and means for transmitting the predicted data and the generated image to a user terminal, thereby enabling the user to visually confirm information about the future state and market value of the product and make an appropriate decision before purchasing.

[0084] The "input means" is a means for a user to input data on the purpose and frequency of use when purchasing a product.

[0085] "Terminal means" refers to means for verifying data entered by a user and transmitting it to a server.

[0086] The "server means" is a means for receiving input data and storing it in a database.

[0087] "Prediction means" refers to a means for predicting the future state of a product using an aging prediction model based on stored data.

[0088] A "price trend prediction means" is a means for predicting the price trend of a product based on past market data.

[0089] The "image generating means" is a means for generating an image that visually shows the predicted future state of the product.

[0090] The "transmission means" is a means for transmitting the prediction data and the generated image to the user's terminal.

[0091] The "display means" is a means for displaying the predicted data and the generated image on the user terminal.

[0092] The "means for calculating a deterioration rate" is a means for calculating a deterioration rate applied to a product based on the user's usage frequency data.

[0093] "Image Processing Library" means a software library for image processing used to visually indicate the aging state of a product.

[0094] The present invention relates to an electronic commerce system that predicts and visually displays the future condition and market value of a product based on data on the product's intended use and frequency of use entered by the user when purchasing the product. The system includes a user terminal, a server, and a database. When purchasing a product, the user enters information on the product's intended use and frequency of use from the user terminal. The terminal is responsible for verifying this input data and sending it to the server. The server stores the received data and uses various prediction models to predict and visualize the product's future condition and price trends. This information is then sent back to the user's terminal so that the user can view it.

[0095] Specifically, a user enters information about the purpose and frequency of use on the details page of an e-commerce site. For example, when purchasing a luxury watch, the user enters information such as "daily use" and "five days a week." This input data is verified by the device and sent to the server. The server stores the received data in a database and uses this data to make predictions using an aging prediction model and a price trend prediction model. Time series analysis and machine learning models are used to predict the aging state and price changes of luxury watches one and five years from now. An image processing library is also used to generate images that visually show the predicted aging state.

[0096] The generated forecast data and visual images are sent from the server to the user's device, allowing the user to understand the product's future state and make a purchasing decision. This information provision improves the user's post-purchase satisfaction and supports appropriate purchasing decisions.

[0097] Specific examples

[0098] For example, when a user purchases a luxury watch on an e-commerce site, they enter "daily use" as the intended use and "five days a week" as the frequency of use. This data is sent by the device to a server, which stores the data and uses it to predict aging and price trends. After one year, the server predicts small scratches and a loss of luster, and after five years, the watch will show clear wear and the plating will peel. The server also predicts that the price will be 900,000 yen after one year and 700,000 yen after five years. Based on this predicted data, the server generates an image of the product's future appearance, which the device displays to the user. Using this information, the user can visually understand the product's future after purchase and make an appropriate decision.

[0099] Prompt Sentence Examples

[0100] Below are some examples of prompt sentences.

[0101] "When purchasing a luxury watch, assume it will be worn daily, five days a week. Using this information, predict the aging and price trends of the watch in one year and five years. Also, generate an image showing its future appearance."

[0102] Based on the above prompt, the generative AI model can be asked to make appropriate predictions and generate visual information.

[0103] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0104] Step 1:

[0105] A user selects a product on an e-commerce site and accesses the product detail page. The user enters information about the purpose and frequency of use, such as "purpose: daily use" and "frequency of use: 5 days a week."

[0106] Input: Usage and frequency data

[0107] Output: Data entered on the user's terminal

[0108] Step 2:

[0109] The terminal validates the data entered by the user. Specifically, it checks the input format and value range to ensure that the input data is correct. For example, it verifies that the purpose is "daily use" and the frequency is "five days a week." If validation is successful, the terminal sends the data to the server.

[0110] Input: User-entered usage and frequency data

[0111] Output: Validated data sent to the server

[0112] Step 3:

[0113] The server receives the data sent from the device and stores it in a database along with information such as product ID, purpose, frequency of use, and user ID. This allows the data necessary for future predictions to be accumulated.

[0114] Input: Verified data sent from the terminal

[0115] Output: Data stored in the database

[0116] Step 4:

[0117] The server uses the data stored in the database to predict the future condition of the product using an aging prediction model. Specifically, it references past usage data and aging data to predict the product's deterioration. For example, if a luxury watch is used daily, five days a week, after one year it may develop small scratches and lose its luster, and after five years it may show clear wear and peeling of the plating.

[0118] Input: Usage and frequency data stored in the database

[0119] Output: Predicted future product state data

[0120] Step 5:

[0121] The server predicts product price trends based on past market data. Specifically, it uses time series analysis and machine learning models to analyze past price history and supply and demand data to predict future prices. For example, if the current price of a luxury watch is 1 million yen, it is predicted that it will be 900,000 yen in one year and 700,000 yen in five years.

[0122] Inputs: Historical market data and aging forecast data

[0123] Output: Predicted price trend data

[0124] Step 6:

[0125] The server uses the forecast data to generate a visual representation of the product's future state. It uses image processing libraries and AI technology to create product images based on the forecast results. For example, it generates images showing the appearance of a watch one year from now and five years from now. The images reflect small scratches, loss of gloss, wear, and peeling plating.

[0126] Input: Predicted future product status data and price transition data

[0127] Output: The generated visual image

[0128] Step 7:

[0129] The server sends the predicted data and generated images to the user's device, which packages the data and sends it to the device in an easily accessible format.

[0130] Input: Prediction data and generated images

[0131] Output: Data sent to the user's device

[0132] Step 8:

[0133] The terminal displays the received forecast data and generated images to the user, allowing the user to visually confirm detailed information about the product's future condition and market value. For example, text information about the aging condition and price trends one and five years from now, along with related images, are displayed.

[0134] Input: Prediction data sent from the server and generated images

[0135] Output: Prediction information and images displayed to the user

[0136] (Application example 1)

[0137] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0138] Conventional e-commerce systems lacked the means to provide detailed information about the future condition and market value of a product when users were considering a purchase, making it difficult for users to make appropriate purchasing decisions. Furthermore, there was also a lack of a means to visually understand the aging state of a product after use and the transition of its market value. As a result, it was not possible to stimulate users' purchasing motivation or to appeal to the benefits of purchasing from a long-term perspective.

[0139] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0140] In this invention, the server includes: input means for inputting data on the purpose and frequency of use of an item when a user purchases it; an information processing device for receiving and saving the input data; prediction means for predicting the future state of the item using a deterioration prediction model based on the saved data and item characteristic data; image generation means for generating an image visually showing the predicted future state; price trend prediction means for predicting price trends based on past market data; transmission means for transmitting the predicted data and the generated image to a user terminal; display means for displaying the predicted data and the generated image; and application means for providing visualized information predicting the future state and market value of the item based on the purpose and frequency of use data of the item. This allows users to obtain detailed information on the future state and market value of the item before purchasing, enabling them to make appropriate purchasing decisions.

[0141] "User" refers to a consumer or user who uses this system to purchase goods.

[0142] "Goods" refers to the goods and products handled in this system.

[0143] "Use" refers to information about how the user intends to use the item they are about to purchase.

[0144] "Frequency of use" refers to information about how often a user uses the item they are about to purchase.

[0145] "Input means" refers to the device or software that allows the user to input data on purpose and frequency of use into the system.

[0146] The term "information processing device" refers to a server or computer that stores data received from a user and performs various calculations.

[0147] "Item characteristic data" refers to data on basic information and characteristics of the items being handled.

[0148] A "deterioration prediction model" refers to an algorithm or mathematical model for predicting the future deterioration state of an item based on its intended use and frequency of use.

[0149] "Prediction means" refers to a function or device for predicting the future state of an item using a deterioration prediction model based on stored data and item characteristic data.

[0150] "Image generation means" refers to a function or device for generating an image that visually shows the predicted future state of an item.

[0151] "Price trend prediction means" refers to a function or device that predicts future price trends of goods based on past market data.

[0152] "Transmission means" refers to a function or device for transmitting prediction data and generated images to a user terminal.

[0153] "User terminal" refers to an electronic device used by a user, such as a computer, smartphone, or tablet.

[0154] "Display means" refers to software and hardware for displaying the predicted data and generated images on the user terminal.

[0155] "Application means" refers to application software that predicts the future condition and market value of an item based on data on the use and frequency of use of the item, and visually presents this to the user.

[0156] This invention is an electronic commerce system that predicts and visually displays the future condition and market value of an item after purchase to a user considering purchasing the item. The system includes a user terminal, an information processing device, and a database.

[0157] User data entry

[0158] Users access the product details page and enter information about the purpose and frequency of use. For example, when purchasing a luxury watch, users enter information such as "daily use" and "five days a week."

[0159] Data transmission and storage

[0160] The user terminal verifies the data entered by the user regarding the purpose and frequency of use to ensure there are no errors. Data that passes verification is sent to the information processing device. The information processing device receives this data and stores it in a database. The stored data includes the item ID, purpose, frequency of use, user ID, etc.

[0161] Item condition prediction

[0162] The information processing device predicts the future condition of an item using a deterioration prediction model based on data on usage and frequency of use stored in a database. For example, a deterioration prediction for a luxury watch used daily five days a week might predict that after one year, small scratches and a loss of luster will appear, and after five years, clear wear and peeling of the coating will be visible.

[0163] Price trend forecast

[0164] The information processing device predicts price trends based on past market data for goods. For example, a luxury watch currently priced at 1 million yen is predicted to have a price of 900,000 yen one year from now, and 700,000 yen five years from now. This prediction is made using time series analysis and machine learning models.

[0165] Future State Visualization

[0166] The information processing device generates an image based on the predicted data that visually represents the future state of the item. For example, it generates an image that reflects the appearance of a watch one year from now, with slight scratches and loss of gloss, and an image that reflects the watch's obvious wear and peeling coating five years from now. To do this, the information processing device uses an image processing library.

[0167] Sending and displaying forecast data and images

[0168] The information processing device transmits the predicted data and the generated image to a user terminal. The user terminal receives the data and image and displays them to the user. The displayed information includes text information about the deterioration state and market value of the item one and five years from now, as well as the generated image of the item's future appearance.

[0169] Specific examples

[0170] For example, when a user purchases a luxury watch, they input "daily use" as the purpose and "five days a week" as the frequency of use. The user's device sends this data to an information processing device, which stores it in a database. The information processing device then predicts deterioration and price trends based on the input data. It predicts that the watch will develop small scratches and lose its luster in one year, and that it will show clear wear and peeling of the coating in five years, and predicts that the price will be 900,000 yen in one year and 700,000 yen in five years. Based on this predicted data, the information processing device generates an image of the future appearance of the item, which the user's device displays to the user. Based on this information, the user can visually understand the future of the item after purchase and make an appropriate decision.

[0171] Prompt Sentence Examples

[0172] A user entered the following information about purpose and frequency of use on the item page:

[0173] Use: Everyday use

[0174] Frequency of use: 5 days a week

[0175] Use this information to predict the condition and market value of the item one and five years from now, along with a visualisation.

[0176] As described above, the present invention provides detailed information about the future state and value of an item when the user is considering purchasing it, thereby enhancing purchasing motivation and supporting appropriate purchasing decisions.

[0177] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0178] Step 1: User Data Entry

[0179] The user accesses the product details page and inputs information about the purpose and frequency of use of the product they are considering purchasing. The input data includes specific usage information such as "daily use" and "five days a week." The user's device receives this input data.

[0180] Input: Information on purpose and frequency of use

[0181] Output: Verified data (use and frequency of use)

[0182] Step 2: Data transmission and storage

[0183] The verified data is sent from the terminal to an information processing device (server). The information processing device stores the received data in a database. The stored data includes the item ID, purpose, frequency of use, user ID, etc.

[0184] Input: Verified data (purpose and frequency of use)

[0185] Output: Data stored in the database

[0186] Step 3: Predicting the condition of the item

[0187] The information processing device predicts the future condition of the item using a deterioration prediction model based on data on usage and frequency of use stored in a database. For example, it uses a machine learning model or a time series analysis algorithm to predict the condition of the item one year or five years from now.

[0188] Input: Data on usage and frequency of use stored in the database

[0189] Output: Predicted future state of the item

[0190] Step 4: Predict price trends

[0191] The information processing device predicts price trends based on past market data for goods. For example, using time series analysis and machine learning models, it predicts that a luxury watch currently priced at 1 million yen will be priced at 900,000 yen one year from now, and 700,000 yen five years from now.

[0192] Input: Product information stored in the database, historical market data

[0193] Output: Predicted price progression

[0194] Step 5: Visualize the future state

[0195] The information processing device uses the predicted data to generate images that visually represent the future state of the item. For example, using an image processing library, it generates an image that shows the appearance of a watch one year from now, with small scratches and a loss of gloss, and the appearance of a watch five years from now, with obvious wear and peeling coating.

[0196] Input: Predicted future state data of the item

[0197] Output: The generated visual image

[0198] Step 6: Send and display forecast data and images

[0199] The information processing device transmits the predicted data and the generated image to a user terminal. The user terminal receives this data and image and displays them to the user. Specifically, text information about the deterioration state and market value of the item one year and five years from now, as well as the generated image of the item's appearance in the future, are displayed.

[0200] Input: Prediction data and generated images

[0201] Output: Predicted data and visual images displayed on the user's device

[0202] Through the above steps, the user can obtain detailed information about the future condition and market value of the item they are considering purchasing, enabling them to make an appropriate purchasing decision.

[0203] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0204] The present invention provides an e-commerce system that predicts and visually displays the future condition and market value of a product based on data on the purpose and frequency of use entered by the user when purchasing the product, and also includes an emotion engine that recognizes the user's emotions to further improve the purchasing experience. This system includes a user terminal, a server, an emotion engine, and a database. The processing of the system's programs is described in detail below.

[0205] User data entry

[0206] A user accesses an e-commerce site and selects the product they want to purchase. They are then taken to the product detail page and enter information about the purpose and frequency of use. For example, when purchasing a luxury watch, a user enters information such as "daily use" and "five days a week."

[0207] Data transmission and storage

[0208] The terminal verifies the data entered by the user regarding purpose and frequency of use to ensure there are no errors. Data that passes verification is sent to the server. The server stores this data in a database. The stored data includes the product ID, purpose, frequency of use, and user ID.

[0209] Emotion recognition by emotion engine

[0210] While the user is entering data, the device uses sensors such as a camera and microphone to analyze the user's facial expressions and tone of voice. The emotion engine uses this data to recognize the user's emotional state (e.g., excitement, anxiety, indifference, etc.).

[0211] Product condition prediction and price trend prediction

[0212] The server uses an aging prediction model to predict the future condition of a product based on stored data on usage and frequency of use. For example, it calculates the deterioration state of a luxury watch one year and five years from now based on daily use five days a week. At the same time, it predicts price trends based on past market data and calculates predicted prices one year and five years from now.

[0213] Future State Visualization

[0214] The server uses the predicted data to generate a visual representation of the product's future state. It uses an image processing library to visualize the product's appearance in one year and five years. For example, the watch's appearance in one year will show minor scratches, while its appearance in five years will show clear wear.

[0215] Customized display based on emotions

[0216] The server customizes the display content based on the user's emotional data received from the emotion engine. For example, if a user seems anxious about a purchase, the server can highlight product warranty information and user reviews. For an excited user, the server can highlight product features and promotional information.

[0217] Sending and displaying forecast data and images

[0218] The server sends the predicted data and generated images to the terminal, which displays them on a user interface. The displayed information includes text information about the product's aging status and market value one and five years from now, generated images of the product's future appearance, and emotion-based customization information.

[0219] Specific examples

[0220] For example, when a user purchases a luxury watch on an e-commerce site, they enter "daily use" as the purpose and "five days a week" as the frequency of use. If the device detects any anxiety about the purchase, the device sends this input data to the server, which stores it in a database. The server then uses the input data to predict aging and price trends, predicting that the watch will develop minor scratches after one year and clear wear after five years. The predicted price trends are 900,000 yen after one year and 700,000 yen after five years. The server uses this predicted data to visualize the product's future appearance, highlighting warranty information and positive user reviews to alleviate the user's anxiety. The device then displays this information to the user, allowing them to confirm the product's future condition and value and make a purchasing decision with confidence.

[0221] In this way, the present invention not only provides users with detailed information about the future condition and value of a product when they are considering a purchase, but also optimizes the user experience based on emotion recognition, improving purchasing motivation and supporting appropriate purchasing decisions.

[0222] The processing flow will be explained below.

[0223] Step 1:

[0224] A user accesses an e-commerce site and selects a product they are interested in. For example, they move to a detail page of a luxury watch and begin to consider purchasing it.

[0225] Step 2:

[0226] The device displays input fields for the purpose and frequency of use on the product detail page, and the user enters information such as "purpose: daily use" and "frequency of use: 5 days a week."

[0227] Step 3:

[0228] The terminal validates the user's input data, checking for missing or malformed data, and data that passes validation proceeds to the next step.

[0229] Step 4:

[0230] The terminal transmits the input data on purpose and frequency of use to the server. The transmitted data includes the product ID, purpose, frequency of use, user ID, etc.

[0231] Step 5:

[0232] The server stores the received data in a database, including product characteristic data, user usage data, and so on.

[0233] Step 6:

[0234] The device uses a camera and microphone to recognize the user's facial expressions and voice while inputting data, and sends this data to an emotion engine to analyze the user's emotional state.

[0235] Step 7:

[0236] The emotion engine analyzes the user's facial expressions and tone of voice to identify emotions (e.g., excitement, anxiety, indifference, etc.), and sends the analysis results to the server.

[0237] Step 8:

[0238] The server uses an aging prediction model to predict the future condition of the product based on the stored data on usage and frequency of use. For example, it calculates wear and tear after one year and five years based on daily use five days a week.

[0239] Step 9:

[0240] The server calls a price trend prediction module based on past market data to predict the market price of the product one year or five years from now. For example, if the current price is 1 million yen, it is predicted that the price will be 900,000 yen one year from now and 700,000 yen five years from now.

[0241] Step 10:

[0242] The server generates images of the future state of the product based on the predicted data, using image processing libraries to visualize what the product will look like in one year and five years, simulating, for example, minor scratches after one year and increased wear after five years.

[0243] Step 11:

[0244] The server customizes the display content based on the emotion data from the emotion engine. For anxious users, it adds reassuring information (guarantees and reviews). For excited users, it emphasizes product features and promotional information.

[0245] Step 12:

[0246] The server transmits the predicted data, customized display content, and generated images to the terminal.

[0247] Step 13:

[0248] The device then displays the received data and images to the user, including customized information based on the aging status after one and five years, market value, and emotions.

[0249] Step 14:

[0250] Users can check the future condition and value of the product based on the displayed information, and can make appropriate purchasing decisions while also referring to customized information based on their emotions.

[0251] Through the above specific processing steps, the system can provide detailed information about the future condition and market value of a product when the user purchases it, as well as customize the display based on the user's emotions, thereby providing a better purchasing experience.

[0252] Example 2

[0253] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0254] In conventional e-commerce systems, when users purchase a product, they are not provided with information about the product's future condition or how its market value will change. This often leaves users feeling unsure about their purchase decision and can make them hesitant to make a final purchase. Furthermore, because the system does not take into account the user's feelings, the purchasing experience remains unimproved.

[0255] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for receiving and saving input data, means for predicting the future state of the product using an aging prediction model based on the saved data and product characteristic data, means for generating an image visually showing the predicted future state, means for predicting price trends based on past market data, means for transmitting the predicted data and the generated image to the user terminal, and means for customizing the display content based on the recognized emotional state. This allows the user to visually check the future state and market value of the product and make a purchase decision with confidence by receiving information according to their emotions.

[0256] 1. "Input means" refers to the interface through which a user inputs data on the purpose and frequency of use when purchasing a product.

[0257] 2. "Server Means" means a computing device for receiving and storing input data.

[0258] 3. "Prediction Means" refers to the function by which the Server Means predicts the future state of the Product using an aging prediction model based on the Stored Data and Product Attribute Data.

[0259] 4. "Image generation means" refers to a function for generating an image that visually shows the predicted future state.

[0260] 5. "Price trend prediction means" refers to a function for predicting price trends based on past market data.

[0261] 6. "Transmission means" refers to the function for transmitting prediction data and generated images to a user terminal.

[0262] 7. "Display means" refers to an interface for a user terminal to display predicted data and generated images.

[0263] 8. "Emotion recognition means" refers to the sensors and analysis functions that enable a device to recognize a user's emotional state by analyzing the user's facial expressions and tone of voice.

[0264] 9. "Customized display means" refers to functionality that allows the server to customize the display content based on the recognized emotional state.

[0265] This invention is a system for an e-commerce system that predicts and visually displays the future condition and market value of a product based on data on the purpose and frequency of use entered by a user when purchasing the product, and recognizes the user's emotions to improve the purchasing experience. This system includes a user terminal, a server, an emotion recognition engine, and a database.

[0266] User data entry

[0267] A user accesses an e-commerce site and selects the product they wish to purchase. They are then taken to the product detail page and enter information about the purpose and frequency of use. For example, when purchasing a luxury watch, they enter information such as "daily use" and "five days a week." An interface such as a web form is used as the input method.

[0268] Data transmission and storage

[0269] The terminal verifies the purpose and frequency of use data entered by the user to ensure there are no errors. A validation function is used for the verification. Data that passes verification is securely sent to the server using the HTTPS protocol. The server saves this data in a database. The saved data includes the product ID, purpose, frequency of use, and user ID. An RDBMS such as MySQL or PostgreSQL is used as the database.

[0270] Emotion recognition by emotion engine

[0271] While the user is entering data, the device uses sensors such as a camera and microphone to analyze the user's facial expressions and tone of voice. This is done using libraries such as OpenCV. The emotion recognition engine uses machine learning models such as TensorFlow and PyTorch to recognize the user's emotional state (e.g., excitement, anxiety, indifference, etc.).

[0272] Product condition prediction and price trend prediction

[0273] The server uses an aging prediction model to predict the future condition of a product based on the stored data on usage and frequency of use. Libraries such as scikit-learn are used for aging prediction. For example, the state of deterioration of a luxury watch after one year and five years is calculated. At the same time, price trends are predicted based on past market data, and a generative AI model is used to calculate predicted prices after one year and five years.

[0274] Future State Visualization

[0275] The server uses the predicted data to generate a visual representation of the product's future state. PIL (Python Imaging Library) is used for image processing, visualizing the product's appearance in one year and five years. For example, the watch's appearance in one year will show minor scratches, while its appearance in five years will show clear wear.

[0276] Customized display based on emotions

[0277] The server customizes the display content based on the user's emotional data received from the emotion engine. For users who appear anxious about the purchase, product warranty information and user reviews are emphasized. On the other hand, for users who are excited, product features and promotional information are emphasized.

[0278] Sending and displaying forecast data and images

[0279] The server sends the predicted data and generated images to the terminal. REST API is used to send the data, and the data is encoded in JSON format. The terminal displays this data and images on a user interface. The information includes text information about the aging status and market value of the product one and five years from now, generated images of the product's future appearance, and emotion-based customization information. As a specific example of how the data is displayed, the predicted information and visualized images are displayed on a web page using HTML and CSS.

[0280] Specific examples

[0281] For example, when a user purchases a luxury watch on an e-commerce site, if the user enters "daily use" as the purpose and "five days a week" as the frequency of use, and anxiety about the purchase is detected, the following processing will be performed.

[0282] 1. The terminal checks the data entered by the user and sends it to the server, which stores it in a database.

[0283] 2. The device uses a camera and microphone to collect the user's facial expressions and voice for emotion recognition. The emotion engine analyzes the data and detects anxiety.

[0284] 3. The server runs an aging prediction model based on usage and frequency of use data, predicting, for example, that the server will develop minor scratches in one year and clear wear in five years. It also predicts that the price will be 900,000 yen in one year and 700,000 yen in five years.

[0285] 4. The server uses an image processing library to generate the future appearance of the product based on the predicted data.

[0286] 5. The server then customizes the display based on the emotional data, emphasizing warranty information and positive word-of-mouth reviews.

[0287] 6. The terminal displays this information on the user interface, allowing the user to check the future condition and value of the product and make a purchasing decision with confidence.

[0288] Prompt Sentence Examples

[0289] Here is an example of inputting the following prompt sentence into a generative AI model to output a detailed explanation of the above system.

[0290] plaintext

[0291] This system is an e-commerce system that predicts and visually displays the future condition and market value of a product based on the usage and frequency of use data entered by the user when purchasing the product. It also includes an emotion engine that recognizes user emotions to improve the purchasing experience. Specifically, a user accesses an e-commerce site, selects the product they want to purchase, and enters information about usage and frequency of use. The terminal then verifies the data, sends it to the server, and stores it. The server then uses this data to predict and visualize aging and price trends. Furthermore, the emotion engine recognizes the user's emotions, and the server customizes the display based on those emotions. Finally, the predicted data and the generated image are sent to the terminal and displayed to the user. Please provide a detailed explanation, including examples and prompt sentences.

[0292] In this way, the present invention not only provides users with detailed information about the future condition and value of a product when they are considering a purchase, but also optimizes the user experience based on emotion recognition, improving purchasing motivation and supporting appropriate purchasing decisions.

[0293] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0294] Step 1:

[0295] A user accesses an e-commerce site, selects a product they wish to purchase, and inputs information about the purpose and frequency of use through an input means.

[0296] Input: User input data on usage and frequency of use.

[0297] Output: The input data is sent to the terminal and prepared for validation.

[0298] Specific actions: Enter details such as "daily use" and "5 days a week" into the web form and click the submit button.

[0299] Step 2:

[0300] The terminal validates the data entered by the user to ensure there are no errors. It uses validation functions to check whether the entered data is in the correct format.

[0301] Input: Usage and frequency data entered by the user.

[0302] Output: Validated data.

[0303] Specific behavior: Validation functions are used to check for blank spaces and formatting. If validation is successful, the data is ready to be sent to the server.

[0304] Step 3:

[0305] The terminal sends the data that passes the verification to the server using the HTTPS protocol, ensuring data security.

[0306] Input: Validated usage and frequency data.

[0307] Output: The user's data sent to the server.

[0308] Specific operation: Data is sent via the HTTPS protocol, and the server returns a receipt confirmation response to the terminal.

[0309] Step 4:

[0310] The server stores the received data in a database, which includes the product ID, purpose, frequency of use, and user ID.

[0311] Input: Usage and frequency data sent to the server.

[0312] Output: User data stored in the database.

[0313] Specific operation: Saves data using an RDBMS such as MySQL or PostgreSQL. Notifies the device that the save was successful.

[0314] Step 5:

[0315] The device uses a camera and microphone to collect the user's facial expressions and tone of voice while they are entering data. This data is then analyzed and an emotion engine is used to recognize their emotional state.

[0316] Input: User's facial expression data and voice data.

[0317] Output: The perceived emotional state of the user (e.g., excited, anxious, apathetic, etc.).

[0318] Specific operation: Facial recognition is performed using OpenCV, and voice tone is analyzed using a voice analysis API. Based on the analysis, emotions are classified using an emotion engine (using TensorFlow and PyTorch).

[0319] Step 6:

[0320] The server uses an aging prediction model to predict the future state of the product based on the stored data and product characteristic data, and also predicts price trends based on past market data.

[0321] Inputs: Usage and frequency data, product characteristics data, historical market data.

[0322] Output: Forecast data of future product state and price trend forecast data.

[0323] Specific operation: Aging prediction is performed using scikit-learn, and price trends are calculated using a generative AI model. For example, it predicts that there will be small scratches after one year and clear wear after five years, and predicts that the price will be 900,000 yen after one year and 700,000 yen after five years.

[0324] Step 7:

[0325] The server generates an image that visually shows the future state of the product based on the predicted data.

[0326] Input: Aging forecast data, price trend forecast data.

[0327] Output: An image showing the future state of the product.

[0328] What it does: It uses PIL (Python Imaging Library) to visualize what a product will look like after one year and five years. For example, the image of a watch after one year will show minor scratches, while the image after five years will show obvious wear.

[0329] Step 8:

[0330] The server customizes the display content based on the emotion data received from the emotion engine.

[0331] Input: Recognized user emotion data.

[0332] Output: Customized display content.

[0333] What it does: If anxiety is detected, warranty information and positive reviews are highlighted, and if an excited user is detected, product features and promotional information are highlighted.

[0334] Step 9:

[0335] The server sends the predicted data and the generated image to the terminal, which displays the data.

[0336] Inputs: Forecast data, future state image, customized display information.

[0337] Output: Information displayed in the user interface.

[0338] Specific operation: Data is encoded in JSON format and sent via the REST API. The device displays the received data in a user interface using HTML and CSS.

[0339] Through these steps, the present invention provides detailed information about the future condition and value of a product when the user is considering purchasing it, and provides a customized display based on emotions, thereby improving the user's purchasing experience.

[0340] (Application example 2)

[0341] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0342] In conventional e-commerce systems, users have limited means of predicting the future condition or market value of a product when purchasing it, which often leaves users anxious about the value and condition of the product after purchase. Furthermore, there is no means to provide a purchasing experience that takes into account the user's emotional state, making it difficult to adequately support purchasing behavior that is influenced by emotions such as anxiety or excitement. This poses a challenge in improving users' purchasing motivation and post-purchase satisfaction.

[0343] The specification process by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving and saving input data, means for predicting the future state of the product using an aging prediction model, means for generating an image visually showing the predicted future state, means for recognizing the emotional state of the user by analyzing the user's facial expression and voice, and means for customizing the display content based on the emotional state obtained by the emotion recognition means. This makes it possible to predict the future state and market value of a product when the user purchases it, and not only to visually confirm the results, but also to provide a purchasing experience that is tailored to the user's emotional state.

[0344] A "user" is an entity that uses the system to purchase or evaluate products.

[0345] "Products" are goods and services provided to users through electronic commerce.

[0346] "Use" refers to the purpose or intention of how the user will use the product.

[0347] "Frequency of use" is data indicating how often or how many times a user uses a product.

[0348] "Input means" refers to an interface that a user uses to input data about purpose and frequency of use into the system.

[0349] The "server means" is a computer system that stores data received from users and performs various processes based on the data.

[0350] The "prediction means" is a function that predicts the future state of a product using an aging prediction model based on the stored data and product characteristic data.

[0351] The "image generation means" is a function that generates an image that visually shows the predicted future state of the product.

[0352] The "price trend prediction means" is a function that predicts the future market value of a product based on past market data.

[0353] The "transmission means" is a function for transmitting predicted data and generated images from the server to the user terminal.

[0354] The "display means" is an interface that displays the predicted data received by the user terminal and the generated image to the user.

[0355] The "emotion recognition means" is a function that analyzes the user's facial expressions and voice to recognize the user's emotional state.

[0356] The "customization means" is a function for customizing the display content based on the emotional state obtained by the emotion recognition means.

[0357] System Overview

[0358] The present invention relates to an electronic commerce system that predicts and visually displays the future condition and market value of a product based on data on the purpose and frequency of use entered by a user when purchasing the product. The system includes a user terminal, a server, an emotion engine, and a database. It also includes emotion recognition means that recognizes the user's emotions to improve the purchasing experience.

[0359] Hardware and Software Configuration

[0360] User devices: Smartphones, tablets, PCs, etc. are applicable. Users input their usage and frequency of use, and the results of predictions of future conditions and price trends are displayed. The devices are equipped with cameras and microphones, and also have an emotion engine that analyzes the user's facial expressions and voice.

[0361] Server: A computer system that receives, stores, and processes data. The server is installed with an aging prediction model, a price trend prediction model, an image processing library, an emotion recognition library, etc.

[0362] Database: A database system for storing product characteristics data, user-entered data, and historical market data.

[0363] Specific details of data processing

[0364] 1. User data entry

[0365] A user accesses an online shopping site and goes to the details page of a product they are considering purchasing. On this page, they enter information about the purpose and frequency of use. For example, if they are purchasing a luxury watch, they enter data such as "daily use" and "five days a week."

[0366] 2. Data transmission and storage

[0367] The user terminal verifies the entered data to ensure there are no errors. After verification, this data is sent to the server, which stores it in a database. The stored data includes the product ID, purpose, frequency of use, and user ID.

[0368] 3. Emotion Recognition

[0369] Using the camera and microphone on the user's device, the system analyzes the user's facial expressions and voice to recognize the user's emotional state. An emotion recognition library (e.g., EmotionRecognition library) is used to determine the user's emotional state (e.g., excitement, anxiety, indifference, etc.).

[0370] 4. Product condition prediction and price trend prediction

[0371] The server uses an aging prediction model to predict the future condition of a product based on the stored data on usage and frequency of use. For example, it calculates the deterioration state of a luxury watch one year and five years from now. At the same time, it predicts price trends based on past market data and calculates prices one year and five years from now.

[0372] 5. Future State Visualization

[0373] The server generates an image that visually represents the future state of the product based on the predicted data. It uses an image processing library (e.g., OpenCV or matplotlib) to visualize the appearance of the product after one year and after five years. For example, it generates an image that shows slight scratches after one year and clear wear after five years.

[0374] 6. Emotion-based customization

[0375] The server customizes the display content based on the user's emotional data received from the emotion recognition means. For example, for a user who appears anxious, product warranty information and positive user reviews are emphasized. For an excited user, product features and promotional information are emphasized.

[0376] 7. Sending and displaying predicted data and images

[0377] The server sends the predicted data and generated images to the user's device, which then displays them on a user interface. The displayed information includes text information about the product's aging status and market value one and five years from now, generated images of the product's future appearance, and emotion-based customization information.

[0378] Specific examples

[0379] For example, when a user purchases a luxury watch, they enter "daily use" as the purpose and "five days a week" as the frequency of use, and if they have concerns about the purchase, the server saves the entered data in a database. Next, it predicts aging and price trends, predicting that small scratches will appear after one year and clear wear after five years. The price trends are predicted to be 900,000 yen after one year and 700,000 yen after five years. Based on this predicted data, the server visualizes the future appearance of the product, and also highlights warranty information and positive user reviews to alleviate the user's concerns.

[0380] Prompt Sentence Examples

[0381] "Predict what a luxury watch will look like in one year and five years if used daily, five days a week."

[0382] In this way, the present invention provides detailed information about the future condition and value of a product at the user's purchasing stage, as well as a personalized purchasing experience based on emotion recognition.

[0383] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0384] Step 1:

[0385] A user accesses an online shopping site and navigates to the details page of a product they are considering purchasing. On the product details page, they input information about the product's purpose and frequency of use. This input data includes the product's purpose (e.g., "daily use") and frequency of use (e.g., "five days a week"). The input data is collected by the device.

[0386] Step 2:

[0387] The terminal verifies the data entered by the user and checks for errors. After verification, it sends the correct data to the server. The data sent includes the product ID, purpose, frequency of use, and user ID. The data processing performed here is a consistency check of the input data.

[0388] Step 3:

[0389] The server stores the received data in a database. The stored data includes the product ID, purpose, frequency of use, and user ID. The data calculation here is the process of writing to the database.

[0390] Step 4:

[0391] While the user is inputting data, the device uses a camera and microphone to collect the user's facial expressions and voice. This data is input to an emotion recognition module, which analyzes the user's emotional state (e.g., excitement, anxiety, joy, etc.). The analysis result (emotional state) is returned to the device.

[0392] Step 5:

[0393] The server uses an aging prediction model to predict the future condition of the product based on the stored data on usage and frequency of use. By combining the input data (usage, frequency of use) with product characteristic data, it calculates the deterioration state of the product one year or five years from now. The predicted result provides the product's condition one year or five years from now (e.g., minor scratches, wear, etc.).

[0394] Step 6:

[0395] The server runs a price trend prediction model based on past market data. The input data includes the product ID and current market price, and the server predicts future prices by referencing past market data. The resulting prediction is the market value of the product one or five years from now (e.g., 900,000 yen in one year, 700,000 yen in five years).

[0396] Step 7:

[0397] The server generates images to visually represent the predicted future state of the product. It uses image processing libraries (e.g., OpenCV and matplotlib) to generate images that recreate the appearance of the product one year and five years from now. The generated images are stored on the server.

[0398] Step 8:

[0399] The server customizes the display content based on the emotion data received from the emotion recognition module, for example, highlighting warranty information and positive reviews for anxious users and highlighting features and promotions for excited users.

[0400] Step 9:

[0401] The server transmits the predicted data and the generated image to the terminal, including the future aging state of the product, its market value, the generated appearance image, and customization information.

[0402] Step 10:

[0403] The device displays the received forecast data and the generated image on the user interface, allowing users to visually check the product's condition and market value one or five years from now, allowing them to make purchasing decisions with confidence.

[0404] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0405] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0406] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0407] [Second embodiment]

[0408] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0409] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0410] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0411] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0412] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0413] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0414] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0415] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0416] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[0417] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0418] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0419] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0420] The present invention relates to an electronic commerce system that predicts and visually displays the future condition and market value of a product based on data on the purpose and frequency of use entered by a user when purchasing the product. This system includes a user terminal, a server, and a database. The processing of the system's program is explained below in natural language.

[0421] User data entry

[0422] A user selects a product on an e-commerce site and accesses the product's details page. On this details page, the user enters information about the purpose and frequency of use. For example, when purchasing a luxury watch, the user enters information such as "daily use" and "five days a week."

[0423] Data transmission and storage

[0424] The terminal verifies the data entered by the user regarding purpose and frequency of use to ensure there are no errors. Data that passes verification is sent to the server. The server receives this data and stores it in a database. The stored data includes the product ID, purpose, frequency of use, user ID, etc.

[0425] Product condition prediction

[0426] The server uses an aging prediction model to predict the future condition of a product based on data on usage and frequency of use stored in the database. For example, the aging prediction for a luxury watch used daily five days a week predicts that after one year, small scratches and a decrease in luster will appear, and after five years, there will be obvious wear and tear and peeling of the plating.

[0427] Price trend forecast

[0428] The server predicts price trends based on past market data for a product. For example, a luxury watch currently priced at 1 million yen is predicted to be priced at 900,000 yen one year from now, and 700,000 yen five years from now. This prediction is made using time series analysis and machine learning models.

[0429] Future State Visualization

[0430] The server uses the predicted data to generate a visual image of the product's future state. For example, it might generate an image of a watch's appearance in one year, showing slight scratches and a loss of luster, and an image of a watch in five years, showing obvious wear and peeling plating. To do this, the server uses an image processing library.

[0431] Sending and displaying forecast data and images

[0432] The server sends the predicted data and generated images to the user's terminal, which receives the data and images and displays them to the user. The displayed information includes text information about the product's aging condition and market value one and five years from now, as well as generated images of the product's future appearance.

[0433] Specific examples

[0434] For example, when a user purchases a luxury watch on an e-commerce site, they enter "daily use" as the intended use and "five days a week" as the frequency of use. The device sends this data to the server, which stores it in a database. The server then uses the entered data to predict aging and price trends. It predicts that after one year, small scratches will appear and the luster will decrease, and after five years, there will be clear wear and peeling of the plating. The server predicts that the price will be 900,000 yen after one year and 700,000 yen after five years. Based on this predicted data, the server generates an image of the product's future appearance, which the device displays to the user. Using this information, the user can visually understand the product's future after purchase and make an appropriate decision.

[0435] In this way, the present invention provides detailed information about the future condition and value of a product when the user is considering purchasing it, thereby enhancing purchasing motivation and supporting appropriate purchasing decisions.

[0436] The processing flow will be explained below.

[0437] Step 1:

[0438] A user accesses an e-commerce site and selects a product. They are taken to the details page of the selected product and enter information such as the purpose and frequency of use.

[0439] Step 2:

[0440] The device validates the purpose and frequency of use data entered by the user, for example by checking that the data is complete and in the correct format.

[0441] Step 3:

[0442] The terminal sends the data that passes the verification to the server, including the product ID, purpose, frequency of use, and user ID.

[0443] Step 4:

[0444] The server stores the received data in a database, where information on the purpose and frequency of use of each product is organized and stored.

[0445] Step 5:

[0446] The server calls the condition prediction module based on the stored data and uses an aging prediction model to predict the future condition of the product. For example, it calculates the deterioration state based on daily use, 5 days a week.

[0447] Step 6:

[0448] The server calls the price trend prediction module based on past market data, and uses time series analysis and machine learning models to predict the market price of the product one or five years from now.

[0449] Step 7:

[0450] The server uses the predicted data to generate images of the future state of the product, using image processing libraries to visualize what the product will look like in one year or five years.

[0451] Step 8:

[0452] The server transmits the forecast data and the generated image to the terminal, including the aging state, price transition, and the generated image.

[0453] Step 9:

[0454] The terminal displays the received data and images on a user interface, including the aging condition and market price one and five years from now, as well as an image of the product's future appearance.

[0455] Step 10:

[0456] Users can check the future condition and value of the product based on the displayed information, which can be used as reference when making purchasing decisions or collecting items.

[0457] Through the above steps, the user can get a detailed understanding of the future condition and market value of the product at the time of purchase, enabling them to make an appropriate selection.

[0458] Example 1

[0459] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0460] In conventional e-commerce systems, it is difficult for users to fully understand the future condition and market value of a product when purchasing it. This makes it difficult for users to make appropriate decisions regarding future use, which can affect their post-purchase satisfaction. The present invention aims to support users in making appropriate purchasing decisions and increase user satisfaction by predicting and visually displaying the future condition and market value of a product when they are considering purchasing it.

[0461] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0462] In this invention, the server includes means for receiving and storing the transmitted data, means for predicting the future state of the product using an aging prediction model based on the stored data, means for predicting price trends based on past market data, means for generating an image visually showing the predicted future state, and means for transmitting the predicted data and the generated image to a user terminal, thereby enabling the user to visually confirm information about the future state and market value of the product and make an appropriate decision before purchasing.

[0463] The "input means" is a means for a user to input data on the purpose and frequency of use when purchasing a product.

[0464] "Terminal means" refers to means for verifying data entered by a user and transmitting it to a server.

[0465] The "server means" is a means for receiving input data and storing it in a database.

[0466] "Prediction means" refers to a means for predicting the future state of a product using an aging prediction model based on stored data.

[0467] A "price trend prediction means" is a means for predicting the price trend of a product based on past market data.

[0468] The "image generating means" is a means for generating an image that visually shows the predicted future state of the product.

[0469] The "transmission means" is a means for transmitting the prediction data and the generated image to the user's terminal.

[0470] The "display means" is a means for displaying the predicted data and the generated image on the user terminal.

[0471] The "means for calculating a deterioration rate" is a means for calculating a deterioration rate applied to a product based on the user's usage frequency data.

[0472] "Image Processing Library" means a software library for image processing used to visually indicate the aging state of a product.

[0473] The present invention relates to an electronic commerce system that predicts and visually displays the future condition and market value of a product based on data on the product's intended use and frequency of use entered by the user when purchasing the product. The system includes a user terminal, a server, and a database. When purchasing a product, the user enters information on the product's intended use and frequency of use from the user terminal. The terminal is responsible for verifying this input data and sending it to the server. The server stores the received data and uses various prediction models to predict and visualize the product's future condition and price trends. This information is then sent back to the user's terminal so that the user can view it.

[0474] Specifically, a user enters information about the purpose and frequency of use on the details page of an e-commerce site. For example, when purchasing a luxury watch, the user enters information such as "daily use" and "five days a week." This input data is verified by the device and sent to the server. The server stores the received data in a database and uses this data to make predictions using an aging prediction model and a price trend prediction model. Time series analysis and machine learning models are used to predict the aging state and price changes of luxury watches one and five years from now. An image processing library is also used to generate images that visually show the predicted aging state.

[0475] The generated forecast data and visual images are sent from the server to the user's device, allowing the user to understand the product's future state and make a purchasing decision. This information provision improves the user's post-purchase satisfaction and supports appropriate purchasing decisions.

[0476] Specific examples

[0477] For example, when a user purchases a luxury watch on an e-commerce site, they enter "daily use" as the intended use and "five days a week" as the frequency of use. This data is sent by the device to a server, which stores the data and uses it to predict aging and price trends. After one year, the server predicts small scratches and a loss of luster, and after five years, the watch will show clear wear and the plating will peel. The server also predicts that the price will be 900,000 yen after one year and 700,000 yen after five years. Based on this predicted data, the server generates an image of the product's future appearance, which the device displays to the user. Using this information, the user can visually understand the product's future after purchase and make an appropriate decision.

[0478] Prompt Sentence Examples

[0479] Below are some examples of prompt sentences.

[0480] "When purchasing a luxury watch, assume it will be worn daily, five days a week. Using this information, predict the aging and price trends of the watch in one year and five years. Also, generate an image showing its future appearance."

[0481] Based on the above prompt, the generative AI model can be asked to make appropriate predictions and generate visual information.

[0482] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0483] Step 1:

[0484] A user selects a product on an e-commerce site and accesses the product detail page. The user enters information about the purpose and frequency of use, such as "purpose: daily use" and "frequency of use: 5 days a week."

[0485] Input: Usage and frequency data

[0486] Output: Data entered on the user's terminal

[0487] Step 2:

[0488] The terminal validates the data entered by the user. Specifically, it checks the input format and value range to ensure that the input data is correct. For example, it verifies that the purpose is "daily use" and the frequency is "five days a week." If validation is successful, the terminal sends the data to the server.

[0489] Input: User-entered usage and frequency data

[0490] Output: Validated data sent to the server

[0491] Step 3:

[0492] The server receives the data sent from the device and stores it in a database along with information such as product ID, purpose, frequency of use, and user ID. This allows the data necessary for future predictions to be accumulated.

[0493] Input: Verified data sent from the terminal

[0494] Output: Data stored in the database

[0495] Step 4:

[0496] The server uses the data stored in the database to predict the future condition of the product using an aging prediction model. Specifically, it references past usage data and aging data to predict the product's deterioration. For example, if a luxury watch is used daily, five days a week, after one year it may develop small scratches and lose its luster, and after five years it may show clear wear and peeling of the plating.

[0497] Input: Usage and frequency data stored in the database

[0498] Output: Predicted future product state data

[0499] Step 5:

[0500] The server predicts product price trends based on past market data. Specifically, it uses time series analysis and machine learning models to analyze past price history and supply and demand data to predict future prices. For example, if the current price of a luxury watch is 1 million yen, it is predicted that it will be 900,000 yen in one year and 700,000 yen in five years.

[0501] Inputs: Historical market data and aging forecast data

[0502] Output: Predicted price trend data

[0503] Step 6:

[0504] The server uses the forecast data to generate a visual representation of the product's future state. It uses image processing libraries and AI technology to create product images based on the forecast results. For example, it generates images showing the appearance of a watch one year from now and five years from now. The images reflect small scratches, loss of gloss, wear, and peeling plating.

[0505] Input: Predicted future product status data and price transition data

[0506] Output: The generated visual image

[0507] Step 7:

[0508] The server sends the predicted data and generated images to the user's device, which packages the data and sends it to the device in an easily accessible format.

[0509] Input: Prediction data and generated images

[0510] Output: Data sent to the user's device

[0511] Step 8:

[0512] The terminal displays the received forecast data and generated images to the user, allowing the user to visually confirm detailed information about the product's future condition and market value. For example, text information about the aging condition and price trends one and five years from now, along with related images, are displayed.

[0513] Input: Prediction data sent from the server and generated images

[0514] Output: Prediction information and images displayed to the user

[0515] (Application example 1)

[0516] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0517] Conventional e-commerce systems lacked the means to provide detailed information about the future condition and market value of a product when users were considering a purchase, making it difficult for users to make appropriate purchasing decisions. Furthermore, there was also a lack of a means to visually understand the aging state of a product after use and the transition of its market value. As a result, it was not possible to stimulate users' purchasing motivation or to appeal to the benefits of purchasing from a long-term perspective.

[0518] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0519] In this invention, the server includes: input means for inputting data on the purpose and frequency of use of an item when a user purchases it; an information processing device for receiving and saving the input data; prediction means for predicting the future state of the item using a deterioration prediction model based on the saved data and item characteristic data; image generation means for generating an image visually showing the predicted future state; price trend prediction means for predicting price trends based on past market data; transmission means for transmitting the predicted data and the generated image to a user terminal; display means for displaying the predicted data and the generated image; and application means for providing visualized information predicting the future state and market value of the item based on the purpose and frequency of use data of the item. This allows users to obtain detailed information on the future state and market value of the item before purchasing, enabling them to make appropriate purchasing decisions.

[0520] "User" refers to a consumer or user who uses this system to purchase goods.

[0521] "Goods" refers to the goods and products handled in this system.

[0522] "Use" refers to information about how the user intends to use the item they are about to purchase.

[0523] "Frequency of use" refers to information about how often a user uses the item they are about to purchase.

[0524] "Input means" refers to the device or software that allows the user to input data on purpose and frequency of use into the system.

[0525] The term "information processing device" refers to a server or computer that stores data received from a user and performs various calculations.

[0526] "Item characteristic data" refers to data on basic information and characteristics of the items being handled.

[0527] A "deterioration prediction model" refers to an algorithm or mathematical model for predicting the future deterioration state of an item based on its intended use and frequency of use.

[0528] "Prediction means" refers to a function or device for predicting the future state of an item using a deterioration prediction model based on stored data and item characteristic data.

[0529] "Image generation means" refers to a function or device for generating an image that visually shows the predicted future state of an item.

[0530] "Price trend prediction means" refers to a function or device that predicts future price trends of goods based on past market data.

[0531] "Transmission means" refers to a function or device for transmitting prediction data and generated images to a user terminal.

[0532] "User terminal" refers to an electronic device used by a user, such as a computer, smartphone, or tablet.

[0533] "Display means" refers to software and hardware for displaying the predicted data and generated images on the user terminal.

[0534] "Application means" refers to application software that predicts the future condition and market value of an item based on data on the use and frequency of use of the item, and visually presents this to the user.

[0535] This invention is an electronic commerce system that predicts and visually displays the future condition and market value of an item after purchase to a user considering purchasing the item. The system includes a user terminal, an information processing device, and a database.

[0536] User data entry

[0537] Users access the product details page and enter information about the purpose and frequency of use. For example, when purchasing a luxury watch, users enter information such as "daily use" and "five days a week."

[0538] Data transmission and storage

[0539] The user terminal verifies the data entered by the user regarding the purpose and frequency of use to ensure there are no errors. Data that passes verification is sent to the information processing device. The information processing device receives this data and stores it in a database. The stored data includes the item ID, purpose, frequency of use, user ID, etc.

[0540] Item condition prediction

[0541] The information processing device predicts the future condition of an item using a deterioration prediction model based on data on usage and frequency of use stored in a database. For example, a deterioration prediction for a luxury watch used daily five days a week might predict that after one year, small scratches and a loss of luster will appear, and after five years, clear wear and peeling of the coating will be visible.

[0542] Price trend forecast

[0543] The information processing device predicts price trends based on past market data for goods. For example, a luxury watch currently priced at 1 million yen is predicted to have a price of 900,000 yen one year from now, and 700,000 yen five years from now. This prediction is made using time series analysis and machine learning models.

[0544] Future State Visualization

[0545] The information processing device generates an image based on the predicted data that visually represents the future state of the item. For example, it generates an image that reflects the appearance of a watch one year from now, with slight scratches and loss of gloss, and an image that reflects the watch's obvious wear and peeling coating five years from now. To do this, the information processing device uses an image processing library.

[0546] Sending and displaying forecast data and images

[0547] The information processing device transmits the predicted data and the generated image to a user terminal. The user terminal receives the data and image and displays them to the user. The displayed information includes text information about the deterioration state and market value of the item one and five years from now, as well as the generated image of the item's future appearance.

[0548] Specific examples

[0549] For example, when a user purchases a luxury watch, they input "daily use" as the purpose and "five days a week" as the frequency of use. The user's device sends this data to an information processing device, which stores it in a database. The information processing device then predicts deterioration and price trends based on the input data. It predicts that the watch will develop small scratches and lose its luster in one year, and that it will show clear wear and peeling of the coating in five years, and predicts that the price will be 900,000 yen in one year and 700,000 yen in five years. Based on this predicted data, the information processing device generates an image of the future appearance of the item, which the user's device displays to the user. Based on this information, the user can visually understand the future of the item after purchase and make an appropriate decision.

[0550] Prompt Sentence Examples

[0551] A user entered the following information about purpose and frequency of use on the item page:

[0552] Use: Everyday use

[0553] Frequency of use: 5 days a week

[0554] Use this information to predict the condition and market value of the item one and five years from now, along with a visualisation.

[0555] As described above, the present invention provides detailed information about the future state and value of an item when the user is considering purchasing it, thereby enhancing purchasing motivation and supporting appropriate purchasing decisions.

[0556] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0557] Step 1: User Data Entry

[0558] The user accesses the product details page and inputs information about the purpose and frequency of use of the product they are considering purchasing. The input data includes specific usage information such as "daily use" and "five days a week." The user's device receives this input data.

[0559] Input: Information on purpose and frequency of use

[0560] Output: Verified data (use and frequency of use)

[0561] Step 2: Data transmission and storage

[0562] The verified data is sent from the terminal to an information processing device (server). The information processing device stores the received data in a database. The stored data includes the item ID, purpose, frequency of use, user ID, etc.

[0563] Input: Verified data (purpose and frequency of use)

[0564] Output: Data stored in the database

[0565] Step 3: Predicting the condition of the item

[0566] The information processing device predicts the future condition of the item using a deterioration prediction model based on data on usage and frequency of use stored in a database. For example, it uses a machine learning model or a time series analysis algorithm to predict the condition of the item one year or five years from now.

[0567] Input: Data on usage and frequency of use stored in the database

[0568] Output: Predicted future state of the item

[0569] Step 4: Predict price trends

[0570] The information processing device predicts price trends based on past market data for goods. For example, using time series analysis and machine learning models, it predicts that a luxury watch currently priced at 1 million yen will be priced at 900,000 yen one year from now, and 700,000 yen five years from now.

[0571] Input: Product information stored in the database, historical market data

[0572] Output: Predicted price progression

[0573] Step 5: Visualize the future state

[0574] The information processing device uses the predicted data to generate images that visually represent the future state of the item. For example, using an image processing library, it generates an image that shows the appearance of a watch one year from now, with small scratches and a loss of gloss, and the appearance of a watch five years from now, with obvious wear and peeling coating.

[0575] Input: Predicted future state data of the item

[0576] Output: The generated visual image

[0577] Step 6: Send and display forecast data and images

[0578] The information processing device transmits the predicted data and the generated image to a user terminal. The user terminal receives this data and image and displays them to the user. Specifically, text information about the deterioration state and market value of the item one year and five years from now, as well as the generated image of the item's appearance in the future, are displayed.

[0579] Input: Prediction data and generated images

[0580] Output: Predicted data and visual images displayed on the user's device

[0581] Through the above steps, the user can obtain detailed information about the future condition and market value of the item they are considering purchasing, enabling them to make an appropriate purchasing decision.

[0582] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0583] The present invention provides an e-commerce system that predicts and visually displays the future condition and market value of a product based on data on the purpose and frequency of use entered by the user when purchasing the product, and also includes an emotion engine that recognizes the user's emotions to further improve the purchasing experience. This system includes a user terminal, a server, an emotion engine, and a database. The processing of the system's programs is described in detail below.

[0584] User data entry

[0585] A user accesses an e-commerce site and selects the product they want to purchase. They are then taken to the product detail page and enter information about the purpose and frequency of use. For example, when purchasing a luxury watch, a user enters information such as "daily use" and "five days a week."

[0586] Data transmission and storage

[0587] The terminal verifies the data entered by the user regarding purpose and frequency of use to ensure there are no errors. Data that passes verification is sent to the server. The server stores this data in a database. The stored data includes the product ID, purpose, frequency of use, and user ID.

[0588] Emotion recognition by emotion engine

[0589] While the user is entering data, the device uses sensors such as a camera and microphone to analyze the user's facial expressions and tone of voice. The emotion engine uses this data to recognize the user's emotional state (e.g., excitement, anxiety, indifference, etc.).

[0590] Product condition prediction and price trend prediction

[0591] The server uses an aging prediction model to predict the future condition of a product based on stored data on usage and frequency of use. For example, it calculates the deterioration state of a luxury watch one year and five years from now based on daily use five days a week. At the same time, it predicts price trends based on past market data and calculates predicted prices one year and five years from now.

[0592] Future State Visualization

[0593] The server uses the predicted data to generate a visual representation of the product's future state. It uses an image processing library to visualize the product's appearance in one year and five years. For example, the watch's appearance in one year will show minor scratches, while its appearance in five years will show clear wear.

[0594] Customized display based on emotions

[0595] The server customizes the display content based on the user's emotional data received from the emotion engine. For example, if a user seems anxious about a purchase, the server can highlight product warranty information and user reviews. For an excited user, the server can highlight product features and promotional information.

[0596] Sending and displaying forecast data and images

[0597] The server sends the predicted data and generated images to the terminal, which displays them on a user interface. The displayed information includes text information about the product's aging status and market value one and five years from now, generated images of the product's future appearance, and emotion-based customization information.

[0598] Specific examples

[0599] For example, when a user purchases a luxury watch on an e-commerce site, they enter "daily use" as the purpose and "five days a week" as the frequency of use. If the device detects any anxiety about the purchase, the device sends this input data to the server, which stores it in a database. The server then uses the input data to predict aging and price trends, predicting that the watch will develop minor scratches after one year and clear wear after five years. The predicted price trends are 900,000 yen after one year and 700,000 yen after five years. The server uses this predicted data to visualize the product's future appearance, highlighting warranty information and positive user reviews to alleviate the user's anxiety. The device then displays this information to the user, allowing them to confirm the product's future condition and value and make a purchasing decision with confidence.

[0600] In this way, the present invention not only provides users with detailed information about the future condition and value of a product when they are considering a purchase, but also optimizes the user experience based on emotion recognition, improving purchasing motivation and supporting appropriate purchasing decisions.

[0601] The processing flow will be explained below.

[0602] Step 1:

[0603] A user accesses an e-commerce site and selects a product they are interested in. For example, they move to a detail page of a luxury watch and begin to consider purchasing it.

[0604] Step 2:

[0605] The device displays input fields for the purpose and frequency of use on the product detail page, and the user enters information such as "purpose: daily use" and "frequency of use: 5 days a week."

[0606] Step 3:

[0607] The terminal validates the user's input data, checking for missing or malformed data, and data that passes validation proceeds to the next step.

[0608] Step 4:

[0609] The terminal transmits the input data on purpose and frequency of use to the server. The transmitted data includes the product ID, purpose, frequency of use, user ID, etc.

[0610] Step 5:

[0611] The server stores the received data in a database, including product characteristic data, user usage data, and so on.

[0612] Step 6:

[0613] The device uses a camera and microphone to recognize the user's facial expressions and voice while inputting data, and sends this data to an emotion engine to analyze the user's emotional state.

[0614] Step 7:

[0615] The emotion engine analyzes the user's facial expressions and tone of voice to identify emotions (e.g., excitement, anxiety, indifference, etc.), and sends the analysis results to the server.

[0616] Step 8:

[0617] The server uses an aging prediction model to predict the future condition of the product based on the stored data on usage and frequency of use. For example, it calculates wear and tear after one year and five years based on daily use five days a week.

[0618] Step 9:

[0619] The server calls a price trend prediction module based on past market data to predict the market price of the product one year or five years from now. For example, if the current price is 1 million yen, it is predicted that the price will be 900,000 yen one year from now and 700,000 yen five years from now.

[0620] Step 10:

[0621] The server generates images of the future state of the product based on the predicted data, using image processing libraries to visualize what the product will look like in one year and five years, simulating, for example, minor scratches after one year and increased wear after five years.

[0622] Step 11:

[0623] The server customizes the display content based on the emotion data from the emotion engine. For anxious users, it adds reassuring information (guarantees and reviews). For excited users, it emphasizes product features and promotional information.

[0624] Step 12:

[0625] The server transmits the predicted data, customized display content, and generated images to the terminal.

[0626] Step 13:

[0627] The device then displays the received data and images to the user, including customized information based on the aging status after one and five years, market value, and emotions.

[0628] Step 14:

[0629] Users can check the future condition and value of the product based on the displayed information, and can make appropriate purchasing decisions while also referring to customized information based on their emotions.

[0630] Through the above specific processing steps, the system can provide detailed information about the future condition and market value of a product when the user purchases it, as well as customize the display based on the user's emotions, thereby providing a better purchasing experience.

[0631] Example 2

[0632] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0633] In conventional e-commerce systems, when users purchase a product, they are not provided with information about the product's future condition or how its market value will change. This often leaves users feeling unsure about their purchase decision and can make them hesitant to make a final purchase. Furthermore, because the system does not take into account the user's feelings, the purchasing experience remains unimproved.

[0634] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for receiving and saving input data, means for predicting the future state of the product using an aging prediction model based on the saved data and product characteristic data, means for generating an image visually showing the predicted future state, means for predicting price trends based on past market data, means for transmitting the predicted data and the generated image to the user terminal, and means for customizing the display content based on the recognized emotional state. This allows the user to visually check the future state and market value of the product and make a purchase decision with confidence by receiving information according to their emotions.

[0635] 1. "Input means" refers to the interface through which a user inputs data on the purpose and frequency of use when purchasing a product.

[0636] 2. "Server Means" means a computing device for receiving and storing input data.

[0637] 3. "Prediction Means" refers to the function by which the Server Means predicts the future state of the Product using an aging prediction model based on the Stored Data and Product Attribute Data.

[0638] 4. "Image generation means" refers to a function for generating an image that visually shows the predicted future state.

[0639] 5. "Price trend prediction means" refers to a function for predicting price trends based on past market data.

[0640] 6. "Transmission means" refers to the function for transmitting prediction data and generated images to a user terminal.

[0641] 7. "Display means" refers to an interface for a user terminal to display predicted data and generated images.

[0642] 8. "Emotion recognition means" refers to the sensors and analysis functions that enable a device to recognize a user's emotional state by analyzing the user's facial expressions and tone of voice.

[0643] 9. "Customized display means" refers to functionality that allows the server to customize the display content based on the recognized emotional state.

[0644] This invention is a system for an e-commerce system that predicts and visually displays the future condition and market value of a product based on data on the purpose and frequency of use entered by a user when purchasing the product, and recognizes the user's emotions to improve the purchasing experience. This system includes a user terminal, a server, an emotion recognition engine, and a database.

[0645] User data entry

[0646] A user accesses an e-commerce site and selects the product they wish to purchase. They are then taken to the product detail page and enter information about the purpose and frequency of use. For example, when purchasing a luxury watch, they enter information such as "daily use" and "five days a week." An interface such as a web form is used as the input method.

[0647] Data transmission and storage

[0648] The terminal verifies the purpose and frequency of use data entered by the user to ensure there are no errors. A validation function is used for the verification. Data that passes verification is securely sent to the server using the HTTPS protocol. The server saves this data in a database. The saved data includes the product ID, purpose, frequency of use, and user ID. An RDBMS such as MySQL or PostgreSQL is used as the database.

[0649] Emotion recognition by emotion engine

[0650] While the user is entering data, the device uses sensors such as a camera and microphone to analyze the user's facial expressions and tone of voice. This is done using libraries such as OpenCV. The emotion recognition engine uses machine learning models such as TensorFlow and PyTorch to recognize the user's emotional state (e.g., excitement, anxiety, indifference, etc.).

[0651] Product condition prediction and price trend prediction

[0652] The server uses an aging prediction model to predict the future condition of a product based on the stored data on usage and frequency of use. Libraries such as scikit-learn are used for aging prediction. For example, the state of deterioration of a luxury watch after one year and five years is calculated. At the same time, price trends are predicted based on past market data, and a generative AI model is used to calculate predicted prices after one year and five years.

[0653] Future State Visualization

[0654] The server uses the predicted data to generate a visual representation of the product's future state. PIL (Python Imaging Library) is used for image processing, visualizing the product's appearance in one year and five years. For example, the watch's appearance in one year will show minor scratches, while its appearance in five years will show clear wear.

[0655] Customized display based on emotions

[0656] The server customizes the display content based on the user's emotional data received from the emotion engine. For users who appear anxious about the purchase, product warranty information and user reviews are emphasized. On the other hand, for users who are excited, product features and promotional information are emphasized.

[0657] Sending and displaying forecast data and images

[0658] The server sends the predicted data and generated images to the terminal. REST API is used to send the data, and the data is encoded in JSON format. The terminal displays this data and images on a user interface. The information includes text information about the aging status and market value of the product one and five years from now, generated images of the product's future appearance, and emotion-based customization information. As a specific example of how the data is displayed, the predicted information and visualized images are displayed on a web page using HTML and CSS.

[0659] Specific examples

[0660] For example, when a user purchases a luxury watch on an e-commerce site, if the user enters "daily use" as the purpose and "five days a week" as the frequency of use, and anxiety about the purchase is detected, the following processing will be performed.

[0661] 1. The terminal checks the data entered by the user and sends it to the server, which stores it in a database.

[0662] 2. The device uses a camera and microphone to collect the user's facial expressions and voice for emotion recognition. The emotion engine analyzes the data and detects anxiety.

[0663] 3. The server runs an aging prediction model based on usage and frequency of use data, predicting, for example, that the server will develop minor scratches in one year and clear wear in five years. It also predicts that the price will be 900,000 yen in one year and 700,000 yen in five years.

[0664] 4. The server uses an image processing library to generate the future appearance of the product based on the predicted data.

[0665] 5. The server then customizes the display based on the emotional data, emphasizing warranty information and positive word-of-mouth reviews.

[0666] 6. The terminal displays this information on the user interface, allowing the user to check the future condition and value of the product and make a purchasing decision with confidence.

[0667] Prompt Sentence Examples

[0668] Here is an example of inputting the following prompt sentence into a generative AI model to output a detailed explanation of the above system.

[0669] plaintext

[0670] This system is an e-commerce system that predicts and visually displays the future condition and market value of a product based on the usage and frequency of use data entered by the user when purchasing the product. It also includes an emotion engine that recognizes user emotions to improve the purchasing experience. Specifically, a user accesses an e-commerce site, selects the product they want to purchase, and enters information about usage and frequency of use. The terminal then verifies the data, sends it to the server, and stores it. The server then uses this data to predict and visualize aging and price trends. Furthermore, the emotion engine recognizes the user's emotions, and the server customizes the display based on those emotions. Finally, the predicted data and the generated image are sent to the terminal and displayed to the user. Please provide a detailed explanation, including examples and prompt sentences.

[0671] In this way, the present invention not only provides users with detailed information about the future condition and value of a product when they are considering a purchase, but also optimizes the user experience based on emotion recognition, improving purchasing motivation and supporting appropriate purchasing decisions.

[0672] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0673] Step 1:

[0674] A user accesses an e-commerce site, selects a product they wish to purchase, and inputs information about the purpose and frequency of use through an input means.

[0675] Input: User input data on usage and frequency of use.

[0676] Output: The input data is sent to the terminal and prepared for validation.

[0677] Specific actions: Enter details such as "daily use" and "5 days a week" into the web form and click the submit button.

[0678] Step 2:

[0679] The terminal validates the data entered by the user to ensure there are no errors. It uses validation functions to check whether the entered data is in the correct format.

[0680] Input: Usage and frequency data entered by the user.

[0681] Output: Validated data.

[0682] Specific behavior: Validation functions are used to check for blank spaces and formatting. If validation is successful, the data is ready to be sent to the server.

[0683] Step 3:

[0684] The terminal sends the data that passes the verification to the server using the HTTPS protocol, ensuring data security.

[0685] Input: Validated usage and frequency data.

[0686] Output: The user's data sent to the server.

[0687] Specific operation: Data is sent via the HTTPS protocol, and the server returns a receipt confirmation response to the terminal.

[0688] Step 4:

[0689] The server stores the received data in a database, which includes the product ID, purpose, frequency of use, and user ID.

[0690] Input: Usage and frequency data sent to the server.

[0691] Output: User data stored in the database.

[0692] Specific operation: Saves data using an RDBMS such as MySQL or PostgreSQL. Notifies the device that the save was successful.

[0693] Step 5:

[0694] The device uses a camera and microphone to collect the user's facial expressions and tone of voice while they are entering data. This data is then analyzed and an emotion engine is used to recognize their emotional state.

[0695] Input: User's facial expression data and voice data.

[0696] Output: The perceived emotional state of the user (e.g., excited, anxious, apathetic, etc.).

[0697] Specific operation: Facial recognition is performed using OpenCV, and voice tone is analyzed using a voice analysis API. Based on the analysis, emotions are classified using an emotion engine (using TensorFlow and PyTorch).

[0698] Step 6:

[0699] The server uses an aging prediction model to predict the future state of the product based on the stored data and product characteristic data, and also predicts price trends based on past market data.

[0700] Inputs: Usage and frequency data, product characteristics data, historical market data.

[0701] Output: Forecast data of future product state and price trend forecast data.

[0702] Specific operation: Aging prediction is performed using scikit-learn, and price trends are calculated using a generative AI model. For example, it predicts that there will be small scratches after one year and clear wear after five years, and predicts that the price will be 900,000 yen after one year and 700,000 yen after five years.

[0703] Step 7:

[0704] The server generates an image that visually shows the future state of the product based on the predicted data.

[0705] Input: Aging forecast data, price trend forecast data.

[0706] Output: An image showing the future state of the product.

[0707] What it does: It uses PIL (Python Imaging Library) to visualize what a product will look like after one year and five years. For example, the image of a watch after one year will show minor scratches, while the image after five years will show obvious wear.

[0708] Step 8:

[0709] The server customizes the display content based on the emotion data received from the emotion engine.

[0710] Input: Recognized user emotion data.

[0711] Output: Customized display content.

[0712] What it does: If anxiety is detected, warranty information and positive reviews are highlighted, and if an excited user is detected, product features and promotional information are highlighted.

[0713] Step 9:

[0714] The server sends the predicted data and the generated image to the terminal, which displays the data.

[0715] Inputs: Forecast data, future state image, customized display information.

[0716] Output: Information displayed in the user interface.

[0717] Specific operation: Data is encoded in JSON format and sent via the REST API. The device displays the received data in a user interface using HTML and CSS.

[0718] Through these steps, the present invention provides detailed information about the future condition and value of a product when the user is considering purchasing it, and provides a customized display based on emotions, thereby improving the user's purchasing experience.

[0719] (Application example 2)

[0720] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0721] In conventional e-commerce systems, users have limited means of predicting the future condition or market value of a product when purchasing it, which often leaves users anxious about the value and condition of the product after purchase. Furthermore, there is no means to provide a purchasing experience that takes into account the user's emotional state, making it difficult to adequately support purchasing behavior that is influenced by emotions such as anxiety or excitement. This poses a challenge in improving users' purchasing motivation and post-purchase satisfaction.

[0722] The specification process by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving and saving input data, means for predicting the future state of the product using an aging prediction model, means for generating an image visually showing the predicted future state, means for recognizing the emotional state of the user by analyzing the user's facial expression and voice, and means for customizing the display content based on the emotional state obtained by the emotion recognition means. This makes it possible to predict the future state and market value of a product when the user purchases it, and not only to visually confirm the results, but also to provide a purchasing experience that is tailored to the user's emotional state.

[0723] A "user" is an entity that uses the system to purchase or evaluate products.

[0724] "Products" are goods and services provided to users through electronic commerce.

[0725] "Use" refers to the purpose or intention of how the user will use the product.

[0726] "Frequency of use" is data indicating how often or how many times a user uses a product.

[0727] "Input means" refers to an interface that a user uses to input data about purpose and frequency of use into the system.

[0728] The "server means" is a computer system that stores data received from users and performs various processes based on the data.

[0729] The "prediction means" is a function that predicts the future state of a product using an aging prediction model based on the stored data and product characteristic data.

[0730] The "image generation means" is a function that generates an image that visually shows the predicted future state of the product.

[0731] The "price trend prediction means" is a function that predicts the future market value of a product based on past market data.

[0732] The "transmission means" is a function for transmitting predicted data and generated images from the server to the user terminal.

[0733] The "display means" is an interface that displays the predicted data received by the user terminal and the generated image to the user.

[0734] The "emotion recognition means" is a function that analyzes the user's facial expressions and voice to recognize the user's emotional state.

[0735] The "customization means" is a function for customizing the display content based on the emotional state obtained by the emotion recognition means.

[0736] System Overview

[0737] The present invention relates to an electronic commerce system that predicts and visually displays the future condition and market value of a product based on data on the purpose and frequency of use entered by a user when purchasing the product. The system includes a user terminal, a server, an emotion engine, and a database. It also includes emotion recognition means that recognizes the user's emotions to improve the purchasing experience.

[0738] Hardware and Software Configuration

[0739] User devices: Smartphones, tablets, PCs, etc. are applicable. Users input their usage and frequency of use, and the results of predictions of future conditions and price trends are displayed. The devices are equipped with cameras and microphones, and also have an emotion engine that analyzes the user's facial expressions and voice.

[0740] Server: A computer system that receives, stores, and processes data. The server is installed with an aging prediction model, a price trend prediction model, an image processing library, an emotion recognition library, etc.

[0741] Database: A database system for storing product characteristics data, user-entered data, and historical market data.

[0742] Specific details of data processing

[0743] 1. User data entry

[0744] A user accesses an online shopping site and goes to the details page of a product they are considering purchasing. On this page, they enter information about the purpose and frequency of use. For example, if they are purchasing a luxury watch, they enter data such as "daily use" and "five days a week."

[0745] 2. Data transmission and storage

[0746] The user terminal verifies the entered data to ensure there are no errors. After verification, this data is sent to the server, which stores it in a database. The stored data includes the product ID, purpose, frequency of use, and user ID.

[0747] 3. Emotion Recognition

[0748] Using the camera and microphone on the user's device, the system analyzes the user's facial expressions and voice to recognize the user's emotional state. An emotion recognition library (e.g., EmotionRecognition library) is used to determine the user's emotional state (e.g., excitement, anxiety, indifference, etc.).

[0749] 4. Product condition prediction and price trend prediction

[0750] The server uses an aging prediction model to predict the future condition of a product based on the stored data on usage and frequency of use. For example, it calculates the deterioration state of a luxury watch one year and five years from now. At the same time, it predicts price trends based on past market data and calculates prices one year and five years from now.

[0751] 5. Future State Visualization

[0752] The server generates an image that visually represents the future state of the product based on the predicted data. It uses an image processing library (e.g., OpenCV or matplotlib) to visualize the appearance of the product after one year and after five years. For example, it generates an image that shows slight scratches after one year and clear wear after five years.

[0753] 6. Emotion-based customization

[0754] The server customizes the display content based on the user's emotional data received from the emotion recognition means. For example, for a user who appears anxious, product warranty information and positive user reviews are emphasized. For an excited user, product features and promotional information are emphasized.

[0755] 7. Sending and displaying predicted data and images

[0756] The server sends the predicted data and generated images to the user's device, which then displays them on a user interface. The displayed information includes text information about the product's aging status and market value one and five years from now, generated images of the product's future appearance, and emotion-based customization information.

[0757] Specific examples

[0758] For example, when a user purchases a luxury watch, they enter "daily use" as the purpose and "five days a week" as the frequency of use, and if they have concerns about the purchase, the server saves the entered data in a database. Next, it predicts aging and price trends, predicting that small scratches will appear after one year and clear wear after five years. The price trends are predicted to be 900,000 yen after one year and 700,000 yen after five years. Based on this predicted data, the server visualizes the future appearance of the product, and also highlights warranty information and positive user reviews to alleviate the user's concerns.

[0759] Prompt Sentence Examples

[0760] "Predict what a luxury watch will look like in one year and five years if used daily, five days a week."

[0761] In this way, the present invention provides detailed information about the future condition and value of a product at the user's purchasing stage, as well as a personalized purchasing experience based on emotion recognition.

[0762] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0763] Step 1:

[0764] A user accesses an online shopping site and navigates to the details page of a product they are considering purchasing. On the product details page, they input information about the product's purpose and frequency of use. This input data includes the product's purpose (e.g., "daily use") and frequency of use (e.g., "five days a week"). The input data is collected by the device.

[0765] Step 2:

[0766] The terminal verifies the data entered by the user and checks for errors. After verification, it sends the correct data to the server. The data sent includes the product ID, purpose, frequency of use, and user ID. The data processing performed here is a consistency check of the input data.

[0767] Step 3:

[0768] The server stores the received data in a database. The stored data includes the product ID, purpose, frequency of use, and user ID. The data calculation here is the process of writing to the database.

[0769] Step 4:

[0770] While the user is inputting data, the device uses a camera and microphone to collect the user's facial expressions and voice. This data is input to an emotion recognition module, which analyzes the user's emotional state (e.g., excitement, anxiety, joy, etc.). The analysis result (emotional state) is returned to the device.

[0771] Step 5:

[0772] The server uses an aging prediction model to predict the future condition of the product based on the stored data on usage and frequency of use. By combining the input data (usage, frequency of use) with product characteristic data, it calculates the deterioration state of the product one year or five years from now. The predicted result provides the product's condition one year or five years from now (e.g., minor scratches, wear, etc.).

[0773] Step 6:

[0774] The server runs a price trend prediction model based on past market data. The input data includes the product ID and current market price, and the server predicts future prices by referencing past market data. The resulting prediction is the market value of the product one or five years from now (e.g., 900,000 yen in one year, 700,000 yen in five years).

[0775] Step 7:

[0776] The server generates images to visually represent the predicted future state of the product. It uses image processing libraries (e.g., OpenCV and matplotlib) to generate images that recreate the appearance of the product one year and five years from now. The generated images are stored on the server.

[0777] Step 8:

[0778] The server customizes the display content based on the emotion data received from the emotion recognition module, for example, highlighting warranty information and positive reviews for anxious users and highlighting features and promotions for excited users.

[0779] Step 9:

[0780] The server transmits the predicted data and the generated image to the terminal, including the future aging state of the product, its market value, the generated appearance image, and customization information.

[0781] Step 10:

[0782] The device displays the received forecast data and the generated image on the user interface, allowing users to visually check the product's condition and market value one or five years from now, allowing them to make purchasing decisions with confidence.

[0783] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0784] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0785] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0786] [Third embodiment]

[0787] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0788] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0789] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0790] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0791] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0792] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0793] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0794] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0795] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[0796] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0797] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0798] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0799] The present invention relates to an electronic commerce system that predicts and visually displays the future condition and market value of a product based on data on the purpose and frequency of use entered by a user when purchasing the product. This system includes a user terminal, a server, and a database. The processing of the system's program is explained below in natural language.

[0800] User data entry

[0801] A user selects a product on an e-commerce site and accesses the product's details page. On this details page, the user enters information about the purpose and frequency of use. For example, when purchasing a luxury watch, the user enters information such as "daily use" and "five days a week."

[0802] Data transmission and storage

[0803] The terminal verifies the data entered by the user regarding purpose and frequency of use to ensure there are no errors. Data that passes verification is sent to the server. The server receives this data and stores it in a database. The stored data includes the product ID, purpose, frequency of use, user ID, etc.

[0804] Product condition prediction

[0805] The server uses an aging prediction model to predict the future condition of a product based on data on usage and frequency of use stored in the database. For example, the aging prediction for a luxury watch used daily five days a week predicts that after one year, small scratches and a decrease in luster will appear, and after five years, there will be obvious wear and tear and peeling of the plating.

[0806] Price trend forecast

[0807] The server predicts price trends based on past market data for a product. For example, a luxury watch currently priced at 1 million yen is predicted to be priced at 900,000 yen one year from now, and 700,000 yen five years from now. This prediction is made using time series analysis and machine learning models.

[0808] Future State Visualization

[0809] The server uses the predicted data to generate a visual image of the product's future state. For example, it might generate an image of a watch's appearance in one year, showing slight scratches and a loss of luster, and an image of a watch in five years, showing obvious wear and peeling plating. To do this, the server uses an image processing library.

[0810] Sending and displaying forecast data and images

[0811] The server sends the predicted data and generated images to the user's terminal, which receives the data and images and displays them to the user. The displayed information includes text information about the product's aging condition and market value one and five years from now, as well as generated images of the product's future appearance.

[0812] Specific examples

[0813] For example, when a user purchases a luxury watch on an e-commerce site, they enter "daily use" as the intended use and "five days a week" as the frequency of use. The device sends this data to the server, which stores it in a database. The server then uses the entered data to predict aging and price trends. It predicts that after one year, small scratches will appear and the luster will decrease, and after five years, there will be clear wear and peeling of the plating. The server predicts that the price will be 900,000 yen after one year and 700,000 yen after five years. Based on this predicted data, the server generates an image of the product's future appearance, which the device displays to the user. Using this information, the user can visually understand the product's future after purchase and make an appropriate decision.

[0814] In this way, the present invention provides detailed information about the future condition and value of a product when the user is considering purchasing it, thereby enhancing purchasing motivation and supporting appropriate purchasing decisions.

[0815] The processing flow will be explained below.

[0816] Step 1:

[0817] A user accesses an e-commerce site and selects a product. They are taken to the details page of the selected product and enter information such as the purpose and frequency of use.

[0818] Step 2:

[0819] The device validates the purpose and frequency of use data entered by the user, for example by checking that the data is complete and in the correct format.

[0820] Step 3:

[0821] The terminal sends the data that passes the verification to the server, including the product ID, purpose, frequency of use, and user ID.

[0822] Step 4:

[0823] The server stores the received data in a database, where information on the purpose and frequency of use of each product is organized and stored.

[0824] Step 5:

[0825] The server calls the condition prediction module based on the stored data and uses an aging prediction model to predict the future condition of the product. For example, it calculates the deterioration state based on daily use, 5 days a week.

[0826] Step 6:

[0827] The server calls the price trend prediction module based on past market data, and uses time series analysis and machine learning models to predict the market price of the product one or five years from now.

[0828] Step 7:

[0829] The server uses the predicted data to generate images of the future state of the product, using image processing libraries to visualize what the product will look like in one year or five years.

[0830] Step 8:

[0831] The server transmits the forecast data and the generated image to the terminal, including the aging state, price transition, and the generated image.

[0832] Step 9:

[0833] The terminal displays the received data and images on a user interface, including the aging condition and market price one and five years from now, as well as an image of the product's future appearance.

[0834] Step 10:

[0835] Users can check the future condition and value of the product based on the displayed information, which can be used as reference when making purchasing decisions or collecting items.

[0836] Through the above steps, the user can get a detailed understanding of the future condition and market value of the product at the time of purchase, enabling them to make an appropriate selection.

[0837] Example 1

[0838] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0839] In conventional e-commerce systems, it is difficult for users to fully understand the future condition and market value of a product when purchasing it. This makes it difficult for users to make appropriate decisions regarding future use, which can affect their post-purchase satisfaction. The present invention aims to support users in making appropriate purchasing decisions and increase user satisfaction by predicting and visually displaying the future condition and market value of a product when they are considering purchasing it.

[0840] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0841] In this invention, the server includes means for receiving and storing the transmitted data, means for predicting the future state of the product using an aging prediction model based on the stored data, means for predicting price trends based on past market data, means for generating an image visually showing the predicted future state, and means for transmitting the predicted data and the generated image to a user terminal, thereby enabling the user to visually confirm information about the future state and market value of the product and make an appropriate decision before purchasing.

[0842] The "input means" is a means for a user to input data on the purpose and frequency of use when purchasing a product.

[0843] "Terminal means" refers to means for verifying data entered by a user and transmitting it to a server.

[0844] The "server means" is a means for receiving input data and storing it in a database.

[0845] "Prediction means" refers to a means for predicting the future state of a product using an aging prediction model based on stored data.

[0846] A "price trend prediction means" is a means for predicting the price trend of a product based on past market data.

[0847] The "image generating means" is a means for generating an image that visually shows the predicted future state of the product.

[0848] The "transmission means" is a means for transmitting the prediction data and the generated image to the user's terminal.

[0849] The "display means" is a means for displaying the predicted data and the generated image on the user terminal.

[0850] The "means for calculating a deterioration rate" is a means for calculating a deterioration rate applied to a product based on the user's usage frequency data.

[0851] "Image Processing Library" means a software library for image processing used to visually indicate the aging state of a product.

[0852] The present invention relates to an electronic commerce system that predicts and visually displays the future condition and market value of a product based on data on the product's intended use and frequency of use entered by the user when purchasing the product. The system includes a user terminal, a server, and a database. When purchasing a product, the user enters information on the product's intended use and frequency of use from the user terminal. The terminal is responsible for verifying this input data and sending it to the server. The server stores the received data and uses various prediction models to predict and visualize the product's future condition and price trends. This information is then sent back to the user's terminal so that the user can view it.

[0853] Specifically, a user enters information about the purpose and frequency of use on the details page of an e-commerce site. For example, when purchasing a luxury watch, the user enters information such as "daily use" and "five days a week." This input data is verified by the device and sent to the server. The server stores the received data in a database and uses this data to make predictions using an aging prediction model and a price trend prediction model. Time series analysis and machine learning models are used to predict the aging state and price changes of luxury watches one and five years from now. An image processing library is also used to generate images that visually show the predicted aging state.

[0854] The generated forecast data and visual images are sent from the server to the user's device, allowing the user to understand the product's future state and make a purchasing decision. This information provision improves the user's post-purchase satisfaction and supports appropriate purchasing decisions.

[0855] Specific examples

[0856] For example, when a user purchases a luxury watch on an e-commerce site, they enter "daily use" as the intended use and "five days a week" as the frequency of use. This data is sent by the device to a server, which stores the data and uses it to predict aging and price trends. After one year, the server predicts small scratches and a loss of luster, and after five years, the watch will show clear wear and the plating will peel. The server also predicts that the price will be 900,000 yen after one year and 700,000 yen after five years. Based on this predicted data, the server generates an image of the product's future appearance, which the device displays to the user. Using this information, the user can visually understand the product's future after purchase and make an appropriate decision.

[0857] Prompt Sentence Examples

[0858] Below are some examples of prompt sentences.

[0859] "When purchasing a luxury watch, assume it will be worn daily, five days a week. Using this information, predict the aging and price trends of the watch in one year and five years. Also, generate an image showing its future appearance."

[0860] Based on the above prompt, the generative AI model can be asked to make appropriate predictions and generate visual information.

[0861] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0862] Step 1:

[0863] A user selects a product on an e-commerce site and accesses the product detail page. The user enters information about the purpose and frequency of use, such as "purpose: daily use" and "frequency of use: 5 days a week."

[0864] Input: Usage and frequency data

[0865] Output: Data entered on the user's terminal

[0866] Step 2:

[0867] The terminal validates the data entered by the user. Specifically, it checks the input format and value range to ensure that the input data is correct. For example, it verifies that the purpose is "daily use" and the frequency is "five days a week." If validation is successful, the terminal sends the data to the server.

[0868] Input: User-entered usage and frequency data

[0869] Output: Validated data sent to the server

[0870] Step 3:

[0871] The server receives the data sent from the device and stores it in a database along with information such as product ID, purpose, frequency of use, and user ID. This allows the data necessary for future predictions to be accumulated.

[0872] Input: Verified data sent from the terminal

[0873] Output: Data stored in the database

[0874] Step 4:

[0875] The server uses the data stored in the database to predict the future condition of the product using an aging prediction model. Specifically, it references past usage data and aging data to predict the product's deterioration. For example, if a luxury watch is used daily, five days a week, after one year it may develop small scratches and lose its luster, and after five years it may show clear wear and peeling of the plating.

[0876] Input: Usage and frequency data stored in the database

[0877] Output: Predicted future product state data

[0878] Step 5:

[0879] The server predicts product price trends based on past market data. Specifically, it uses time series analysis and machine learning models to analyze past price history and supply and demand data to predict future prices. For example, if the current price of a luxury watch is 1 million yen, it is predicted that it will be 900,000 yen in one year and 700,000 yen in five years.

[0880] Inputs: Historical market data and aging forecast data

[0881] Output: Predicted price trend data

[0882] Step 6:

[0883] The server uses the forecast data to generate a visual representation of the product's future state. It uses image processing libraries and AI technology to create product images based on the forecast results. For example, it generates images showing the appearance of a watch one year from now and five years from now. The images reflect small scratches, loss of gloss, wear, and peeling plating.

[0884] Input: Predicted future product status data and price transition data

[0885] Output: The generated visual image

[0886] Step 7:

[0887] The server sends the predicted data and generated images to the user's device, which packages the data and sends it to the device in an easily accessible format.

[0888] Input: Prediction data and generated images

[0889] Output: Data sent to the user's device

[0890] Step 8:

[0891] The terminal displays the received forecast data and generated images to the user, allowing the user to visually confirm detailed information about the product's future condition and market value. For example, text information about the aging condition and price trends one and five years from now, along with related images, are displayed.

[0892] Input: Prediction data sent from the server and generated images

[0893] Output: Prediction information and images displayed to the user

[0894] (Application example 1)

[0895] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0896] Conventional e-commerce systems lacked the means to provide detailed information about the future condition and market value of a product when users were considering a purchase, making it difficult for users to make appropriate purchasing decisions. Furthermore, there was also a lack of a means to visually understand the aging state of a product after use and the transition of its market value. As a result, it was not possible to stimulate users' purchasing motivation or to appeal to the benefits of purchasing from a long-term perspective.

[0897] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0898] In this invention, the server includes: input means for inputting data on the purpose and frequency of use of an item when a user purchases it; an information processing device for receiving and saving the input data; prediction means for predicting the future state of the item using a deterioration prediction model based on the saved data and item characteristic data; image generation means for generating an image visually showing the predicted future state; price trend prediction means for predicting price trends based on past market data; transmission means for transmitting the predicted data and the generated image to a user terminal; display means for displaying the predicted data and the generated image; and application means for providing visualized information predicting the future state and market value of the item based on the purpose and frequency of use data of the item. This allows users to obtain detailed information on the future state and market value of the item before purchasing, enabling them to make appropriate purchasing decisions.

[0899] "User" refers to a consumer or user who uses this system to purchase goods.

[0900] "Goods" refers to the goods and products handled in this system.

[0901] "Use" refers to information about how the user intends to use the item they are about to purchase.

[0902] "Frequency of use" refers to information about how often a user uses the item they are about to purchase.

[0903] "Input means" refers to the device or software that allows the user to input data on purpose and frequency of use into the system.

[0904] The term "information processing device" refers to a server or computer that stores data received from a user and performs various calculations.

[0905] "Item characteristic data" refers to data on basic information and characteristics of the items being handled.

[0906] A "deterioration prediction model" refers to an algorithm or mathematical model for predicting the future deterioration state of an item based on its intended use and frequency of use.

[0907] "Prediction means" refers to a function or device for predicting the future state of an item using a deterioration prediction model based on stored data and item characteristic data.

[0908] "Image generation means" refers to a function or device for generating an image that visually shows the predicted future state of an item.

[0909] "Price trend prediction means" refers to a function or device that predicts future price trends of goods based on past market data.

[0910] "Transmission means" refers to a function or device for transmitting prediction data and generated images to a user terminal.

[0911] "User terminal" refers to an electronic device used by a user, such as a computer, smartphone, or tablet.

[0912] "Display means" refers to software and hardware for displaying the predicted data and generated images on the user terminal.

[0913] "Application means" refers to application software that predicts the future condition and market value of an item based on data on the use and frequency of use of the item, and visually presents this to the user.

[0914] This invention is an electronic commerce system that predicts and visually displays the future condition and market value of an item after purchase to a user considering purchasing the item. The system includes a user terminal, an information processing device, and a database.

[0915] User data entry

[0916] Users access the product details page and enter information about the purpose and frequency of use. For example, when purchasing a luxury watch, users enter information such as "daily use" and "five days a week."

[0917] Data transmission and storage

[0918] The user terminal verifies the data entered by the user regarding the purpose and frequency of use to ensure there are no errors. Data that passes verification is sent to the information processing device. The information processing device receives this data and stores it in a database. The stored data includes the item ID, purpose, frequency of use, user ID, etc.

[0919] Item condition prediction

[0920] The information processing device predicts the future condition of an item using a deterioration prediction model based on data on usage and frequency of use stored in a database. For example, a deterioration prediction for a luxury watch used daily five days a week might predict that after one year, small scratches and a loss of luster will appear, and after five years, clear wear and peeling of the coating will be visible.

[0921] Price trend forecast

[0922] The information processing device predicts price trends based on past market data for goods. For example, a luxury watch currently priced at 1 million yen is predicted to have a price of 900,000 yen one year from now, and 700,000 yen five years from now. This prediction is made using time series analysis and machine learning models.

[0923] Future State Visualization

[0924] The information processing device generates an image based on the predicted data that visually represents the future state of the item. For example, it generates an image that reflects the appearance of a watch one year from now, with slight scratches and loss of gloss, and an image that reflects the watch's obvious wear and peeling coating five years from now. To do this, the information processing device uses an image processing library.

[0925] Sending and displaying forecast data and images

[0926] The information processing device transmits the predicted data and the generated image to a user terminal. The user terminal receives the data and image and displays them to the user. The displayed information includes text information about the deterioration state and market value of the item one and five years from now, as well as the generated image of the item's future appearance.

[0927] Specific examples

[0928] For example, when a user purchases a luxury watch, they input "daily use" as the purpose and "five days a week" as the frequency of use. The user's device sends this data to an information processing device, which stores it in a database. The information processing device then predicts deterioration and price trends based on the input data. It predicts that the watch will develop small scratches and lose its luster in one year, and that it will show clear wear and peeling of the coating in five years, and predicts that the price will be 900,000 yen in one year and 700,000 yen in five years. Based on this predicted data, the information processing device generates an image of the future appearance of the item, which the user's device displays to the user. Based on this information, the user can visually understand the future of the item after purchase and make an appropriate decision.

[0929] Prompt Sentence Examples

[0930] A user entered the following information about purpose and frequency of use on the item page:

[0931] Use: Everyday use

[0932] Frequency of use: 5 days a week

[0933] Use this information to predict the condition and market value of the item one and five years from now, along with a visualisation.

[0934] As described above, the present invention provides detailed information about the future state and value of an item when the user is considering purchasing it, thereby enhancing purchasing motivation and supporting appropriate purchasing decisions.

[0935] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0936] Step 1: User Data Entry

[0937] The user accesses the product details page and inputs information about the purpose and frequency of use of the product they are considering purchasing. The input data includes specific usage information such as "daily use" and "five days a week." The user's device receives this input data.

[0938] Input: Information on purpose and frequency of use

[0939] Output: Verified data (use and frequency of use)

[0940] Step 2: Data transmission and storage

[0941] The verified data is sent from the terminal to an information processing device (server). The information processing device stores the received data in a database. The stored data includes the item ID, purpose, frequency of use, user ID, etc.

[0942] Input: Verified data (purpose and frequency of use)

[0943] Output: Data stored in the database

[0944] Step 3: Predicting the condition of the item

[0945] The information processing device predicts the future condition of the item using a deterioration prediction model based on data on usage and frequency of use stored in a database. For example, it uses a machine learning model or a time series analysis algorithm to predict the condition of the item one year or five years from now.

[0946] Input: Data on usage and frequency of use stored in the database

[0947] Output: Predicted future state of the item

[0948] Step 4: Predict price trends

[0949] The information processing device predicts price trends based on past market data for goods. For example, using time series analysis and machine learning models, it predicts that a luxury watch currently priced at 1 million yen will be priced at 900,000 yen one year from now, and 700,000 yen five years from now.

[0950] Input: Product information stored in the database, historical market data

[0951] Output: Predicted price progression

[0952] Step 5: Visualize the future state

[0953] The information processing device uses the predicted data to generate images that visually represent the future state of the item. For example, using an image processing library, it generates an image that shows the appearance of a watch one year from now, with small scratches and a loss of gloss, and the appearance of a watch five years from now, with obvious wear and peeling coating.

[0954] Input: Predicted future state data of the item

[0955] Output: The generated visual image

[0956] Step 6: Send and display forecast data and images

[0957] The information processing device transmits the predicted data and the generated image to a user terminal. The user terminal receives this data and image and displays them to the user. Specifically, text information about the deterioration state and market value of the item one year and five years from now, as well as the generated image of the item's appearance in the future, are displayed.

[0958] Input: Prediction data and generated images

[0959] Output: Predicted data and visual images displayed on the user's device

[0960] Through the above steps, the user can obtain detailed information about the future condition and market value of the item they are considering purchasing, enabling them to make an appropriate purchasing decision.

[0961] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0962] The present invention provides an e-commerce system that predicts and visually displays the future condition and market value of a product based on data on the purpose and frequency of use entered by the user when purchasing the product, and also includes an emotion engine that recognizes the user's emotions to further improve the purchasing experience. This system includes a user terminal, a server, an emotion engine, and a database. The processing of the system's programs is described in detail below.

[0963] User data entry

[0964] A user accesses an e-commerce site and selects the product they want to purchase. They are then taken to the product detail page and enter information about the purpose and frequency of use. For example, when purchasing a luxury watch, a user enters information such as "daily use" and "five days a week."

[0965] Data transmission and storage

[0966] The terminal verifies the data entered by the user regarding purpose and frequency of use to ensure there are no errors. Data that passes verification is sent to the server. The server stores this data in a database. The stored data includes the product ID, purpose, frequency of use, and user ID.

[0967] Emotion recognition by emotion engine

[0968] While the user is entering data, the device uses sensors such as a camera and microphone to analyze the user's facial expressions and tone of voice. The emotion engine uses this data to recognize the user's emotional state (e.g., excitement, anxiety, indifference, etc.).

[0969] Product condition prediction and price trend prediction

[0970] The server uses an aging prediction model to predict the future condition of a product based on stored data on usage and frequency of use. For example, it calculates the deterioration state of a luxury watch one year and five years from now based on daily use five days a week. At the same time, it predicts price trends based on past market data and calculates predicted prices one year and five years from now.

[0971] Future State Visualization

[0972] The server uses the predicted data to generate a visual representation of the product's future state. It uses an image processing library to visualize the product's appearance in one year and five years. For example, the watch's appearance in one year will show minor scratches, while its appearance in five years will show clear wear.

[0973] Customized display based on emotions

[0974] The server customizes the display content based on the user's emotional data received from the emotion engine. For example, if a user seems anxious about a purchase, the server can highlight product warranty information and user reviews. For an excited user, the server can highlight product features and promotional information.

[0975] Sending and displaying forecast data and images

[0976] The server sends the predicted data and generated images to the terminal, which displays them on a user interface. The displayed information includes text information about the product's aging status and market value one and five years from now, generated images of the product's future appearance, and emotion-based customization information.

[0977] Specific examples

[0978] For example, when a user purchases a luxury watch on an e-commerce site, they enter "daily use" as the purpose and "five days a week" as the frequency of use. If the device detects any anxiety about the purchase, the device sends this input data to the server, which stores it in a database. The server then uses the input data to predict aging and price trends, predicting that the watch will develop minor scratches after one year and clear wear after five years. The predicted price trends are 900,000 yen after one year and 700,000 yen after five years. The server uses this predicted data to visualize the product's future appearance, highlighting warranty information and positive user reviews to alleviate the user's anxiety. The device then displays this information to the user, allowing them to confirm the product's future condition and value and make a purchasing decision with confidence.

[0979] In this way, the present invention not only provides users with detailed information about the future condition and value of a product when they are considering a purchase, but also optimizes the user experience based on emotion recognition, improving purchasing motivation and supporting appropriate purchasing decisions.

[0980] The processing flow will be explained below.

[0981] Step 1:

[0982] A user accesses an e-commerce site and selects a product they are interested in. For example, they move to a detail page of a luxury watch and begin to consider purchasing it.

[0983] Step 2:

[0984] The device displays input fields for the purpose and frequency of use on the product detail page, and the user enters information such as "purpose: daily use" and "frequency of use: 5 days a week."

[0985] Step 3:

[0986] The terminal validates the user's input data, checking for missing or malformed data, and data that passes validation proceeds to the next step.

[0987] Step 4:

[0988] The terminal transmits the input data on purpose and frequency of use to the server. The transmitted data includes the product ID, purpose, frequency of use, user ID, etc.

[0989] Step 5:

[0990] The server stores the received data in a database, including product characteristic data, user usage data, and so on.

[0991] Step 6:

[0992] The device uses a camera and microphone to recognize the user's facial expressions and voice while inputting data, and sends this data to an emotion engine to analyze the user's emotional state.

[0993] Step 7:

[0994] The emotion engine analyzes the user's facial expressions and tone of voice to identify emotions (e.g., excitement, anxiety, indifference, etc.), and sends the analysis results to the server.

[0995] Step 8:

[0996] The server uses an aging prediction model to predict the future condition of the product based on the stored data on usage and frequency of use. For example, it calculates wear and tear after one year and five years based on daily use five days a week.

[0997] Step 9:

[0998] The server calls a price trend prediction module based on past market data to predict the market price of the product one year or five years from now. For example, if the current price is 1 million yen, it is predicted that the price will be 900,000 yen one year from now and 700,000 yen five years from now.

[0999] Step 10:

[1000] The server generates images of the future state of the product based on the predicted data, using image processing libraries to visualize what the product will look like in one year and five years, simulating, for example, minor scratches after one year and increased wear after five years.

[1001] Step 11:

[1002] The server customizes the display content based on the emotion data from the emotion engine. For anxious users, it adds reassuring information (guarantees and reviews). For excited users, it emphasizes product features and promotional information.

[1003] Step 12:

[1004] The server transmits the predicted data, customized display content, and generated images to the terminal.

[1005] Step 13:

[1006] The device then displays the received data and images to the user, including customized information based on the aging status after one and five years, market value, and emotions.

[1007] Step 14:

[1008] Users can check the future condition and value of the product based on the displayed information, and can make appropriate purchasing decisions while also referring to customized information based on their emotions.

[1009] Through the above specific processing steps, the system can provide detailed information about the future condition and market value of a product when the user purchases it, as well as customize the display based on the user's emotions, thereby providing a better purchasing experience.

[1010] Example 2

[1011] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1012] In conventional e-commerce systems, when users purchase a product, they are not provided with information about the product's future condition or how its market value will change. This often leaves users feeling unsure about their purchase decision and can make them hesitant to make a final purchase. Furthermore, because the system does not take into account the user's feelings, the purchasing experience remains unimproved.

[1013] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for receiving and saving input data, means for predicting the future state of the product using an aging prediction model based on the saved data and product characteristic data, means for generating an image visually showing the predicted future state, means for predicting price trends based on past market data, means for transmitting the predicted data and the generated image to the user terminal, and means for customizing the display content based on the recognized emotional state. This allows the user to visually check the future state and market value of the product and make a purchase decision with confidence by receiving information according to their emotions.

[1014] 1. "Input means" refers to the interface through which a user inputs data on the purpose and frequency of use when purchasing a product.

[1015] 2. "Server Means" means a computing device for receiving and storing input data.

[1016] 3. "Prediction Means" refers to the function by which the Server Means predicts the future state of the Product using an aging prediction model based on the Stored Data and Product Attribute Data.

[1017] 4. "Image generation means" refers to a function for generating an image that visually shows the predicted future state.

[1018] 5. "Price trend prediction means" refers to a function for predicting price trends based on past market data.

[1019] 6. "Transmission means" refers to the function for transmitting prediction data and generated images to a user terminal.

[1020] 7. "Display means" refers to an interface for a user terminal to display predicted data and generated images.

[1021] 8. "Emotion recognition means" refers to the sensors and analysis functions that enable a device to recognize a user's emotional state by analyzing the user's facial expressions and tone of voice.

[1022] 9. "Customized display means" refers to functionality that allows the server to customize the display content based on the recognized emotional state.

[1023] This invention is a system for an e-commerce system that predicts and visually displays the future condition and market value of a product based on data on the purpose and frequency of use entered by a user when purchasing the product, and recognizes the user's emotions to improve the purchasing experience. This system includes a user terminal, a server, an emotion recognition engine, and a database.

[1024] User data entry

[1025] A user accesses an e-commerce site and selects the product they wish to purchase. They are then taken to the product detail page and enter information about the purpose and frequency of use. For example, when purchasing a luxury watch, they enter information such as "daily use" and "five days a week." An interface such as a web form is used as the input method.

[1026] Data transmission and storage

[1027] The terminal verifies the purpose and frequency of use data entered by the user to ensure there are no errors. A validation function is used for the verification. Data that passes verification is securely sent to the server using the HTTPS protocol. The server saves this data in a database. The saved data includes the product ID, purpose, frequency of use, and user ID. An RDBMS such as MySQL or PostgreSQL is used as the database.

[1028] Emotion recognition by emotion engine

[1029] While the user is entering data, the device uses sensors such as a camera and microphone to analyze the user's facial expressions and tone of voice. This is done using libraries such as OpenCV. The emotion recognition engine uses machine learning models such as TensorFlow and PyTorch to recognize the user's emotional state (e.g., excitement, anxiety, indifference, etc.).

[1030] Product condition prediction and price trend prediction

[1031] The server uses an aging prediction model to predict the future condition of a product based on the stored data on usage and frequency of use. Libraries such as scikit-learn are used for aging prediction. For example, the state of deterioration of a luxury watch after one year and five years is calculated. At the same time, price trends are predicted based on past market data, and a generative AI model is used to calculate predicted prices after one year and five years.

[1032] Future State Visualization

[1033] The server uses the predicted data to generate a visual representation of the product's future state. PIL (Python Imaging Library) is used for image processing, visualizing the product's appearance in one year and five years. For example, the watch's appearance in one year will show minor scratches, while its appearance in five years will show clear wear.

[1034] Customized display based on emotions

[1035] The server customizes the display content based on the user's emotional data received from the emotion engine. For users who appear anxious about the purchase, product warranty information and user reviews are emphasized. On the other hand, for users who are excited, product features and promotional information are emphasized.

[1036] Sending and displaying forecast data and images

[1037] The server sends the predicted data and generated images to the terminal. REST API is used to send the data, and the data is encoded in JSON format. The terminal displays this data and images on a user interface. The information includes text information about the aging status and market value of the product one and five years from now, generated images of the product's future appearance, and emotion-based customization information. As a specific example of how the data is displayed, the predicted information and visualized images are displayed on a web page using HTML and CSS.

[1038] Specific examples

[1039] For example, when a user purchases a luxury watch on an e-commerce site, if the user enters "daily use" as the purpose and "five days a week" as the frequency of use, and anxiety about the purchase is detected, the following processing will be performed.

[1040] 1. The terminal checks the data entered by the user and sends it to the server, which stores it in a database.

[1041] 2. The device uses a camera and microphone to collect the user's facial expressions and voice for emotion recognition. The emotion engine analyzes the data and detects anxiety.

[1042] 3. The server runs an aging prediction model based on usage and frequency of use data, predicting, for example, that the server will develop minor scratches in one year and clear wear in five years. It also predicts that the price will be 900,000 yen in one year and 700,000 yen in five years.

[1043] 4. The server uses an image processing library to generate the future appearance of the product based on the predicted data.

[1044] 5. The server then customizes the display based on the emotional data, emphasizing warranty information and positive word-of-mouth reviews.

[1045] 6. The terminal displays this information on the user interface, allowing the user to check the future condition and value of the product and make a purchasing decision with confidence.

[1046] Prompt Sentence Examples

[1047] Here is an example of inputting the following prompt sentence into a generative AI model to output a detailed explanation of the above system.

[1048] plaintext

[1049] This system is an e-commerce system that predicts and visually displays the future condition and market value of a product based on the usage and frequency of use data entered by the user when purchasing the product. It also includes an emotion engine that recognizes user emotions to improve the purchasing experience. Specifically, a user accesses an e-commerce site, selects the product they want to purchase, and enters information about usage and frequency of use. The terminal then verifies the data, sends it to the server, and stores it. The server then uses this data to predict and visualize aging and price trends. Furthermore, the emotion engine recognizes the user's emotions, and the server customizes the display based on those emotions. Finally, the predicted data and the generated image are sent to the terminal and displayed to the user. Please provide a detailed explanation, including examples and prompt sentences.

[1050] In this way, the present invention not only provides users with detailed information about the future condition and value of a product when they are considering a purchase, but also optimizes the user experience based on emotion recognition, improving purchasing motivation and supporting appropriate purchasing decisions.

[1051] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1052] Step 1:

[1053] A user accesses an e-commerce site, selects a product they wish to purchase, and inputs information about the purpose and frequency of use through an input means.

[1054] Input: User input data on usage and frequency of use.

[1055] Output: The input data is sent to the terminal and prepared for validation.

[1056] Specific actions: Enter details such as "daily use" and "5 days a week" into the web form and click the submit button.

[1057] Step 2:

[1058] The terminal validates the data entered by the user to ensure there are no errors. It uses validation functions to check whether the entered data is in the correct format.

[1059] Input: Usage and frequency data entered by the user.

[1060] Output: Validated data.

[1061] Specific behavior: Validation functions are used to check for blank spaces and formatting. If validation is successful, the data is ready to be sent to the server.

[1062] Step 3:

[1063] The terminal sends the data that passes the verification to the server using the HTTPS protocol, ensuring data security.

[1064] Input: Validated usage and frequency data.

[1065] Output: The user's data sent to the server.

[1066] Specific operation: Data is sent via the HTTPS protocol, and the server returns a receipt confirmation response to the terminal.

[1067] Step 4:

[1068] The server stores the received data in a database, which includes the product ID, purpose, frequency of use, and user ID.

[1069] Input: Usage and frequency data sent to the server.

[1070] Output: User data stored in the database.

[1071] Specific operation: Saves data using an RDBMS such as MySQL or PostgreSQL. Notifies the device that the save was successful.

[1072] Step 5:

[1073] The device uses a camera and microphone to collect the user's facial expressions and tone of voice while they are entering data. This data is then analyzed and an emotion engine is used to recognize their emotional state.

[1074] Input: User's facial expression data and voice data.

[1075] Output: The perceived emotional state of the user (e.g., excited, anxious, apathetic, etc.).

[1076] Specific operation: Facial recognition is performed using OpenCV, and voice tone is analyzed using a voice analysis API. Based on the analysis, emotions are classified using an emotion engine (using TensorFlow and PyTorch).

[1077] Step 6:

[1078] The server uses an aging prediction model to predict the future state of the product based on the stored data and product characteristic data, and also predicts price trends based on past market data.

[1079] Inputs: Usage and frequency data, product characteristics data, historical market data.

[1080] Output: Forecast data of future product state and price trend forecast data.

[1081] Specific operation: Aging prediction is performed using scikit-learn, and price trends are calculated using a generative AI model. For example, it predicts that there will be small scratches after one year and clear wear after five years, and predicts that the price will be 900,000 yen after one year and 700,000 yen after five years.

[1082] Step 7:

[1083] The server generates an image that visually shows the future state of the product based on the predicted data.

[1084] Input: Aging forecast data, price trend forecast data.

[1085] Output: An image showing the future state of the product.

[1086] What it does: It uses PIL (Python Imaging Library) to visualize what a product will look like after one year and five years. For example, the image of a watch after one year will show minor scratches, while the image after five years will show obvious wear.

[1087] Step 8:

[1088] The server customizes the display content based on the emotion data received from the emotion engine.

[1089] Input: Recognized user emotion data.

[1090] Output: Customized display content.

[1091] What it does: If anxiety is detected, warranty information and positive reviews are highlighted, and if an excited user is detected, product features and promotional information are highlighted.

[1092] Step 9:

[1093] The server sends the predicted data and the generated image to the terminal, which displays the data.

[1094] Inputs: Forecast data, future state image, customized display information.

[1095] Output: Information displayed in the user interface.

[1096] Specific operation: Data is encoded in JSON format and sent via the REST API. The device displays the received data in a user interface using HTML and CSS.

[1097] Through these steps, the present invention provides detailed information about the future condition and value of a product when the user is considering purchasing it, and provides a customized display based on emotions, thereby improving the user's purchasing experience.

[1098] (Application example 2)

[1099] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1100] In conventional e-commerce systems, users have limited means of predicting the future condition or market value of a product when purchasing it, which often leaves users anxious about the value and condition of the product after purchase. Furthermore, there is no means to provide a purchasing experience that takes into account the user's emotional state, making it difficult to adequately support purchasing behavior that is influenced by emotions such as anxiety or excitement. This poses a challenge in improving users' purchasing motivation and post-purchase satisfaction.

[1101] The specification process by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving and saving input data, means for predicting the future state of the product using an aging prediction model, means for generating an image visually showing the predicted future state, means for recognizing the emotional state of the user by analyzing the user's facial expression and voice, and means for customizing the display content based on the emotional state obtained by the emotion recognition means. This makes it possible to predict the future state and market value of a product when the user purchases it, and not only to visually confirm the results, but also to provide a purchasing experience that is tailored to the user's emotional state.

[1102] A "user" is an entity that uses the system to purchase or evaluate products.

[1103] "Products" are goods and services provided to users through electronic commerce.

[1104] "Use" refers to the purpose or intention of how the user will use the product.

[1105] "Frequency of use" is data indicating how often or how many times a user uses a product.

[1106] "Input means" refers to an interface that a user uses to input data about purpose and frequency of use into the system.

[1107] The "server means" is a computer system that stores data received from users and performs various processes based on the data.

[1108] The "prediction means" is a function that predicts the future state of a product using an aging prediction model based on the stored data and product characteristic data.

[1109] The "image generation means" is a function that generates an image that visually shows the predicted future state of the product.

[1110] The "price trend prediction means" is a function that predicts the future market value of a product based on past market data.

[1111] The "transmission means" is a function for transmitting predicted data and generated images from the server to the user terminal.

[1112] The "display means" is an interface that displays the predicted data received by the user terminal and the generated image to the user.

[1113] The "emotion recognition means" is a function that analyzes the user's facial expressions and voice to recognize the user's emotional state.

[1114] The "customization means" is a function for customizing the display content based on the emotional state obtained by the emotion recognition means.

[1115] System Overview

[1116] The present invention relates to an electronic commerce system that predicts and visually displays the future condition and market value of a product based on data on the purpose and frequency of use entered by a user when purchasing the product. The system includes a user terminal, a server, an emotion engine, and a database. It also includes emotion recognition means that recognizes the user's emotions to improve the purchasing experience.

[1117] Hardware and Software Configuration

[1118] User devices: Smartphones, tablets, PCs, etc. are applicable. Users input their usage and frequency of use, and the results of predictions of future conditions and price trends are displayed. The devices are equipped with cameras and microphones, and also have an emotion engine that analyzes the user's facial expressions and voice.

[1119] Server: A computer system that receives, stores, and processes data. The server is installed with an aging prediction model, a price trend prediction model, an image processing library, an emotion recognition library, etc.

[1120] Database: A database system for storing product characteristics data, user-entered data, and historical market data.

[1121] Specific details of data processing

[1122] 1. User data entry

[1123] A user accesses an online shopping site and goes to the details page of a product they are considering purchasing. On this page, they enter information about the purpose and frequency of use. For example, if they are purchasing a luxury watch, they enter data such as "daily use" and "five days a week."

[1124] 2. Data transmission and storage

[1125] The user terminal verifies the entered data to ensure there are no errors. After verification, this data is sent to the server, which stores it in a database. The stored data includes the product ID, purpose, frequency of use, and user ID.

[1126] 3. Emotion Recognition

[1127] Using the camera and microphone on the user's device, the system analyzes the user's facial expressions and voice to recognize the user's emotional state. An emotion recognition library (e.g., EmotionRecognition library) is used to determine the user's emotional state (e.g., excitement, anxiety, indifference, etc.).

[1128] 4. Product condition prediction and price trend prediction

[1129] The server uses an aging prediction model to predict the future condition of a product based on the stored data on usage and frequency of use. For example, it calculates the deterioration state of a luxury watch one year and five years from now. At the same time, it predicts price trends based on past market data and calculates prices one year and five years from now.

[1130] 5. Future State Visualization

[1131] The server generates an image that visually represents the future state of the product based on the predicted data. It uses an image processing library (e.g., OpenCV or matplotlib) to visualize the appearance of the product after one year and after five years. For example, it generates an image that shows slight scratches after one year and clear wear after five years.

[1132] 6. Emotion-based customization

[1133] The server customizes the display content based on the user's emotional data received from the emotion recognition means. For example, for a user who appears anxious, product warranty information and positive user reviews are emphasized. For an excited user, product features and promotional information are emphasized.

[1134] 7. Sending and displaying predicted data and images

[1135] The server sends the predicted data and generated images to the user's device, which then displays them on a user interface. The displayed information includes text information about the product's aging status and market value one and five years from now, generated images of the product's future appearance, and emotion-based customization information.

[1136] Specific examples

[1137] For example, when a user purchases a luxury watch, they enter "daily use" as the purpose and "five days a week" as the frequency of use, and if they have concerns about the purchase, the server saves the entered data in a database. Next, it predicts aging and price trends, predicting that small scratches will appear after one year and clear wear after five years. The price trends are predicted to be 900,000 yen after one year and 700,000 yen after five years. Based on this predicted data, the server visualizes the future appearance of the product, and also highlights warranty information and positive user reviews to alleviate the user's concerns.

[1138] Prompt Sentence Examples

[1139] "Predict what a luxury watch will look like in one year and five years if used daily, five days a week."

[1140] In this way, the present invention provides detailed information about the future condition and value of a product at the user's purchasing stage, as well as a personalized purchasing experience based on emotion recognition.

[1141] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1142] Step 1:

[1143] A user accesses an online shopping site and navigates to the details page of a product they are considering purchasing. On the product details page, they input information about the product's purpose and frequency of use. This input data includes the product's purpose (e.g., "daily use") and frequency of use (e.g., "five days a week"). The input data is collected by the device.

[1144] Step 2:

[1145] The terminal verifies the data entered by the user and checks for errors. After verification, it sends the correct data to the server. The data sent includes the product ID, purpose, frequency of use, and user ID. The data processing performed here is a consistency check of the input data.

[1146] Step 3:

[1147] The server stores the received data in a database. The stored data includes the product ID, purpose, frequency of use, and user ID. The data calculation here is the process of writing to the database.

[1148] Step 4:

[1149] While the user is inputting data, the device uses a camera and microphone to collect the user's facial expressions and voice. This data is input to an emotion recognition module, which analyzes the user's emotional state (e.g., excitement, anxiety, joy, etc.). The analysis result (emotional state) is returned to the device.

[1150] Step 5:

[1151] The server uses an aging prediction model to predict the future condition of the product based on the stored data on usage and frequency of use. By combining the input data (usage, frequency of use) with product characteristic data, it calculates the deterioration state of the product one year or five years from now. The predicted result provides the product's condition one year or five years from now (e.g., minor scratches, wear, etc.).

[1152] Step 6:

[1153] The server runs a price trend prediction model based on past market data. The input data includes the product ID and current market price, and the server predicts future prices by referencing past market data. The resulting prediction is the market value of the product one or five years from now (e.g., 900,000 yen in one year, 700,000 yen in five years).

[1154] Step 7:

[1155] The server generates images to visually represent the predicted future state of the product. It uses image processing libraries (e.g., OpenCV and matplotlib) to generate images that recreate the appearance of the product one year and five years from now. The generated images are stored on the server.

[1156] Step 8:

[1157] The server customizes the display content based on the emotion data received from the emotion recognition module, for example, highlighting warranty information and positive reviews for anxious users and highlighting features and promotions for excited users.

[1158] Step 9:

[1159] The server transmits the predicted data and the generated image to the terminal, including the future aging state of the product, its market value, the generated appearance image, and customization information.

[1160] Step 10:

[1161] The device displays the received forecast data and the generated image on the user interface, allowing users to visually check the product's condition and market value one or five years from now, allowing them to make purchasing decisions with confidence.

[1162] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1163] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1164] 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 the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1165] [Fourth embodiment]

[1166] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1167] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1168] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1169] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1170] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1171] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1172] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1173] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1174] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1175] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[1176] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1177] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1178] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1179] The present invention relates to an electronic commerce system that predicts and visually displays the future condition and market value of a product based on data on the purpose and frequency of use entered by a user when purchasing the product. This system includes a user terminal, a server, and a database. The processing of the system's program is explained below in natural language.

[1180] User data entry

[1181] A user selects a product on an e-commerce site and accesses the product's details page. On this details page, the user enters information about the purpose and frequency of use. For example, when purchasing a luxury watch, the user enters information such as "daily use" and "five days a week."

[1182] Data transmission and storage

[1183] The terminal verifies the data entered by the user regarding purpose and frequency of use to ensure there are no errors. Data that passes verification is sent to the server. The server receives this data and stores it in a database. The stored data includes the product ID, purpose, frequency of use, user ID, etc.

[1184] Product condition prediction

[1185] The server uses an aging prediction model to predict the future condition of a product based on data on usage and frequency of use stored in the database. For example, the aging prediction for a luxury watch used daily five days a week predicts that after one year, small scratches and a decrease in luster will appear, and after five years, there will be obvious wear and tear and peeling of the plating.

[1186] Price trend forecast

[1187] The server predicts price trends based on past market data for a product. For example, a luxury watch currently priced at 1 million yen is predicted to be priced at 900,000 yen one year from now, and 700,000 yen five years from now. This prediction is made using time series analysis and machine learning models.

[1188] Future State Visualization

[1189] The server uses the predicted data to generate a visual image of the product's future state. For example, it might generate an image of a watch's appearance in one year, showing slight scratches and a loss of luster, and an image of a watch in five years, showing obvious wear and peeling plating. To do this, the server uses an image processing library.

[1190] Sending and displaying forecast data and images

[1191] The server sends the predicted data and generated images to the user's terminal, which receives the data and images and displays them to the user. The displayed information includes text information about the product's aging condition and market value one and five years from now, as well as generated images of the product's future appearance.

[1192] Specific examples

[1193] For example, when a user purchases a luxury watch on an e-commerce site, they enter "daily use" as the intended use and "five days a week" as the frequency of use. The device sends this data to the server, which stores it in a database. The server then uses the entered data to predict aging and price trends. It predicts that after one year, small scratches will appear and the luster will decrease, and after five years, there will be clear wear and peeling of the plating. The server predicts that the price will be 900,000 yen after one year and 700,000 yen after five years. Based on this predicted data, the server generates an image of the product's future appearance, which the device displays to the user. Using this information, the user can visually understand the product's future after purchase and make an appropriate decision.

[1194] In this way, the present invention provides detailed information about the future condition and value of a product when the user is considering purchasing it, thereby enhancing purchasing motivation and supporting appropriate purchasing decisions.

[1195] The processing flow will be explained below.

[1196] Step 1:

[1197] A user accesses an e-commerce site and selects a product. They are taken to the details page of the selected product and enter information such as the purpose and frequency of use.

[1198] Step 2:

[1199] The device validates the purpose and frequency of use data entered by the user, for example by checking that the data is complete and in the correct format.

[1200] Step 3:

[1201] The terminal sends the data that passes the verification to the server, including the product ID, purpose, frequency of use, and user ID.

[1202] Step 4:

[1203] The server stores the received data in a database, where information on the purpose and frequency of use of each product is organized and stored.

[1204] Step 5:

[1205] The server calls the condition prediction module based on the stored data and uses an aging prediction model to predict the future condition of the product. For example, it calculates the deterioration state based on daily use, 5 days a week.

[1206] Step 6:

[1207] The server calls the price trend prediction module based on past market data, and uses time series analysis and machine learning models to predict the market price of the product one or five years from now.

[1208] Step 7:

[1209] The server uses the predicted data to generate images of the future state of the product, using image processing libraries to visualize what the product will look like in one year or five years.

[1210] Step 8:

[1211] The server transmits the forecast data and the generated image to the terminal, including the aging state, price transition, and the generated image.

[1212] Step 9:

[1213] The terminal displays the received data and images on a user interface, including the aging condition and market price one and five years from now, as well as an image of the product's future appearance.

[1214] Step 10:

[1215] Users can check the future condition and value of the product based on the displayed information, which can be used as reference when making purchasing decisions or collecting items.

[1216] Through the above steps, the user can get a detailed understanding of the future condition and market value of the product at the time of purchase, enabling them to make an appropriate selection.

[1217] Example 1

[1218] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1219] In conventional e-commerce systems, it is difficult for users to fully understand the future condition and market value of a product when purchasing it. This makes it difficult for users to make appropriate decisions regarding future use, which can affect their post-purchase satisfaction. The present invention aims to support users in making appropriate purchasing decisions and increase user satisfaction by predicting and visually displaying the future condition and market value of a product when they are considering purchasing it.

[1220] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1221] In this invention, the server includes means for receiving and storing the transmitted data, means for predicting the future state of the product using an aging prediction model based on the stored data, means for predicting price trends based on past market data, means for generating an image visually showing the predicted future state, and means for transmitting the predicted data and the generated image to a user terminal, thereby enabling the user to visually confirm information about the future state and market value of the product and make an appropriate decision before purchasing.

[1222] The "input means" is a means for a user to input data on the purpose and frequency of use when purchasing a product.

[1223] "Terminal means" refers to means for verifying data entered by a user and transmitting it to a server.

[1224] The "server means" is a means for receiving input data and storing it in a database.

[1225] "Prediction means" refers to a means for predicting the future state of a product using an aging prediction model based on stored data.

[1226] A "price trend prediction means" is a means for predicting the price trend of a product based on past market data.

[1227] The "image generating means" is a means for generating an image that visually shows the predicted future state of the product.

[1228] The "transmission means" is a means for transmitting the prediction data and the generated image to the user's terminal.

[1229] The "display means" is a means for displaying the predicted data and the generated image on the user terminal.

[1230] The "means for calculating a deterioration rate" is a means for calculating a deterioration rate applied to a product based on the user's usage frequency data.

[1231] "Image Processing Library" means a software library for image processing used to visually indicate the aging state of a product.

[1232] The present invention relates to an electronic commerce system that predicts and visually displays the future condition and market value of a product based on data on the product's intended use and frequency of use entered by the user when purchasing the product. The system includes a user terminal, a server, and a database. When purchasing a product, the user enters information on the product's intended use and frequency of use from the user terminal. The terminal is responsible for verifying this input data and sending it to the server. The server stores the received data and uses various prediction models to predict and visualize the product's future condition and price trends. This information is then sent back to the user's terminal so that the user can view it.

[1233] Specifically, a user enters information about the purpose and frequency of use on the details page of an e-commerce site. For example, when purchasing a luxury watch, the user enters information such as "daily use" and "five days a week." This input data is verified by the device and sent to the server. The server stores the received data in a database and uses this data to make predictions using an aging prediction model and a price trend prediction model. Time series analysis and machine learning models are used to predict the aging state and price changes of luxury watches one and five years from now. An image processing library is also used to generate images that visually show the predicted aging state.

[1234] The generated forecast data and visual images are sent from the server to the user's device, allowing the user to understand the product's future state and make a purchasing decision. This information provision improves the user's post-purchase satisfaction and supports appropriate purchasing decisions.

[1235] Specific examples

[1236] For example, when a user purchases a luxury watch on an e-commerce site, they enter "daily use" as the intended use and "five days a week" as the frequency of use. This data is sent by the device to a server, which stores the data and uses it to predict aging and price trends. After one year, the server predicts small scratches and a loss of luster, and after five years, the watch will show clear wear and the plating will peel. The server also predicts that the price will be 900,000 yen after one year and 700,000 yen after five years. Based on this predicted data, the server generates an image of the product's future appearance, which the device displays to the user. Using this information, the user can visually understand the product's future after purchase and make an appropriate decision.

[1237] Prompt Sentence Examples

[1238] Below are some examples of prompt sentences.

[1239] "When purchasing a luxury watch, assume it will be worn daily, five days a week. Using this information, predict the aging and price trends of the watch in one year and five years. Also, generate an image showing its future appearance."

[1240] Based on the above prompt, the generative AI model can be asked to make appropriate predictions and generate visual information.

[1241] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1242] Step 1:

[1243] A user selects a product on an e-commerce site and accesses the product detail page. The user enters information about the purpose and frequency of use, such as "purpose: daily use" and "frequency of use: 5 days a week."

[1244] Input: Usage and frequency data

[1245] Output: Data entered on the user's terminal

[1246] Step 2:

[1247] The terminal validates the data entered by the user. Specifically, it checks the input format and value range to ensure that the input data is correct. For example, it verifies that the purpose is "daily use" and the frequency is "five days a week." If validation is successful, the terminal sends the data to the server.

[1248] Input: User-entered usage and frequency data

[1249] Output: Validated data sent to the server

[1250] Step 3:

[1251] The server receives the data sent from the device and stores it in a database along with information such as product ID, purpose, frequency of use, and user ID. This allows the data necessary for future predictions to be accumulated.

[1252] Input: Verified data sent from the terminal

[1253] Output: Data stored in the database

[1254] Step 4:

[1255] The server uses the data stored in the database to predict the future condition of the product using an aging prediction model. Specifically, it references past usage data and aging data to predict the product's deterioration. For example, if a luxury watch is used daily, five days a week, after one year it may develop small scratches and lose its luster, and after five years it may show clear wear and peeling of the plating.

[1256] Input: Usage and frequency data stored in the database

[1257] Output: Predicted future product state data

[1258] Step 5:

[1259] The server predicts product price trends based on past market data. Specifically, it uses time series analysis and machine learning models to analyze past price history and supply and demand data to predict future prices. For example, if the current price of a luxury watch is 1 million yen, it is predicted that it will be 900,000 yen in one year and 700,000 yen in five years.

[1260] Inputs: Historical market data and aging forecast data

[1261] Output: Predicted price trend data

[1262] Step 6:

[1263] The server uses the forecast data to generate a visual representation of the product's future state. It uses image processing libraries and AI technology to create product images based on the forecast results. For example, it generates images showing the appearance of a watch one year from now and five years from now. The images reflect small scratches, loss of gloss, wear, and peeling plating.

[1264] Input: Predicted future product status data and price transition data

[1265] Output: The generated visual image

[1266] Step 7:

[1267] The server sends the predicted data and generated images to the user's device, which packages the data and sends it to the device in an easily accessible format.

[1268] Input: Prediction data and generated images

[1269] Output: Data sent to the user's device

[1270] Step 8:

[1271] The terminal displays the received forecast data and generated images to the user, allowing the user to visually confirm detailed information about the product's future condition and market value. For example, text information about the aging condition and price trends one and five years from now, along with related images, are displayed.

[1272] Input: Prediction data sent from the server and generated images

[1273] Output: Prediction information and images displayed to the user

[1274] (Application example 1)

[1275] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1276] Conventional e-commerce systems lacked the means to provide detailed information about the future condition and market value of a product when users were considering a purchase, making it difficult for users to make appropriate purchasing decisions. Furthermore, there was also a lack of a means to visually understand the aging state of a product after use and the transition of its market value. As a result, it was not possible to stimulate users' purchasing motivation or to appeal to the benefits of purchasing from a long-term perspective.

[1277] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1278] In this invention, the server includes: input means for inputting data on the purpose and frequency of use of an item when a user purchases it; an information processing device for receiving and saving the input data; prediction means for predicting the future state of the item using a deterioration prediction model based on the saved data and item characteristic data; image generation means for generating an image visually showing the predicted future state; price trend prediction means for predicting price trends based on past market data; transmission means for transmitting the predicted data and the generated image to a user terminal; display means for displaying the predicted data and the generated image; and application means for providing visualized information predicting the future state and market value of the item based on the purpose and frequency of use data of the item. This allows users to obtain detailed information on the future state and market value of the item before purchasing, enabling them to make appropriate purchasing decisions.

[1279] "User" refers to a consumer or user who uses this system to purchase goods.

[1280] "Goods" refers to the goods and products handled in this system.

[1281] "Use" refers to information about how the user intends to use the item they are about to purchase.

[1282] "Frequency of use" refers to information about how often a user uses the item they are about to purchase.

[1283] "Input means" refers to the device or software that allows the user to input data on purpose and frequency of use into the system.

[1284] The term "information processing device" refers to a server or computer that stores data received from a user and performs various calculations.

[1285] "Item characteristic data" refers to data on basic information and characteristics of the items being handled.

[1286] A "deterioration prediction model" refers to an algorithm or mathematical model for predicting the future deterioration state of an item based on its intended use and frequency of use.

[1287] "Prediction means" refers to a function or device for predicting the future state of an item using a deterioration prediction model based on stored data and item characteristic data.

[1288] "Image generation means" refers to a function or device for generating an image that visually shows the predicted future state of an item.

[1289] "Price trend prediction means" refers to a function or device that predicts future price trends of goods based on past market data.

[1290] "Transmission means" refers to a function or device for transmitting prediction data and generated images to a user terminal.

[1291] "User terminal" refers to an electronic device used by a user, such as a computer, smartphone, or tablet.

[1292] "Display means" refers to software and hardware for displaying the predicted data and generated images on the user terminal.

[1293] "Application means" refers to application software that predicts the future condition and market value of an item based on data on the use and frequency of use of the item, and visually presents this to the user.

[1294] This invention is an electronic commerce system that predicts and visually displays the future condition and market value of an item after purchase to a user considering purchasing the item. The system includes a user terminal, an information processing device, and a database.

[1295] User data entry

[1296] Users access the product details page and enter information about the purpose and frequency of use. For example, when purchasing a luxury watch, users enter information such as "daily use" and "five days a week."

[1297] Data transmission and storage

[1298] The user terminal verifies the data entered by the user regarding the purpose and frequency of use to ensure there are no errors. Data that passes verification is sent to the information processing device. The information processing device receives this data and stores it in a database. The stored data includes the item ID, purpose, frequency of use, user ID, etc.

[1299] Item condition prediction

[1300] The information processing device predicts the future condition of an item using a deterioration prediction model based on data on usage and frequency of use stored in a database. For example, a deterioration prediction for a luxury watch used daily five days a week might predict that after one year, small scratches and a loss of luster will appear, and after five years, clear wear and peeling of the coating will be visible.

[1301] Price trend forecast

[1302] The information processing device predicts price trends based on past market data for goods. For example, a luxury watch currently priced at 1 million yen is predicted to have a price of 900,000 yen one year from now, and 700,000 yen five years from now. This prediction is made using time series analysis and machine learning models.

[1303] Future State Visualization

[1304] The information processing device generates an image based on the predicted data that visually represents the future state of the item. For example, it generates an image that reflects the appearance of a watch one year from now, with slight scratches and loss of gloss, and an image that reflects the watch's obvious wear and peeling coating five years from now. To do this, the information processing device uses an image processing library.

[1305] Sending and displaying forecast data and images

[1306] The information processing device transmits the predicted data and the generated image to a user terminal. The user terminal receives the data and image and displays them to the user. The displayed information includes text information about the deterioration state and market value of the item one and five years from now, as well as the generated image of the item's future appearance.

[1307] Specific examples

[1308] For example, when a user purchases a luxury watch, they input "daily use" as the purpose and "five days a week" as the frequency of use. The user's device sends this data to an information processing device, which stores it in a database. The information processing device then predicts deterioration and price trends based on the input data. It predicts that the watch will develop small scratches and lose its luster in one year, and that it will show clear wear and peeling of the coating in five years, and predicts that the price will be 900,000 yen in one year and 700,000 yen in five years. Based on this predicted data, the information processing device generates an image of the future appearance of the item, which the user's device displays to the user. Based on this information, the user can visually understand the future of the item after purchase and make an appropriate decision.

[1309] Prompt Sentence Examples

[1310] A user entered the following information about purpose and frequency of use on the item page:

[1311] Use: Everyday use

[1312] Frequency of use: 5 days a week

[1313] Use this information to predict the condition and market value of the item one and five years from now, along with a visualisation.

[1314] As described above, the present invention provides detailed information about the future state and value of an item when the user is considering purchasing it, thereby enhancing purchasing motivation and supporting appropriate purchasing decisions.

[1315] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1316] Step 1: User Data Entry

[1317] The user accesses the product details page and inputs information about the purpose and frequency of use of the product they are considering purchasing. The input data includes specific usage information such as "daily use" and "five days a week." The user's device receives this input data.

[1318] Input: Information on purpose and frequency of use

[1319] Output: Verified data (use and frequency of use)

[1320] Step 2: Data transmission and storage

[1321] The verified data is sent from the terminal to an information processing device (server). The information processing device stores the received data in a database. The stored data includes the item ID, purpose, frequency of use, user ID, etc.

[1322] Input: Verified data (purpose and frequency of use)

[1323] Output: Data stored in the database

[1324] Step 3: Predicting the condition of the item

[1325] The information processing device predicts the future condition of the item using a deterioration prediction model based on data on usage and frequency of use stored in a database. For example, it uses a machine learning model or a time series analysis algorithm to predict the condition of the item one year or five years from now.

[1326] Input: Data on usage and frequency of use stored in the database

[1327] Output: Predicted future state of the item

[1328] Step 4: Predict price trends

[1329] The information processing device predicts price trends based on past market data for goods. For example, using time series analysis and machine learning models, it predicts that a luxury watch currently priced at 1 million yen will be priced at 900,000 yen one year from now, and 700,000 yen five years from now.

[1330] Input: Product information stored in the database, historical market data

[1331] Output: Predicted price progression

[1332] Step 5: Visualize the future state

[1333] The information processing device uses the predicted data to generate images that visually represent the future state of the item. For example, using an image processing library, it generates an image that shows the appearance of a watch one year from now, with small scratches and a loss of gloss, and the appearance of a watch five years from now, with obvious wear and peeling coating.

[1334] Input: Predicted future state data of the item

[1335] Output: The generated visual image

[1336] Step 6: Send and display forecast data and images

[1337] The information processing device transmits the predicted data and the generated image to a user terminal. The user terminal receives this data and image and displays them to the user. Specifically, text information about the deterioration state and market value of the item one year and five years from now, as well as the generated image of the item's appearance in the future, are displayed.

[1338] Input: Prediction data and generated images

[1339] Output: Predicted data and visual images displayed on the user's device

[1340] Through the above steps, the user can obtain detailed information about the future condition and market value of the item they are considering purchasing, enabling them to make an appropriate purchasing decision.

[1341] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1342] The present invention provides an e-commerce system that predicts and visually displays the future condition and market value of a product based on data on the purpose and frequency of use entered by the user when purchasing the product, and also includes an emotion engine that recognizes the user's emotions to further improve the purchasing experience. This system includes a user terminal, a server, an emotion engine, and a database. The processing of the system's programs is described in detail below.

[1343] User data entry

[1344] A user accesses an e-commerce site and selects the product they want to purchase. They are then taken to the product detail page and enter information about the purpose and frequency of use. For example, when purchasing a luxury watch, a user enters information such as "daily use" and "five days a week."

[1345] Data transmission and storage

[1346] The terminal verifies the data entered by the user regarding purpose and frequency of use to ensure there are no errors. Data that passes verification is sent to the server. The server stores this data in a database. The stored data includes the product ID, purpose, frequency of use, and user ID.

[1347] Emotion recognition by emotion engine

[1348] While the user is entering data, the device uses sensors such as a camera and microphone to analyze the user's facial expressions and tone of voice. The emotion engine uses this data to recognize the user's emotional state (e.g., excitement, anxiety, indifference, etc.).

[1349] Product condition prediction and price trend prediction

[1350] The server uses an aging prediction model to predict the future condition of a product based on stored data on usage and frequency of use. For example, it calculates the deterioration state of a luxury watch one year and five years from now based on daily use five days a week. At the same time, it predicts price trends based on past market data and calculates predicted prices one year and five years from now.

[1351] Future State Visualization

[1352] The server uses the predicted data to generate a visual representation of the product's future state. It uses an image processing library to visualize the product's appearance in one year and five years. For example, the watch's appearance in one year will show minor scratches, while its appearance in five years will show clear wear.

[1353] Customized display based on emotions

[1354] The server customizes the display content based on the user's emotional data received from the emotion engine. For example, if a user seems anxious about a purchase, the server can highlight product warranty information and user reviews. For an excited user, the server can highlight product features and promotional information.

[1355] Sending and displaying forecast data and images

[1356] The server sends the predicted data and generated images to the terminal, which displays them on a user interface. The displayed information includes text information about the product's aging status and market value one and five years from now, generated images of the product's future appearance, and emotion-based customization information.

[1357] Specific examples

[1358] For example, when a user purchases a luxury watch on an e-commerce site, they enter "daily use" as the purpose and "five days a week" as the frequency of use. If the device detects any anxiety about the purchase, the device sends this input data to the server, which stores it in a database. The server then uses the input data to predict aging and price trends, predicting that the watch will develop minor scratches after one year and clear wear after five years. The predicted price trends are 900,000 yen after one year and 700,000 yen after five years. The server uses this predicted data to visualize the product's future appearance, highlighting warranty information and positive user reviews to alleviate the user's anxiety. The device then displays this information to the user, allowing them to confirm the product's future condition and value and make a purchasing decision with confidence.

[1359] In this way, the present invention not only provides users with detailed information about the future condition and value of a product when they are considering a purchase, but also optimizes the user experience based on emotion recognition, improving purchasing motivation and supporting appropriate purchasing decisions.

[1360] The processing flow will be explained below.

[1361] Step 1:

[1362] A user accesses an e-commerce site and selects a product they are interested in. For example, they move to a detail page of a luxury watch and begin to consider purchasing it.

[1363] Step 2:

[1364] The device displays input fields for the purpose and frequency of use on the product detail page, and the user enters information such as "purpose: daily use" and "frequency of use: 5 days a week."

[1365] Step 3:

[1366] The terminal validates the user's input data, checking for missing or malformed data, and data that passes validation proceeds to the next step.

[1367] Step 4:

[1368] The terminal transmits the input data on purpose and frequency of use to the server. The transmitted data includes the product ID, purpose, frequency of use, user ID, etc.

[1369] Step 5:

[1370] The server stores the received data in a database, including product characteristic data, user usage data, and so on.

[1371] Step 6:

[1372] The device uses a camera and microphone to recognize the user's facial expressions and voice while inputting data, and sends this data to an emotion engine to analyze the user's emotional state.

[1373] Step 7:

[1374] The emotion engine analyzes the user's facial expressions and tone of voice to identify emotions (e.g., excitement, anxiety, indifference, etc.), and sends the analysis results to the server.

[1375] Step 8:

[1376] The server uses an aging prediction model to predict the future condition of the product based on the stored data on usage and frequency of use. For example, it calculates wear and tear after one year and five years based on daily use five days a week.

[1377] Step 9:

[1378] The server calls a price trend prediction module based on past market data to predict the market price of the product one year or five years from now. For example, if the current price is 1 million yen, it is predicted that the price will be 900,000 yen one year from now and 700,000 yen five years from now.

[1379] Step 10:

[1380] The server generates images of the future state of the product based on the predicted data, using image processing libraries to visualize what the product will look like in one year and five years, simulating, for example, minor scratches after one year and increased wear after five years.

[1381] Step 11:

[1382] The server customizes the display content based on the emotion data from the emotion engine. For anxious users, it adds reassuring information (guarantees and reviews). For excited users, it emphasizes product features and promotional information.

[1383] Step 12:

[1384] The server transmits the predicted data, customized display content, and generated images to the terminal.

[1385] Step 13:

[1386] The device then displays the received data and images to the user, including customized information based on the aging status after one and five years, market value, and emotions.

[1387] Step 14:

[1388] Users can check the future condition and value of the product based on the displayed information, and can make appropriate purchasing decisions while also referring to customized information based on their emotions.

[1389] Through the above specific processing steps, the system can provide detailed information about the future condition and market value of a product when the user purchases it, as well as customize the display based on the user's emotions, thereby providing a better purchasing experience.

[1390] Example 2

[1391] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1392] In conventional e-commerce systems, when users purchase a product, they are not provided with information about the product's future condition or how its market value will change. This often leaves users feeling unsure about their purchase decision and can make them hesitant to make a final purchase. Furthermore, because the system does not take into account the user's feelings, the purchasing experience remains unimproved.

[1393] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for receiving and saving input data, means for predicting the future state of the product using an aging prediction model based on the saved data and product characteristic data, means for generating an image visually showing the predicted future state, means for predicting price trends based on past market data, means for transmitting the predicted data and the generated image to the user terminal, and means for customizing the display content based on the recognized emotional state. This allows the user to visually check the future state and market value of the product and make a purchase decision with confidence by receiving information according to their emotions.

[1394] 1. "Input means" refers to the interface through which a user inputs data on the purpose and frequency of use when purchasing a product.

[1395] 2. "Server Means" means a computing device for receiving and storing input data.

[1396] 3. "Prediction Means" refers to the function by which the Server Means predicts the future state of the Product using an aging prediction model based on the Stored Data and Product Attribute Data.

[1397] 4. "Image generation means" refers to a function for generating an image that visually shows the predicted future state.

[1398] 5. "Price trend prediction means" refers to a function for predicting price trends based on past market data.

[1399] 6. "Transmission means" refers to the function for transmitting prediction data and generated images to a user terminal.

[1400] 7. "Display means" refers to an interface for a user terminal to display predicted data and generated images.

[1401] 8. "Emotion recognition means" refers to the sensors and analysis functions that enable a device to recognize a user's emotional state by analyzing the user's facial expressions and tone of voice.

[1402] 9. "Customized display means" refers to functionality that allows the server to customize the display content based on the recognized emotional state.

[1403] This invention is a system for an e-commerce system that predicts and visually displays the future condition and market value of a product based on data on the purpose and frequency of use entered by a user when purchasing the product, and recognizes the user's emotions to improve the purchasing experience. This system includes a user terminal, a server, an emotion recognition engine, and a database.

[1404] User data entry

[1405] A user accesses an e-commerce site and selects the product they wish to purchase. They are then taken to the product detail page and enter information about the purpose and frequency of use. For example, when purchasing a luxury watch, they enter information such as "daily use" and "five days a week." An interface such as a web form is used as the input method.

[1406] Data transmission and storage

[1407] The terminal verifies the purpose and frequency of use data entered by the user to ensure there are no errors. A validation function is used for the verification. Data that passes verification is securely sent to the server using the HTTPS protocol. The server saves this data in a database. The saved data includes the product ID, purpose, frequency of use, and user ID. An RDBMS such as MySQL or PostgreSQL is used as the database.

[1408] Emotion recognition by emotion engine

[1409] While the user is entering data, the device uses sensors such as a camera and microphone to analyze the user's facial expressions and tone of voice. This is done using libraries such as OpenCV. The emotion recognition engine uses machine learning models such as TensorFlow and PyTorch to recognize the user's emotional state (e.g., excitement, anxiety, indifference, etc.).

[1410] Product condition prediction and price trend prediction

[1411] The server uses an aging prediction model to predict the future condition of a product based on the stored data on usage and frequency of use. Libraries such as scikit-learn are used for aging prediction. For example, the state of deterioration of a luxury watch after one year and five years is calculated. At the same time, price trends are predicted based on past market data, and a generative AI model is used to calculate predicted prices after one year and five years.

[1412] Future State Visualization

[1413] The server uses the predicted data to generate a visual representation of the product's future state. PIL (Python Imaging Library) is used for image processing, visualizing the product's appearance in one year and five years. For example, the watch's appearance in one year will show minor scratches, while its appearance in five years will show clear wear.

[1414] Customized display based on emotions

[1415] The server customizes the display content based on the user's emotional data received from the emotion engine. For users who appear anxious about the purchase, product warranty information and user reviews are emphasized. On the other hand, for users who are excited, product features and promotional information are emphasized.

[1416] Sending and displaying forecast data and images

[1417] The server sends the predicted data and generated images to the terminal. REST API is used to send the data, and the data is encoded in JSON format. The terminal displays this data and images on a user interface. The information includes text information about the aging status and market value of the product one and five years from now, generated images of the product's future appearance, and emotion-based customization information. As a specific example of how the data is displayed, the predicted information and visualized images are displayed on a web page using HTML and CSS.

[1418] Specific examples

[1419] For example, when a user purchases a luxury watch on an e-commerce site, if the user enters "daily use" as the purpose and "five days a week" as the frequency of use, and anxiety about the purchase is detected, the following processing will be performed.

[1420] 1. The terminal checks the data entered by the user and sends it to the server, which stores it in a database.

[1421] 2. The device uses a camera and microphone to collect the user's facial expressions and voice for emotion recognition. The emotion engine analyzes the data and detects anxiety.

[1422] 3. The server runs an aging prediction model based on usage and frequency of use data, predicting, for example, that the server will develop minor scratches in one year and clear wear in five years. It also predicts that the price will be 900,000 yen in one year and 700,000 yen in five years.

[1423] 4. The server uses an image processing library to generate the future appearance of the product based on the predicted data.

[1424] 5. The server then customizes the display based on the emotional data, emphasizing warranty information and positive word-of-mouth reviews.

[1425] 6. The terminal displays this information on the user interface, allowing the user to check the future condition and value of the product and make a purchasing decision with confidence.

[1426] Prompt Sentence Examples

[1427] Here is an example of inputting the following prompt sentence into a generative AI model to output a detailed explanation of the above system.

[1428] plaintext

[1429] This system is an e-commerce system that predicts and visually displays the future condition and market value of a product based on the usage and frequency of use data entered by the user when purchasing the product. It also includes an emotion engine that recognizes user emotions to improve the purchasing experience. Specifically, a user accesses an e-commerce site, selects the product they want to purchase, and enters information about usage and frequency of use. The terminal then verifies the data, sends it to the server, and stores it. The server then uses this data to predict and visualize aging and price trends. Furthermore, the emotion engine recognizes the user's emotions, and the server customizes the display based on those emotions. Finally, the predicted data and the generated image are sent to the terminal and displayed to the user. Please provide a detailed explanation, including examples and prompt sentences.

[1430] In this way, the present invention not only provides users with detailed information about the future condition and value of a product when they are considering a purchase, but also optimizes the user experience based on emotion recognition, improving purchasing motivation and supporting appropriate purchasing decisions.

[1431] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1432] Step 1:

[1433] A user accesses an e-commerce site, selects a product they wish to purchase, and inputs information about the purpose and frequency of use through an input means.

[1434] Input: User input data on usage and frequency of use.

[1435] Output: The input data is sent to the terminal and prepared for validation.

[1436] Specific actions: Enter details such as "daily use" and "5 days a week" into the web form and click the submit button.

[1437] Step 2:

[1438] The terminal validates the data entered by the user to ensure there are no errors. It uses validation functions to check whether the entered data is in the correct format.

[1439] Input: Usage and frequency data entered by the user.

[1440] Output: Validated data.

[1441] Specific behavior: Validation functions are used to check for blank spaces and formatting. If validation is successful, the data is ready to be sent to the server.

[1442] Step 3:

[1443] The terminal sends the data that passes the verification to the server using the HTTPS protocol, ensuring data security.

[1444] Input: Validated usage and frequency data.

[1445] Output: The user's data sent to the server.

[1446] Specific operation: Data is sent via the HTTPS protocol, and the server returns a receipt confirmation response to the terminal.

[1447] Step 4:

[1448] The server stores the received data in a database, which includes the product ID, purpose, frequency of use, and user ID.

[1449] Input: Usage and frequency data sent to the server.

[1450] Output: User data stored in the database.

[1451] Specific operation: Saves data using an RDBMS such as MySQL or PostgreSQL. Notifies the device that the save was successful.

[1452] Step 5:

[1453] The device uses a camera and microphone to collect the user's facial expressions and tone of voice while they are entering data. This data is then analyzed and an emotion engine is used to recognize their emotional state.

[1454] Input: User's facial expression data and voice data.

[1455] Output: The perceived emotional state of the user (e.g., excited, anxious, apathetic, etc.).

[1456] Specific operation: Facial recognition is performed using OpenCV, and voice tone is analyzed using a voice analysis API. Based on the analysis, emotions are classified using an emotion engine (using TensorFlow and PyTorch).

[1457] Step 6:

[1458] The server uses an aging prediction model to predict the future state of the product based on the stored data and product characteristic data, and also predicts price trends based on past market data.

[1459] Inputs: Usage and frequency data, product characteristics data, historical market data.

[1460] Output: Forecast data of future product state and price trend forecast data.

[1461] Specific operation: Aging prediction is performed using scikit-learn, and price trends are calculated using a generative AI model. For example, it predicts that there will be small scratches after one year and clear wear after five years, and predicts that the price will be 900,000 yen after one year and 700,000 yen after five years.

[1462] Step 7:

[1463] The server generates an image that visually shows the future state of the product based on the predicted data.

[1464] Input: Aging forecast data, price trend forecast data.

[1465] Output: An image showing the future state of the product.

[1466] What it does: It uses PIL (Python Imaging Library) to visualize what a product will look like after one year and five years. For example, the image of a watch after one year will show minor scratches, while the image after five years will show obvious wear.

[1467] Step 8:

[1468] The server customizes the display content based on the emotion data received from the emotion engine.

[1469] Input: Recognized user emotion data.

[1470] Output: Customized display content.

[1471] What it does: If anxiety is detected, warranty information and positive reviews are highlighted, and if an excited user is detected, product features and promotional information are highlighted.

[1472] Step 9:

[1473] The server sends the predicted data and the generated image to the terminal, which displays the data.

[1474] Inputs: Forecast data, future state image, customized display information.

[1475] Output: Information displayed in the user interface.

[1476] Specific operation: Data is encoded in JSON format and sent via the REST API. The device displays the received data in a user interface using HTML and CSS.

[1477] Through these steps, the present invention provides detailed information about the future condition and value of a product when the user is considering purchasing it, and provides a customized display based on emotions, thereby improving the user's purchasing experience.

[1478] (Application example 2)

[1479] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1480] In conventional e-commerce systems, users have limited means of predicting the future condition or market value of a product when purchasing it, which often leaves users anxious about the value and condition of the product after purchase. Furthermore, there is no means to provide a purchasing experience that takes into account the user's emotional state, making it difficult to adequately support purchasing behavior that is influenced by emotions such as anxiety or excitement. This poses a challenge in improving users' purchasing motivation and post-purchase satisfaction.

[1481] The specification process by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving and saving input data, means for predicting the future state of the product using an aging prediction model, means for generating an image visually showing the predicted future state, means for recognizing the emotional state of the user by analyzing the user's facial expression and voice, and means for customizing the display content based on the emotional state obtained by the emotion recognition means. This makes it possible to predict the future state and market value of a product when the user purchases it, and not only to visually confirm the results, but also to provide a purchasing experience that is tailored to the user's emotional state.

[1482] A "user" is an entity that uses the system to purchase or evaluate products.

[1483] "Products" are goods and services provided to users through electronic commerce.

[1484] "Use" refers to the purpose or intention of how the user will use the product.

[1485] "Frequency of use" is data indicating how often or how many times a user uses a product.

[1486] "Input means" refers to an interface that a user uses to input data about purpose and frequency of use into the system.

[1487] The "server means" is a computer system that stores data received from users and performs various processes based on the data.

[1488] The "prediction means" is a function that predicts the future state of a product using an aging prediction model based on the stored data and product characteristic data.

[1489] The "image generation means" is a function that generates an image that visually shows the predicted future state of the product.

[1490] The "price trend prediction means" is a function that predicts the future market value of a product based on past market data.

[1491] The "transmission means" is a function for transmitting predicted data and generated images from the server to the user terminal.

[1492] The "display means" is an interface that displays the predicted data received by the user terminal and the generated image to the user.

[1493] The "emotion recognition means" is a function that analyzes the user's facial expressions and voice to recognize the user's emotional state.

[1494] The "customization means" is a function for customizing the display content based on the emotional state obtained by the emotion recognition means.

[1495] System Overview

[1496] The present invention relates to an electronic commerce system that predicts and visually displays the future condition and market value of a product based on data on the purpose and frequency of use entered by a user when purchasing the product. The system includes a user terminal, a server, an emotion engine, and a database. It also includes emotion recognition means that recognizes the user's emotions to improve the purchasing experience.

[1497] Hardware and Software Configuration

[1498] User devices: Smartphones, tablets, PCs, etc. are applicable. Users input their usage and frequency of use, and the results of predictions of future conditions and price trends are displayed. The devices are equipped with cameras and microphones, and also have an emotion engine that analyzes the user's facial expressions and voice.

[1499] Server: A computer system that receives, stores, and processes data. The server is installed with an aging prediction model, a price trend prediction model, an image processing library, an emotion recognition library, etc.

[1500] Database: A database system for storing product characteristics data, user-entered data, and historical market data.

[1501] Specific details of data processing

[1502] 1. User data entry

[1503] A user accesses an online shopping site and goes to the details page of a product they are considering purchasing. On this page, they enter information about the purpose and frequency of use. For example, if they are purchasing a luxury watch, they enter data such as "daily use" and "five days a week."

[1504] 2. Data transmission and storage

[1505] The user terminal verifies the entered data to ensure there are no errors. After verification, this data is sent to the server, which stores it in a database. The stored data includes the product ID, purpose, frequency of use, and user ID.

[1506] 3. Emotion Recognition

[1507] Using the camera and microphone on the user's device, the system analyzes the user's facial expressions and voice to recognize the user's emotional state. An emotion recognition library (e.g., EmotionRecognition library) is used to determine the user's emotional state (e.g., excitement, anxiety, indifference, etc.).

[1508] 4. Product condition prediction and price trend prediction

[1509] The server uses an aging prediction model to predict the future condition of a product based on the stored data on usage and frequency of use. For example, it calculates the deterioration state of a luxury watch one year and five years from now. At the same time, it predicts price trends based on past market data and calculates prices one year and five years from now.

[1510] 5. Future State Visualization

[1511] The server generates an image that visually represents the future state of the product based on the predicted data. It uses an image processing library (e.g., OpenCV or matplotlib) to visualize the appearance of the product after one year and after five years. For example, it generates an image that shows slight scratches after one year and clear wear after five years.

[1512] 6. Emotion-based customization

[1513] The server customizes the display content based on the user's emotional data received from the emotion recognition means. For example, for a user who appears anxious, product warranty information and positive user reviews are emphasized. For an excited user, product features and promotional information are emphasized.

[1514] 7. Sending and displaying predicted data and images

[1515] The server sends the predicted data and generated images to the user's device, which then displays them on a user interface. The displayed information includes text information about the product's aging status and market value one and five years from now, generated images of the product's future appearance, and emotion-based customization information.

[1516] Specific examples

[1517] For example, when a user purchases a luxury watch, they enter "daily use" as the purpose and "five days a week" as the frequency of use, and if they have concerns about the purchase, the server saves the entered data in a database. Next, it predicts aging and price trends, predicting that small scratches will appear after one year and clear wear after five years. The price trends are predicted to be 900,000 yen after one year and 700,000 yen after five years. Based on this predicted data, the server visualizes the future appearance of the product, and also highlights warranty information and positive user reviews to alleviate the user's concerns.

[1518] Prompt Sentence Examples

[1519] "Predict what a luxury watch will look like in one year and five years if used daily, five days a week."

[1520] In this way, the present invention provides detailed information about the future condition and value of a product at the user's purchasing stage, as well as a personalized purchasing experience based on emotion recognition.

[1521] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1522] Step 1:

[1523] A user accesses an online shopping site and navigates to the details page of a product they are considering purchasing. On the product details page, they input information about the product's purpose and frequency of use. This input data includes the product's purpose (e.g., "daily use") and frequency of use (e.g., "five days a week"). The input data is collected by the device.

[1524] Step 2:

[1525] The terminal verifies the data entered by the user and checks for errors. After verification, it sends the correct data to the server. The data sent includes the product ID, purpose, frequency of use, and user ID. The data processing performed here is a consistency check of the input data.

[1526] Step 3:

[1527] The server stores the received data in a database. The stored data includes the product ID, purpose, frequency of use, and user ID. The data calculation here is the process of writing to the database.

[1528] Step 4:

[1529] While the user is inputting data, the device uses a camera and microphone to collect the user's facial expressions and voice. This data is input to an emotion recognition module, which analyzes the user's emotional state (e.g., excitement, anxiety, joy, etc.). The analysis result (emotional state) is returned to the device.

[1530] Step 5:

[1531] The server uses an aging prediction model to predict the future condition of the product based on the stored data on usage and frequency of use. By combining the input data (usage, frequency of use) with product characteristic data, it calculates the deterioration state of the product one year or five years from now. The predicted result provides the product's condition one year or five years from now (e.g., minor scratches, wear, etc.).

[1532] Step 6:

[1533] The server runs a price trend prediction model based on past market data. The input data includes the product ID and current market price, and the server predicts future prices by referencing past market data. The resulting prediction is the market value of the product one or five years from now (e.g., 900,000 yen in one year, 700,000 yen in five years).

[1534] Step 7:

[1535] The server generates images to visually represent the predicted future state of the product. It uses image processing libraries (e.g., OpenCV and matplotlib) to generate images that recreate the appearance of the product one year and five years from now. The generated images are stored on the server.

[1536] Step 8:

[1537] The server customizes the display content based on the emotion data received from the emotion recognition module, for example, highlighting warranty information and positive reviews for anxious users and highlighting features and promotions for excited users.

[1538] Step 9:

[1539] The server transmits the predicted data and the generated image to the terminal, including the future aging state of the product, its market value, the generated appearance image, and customization information.

[1540] Step 10:

[1541] The device displays the received forecast data and the generated image on the user interface, allowing users to visually check the product's condition and market value one or five years from now, allowing them to make purchasing decisions with confidence.

[1542] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1543] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1544] 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 the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1545] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1546] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1547] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1548] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1549] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1550] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1551] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1552] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1553] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1554] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1555] 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.

[1556] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1557] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1558] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1559] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1560] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1561] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1562] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1563] The following is further disclosed regarding the above embodiment.

[1564] (Claim 1)

[1565] an input means for inputting data on the purpose and frequency of use when a user purchases a product;

[1566] a server means for receiving and storing input data;

[1567] a prediction means for predicting a future state of the product using an aging prediction model based on the stored data and the product characteristic data;

[1568] an image generating means for generating an image visually showing the predicted ...

Claims

1. an input means for inputting data on the purpose and frequency of use when a user purchases a product; a server means for receiving and storing input data; a prediction means for predicting a future state of the product using an aging prediction model based on the stored data and the product characteristic data; an image generating means for generating an image visually showing the predicted future state; The server means comprises a price transition prediction means for predicting price transitions based on past market data; a transmitting means for transmitting the prediction data and the generated image to a user terminal; a display means for displaying the predicted data and the generated image in the user terminal; A system including:

2. 2. The system according to claim 1, wherein the server means includes means for calculating a deterioration rate applied to the product based on the user's usage frequency data.

3. 2. The system of claim 1, wherein the server means includes means for using an image processing library to visually indicate the aging state of the product.

Images