system

Abstract

We provide the system. [Solution] A first method for identifying items using image recognition technology, A second means of obtaining information about the aforementioned article from a database, A third method for evaluating the environmental impact and calculating the eco-level based on the aforementioned information, A fourth means of presenting the aforementioned eco-level to the user, A fifth means for taking an image of an object and performing image preprocessing, A sixth means of visually displaying evaluation results for users to understand the environmental impact of goods, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] With the growing concern about environmental issues, many consumers have the problem that it is difficult to make conscious choices because the specific means of action are not clear. In particular, in daily shopping, there is a lack of means to quickly and easily determine the degree to which products consider the environment, resulting in the problem that sustainable consumption behavior does not progress.

Means for Solving the Problems

[0005] This invention provides a system that uses image recognition technology to identify items, evaluates their environmental impact from the acquired information, and calculates their eco-level. This allows consumers to instantly check the environmental friendliness level of a product simply by taking a picture of it with a smartphone or other device, supporting them in making sustainable consumption choices. By using a database on the server side, the system enables the provision of detailed product information and calculation of eco-levels, and presents the results visually to the user, allowing for intuitive understanding of the information.

[0006] "Image recognition technology" is a technology that identifies and analyzes objects from digital images and videos, and uses machine learning and computer vision algorithms.

[0007] "Goods" refer to products or manufactured goods that consumers consider purchasing, and which have characteristics such as labels and shapes.

[0008] "Information" refers to data related to a product, such as its ingredients, manufacturing methods, and the company's environmental policies, which are stored in a database.

[0009] A "database" is an electronic storage system in which specific information is systematically stored, enabling high-speed retrieval and management of that information.

[0010] "Environmental impact" refers to the environmental impact resulting from the manufacture, use, and disposal of goods, and is evaluated based on resource consumption and the amount of emissions.

[0011] "Eco-level" is an indicator that shows the degree of environmental consideration in an item, and it evaluates the environmental impact of a product and expresses it as a numerical value or star rating.

[0012] "Users" refer to consumers who operate the system and use terminals to obtain product information and eco-level data.

[0013] A "terminal" refers to an electronic device used by a user, such as a smartphone or tablet, which is a device that acquires images and displays data. [Brief explanation of the drawing]

[0014] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Mode for Carrying Out the Invention

[0015] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, a processor with a reference numeral (hereinafter simply referred to as "processor") may be one arithmetic unit or a combination of a plurality of arithmetic units. Also, the processor may be one type of arithmetic unit or a combination of a plurality of types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0018] In the following embodiments, a RAM (Random Access Memory) with a reference numeral is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] In the following embodiments, a storage with a reference numeral is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0020] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0021] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0025] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0026] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0027] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0029] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0032] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0033] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0034] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0035] The system based on this invention is for consumers to instantly determine the environmental friendliness of items they are considering purchasing, and consists of a terminal, a server, and a database.

[0036] Users take photos of products while shopping using devices such as smartphones and tablets. The devices have camera functions and capture image data of product labels and shapes. The captured image data is pre-processed on the device and then sent to the server.

[0037] On the server, image recognition technology is used to identify products. Specifically, AI-powered character recognition and shape matching algorithms are used to analyze the characteristics of the items. This identifies which information in the database corresponds to the item in question. This information includes product name, manufacturer, ingredients, and environmental policy.

[0038] Based on the identified product information, the server assesses the environmental impact and calculates the eco-level. Evaluation criteria include the carbon footprint during the manufacturing process, the renewable nature of the materials used, and the product's lifecycle. This allows for a quantitative measurement of the environmental considerations of each product.

[0039] The calculated eco-level is formatted as a numerical score or star rating and sent to the device. The device then presents the received eco-level and related information to the user. Visual displays using graphs and icons are used to ensure intuitive understanding.

[0040] As a concrete example, if a user takes a picture of a shampoo bottle in a supermarket, the server analyzes the shampoo's ingredient information and manufacturing process, and the resulting eco-level is displayed to the user on the device. Based on this result, the user can then decide whether or not to purchase the shampoo.

[0041] Thus, the system of the present invention provides consumers with an innovative means of supporting user-friendly and environmentally conscious choices.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] The user launches the app on their device and takes a picture of the product they are interested in with their camera.

[0045] Step 2:

[0046] The device acquires the captured image data and performs preprocessing to adjust the image resolution and contrast. This process improves the recognition accuracy on the server.

[0047] Step 3:

[0048] The terminal sends the pre-processed image data to the server. Here, data compression is performed to improve communication efficiency.

[0049] Step 4:

[0050] The server uses image recognition technology based on machine learning models to analyze the received image data, identifying product labels and shapes. This analysis identifies the product and matches it with the corresponding information in the database.

[0051] Step 5:

[0052] The server retrieves the identified product information from the database. This information includes the product's ingredients, manufacturer, and environmental policy.

[0053] Step 6:

[0054] The server executes an environmental impact algorithm based on the acquired product information. This algorithm evaluates the environmental impact associated with the manufacturing and disposal of products and calculates the eco-level.

[0055] Step 7:

[0056] The server formats the calculated eco-level as a numerical score or star rating, converting it into an easy-to-understand form.

[0057] Step 8:

[0058] The server sends the formatted eco-level and related information to the terminal.

[0059] Step 9:

[0060] The terminal displays the received information and provides the user with the product's eco-level and related information. Graphs and icons are used to make it visually easy to understand.

[0061] Step 10:

[0062] Users make environmentally conscious product selections based on the information provided. This information serves as a basis for their purchasing decisions.

[0063] (Example 1)

[0064] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0065] Consumers often have limited means to quickly and intuitively understand the environmental impact of products when shopping. In particular, it is difficult to obtain information on environmental impact immediately when selecting products, and it is not easy to include environmental considerations in selection criteria. The present invention aims to provide a system that enables consumers to evaluate the environmental impact of products in real time and to support them in making more sustainable choices.

[0066] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0067] In this invention, the server includes means for acquiring visual information of an item using an image acquisition device, means for preprocessing the acquired visual information to remove noise and extract features, means for analyzing the preprocessed visual information and identifying the item using image recognition technology, means for acquiring information of the identified item from an information recording device, means for evaluating the environmental impact based on the acquired information and calculating the eco-level, and means for presenting the calculated eco-level to the user in a visually interpretable form via a display device. As a result, consumers can quickly evaluate the environmental impact of an item and make environmentally conscious choices simply by taking a picture of the item while shopping.

[0068] An "image acquisition device" is a device for recording visual information of an object, and usually includes optical equipment such as a camera.

[0069] "Visual information" refers to image data and visual characteristics related to an item, including information such as product labels and shape.

[0070] "Preprocessing" is the process of removing noise from acquired visual information and extracting features necessary to improve the accuracy of the analysis.

[0071] "Image recognition technology" is a technology that analyzes visual information and identifies objects based on specific features, and includes the analysis of textual and shape information.

[0072] An "information recording device" is a device for storing various types of information about a specified item, and includes databases and the like.

[0073] "Means for evaluating environmental impact" refers to technologies or processes for quantitatively evaluating the impact of an item on the environment based on its manufacturing process and the characteristics of the materials used.

[0074] "Eco-level" is a numerical or graphical indicator that shows the degree to which an item has an impact on the environment.

[0075] A "display device" is a device that presents the calculated eco-level in a way that allows users to visually confirm it, and generally includes smartphone screens, etc.

[0076] This invention provides a system that allows consumers to instantly evaluate the environmental impact of products while shopping. Users utilize devices such as smartphones and tablets. These devices can acquire visual information about products by utilizing their built-in cameras. The visual information obtained by this image acquisition device is preprocessed on the device. Specifically, noise reduction and feature extraction are performed to improve the accuracy of the analysis of the visual information.

[0077] The terminal transmits pre-processed visual information to the server. The server uses image recognition technology to identify items. This technology identifies products through character recognition and analysis of shape information. The identified product information is compared with data stored in the information recording device, and detailed information about the corresponding product is obtained.

[0078] The server evaluates the environmental impact of acquired product details and calculates an eco-level. The eco-level is an indicator of the product's environmental impact, using manufacturing processes and material recyclability as evaluation criteria. Finally, the calculated eco-level is presented to the user via the terminal's display. Users can visually confirm this and use it as a selection criterion when shopping.

[0079] As a concrete example, when a user takes a picture of a shampoo bottle in a supermarket, the server analyzes the shampoo's ingredients and manufacturing process. Based on these results, an eco-level is calculated and sent to the user's device, which then displays this information to the user in a visual format such as graphs or icons. This allows the user to make a decision about whether or not to purchase the shampoo from an environmental perspective.

[0080] Examples of prompts for the generating AI model include, "What information would be helpful when you want to know the environmental friendliness of a product you plan to buy at the supermarket?" and "How would you like to use the eco-level rating provided by this system in your daily shopping?" This makes it possible to provide information that addresses more specific user needs.

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

[0082] Step 1:

[0083] The user takes a photo of an item of interest using a smartphone or tablet. The input is image data of the item, and the output is an image file generated on the device. Specifically, the user launches the camera app, positions the product's label and shape within the screen, and presses the shutter button to capture the image.

[0084] Step 2:

[0085] The device performs preprocessing on the acquired image data. Here, the input is raw image data, and the output is image data with noise removed and features enhanced. Specifically, the device adjusts the contrast and brightness of the image, trims off unnecessary backgrounds, and emphasizes labels and shape information.

[0086] Step 3:

[0087] The terminal sends pre-processed image data to the server. The input is the pre-processed image data, and the output is the state after the image data has been sent to the server. Specifically, the terminal sends the image to a particular endpoint via internet communication.

[0088] Step 4:

[0089] The server analyzes the received image data and identifies the product using image recognition technology. The input is pre-processed image data, and the output is product identification information. Specifically, the server extracts the text from the label using OCR technology and determines the shape of the product using a shape matching algorithm.

[0090] Step 5:

[0091] The server retrieves product details from the information storage device based on the identified product information. The input is the identified product ID, and the output is the product details. Specifically, the server executes a database query using the product ID to retrieve the data for the corresponding product.

[0092] Step 6:

[0093] The server evaluates the environmental impact based on the acquired product information and calculates the eco-level. The input is detailed product information, and the output is the product's eco-level score. Specifically, the server analyzes the product's manufacturing process data and material information and applies an algorithm to quantify its environmental impact.

[0094] Step 7:

[0095] The server sends the calculated eco-level to the terminal. The input is the eco-level score, and the output is the completion of sending the score to the user terminal. Specifically, the server converts the score into a data format and sends it to the terminal.

[0096] Step 8:

[0097] The terminal displays the received eco-level to the user. The input is the eco-level score, and the output is an eco-level display in a format that is easy for the user to understand. Specifically, the terminal converts the score into a graph or icon format and displays it on the screen to inform the user of the product's environmental friendliness.

[0098] (Application Example 1)

[0099] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0100] In recent years, consumers have placed great importance on environmental considerations when choosing products. However, with so many products on the market, there is a lack of information to understand the environmental impact of each individual product. Therefore, there is a need to provide consumers with a means to quickly and easily check the degree of environmental consideration in products when shopping. This invention aims to meet such consumer needs.

[0101] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0102] In this invention, the server includes means for identifying an item using image recognition technology, means for obtaining information about the item from a database, and means for evaluating the environmental impact based on the information and calculating the eco-level. This makes it possible for consumers to instantly determine the environmental friendliness of a product they are considering purchasing.

[0103] "Image recognition technology" is a technology that analyzes digital image data to identify objects and textual information.

[0104] "Goods" refers to products or goods that consumers consider purchasing or using.

[0105] A "database" is a system for systematically storing specific information and for retrieving or managing that data as needed.

[0106] "Environmental impact" refers to the impact a product has on the environment throughout its entire lifecycle, and includes factors such as carbon footprint and recyclability.

[0107] "Eco-level" is an evaluation index that quantifies the degree of environmental impact of an item, and is expressed in a format that consumers can easily understand.

[0108] "Image preprocessing" refers to the process of preparing captured image data to a state suitable for analysis.

[0109] "Visual display" means providing information visually in a way that makes it easy for users to intuitively understand the information.

[0110] The system that realizes this invention mainly consists of a terminal, a server, and a database. The user uses a terminal such as a smartphone or tablet to take pictures of items they are considering purchasing. The terminal is equipped with a camera that utilizes image recognition technology and software for preprocessing. Specifically, the terminal uses OpenCV to preprocess the captured image data and extract label and shape features.

[0111] The processed image data is sent to a server. The server uses AI models such as TENSORFLOW® and PyTorch to perform image recognition and identify the items. Information about the identified items is retrieved from database systems such as AWS® DynamoDB and MySQL®. Based on the retrieved information, the server evaluates the environmental impact of the items and calculates their eco-level. Criteria such as carbon footprint and renewables are used in this evaluation.

[0112] The calculated eco-level is displayed on the device in a visually and intuitively easy-to-understand format. Users can check the environmental friendliness of a product based on the eco-level, which is displayed as a star rating or numerical score, and decide whether or not to purchase it.

[0113] For example, when a user visits a supermarket and scans the label of an item they're interested in using their smartphone before putting it in their cart, the server instantly calculates the eco-level of that item and assesses its environmental impact. This is achieved through prompts sent to a generative AI model, such as, "Scan the label on this tomato, assess its environmental impact, and tell me its eco-level."

[0114] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0115] Step 1:

[0116] The user takes a picture of an item they are considering purchasing using the device's camera. The input obtained is digital image data. This image data is preprocessed on the device using the OpenCV library to extract labels and shape features. The output is the processed image data.

[0117] Step 2:

[0118] The terminal sends pre-processed image data to the server. The input here is the processed image data sent from the terminal. The server analyzes this data using TensorFlow or PyTorch and identifies objects using image recognition technology. The output is the identification information of the identified objects.

[0119] Step 3:

[0120] The server retrieves detailed information about identified items from databases such as AWS DynamoDB or MySQL, based on the item's identification information. The input is the item's identification information, and the server retrieves information related to the item's environmental impact through database access. The output is the item's detailed information.

[0121] Step 4:

[0122] The server calculates the environmental impact and eco-level based on the detailed information of the retrieved items, using algorithms that evaluate carbon footprint and material renewableness. The input is detailed information of the items, and the output is the eco-level, such as a numerical score or star rating.

[0123] Step 5:

[0124] The server sends the calculated eco-level to the terminal. The terminal displays this information to the user. The input is the eco-level sent from the server, and the output is a display interface that the user can visually confirm. This allows the user to intuitively understand the environmental friendliness of a product and make a purchase decision.

[0125] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0126] The system based on this invention is designed to personalize the consumer's shopping experience and instantly determine the degree of environmental consideration. This system consists of a terminal, a server, a database, and an emotion engine.

[0127] Users begin by taking photos of items they are interested in while shopping using a device such as a smartphone or tablet. The device has a camera function and captures the labels and shapes of the items as image data. The captured image data is pre-processed on the device before being sent to the server. Pre-processing includes noise reduction and image optimization.

[0128] The server identifies items from the transmitted image data using image recognition technology. Specifically, it uses an AI model to analyze the product's label information and shape, and matches it with information in a database. This allows it to obtain detailed information about the item, such as its ingredients, manufacturer, and environmental policy.

[0129] The server calculates the eco-level using an environmental impact algorithm based on identified product information. This eco-level evaluates the carbon footprint of the product's manufacturing process, the renewable nature of the materials, and other factors. The calculated eco-level is formatted as a numerical score or star rating and presented to customers in an easily understandable way.

[0130] Furthermore, the device utilizes its built-in camera and biosensors to analyze the user's emotional state in real time. The emotion engine collects and analyzes data such as the user's facial expressions, voice tone, and heart rate. This allows it to understand the user's emotional state, and the obtained emotional information is sent to the server.

[0131] Based on the information provided by the emotion engine, the server displays information and recommends products that match the user's emotions. For example, it can provide more personalized information, such as suggesting environmentally friendly alternatives to users who show sensitive emotions towards the environment.

[0132] For example, if a user takes a photo of shampoo in a supermarket and the emotion engine detects that the user is showing a happy expression, the system will display not only the shampoo's eco-friendliness level but also positive messages and personalized product recommendations to encourage purchase. Based on these results, users can make informed product choices and engage in environmentally conscious and smart consumption.

[0133] Thus, by combining emotion recognition capabilities, the system of the present invention provides a powerful means to support consumers in making more personalized and environmentally conscious choices.

[0134] The following describes the processing flow.

[0135] Step 1:

[0136] The user launches the app on their device and takes a picture of an item they are interested in in the store. The device then retrieves the captured image data.

[0137] Step 2:

[0138] The device processes the image data and adjusts it to the required resolution and contrast. This pre-processing improves the accuracy of image recognition.

[0139] Step 3:

[0140] The terminal sends pre-processed image data to the server. Data compression technology is used to reduce the amount of data transmitted.

[0141] Step 4:

[0142] The server executes an image recognition algorithm based on the received image data to analyze the product's label and shape. The analyzed features are then compared with a database to identify the product.

[0143] Step 5:

[0144] The server retrieves identified product information from the database. This information includes product ingredients, manufacturer information, and environmental policies.

[0145] Step 6:

[0146] The server uses the acquired product information to run an environmental impact assessment algorithm and calculate the eco-level. This calculation is based on the product's lifecycle and the sustainability of its materials.

[0147] Step 7:

[0148] The server calculates the eco-level, converts it into a score, and sends it to the terminal. Before sending, the information is formatted to be easily understood by the user.

[0149] Step 8:

[0150] Before displaying eco-level information, the device activates an emotion engine to analyze emotional data from the user's facial expressions and voice. This data may also utilize biosensors.

[0151] Step 9:

[0152] The device sends the collected emotional data to the server, which allows for an evaluation of the user's current emotional state.

[0153] Step 10:

[0154] The server analyzes emotional data and adjusts the displayed information and product recommendations to suit the user's emotions. Messages and options are generated according to the user's feelings.

[0155] Step 11:

[0156] The device displays the final eco-level and personalized, emotion-based messages to the user, providing information to help them make a purchase decision.

[0157] Step 12:

[0158] Users select products and engage in environmentally conscious consumption behavior based on the information and recommendations presented.

[0159] (Example 2)

[0160] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0161] In recent years, there has been a growing need for support in consumers' shopping behavior to help them choose products that align with environmental considerations and their personal feelings. However, conventional systems have been insufficient in assessing environmental impact and have been unable to suggest product information based on the user's emotional state. As a result, consumers have faced challenges in making appropriate product choices due to information overload and inappropriate information provision.

[0162] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0163] In this invention, the server includes means for acquiring and pre-processing image data, means for identifying articles using information processing technology with the pre-processed image data, and means for acquiring information about the identified articles from an information storage device. This enables the provision of accurate environmental assessment and sentiment-based product information to support consumer choices.

[0164] "Image data" refers to information captured by a camera device, including visual information about the label and shape of an item.

[0165] "Preprocessing" refers to improvement processes performed on image data, including noise reduction and image quality optimization.

[0166] "Information processing technology" refers to technologies for data analysis using computers, and in particular, includes technologies for identifying items using AI models.

[0167] "Goods" are products sold in the market that include visual information on their labels and packaging.

[0168] An "information storage device" is a device for storing information such as databases, and provides detailed information about items.

[0169] "Environmental impact" refers to the effects that goods have on the environment, including the carbon footprint of the manufacturing process and the sustainability of materials.

[0170] A "sustainability index" is a numerical or evaluation indicator that represents the degree of environmental consideration of an item.

[0171] "Emotional state" refers to the psychological and emotional state of a user, as analyzed from their facial expressions, tone of voice, heart rate, and other factors.

[0172] This invention is a system that uses terminals, servers, and necessary algorithms to personalize the consumer's shopping experience and further assess the degree of environmental consideration. Specifically, the user uses a terminal such as a smartphone or tablet and acquires images of products of interest using the camera. These images undergo pre-processing on the terminal, such as noise reduction and image quality adjustment. Open-source image processing libraries are commonly used for this processing.

[0173] Pre-processed images are sent from the terminal to the server. The server analyzes the image data using information processing technology that utilizes a generative AI model. Specifically, the AI ​​model recognizes the product's label information and shape and compares it with a database. In this process, detailed information about the item, such as ingredients, manufacturer, and environmental policy, is extracted.

[0174] The server then uses the acquired information to calculate a sustainability index. This calculation takes into account the carbon footprint of the product's manufacturing process and the sustainability of the materials used. The calculated sustainability index is returned to the device in a format that is easy for the user to understand, such as a star rating or score.

[0175] Furthermore, the device incorporates biosensors to analyze the user's emotional state. Cameras and voice input devices collect data such as the user's facial expressions, voice, and heart rate, allowing for real-time monitoring of their emotional state. This emotional information is sent to a server, which then provides the user with optimal product information based on their emotions.

[0176] As a concrete example, a user can take a picture of a shampoo product, and the system can calculate and display the product's sustainability index. Simultaneously, based on sentiment analysis, it can suggest more environmentally friendly alternative products. This allows users to make more informed purchasing decisions and supports sustainable consumption behavior.

[0177] An example of a prompt message would be, "Please tell us the eco-level of the products the user is interested in. Please also provide product recommendations based on sentiment analysis."

[0178] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0179] Step 1:

[0180] The user takes a picture of a product they are interested in using the device's camera. This image becomes the input data. The device uses an image processing library to perform pre-processing such as noise reduction and image quality adjustment. This results in outputting image data that is easy to analyze.

[0181] Step 2:

[0182] The terminal sends pre-processed image data to the server. The server analyzes the image data using an image recognition generative AI model. In this process, it identifies product label information and shape, and obtains product information by comparing it with a database. Detailed information about the product is then output.

[0183] Step 3:

[0184] The server calculates sustainability indicators using acquired product information. Input data includes information about the product's manufacturing process and materials used. An environmental impact algorithm evaluates data such as carbon footprint and renewables, and outputs sustainability indicators in the form of numerical values ​​or star ratings.

[0185] Step 4:

[0186] The device uses its built-in camera and biosensors to collect data on the user's facial expressions and heart rate. This data serves as input for analyzing their emotional state. The device then uses emotion analysis software to understand the user's emotional state in real time and sends the analysis results to a server. The output is information indicating the user's emotional state.

[0187] Step 5:

[0188] The server provides users with optimal product information based on the analysis of their emotional state. Input data includes emotional information and product sustainability indicators. Based on this information, it recommends products or alternatives that match the user's emotional state and sends this information to the terminal. The terminal then displays this information to the user, supporting their purchasing decision. This process enables users to engage in more environmentally conscious consumption.

[0189] (Application Example 2)

[0190] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0191] In recent years, while consumers have shown increased interest in environmentally conscious product choices, they face the challenge of selecting the right product from a wide range of options. Furthermore, the inability to provide product recommendations tailored to consumers' immediate emotional states makes it difficult to offer personalized shopping experiences.

[0192] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0193] In this invention, the server includes means for identifying an item using image recognition technology, means for acquiring information about the item from an information storage medium, means for evaluating the environmental impact based on the information and calculating the eco-level, and means for analyzing the user's emotional state and presenting information to the user based on the analysis results. This makes it possible for consumers to easily select products with a low environmental impact and to obtain personalized information that corresponds to their emotional state.

[0194] "Image recognition technology" is a technology that allows computers or machines to extract and analyze specific information from image data.

[0195] "Goods" refers to merchandise or products that consumers purchase or use.

[0196] An "information storage medium" is a recording device that includes servers and databases for storing and managing data and information.

[0197] "Environmental burden" refers to the degree of impact or damage to the natural environment resulting from the production or use of goods.

[0198] "Eco-level" is an index that indicates the degree of environmental consideration based on the manufacturing process and materials of a product.

[0199] "User" refers to a consumer or end-user who utilizes the functions of this system.

[0200] "Emotional state" refers to the user's current emotions and psychological condition, derived from their facial expressions and biometric data.

[0201] "Information presentation" refers to the act of providing users with necessary data and knowledge using methods such as visuals and audio.

[0202] The system for realizing this invention mainly consists of a terminal, a server, a database, and an emotion analysis engine. Users can scan product barcodes and labels using their smartphone camera in physical stores. The terminal analyzes the acquired image data using an image processing library such as OpenCV and digitizes the barcode information.

[0203] The terminal then sends the analyzed data to the server. The server uses the Google® Cloud Vision API to verify the barcode information and retrieves product data from the information storage medium. This data includes product ingredient information, manufacturer information, and details about environmental policies. Based on this data, the server uses an environmental impact algorithm implemented in Python to calculate the product's eco-level and presents it to the user in a numerical format.

[0204] Meanwhile, the device uses its built-in camera and biosensors to analyze the user's emotional state in real time. By utilizing Microsoft® Azure®'s Emotion Recognition API, it identifies emotions based on the user's facial expressions and heart rate data. Based on this emotional information, the server uses a generative AI model to recommend the most suitable products to the user. If the emotional state is positive, it recommends similar products with a lower environmental impact and displays a message to encourage purchase.

[0205] For example, when a user picks up an organic shampoo in a physical store, the application provides information such as, "This shampoo is organically certified and made with environmentally friendly ingredients." If the user shows a happy expression, the server provides personalized recommendations such as, "We also have a conditioner that works well in conjunction with this shampoo."

[0206] Use the following prompts for the generative AI model:

[0207] "Please consider the environmental considerations and suggested alternative product display scenarios when a user scans a product's barcode in a physical store."

[0208] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0209] Step 1:

[0210] The terminal works by having the user photograph the product's barcode or label using their smartphone camera. The image data acquired from the camera is used as input. The terminal uses the OpenCV library to remove noise from the image data and recognize the barcode shape and label information. The output is digitized barcode information.

[0211] Step 2:

[0212] The terminal sends the barcode information acquired in Step 1 to the server. The server uses the received barcode information to call the Google Cloud Vision API and retrieve product information. The input is barcode information, and the output is data that includes ingredient information, manufacturer information, and environmental policy related to the product. The server compares this information with historical data acquired from the information storage medium and selects the most appropriate product information.

[0213] Step 3:

[0214] Based on the product information obtained in Step 2, the server calculates the eco-level using an environmental impact algorithm implemented in Python. Product information is used as input, and a numerical eco-level is obtained as output. The server saves this eco-level to a database and prepares to display it in a visually easy-to-understand format for the user.

[0215] Step 4:

[0216] The device uses its built-in camera and biosensors to acquire the user's emotional state in real time. By utilizing Microsoft Azure's Emotion Recognition API, it takes the user's facial image and heart rate data as input and obtains data indicating their emotional state as output. This allows the device to analyze the user's psychological state and lay the foundation for providing a personalized experience.

[0217] Step 5:

[0218] The server uses the emotional state data obtained in step 4 to generate optimal product information and alternatives for the user using a generative AI model. It utilizes emotional state and eco-level as input and generates personalized recommendations and purchase-promoting messages for the user as output. The prompt used is: "Consider a scenario where the user scans a product barcode in a physical store and is shown the environmental friendliness and recommended alternatives." The server sends this information to the terminal and presents it visually to the user.

[0219] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0220] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include those described above. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions shown by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0221] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0222] [Second Embodiment]

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

[0224] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0225] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0226] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0227] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0228] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0229] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0230] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0231] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0232] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0233] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0234] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0235] The system based on this invention is for consumers to instantly determine the environmental friendliness of items they are considering purchasing, and consists of a terminal, a server, and a database.

[0236] Users take photos of products while shopping using devices such as smartphones and tablets. The devices have camera functions and capture image data of product labels and shapes. The captured image data is pre-processed on the device and then sent to the server.

[0237] On the server, image recognition technology is used to identify products. Specifically, AI-powered character recognition and shape matching algorithms are used to analyze the characteristics of the items. This identifies which information in the database corresponds to the item in question. This information includes product name, manufacturer, ingredients, and environmental policy.

[0238] Based on the identified product information, the server assesses the environmental impact and calculates the eco-level. Evaluation criteria include the carbon footprint during the manufacturing process, the renewable nature of the materials used, and the product's lifecycle. This allows for a quantitative measurement of the environmental considerations of each product.

[0239] The calculated eco-level is formatted as a numerical score or star rating and sent to the device. The device then presents the received eco-level and related information to the user. Visual displays using graphs and icons are used to ensure intuitive understanding.

[0240] As a concrete example, if a user takes a picture of a shampoo bottle in a supermarket, the server analyzes the shampoo's ingredient information and manufacturing process, and the resulting eco-level is displayed to the user on the device. Based on this result, the user can then decide whether or not to purchase the shampoo.

[0241] Thus, the system of the present invention provides consumers with an innovative means of supporting user-friendly and environmentally conscious choices.

[0242] The following describes the processing flow.

[0243] Step 1:

[0244] The user launches the app on their device and takes a picture of the product they are interested in with their camera.

[0245] Step 2:

[0246] The device acquires the captured image data and performs preprocessing to adjust the image resolution and contrast. This process improves the recognition accuracy on the server.

[0247] Step 3:

[0248] The terminal sends the pre-processed image data to the server. Here, data compression is performed to improve communication efficiency.

[0249] Step 4:

[0250] The server uses image recognition technology based on machine learning models to analyze the received image data, identifying product labels and shapes. This analysis identifies the product and matches it with the corresponding information in the database.

[0251] Step 5:

[0252] The server retrieves the identified product information from the database. This information includes the product's ingredients, manufacturer, and environmental policy.

[0253] Step 6:

[0254] The server executes an environmental impact algorithm based on the acquired product information. This algorithm evaluates the environmental impact associated with the manufacturing and disposal of products and calculates the eco-level.

[0255] Step 7:

[0256] The server formats the calculated eco-level as a numerical score or star rating, converting it into an easy-to-understand form.

[0257] Step 8:

[0258] The server sends the formatted eco-level and related information to the terminal.

[0259] Step 9:

[0260] The terminal displays the received information and provides the user with the product's eco-level and related information. Graphs and icons are used to make it visually easy to understand.

[0261] Step 10:

[0262] Users make environmentally conscious product selections based on the information provided. This information serves as a basis for their purchasing decisions.

[0263] (Example 1)

[0264] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0265] Consumers often have limited means to quickly and intuitively understand the environmental impact of products when shopping. In particular, it is difficult to obtain information on environmental impact immediately when selecting products, and it is not easy to include environmental considerations in selection criteria. The present invention aims to provide a system that enables consumers to evaluate the environmental impact of products in real time and to support them in making more sustainable choices.

[0266] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0267] In this invention, the server includes means for acquiring visual information of an item using an image acquisition device, means for preprocessing the acquired visual information to remove noise and extract features, means for analyzing the preprocessed visual information and identifying the item using image recognition technology, means for acquiring information of the identified item from an information recording device, means for evaluating the environmental impact based on the acquired information and calculating the eco-level, and means for presenting the calculated eco-level to the user in a visually interpretable form via a display device. As a result, consumers can quickly evaluate the environmental impact of an item and make environmentally conscious choices simply by taking a picture of the item while shopping.

[0268] An "image acquisition device" is a device for recording visual information of an object, and usually includes optical equipment such as a camera.

[0269] "Visual information" refers to image data and visual characteristics related to an item, including information such as product labels and shape.

[0270] "Preprocessing" is the process of removing noise from acquired visual information and extracting features necessary to improve the accuracy of the analysis.

[0271] "Image recognition technology" is a technology that analyzes visual information and identifies objects based on specific features, and includes the analysis of textual and shape information.

[0272] An "information recording device" is a device for storing various types of information about a specified item, and includes databases and the like.

[0273] "Means for evaluating environmental impact" refers to technologies or processes for quantitatively evaluating the impact of an item on the environment based on its manufacturing process and the characteristics of the materials used.

[0274] "Eco-level" is a numerical or graphical indicator that shows the degree to which an item has an impact on the environment.

[0275] A "display device" is a device that presents the calculated eco-level in a way that allows users to visually confirm it, and generally includes smartphone screens, etc.

[0276] This invention provides a system that allows consumers to instantly evaluate the environmental impact of products while shopping. Users utilize devices such as smartphones and tablets. These devices can acquire visual information about products by utilizing their built-in cameras. The visual information obtained by this image acquisition device is preprocessed on the device. Specifically, noise reduction and feature extraction are performed to improve the accuracy of the analysis of the visual information.

[0277] The terminal transmits pre-processed visual information to the server. The server uses image recognition technology to identify items. This technology identifies products through character recognition and analysis of shape information. The identified product information is compared with data stored in the information recording device, and detailed information about the corresponding product is obtained.

[0278] The server evaluates the environmental impact of acquired product details and calculates an eco-level. The eco-level is an indicator of the product's environmental impact, using manufacturing processes and material recyclability as evaluation criteria. Finally, the calculated eco-level is presented to the user via the terminal's display. Users can visually confirm this and use it as a selection criterion when shopping.

[0279] As a concrete example, when a user takes a picture of a shampoo bottle in a supermarket, the server analyzes the shampoo's ingredients and manufacturing process. Based on these results, an eco-level is calculated and sent to the user's device, which then displays this information to the user in a visual format such as graphs or icons. This allows the user to make a decision about whether or not to purchase the shampoo from an environmental perspective.

[0280] Examples of prompts for the generating AI model include, "What information would be helpful when you want to know the environmental friendliness of a product you plan to buy at the supermarket?" and "How would you like to use the eco-level rating provided by this system in your daily shopping?" This makes it possible to provide information that addresses more specific user needs.

[0281] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0282] Step 1:

[0283] The user takes a photo of an interesting product using a smartphone or tablet. The input is the image data of the item, and an image file is generated as the output on the terminal. As a specific operation, the user launches the camera app, fits the product label and shape within the screen, and presses the shutter button to obtain the image.

[0284] Step 2:

[0285] The terminal performs preprocessing on the acquired image data. Here, the input is the raw image data, and the output is the image data with noise removed and features enhanced. As a specific operation, the terminal adjusts the contrast and brightness of the image, trims the unnecessary background, and emphasizes the label and shape information.

[0286] Step 3:

[0287] The terminal sends the preprocessed image data to the server. The input is the preprocessed image data, and the output is the state where the transmission of the image data to the server is completed. As a specific operation, the terminal sends the image to a specific endpoint through Internet communication.

[0288] Step 4:

[0289] The server analyzes the received image data and identifies the product using image recognition technology. The input is the preprocessed image data, and the output is the product identification information. As a specific operation, the server uses OCR technology to extract the text of the label and discriminates the shape of the product using a shape matching algorithm.

[0290] Step 5:

[0291] The server obtains the product details from the information recording device based on the identified product information. The input is the identified product ID, and the output is the detailed information of the product. As a specific operation, the server executes a database query using the product ID and retrieves the data of the corresponding product.

[0292] Step 6:

[0293] The server evaluates the environmental impact based on the acquired product information and calculates the eco-level. The input is detailed product information, and the output is the product's eco-level score. Specifically, the server analyzes the product's manufacturing process data and material information and applies an algorithm to quantify its environmental impact.

[0294] Step 7:

[0295] The server sends the calculated eco-level to the terminal. The input is the eco-level score, and the output is the completion of sending the score to the user terminal. Specifically, the server converts the score into a data format and sends it to the terminal.

[0296] Step 8:

[0297] The terminal displays the received eco-level to the user. The input is the eco-level score, and the output is an eco-level display in a format that is easy for the user to understand. Specifically, the terminal converts the score into a graph or icon format and displays it on the screen to inform the user of the product's environmental friendliness.

[0298] (Application Example 1)

[0299] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0300] In recent years, consumers have placed great importance on environmental considerations when choosing products. However, with so many products on the market, there is a lack of information to understand the environmental impact of each individual product. Therefore, there is a need to provide consumers with a means to quickly and easily check the degree of environmental consideration in products when shopping. This invention aims to meet such consumer needs.

[0301] The specific processing by the specific processing unit 290 of the data processing apparatus 12 in Application Example 1 is realized by the following means.

[0302] In this invention, the server includes means for identifying an article using image recognition technology, means for obtaining information of the article from a database, and means for evaluating an environmental load based on the information and calculating an eco-level. Thereby, it becomes possible to immediately determine the environmental consideration level of a product that a consumer is considering purchasing.

[0303] "Image recognition technology" is a technology for analyzing digital image data and identifying articles or character information.

[0304] "Article" refers to a product or commodity that a consumer is considering purchasing or using.

[0305] "Database" is a system for systematically storing specific information and obtaining or managing data as needed.

[0306] "Environmental load" refers to the impact that a product gives to the environment throughout its entire life cycle, including carbon footprint and recyclability.

[0307] "Eco-level" is an evaluation index that quantifies the degree of the load that an article imposes on the environment and is expressed in a form that is easily understandable by consumers.

[0308] "Image preprocessing" is a process for preparing the photographed image data in a state suitable for analysis.

[0309] [[ID=3,2]]"Visually display" means providing information visually so that users can intuitively understand the information easily. <*

[0310] The system that realizes this invention mainly consists of a terminal, a server, and a database. The user uses a terminal such as a smartphone or tablet to take pictures of items they are considering purchasing. The terminal is equipped with a camera that utilizes image recognition technology and software for preprocessing. Specifically, the terminal uses OpenCV to preprocess the captured image data and extract label and shape features.

[0311] The processed image data is sent to a server. The server uses AI models such as TensorFlow and PyTorch to identify objects using image recognition technology. Information about the identified objects is retrieved from database systems such as AWS DynamoDB and MySQL. Based on the retrieved information, the server evaluates the environmental impact of the objects and calculates their eco-level. Criteria such as carbon footprint and renewables are used in this evaluation.

[0312] The calculated eco-level is displayed on the device in a visually and intuitively easy-to-understand format. Users can check the environmental friendliness of a product based on the eco-level, which is displayed as a star rating or numerical score, and decide whether or not to purchase it.

[0313] For example, when a user visits a supermarket and scans the label of an item they're interested in using their smartphone before putting it in their cart, the server instantly calculates the eco-level of that item and assesses its environmental impact. This is achieved through prompts sent to a generative AI model, such as, "Scan the label on this tomato, assess its environmental impact, and tell me its eco-level."

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

[0315] Step 1:

[0316] The user takes a picture of an item they are considering purchasing using the device's camera. The input obtained is digital image data. This image data is preprocessed on the device using the OpenCV library to extract labels and shape features. The output is the processed image data.

[0317] Step 2:

[0318] The terminal sends pre-processed image data to the server. The input here is the processed image data sent from the terminal. The server analyzes this data using TensorFlow or PyTorch and identifies objects using image recognition technology. The output is the identification information of the identified objects.

[0319] Step 3:

[0320] The server retrieves detailed information about identified items from databases such as AWS DynamoDB or MySQL, based on the item's identification information. The input is the item's identification information, and the server retrieves information related to the item's environmental impact through database access. The output is the item's detailed information.

[0321] Step 4:

[0322] The server calculates the environmental impact and eco-level based on the detailed information of the retrieved items, using algorithms that evaluate carbon footprint and material renewableness. The input is detailed information of the items, and the output is the eco-level, such as a numerical score or star rating.

[0323] Step 5:

[0324] The server sends the calculated eco-level to the terminal. The terminal displays this information to the user. The input is the eco-level sent from the server, and the output is a display interface that the user can visually confirm. This allows the user to intuitively understand the environmental friendliness of a product and make a purchase decision.

[0325] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0326] The system based on this invention is designed to personalize the consumer's shopping experience and instantly determine the degree of environmental consideration. This system consists of a terminal, a server, a database, and an emotion engine.

[0327] Users begin by taking photos of items they are interested in while shopping using a device such as a smartphone or tablet. The device has a camera function and captures the labels and shapes of the items as image data. The captured image data is pre-processed on the device before being sent to the server. Pre-processing includes noise reduction and image optimization.

[0328] The server identifies items from the transmitted image data using image recognition technology. Specifically, it uses an AI model to analyze the product's label information and shape, and matches it with information in a database. This allows it to obtain detailed information about the item, such as its ingredients, manufacturer, and environmental policy.

[0329] The server calculates the eco-level using an environmental impact algorithm based on identified product information. This eco-level evaluates the carbon footprint of the product's manufacturing process, the renewable nature of the materials, and other factors. The calculated eco-level is formatted as a numerical score or star rating and presented to customers in an easily understandable way.

[0330] Furthermore, the device utilizes its built-in camera and biosensors to analyze the user's emotional state in real time. The emotion engine collects and analyzes data such as the user's facial expressions, voice tone, and heart rate. This allows it to understand the user's emotional state, and the obtained emotional information is sent to the server.

[0331] Based on the information provided by the emotion engine, the server displays information and recommends products that match the user's emotions. For example, it can provide more personalized information, such as suggesting environmentally friendly alternatives to users who show sensitive emotions towards the environment.

[0332] For example, if a user takes a photo of shampoo in a supermarket and the emotion engine detects that the user is showing a happy expression, the system will display not only the shampoo's eco-friendliness level but also positive messages and personalized product recommendations to encourage purchase. Based on these results, users can make informed product choices and engage in environmentally conscious and smart consumption.

[0333] Thus, by combining emotion recognition capabilities, the system of the present invention provides a powerful means to support consumers in making more personalized and environmentally conscious choices.

[0334] The following describes the processing flow.

[0335] Step 1:

[0336] The user launches the app on their device and takes a picture of an item they are interested in in the store. The device then retrieves the captured image data.

[0337] Step 2:

[0338] The device processes the image data and adjusts it to the required resolution and contrast. This pre-processing improves the accuracy of image recognition.

[0339] Step 3:

[0340] The terminal sends pre-processed image data to the server. Data compression technology is used to reduce the amount of data transmitted.

[0341] Step 4:

[0342] The server executes an image recognition algorithm based on the received image data to analyze the product's label and shape. The analyzed features are then compared with a database to identify the product.

[0343] Step 5:

[0344] The server retrieves identified product information from the database. This information includes product ingredients, manufacturer information, and environmental policies.

[0345] Step 6:

[0346] The server uses the acquired product information to run an environmental impact assessment algorithm and calculate the eco-level. This calculation is based on the product's lifecycle and the sustainability of its materials.

[0347] Step 7:

[0348] The server calculates the eco-level, converts it into a score, and sends it to the terminal. Before sending, the information is formatted to be easily understood by the user.

[0349] Step 8:

[0350] Before displaying eco-level information, the device activates an emotion engine to analyze emotional data from the user's facial expressions and voice. This data may also utilize biosensors.

[0351] Step 9:

[0352] The device sends the collected emotional data to the server, which allows for an evaluation of the user's current emotional state.

[0353] Step 10:

[0354] The server analyzes emotional data and adjusts the displayed information and product recommendations to suit the user's emotions. Messages and options are generated according to the user's feelings.

[0355] Step 11:

[0356] The device displays the final eco-level and personalized, emotion-based messages to the user, providing information to help them make a purchase decision.

[0357] Step 12:

[0358] Users select products and engage in environmentally conscious consumption behavior based on the information and recommendations presented.

[0359] (Example 2)

[0360] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0361] In recent years, there has been a growing need for support in consumers' shopping behavior to help them choose products that align with environmental considerations and their personal feelings. However, conventional systems have been insufficient in assessing environmental impact and have been unable to suggest product information based on the user's emotional state. As a result, consumers have faced challenges in making appropriate product choices due to information overload and inappropriate information provision.

[0362] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0363] In this invention, the server includes means for acquiring and pre-processing image data, means for identifying articles using information processing technology with the pre-processed image data, and means for acquiring information about the identified articles from an information storage device. This enables the provision of accurate environmental assessment and sentiment-based product information to support consumer choices.

[0364] "Image data" refers to information captured by a camera device, including visual information about the label and shape of an item.

[0365] "Preprocessing" refers to improvement processes performed on image data, including noise reduction and image quality optimization.

[0366] "Information processing technology" refers to technologies for data analysis using computers, and in particular, includes technologies for identifying items using AI models.

[0367] "Goods" are products sold in the market that include visual information on their labels and packaging.

[0368] An "information storage device" is a device for storing information such as databases, and provides detailed information about items.

[0369] "Environmental impact" refers to the effects that goods have on the environment, including the carbon footprint of the manufacturing process and the sustainability of materials.

[0370] A "sustainability index" is a numerical or evaluation indicator that represents the degree of environmental consideration of an item.

[0371] "Emotional state" refers to the psychological and emotional state of a user, as analyzed from their facial expressions, tone of voice, heart rate, and other factors.

[0372] This invention is a system that uses terminals, servers, and necessary algorithms to personalize the consumer's shopping experience and further assess the degree of environmental consideration. Specifically, the user uses a terminal such as a smartphone or tablet and acquires images of products of interest using the camera. These images undergo pre-processing on the terminal, such as noise reduction and image quality adjustment. Open-source image processing libraries are commonly used for this processing.

[0373] Pre-processed images are sent from the terminal to the server. The server analyzes the image data using information processing technology that utilizes a generative AI model. Specifically, the AI ​​model recognizes the product's label information and shape and compares it with a database. In this process, detailed information about the item, such as ingredients, manufacturer, and environmental policy, is extracted.

[0374] The server then uses the acquired information to calculate a sustainability index. This calculation takes into account the carbon footprint of the product's manufacturing process and the sustainability of the materials used. The calculated sustainability index is returned to the device in a format that is easy for the user to understand, such as a star rating or score.

[0375] Furthermore, the device incorporates biosensors to analyze the user's emotional state. Cameras and voice input devices collect data such as the user's facial expressions, voice, and heart rate, allowing for real-time monitoring of their emotional state. This emotional information is sent to a server, which then provides the user with optimal product information based on their emotions.

[0376] As a concrete example, a user can take a picture of a shampoo product, and the system can calculate and display the product's sustainability index. Simultaneously, based on sentiment analysis, it can suggest more environmentally friendly alternative products. This allows users to make more informed purchasing decisions and supports sustainable consumption behavior.

[0377] An example of a prompt message would be, "Please tell us the eco-level of the products the user is interested in. Please also provide product recommendations based on sentiment analysis."

[0378] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0379] Step 1:

[0380] The user takes a picture of a product they are interested in using the device's camera. This image becomes the input data. The device uses an image processing library to perform pre-processing such as noise reduction and image quality adjustment. This results in outputting image data that is easy to analyze.

[0381] Step 2:

[0382] The terminal sends pre-processed image data to the server. The server analyzes the image data using an image recognition generative AI model. In this process, it identifies product label information and shape, and obtains product information by comparing it with a database. Detailed information about the product is then output.

[0383] Step 3:

[0384] The server calculates sustainability indicators using acquired product information. Input data includes information about the product's manufacturing process and materials used. An environmental impact algorithm evaluates data such as carbon footprint and renewables, and outputs sustainability indicators in the form of numerical values ​​or star ratings.

[0385] Step 4:

[0386] The device uses its built-in camera and biosensors to collect data on the user's facial expressions and heart rate. This data serves as input for analyzing their emotional state. The device then uses emotion analysis software to understand the user's emotional state in real time and sends the analysis results to a server. The output is information indicating the user's emotional state.

[0387] Step 5:

[0388] The server provides users with optimal product information based on the analysis of their emotional state. Input data includes emotional information and product sustainability indicators. Based on this information, it recommends products or alternatives that match the user's emotional state and sends this information to the terminal. The terminal then displays this information to the user, supporting their purchasing decision. This process enables users to engage in more environmentally conscious consumption.

[0389] (Application Example 2)

[0390] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0391] In recent years, while consumers have shown increased interest in environmentally conscious product choices, they face the challenge of selecting the right product from a wide range of options. Furthermore, the inability to provide product recommendations tailored to consumers' immediate emotional states makes it difficult to offer personalized shopping experiences.

[0392] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0393] In this invention, the server includes means for identifying an item using image recognition technology, means for acquiring information about the item from an information storage medium, means for evaluating the environmental impact based on the information and calculating the eco-level, and means for analyzing the user's emotional state and presenting information to the user based on the analysis results. This makes it possible for consumers to easily select products with a low environmental impact and to obtain personalized information that corresponds to their emotional state.

[0394] "Image recognition technology" is a technology that allows computers or machines to extract and analyze specific information from image data.

[0395] "Goods" refers to merchandise or products that consumers purchase or use.

[0396] An "information storage medium" is a recording device that includes servers and databases for storing and managing data and information.

[0397] "Environmental burden" refers to the degree of impact or damage to the natural environment resulting from the production or use of goods.

[0398] "Eco-level" is an index that indicates the degree of environmental consideration based on the manufacturing process and materials of a product.

[0399] "User" refers to a consumer or end-user who utilizes the functions of this system.

[0400] "Emotional state" refers to the user's current emotions and psychological condition, derived from their facial expressions and biometric data.

[0401] "Information presentation" refers to the act of providing users with necessary data and knowledge using methods such as visuals and audio.

[0402] The system for realizing this invention mainly consists of a terminal, a server, a database, and an emotion analysis engine. Users can scan product barcodes and labels using their smartphone camera in physical stores. The terminal analyzes the acquired image data using an image processing library such as OpenCV and digitizes the barcode information.

[0403] The terminal then sends the analyzed data to the server. The server uses the Google Cloud Vision API to verify the barcode information and retrieves product data from the information storage medium. This data includes product ingredient information, manufacturer information, and details about environmental policies. Based on this data, the server uses an environmental impact algorithm implemented in Python to calculate the product's eco-level and presents it to the user in a numerical format.

[0404] Meanwhile, the device uses its built-in camera and biosensors to analyze the user's emotional state in real time. By utilizing Microsoft Azure's Emotion Recognition API, it identifies emotions based on the user's facial expressions and heart rate data. Based on this emotional information, the server uses a generative AI model to recommend the most suitable products to the user. If the emotional state is positive, it recommends similar products with a lower environmental impact and displays a message to encourage purchase.

[0405] For example, when a user picks up an organic shampoo in a physical store, the application provides information such as, "This shampoo is organically certified and made with environmentally friendly ingredients." If the user shows a happy expression, the server provides personalized recommendations such as, "We also have a conditioner that works well in conjunction with this shampoo."

[0406] Use the following prompts for the generative AI model:

[0407] "Please consider the environmental considerations and suggested alternative product display scenarios when a user scans a product's barcode in a physical store."

[0408] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0409] Step 1:

[0410] The terminal works by having the user photograph the product's barcode or label using their smartphone camera. The image data acquired from the camera is used as input. The terminal uses the OpenCV library to remove noise from the image data and recognize the barcode shape and label information. The output is digitized barcode information.

[0411] Step 2:

[0412] The terminal sends the barcode information acquired in Step 1 to the server. The server uses the received barcode information to call the Google Cloud Vision API and retrieve product information. The input is barcode information, and the output is data that includes ingredient information, manufacturer information, and environmental policy related to the product. The server compares this information with historical data acquired from the information storage medium and selects the most appropriate product information.

[0413] Step 3:

[0414] Based on the product information obtained in Step 2, the server calculates the eco-level using an environmental impact algorithm implemented in Python. Product information is used as input, and a numerical eco-level is obtained as output. The server saves this eco-level to a database and prepares to display it in a visually easy-to-understand format for the user.

[0415] Step 4:

[0416] The device uses its built-in camera and biosensors to acquire the user's emotional state in real time. By utilizing Microsoft Azure's Emotion Recognition API, it takes the user's facial image and heart rate data as input and obtains data indicating their emotional state as output. This allows the device to analyze the user's psychological state and lay the foundation for providing a personalized experience.

[0417] Step 5:

[0418] The server uses the emotional state data obtained in step 4 to generate optimal product information and alternatives for the user using a generative AI model. It utilizes emotional state and eco-level as input and generates personalized recommendations and purchase-promoting messages for the user as output. The prompt used is: "Consider a scenario where the user scans a product barcode in a physical store and is shown the environmental friendliness and recommended alternatives." The server sends this information to the terminal and presents it visually to the user.

[0419] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0420] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include those described above. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions shown by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0421] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0422] [Third Embodiment]

[0423] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0424] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0425] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0426] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0427] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0428] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0429] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0430] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0431] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0432] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0433] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0434] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0435] The system based on this invention is for consumers to instantly determine the environmental friendliness of items they are considering purchasing, and consists of a terminal, a server, and a database.

[0436] Users take photos of products while shopping using devices such as smartphones and tablets. The devices have camera functions and capture image data of product labels and shapes. The captured image data is pre-processed on the device and then sent to the server.

[0437] On the server, image recognition technology is used to identify products. Specifically, AI-powered character recognition and shape matching algorithms are used to analyze the characteristics of the items. This identifies which information in the database corresponds to the item in question. This information includes product name, manufacturer, ingredients, and environmental policy.

[0438] Based on the identified product information, the server assesses the environmental impact and calculates the eco-level. Evaluation criteria include the carbon footprint during the manufacturing process, the renewable nature of the materials used, and the product's lifecycle. This allows for a quantitative measurement of the environmental considerations of each product.

[0439] The calculated eco-level is formatted as a numerical score or star rating and sent to the device. The device then presents the received eco-level and related information to the user. Visual displays using graphs and icons are used to ensure intuitive understanding.

[0440] As a concrete example, if a user takes a picture of a shampoo bottle in a supermarket, the server analyzes the shampoo's ingredient information and manufacturing process, and the resulting eco-level is displayed to the user on the device. Based on this result, the user can then decide whether or not to purchase the shampoo.

[0441] Thus, the system of the present invention provides consumers with an innovative means of supporting user-friendly and environmentally conscious choices.

[0442] The following describes the processing flow.

[0443] Step 1:

[0444] The user launches the app on their device and takes a picture of the product they are interested in with their camera.

[0445] Step 2:

[0446] The device acquires the captured image data and performs preprocessing to adjust the image resolution and contrast. This process improves the recognition accuracy on the server.

[0447] Step 3:

[0448] The terminal sends the pre-processed image data to the server. Here, data compression is performed to improve communication efficiency.

[0449] Step 4:

[0450] The server uses image recognition technology based on machine learning models to analyze the received image data, identifying product labels and shapes. This analysis identifies the product and matches it with the corresponding information in the database.

[0451] Step 5:

[0452] The server retrieves the identified product information from the database. This information includes the product's ingredients, manufacturer, and environmental policy.

[0453] Step 6:

[0454] The server executes an environmental impact algorithm based on the acquired product information. This algorithm evaluates the environmental impact associated with the manufacturing and disposal of products and calculates the eco-level.

[0455] Step 7:

[0456] The server formats the calculated eco-level as a numerical score or star rating, converting it into an easy-to-understand form.

[0457] Step 8:

[0458] The server sends the formatted eco-level and related information to the terminal.

[0459] Step 9:

[0460] The terminal displays the received information and provides the user with the product's eco-level and related information. Graphs and icons are used to make it visually easy to understand.

[0461] Step 10:

[0462] Users make environmentally conscious product selections based on the information provided. This information serves as a basis for their purchasing decisions.

[0463] (Example 1)

[0464] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0465] Consumers often have limited means to quickly and intuitively understand the environmental impact of products when shopping. In particular, it is difficult to obtain information on environmental impact immediately when selecting products, and it is not easy to include environmental considerations in selection criteria. The present invention aims to provide a system that enables consumers to evaluate the environmental impact of products in real time and to support them in making more sustainable choices.

[0466] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0467] In this invention, the server includes means for acquiring visual information of an item using an image acquisition device, means for preprocessing the acquired visual information to remove noise and extract features, means for analyzing the preprocessed visual information and identifying the item using image recognition technology, means for acquiring information of the identified item from an information recording device, means for evaluating the environmental impact based on the acquired information and calculating the eco-level, and means for presenting the calculated eco-level to the user in a visually interpretable form via a display device. As a result, consumers can quickly evaluate the environmental impact of an item and make environmentally conscious choices simply by taking a picture of the item while shopping.

[0468] An "image acquisition device" is a device for recording visual information of an object, and usually includes optical equipment such as a camera.

[0469] "Visual information" refers to image data and visual characteristics related to an item, including information such as product labels and shape.

[0470] "Preprocessing" is the process of removing noise from acquired visual information and extracting features necessary to improve the accuracy of the analysis.

[0471] "Image recognition technology" is a technology that analyzes visual information and identifies objects based on specific features, and includes the analysis of textual and shape information.

[0472] An "information recording device" is a device for storing various types of information about a specified item, and includes databases and the like.

[0473] "Means for evaluating environmental impact" refers to technologies or processes for quantitatively evaluating the impact of an item on the environment based on its manufacturing process and the characteristics of the materials used.

[0474] "Eco-level" is a numerical or graphical indicator that shows the degree to which an item has an impact on the environment.

[0475] A "display device" is a device that presents the calculated eco-level in a way that allows users to visually confirm it, and generally includes smartphone screens, etc.

[0476] This invention provides a system that allows consumers to instantly evaluate the environmental impact of products while shopping. Users utilize devices such as smartphones and tablets. These devices can acquire visual information about products by utilizing their built-in cameras. The visual information obtained by this image acquisition device is preprocessed on the device. Specifically, noise reduction and feature extraction are performed to improve the accuracy of the analysis of the visual information.

[0477] The terminal transmits pre-processed visual information to the server. The server uses image recognition technology to identify items. This technology identifies products through character recognition and analysis of shape information. The identified product information is compared with data stored in the information recording device, and detailed information about the corresponding product is obtained.

[0478] The server evaluates the environmental impact of acquired product details and calculates an eco-level. The eco-level is an indicator of the product's environmental impact, using manufacturing processes and material recyclability as evaluation criteria. Finally, the calculated eco-level is presented to the user via the terminal's display. Users can visually confirm this and use it as a selection criterion when shopping.

[0479] As a concrete example, when a user takes a picture of a shampoo bottle in a supermarket, the server analyzes the shampoo's ingredients and manufacturing process. Based on these results, an eco-level is calculated and sent to the user's device, which then displays this information to the user in a visual format such as graphs or icons. This allows the user to make a decision about whether or not to purchase the shampoo from an environmental perspective.

[0480] Examples of prompts for the generating AI model include, "What information would be helpful when you want to know the environmental friendliness of a product you plan to buy at the supermarket?" and "How would you like to use the eco-level rating provided by this system in your daily shopping?" This makes it possible to provide information that addresses more specific user needs.

[0481] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0482] Step 1:

[0483] The user takes a photo of an item of interest using a smartphone or tablet. The input is image data of the item, and the output is an image file generated on the device. Specifically, the user launches the camera app, positions the product's label and shape within the screen, and presses the shutter button to capture the image.

[0484] Step 2:

[0485] The device performs preprocessing on the acquired image data. Here, the input is raw image data, and the output is image data with noise removed and features enhanced. Specifically, the device adjusts the contrast and brightness of the image, trims off unnecessary backgrounds, and emphasizes labels and shape information.

[0486] Step 3:

[0487] The terminal sends pre-processed image data to the server. The input is the pre-processed image data, and the output is the state after the image data has been sent to the server. Specifically, the terminal sends the image to a particular endpoint via internet communication.

[0488] Step 4:

[0489] The server analyzes the received image data and identifies the product using image recognition technology. The input is pre-processed image data, and the output is product identification information. Specifically, the server extracts the text from the label using OCR technology and determines the shape of the product using a shape matching algorithm.

[0490] Step 5:

[0491] The server retrieves product details from the information storage device based on the identified product information. The input is the identified product ID, and the output is the product details. Specifically, the server executes a database query using the product ID to retrieve the data for the corresponding product.

[0492] Step 6:

[0493] The server evaluates the environmental impact based on the acquired product information and calculates the eco-level. The input is detailed product information, and the output is the product's eco-level score. Specifically, the server analyzes the product's manufacturing process data and material information and applies an algorithm to quantify its environmental impact.

[0494] Step 7:

[0495] The server sends the calculated eco-level to the terminal. The input is the eco-level score, and the output is the completion of sending the score to the user terminal. Specifically, the server converts the score into a data format and sends it to the terminal.

[0496] Step 8:

[0497] The terminal displays the received eco-level to the user. The input is the eco-level score, and the output is an eco-level display in a format that is easy for the user to understand. Specifically, the terminal converts the score into a graph or icon format and displays it on the screen to inform the user of the product's environmental friendliness.

[0498] (Application Example 1)

[0499] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0500] In recent years, consumers have placed great importance on environmental considerations when choosing products. However, with so many products on the market, there is a lack of information to understand the environmental impact of each individual product. Therefore, there is a need to provide consumers with a means to quickly and easily check the degree of environmental consideration in products when shopping. This invention aims to meet such consumer needs.

[0501] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0502] In this invention, the server includes means for identifying an item using image recognition technology, means for obtaining information about the item from a database, and means for evaluating the environmental impact based on the information and calculating the eco-level. This makes it possible for consumers to instantly determine the environmental friendliness of a product they are considering purchasing.

[0503] "Image recognition technology" is a technology that analyzes digital image data to identify objects and textual information.

[0504] "Goods" refers to products or goods that consumers consider purchasing or using.

[0505] A "database" is a system for systematically storing specific information and for retrieving or managing that data as needed.

[0506] "Environmental impact" refers to the impact a product has on the environment throughout its entire lifecycle, and includes factors such as carbon footprint and recyclability.

[0507] "Eco-level" is an evaluation index that quantifies the degree of environmental impact of an item, and is expressed in a format that consumers can easily understand.

[0508] "Image preprocessing" refers to the process of preparing captured image data to a state suitable for analysis.

[0509] "Visual display" means providing information visually in a way that makes it easy for users to intuitively understand the information.

[0510] The system that realizes this invention mainly consists of a terminal, a server, and a database. The user uses a terminal such as a smartphone or tablet to take pictures of items they are considering purchasing. The terminal is equipped with a camera that utilizes image recognition technology and software for preprocessing. Specifically, the terminal uses OpenCV to preprocess the captured image data and extract label and shape features.

[0511] The processed image data is sent to a server. The server uses AI models such as TensorFlow and PyTorch to identify objects using image recognition technology. Information about the identified objects is retrieved from database systems such as AWS DynamoDB and MySQL. Based on the retrieved information, the server evaluates the environmental impact of the objects and calculates their eco-level. Criteria such as carbon footprint and renewables are used in this evaluation.

[0512] The calculated eco-level is displayed on the device in a visually and intuitively easy-to-understand format. Users can check the environmental friendliness of a product based on the eco-level, which is displayed as a star rating or numerical score, and decide whether or not to purchase it.

[0513] For example, when a user visits a supermarket and scans the label of an item they're interested in using their smartphone before putting it in their cart, the server instantly calculates the eco-level of that item and assesses its environmental impact. This is achieved through prompts sent to a generative AI model, such as, "Scan the label on this tomato, assess its environmental impact, and tell me its eco-level."

[0514] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0515] Step 1:

[0516] The user takes a picture of an item they are considering purchasing using the device's camera. The input obtained is digital image data. This image data is preprocessed on the device using the OpenCV library to extract labels and shape features. The output is the processed image data.

[0517] Step 2:

[0518] The terminal sends pre-processed image data to the server. The input here is the processed image data sent from the terminal. The server analyzes this data using TensorFlow or PyTorch and identifies objects using image recognition technology. The output is the identification information of the identified objects.

[0519] Step 3:

[0520] The server retrieves detailed information about identified items from databases such as AWS DynamoDB or MySQL, based on the item's identification information. The input is the item's identification information, and the server retrieves information related to the item's environmental impact through database access. The output is the item's detailed information.

[0521] Step 4:

[0522] The server calculates the environmental impact and eco-level based on the detailed information of the retrieved items, using algorithms that evaluate carbon footprint and material renewableness. The input is detailed information of the items, and the output is the eco-level, such as a numerical score or star rating.

[0523] Step 5:

[0524] The server sends the calculated eco-level to the terminal. The terminal displays this information to the user. The input is the eco-level sent from the server, and the output is a display interface that the user can visually confirm. This allows the user to intuitively understand the environmental friendliness of a product and make a purchase decision.

[0525] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0526] The system based on this invention is designed to personalize the consumer's shopping experience and instantly determine the degree of environmental consideration. This system consists of a terminal, a server, a database, and an emotion engine.

[0527] Users begin by taking photos of items they are interested in while shopping using a device such as a smartphone or tablet. The device has a camera function and captures the labels and shapes of the items as image data. The captured image data is pre-processed on the device before being sent to the server. Pre-processing includes noise reduction and image optimization.

[0528] The server identifies items from the transmitted image data using image recognition technology. Specifically, it uses an AI model to analyze the product's label information and shape, and matches it with information in a database. This allows it to obtain detailed information about the item, such as its ingredients, manufacturer, and environmental policy.

[0529] The server calculates the eco-level using an environmental impact algorithm based on identified product information. This eco-level evaluates the carbon footprint of the product's manufacturing process, the renewable nature of the materials, and other factors. The calculated eco-level is formatted as a numerical score or star rating and presented to customers in an easily understandable way.

[0530] Furthermore, the device utilizes its built-in camera and biosensors to analyze the user's emotional state in real time. The emotion engine collects and analyzes data such as the user's facial expressions, voice tone, and heart rate. This allows it to understand the user's emotional state, and the obtained emotional information is sent to the server.

[0531] Based on the information provided by the emotion engine, the server displays information and recommends products that match the user's emotions. For example, it can provide more personalized information, such as suggesting environmentally friendly alternatives to users who show sensitive emotions towards the environment.

[0532] For example, if a user takes a photo of shampoo in a supermarket and the emotion engine detects that the user is showing a happy expression, the system will display not only the shampoo's eco-friendliness level but also positive messages and personalized product recommendations to encourage purchase. Based on these results, users can make informed product choices and engage in environmentally conscious and smart consumption.

[0533] Thus, by combining emotion recognition capabilities, the system of the present invention provides a powerful means to support consumers in making more personalized and environmentally conscious choices.

[0534] The following describes the processing flow.

[0535] Step 1:

[0536] The user launches the app on their device and takes a picture of an item they are interested in in the store. The device then retrieves the captured image data.

[0537] Step 2:

[0538] The device processes the image data and adjusts it to the required resolution and contrast. This pre-processing improves the accuracy of image recognition.

[0539] Step 3:

[0540] The terminal sends pre-processed image data to the server. Data compression technology is used to reduce the amount of data transmitted.

[0541] Step 4:

[0542] The server executes an image recognition algorithm based on the received image data to analyze the product's label and shape. The analyzed features are then compared with a database to identify the product.

[0543] Step 5:

[0544] The server retrieves identified product information from the database. This information includes product ingredients, manufacturer information, and environmental policies.

[0545] Step 6:

[0546] The server uses the acquired product information to run an environmental impact assessment algorithm and calculate the eco-level. This calculation is based on the product's lifecycle and the sustainability of its materials.

[0547] Step 7:

[0548] The server calculates the eco-level, converts it into a score, and sends it to the terminal. Before sending, the information is formatted to be easily understood by the user.

[0549] Step 8:

[0550] Before displaying eco-level information, the device activates an emotion engine to analyze emotional data from the user's facial expressions and voice. This data may also utilize biosensors.

[0551] Step 9:

[0552] The device sends the collected emotional data to the server, which allows for an evaluation of the user's current emotional state.

[0553] Step 10:

[0554] The server analyzes emotional data and adjusts the displayed information and product recommendations to suit the user's emotions. Messages and options are generated according to the user's feelings.

[0555] Step 11:

[0556] The device displays the final eco-level and personalized, emotion-based messages to the user, providing information to help them make a purchase decision.

[0557] Step 12:

[0558] Users select products and engage in environmentally conscious consumption behavior based on the information and recommendations presented.

[0559] (Example 2)

[0560] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0561] In recent years, there has been a growing need for support in consumers' shopping behavior to help them choose products that align with environmental considerations and their personal feelings. However, conventional systems have been insufficient in assessing environmental impact and have been unable to suggest product information based on the user's emotional state. As a result, consumers have faced challenges in making appropriate product choices due to information overload and inappropriate information provision.

[0562] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0563] In this invention, the server includes means for acquiring and pre-processing image data, means for identifying articles using information processing technology with the pre-processed image data, and means for acquiring information about the identified articles from an information storage device. This enables the provision of accurate environmental assessment and sentiment-based product information to support consumer choices.

[0564] "Image data" refers to information captured by a camera device, including visual information about the label and shape of an item.

[0565] "Preprocessing" refers to improvement processes performed on image data, including noise reduction and image quality optimization.

[0566] "Information processing technology" refers to technologies for data analysis using computers, and in particular, includes technologies for identifying items using AI models.

[0567] "Goods" are products sold in the market that include visual information on their labels and packaging.

[0568] An "information storage device" is a device for storing information such as databases, and provides detailed information about items.

[0569] "Environmental impact" refers to the effects that goods have on the environment, including the carbon footprint of the manufacturing process and the sustainability of materials.

[0570] A "sustainability index" is a numerical or evaluation indicator that represents the degree of environmental consideration of an item.

[0571] "Emotional state" refers to the psychological and emotional state of a user, as analyzed from their facial expressions, tone of voice, heart rate, and other factors.

[0572] This invention is a system that uses terminals, servers, and necessary algorithms to personalize the consumer's shopping experience and further assess the degree of environmental consideration. Specifically, the user uses a terminal such as a smartphone or tablet and acquires images of products of interest using the camera. These images undergo pre-processing on the terminal, such as noise reduction and image quality adjustment. Open-source image processing libraries are commonly used for this processing.

[0573] Pre-processed images are sent from the terminal to the server. The server analyzes the image data using information processing technology that utilizes a generative AI model. Specifically, the AI ​​model recognizes the product's label information and shape and compares it with a database. In this process, detailed information about the item, such as ingredients, manufacturer, and environmental policy, is extracted.

[0574] The server then uses the acquired information to calculate a sustainability index. This calculation takes into account the carbon footprint of the product's manufacturing process and the sustainability of the materials used. The calculated sustainability index is returned to the device in a format that is easy for the user to understand, such as a star rating or score.

[0575] Furthermore, the device incorporates biosensors to analyze the user's emotional state. Cameras and voice input devices collect data such as the user's facial expressions, voice, and heart rate, allowing for real-time monitoring of their emotional state. This emotional information is sent to a server, which then provides the user with optimal product information based on their emotions.

[0576] As a concrete example, a user can take a picture of a shampoo product, and the system can calculate and display the product's sustainability index. Simultaneously, based on sentiment analysis, it can suggest more environmentally friendly alternative products. This allows users to make more informed purchasing decisions and supports sustainable consumption behavior.

[0577] An example of a prompt message would be, "Please tell us the eco-level of the products the user is interested in. Please also provide product recommendations based on sentiment analysis."

[0578] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0579] Step 1:

[0580] The user takes a picture of a product they are interested in using the device's camera. This image becomes the input data. The device uses an image processing library to perform pre-processing such as noise reduction and image quality adjustment. This results in outputting image data that is easy to analyze.

[0581] Step 2:

[0582] The terminal sends pre-processed image data to the server. The server analyzes the image data using an image recognition generative AI model. In this process, it identifies product label information and shape, and obtains product information by comparing it with a database. Detailed information about the product is then output.

[0583] Step 3:

[0584] The server calculates sustainability indicators using acquired product information. Input data includes information about the product's manufacturing process and materials used. An environmental impact algorithm evaluates data such as carbon footprint and renewables, and outputs sustainability indicators in the form of numerical values ​​or star ratings.

[0585] Step 4:

[0586] The device uses its built-in camera and biosensors to collect data on the user's facial expressions and heart rate. This data serves as input for analyzing their emotional state. The device then uses emotion analysis software to understand the user's emotional state in real time and sends the analysis results to a server. The output is information indicating the user's emotional state.

[0587] Step 5:

[0588] The server provides users with optimal product information based on the analysis of their emotional state. Input data includes emotional information and product sustainability indicators. Based on this information, it recommends products or alternatives that match the user's emotional state and sends this information to the terminal. The terminal then displays this information to the user, supporting their purchasing decision. This process enables users to engage in more environmentally conscious consumption.

[0589] (Application Example 2)

[0590] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0591] In recent years, while consumers have shown increased interest in environmentally conscious product choices, they face the challenge of selecting the right product from a wide range of options. Furthermore, the inability to provide product recommendations tailored to consumers' immediate emotional states makes it difficult to offer personalized shopping experiences.

[0592] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0593] In this invention, the server includes means for identifying an item using image recognition technology, means for acquiring information about the item from an information storage medium, means for evaluating the environmental impact based on the information and calculating the eco-level, and means for analyzing the user's emotional state and presenting information to the user based on the analysis results. This makes it possible for consumers to easily select products with a low environmental impact and to obtain personalized information that corresponds to their emotional state.

[0594] "Image recognition technology" is a technology that allows computers or machines to extract and analyze specific information from image data.

[0595] "Goods" refers to merchandise or products that consumers purchase or use.

[0596] An "information storage medium" is a recording device that includes servers and databases for storing and managing data and information.

[0597] "Environmental burden" refers to the degree of impact or damage to the natural environment resulting from the production or use of goods.

[0598] "Eco-level" is an index that indicates the degree of environmental consideration based on the manufacturing process and materials of a product.

[0599] "User" refers to a consumer or end-user who utilizes the functions of this system.

[0600] "Emotional state" refers to the user's current emotions and psychological condition, derived from their facial expressions and biometric data.

[0601] "Information presentation" refers to the act of providing users with necessary data and knowledge using methods such as visuals and audio.

[0602] The system for realizing this invention mainly consists of a terminal, a server, a database, and an emotion analysis engine. Users can scan product barcodes and labels using their smartphone camera in physical stores. The terminal analyzes the acquired image data using an image processing library such as OpenCV and digitizes the barcode information.

[0603] The terminal then sends the analyzed data to the server. The server uses the Google Cloud Vision API to verify the barcode information and retrieves product data from the information storage medium. This data includes product ingredient information, manufacturer information, and details about environmental policies. Based on this data, the server uses an environmental impact algorithm implemented in Python to calculate the product's eco-level and presents it to the user in a numerical format.

[0604] Meanwhile, the device uses its built-in camera and biosensors to analyze the user's emotional state in real time. By utilizing Microsoft Azure's Emotion Recognition API, it identifies emotions based on the user's facial expressions and heart rate data. Based on this emotional information, the server uses a generative AI model to recommend the most suitable products to the user. If the emotional state is positive, it recommends similar products with a lower environmental impact and displays a message to encourage purchase.

[0605] For example, when a user picks up an organic shampoo in a physical store, the application provides information such as, "This shampoo is organically certified and made with environmentally friendly ingredients." If the user shows a happy expression, the server provides personalized recommendations such as, "We also have a conditioner that works well in conjunction with this shampoo."

[0606] Use the following prompts for the generative AI model:

[0607] "Please consider the environmental considerations and suggested alternative product display scenarios when a user scans a product's barcode in a physical store."

[0608] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0609] Step 1:

[0610] The terminal works by having the user photograph the product's barcode or label using their smartphone camera. The image data acquired from the camera is used as input. The terminal uses the OpenCV library to remove noise from the image data and recognize the barcode shape and label information. The output is digitized barcode information.

[0611] Step 2:

[0612] The terminal sends the barcode information acquired in Step 1 to the server. The server uses the received barcode information to call the Google Cloud Vision API and retrieve product information. The input is barcode information, and the output is data that includes ingredient information, manufacturer information, and environmental policy related to the product. The server compares this information with historical data acquired from the information storage medium and selects the most appropriate product information.

[0613] Step 3:

[0614] Based on the product information obtained in Step 2, the server calculates the eco-level using an environmental impact algorithm implemented in Python. Product information is used as input, and a numerical eco-level is obtained as output. The server saves this eco-level to a database and prepares to display it in a visually easy-to-understand format for the user.

[0615] Step 4:

[0616] The device uses its built-in camera and biosensors to acquire the user's emotional state in real time. By utilizing Microsoft Azure's Emotion Recognition API, it takes the user's facial image and heart rate data as input and obtains data indicating their emotional state as output. This allows the device to analyze the user's psychological state and lay the foundation for providing a personalized experience.

[0617] Step 5:

[0618] The server uses the emotional state data obtained in step 4 to generate optimal product information and alternatives for the user using a generative AI model. It utilizes emotional state and eco-level as input and generates personalized recommendations and purchase-promoting messages for the user as output. The prompt used is: "Consider a scenario where the user scans a product barcode in a physical store and is shown the environmental friendliness and recommended alternatives." The server sends this information to the terminal and presents it visually to the user.

[0619] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0620] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include those described above. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions shown by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0621] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0622] [Fourth Embodiment]

[0623] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0624] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0625] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0626] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0627] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0628] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0629] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0630] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0631] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0632] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0633] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0634] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0635] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0636] The system based on this invention is for consumers to instantly determine the environmental friendliness of items they are considering purchasing, and consists of a terminal, a server, and a database.

[0637] Users take photos of products while shopping using devices such as smartphones and tablets. The devices have camera functions and capture image data of product labels and shapes. The captured image data is pre-processed on the device and then sent to the server.

[0638] On the server, image recognition technology is used to identify products. Specifically, AI-powered character recognition and shape matching algorithms are used to analyze the characteristics of the items. This identifies which information in the database corresponds to the item in question. This information includes product name, manufacturer, ingredients, and environmental policy.

[0639] Based on the identified product information, the server assesses the environmental impact and calculates the eco-level. Evaluation criteria include the carbon footprint during the manufacturing process, the renewable nature of the materials used, and the product's lifecycle. This allows for a quantitative measurement of the environmental considerations of each product.

[0640] The calculated eco-level is formatted as a numerical score or star rating and sent to the device. The device then presents the received eco-level and related information to the user. Visual displays using graphs and icons are used to ensure intuitive understanding.

[0641] As a concrete example, if a user takes a picture of a shampoo bottle in a supermarket, the server analyzes the shampoo's ingredient information and manufacturing process, and the resulting eco-level is displayed to the user on the device. Based on this result, the user can then decide whether or not to purchase the shampoo.

[0642] Thus, the system of the present invention provides consumers with an innovative means of supporting user-friendly and environmentally conscious choices.

[0643] The following describes the processing flow.

[0644] Step 1:

[0645] The user launches the app on their device and takes a picture of the product they are interested in with their camera.

[0646] Step 2:

[0647] The device acquires the captured image data and performs preprocessing to adjust the image resolution and contrast. This process improves the recognition accuracy on the server.

[0648] Step 3:

[0649] The terminal sends the pre-processed image data to the server. Here, data compression is performed to improve communication efficiency.

[0650] Step 4:

[0651] The server uses image recognition technology based on machine learning models to analyze the received image data, identifying product labels and shapes. This analysis identifies the product and matches it with the corresponding information in the database.

[0652] Step 5:

[0653] The server retrieves the identified product information from the database. This information includes the product's ingredients, manufacturer, and environmental policy.

[0654] Step 6:

[0655] The server executes an environmental impact algorithm based on the acquired product information. This algorithm evaluates the environmental impact associated with the manufacturing and disposal of products and calculates the eco-level.

[0656] Step 7:

[0657] The server formats the calculated eco-level as a numerical score or star rating, converting it into an easy-to-understand form.

[0658] Step 8:

[0659] The server sends the formatted eco-level and related information to the terminal.

[0660] Step 9:

[0661] The terminal displays the received information and provides the user with the product's eco-level and related information. Graphs and icons are used to make it visually easy to understand.

[0662] Step 10:

[0663] Users make environmentally conscious product selections based on the information provided. This information serves as a basis for their purchasing decisions.

[0664] (Example 1)

[0665] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0666] Consumers often have limited means to quickly and intuitively understand the environmental impact of products when shopping. In particular, it is difficult to obtain information on environmental impact immediately when selecting products, and it is not easy to include environmental considerations in selection criteria. The present invention aims to provide a system that enables consumers to evaluate the environmental impact of products in real time and to support them in making more sustainable choices.

[0667] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0668] In this invention, the server includes means for acquiring visual information of an item using an image acquisition device, means for preprocessing the acquired visual information to remove noise and extract features, means for analyzing the preprocessed visual information and identifying the item using image recognition technology, means for acquiring information of the identified item from an information recording device, means for evaluating the environmental impact based on the acquired information and calculating the eco-level, and means for presenting the calculated eco-level to the user in a visually interpretable form via a display device. As a result, consumers can quickly evaluate the environmental impact of an item and make environmentally conscious choices simply by taking a picture of the item while shopping.

[0669] An "image acquisition device" is a device for recording visual information of an object, and usually includes optical equipment such as a camera.

[0670] "Visual information" refers to image data and visual characteristics related to an item, including information such as product labels and shape.

[0671] "Preprocessing" is the process of removing noise from acquired visual information and extracting features necessary to improve the accuracy of the analysis.

[0672] "Image recognition technology" is a technology that analyzes visual information and identifies objects based on specific features, and includes the analysis of textual and shape information.

[0673] An "information recording device" is a device for storing various types of information about a specified item, and includes databases and the like.

[0674] "Means for evaluating environmental impact" refers to technologies or processes for quantitatively evaluating the impact of an item on the environment based on its manufacturing process and the characteristics of the materials used.

[0675] "Eco-level" is a numerical or graphical indicator that shows the degree to which an item has an impact on the environment.

[0676] A "display device" is a device that presents the calculated eco-level in a way that allows users to visually confirm it, and generally includes smartphone screens, etc.

[0677] This invention provides a system that allows consumers to instantly evaluate the environmental impact of products while shopping. Users utilize devices such as smartphones and tablets. These devices can acquire visual information about products by utilizing their built-in cameras. The visual information obtained by this image acquisition device is preprocessed on the device. Specifically, noise reduction and feature extraction are performed to improve the accuracy of the analysis of the visual information.

[0678] The terminal transmits pre-processed visual information to the server. The server uses image recognition technology to identify items. This technology identifies products through character recognition and analysis of shape information. The identified product information is compared with data stored in the information recording device, and detailed information about the corresponding product is obtained.

[0679] The server evaluates the environmental impact of acquired product details and calculates an eco-level. The eco-level is an indicator of the product's environmental impact, using manufacturing processes and material recyclability as evaluation criteria. Finally, the calculated eco-level is presented to the user via the terminal's display. Users can visually confirm this and use it as a selection criterion when shopping.

[0680] As a concrete example, when a user takes a picture of a shampoo bottle in a supermarket, the server analyzes the shampoo's ingredients and manufacturing process. Based on these results, an eco-level is calculated and sent to the user's device, which then displays this information to the user in a visual format such as graphs or icons. This allows the user to make a decision about whether or not to purchase the shampoo from an environmental perspective.

[0681] Examples of prompts for the generating AI model include, "What information would be helpful when you want to know the environmental friendliness of a product you plan to buy at the supermarket?" and "How would you like to use the eco-level rating provided by this system in your daily shopping?" This makes it possible to provide information that addresses more specific user needs.

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

[0683] Step 1:

[0684] The user takes a photo of an item of interest using a smartphone or tablet. The input is image data of the item, and the output is an image file generated on the device. Specifically, the user launches the camera app, positions the product's label and shape within the screen, and presses the shutter button to capture the image.

[0685] Step 2:

[0686] The device performs preprocessing on the acquired image data. Here, the input is raw image data, and the output is image data with noise removed and features enhanced. Specifically, the device adjusts the contrast and brightness of the image, trims off unnecessary backgrounds, and emphasizes labels and shape information.

[0687] Step 3:

[0688] The terminal sends pre-processed image data to the server. The input is the pre-processed image data, and the output is the state after the image data has been sent to the server. Specifically, the terminal sends the image to a particular endpoint via internet communication.

[0689] Step 4:

[0690] The server analyzes the received image data and identifies the product using image recognition technology. The input is pre-processed image data, and the output is product identification information. Specifically, the server extracts the text from the label using OCR technology and determines the shape of the product using a shape matching algorithm.

[0691] Step 5:

[0692] The server retrieves product details from the information storage device based on the identified product information. The input is the identified product ID, and the output is the product details. Specifically, the server executes a database query using the product ID to retrieve the data for the corresponding product.

[0693] Step 6:

[0694] The server evaluates the environmental impact based on the acquired product information and calculates the eco-level. The input is detailed product information, and the output is the product's eco-level score. Specifically, the server analyzes the product's manufacturing process data and material information and applies an algorithm to quantify its environmental impact.

[0695] Step 7:

[0696] The server sends the calculated eco-level to the terminal. The input is the eco-level score, and the output is the completion of sending the score to the user terminal. Specifically, the server converts the score into a data format and sends it to the terminal.

[0697] Step 8:

[0698] The terminal displays the received eco-level to the user. The input is the eco-level score, and the output is an eco-level display in a format that is easy for the user to understand. Specifically, the terminal converts the score into a graph or icon format and displays it on the screen to inform the user of the product's environmental friendliness.

[0699] (Application Example 1)

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

[0701] In recent years, consumers have placed great importance on environmental considerations when choosing products. However, with so many products on the market, there is a lack of information to understand the environmental impact of each individual product. Therefore, there is a need to provide consumers with a means to quickly and easily check the degree of environmental consideration in products when shopping. This invention aims to meet such consumer needs.

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

[0703] In this invention, the server includes means for identifying an item using image recognition technology, means for obtaining information about the item from a database, and means for evaluating the environmental impact based on the information and calculating the eco-level. This makes it possible for consumers to instantly determine the environmental friendliness of a product they are considering purchasing.

[0704] "Image recognition technology" is a technology that analyzes digital image data to identify objects and textual information.

[0705] "Goods" refers to products or goods that consumers consider purchasing or using.

[0706] A "database" is a system for systematically storing specific information and for retrieving or managing that data as needed.

[0707] "Environmental impact" refers to the impact a product has on the environment throughout its entire lifecycle, and includes factors such as carbon footprint and recyclability.

[0708] "Eco-level" is an evaluation index that quantifies the degree of environmental impact of an item, and is expressed in a format that consumers can easily understand.

[0709] "Image preprocessing" refers to the process of preparing captured image data to a state suitable for analysis.

[0710] "Visual display" means providing information visually in a way that makes it easy for users to intuitively understand the information.

[0711] The system that realizes this invention mainly consists of a terminal, a server, and a database. The user uses a terminal such as a smartphone or tablet to take pictures of items they are considering purchasing. The terminal is equipped with a camera that utilizes image recognition technology and software for preprocessing. Specifically, the terminal uses OpenCV to preprocess the captured image data and extract label and shape features.

[0712] The processed image data is sent to a server. The server uses AI models such as TensorFlow and PyTorch to identify objects using image recognition technology. Information about the identified objects is retrieved from database systems such as AWS DynamoDB and MySQL. Based on the retrieved information, the server evaluates the environmental impact of the objects and calculates their eco-level. Criteria such as carbon footprint and renewables are used in this evaluation.

[0713] The calculated eco-level is displayed on the device in a visually and intuitively easy-to-understand format. Users can check the environmental friendliness of a product based on the eco-level, which is displayed as a star rating or numerical score, and decide whether or not to purchase it.

[0714] For example, when a user visits a supermarket and scans the label of an item they're interested in using their smartphone before putting it in their cart, the server instantly calculates the eco-level of that item and assesses its environmental impact. This is achieved through prompts sent to a generative AI model, such as, "Scan the label on this tomato, assess its environmental impact, and tell me its eco-level."

[0715] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0716] Step 1:

[0717] The user takes a picture of an item they are considering purchasing using the device's camera. The input obtained is digital image data. This image data is preprocessed on the device using the OpenCV library to extract labels and shape features. The output is the processed image data.

[0718] Step 2:

[0719] The terminal sends pre-processed image data to the server. The input here is the processed image data sent from the terminal. The server analyzes this data using TensorFlow or PyTorch and identifies objects using image recognition technology. The output is the identification information of the identified objects.

[0720] Step 3:

[0721] The server retrieves detailed information about identified items from databases such as AWS DynamoDB or MySQL, based on the item's identification information. The input is the item's identification information, and the server retrieves information related to the item's environmental impact through database access. The output is the item's detailed information.

[0722] Step 4:

[0723] The server calculates the environmental impact and eco-level based on the detailed information of the retrieved items, using algorithms that evaluate carbon footprint and material renewableness. The input is detailed information of the items, and the output is the eco-level, such as a numerical score or star rating.

[0724] Step 5:

[0725] The server sends the calculated eco-level to the terminal. The terminal displays this information to the user. The input is the eco-level sent from the server, and the output is a display interface that the user can visually confirm. This allows the user to intuitively understand the environmental friendliness of a product and make a purchase decision.

[0726] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0727] The system based on this invention is designed to personalize the consumer's shopping experience and instantly determine the degree of environmental consideration. This system consists of a terminal, a server, a database, and an emotion engine.

[0728] Users begin by taking photos of items they are interested in while shopping using a device such as a smartphone or tablet. The device has a camera function and captures the labels and shapes of the items as image data. The captured image data is pre-processed on the device before being sent to the server. Pre-processing includes noise reduction and image optimization.

[0729] The server identifies items from the transmitted image data using image recognition technology. Specifically, it uses an AI model to analyze the product's label information and shape, and matches it with information in a database. This allows it to obtain detailed information about the item, such as its ingredients, manufacturer, and environmental policy.

[0730] The server calculates the eco-level using an environmental impact algorithm based on identified product information. This eco-level evaluates the carbon footprint of the product's manufacturing process, the renewable nature of the materials, and other factors. The calculated eco-level is formatted as a numerical score or star rating and presented to customers in an easily understandable way.

[0731] Furthermore, the device utilizes its built-in camera and biosensors to analyze the user's emotional state in real time. The emotion engine collects and analyzes data such as the user's facial expressions, voice tone, and heart rate. This allows it to understand the user's emotional state, and the obtained emotional information is sent to the server.

[0732] Based on the information provided by the emotion engine, the server displays information and recommends products that match the user's emotions. For example, it can provide more personalized information, such as suggesting environmentally friendly alternatives to users who show sensitive emotions towards the environment.

[0733] For example, if a user takes a photo of shampoo in a supermarket and the emotion engine detects that the user is showing a happy expression, the system will display not only the shampoo's eco-friendliness level but also positive messages and personalized product recommendations to encourage purchase. Based on these results, users can make informed product choices and engage in environmentally conscious and smart consumption.

[0734] Thus, by combining emotion recognition capabilities, the system of the present invention provides a powerful means to support consumers in making more personalized and environmentally conscious choices.

[0735] The following describes the processing flow.

[0736] Step 1:

[0737] The user launches the app on their device and takes a picture of an item they are interested in in the store. The device then retrieves the captured image data.

[0738] Step 2:

[0739] The device processes the image data and adjusts it to the required resolution and contrast. This pre-processing improves the accuracy of image recognition.

[0740] Step 3:

[0741] The terminal sends pre-processed image data to the server. Data compression technology is used to reduce the amount of data transmitted.

[0742] Step 4:

[0743] The server executes an image recognition algorithm based on the received image data to analyze the product's label and shape. The analyzed features are then compared with a database to identify the product.

[0744] Step 5:

[0745] The server retrieves identified product information from the database. This information includes product ingredients, manufacturer information, and environmental policies.

[0746] Step 6:

[0747] The server uses the acquired product information to run an environmental impact assessment algorithm and calculate the eco-level. This calculation is based on the product's lifecycle and the sustainability of its materials.

[0748] Step 7:

[0749] The server calculates the eco-level, converts it into a score, and sends it to the terminal. Before sending, the information is formatted to be easily understood by the user.

[0750] Step 8:

[0751] Before displaying eco-level information, the device activates an emotion engine to analyze emotional data from the user's facial expressions and voice. This data may also utilize biosensors.

[0752] Step 9:

[0753] The device sends the collected emotional data to the server, which allows for an evaluation of the user's current emotional state.

[0754] Step 10:

[0755] The server analyzes emotional data and adjusts the displayed information and product recommendations to suit the user's emotions. Messages and options are generated according to the user's feelings.

[0756] Step 11:

[0757] The device displays the final eco-level and personalized, emotion-based messages to the user, providing information to help them make a purchase decision.

[0758] Step 12:

[0759] Users select products and engage in environmentally conscious consumption behavior based on the information and recommendations presented.

[0760] (Example 2)

[0761] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0762] In recent years, there has been a growing need for support in consumers' shopping behavior to help them choose products that align with environmental considerations and their personal feelings. However, conventional systems have been insufficient in assessing environmental impact and have been unable to suggest product information based on the user's emotional state. As a result, consumers have faced challenges in making appropriate product choices due to information overload and inappropriate information provision.

[0763] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0764] In this invention, the server includes means for acquiring and pre-processing image data, means for identifying articles using information processing technology with the pre-processed image data, and means for acquiring information about the identified articles from an information storage device. This enables the provision of accurate environmental assessment and sentiment-based product information to support consumer choices.

[0765] "Image data" refers to information captured by a camera device, including visual information about the label and shape of an item.

[0766] "Preprocessing" refers to improvement processes performed on image data, including noise reduction and image quality optimization.

[0767] "Information processing technology" refers to technologies for data analysis using computers, and in particular, includes technologies for identifying items using AI models.

[0768] "Goods" are products sold in the market that include visual information on their labels and packaging.

[0769] An "information storage device" is a device for storing information such as databases, and provides detailed information about items.

[0770] "Environmental impact" refers to the effects that goods have on the environment, including the carbon footprint of the manufacturing process and the sustainability of materials.

[0771] A "sustainability index" is a numerical or evaluation indicator that represents the degree of environmental consideration of an item.

[0772] "Emotional state" refers to the psychological and emotional state of a user, as analyzed from their facial expressions, tone of voice, heart rate, and other factors.

[0773] This invention is a system that uses terminals, servers, and necessary algorithms to personalize the consumer's shopping experience and further assess the degree of environmental consideration. Specifically, the user uses a terminal such as a smartphone or tablet and acquires images of products of interest using the camera. These images undergo pre-processing on the terminal, such as noise reduction and image quality adjustment. Open-source image processing libraries are commonly used for this processing.

[0774] Pre-processed images are sent from the terminal to the server. The server analyzes the image data using information processing technology that utilizes a generative AI model. Specifically, the AI ​​model recognizes the product's label information and shape and compares it with a database. In this process, detailed information about the item, such as ingredients, manufacturer, and environmental policy, is extracted.

[0775] The server then uses the acquired information to calculate a sustainability index. This calculation takes into account the carbon footprint of the product's manufacturing process and the sustainability of the materials used. The calculated sustainability index is returned to the device in a format that is easy for the user to understand, such as a star rating or score.

[0776] Furthermore, the device incorporates biosensors to analyze the user's emotional state. Cameras and voice input devices collect data such as the user's facial expressions, voice, and heart rate, allowing for real-time monitoring of their emotional state. This emotional information is sent to a server, which then provides the user with optimal product information based on their emotions.

[0777] As a concrete example, a user can take a picture of a shampoo product, and the system can calculate and display the product's sustainability index. Simultaneously, based on sentiment analysis, it can suggest more environmentally friendly alternative products. This allows users to make more informed purchasing decisions and supports sustainable consumption behavior.

[0778] An example of a prompt message would be, "Please tell us the eco-level of the products the user is interested in. Please also provide product recommendations based on sentiment analysis."

[0779] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0780] Step 1:

[0781] The user takes a picture of a product they are interested in using the device's camera. This image becomes the input data. The device uses an image processing library to perform pre-processing such as noise reduction and image quality adjustment. This results in outputting image data that is easy to analyze.

[0782] Step 2:

[0783] The terminal sends pre-processed image data to the server. The server analyzes the image data using an image recognition generative AI model. In this process, it identifies product label information and shape, and obtains product information by comparing it with a database. Detailed information about the product is then output.

[0784] Step 3:

[0785] The server calculates sustainability indicators using acquired product information. Input data includes information about the product's manufacturing process and materials used. An environmental impact algorithm evaluates data such as carbon footprint and renewables, and outputs sustainability indicators in the form of numerical values ​​or star ratings.

[0786] Step 4:

[0787] The device uses its built-in camera and biosensors to collect data on the user's facial expressions and heart rate. This data serves as input for analyzing their emotional state. The device then uses emotion analysis software to understand the user's emotional state in real time and sends the analysis results to a server. The output is information indicating the user's emotional state.

[0788] Step 5:

[0789] The server provides users with optimal product information based on the analysis of their emotional state. Input data includes emotional information and product sustainability indicators. Based on this information, it recommends products or alternatives that match the user's emotional state and sends this information to the terminal. The terminal then displays this information to the user, supporting their purchasing decision. This process enables users to engage in more environmentally conscious consumption.

[0790] (Application Example 2)

[0791] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0792] In recent years, while consumers have shown increased interest in environmentally conscious product choices, they face the challenge of selecting the right product from a wide range of options. Furthermore, the inability to provide product recommendations tailored to consumers' immediate emotional states makes it difficult to offer personalized shopping experiences.

[0793] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0794] In this invention, the server includes means for identifying an item using image recognition technology, means for acquiring information about the item from an information storage medium, means for evaluating the environmental impact based on the information and calculating the eco-level, and means for analyzing the user's emotional state and presenting information to the user based on the analysis results. This makes it possible for consumers to easily select products with a low environmental impact and to obtain personalized information that corresponds to their emotional state.

[0795] "Image recognition technology" is a technology that allows computers or machines to extract and analyze specific information from image data.

[0796] "Goods" refers to merchandise or products that consumers purchase or use.

[0797] An "information storage medium" is a recording device that includes servers and databases for storing and managing data and information.

[0798] "Environmental burden" refers to the degree of impact or damage to the natural environment resulting from the production or use of goods.

[0799] "Eco-level" is an index that indicates the degree of environmental consideration based on the manufacturing process and materials of a product.

[0800] "User" refers to a consumer or end-user who utilizes the functions of this system.

[0801] "Emotional state" refers to the user's current emotions and psychological condition, derived from their facial expressions and biometric data.

[0802] "Information presentation" refers to the act of providing users with necessary data and knowledge using methods such as visuals and audio.

[0803] The system for realizing this invention mainly consists of a terminal, a server, a database, and an emotion analysis engine. Users can scan product barcodes and labels using their smartphone camera in physical stores. The terminal analyzes the acquired image data using an image processing library such as OpenCV and digitizes the barcode information.

[0804] The terminal then sends the analyzed data to the server. The server uses the Google Cloud Vision API to verify the barcode information and retrieves product data from the information storage medium. This data includes product ingredient information, manufacturer information, and details about environmental policies. Based on this data, the server uses an environmental impact algorithm implemented in Python to calculate the product's eco-level and presents it to the user in a numerical format.

[0805] Meanwhile, the device uses its built-in camera and biosensors to analyze the user's emotional state in real time. By utilizing Microsoft Azure's Emotion Recognition API, it identifies emotions based on the user's facial expressions and heart rate data. Based on this emotional information, the server uses a generative AI model to recommend the most suitable products to the user. If the emotional state is positive, it recommends similar products with a lower environmental impact and displays a message to encourage purchase.

[0806] For example, when a user picks up an organic shampoo in a physical store, the application provides information such as, "This shampoo is organically certified and made with environmentally friendly ingredients." If the user shows a happy expression, the server provides personalized recommendations such as, "We also have a conditioner that works well in conjunction with this shampoo."

[0807] Use the following prompts for the generative AI model:

[0808] "Please consider the environmental considerations and suggested alternative product display scenarios when a user scans a product's barcode in a physical store."

[0809] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0810] Step 1:

[0811] The terminal works by having the user photograph the product's barcode or label using their smartphone camera. The image data acquired from the camera is used as input. The terminal uses the OpenCV library to remove noise from the image data and recognize the barcode shape and label information. The output is digitized barcode information.

[0812] Step 2:

[0813] The terminal sends the barcode information acquired in Step 1 to the server. The server uses the received barcode information to call the Google Cloud Vision API and retrieve product information. The input is barcode information, and the output is data that includes ingredient information, manufacturer information, and environmental policy related to the product. The server compares this information with historical data acquired from the information storage medium and selects the most appropriate product information.

[0814] Step 3:

[0815] Based on the product information obtained in Step 2, the server calculates the eco-level using an environmental impact algorithm implemented in Python. Product information is used as input, and a numerical eco-level is obtained as output. The server saves this eco-level to a database and prepares to display it in a visually easy-to-understand format for the user.

[0816] Step 4:

[0817] The device uses its built-in camera and biosensors to acquire the user's emotional state in real time. By utilizing Microsoft Azure's Emotion Recognition API, it takes the user's facial image and heart rate data as input and obtains data indicating their emotional state as output. This allows the device to analyze the user's psychological state and lay the foundation for providing a personalized experience.

[0818] Step 5:

[0819] The server uses the emotional state data obtained in step 4 to generate optimal product information and alternatives for the user using a generative AI model. It utilizes emotional state and eco-level as input and generates personalized recommendations and purchase-promoting messages for the user as output. The prompt used is: "Consider a scenario where the user scans a product barcode in a physical store and is shown the environmental friendliness and recommended alternatives." The server sends this information to the terminal and presents it visually to the user.

[0820] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0821] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include those described above. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions shown by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0822] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0823] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0824] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0825] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0826] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0827] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0828] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0829] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0830] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0831] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0832] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0833] 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.

[0834] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0835] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0836] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0837] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0838] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0839] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0840] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0841] The following is further disclosed regarding the embodiments described above.

[0842] (Claim 1)

[0843] A first method for identifying items using image recognition technology,

[0844] A second means of obtaining information about the aforementioned article from a database,

[0845] A third method for evaluating the environmental impact and calculating the eco-level based on the aforementioned information,

[0846] A fourth means of presenting the aforementioned eco-level to the user,

[0847] A system that includes this.

[0848] (Claim 2)

[0849] The system according to claim 1, characterized in that the first means analyzes label information from captured image data.

[0850] (Claim 3)

[0851] The system according to claim 1, characterized in that the third means evaluates the manufacturing process of an article and the recyclability of the material.

[0852] "Example 1"

[0853] (Claim 1)

[0854] A means for acquiring visual information of an object using an image acquisition device,

[0855] A means for preprocessing acquired visual information to perform noise reduction and feature extraction,

[0856] A means for analyzing pre-processed visual information and identifying an item using image recognition technology,

[0857] A means for obtaining information about the identified item from an information recording device,

[0858] A means for evaluating the environmental impact and calculating the eco-level based on the acquired information,

[0859] A means of presenting the calculated eco-level to the user in a visually interpretable form via a display device,

[0860] A system that includes this.

[0861] (Claim 2)

[0862] The system according to claim 1, characterized in that the image recognition technology analyzes character information and shape information from captured visual information.

[0863] (Claim 3)

[0864] The system according to claim 1, characterized in that the means for evaluating the environmental burden calculates carbon emissions and the renewableness of materials in the manufacturing process of an article.

[0865] "Application Example 1"

[0866] (Claim 1)

[0867] A first method for identifying items using image recognition technology,

[0868] A second means of obtaining information about the aforementioned article from a database,

[0869] A third method for evaluating the environmental impact and calculating the eco-level based on the aforementioned information,

[0870] A fourth means of presenting the aforementioned eco-level to the user,

[0871] A fifth means for taking an image of an object and performing image preprocessing,

[0872] A sixth means of visually displaying evaluation results for users to understand the environmental impact of goods,

[0873] A system that includes this.

[0874] (Claim 2)

[0875] The system according to claim 1, characterized in that the first means analyzes label information from captured image data and identifies an article.

[0876] (Claim 3)

[0877] The system according to claim 1, characterized in that the third means evaluates the manufacturing process and the recyclability of the materials of an article and presents the results in a manner that is easily understandable to the user.

[0878] "Example 2 of combining an emotion engine"

[0879] (Claim 1)

[0880] The first method involves acquiring image data and performing preprocessing,

[0881] A second means for identifying an article using information processing technology with the aforementioned pre-processed image data,

[0882] A third means for obtaining information about the identified article from an information storage device,

[0883] Based on the aforementioned information, a fourth method for evaluating environmental impact and calculating sustainability indicators is provided.

[0884] A fifth means of presenting the aforementioned sustainability indicators to users,

[0885] A sixth means of analyzing the user's emotional state and providing information based on the analysis results,

[0886] A system that includes this.

[0887] (Claim 2)

[0888] The system according to claim 1, characterized in that the second means analyzes label information and appearance from captured image data.

[0889] (Claim 3)

[0890] The system according to claim 1, characterized in that the fourth means evaluates the sustainability of the manufacturing process and materials of an article.

[0891] "Application example 2 when combining with an emotional engine"

[0892] (Claim 1)

[0893] A first method for identifying items using image recognition technology,

[0894] A second means for obtaining information about the aforementioned article from an information storage medium,

[0895] A third method for evaluating the environmental impact and calculating the eco-level based on the aforementioned information,

[0896] A fourth means of presenting the aforementioned eco-level to the user,

[0897] A fifth method involves analyzing the user's emotional state and presenting information to the user based on the analysis results.

[0898] A system that includes this.

[0899] (Claim 2)

[0900] The system according to claim 1, characterized in that the first means analyzes identification information from captured image data.

[0901] (Claim 3)

[0902] The system according to claim 1, characterized in that the third means evaluates the manufacturing process of an article and the recyclability of the material. [Explanation of symbols]

[0903] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A first method for identifying items using image recognition technology, A second means of obtaining information about the aforementioned article from a database, A third method for evaluating the environmental impact and calculating the eco-level based on the aforementioned information, A fourth means of presenting the aforementioned eco-level to the user, A fifth means for taking an image of an object and performing image preprocessing, A sixth means of visually displaying evaluation results for users to understand the environmental impact of goods, A system that includes this.

2. The system according to claim 1, characterized in that the first means analyzes label information from captured image data and identifies an article.

3. The system according to claim 1, characterized in that the third means evaluates the manufacturing process and the recyclability of the materials of an article and presents the results in a manner that is easy for the user to understand.

Images