system

Abstract

We provide the system. [Solution] An image processing device receives an image of a gold coin or currency and extracts features from the image, A means for applying an analytical algorithm that quantifies the reliability of a product based on the aforementioned characteristics, A means for outputting the aforementioned reliability and presenting it to the purchaser via a display device, A method for recalculating the reliability based on images retaken after the transaction and determining whether the transaction will be completed, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In transactions of coins and currencies using image processing technology, there is a concern about the risk of purchasing counterfeits. Conventional methods require specialized knowledge and experience to distinguish genuine and fake items, making it difficult for ordinary consumers to use intuitively. Therefore, a method for easily realizing safe and reliable transactions is required.

Means for Solving the Problems

[0005] This invention provides a means for receiving an image of a gold coin or currency using an image processing device and extracting various features of the image. Based on the extracted features, an analysis algorithm is applied to quantify the reliability of the product and present it to the buyer via a display device. This system can re-evaluate the product based on images recaptured after the transaction and decide whether to proceed with the transaction based on the change in reliability. It also includes functions to instruct appropriate shooting conditions and manage return procedures. As a result, users can conduct safe and secure online transactions without requiring specialized knowledge.

[0006] An "image processing device" is a device that receives image data and performs analysis and processing on it.

[0007] A "gold coin" is a metal coin that is traded primarily for investment and collection purposes.

[0008] "Money" refers to public currency used in general transactions.

[0009] "Features" refer to specific patterns or attributes within an image, and are the information that forms the basis for analysis.

[0010] An "analysis algorithm" is a series of computational procedures used to analyze data for a specific purpose and derive results.

[0011] "Reliability" is an index that numerically evaluates the likelihood that a product is genuine.

[0012] A "display device" is a device used to visually output information generated by a computer.

[0013] "Transaction" refers to the entire process involved in buying and selling goods.

[0014] "Re-photography" refers to the act of photographing a subject that has already been photographed, but on a different occasion.

[0015] "Return procedures" refer to a series of processes where a purchaser returns the purchased goods to the original seller to receive a refund or exchange of the goods.

Brief Explanation of Drawings

[0016] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It 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] It shows an emotion map where multiple emotions are mapped. [Figure 10] It shows an emotion map where multiple emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [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.

Embodiments for Carrying out the Invention

[0017] 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.

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

[0019] 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.

[0020] 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.

[0021] 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.

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

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0024] [First Embodiment]

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

[0026] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0027] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0029] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0033] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0034] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0035] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0036] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0037] To implement the system of the present invention, the user first needs a smartphone or camera to photograph gold coins or currency. The user uses this device to take pictures of the items they wish to sell and uploads the images to the platform through a designated application. By adhering to the shooting guidelines provided by the device, images that allow for more accurate analysis can be obtained.

[0038] The server receives the uploaded image and analyzes it using image recognition AI. This AI extracts features such as "pattern," "depth of engraving," "size," and "color / light reflection" from the image and generates numerical data to evaluate the likelihood that the product is authentic. This data is quantified as a confidence score and presented to the user again via the device.

[0039] The program can be described in natural language as follows: First, the user's device sends an image it has captured to the server. This image is then analyzed by an image processing unit on the server. The AI ​​extracts features from the image by comparing them with pre-trained data and uses these features to evaluate the reliability of the product. The resulting reliability score becomes an important criterion for the user to judge the quality of the product.

[0040] As a concrete example, suppose a user is trying to sell a specific commemorative coin from the 19th century. The user takes photos of the front and back of the coin from various angles and uploads the images to the platform. The server uses AI to analyze these images and compare their features with past data. In this process, the AI ​​determines whether the coin's design, color, and size match known genuine standards and generates a high confidence score. As a result, buyers can use the displayed score as a reference to safely purchase the item.

[0041] After receiving the item, the user (buyer) takes another picture and sends it to the server, allowing the system to re-verify if it matches the initial rating. If the scores match, the transaction is successfully completed; if they do not match, the server guides the user through the appropriate return procedure, further increasing user confidence.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] Users take appropriate images of gold coins or currency using their smartphone cameras. Following the shooting guidelines will enable more accurate analysis.

[0045] Step 2:

[0046] The device uploads captured images to the server via the platform's application. Image data is transmitted at high resolution, and the detail of the image contributes to the accuracy of the analysis.

[0047] Step 3:

[0048] The server receives the uploaded images and starts the analysis process using image recognition AI. The AI ​​first preprocesses the images, performing noise reduction and image shaping.

[0049] Step 4:

[0050] The server's AI extracts features from the image. At this stage, it quantitatively identifies attributes such as "pattern," "depth of engraving," "size," and "color / light reflection."

[0051] Step 5:

[0052] The server applies an analytical algorithm to evaluate the reliability of a product using feature data. The AI ​​model determines the likelihood of a product being genuine by comparing it with past data and generates a reliability score.

[0053] Step 6:

[0054] The server sends the calculated confidence score to the user's device. The user reviews this score on their device and makes a purchase decision.

[0055] Step 7:

[0056] After a user purchases a product and receives it, they take another picture of it with their device and upload it to the server. This reassessment is performed to verify consistency with the initial scoring.

[0057] Step 8:

[0058] The server re-analyzes the image and generates a new confidence score. If this score matches the initial score, the transaction is confirmed as successful. If a discrepancy is detected, the server will suggest a return procedure to the user and proceed with the necessary steps.

[0059] (Example 1)

[0060] 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."

[0061] In traditional transactions involving valuable goods, objectively and quickly assessing the reliability of a product is difficult, often causing anxiety for both buyers and sellers. Furthermore, there are insufficient means to reconfirm the reliability of a product after the transaction, making it difficult to ensure the security of the transaction.

[0062] 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.

[0063] In this invention, the server includes means for an image processing device to receive an image of a valuable article and extract features from the image; means for applying an analysis method to quantify the reliability of the article based on the features; and means for outputting the reliability and presenting it to the user via a visual display device. This makes it possible to quantify the reliability of a product using features in the image and to verify the quality of the product at the time of and after the transaction.

[0064] An "image processing device" is a device that receives visually acquired data, extracts specific features from that data, and performs various processing operations on it.

[0065] "Items of value" refers to goods or similar items that have financial or historical value and are normally traded in the general market.

[0066] "Features" refer to the characteristics and attributes of an item or image, including specific elements such as shape, color, light reflection, and size.

[0067] "Reliability" is an indicator that shows the authenticity and quality of an item as evaluated by analytical methods, and it is an indicator that can be expressed numerically.

[0068] "Analysis method" refers to algorithms and techniques used to understand and evaluate data according to a specific purpose.

[0069] A "visual display device" refers to a device such as a monitor or screen that visually communicates processing results to the user.

[0070] "Users" are entities that operate the system or receive its results, and generally include buyers and sellers who trade goods.

[0071] To implement this system, users first need photographic equipment to take pictures of valuable items. Users use smartphones or cameras to photograph the items and upload the images to a server-based platform via a designated application. The device provides shooting guidance, enabling users to obtain high-quality images by following it.

[0072] The server receives the uploaded images and performs analysis using image recognition AI in the image processing unit. This image recognition AI extracts image features based on a pre-trained dataset and evaluates the authenticity of the items. Specifically, it quantifies features such as "pattern," "depth of engraving," "size," and "color / light reflectivity," and displays them as a confidence score.

[0073] Once a confidence score is generated, the server sends the result back to the terminal, allowing the user to view it through a visual display. The user can use this score to assess the quality of the item and make a decision about whether to sell or buy it.

[0074] As a concrete example, consider a scenario where a user sells a specific commemorative coin from the 19th century. In this case, the user takes photos of both sides of the coin from various angles and uploads the images to the platform. The server uses AI to analyze these images and compare them with historical data. The AI ​​verifies whether the coin's design, color, and size match known genuine standards and generates a high confidence score.

[0075] An example of a prompt message might be, "I would like to sell a 19th-century commemorative coin. Please evaluate the reliability score of the item." This system allows for a quick and objective assessment of the reliability of valuable items, enabling users to conduct transactions with confidence.

[0076] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0077] Step 1:

[0078] The user takes a picture of a valuable item. A smartphone or digital camera is used as the camera. The device provides shooting guidance, including instructions on lighting and shooting distance. Based on this guidance, the user takes the image under optimal conditions and uploads it to the system's platform. The input is an image of the item, and the output is an image file stored within the platform.

[0079] Step 2:

[0080] The server receives images sent from the user's terminal. The received images are sent to an image processing device for pre-processing. Specifically, this pre-processing includes format conversion and resolution adjustment. The input is the uploaded image file, and the output is an image file that has been prepared for analysis.

[0081] Step 3:

[0082] The server inputs the pre-processed image data into the image recognition AI. This AI compares the image with a pre-trained dataset and extracts features such as "pattern," "depth of carving," "size," and "color / light reflection" from the image. The input is processed image data, and the output is data quantified as features.

[0083] Step 4:

[0084] The server analyzes feature data quantified by AI and generates a confidence score for the item. Using a generative AI model, it quantifies the likelihood that an item is genuine by comparing past data with current features. The input is feature data, and the output is a numerical score indicating confidence.

[0085] Step 5:

[0086] The server sends the generated confidence score to the user's terminal. The terminal displays the score on a visual display device for the user to review. The user uses this score to evaluate the quality of the goods and decide whether to proceed with the transaction. The input is the confidence score, and the output is a score display that the user can visually review.

[0087] Step 6:

[0088] After the transaction is complete and the user receives the item, they take another picture of the item and send the image to the server. The server then re-evaluates the confidence score using the same process. By taking the picture under the same conditions as when the purchase was made, a highly reproducible evaluation is possible. The input is the re-taken image, and the output is the recalculated confidence score.

[0089] Step 7:

[0090] The server compares the transaction score with the re-evaluated score. If they match, the transaction is considered successful, and the process is completed. If they do not match, the server provides the user with return procedures and supports secure transactions. The input is the confidence score at the time of the transaction and at the time of re-evaluation, and the output is whether the transaction was successful or not, along with any necessary instructions.

[0091] (Application Example 1)

[0092] 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."

[0093] A challenge exists in that it is difficult for buyers to quickly and accurately determine whether an item is genuine when purchasing it in a store. This creates a risk of purchasing counterfeit goods, making it difficult to ensure buyer trust. Furthermore, it is crucial to reconfirm the authenticity of goods after the transaction and to have efficient return procedures in case of discrepancies.

[0094] 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.

[0095] In this invention, the server includes means for an image processing device to receive an image of an object and extract features from the image; means for applying an analysis algorithm that quantifies the reliability of the product based on the features; and means for outputting the reliability and presenting it to the consumer via a visual display device. As a result, buyers can verify the reliability of the product in real time, reducing the risk of purchasing counterfeit goods and enabling them to conduct transactions with peace of mind. Furthermore, it allows for a re-evaluation of the product after the transaction and prompt and appropriate return procedures.

[0096] An "image processing device" is an electronic device that receives images of objects and extracts specific features from those images.

[0097] "Features" refer to physical or visual elements extracted from an image of an object, such as "pattern," "depth of engraving," "size," and "color / reflection of light."

[0098] An "analysis algorithm" is a calculation method used to quantify the reliability of a product based on the extracted features.

[0099] "Reliability" is a value calculated by an analytical algorithm and serves as a numerical standard for evaluating the authenticity and quality of an item.

[0100] A "visual display device" is a display device used to present images and information to the user's visual sense.

[0101] "Consumer" refers to customers or users who purchase or trade goods.

[0102] An "object" refers to a specific product or item that is the subject of image processing or reliability evaluation.

[0103] A "cloud server" is a server located in a remote location for processing and recording data via the internet.

[0104] "Real-time" refers to a temporal continuity in which processing takes place in an instantaneous response the moment an operation or input is made.

[0105] The server first analyzes the image of the object received by the image processing device. Smart glasses and smartphones are used as image processing devices in this process. These devices use machine learning libraries such as TENSORFLOW® and OpenCV, as well as image processing tools, to extract features such as "pattern," "depth of engraving," "size," and "color / light reflection" from the image.

[0106] Next, the server uses this information to quantify the reliability of the product using a pre-configured analysis algorithm. This reliability score is provided to the consumer in real time and presented via a visual display device.

[0107] Based on this information, users (consumers) can verify the authenticity of products in stores and make an immediate decision about whether or not to purchase them. Furthermore, after a transaction, the item is photographed again and resent to the server, allowing for a confirmation of consistency with the initial evaluation and a reassessment of the product's authenticity. This improves transaction security, and if a discrepancy is found, the server guides the user through the appropriate return procedure.

[0108] As a concrete example, when a salesperson in a store shows a consumer a 17th-century coin, the salesperson scans the coin with smart glasses. The consumer can then see the confidence score displayed on the glasses, allowing them to make a purchase decision with confidence on the spot. In this way, by utilizing generative AI models, the authenticity of objects can be verified, enabling fast and reliable transactions.

[0109] An example of a prompt message would be: "When selling 17th-century gold coins, how can I analyze images taken with smart glasses and display a product reliability score to the user in real time?"

[0110] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0111] Step 1:

[0112] The device captures images of an object. The device uses smart glasses or a smartphone to input images of the object. The user points the device at the object and acquires images from various angles.

[0113] Step 2:

[0114] The device sends the image it captures to the server. The input here is the image captured by the device, and the output is the data arriving at the server. The device uploads the image file to the server via the internet.

[0115] Step 3:

[0116] The server analyzes the images it receives. The input is an image file, and data processing is performed using tools such as OpenCV and TensorFlow to extract features. The output is the extracted feature data.

[0117] Step 4:

[0118] The server applies an analysis algorithm based on the feature data. The input is the extracted features, and a generative AI model is used to perform data calculations to determine the confidence level of the image. The output is the confidence score.

[0119] Step 5:

[0120] The server sends a confidence score to the terminal. The input is the confidence score obtained through analysis, and the output is the data displayed on the terminal's visual display device. The server quickly returns the score to the terminal and provides information to the user.

[0121] Step 6:

[0122] The user checks the confidence score and makes a purchase decision. The input is the displayed confidence score, and the output is the choice to purchase or decline. The user proceeds with the transaction based on the data displayed on the screen.

[0123] Step 7:

[0124] After the transaction, the device takes another picture of the object and sends it to the server. The input here is the newly taken image, which is sent to the server for re-evaluation.

[0125] Step 8:

[0126] The server re-evaluates the image based on the recaptured image and compares it to the previous evaluation. The input is the retransmitted image, and data processing is performed to check if the confidence levels match. The output is the evaluation result.

[0127] Step 9:

[0128] The server sends the re-evaluation results to the terminal and guides the user through the appropriate return procedure if necessary. The input here is the re-evaluation result, and the output is a notification to the user or instructions for the return procedure.

[0129] 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.

[0130] This invention begins with a user taking a picture of a gold coin or currency with a smartphone or camera and uploading the image to the platform. The user receives assistance via an image processing device, following shooting guidance to obtain an image that is easier to analyze. This image is sent to a server, where it is analyzed by an image recognition AI.

[0131] The server extracts features such as "pattern," "depth of engraving," "size," and "color / light reflection" from the image, and evaluates and quantifies the reliability of the product. This reliability score is presented to the buyer via their device to help them make a purchase decision.

[0132] Furthermore, an emotion engine is incorporated that analyzes the user's emotions and provides an interface that reinforces their purchase intent when it decreases. This engine recognizes emotions from the user's facial expressions and voice, and modifies prompts, visuals, and voice guidance to provide appropriate feedback. In addition, to increase post-transaction satisfaction, it also analyzes the user's emotions when a transaction is canceled and provides support to reduce stress as needed.

[0133] As a concrete example, consider a scenario where a user attempts to purchase a registered 19th-century commemorative coin. If the emotion engine detects that the user is feeling anxious, the server provides specific product descriptions and feedback to reassure them. After the transaction is completed and the product arrives, another emotion analysis is performed to confirm the user's satisfaction and whether there are any problems, and directs them to customer support if necessary.

[0134] This system is designed to highly personalize the user's purchasing experience and provide a reassuring and trustworthy environment at every stage of the transaction. By incorporating an emotional engine in this way, it is possible to improve the quality of transactions and increase user satisfaction.

[0135] The following describes the processing flow.

[0136] Step 1:

[0137] The user takes a picture of a gold coin or currency using their smartphone camera. The device provides shooting guidance, prompting the user to take the picture at the appropriate angle and under suitable lighting conditions.

[0138] Step 2:

[0139] The device uploads the captured image to the server. At the same time, settings regarding the image size and resolution are also sent, setting the criteria for the server to perform the optimal processing.

[0140] Step 3:

[0141] The server analyzes the received images using image recognition AI. First, it removes unnecessary data from the image and extracts features such as "pattern," "depth of engraving," "size," and "color / light reflection."

[0142] Step 4:

[0143] The server applies an analysis algorithm to evaluate the reliability based on the extracted features. It calculates the resulting reliability score and sends the result to the terminal.

[0144] Step 5:

[0145] The device presents the user with a confidence score. At this stage, the emotion engine analyzes the user's emotional state based on their facial expressions and the operation of their input device.

[0146] Step 6:

[0147] Based on data from the emotion engine, the server detects a potential decrease in the user's purchase intent and sends appropriate feedback and advice to the device. This may include visual and auditory changes to encourage purchases.

[0148] Step 7:

[0149] Users make purchasing decisions based on the presented confidence score and additional information. After purchase, when the product arrives, they take another picture and upload it to the server.

[0150] Step 8:

[0151] The server analyzes the recaptured images and compares them to the initial data to confirm consistency in confidence. The sentiment engine also restarts, detecting user satisfaction and dissatisfaction and providing additional support as needed.

[0152] (Example 2)

[0153] 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".

[0154] Conventional currency reliability evaluation systems using image recognition technology failed to adequately address the anxieties and doubts felt by prospective buyers, resulting in a lack of means to ensure reliable transactions. Furthermore, insufficient feedback and support to enhance satisfaction after a transaction was completed contributed to factors that compromised the quality of transactions.

[0155] 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.

[0156] In this invention, the server includes means for an image processing device to receive an image of currency and extract the visual features of the image; means for applying an analysis technique to quantify the reliability of the product based on the features; means for outputting the reliability and presenting it to the prospective buyer via a display device; and means for analyzing the prospective buyer's emotions using an emotion analysis engine and generating feedback to reinforce their willingness to purchase. This makes it possible to personalize the user's purchasing experience and provide a transaction environment that instills a sense of security and trust.

[0157] An "image processing device" is a device that receives images of currency and has the function of extracting the visual features of those images.

[0158] "Visual features" refer to characteristics of the appearance of the coin, such as patterns in the image, depth of engraving, size, color, and how light reflects off it.

[0159] "Analysis techniques" refer to algorithms and methods used to quantify the reliability of a product based on its visual characteristics.

[0160] "Reliability" is an evaluation score regarding the authenticity and value of a product, quantified using analytical techniques.

[0161] A "display device" is a device used to visually present the calculated reliability level to potential buyers.

[0162] An "emotion analysis engine" is a device that analyzes the emotions of potential buyers from their voices and facial expressions, and generates feedback to reinforce their desire to purchase.

[0163] "Feedback" refers to information and messages provided to potential buyers to increase their desire to purchase.

[0164] This invention is a system for analyzing images of currency and evaluating their reliability. Users take images of gold coins or other currency using a smartphone or camera. During shooting, it is recommended to use the guidance provided by the image processing device to acquire images under appropriate shooting conditions. This results in images that allow for detailed feature extraction.

[0165] Once an image is taken, the user uploads it to the platform. The device sends the uploaded image to the server, and image recognition begins. The server uses image recognition AI to analyze the visual features of the image, extracting data such as patterns, depth of carvings, size, color, and light reflection. This analysis utilizes advanced algorithms, including deep learning techniques.

[0166] After analysis, the server quantifies the reliability of the product based on its visual characteristics. This reliability score, generated by the analysis technology, is output from the server and presented to the user via the terminal. This allows the user to make purchase decisions based on reliable information.

[0167] Furthermore, an emotion analysis engine analyzes the user's facial expressions and voice to collect data on their purchase intent. Based on the user's emotions, the server generates and provides feedback that enhances their purchase intent. This prompt includes reassuring information and specific explanations to encourage purchase.

[0168] As a concrete example, when a user attempts to purchase a 19th-century commemorative coin, the AI ​​model is input with an image along with the prompt, "Please create reassuring feedback to alleviate any anxieties about purchasing a 19th-century commemorative coin," which is then used as feedback for the user.

[0169] This system design is expected to allow users to enjoy a personalized purchasing experience and enhance their sense of security and trust at each stage of the transaction.

[0170] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0171] Step 1:

[0172] The user takes pictures of gold coins or currency using a smartphone or camera. During the process, the user receives guidance from the image processing device, specifying appropriate lighting conditions and angles to obtain images with clearly recorded visual features. In this process, the input is the image of the currency taken by the user, and the output is image data suitable for analysis.

[0173] Step 2:

[0174] The user uploads the captured image to the platform. The device then sends the image to the server. After image transmission, the input is an image file of currency, and the output is the image data transferred to the server.

[0175] Step 3:

[0176] The server analyzes the received image of the currency using image recognition AI. The input is image data transferred to the server, and the server performs the specific operation of extracting visual features. Here, data such as the pattern, depth of engraving, size, color, and light reflection within the image are quantified, and the output is the extracted visual feature data.

[0177] Step 4:

[0178] The server applies analytical techniques to extracted visual feature data to quantify the reliability of a product. The input is visual feature data, and the server calculates a reliability evaluation score. Data processing and calculations are performed during this process, and the output is the product's reliability score.

[0179] Step 5:

[0180] The terminal displays a confidence score provided by the server to the user. By receiving the confidence score as input and visually providing confidence information to help with purchase decisions, the output is the confidence score information displayed to the user.

[0181] Step 6:

[0182] The emotion analysis engine analyzes the user's facial expressions and voice data to extract emotional information. The input is the user's emotional data acquired by the terminal, the server performs emotion analysis, and outputs analysis results based on the user's emotions.

[0183] Step 7:

[0184] The server generates feedback to increase purchase intent based on the analysis results. The input is user sentiment analysis data, which generates prompts as specific actions, and then uses a generative AI model to generate feedback. The output is feedback information designed to reinforce the user's purchase intent.

[0185] (Application Example 2)

[0186] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0187] In commercial transactions, failure to adequately address concerns about product reliability or buyer anxiety can result in decreased buyer satisfaction or transaction cancellations. In particular, prompt and appropriate responses are required when users have concerns about a product or when their evaluation of the product changes after the transaction. Furthermore, because it is not possible to provide support tailored to the individual emotions and circumstances of each user, strengthening the support system for purchase decision-making is necessary.

[0188] 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.

[0189] In this invention, the server includes means for an image processing device to receive an image of a gold coin or currency and extract features of the image; means for applying an analysis algorithm that quantifies the reliability of the product based on the features; means for an emotion analysis engine to analyze the user's emotions and provide appropriate feedback; and means for referencing an online database to evaluate user satisfaction after a transaction and suggest improvement measures. This makes it possible to reduce user anxiety at each stage of product transactions, improve buyer satisfaction, and optimize the transaction environment.

[0190] An "image processing device" is a device or system for receiving an image of a gold coin or currency and extracting features from that image.

[0191] "Characteristics" refer to physical or visual attributes of a gold coin or currency, such as "pattern," "depth of engraving," "size," and "color / reflection of light."

[0192] An "analysis algorithm" is a computational method or program used to quantify and evaluate the reliability of a product based on extracted features.

[0193] "Confidence level" is a numerical index that indicates the degree of authenticity or value of a gold coin or currency, estimated from features obtained through image processing.

[0194] An "emotion analysis engine" is a system or program that analyzes a user's facial expressions and voice, recognizes their emotional state, and generates appropriate feedback.

[0195] "Feedback" refers to information and suggestions provided to users to support their purchasing decisions.

[0196] An "online database" is a digital storage system used to store and access user data and evaluation information related to transactions.

[0197] "User satisfaction" is an indicator that shows how satisfied users are with a product or service after a transaction.

[0198] "Improvement measures" refer to strategies or means proposed to enhance user satisfaction.

[0199] The system for realizing this invention includes a series of steps to improve user confidence in gold coin or currency transactions. First, the user takes a picture of the gold coin or currency using a device such as a smartphone and transmits it to an image processing device. This image processing device extracts features from the image and applies an analysis algorithm to calculate confidence. In this process, libraries such as TensorFlow are used to precisely analyze various features of the image.

[0200] The server calculates the confidence level and then presents it to the user via a display device. It also analyzes the user's emotions using an emotion analysis engine and generates situation-appropriate feedback based on facial recognition and voice analysis. This feedback helps reduce user anxiety and allows for confident transactions. Emotion analysis utilizes technologies such as OpenCV and Google® Cloud Speech-to-Text.

[0201] Furthermore, even after a transaction is completed, the server continues to interact with the online database to evaluate user satisfaction. This provides a framework for continuously improving the user experience by suggesting improvement measures as needed.

[0202] For example, a user might take a picture of a coin, upload the image, and then have its trustworthiness calculated. If the user expresses concern, the server may provide additional information to reassure them. Also, if the user is dissatisfied with the condition of the product, the system will immediately follow up and guide them through the process of resolving the issue.

[0203] An example of a prompt message is as follows: "Upload an image of the registered currency, and we want to score its trustworthiness based on its visual characteristics. If the user becomes anxious, please provide specific purchase information to reassure them. For example, continue to check on customer satisfaction after they receive the product."

[0204] In this way, the system enables two-way interaction that meets the individual needs of users, improving the quality of transactions.

[0205] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0206] Step 1:

[0207] The user takes a picture of a gold coin or currency using their device. The image must be of high quality and capture important features. This image will serve as the input data.

[0208] Step 2:

[0209] The terminal transmits an image to the image processing unit. The image processing unit uses digital image processing technology to extract features such as "pattern," "depth of engraving," "size," and "color / light reflection" from the image. This process outputs feature data.

[0210] Step 3:

[0211] The server receives feature data and applies an analysis algorithm to quantify the confidence level of the images. Specifically, it performs data calculations based on the extracted features using a generative AI model, and outputs a confidence score for the product.

[0212] Step 4:

[0213] The server sends a confidence score to the user's device and displays it on the display. The user can then use this score to make a purchase decision.

[0214] Step 5:

[0215] The device collects the user's facial expressions and voice, which are then input into a sentiment analysis engine on the server. The sentiment analysis engine uses OpenCV and Google Cloud Speech-to-Text to analyze the user's emotions and recognize their state. Sentiment data is then generated.

[0216] Step 6:

[0217] The server generates appropriate feedback based on emotional data. This feedback includes specific information and reassurances to alleviate the user's anxiety. This feedback is generated and presented to the user.

[0218] Step 7:

[0219] The server uses an online database to monitor user satisfaction after transactions. By referring to user feedback and reviews, it can generate improvement plans if necessary. Additional support and information are provided during this step.

[0220] Step 8:

[0221] The system provides users with confirmation prompts after receiving or using a product. For example, it can provide prompts to confirm user satisfaction or to check if there are any problems with the product's condition. Based on these prompts, the system can continue to provide support as needed.

[0222] 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.

[0223] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0224] 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.

[0225] [Second Embodiment]

[0226] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0227] 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.

[0228] 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).

[0229] 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.

[0230] 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.

[0231] 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).

[0232] 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.

[0233] 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.

[0234] 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.

[0235] 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.

[0236] 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.

[0237] 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".

[0238] To implement the system of the present invention, the user first needs a smartphone or camera to photograph gold coins or currency. The user uses this device to take pictures of the items they wish to sell and uploads the images to the platform through a designated application. By adhering to the shooting guidelines provided by the device, images that allow for more accurate analysis can be obtained.

[0239] The server receives the uploaded image and analyzes it using image recognition AI. This AI extracts features such as "pattern," "depth of engraving," "size," and "color / light reflection" from the image and generates numerical data to evaluate the likelihood that the product is authentic. This data is quantified as a confidence score and presented to the user again via the device.

[0240] The program can be described in natural language as follows: First, the user's device sends an image it has captured to the server. This image is then analyzed by an image processing unit on the server. The AI ​​extracts features from the image by comparing them with pre-trained data and uses these features to evaluate the reliability of the product. The resulting reliability score becomes an important criterion for the user to judge the quality of the product.

[0241] As a concrete example, suppose a user is trying to sell a specific commemorative coin from the 19th century. The user takes photos of the front and back of the coin from various angles and uploads the images to the platform. The server uses AI to analyze these images and compare their features with past data. In this process, the AI ​​determines whether the coin's design, color, and size match known genuine standards and generates a high confidence score. As a result, buyers can use the displayed score as a reference to safely purchase the item.

[0242] After receiving the item, the user (buyer) takes another picture and sends it to the server, allowing the system to re-verify if it matches the initial rating. If the scores match, the transaction is successfully completed; if they do not match, the server guides the user through the appropriate return procedure, further increasing user confidence.

[0243] The following describes the processing flow.

[0244] Step 1:

[0245] Users take appropriate images of gold coins or currency using their smartphone cameras. Following the shooting guidelines will enable more accurate analysis.

[0246] Step 2:

[0247] The device uploads captured images to the server via the platform's application. Image data is transmitted at high resolution, and the detail of the image contributes to the accuracy of the analysis.

[0248] Step 3:

[0249] The server receives the uploaded images and starts the analysis process using image recognition AI. The AI ​​first preprocesses the images, performing noise reduction and image shaping.

[0250] Step 4:

[0251] The server's AI extracts features from the image. At this stage, it quantitatively identifies attributes such as "pattern," "depth of engraving," "size," and "color / light reflection."

[0252] Step 5:

[0253] The server applies an analytical algorithm to evaluate the reliability of a product using feature data. The AI ​​model determines the likelihood of a product being genuine by comparing it with past data and generates a reliability score.

[0254] Step 6:

[0255] The server sends the calculated confidence score to the user's device. The user reviews this score on their device and makes a purchase decision.

[0256] Step 7:

[0257] After a user purchases a product and receives it, they take another picture of it with their device and upload it to the server. This reassessment is performed to verify consistency with the initial scoring.

[0258] Step 8:

[0259] The server re-analyzes the image and generates a new confidence score. If this score matches the initial score, the transaction is confirmed as successful. If a discrepancy is detected, the server will suggest a return procedure to the user and proceed with the necessary steps.

[0260] (Example 1)

[0261] 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."

[0262] In traditional transactions involving valuable goods, objectively and quickly assessing the reliability of a product is difficult, often causing anxiety for both buyers and sellers. Furthermore, there are insufficient means to reconfirm the reliability of a product after the transaction, making it difficult to ensure the security of the transaction.

[0263] 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.

[0264] In this invention, the server includes means for an image processing device to receive an image of a valuable article and extract features from the image; means for applying an analysis method to quantify the reliability of the article based on the features; and means for outputting the reliability and presenting it to the user via a visual display device. This makes it possible to quantify the reliability of a product using features in the image and to verify the quality of the product at the time of and after the transaction.

[0265] An "image processing device" is a device that receives visually acquired data, extracts specific features from that data, and performs various processing operations on it.

[0266] "Items of value" refers to goods or similar items that have financial or historical value and are normally traded in the general market.

[0267] "Features" refer to the characteristics and attributes of an item or image, including specific elements such as shape, color, light reflection, and size.

[0268] "Reliability" is an indicator that shows the authenticity and quality of an item as evaluated by analytical methods, and it is an indicator that can be expressed numerically.

[0269] "Analysis method" refers to algorithms and techniques used to understand and evaluate data according to a specific purpose.

[0270] A "visual display device" refers to a device such as a monitor or screen that visually communicates processing results to the user.

[0271] "Users" are entities that operate the system or receive its results, and generally include buyers and sellers who trade goods.

[0272] To implement this system, users first need photographic equipment to take pictures of valuable items. Users use smartphones or cameras to photograph the items and upload the images to a server-based platform via a designated application. The device provides shooting guidance, enabling users to obtain high-quality images by following it.

[0273] The server receives the uploaded images and performs analysis using image recognition AI in the image processing unit. This image recognition AI extracts image features based on a pre-trained dataset and evaluates the authenticity of the items. Specifically, it quantifies features such as "pattern," "depth of engraving," "size," and "color / light reflectivity," and displays them as a confidence score.

[0274] Once a confidence score is generated, the server sends the result back to the terminal, allowing the user to view it through a visual display. The user can use this score to assess the quality of the item and make a decision about whether to sell or buy it.

[0275] As a concrete example, consider a scenario where a user sells a specific commemorative coin from the 19th century. In this case, the user takes photos of both sides of the coin from various angles and uploads the images to the platform. The server uses AI to analyze these images and compare them with historical data. The AI ​​verifies whether the coin's design, color, and size match known genuine standards and generates a high confidence score.

[0276] An example of a prompt message might be, "I would like to sell a 19th-century commemorative coin. Please evaluate the reliability score of the item." This system allows for a quick and objective assessment of the reliability of valuable items, enabling users to conduct transactions with confidence.

[0277] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0278] Step 1:

[0279] The user takes a picture of a valuable item. A smartphone or digital camera is used as the camera. The device provides shooting guidance, including instructions on lighting and shooting distance. Based on this guidance, the user takes the image under optimal conditions and uploads it to the system's platform. The input is an image of the item, and the output is an image file stored within the platform.

[0280] Step 2:

[0281] The server receives the image sent from the user's terminal. The received image is sent to an image processing device for preprocessing. Specific operations include image format conversion and resolution adjustment. The input is the uploaded image file, and the output is the image file prepared in a state suitable for analysis.

[0282] Step 3:

[0283] The server inputs the preprocessed image data into the image recognition AI. This AI compares it with the pre-learned dataset and extracts features such as "pattern", "depth of carving", "size", and "color and light reflection condition" from the image. The input is the prepared image data, and the output is the data quantified as feature values.

[0284] Step 4:

[0285] The server analyzes the feature data quantified by the AI and generates a reliability score for the item. By using the generated AI model to match past data with current features, the possibility that the product is genuine is quantified. The input is the feature value data, and the output is the numerical score indicating the reliability.

[0286] Step 5:

[0287] The server sends the generated reliability score to the user's terminal. The terminal outputs the score to a visual display device so that the user can confirm it. The user evaluates the quality of the item based on this score and decides whether to proceed with the transaction. The input is the reliability score, and the output is the score display that the user can visually confirm.

[0288] Step 6:

[0289] After the transaction is complete and the user receives the item, they take another picture of the item and send the image to the server. The server then re-evaluates the confidence score using the same process. By taking the picture under the same conditions as when the purchase was made, a highly reproducible evaluation is possible. The input is the re-taken image, and the output is the recalculated confidence score.

[0290] Step 7:

[0291] The server compares the transaction score with the re-evaluated score. If they match, the transaction is considered successful, and the process is completed. If they do not match, the server provides the user with return procedures and supports secure transactions. The input is the confidence score at the time of the transaction and at the time of re-evaluation, and the output is whether the transaction was successful or not, along with any necessary instructions.

[0292] (Application Example 1)

[0293] 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."

[0294] A challenge exists in that it is difficult for buyers to quickly and accurately determine whether an item is genuine when purchasing it in a store. This creates a risk of purchasing counterfeit goods, making it difficult to ensure buyer trust. Furthermore, it is crucial to reconfirm the authenticity of goods after the transaction and to have efficient return procedures in case of discrepancies.

[0295] 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.

[0296] In this invention, the server includes means for an image processing device to receive an image of an object and extract features from the image; means for applying an analysis algorithm that quantifies the reliability of the product based on the features; and means for outputting the reliability and presenting it to the consumer via a visual display device. As a result, buyers can verify the reliability of the product in real time, reducing the risk of purchasing counterfeit goods and enabling them to conduct transactions with peace of mind. Furthermore, it allows for a re-evaluation of the product after the transaction and prompt and appropriate return procedures.

[0297] An "image processing device" is an electronic device that receives images of objects and extracts specific features from those images.

[0298] "Features" refer to physical or visual elements extracted from an image of an object, such as "pattern," "depth of engraving," "size," and "color / reflection of light."

[0299] An "analysis algorithm" is a calculation method used to quantify the reliability of a product based on the extracted features.

[0300] "Reliability" is a value calculated by an analytical algorithm and serves as a numerical standard for evaluating the authenticity and quality of an item.

[0301] A "visual display device" is a display device used to present images and information to the user's visual sense.

[0302] "Consumer" refers to customers or users who purchase or trade goods.

[0303] An "object" refers to a specific product or item that is the subject of image processing or reliability evaluation.

[0304] A "cloud server" is a server located in a remote location for processing and recording data via the internet.

[0305] "Real-time" refers to the temporal continuity in which processing is carried out in an immediate response to the moment when an operation or input is made.

[0306] First, the server analyzes the image of the object received by the image processing device. As the image processing device, smart glasses or smartphones are used in this process. These devices use machine learning libraries and image processing tools such as TensorFlow and OpenCV to extract features such as "pattern", "depth of carving", "size", and "color / light reflection condition" from the image.

[0307] Next, based on this information, the server utilizes a pre-set analysis algorithm to quantify the reliability of the product. This reliability score is provided to consumers in real-time and presented via a visual display device.

[0308] The user (consumer) can use this information to confirm the authenticity of the product at the store and assist in making an immediate decision on whether to purchase. Also, after the transaction, the item can be re-photographed and re-transmitted to the server to confirm its match with the initial evaluation and re-evaluate the authenticity of the product. This improves the security of the transaction, and if there is a discrepancy, the server will guide the appropriate return procedure.

[0309] As a specific example, when a salesperson shows a 17th-century coin to a consumer at the store, the salesperson scans the coin with smart glasses. The consumer can check the reliability score displayed on the glasses and can make a purchase decision with confidence on the spot. By utilizing the generative AI model in this way, the authenticity of the object can be confirmed, and a quick and reliable transaction can be realized.

[0310] Examples of prompt sentences include forms such as "When selling a 17th-century coin, please teach me a method to analyze the image taken by smart glasses and present the reliability score of the product to the user in real-time."

[0311] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0312] Step 1:

[0313] The device captures images of an object. The device uses smart glasses or a smartphone to input images of the object. The user points the device at the object and acquires images from various angles.

[0314] Step 2:

[0315] The device sends the image it captures to the server. The input here is the image captured by the device, and the output is the data arriving at the server. The device uploads the image file to the server via the internet.

[0316] Step 3:

[0317] The server analyzes the images it receives. The input is an image file, and data processing is performed using tools such as OpenCV and TensorFlow to extract features. The output is the extracted feature data.

[0318] Step 4:

[0319] The server applies an analysis algorithm based on the feature data. The input is the extracted features, and a generative AI model is used to perform data calculations to determine the confidence level of the image. The output is the confidence score.

[0320] Step 5:

[0321] The server sends a confidence score to the terminal. The input is the confidence score obtained through analysis, and the output is the data displayed on the terminal's visual display device. The server quickly returns the score to the terminal and provides information to the user.

[0322] Step 6:

[0323] The user checks the confidence score and makes a purchase decision. The input is the displayed confidence score, and the output is the choice to purchase or decline. The user proceeds with the transaction based on the data displayed on the screen.

[0324] Step 7:

[0325] After the transaction, the device takes another picture of the object and sends it to the server. The input here is the newly taken image, which is sent to the server for re-evaluation.

[0326] Step 8:

[0327] The server re-evaluates the image based on the recaptured image and compares it to the previous evaluation. The input is the retransmitted image, and data processing is performed to check if the confidence levels match. The output is the evaluation result.

[0328] Step 9:

[0329] The server sends the re-evaluation results to the terminal and guides the user through the appropriate return procedure if necessary. The input here is the re-evaluation result, and the output is a notification to the user or instructions for the return procedure.

[0330] 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.

[0331] This invention begins with a user taking a picture of a gold coin or currency with a smartphone or camera and uploading the image to the platform. The user receives assistance via an image processing device, following shooting guidance to obtain an image that is easier to analyze. This image is sent to a server, where it is analyzed by an image recognition AI.

[0332] The server extracts features such as "pattern," "depth of engraving," "size," and "color / light reflection" from the image, and evaluates and quantifies the reliability of the product. This reliability score is presented to the buyer via their device to help them make a purchase decision.

[0333] Furthermore, an emotion engine is incorporated that analyzes the user's emotions and provides an interface that reinforces their purchase intent when it decreases. This engine recognizes emotions from the user's facial expressions and voice, and modifies prompts, visuals, and voice guidance to provide appropriate feedback. In addition, to increase post-transaction satisfaction, it also analyzes the user's emotions when a transaction is canceled and provides support to reduce stress as needed.

[0334] As a concrete example, consider a scenario where a user attempts to purchase a registered 19th-century commemorative coin. If the emotion engine detects that the user is feeling anxious, the server provides specific product descriptions and feedback to reassure them. After the transaction is completed and the product arrives, another emotion analysis is performed to confirm the user's satisfaction and whether there are any problems, and directs them to customer support if necessary.

[0335] This system is designed to highly personalize the user's purchasing experience and provide a reassuring and trustworthy environment at every stage of the transaction. By incorporating an emotional engine in this way, it is possible to improve the quality of transactions and increase user satisfaction.

[0336] The following describes the processing flow.

[0337] Step 1:

[0338] The user takes a picture of a gold coin or currency using their smartphone camera. The device provides shooting guidance, prompting the user to take the picture at the appropriate angle and under suitable lighting conditions.

[0339] Step 2:

[0340] The device uploads the captured image to the server. At the same time, settings regarding the image size and resolution are also sent, setting the criteria for the server to perform the optimal processing.

[0341] Step 3:

[0342] The server analyzes the received images using image recognition AI. First, it removes unnecessary data from the image and extracts features such as "pattern," "depth of engraving," "size," and "color / light reflection."

[0343] Step 4:

[0344] The server applies an analysis algorithm to evaluate the reliability based on the extracted features. It calculates the resulting reliability score and sends the result to the terminal.

[0345] Step 5:

[0346] The device presents the user with a confidence score. At this stage, the emotion engine analyzes the user's emotional state based on their facial expressions and the operation of their input device.

[0347] Step 6:

[0348] Based on data from the emotion engine, the server detects a potential decrease in the user's purchase intent and sends appropriate feedback and advice to the device. This may include visual and auditory changes to encourage purchases.

[0349] Step 7:

[0350] Users make purchasing decisions based on the presented confidence score and additional information. After purchase, when the product arrives, they take another picture and upload it to the server.

[0351] Step 8:

[0352] The server analyzes the recaptured images and compares them to the initial data to confirm consistency in confidence. The sentiment engine also restarts, detecting user satisfaction and dissatisfaction and providing additional support as needed.

[0353] (Example 2)

[0354] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0355] Conventional currency reliability evaluation systems using image recognition technology failed to adequately address the anxieties and doubts felt by prospective buyers, resulting in a lack of means to ensure reliable transactions. Furthermore, insufficient feedback and support to enhance satisfaction after a transaction was completed contributed to factors that compromised the quality of transactions.

[0356] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0357] In this invention, the server includes means for an image processing device to receive an image of currency and extract the visual features of the image; means for applying an analysis technique to quantify the reliability of the product based on the features; means for outputting the reliability and presenting it to the prospective buyer via a display device; and means for analyzing the prospective buyer's emotions using an emotion analysis engine and generating feedback to reinforce their willingness to purchase. This makes it possible to personalize the user's purchasing experience and provide a transaction environment that instills a sense of security and trust.

[0358] An "image processing device" is a device that receives images of currency and has the function of extracting the visual features of those images.

[0359] "Visual features" refer to characteristics of the appearance of the coin, such as patterns in the image, depth of engraving, size, color, and how light reflects off it.

[0360] "Analysis techniques" refer to algorithms and methods used to quantify the reliability of a product based on its visual characteristics.

[0361] "Reliability" is an evaluation score regarding the authenticity and value of a product, quantified using analytical techniques.

[0362] A "display device" is a device used to visually present the calculated reliability level to potential buyers.

[0363] An "emotion analysis engine" is a device that analyzes the emotions of potential buyers from their voices and facial expressions, and generates feedback to reinforce their desire to purchase.

[0364] "Feedback" refers to information and messages provided to potential buyers to increase their desire to purchase.

[0365] This invention is a system for analyzing images of currency and evaluating their reliability. Users take images of gold coins or other currency using a smartphone or camera. During shooting, it is recommended to use the guidance provided by the image processing device to acquire images under appropriate shooting conditions. This results in images that allow for detailed feature extraction.

[0366] Once an image is taken, the user uploads it to the platform. The device sends the uploaded image to the server, and image recognition begins. The server uses image recognition AI to analyze the visual features of the image, extracting data such as patterns, depth of carvings, size, color, and light reflection. This analysis utilizes advanced algorithms, including deep learning techniques.

[0367] After analysis, the server quantifies the reliability of the product based on its visual characteristics. This reliability score, generated by the analysis technology, is output from the server and presented to the user via the terminal. This allows the user to make purchase decisions based on reliable information.

[0368] Furthermore, an emotion analysis engine analyzes the user's facial expressions and voice to collect data on their purchase intent. Based on the user's emotions, the server generates and provides feedback that enhances their purchase intent. This prompt includes reassuring information and specific explanations to encourage purchase.

[0369] As a concrete example, when a user attempts to purchase a 19th-century commemorative coin, the AI ​​model is input with an image along with the prompt, "Please create reassuring feedback to alleviate any anxieties about purchasing a 19th-century commemorative coin," which is then used as feedback for the user.

[0370] This system design is expected to allow users to enjoy a personalized purchasing experience and enhance their sense of security and trust at each stage of the transaction.

[0371] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0372] Step 1:

[0373] The user takes pictures of gold coins or currency using a smartphone or camera. During the process, the user receives guidance from the image processing device, specifying appropriate lighting conditions and angles to obtain images with clearly recorded visual features. In this process, the input is the image of the currency taken by the user, and the output is image data suitable for analysis.

[0374] Step 2:

[0375] The user uploads the captured image to the platform. The device then sends the image to the server. After image transmission, the input is an image file of currency, and the output is the image data transferred to the server.

[0376] Step 3:

[0377] The server analyzes the received image of the currency using image recognition AI. The input is image data transferred to the server, and the server performs the specific operation of extracting visual features. Here, data such as the pattern, depth of engraving, size, color, and light reflection within the image are quantified, and the output is the extracted visual feature data.

[0378] Step 4:

[0379] The server applies analytical techniques to extracted visual feature data to quantify the reliability of a product. The input is visual feature data, and the server calculates a reliability evaluation score. Data processing and calculations are performed during this process, and the output is the product's reliability score.

[0380] Step 5:

[0381] The terminal displays a confidence score provided by the server to the user. By receiving the confidence score as input and visually providing confidence information to help with purchase decisions, the output is the confidence score information displayed to the user.

[0382] Step 6:

[0383] The emotion analysis engine analyzes the user's facial expressions and voice data to extract emotional information. The input is the user's emotional data acquired by the terminal, the server performs emotion analysis, and outputs analysis results based on the user's emotions.

[0384] Step 7:

[0385] The server generates feedback to increase purchase intent based on the analysis results. The input is user sentiment analysis data, which generates prompts as specific actions, and then uses a generative AI model to generate feedback. The output is feedback information designed to reinforce the user's purchase intent.

[0386] (Application Example 2)

[0387] 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."

[0388] In commercial transactions, failure to adequately address concerns about product reliability or buyer anxiety can result in decreased buyer satisfaction or transaction cancellations. In particular, prompt and appropriate responses are required when users have concerns about a product or when their evaluation of the product changes after the transaction. Furthermore, because it is not possible to provide support tailored to the individual emotions and circumstances of each user, strengthening the support system for purchase decision-making is necessary.

[0389] 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.

[0390] In this invention, the server includes means for an image processing device to receive an image of a gold coin or currency and extract features of the image; means for applying an analysis algorithm that quantifies the reliability of the product based on the features; means for an emotion analysis engine to analyze the user's emotions and provide appropriate feedback; and means for referencing an online database to evaluate user satisfaction after a transaction and suggest improvement measures. This makes it possible to reduce user anxiety at each stage of product transactions, improve buyer satisfaction, and optimize the transaction environment.

[0391] An "image processing device" is a device or system for receiving an image of a gold coin or currency and extracting features from that image.

[0392] "Characteristics" refer to physical or visual attributes of a gold coin or currency, such as "pattern," "depth of engraving," "size," and "color / reflection of light."

[0393] An "analysis algorithm" is a computational method or program used to quantify and evaluate the reliability of a product based on extracted features.

[0394] "Confidence level" is a numerical index that indicates the degree of authenticity or value of a gold coin or currency, estimated from features obtained through image processing.

[0395] An "emotion analysis engine" is a system or program that analyzes a user's facial expressions and voice, recognizes their emotional state, and generates appropriate feedback.

[0396] "Feedback" refers to information and suggestions provided to users to support their purchasing decisions.

[0397] An "online database" is a digital storage system used to store and access user data and evaluation information related to transactions.

[0398] "User satisfaction" is an indicator that shows how satisfied users are with a product or service after a transaction.

[0399] "Improvement measures" refer to strategies or means proposed to enhance user satisfaction.

[0400] The system for realizing this invention includes a series of steps to improve user confidence in gold coin or currency transactions. First, the user takes a picture of the gold coin or currency using a device such as a smartphone and transmits it to an image processing device. This image processing device extracts features from the image and applies an analysis algorithm to calculate confidence. In this process, libraries such as TensorFlow are used to precisely analyze various features of the image.

[0401] The server calculates the confidence level and then presents it to the user via a display device. It also analyzes the user's emotions using an emotion analysis engine and generates situation-appropriate feedback based on facial recognition and voice analysis. This feedback helps reduce user anxiety and allows for confident transactions. Emotion analysis utilizes technologies such as OpenCV and Google Cloud Speech-to-Text.

[0402] Furthermore, even after a transaction is completed, the server continues to interact with the online database to evaluate user satisfaction. This provides a framework for continuously improving the user experience by suggesting improvement measures as needed.

[0403] For example, a user might take a picture of a coin, upload the image, and then have its trustworthiness calculated. If the user expresses concern, the server may provide additional information to reassure them. Also, if the user is dissatisfied with the condition of the product, the system will immediately follow up and guide them through the process of resolving the issue.

[0404] An example of a prompt message is as follows: "Upload an image of the registered currency, and we want to score its trustworthiness based on its visual characteristics. If the user becomes anxious, please provide specific purchase information to reassure them. For example, continue to check on customer satisfaction after they receive the product."

[0405] In this way, the system enables two-way interaction that meets the individual needs of users, improving the quality of transactions.

[0406] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0407] Step 1:

[0408] The user takes a picture of a gold coin or currency using their device. The image must be of high quality and capture important features. This image will serve as the input data.

[0409] Step 2:

[0410] The terminal transmits an image to the image processing unit. The image processing unit uses digital image processing technology to extract features such as "pattern," "depth of engraving," "size," and "color / light reflection" from the image. This process outputs feature data.

[0411] Step 3:

[0412] The server receives feature data and applies an analysis algorithm to quantify the confidence level of the images. Specifically, it performs data calculations based on the extracted features using a generative AI model, and outputs a confidence score for the product.

[0413] Step 4:

[0414] The server sends a confidence score to the user's device and displays it on the display. The user can then use this score to make a purchase decision.

[0415] Step 5:

[0416] The device collects the user's facial expressions and voice, which are then input into a sentiment analysis engine on the server. The sentiment analysis engine uses OpenCV and Google Cloud Speech-to-Text to analyze the user's emotions and recognize their state. Sentiment data is then generated.

[0417] Step 6:

[0418] The server generates appropriate feedback based on emotional data. This feedback includes specific information and reassurances to alleviate the user's anxiety. This feedback is generated and presented to the user.

[0419] Step 7:

[0420] The server uses an online database to monitor user satisfaction after transactions. By referring to user feedback and reviews, it can generate improvement plans if necessary. Additional support and information are provided during this step.

[0421] Step 8:

[0422] The system provides users with confirmation prompts after receiving or using a product. For example, it can provide prompts to confirm user satisfaction or to check if there are any problems with the product's condition. Based on these prompts, the system can continue to provide support as needed.

[0423] 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.

[0424] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0425] 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.

[0426] [Third Embodiment]

[0427] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0428] 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.

[0429] 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).

[0430] 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.

[0431] 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.

[0432] 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).

[0433] 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.

[0434] 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.

[0435] 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.

[0436] 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.

[0437] 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.

[0438] 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".

[0439] To implement the system of the present invention, the user first needs a smartphone or camera to photograph gold coins or currency. The user uses this device to take pictures of the items they wish to sell and uploads the images to the platform through a designated application. By adhering to the shooting guidelines provided by the device, images that allow for more accurate analysis can be obtained.

[0440] The server receives the uploaded image and analyzes it using image recognition AI. This AI extracts features such as "pattern," "depth of engraving," "size," and "color / light reflection" from the image and generates numerical data to evaluate the likelihood that the product is authentic. This data is quantified as a confidence score and presented to the user again via the device.

[0441] The program can be described in natural language as follows: First, the user's device sends an image it has captured to the server. This image is then analyzed by an image processing unit on the server. The AI ​​extracts features from the image by comparing them with pre-trained data and uses these features to evaluate the reliability of the product. The resulting reliability score becomes an important criterion for the user to judge the quality of the product.

[0442] As a concrete example, suppose a user is trying to sell a specific commemorative coin from the 19th century. The user takes photos of the front and back of the coin from various angles and uploads the images to the platform. The server uses AI to analyze these images and compare their features with past data. In this process, the AI ​​determines whether the coin's design, color, and size match known genuine standards and generates a high confidence score. As a result, buyers can use the displayed score as a reference to safely purchase the item.

[0443] After receiving the item, the user (buyer) takes another picture and sends it to the server, allowing the system to re-verify if it matches the initial rating. If the scores match, the transaction is successfully completed; if they do not match, the server guides the user through the appropriate return procedure, further increasing user confidence.

[0444] The following describes the processing flow.

[0445] Step 1:

[0446] Users take appropriate images of gold coins or currency using their smartphone cameras. Following the shooting guidelines will enable more accurate analysis.

[0447] Step 2:

[0448] The device uploads captured images to the server via the platform's application. Image data is transmitted at high resolution, and the detail of the image contributes to the accuracy of the analysis.

[0449] Step 3:

[0450] The server receives the uploaded images and starts the analysis process using image recognition AI. The AI ​​first preprocesses the images, performing noise reduction and image shaping.

[0451] Step 4:

[0452] The server's AI extracts features from the image. At this stage, it quantitatively identifies attributes such as "pattern," "depth of engraving," "size," and "color / light reflection."

[0453] Step 5:

[0454] The server applies an analytical algorithm to evaluate the reliability of a product using feature data. The AI ​​model determines the likelihood of a product being genuine by comparing it with past data and generates a reliability score.

[0455] Step 6:

[0456] The server sends the calculated confidence score to the user's device. The user reviews this score on their device and makes a purchase decision.

[0457] Step 7:

[0458] After a user purchases a product and receives it, they take another picture of it with their device and upload it to the server. This reassessment is performed to verify consistency with the initial scoring.

[0459] Step 8:

[0460] The server re-analyzes the image and generates a new confidence score. If this score matches the initial score, the transaction is confirmed as successful. If a discrepancy is detected, the server will suggest a return procedure to the user and proceed with the necessary steps.

[0461] (Example 1)

[0462] 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."

[0463] In traditional transactions involving valuable goods, objectively and quickly assessing the reliability of a product is difficult, often causing anxiety for both buyers and sellers. Furthermore, there are insufficient means to reconfirm the reliability of a product after the transaction, making it difficult to ensure the security of the transaction.

[0464] 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.

[0465] In this invention, the server includes means for an image processing device to receive an image of a valuable article and extract features from the image; means for applying an analysis method to quantify the reliability of the article based on the features; and means for outputting the reliability and presenting it to the user via a visual display device. This makes it possible to quantify the reliability of a product using features in the image and to verify the quality of the product at the time of and after the transaction.

[0466] An "image processing device" is a device that receives visually acquired data, extracts specific features from that data, and performs various processing operations on it.

[0467] "Items of value" refers to goods or similar items that have financial or historical value and are normally traded in the general market.

[0468] "Features" refer to the characteristics and attributes of an item or image, including specific elements such as shape, color, light reflection, and size.

[0469] "Reliability" is an indicator that shows the authenticity and quality of an item as evaluated by analytical methods, and it is an indicator that can be expressed numerically.

[0470] "Analysis method" refers to algorithms and techniques used to understand and evaluate data according to a specific purpose.

[0471] A "visual display device" refers to a device such as a monitor or screen that visually communicates processing results to the user.

[0472] "Users" are entities that operate the system or receive its results, and generally include buyers and sellers who trade goods.

[0473] To implement this system, users first need photographic equipment to take pictures of valuable items. Users use smartphones or cameras to photograph the items and upload the images to a server-based platform via a designated application. The device provides shooting guidance, enabling users to obtain high-quality images by following it.

[0474] The server receives the uploaded images and performs analysis using image recognition AI in the image processing unit. This image recognition AI extracts image features based on a pre-trained dataset and evaluates the authenticity of the items. Specifically, it quantifies features such as "pattern," "depth of engraving," "size," and "color / light reflectivity," and displays them as a confidence score.

[0475] Once a confidence score is generated, the server sends the result back to the terminal, allowing the user to view it through a visual display. The user can use this score to assess the quality of the item and make a decision about whether to sell or buy it.

[0476] As a concrete example, consider a scenario where a user sells a specific commemorative coin from the 19th century. In this case, the user takes photos of both sides of the coin from various angles and uploads the images to the platform. The server uses AI to analyze these images and compare them with historical data. The AI ​​verifies whether the coin's design, color, and size match known genuine standards and generates a high confidence score.

[0477] An example of a prompt message might be, "I would like to sell a 19th-century commemorative coin. Please evaluate the reliability score of the item." This system allows for a quick and objective assessment of the reliability of valuable items, enabling users to conduct transactions with confidence.

[0478] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0479] Step 1:

[0480] The user takes a picture of a valuable item. A smartphone or digital camera is used as the camera. The device provides shooting guidance, including instructions on lighting and shooting distance. Based on this guidance, the user takes the image under optimal conditions and uploads it to the system's platform. The input is an image of the item, and the output is an image file stored within the platform.

[0481] Step 2:

[0482] The server receives images sent from the user's terminal. The received images are sent to an image processing device for pre-processing. Specifically, this pre-processing includes format conversion and resolution adjustment. The input is the uploaded image file, and the output is an image file that has been prepared for analysis.

[0483] Step 3:

[0484] The server inputs the pre-processed image data into the image recognition AI. This AI compares the image with a pre-trained dataset and extracts features such as "pattern," "depth of carving," "size," and "color / light reflection" from the image. The input is processed image data, and the output is data quantified as features.

[0485] Step 4:

[0486] The server analyzes feature data quantified by AI and generates a confidence score for the item. Using a generative AI model, it quantifies the likelihood that an item is genuine by comparing past data with current features. The input is feature data, and the output is a numerical score indicating confidence.

[0487] Step 5:

[0488] The server sends the generated confidence score to the user's terminal. The terminal displays the score on a visual display device for the user to review. The user uses this score to evaluate the quality of the goods and decide whether to proceed with the transaction. The input is the confidence score, and the output is a score display that the user can visually review.

[0489] Step 6:

[0490] After the transaction is complete and the user receives the item, they take another picture of the item and send the image to the server. The server then re-evaluates the confidence score using the same process. By taking the picture under the same conditions as when the purchase was made, a highly reproducible evaluation is possible. The input is the re-taken image, and the output is the recalculated confidence score.

[0491] Step 7:

[0492] The server compares the transaction score with the re-evaluated score. If they match, the transaction is considered successful, and the process is completed. If they do not match, the server provides the user with return procedures and supports secure transactions. The input is the confidence score at the time of the transaction and at the time of re-evaluation, and the output is whether the transaction was successful or not, along with any necessary instructions.

[0493] (Application Example 1)

[0494] 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."

[0495] A challenge exists in that it is difficult for buyers to quickly and accurately determine whether an item is genuine when purchasing it in a store. This creates a risk of purchasing counterfeit goods, making it difficult to ensure buyer trust. Furthermore, it is crucial to reconfirm the authenticity of goods after the transaction and to have efficient return procedures in case of discrepancies.

[0496] 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.

[0497] In this invention, the server includes means for an image processing device to receive an image of an object and extract features from the image; means for applying an analysis algorithm that quantifies the reliability of the product based on the features; and means for outputting the reliability and presenting it to the consumer via a visual display device. As a result, buyers can verify the reliability of the product in real time, reducing the risk of purchasing counterfeit goods and enabling them to conduct transactions with peace of mind. Furthermore, it allows for a re-evaluation of the product after the transaction and prompt and appropriate return procedures.

[0498] An "image processing device" is an electronic device that receives images of objects and extracts specific features from those images.

[0499] "Features" refer to physical or visual elements extracted from an image of an object, such as "pattern," "depth of engraving," "size," and "color / reflection of light."

[0500] An "analysis algorithm" is a calculation method used to quantify the reliability of a product based on the extracted features.

[0501] "Reliability" is a value calculated by an analytical algorithm and serves as a numerical standard for evaluating the authenticity and quality of an item.

[0502] A "visual display device" is a display device used to present images and information to the user's visual sense.

[0503] "Consumer" refers to customers or users who purchase or trade goods.

[0504] An "object" refers to a specific product or item that is the subject of image processing or reliability evaluation.

[0505] A "cloud server" is a server located in a remote location for processing and recording data via the internet.

[0506] "Real-time" refers to a temporal continuity in which processing takes place in an instantaneous response the moment an operation or input is made.

[0507] The server first analyzes the image of the object received by the image processing unit. Smart glasses and smartphones are used as image processing units in this process. These devices use machine learning libraries such as TensorFlow and OpenCV, as well as image processing tools, to extract features such as "pattern," "depth of engraving," "size," and "color / light reflection" from the image.

[0508] Next, the server uses this information to quantify the reliability of the product using a pre-configured analysis algorithm. This reliability score is provided to the consumer in real time and presented via a visual display device.

[0509] Based on this information, users (consumers) can verify the authenticity of products in stores and make an immediate decision about whether or not to purchase them. Furthermore, after a transaction, the item is photographed again and resent to the server, allowing for a confirmation of consistency with the initial evaluation and a reassessment of the product's authenticity. This improves transaction security, and if a discrepancy is found, the server guides the user through the appropriate return procedure.

[0510] As a concrete example, when a salesperson in a store shows a consumer a 17th-century coin, the salesperson scans the coin with smart glasses. The consumer can then see the confidence score displayed on the glasses, allowing them to make a purchase decision with confidence on the spot. In this way, by utilizing generative AI models, the authenticity of objects can be verified, enabling fast and reliable transactions.

[0511] An example of a prompt message would be: "When selling 17th-century gold coins, how can I analyze images taken with smart glasses and display a product reliability score to the user in real time?"

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

[0513] Step 1:

[0514] The device captures images of an object. The device uses smart glasses or a smartphone to input images of the object. The user points the device at the object and acquires images from various angles.

[0515] Step 2:

[0516] The device sends the image it captures to the server. The input here is the image captured by the device, and the output is the data arriving at the server. The device uploads the image file to the server via the internet.

[0517] Step 3:

[0518] The server analyzes the images it receives. The input is an image file, and data processing is performed using tools such as OpenCV and TensorFlow to extract features. The output is the extracted feature data.

[0519] Step 4:

[0520] The server applies an analysis algorithm based on the feature data. The input is the extracted features, and a generative AI model is used to perform data calculations to determine the confidence level of the image. The output is the confidence score.

[0521] Step 5:

[0522] The server sends a confidence score to the terminal. The input is the confidence score obtained through analysis, and the output is the data displayed on the terminal's visual display device. The server quickly returns the score to the terminal and provides information to the user.

[0523] Step 6:

[0524] The user checks the confidence score and makes a purchase decision. The input is the displayed confidence score, and the output is the choice to purchase or decline. The user proceeds with the transaction based on the data displayed on the screen.

[0525] Step 7:

[0526] After the transaction, the device takes another picture of the object and sends it to the server. The input here is the newly taken image, which is sent to the server for re-evaluation.

[0527] Step 8:

[0528] The server re-evaluates the image based on the recaptured image and compares it to the previous evaluation. The input is the retransmitted image, and data processing is performed to check if the confidence levels match. The output is the evaluation result.

[0529] Step 9:

[0530] The server sends the re-evaluation results to the terminal and guides the user through the appropriate return procedure if necessary. The input here is the re-evaluation result, and the output is a notification to the user or instructions for the return procedure.

[0531] 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.

[0532] This invention begins with a user taking a picture of a gold coin or currency with a smartphone or camera and uploading the image to the platform. The user receives assistance via an image processing device, following shooting guidance to obtain an image that is easier to analyze. This image is sent to a server, where it is analyzed by an image recognition AI.

[0533] The server extracts features such as "pattern," "depth of engraving," "size," and "color / light reflection" from the image, and evaluates and quantifies the reliability of the product. This reliability score is presented to the buyer via their device to help them make a purchase decision.

[0534] Furthermore, an emotion engine is incorporated that analyzes the user's emotions and provides an interface that reinforces their purchase intent when it decreases. This engine recognizes emotions from the user's facial expressions and voice, and modifies prompts, visuals, and voice guidance to provide appropriate feedback. In addition, to increase post-transaction satisfaction, it also analyzes the user's emotions when a transaction is canceled and provides support to reduce stress as needed.

[0535] As a concrete example, consider a scenario where a user attempts to purchase a registered 19th-century commemorative coin. If the emotion engine detects that the user is feeling anxious, the server provides specific product descriptions and feedback to reassure them. After the transaction is completed and the product arrives, another emotion analysis is performed to confirm the user's satisfaction and whether there are any problems, and directs them to customer support if necessary.

[0536] This system is designed to highly personalize the user's purchasing experience and provide a reassuring and trustworthy environment at every stage of the transaction. By incorporating an emotional engine in this way, it is possible to improve the quality of transactions and increase user satisfaction.

[0537] The following describes the processing flow.

[0538] Step 1:

[0539] The user takes a picture of a gold coin or currency using their smartphone camera. The device provides shooting guidance, prompting the user to take the picture at the appropriate angle and under suitable lighting conditions.

[0540] Step 2:

[0541] The device uploads the captured image to the server. At the same time, settings regarding the image size and resolution are also sent, setting the criteria for the server to perform the optimal processing.

[0542] Step 3:

[0543] The server analyzes the received images using image recognition AI. First, it removes unnecessary data from the image and extracts features such as "pattern," "depth of engraving," "size," and "color / light reflection."

[0544] Step 4:

[0545] The server applies an analysis algorithm to evaluate the reliability based on the extracted features. It calculates the resulting reliability score and sends the result to the terminal.

[0546] Step 5:

[0547] The device presents the user with a confidence score. At this stage, the emotion engine analyzes the user's emotional state based on their facial expressions and the operation of their input device.

[0548] Step 6:

[0549] Based on data from the emotion engine, the server detects a potential decrease in the user's purchase intent and sends appropriate feedback and advice to the device. This may include visual and auditory changes to encourage purchases.

[0550] Step 7:

[0551] Users make purchasing decisions based on the presented confidence score and additional information. After purchase, when the product arrives, they take another picture and upload it to the server.

[0552] Step 8:

[0553] The server analyzes the recaptured images and compares them to the initial data to confirm consistency in confidence. The sentiment engine also restarts, detecting user satisfaction and dissatisfaction and providing additional support as needed.

[0554] (Example 2)

[0555] 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."

[0556] Conventional currency reliability evaluation systems using image recognition technology failed to adequately address the anxieties and doubts felt by prospective buyers, resulting in a lack of means to ensure reliable transactions. Furthermore, insufficient feedback and support to enhance satisfaction after a transaction was completed contributed to factors that compromised the quality of transactions.

[0557] 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.

[0558] In this invention, the server includes means for an image processing device to receive an image of currency and extract the visual features of the image; means for applying an analysis technique to quantify the reliability of the product based on the features; means for outputting the reliability and presenting it to the prospective buyer via a display device; and means for analyzing the prospective buyer's emotions using an emotion analysis engine and generating feedback to reinforce their willingness to purchase. This makes it possible to personalize the user's purchasing experience and provide a transaction environment that instills a sense of security and trust.

[0559] An "image processing device" is a device that receives images of currency and has the function of extracting the visual features of those images.

[0560] "Visual features" refer to characteristics of the appearance of the coin, such as patterns in the image, depth of engraving, size, color, and how light reflects off it.

[0561] "Analysis techniques" refer to algorithms and methods used to quantify the reliability of a product based on its visual characteristics.

[0562] "Reliability" is an evaluation score regarding the authenticity and value of a product, quantified using analytical techniques.

[0563] A "display device" is a device used to visually present the calculated reliability level to potential buyers.

[0564] An "emotion analysis engine" is a device that analyzes the emotions of potential buyers from their voices and facial expressions, and generates feedback to reinforce their desire to purchase.

[0565] "Feedback" refers to information and messages provided to potential buyers to increase their desire to purchase.

[0566] This invention is a system for analyzing images of currency and evaluating their reliability. Users take images of gold coins or other currency using a smartphone or camera. During shooting, it is recommended to use the guidance provided by the image processing device to acquire images under appropriate shooting conditions. This results in images that allow for detailed feature extraction.

[0567] Once an image is taken, the user uploads it to the platform. The device sends the uploaded image to the server, and image recognition begins. The server uses image recognition AI to analyze the visual features of the image, extracting data such as patterns, depth of carvings, size, color, and light reflection. This analysis utilizes advanced algorithms, including deep learning techniques.

[0568] After analysis, the server quantifies the reliability of the product based on its visual characteristics. This reliability score, generated by the analysis technology, is output from the server and presented to the user via the terminal. This allows the user to make purchase decisions based on reliable information.

[0569] Furthermore, an emotion analysis engine analyzes the user's facial expressions and voice to collect data on their purchase intent. Based on the user's emotions, the server generates and provides feedback that enhances their purchase intent. This prompt includes reassuring information and specific explanations to encourage purchase.

[0570] As a concrete example, when a user attempts to purchase a 19th-century commemorative coin, the AI ​​model is input with an image along with the prompt, "Please create reassuring feedback to alleviate any anxieties about purchasing a 19th-century commemorative coin," which is then used as feedback for the user.

[0571] This system design is expected to allow users to enjoy a personalized purchasing experience and enhance their sense of security and trust at each stage of the transaction.

[0572] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0573] Step 1:

[0574] The user takes pictures of gold coins or currency using a smartphone or camera. During the process, the user receives guidance from the image processing device, specifying appropriate lighting conditions and angles to obtain images with clearly recorded visual features. In this process, the input is the image of the currency taken by the user, and the output is image data suitable for analysis.

[0575] Step 2:

[0576] The user uploads the captured image to the platform. The device then sends the image to the server. After image transmission, the input is an image file of currency, and the output is the image data transferred to the server.

[0577] Step 3:

[0578] The server analyzes the received image of the currency using image recognition AI. The input is image data transferred to the server, and the server performs the specific operation of extracting visual features. Here, data such as the pattern, depth of engraving, size, color, and light reflection within the image are quantified, and the output is the extracted visual feature data.

[0579] Step 4:

[0580] The server applies analytical techniques to extracted visual feature data to quantify the reliability of a product. The input is visual feature data, and the server calculates a reliability evaluation score. Data processing and calculations are performed during this process, and the output is the product's reliability score.

[0581] Step 5:

[0582] The terminal displays a confidence score provided by the server to the user. By receiving the confidence score as input and visually providing confidence information to help with purchase decisions, the output is the confidence score information displayed to the user.

[0583] Step 6:

[0584] The emotion analysis engine analyzes the user's facial expressions and voice data to extract emotional information. The input is the user's emotional data acquired by the terminal, the server performs emotion analysis, and outputs analysis results based on the user's emotions.

[0585] Step 7:

[0586] The server generates feedback to increase purchase intent based on the analysis results. The input is user sentiment analysis data, which generates prompts as specific actions, and then uses a generative AI model to generate feedback. The output is feedback information designed to reinforce the user's purchase intent.

[0587] (Application Example 2)

[0588] 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."

[0589] In commercial transactions, failure to adequately address concerns about product reliability or buyer anxiety can result in decreased buyer satisfaction or transaction cancellations. In particular, prompt and appropriate responses are required when users have concerns about a product or when their evaluation of the product changes after the transaction. Furthermore, because it is not possible to provide support tailored to the individual emotions and circumstances of each user, strengthening the support system for purchase decision-making is necessary.

[0590] 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.

[0591] In this invention, the server includes means for an image processing device to receive an image of a gold coin or currency and extract features of the image; means for applying an analysis algorithm that quantifies the reliability of the product based on the features; means for an emotion analysis engine to analyze the user's emotions and provide appropriate feedback; and means for referencing an online database to evaluate user satisfaction after a transaction and suggest improvement measures. This makes it possible to reduce user anxiety at each stage of product transactions, improve buyer satisfaction, and optimize the transaction environment.

[0592] An "image processing device" is a device or system for receiving an image of a gold coin or currency and extracting features from that image.

[0593] "Characteristics" refer to physical or visual attributes of a gold coin or currency, such as "pattern," "depth of engraving," "size," and "color / reflection of light."

[0594] An "analysis algorithm" is a computational method or program used to quantify and evaluate the reliability of a product based on extracted features.

[0595] "Confidence level" is a numerical index that indicates the degree of authenticity or value of a gold coin or currency, estimated from features obtained through image processing.

[0596] An "emotion analysis engine" is a system or program that analyzes a user's facial expressions and voice, recognizes their emotional state, and generates appropriate feedback.

[0597] "Feedback" refers to information and suggestions provided to users to support their purchasing decisions.

[0598] An "online database" is a digital storage system used to store and access user data and evaluation information related to transactions.

[0599] "User satisfaction" is an indicator that shows how satisfied users are with a product or service after a transaction.

[0600] "Improvement measures" refer to strategies or means proposed to enhance user satisfaction.

[0601] The system for realizing this invention includes a series of steps to improve user confidence in gold coin or currency transactions. First, the user takes a picture of the gold coin or currency using a device such as a smartphone and transmits it to an image processing device. This image processing device extracts features from the image and applies an analysis algorithm to calculate confidence. In this process, libraries such as TensorFlow are used to precisely analyze various features of the image.

[0602] The server calculates the confidence level and then presents it to the user via a display device. It also analyzes the user's emotions using an emotion analysis engine and generates situation-appropriate feedback based on facial recognition and voice analysis. This feedback helps reduce user anxiety and allows for confident transactions. Emotion analysis utilizes technologies such as OpenCV and Google Cloud Speech-to-Text.

[0603] Furthermore, even after a transaction is completed, the server continues to interact with the online database to evaluate user satisfaction. This provides a framework for continuously improving the user experience by suggesting improvement measures as needed.

[0604] For example, a user might take a picture of a coin, upload the image, and then have its trustworthiness calculated. If the user expresses concern, the server may provide additional information to reassure them. Also, if the user is dissatisfied with the condition of the product, the system will immediately follow up and guide them through the process of resolving the issue.

[0605] An example of a prompt message is as follows: "Upload an image of the registered currency, and we want to score its trustworthiness based on its visual characteristics. If the user becomes anxious, please provide specific purchase information to reassure them. For example, continue to check on customer satisfaction after they receive the product."

[0606] In this way, the system enables two-way interaction that meets the individual needs of users, improving the quality of transactions.

[0607] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0608] Step 1:

[0609] The user takes a picture of a gold coin or currency using their device. The image must be of high quality and capture important features. This image will serve as the input data.

[0610] Step 2:

[0611] The terminal transmits an image to the image processing unit. The image processing unit uses digital image processing technology to extract features such as "pattern," "depth of engraving," "size," and "color / light reflection" from the image. This process outputs feature data.

[0612] Step 3:

[0613] The server receives feature data and applies an analysis algorithm to quantify the confidence level of the images. Specifically, it performs data calculations based on the extracted features using a generative AI model, and outputs a confidence score for the product.

[0614] Step 4:

[0615] The server sends a confidence score to the user's device and displays it on the display. The user can then use this score to make a purchase decision.

[0616] Step 5:

[0617] The device collects the user's facial expressions and voice, which are then input into a sentiment analysis engine on the server. The sentiment analysis engine uses OpenCV and Google Cloud Speech-to-Text to analyze the user's emotions and recognize their state. Sentiment data is then generated.

[0618] Step 6:

[0619] The server generates appropriate feedback based on emotional data. This feedback includes specific information and reassurances to alleviate the user's anxiety. This feedback is generated and presented to the user.

[0620] Step 7:

[0621] The server uses an online database to monitor user satisfaction after transactions. By referring to user feedback and reviews, it can generate improvement plans if necessary. Additional support and information are provided during this step.

[0622] Step 8:

[0623] The system provides users with confirmation prompts after receiving or using a product. For example, it can provide prompts to confirm user satisfaction or to check if there are any problems with the product's condition. Based on these prompts, the system can continue to provide support as needed.

[0624] 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.

[0625] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0626] 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.

[0627] [Fourth Embodiment]

[0628] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0629] 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.

[0630] 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).

[0631] 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.

[0632] 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.

[0633] 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).

[0634] 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.

[0635] 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.

[0636] 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.

[0637] 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.

[0638] 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.

[0639] 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.

[0640] 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".

[0641] To implement the system of the present invention, the user first needs a smartphone or camera to photograph gold coins or currency. The user uses this device to take pictures of the items they wish to sell and uploads the images to the platform through a designated application. By adhering to the shooting guidelines provided by the device, images that allow for more accurate analysis can be obtained.

[0642] The server receives the uploaded image and analyzes it using image recognition AI. This AI extracts features such as "pattern," "depth of engraving," "size," and "color / light reflection" from the image and generates numerical data to evaluate the likelihood that the product is authentic. This data is quantified as a confidence score and presented to the user again via the device.

[0643] The program can be described in natural language as follows: First, the user's device sends an image it has captured to the server. This image is then analyzed by an image processing unit on the server. The AI ​​extracts features from the image by comparing them with pre-trained data and uses these features to evaluate the reliability of the product. The resulting reliability score becomes an important criterion for the user to judge the quality of the product.

[0644] As a concrete example, suppose a user is trying to sell a specific commemorative coin from the 19th century. The user takes photos of the front and back of the coin from various angles and uploads the images to the platform. The server uses AI to analyze these images and compare their features with past data. In this process, the AI ​​determines whether the coin's design, color, and size match known genuine standards and generates a high confidence score. As a result, buyers can use the displayed score as a reference to safely purchase the item.

[0645] After receiving the item, the user (buyer) takes another picture and sends it to the server, allowing the system to re-verify if it matches the initial rating. If the scores match, the transaction is successfully completed; if they do not match, the server guides the user through the appropriate return procedure, further increasing user confidence.

[0646] The following describes the processing flow.

[0647] Step 1:

[0648] Users take appropriate images of gold coins or currency using their smartphone cameras. Following the shooting guidelines will enable more accurate analysis.

[0649] Step 2:

[0650] The device uploads captured images to the server via the platform's application. Image data is transmitted at high resolution, and the detail of the image contributes to the accuracy of the analysis.

[0651] Step 3:

[0652] The server receives the uploaded images and starts the analysis process using image recognition AI. The AI ​​first preprocesses the images, performing noise reduction and image shaping.

[0653] Step 4:

[0654] The server's AI extracts features from the image. At this stage, it quantitatively identifies attributes such as "pattern," "depth of engraving," "size," and "color / light reflection."

[0655] Step 5:

[0656] The server applies an analytical algorithm to evaluate the reliability of a product using feature data. The AI ​​model determines the likelihood of a product being genuine by comparing it with past data and generates a reliability score.

[0657] Step 6:

[0658] The server sends the calculated confidence score to the user's device. The user reviews this score on their device and makes a purchase decision.

[0659] Step 7:

[0660] After a user purchases a product and receives it, they take another picture of it with their device and upload it to the server. This reassessment is performed to verify consistency with the initial scoring.

[0661] Step 8:

[0662] The server re-analyzes the image and generates a new confidence score. If this score matches the initial score, the transaction is confirmed as successful. If a discrepancy is detected, the server will suggest a return procedure to the user and proceed with the necessary steps.

[0663] (Example 1)

[0664] 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".

[0665] In traditional transactions involving valuable goods, objectively and quickly assessing the reliability of a product is difficult, often causing anxiety for both buyers and sellers. Furthermore, there are insufficient means to reconfirm the reliability of a product after the transaction, making it difficult to ensure the security of the transaction.

[0666] 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.

[0667] In this invention, the server includes means for an image processing device to receive an image of a valuable article and extract features from the image; means for applying an analysis method to quantify the reliability of the article based on the features; and means for outputting the reliability and presenting it to the user via a visual display device. This makes it possible to quantify the reliability of a product using features in the image and to verify the quality of the product at the time of and after the transaction.

[0668] An "image processing device" is a device that receives visually acquired data, extracts specific features from that data, and performs various processing operations on it.

[0669] "Items of value" refers to goods or similar items that have financial or historical value and are normally traded in the general market.

[0670] "Features" refer to the characteristics and attributes of an item or image, including specific elements such as shape, color, light reflection, and size.

[0671] "Reliability" is an indicator that shows the authenticity and quality of an item as evaluated by analytical methods, and it is an indicator that can be expressed numerically.

[0672] "Analysis method" refers to algorithms and techniques used to understand and evaluate data according to a specific purpose.

[0673] A "visual display device" refers to a device such as a monitor or screen that visually communicates processing results to the user.

[0674] "Users" are entities that operate the system or receive its results, and generally include buyers and sellers who trade goods.

[0675] To implement this system, users first need photographic equipment to take pictures of valuable items. Users use smartphones or cameras to photograph the items and upload the images to a server-based platform via a designated application. The device provides shooting guidance, enabling users to obtain high-quality images by following it.

[0676] The server receives the uploaded images and performs analysis using image recognition AI in the image processing unit. This image recognition AI extracts image features based on a pre-trained dataset and evaluates the authenticity of the items. Specifically, it quantifies features such as "pattern," "depth of engraving," "size," and "color / light reflectivity," and displays them as a confidence score.

[0677] Once a confidence score is generated, the server sends the result back to the terminal, allowing the user to view it through a visual display. The user can use this score to assess the quality of the item and make a decision about whether to sell or buy it.

[0678] As a concrete example, consider a scenario where a user sells a specific commemorative coin from the 19th century. In this case, the user takes photos of both sides of the coin from various angles and uploads the images to the platform. The server uses AI to analyze these images and compare them with historical data. The AI ​​verifies whether the coin's design, color, and size match known genuine standards and generates a high confidence score.

[0679] An example of a prompt message might be, "I would like to sell a 19th-century commemorative coin. Please evaluate the reliability score of the item." This system allows for a quick and objective assessment of the reliability of valuable items, enabling users to conduct transactions with confidence.

[0680] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0681] Step 1:

[0682] The user takes a picture of a valuable item. A smartphone or digital camera is used as the camera. The device provides shooting guidance, including instructions on lighting and shooting distance. Based on this guidance, the user takes the image under optimal conditions and uploads it to the system's platform. The input is an image of the item, and the output is an image file stored within the platform.

[0683] Step 2:

[0684] The server receives images sent from the user's terminal. The received images are sent to an image processing device for pre-processing. Specifically, this pre-processing includes format conversion and resolution adjustment. The input is the uploaded image file, and the output is an image file that has been prepared for analysis.

[0685] Step 3:

[0686] The server inputs the pre-processed image data into the image recognition AI. This AI compares the image with a pre-trained dataset and extracts features such as "pattern," "depth of carving," "size," and "color / light reflection" from the image. The input is processed image data, and the output is data quantified as features.

[0687] Step 4:

[0688] The server analyzes feature data quantified by AI and generates a confidence score for the item. Using a generative AI model, it quantifies the likelihood that an item is genuine by comparing past data with current features. The input is feature data, and the output is a numerical score indicating confidence.

[0689] Step 5:

[0690] The server sends the generated confidence score to the user's terminal. The terminal displays the score on a visual display device for the user to review. The user uses this score to evaluate the quality of the goods and decide whether to proceed with the transaction. The input is the confidence score, and the output is a score display that the user can visually review.

[0691] Step 6:

[0692] After the transaction is complete and the user receives the item, they take another picture of the item and send the image to the server. The server then re-evaluates the confidence score using the same process. By taking the picture under the same conditions as when the purchase was made, a highly reproducible evaluation is possible. The input is the re-taken image, and the output is the recalculated confidence score.

[0693] Step 7:

[0694] The server compares the transaction score with the re-evaluated score. If they match, the transaction is considered successful, and the process is completed. If they do not match, the server provides the user with return procedures and supports secure transactions. The input is the confidence score at the time of the transaction and at the time of re-evaluation, and the output is whether the transaction was successful or not, along with any necessary instructions.

[0695] (Application Example 1)

[0696] 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".

[0697] A challenge exists in that it is difficult for buyers to quickly and accurately determine whether an item is genuine when purchasing it in a store. This creates a risk of purchasing counterfeit goods, making it difficult to ensure buyer trust. Furthermore, it is crucial to reconfirm the authenticity of goods after the transaction and to have efficient return procedures in case of discrepancies.

[0698] 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.

[0699] In this invention, the server includes means for an image processing device to receive an image of an object and extract features from the image; means for applying an analysis algorithm that quantifies the reliability of the product based on the features; and means for outputting the reliability and presenting it to the consumer via a visual display device. As a result, buyers can verify the reliability of the product in real time, reducing the risk of purchasing counterfeit goods and enabling them to conduct transactions with peace of mind. Furthermore, it allows for a re-evaluation of the product after the transaction and prompt and appropriate return procedures.

[0700] An "image processing device" is an electronic device that receives images of objects and extracts specific features from those images.

[0701] "Features" refer to physical or visual elements extracted from an image of an object, such as "pattern," "depth of engraving," "size," and "color / reflection of light."

[0702] An "analysis algorithm" is a calculation method used to quantify the reliability of a product based on the extracted features.

[0703] "Reliability" is a value calculated by an analytical algorithm and serves as a numerical standard for evaluating the authenticity and quality of an item.

[0704] A "visual display device" is a display device used to present images and information to the user's visual sense.

[0705] "Consumer" refers to customers or users who purchase or trade goods.

[0706] An "object" refers to a specific product or item that is the subject of image processing or reliability evaluation.

[0707] A "cloud server" is a server located in a remote location for processing and recording data via the internet.

[0708] "Real-time" refers to a temporal continuity in which processing takes place in an instantaneous response the moment an operation or input is made.

[0709] The server first analyzes the image of the object received by the image processing unit. Smart glasses and smartphones are used as image processing units in this process. These devices use machine learning libraries such as TensorFlow and OpenCV, as well as image processing tools, to extract features such as "pattern," "depth of engraving," "size," and "color / light reflection" from the image.

[0710] Next, the server uses this information to quantify the reliability of the product using a pre-configured analysis algorithm. This reliability score is provided to the consumer in real time and presented via a visual display device.

[0711] Based on this information, users (consumers) can verify the authenticity of products in stores and make an immediate decision about whether or not to purchase them. Furthermore, after a transaction, the item is photographed again and resent to the server, allowing for a confirmation of consistency with the initial evaluation and a reassessment of the product's authenticity. This improves transaction security, and if a discrepancy is found, the server guides the user through the appropriate return procedure.

[0712] As a concrete example, when a salesperson in a store shows a consumer a 17th-century coin, the salesperson scans the coin with smart glasses. The consumer can then see the confidence score displayed on the glasses, allowing them to make a purchase decision with confidence on the spot. In this way, by utilizing generative AI models, the authenticity of objects can be verified, enabling fast and reliable transactions.

[0713] An example of a prompt message would be: "When selling 17th-century gold coins, how can I analyze images taken with smart glasses and display a product reliability score to the user in real time?"

[0714] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0715] Step 1:

[0716] The device captures images of an object. The device uses smart glasses or a smartphone to input images of the object. The user points the device at the object and acquires images from various angles.

[0717] Step 2:

[0718] The device sends the image it captures to the server. The input here is the image captured by the device, and the output is the data arriving at the server. The device uploads the image file to the server via the internet.

[0719] Step 3:

[0720] The server analyzes the images it receives. The input is an image file, and data processing is performed using tools such as OpenCV and TensorFlow to extract features. The output is the extracted feature data.

[0721] Step 4:

[0722] The server applies an analysis algorithm based on the feature data. The input is the extracted features, and a generative AI model is used to perform data calculations to determine the confidence level of the image. The output is the confidence score.

[0723] Step 5:

[0724] The server sends a confidence score to the terminal. The input is the confidence score obtained through analysis, and the output is the data displayed on the terminal's visual display device. The server quickly returns the score to the terminal and provides information to the user.

[0725] Step 6:

[0726] The user checks the confidence score and makes a purchase decision. The input is the displayed confidence score, and the output is the choice to purchase or decline. The user proceeds with the transaction based on the data displayed on the screen.

[0727] Step 7:

[0728] After the transaction, the device takes another picture of the object and sends it to the server. The input here is the newly taken image, which is sent to the server for re-evaluation.

[0729] Step 8:

[0730] The server re-evaluates the image based on the recaptured image and compares it to the previous evaluation. The input is the retransmitted image, and data processing is performed to check if the confidence levels match. The output is the evaluation result.

[0731] Step 9:

[0732] The server sends the re-evaluation results to the terminal and guides the user through the appropriate return procedure if necessary. The input here is the re-evaluation result, and the output is a notification to the user or instructions for the return procedure.

[0733] 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.

[0734] This invention begins with a user taking a picture of a gold coin or currency with a smartphone or camera and uploading the image to the platform. The user receives assistance via an image processing device, following shooting guidance to obtain an image that is easier to analyze. This image is sent to a server, where it is analyzed by an image recognition AI.

[0735] The server extracts features such as "pattern," "depth of engraving," "size," and "color / light reflection" from the image, and evaluates and quantifies the reliability of the product. This reliability score is presented to the buyer via their device to help them make a purchase decision.

[0736] Furthermore, an emotion engine is incorporated that analyzes the user's emotions and provides an interface that reinforces their purchase intent when it decreases. This engine recognizes emotions from the user's facial expressions and voice, and modifies prompts, visuals, and voice guidance to provide appropriate feedback. In addition, to increase post-transaction satisfaction, it also analyzes the user's emotions when a transaction is canceled and provides support to reduce stress as needed.

[0737] As a concrete example, consider a scenario where a user attempts to purchase a registered 19th-century commemorative coin. If the emotion engine detects that the user is feeling anxious, the server provides specific product descriptions and feedback to reassure them. After the transaction is completed and the product arrives, another emotion analysis is performed to confirm the user's satisfaction and whether there are any problems, and directs them to customer support if necessary.

[0738] This system is designed to highly personalize the user's purchasing experience and provide a reassuring and trustworthy environment at every stage of the transaction. By incorporating an emotional engine in this way, it is possible to improve the quality of transactions and increase user satisfaction.

[0739] The following describes the processing flow.

[0740] Step 1:

[0741] The user takes a picture of a gold coin or currency using their smartphone camera. The device provides shooting guidance, prompting the user to take the picture at the appropriate angle and under suitable lighting conditions.

[0742] Step 2:

[0743] The device uploads the captured image to the server. At the same time, settings regarding the image size and resolution are also sent, setting the criteria for the server to perform the optimal processing.

[0744] Step 3:

[0745] The server analyzes the received images using image recognition AI. First, it removes unnecessary data from the image and extracts features such as "pattern," "depth of engraving," "size," and "color / light reflection."

[0746] Step 4:

[0747] The server applies an analysis algorithm to evaluate the reliability based on the extracted features. It calculates the resulting reliability score and sends the result to the terminal.

[0748] Step 5:

[0749] The device presents the user with a confidence score. At this stage, the emotion engine analyzes the user's emotional state based on their facial expressions and the operation of their input device.

[0750] Step 6:

[0751] Based on data from the emotion engine, the server detects a potential decrease in the user's purchase intent and sends appropriate feedback and advice to the device. This may include visual and auditory changes to encourage purchases.

[0752] Step 7:

[0753] Users make purchasing decisions based on the presented confidence score and additional information. After purchase, when the product arrives, they take another picture and upload it to the server.

[0754] Step 8:

[0755] The server analyzes the recaptured images and compares them to the initial data to confirm consistency in confidence. The sentiment engine also restarts, detecting user satisfaction and dissatisfaction and providing additional support as needed.

[0756] (Example 2)

[0757] 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".

[0758] Conventional currency reliability evaluation systems using image recognition technology failed to adequately address the anxieties and doubts felt by prospective buyers, resulting in a lack of means to ensure reliable transactions. Furthermore, insufficient feedback and support to enhance satisfaction after a transaction was completed contributed to factors that compromised the quality of transactions.

[0759] 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.

[0760] In this invention, the server includes means for an image processing device to receive an image of currency and extract the visual features of the image; means for applying an analysis technique to quantify the reliability of the product based on the features; means for outputting the reliability and presenting it to the prospective buyer via a display device; and means for analyzing the prospective buyer's emotions using an emotion analysis engine and generating feedback to reinforce their willingness to purchase. This makes it possible to personalize the user's purchasing experience and provide a transaction environment that instills a sense of security and trust.

[0761] An "image processing device" is a device that receives images of currency and has the function of extracting the visual features of those images.

[0762] "Visual features" refer to characteristics of the appearance of the coin, such as patterns in the image, depth of engraving, size, color, and how light reflects off it.

[0763] "Analysis techniques" refer to algorithms and methods used to quantify the reliability of a product based on its visual characteristics.

[0764] "Reliability" is an evaluation score regarding the authenticity and value of a product, quantified using analytical techniques.

[0765] A "display device" is a device used to visually present the calculated reliability level to potential buyers.

[0766] An "emotion analysis engine" is a device that analyzes the emotions of potential buyers from their voices and facial expressions, and generates feedback to reinforce their desire to purchase.

[0767] "Feedback" refers to information and messages provided to potential buyers to increase their desire to purchase.

[0768] This invention is a system for analyzing images of currency and evaluating their reliability. Users take images of gold coins or other currency using a smartphone or camera. During shooting, it is recommended to use the guidance provided by the image processing device to acquire images under appropriate shooting conditions. This results in images that allow for detailed feature extraction.

[0769] Once an image is taken, the user uploads it to the platform. The device sends the uploaded image to the server, and image recognition begins. The server uses image recognition AI to analyze the visual features of the image, extracting data such as patterns, depth of carvings, size, color, and light reflection. This analysis utilizes advanced algorithms, including deep learning techniques.

[0770] After analysis, the server quantifies the reliability of the product based on its visual characteristics. This reliability score, generated by the analysis technology, is output from the server and presented to the user via the terminal. This allows the user to make purchase decisions based on reliable information.

[0771] Furthermore, an emotion analysis engine analyzes the user's facial expressions and voice to collect data on their purchase intent. Based on the user's emotions, the server generates and provides feedback that enhances their purchase intent. This prompt includes reassuring information and specific explanations to encourage purchase.

[0772] As a concrete example, when a user attempts to purchase a 19th-century commemorative coin, the AI ​​model is input with an image along with the prompt, "Please create reassuring feedback to alleviate any anxieties about purchasing a 19th-century commemorative coin," which is then used as feedback for the user.

[0773] This system design is expected to allow users to enjoy a personalized purchasing experience and enhance their sense of security and trust at each stage of the transaction.

[0774] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0775] Step 1:

[0776] The user takes pictures of gold coins or currency using a smartphone or camera. During the process, the user receives guidance from the image processing device, specifying appropriate lighting conditions and angles to obtain images with clearly recorded visual features. In this process, the input is the image of the currency taken by the user, and the output is image data suitable for analysis.

[0777] Step 2:

[0778] The user uploads the captured image to the platform. The device then sends the image to the server. After image transmission, the input is an image file of currency, and the output is the image data transferred to the server.

[0779] Step 3:

[0780] The server analyzes the received image of the currency using image recognition AI. The input is image data transferred to the server, and the server performs the specific operation of extracting visual features. Here, data such as the pattern, depth of engraving, size, color, and light reflection within the image are quantified, and the output is the extracted visual feature data.

[0781] Step 4:

[0782] The server applies analytical techniques to extracted visual feature data to quantify the reliability of a product. The input is visual feature data, and the server calculates a reliability evaluation score. Data processing and calculations are performed during this process, and the output is the product's reliability score.

[0783] Step 5:

[0784] The terminal displays a confidence score provided by the server to the user. By receiving the confidence score as input and visually providing confidence information to help with purchase decisions, the output is the confidence score information displayed to the user.

[0785] Step 6:

[0786] The emotion analysis engine analyzes the user's facial expressions and voice data to extract emotional information. The input is the user's emotional data acquired by the terminal, the server performs emotion analysis, and outputs analysis results based on the user's emotions.

[0787] Step 7:

[0788] The server generates feedback to increase purchase intent based on the analysis results. The input is user sentiment analysis data, which generates prompts as specific actions, and then uses a generative AI model to generate feedback. The output is feedback information designed to reinforce the user's purchase intent.

[0789] (Application Example 2)

[0790] 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".

[0791] In commercial transactions, failure to adequately address concerns about product reliability or buyer anxiety can result in decreased buyer satisfaction or transaction cancellations. In particular, prompt and appropriate responses are required when users have concerns about a product or when their evaluation of the product changes after the transaction. Furthermore, because it is not possible to provide support tailored to the individual emotions and circumstances of each user, strengthening the support system for purchase decision-making is necessary.

[0792] 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.

[0793] In this invention, the server includes means for an image processing device to receive an image of a gold coin or currency and extract features of the image; means for applying an analysis algorithm that quantifies the reliability of the product based on the features; means for an emotion analysis engine to analyze the user's emotions and provide appropriate feedback; and means for referencing an online database to evaluate user satisfaction after a transaction and suggest improvement measures. This makes it possible to reduce user anxiety at each stage of product transactions, improve buyer satisfaction, and optimize the transaction environment.

[0794] An "image processing device" is a device or system for receiving an image of a gold coin or currency and extracting features from that image.

[0795] "Characteristics" refer to physical or visual attributes of a gold coin or currency, such as "pattern," "depth of engraving," "size," and "color / reflection of light."

[0796] An "analysis algorithm" is a computational method or program used to quantify and evaluate the reliability of a product based on extracted features.

[0797] "Confidence level" is a numerical index that indicates the degree of authenticity or value of a gold coin or currency, estimated from features obtained through image processing.

[0798] An "emotion analysis engine" is a system or program that analyzes a user's facial expressions and voice, recognizes their emotional state, and generates appropriate feedback.

[0799] "Feedback" refers to information and suggestions provided to users to support their purchasing decisions.

[0800] An "online database" is a digital storage system used to store and access user data and evaluation information related to transactions.

[0801] "User satisfaction" is an indicator that shows how satisfied users are with a product or service after a transaction.

[0802] "Improvement measures" refer to strategies or means proposed to enhance user satisfaction.

[0803] The system for realizing this invention includes a series of steps to improve user confidence in gold coin or currency transactions. First, the user takes a picture of the gold coin or currency using a device such as a smartphone and transmits it to an image processing device. This image processing device extracts features from the image and applies an analysis algorithm to calculate confidence. In this process, libraries such as TensorFlow are used to precisely analyze various features of the image.

[0804] The server calculates the confidence level and then presents it to the user via a display device. It also analyzes the user's emotions using an emotion analysis engine and generates situation-appropriate feedback based on facial recognition and voice analysis. This feedback helps reduce user anxiety and allows for confident transactions. Emotion analysis utilizes technologies such as OpenCV and Google Cloud Speech-to-Text.

[0805] Furthermore, even after a transaction is completed, the server continues to interact with the online database to evaluate user satisfaction. This provides a framework for continuously improving the user experience by suggesting improvement measures as needed.

[0806] For example, a user might take a picture of a coin, upload the image, and then have its trustworthiness calculated. If the user expresses concern, the server may provide additional information to reassure them. Also, if the user is dissatisfied with the condition of the product, the system will immediately follow up and guide them through the process of resolving the issue.

[0807] An example of a prompt message is as follows: "Upload an image of the registered currency, and we want to score its trustworthiness based on its visual characteristics. If the user becomes anxious, please provide specific purchase information to reassure them. For example, continue to check on customer satisfaction after they receive the product."

[0808] In this way, the system enables two-way interaction that meets the individual needs of users, improving the quality of transactions.

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

[0810] Step 1:

[0811] The user takes a picture of a gold coin or currency using their device. The image must be of high quality and capture important features. This image will serve as the input data.

[0812] Step 2:

[0813] The terminal transmits an image to the image processing unit. The image processing unit uses digital image processing technology to extract features such as "pattern," "depth of engraving," "size," and "color / light reflection" from the image. This process outputs feature data.

[0814] Step 3:

[0815] The server receives feature data and applies an analysis algorithm to quantify the confidence level of the images. Specifically, it performs data calculations based on the extracted features using a generative AI model, and outputs a confidence score for the product.

[0816] Step 4:

[0817] The server sends a confidence score to the user's device and displays it on the display. The user can then use this score to make a purchase decision.

[0818] Step 5:

[0819] The device collects the user's facial expressions and voice, which are then input into a sentiment analysis engine on the server. The sentiment analysis engine uses OpenCV and Google Cloud Speech-to-Text to analyze the user's emotions and recognize their state. Sentiment data is then generated.

[0820] Step 6:

[0821] The server generates appropriate feedback based on emotional data. This feedback includes specific information and reassurances to alleviate the user's anxiety. This feedback is generated and presented to the user.

[0822] Step 7:

[0823] The server uses an online database to monitor user satisfaction after transactions. By referring to user feedback and reviews, it can generate improvement plans if necessary. Additional support and information are provided during this step.

[0824] Step 8:

[0825] The system provides users with confirmation prompts after receiving or using a product. For example, it can provide prompts to confirm user satisfaction or to check if there are any problems with the product's condition. Based on these prompts, the system can continue to provide support as needed.

[0826] 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.

[0827] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0828] 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.

[0829] 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.

[0830] 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.

[0831] 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.

[0832] 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.

[0833] 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.

[0834] 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."

[0835] 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.

[0836] 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.

[0837] 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.

[0838] 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.

[0839] 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.

[0840] 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.

[0841] 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.

[0842] 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.

[0843] 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.

[0844] 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.

[0845] 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.

[0846] 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.

[0847] The following is further disclosed regarding the embodiments described above.

[0848] (Claim 1)

[0849] An image processing device receives an image of a gold coin or currency and extracts features from the image,

[0850] A means for applying an analytical algorithm that quantifies the reliability of a product based on the aforementioned characteristics,

[0851] A means for outputting the aforementioned reliability and presenting it to the purchaser via a display device,

[0852] A method for recalculating the reliability based on images retaken after the transaction and determining whether the transaction will be completed,

[0853] A system that includes this.

[0854] (Claim 2)

[0855] The system according to claim 1, characterized in that the image processing device provides means for providing shooting guidance and assisting in acquiring images under appropriate shooting conditions.

[0856] (Claim 3)

[0857] The system according to claim 1, characterized in that it includes means for sending notifications to buyers and sellers when a transaction is canceled and for managing the return process.

[0858] "Example 1"

[0859] (Claim 1)

[0860] An image processing device receives an image of an article of value and extracts features from the image,

[0861] A means for applying an analysis method that quantifies the reliability of an item based on the aforementioned characteristics,

[0862] A means for outputting the aforementioned reliability and presenting it to the user via a visual display device,

[0863] A means of determining the success or failure of a transaction by recalculating the generated confidence score based on the received image again,

[0864] A system that includes this.

[0865] (Claim 2)

[0866] The system according to claim 1, characterized in that the image processing device provides means for providing shooting guidelines and assisting in image acquisition under appropriate conditions.

[0867] (Claim 3)

[0868] The system according to claim 1, characterized in that it includes means for sending notifications to users and suppliers when a transaction is not completed and for managing return procedures.

[0869] "Application Example 1"

[0870] (Claim 1)

[0871] An image processing device receives an image of an object and extracts features from the image,

[0872] A means for applying an analytical algorithm that quantifies the reliability of a product based on the aforementioned characteristics,

[0873] A means for outputting the aforementioned reliability and presenting it to the consumer via a visual display device,

[0874] A method for recalculating the reliability based on images retaken after the transaction and determining whether the transaction will be completed,

[0875] A means of capturing product images through a visual display and presenting the level of reliability to consumers in real time,

[0876] A system that includes this.

[0877] (Claim 2)

[0878] The system according to claim 1, characterized in that the image processing device provides means for providing shooting guidance and assisting in acquiring images under appropriate shooting conditions.

[0879] (Claim 3)

[0880] The system according to claim 1, characterized by having means for sending notifications to consumers and suppliers when a transaction is canceled and for managing return procedures.

[0881] "Example 2 of combining an emotion engine"

[0882] (Claim 1)

[0883] An image processing device receives an image of a coin and extracts the visual features of the image,

[0884] A means for applying an analytical technique that quantifies the reliability of a product based on the aforementioned characteristics,

[0885] A means for outputting the aforementioned reliability and presenting it to prospective buyers via a display device,

[0886] A means of analyzing the emotions of potential buyers using an emotion analysis engine and generating feedback to reinforce their purchasing intent,

[0887] A method for recalculating the reliability based on images retaken after the transaction and determining whether the transaction will be completed,

[0888] A system that includes this.

[0889] (Claim 2)

[0890] The system according to claim 1, characterized in that the image processing device provides means for providing shooting guidance and assisting in acquiring images under appropriate shooting conditions.

[0891] (Claim 3)

[0892] The system according to claim 1, characterized in that it includes means for sending notifications to prospective buyers and sellers when a transaction is canceled and for managing the return process.

[0893] "Application example 2 when combining with an emotional engine"

[0894] (Claim 1)

[0895] An image processing device receives an image of a gold coin or currency and extracts features from the image,

[0896] A means for applying an analytical algorithm that quantifies the reliability of a product based on the aforementioned characteristics,

[0897] A means for outputting the aforementioned reliability and presenting it to the purchaser via a display device,

[0898] A means by which an emotion analysis engine analyzes the user's emotions and provides appropriate feedback,

[0899] A means of evaluating post-transaction user satisfaction by referring to an online database and proposing improvement measures,

[0900] A system that includes this.

[0901] (Claim 2)

[0902] The system according to claim 1, characterized in that the image processing device provides means for providing shooting guidance and assisting in acquiring images under appropriate shooting conditions.

[0903] (Claim 3)

[0904] The system according to claim 1, characterized by having means to send notifications to buyers and sellers when a transaction is canceled, manage return procedures, and provide support to reduce user stress. [Explanation of Symbols]

[0905] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. An image processing device receives an image of a gold coin or currency and extracts features from the image, A means for applying an analytical algorithm that quantifies the reliability of a product based on the aforementioned characteristics, A means for outputting the aforementioned reliability and presenting it to the purchaser via a display device, A method for recalculating the reliability based on images retaken after the transaction and determining whether the transaction will be completed, A system that includes this.

2. The system according to claim 1, characterized in that the image processing device provides means for providing shooting guidance and assisting in acquiring images under appropriate shooting conditions.

3. The system according to claim 1, characterized in that it includes means for sending notifications to buyers and sellers when a transaction is canceled and for managing the return process.

Images