Footwear authenticity determination system using pressure distribution image analysis

Abstract

To provide systems and methods for authenticating footwear based on pressure distribution image analysis.SOLUTION: A method for authenticating footwear using pressure distribution image analysis includes: receiving a request to authenticate footwear; and accessing a pressure distribution image for the footwear. The pressure distribution image comprises a pressure distribution represented by colors that is generated based on a preset force applied to the footwear. The method includes: executing analysis on the pressure distribution image including comparing the pressure distribution image to a pressure distribution image of an authentic version of the footwear; determining whether the pressure distribution image is within an authenticity threshold of the pressure distribution image of the authentic version; and presenting, based on the pressure distribution image being within the authenticity threshold, an index of authenticity of the footwear.SELECTED DRAWING: Figure 4

Description

Technical Field

[0001] The subject matter disclosed herein generally relates to authenticating footwear. Specifically, the present disclosure addresses systems and methods for authenticating footwear using pressure distribution image analysis.

Background Art

[0002] Vendors typically hesitate to sell high-end or luxury goods when they receive the same item back in the case of a return and are not confident that the item is not a counterfeit. Similarly, purchasers hesitate to buy high-end or luxury goods when they are not confident that the item is genuine. This level of trust is extremely important for categories such as designer shoes or branded sneakers and athletic shoes (collectively referred to as "footwear"). These types of goods are often counterfeited to a level where only experts can authenticate them.

Brief Description of the Drawings

[0003] [Figure 1] FIG. 1 is a diagram showing an exemplary network environment suitable for authenticating footwear based on pressure distribution image analysis, according to an exemplary embodiment. [Figure 2] FIG. 2 is a diagram showing multiple components of an authenticity determination system for authenticating footwear based on pressure distribution image analysis, according to an exemplary embodiment. [Figure 3A] FIG. 3A is an exemplary image of pressure distribution heatmaps for both a right shoe and a left shoe. [Figure 3B] FIG. 3B is an exemplary image of pedobarography measurement images for both a right shoe and a left shoe. [Figure 4] FIG. 4 is a flowchart showing multiple operations of a method for authenticating footwear using pressure distribution image analysis, according to an exemplary embodiment. [Figure 5]Figure 5 is a flowchart showing multiple operations of a method for performing statistical pressure distribution image analysis according to an exemplary embodiment. [Figure 6] Figure 6 is a flowchart showing multiple operations of a method for performing machine learning pressure distribution image analysis according to an exemplary embodiment. [Figure 7] Figure 7 is a block diagram showing several components of a machine capable of reading multiple instructions from a machine storage medium and performing any one or more of the methods described herein, as in several examples. [Modes for carrying out the invention]

[0004] The following description describes systems, methods, techniques, instruction sequences, and computing machine program products that illustrate multiple examples of this subject. The following description includes numerous specific details for illustrative purposes to provide an understanding of the various examples of this subject. However, it will be apparent to those skilled in the art that the various examples of this subject can be implemented without some or other of these specific details. The examples are merely representative of possible variations. Unless otherwise specified, multiple structures (e.g., multiple structural components) are arbitrary and may be combined or subdivided, and multiple operations (e.g., in procedures, algorithms, or other functions) may have their sequences changed, combined, or subdivided.

[0005] An exemplary embodiment addresses the technical problem of determining the authenticity of footwear in a computationally efficient manner based on pressure distribution image analysis. When counterfeiting designer or branded footwear, counterfeiters attempt to make the counterfeit footwear look like the genuine article. However, the materials typically used are not identical, especially when the material is inside the footwear, for example, in the sole.

[0006] Furthermore, many designer and brand-name footwear brands hold patents for technologies related to their soles. In particular, these technologies are aimed at distributing the weight of the individual wearing the footwear in a way that attempts to maximize comfort. Therefore, several exemplary embodiments test the pressure distribution on the footwear (e.g., the sole) to determine whether a shoe is genuine or counterfeit. For explanatory purposes, the terms “footwear” and “shoes” are used interchangeably.

[0007] To test a shoe, a pressure distribution image of the shoe is analyzed. In some cases, the pressure distribution image is generated using a pressure measuring machine equipped with an artificial foot (or similar device), the artificial foot (or similar device) having multiple sensors on it. The artificial foot applies a preset force to the shoe, which is the same force applied to a real shoe. The multiple sensors can respond by detecting the pressure generated by the sole, and the pressure measurements can be recorded in the form of an image. The image can be a pressure distribution heat map or a pedobarographic measurement image. In several further embodiments, the pressure may be recorded in the form of a table, matrix, or other form of pressure distribution data without a graphical representation.

[0008] The image analysis authenticity determination system then analyzes the pressure distribution image by comparing it to a pressure distribution image of a real shoe. In several further embodiments, the authenticity determination system may similarly analyze a pressure distribution table or row instead of an image. In several exemplary embodiments, the analysis may be performed using either a statistical model or a machine learning model. In some cases, the statistical model is a statistical color model configured to detect specific colors (and their locations) within the pressure distribution image. The dataset of specific colors / locations is then compared to a dataset of the real shoe being tested, and the comparison can determine whether it falls within an authenticity threshold.

[0009] In the case of machine learning models, pressure distribution images can be applied to a machine learning model trained using training data derived from pressure distribution images of real and counterfeit footwear. The probability that the test footwear is real is output by the machine learning model and compared to a probability (authenticity) threshold. If the authenticity (probability) threshold is met, the test footwear is labeled as real. In some cases, the machine learning model comprises one or more neural networks (e.g., convolutional neural networks: CNNs) trained to determine the similarity (e.g., multiple similarity scores) between the pressure distribution images of the test shoe and the pressure distribution images of real shoes. The multiple similarity scores are compared to the authenticity threshold.

[0010] Figure 1 shows an exemplary network environment 100 suitable for authenticating footwear based on pressure distribution image analysis, according to an exemplary embodiment. The network system 102 provides server-side functionality to client devices 106 via a communication network 104 (e.g., the Internet, a wireless network, a cellular network, or a wide area network (WAN)). The network system 102 is configured to authenticate footwear using either a statistical model or a machine learning model to analyze multiple pressure distribution images, as will be described in more detail below.

[0011] In various cases, the client device 106 is a device associated with the user account of a user of the network system 102 who wishes to verify that the footwear they own / will own is authentic. For example, the user may be a seller who wishes to verify that returned shoes are the same authentic shoes as the ones sold. In other cases, the client device 106 is a device associated with the user account of a buyer of the network system 102 who wishes to guarantee that the shoes they purchased are authentic. Furthermore, the client device 106 may be a device of an operator associated with an intermediary that determines the authenticity of the footwear on behalf of the seller or buyer. In some cases, the intermediary may be part of the same entity that controls the network system 102.

[0012] The client device 106 includes one or more client applications 108 that communicate with the network system 102 for additional functionality. For example, the client application 108 may be a local version of an application or component of the network system 102. Alternatively, the client application 108 exchanges data with one or more corresponding components / applications in the network system 102. The client application 108 may be provided by the network system 102 and / or downloaded to the client device 106. A request for authenticity determination of footwear can be sent via the client application 108. In response, the client application 108 receives an indicator of whether the shoes are authentic after evaluation.

[0013] In certain embodiments, the client application 108 includes a truth determination component that exchanges data with the network system 102. In some cases, the client application 108 operates with or triggers a pressure measuring machine comprising an artificial foot (or similar device) having multiple sensors that apply a predetermined force to a shoe to be determined as truth (also referred to herein as “test shoe” or “test footwear”) to generate pressure distribution data or images, and transmit the pressure distribution data or images to the network system 102 for analysis. In certain embodiments, the pressure measuring machine may be part of the client device 106. In other embodiments, the pressure measuring device is a standalone device that can be communicably coupled to the client device 106 and the network system 102 (e.g., via network 104). In these cases, the pressure measuring device may be associated with (e.g., owned by) the same entity that controls the network system 102.

[0014] The client device 106 interfaces with the network system 102 via a connection to the network 104. Depending on the configuration of the client device 106, various types of connections and any of the network 104 can be used. For example, this connection may be a CDMA (Code Division Multiple Access) connection, a GSM (Global System for Mobile communications) (registered trademark) connection, or another type of cellular connection. Such a connection may implement any of various types of data transfer technologies, such as single-carrier radio transmission technology (1xRTT), EVDO (Evolution-Data Optimized) technology, GPRS (General Packet Radio Service) technology, EDGE (Enhanced Data rates for GSM Evolution) technology, or other data transfer technologies (e.g., 4th generation wireless, 4G network, 5G network). When such technology is used, the network 104 includes a cellular network with multiple cell sites having overlapping geographic coverage, interconnected by cellular telephone exchanges. These cellular telephone exchanges are connected to a network backbone (for example, a public switched telephone network (PSTN), a packet-switched data network, or another type of network).

[0015] In another example, the connection to network 104 is a Wireless Fidelity (Wi-Fi, IEEE 802.11x type) connection, a WiMAX (Worldwide Interoperability for Microwave Access) connection, or another type of wireless data connection. In such an example, network 104 includes one or more wireless access points connected to a local area network (LAN), a wide area network (WAN), the Internet, or another packet-switched data network. In yet another example, the connection to network 104 is a wired connection (e.g., an Ethernet® link), and network 104 is a LAN, WAN, the Internet, or another packet-switched data network. Thus, a variety of different configurations are explicitly considered.

[0016] The client device 106 may include, but is not limited to, a smartphone, tablet, laptop, multiprocessor system, microprocessor-based or programmable consumer electronics, game console, set-top box, server, or any other communication device that can access the network system 102. The client device 106 may include a display component (not shown) for displaying information (e.g., in the form of a user interface) that includes an indicator of whether the footwear is genuine or not. The client device 106 may be run by a human user and / or a machine user.

[0017] Referring specifically to the network system 102, an application programming interface (API) server 110 and a web server 112 are coupled to one or more networking servers 114, providing programmatic and web interfaces, respectively. One or more networking servers 114 host various systems, including a truth determination system 116, which comprises multiple components and can be embodied as hardware, software, firmware, or any combination thereof. The truth determination system 116 will be described in more detail with reference to Figure 2.

[0018] One or more networking servers 114 are connected to one or more database servers 118 that enable access to one or more storage repositories or data storage devices 120. The data storage device 120 is a storage device that stores, for example, user accounts including user profiles (such as buyers, sellers, or intermediaries) and goods associated with the user accounts (such as footwear owned, sold, or purchased by the user).

[0019] Any of the systems, servers, data storage devices, or devices shown in or associated with Figure 1 (collectively referred to as the “Multiple Components”) may be, include, or otherwise implement a special-purpose (e.g., special or otherwise non-general-purpose) computer that can be modified to perform one or more of the functions described herein for that system or machine (e.g., composed of or programmed with software such as one or more software components of applications, operating systems, firmware, middleware, or other programs). For example, a special-purpose computer system capable of implementing any one or more of the methods described herein is described below with respect to Figure 7, and such a special-purpose computer is a means of performing any one or more of the methods described herein. Within the art of such special-purpose computers, a special-purpose computer modified by the multiple structures described herein to perform the multiple functions described herein is technically improved compared to other special-purpose computers that lack the multiple structures described herein or are otherwise unable to perform the multiple functions described herein. Therefore, the special-purpose machines configured according to the systems and methods described herein offer improvements to the technology of similar special-purpose machines.

[0020] Furthermore, any two or more of the components shown in Figure 1 may be combined, and any single component whose functions are described herein may be subdivided among multiple components. In alternative examples, the functions of a particular system may be embodied in different systems. For example, any number of client devices 106 or data storage devices 120 may be embodied within the network environment 100. Although only a single network system 102 is shown, alternatively, two or more network systems 102 may be included (e.g., localized to a specific region).

[0021] Figure 2 shows the components of an authenticity determination system 116 that determines the authenticity of footwear based on pressure distribution image analysis, according to an exemplary embodiment. The authenticity determination system 116 accesses a pressure distribution image of a test shoe, analyzes the pressure distribution image, and determines whether the pressure distribution image is within an authenticity threshold of a pressure distribution image of a real shoe. To enable these operations, the authenticity determination system 116 comprises a communication component 202, an image analysis component 204, an image data storage device 206, and an image analysis system, all configured to communicate with each other (e.g., via a bus, shared memory, or switch). The multiple image analysis systems include a statistical system 208 and a machine learning system 210. In some embodiments, the authenticity determination system 116 may include the statistical system 208 but not the machine learning system 210, or it may include the machine learning system 210 but not the statistical system 208.

[0022] The communication component 202 is configured to exchange data with other components of the network environment 100. Accordingly, the communication component 202 receives a request for authenticating footwear from the client application 108 operating on the client device 106. In some cases, the request includes a pressure distribution image of the footwear to be authenticated. In other cases, the request indicates a pressure distribution image to be retrieved for analysis (e.g., previously uploaded to the image data storage device 206). The request can also include details of the footwear, including the manufacturing year or purchase year, brand, model name, and / or serial number. After analysis by the authenticity determination system 116, the communication component 202 can send a response to a query that includes an indicator of the authenticity of the footwear.

[0023] In multiple embodiments where the pressure distribution image of the test footwear has been previously uploaded or uploaded by another entity (e.g., an intermediary testing the product), the image analysis component 204 reads the pressure distribution image of the test footwear from the image data storage device 206. In an exemplary multiple cases, the image data store 206 can also include a database of pressure distribution images for different brands and models of genuine footwear. These pressure distribution images for different brands and models of genuine footwear can be used for comparison and for training a machine learning model, as described in more detail below. Periodically, new pressure distribution images are received (e.g., with new product releases) and the machine learning model is retrained.

[0024] In some cases, the pressure distribution image may include a heat map of the foot pressure measured by a pressure measurement machine at a plurality of different positions on the sole. FIG. 3A shows an example of the pressure distribution heat maps of both the right shoe and the left shoe. In some cases, the pressure distribution image includes a pedobarographic measurement image as shown in FIG. 3B. Different colors correspond to different pressure measurement values. In FIGS. 3A and 3B, "R" indicates red, "G" indicates medium green, and "B" indicates dark blue. For example, red can indicate greater than 195 kilopascals (kPa), medium green can indicate 100 kPa, and dark blue can indicate 10 kPa.

[0025] The plurality of pressure measurement values in both types of pressure distribution images are obtained from a pressure measurement machine. The pressure measurement machine includes an artificial foot or a similar device having a plurality of pressure sensors arranged at a plurality of different positions. When the artificial foot applies a preset amount of force to the shoe, the plurality of pressure sensors obtain the amount of pressure at each position. Then, using the amount of pressure at each position, a pressure distribution image can be generated.

[0026] In a further embodiment, the pressure distribution image may comprise a video of the dynamic pressure distribution in a process performed using a pressure measurement machine. In this process, the sole absorbs energy and then transmits the energy through the sole to a plurality of sensors. Accordingly, the plurality of sensors of the pressure measurement machine continuously obtain a plurality of pressure measurement values in that process. The continuous pressure measurement values (e.g., dynamic pressure measurement values) are compiled into a video or a data stream, whereby, for each time t, different heat maps or pressure distribution tables or matrices can be generated.

[0027] In some cases, the image analysis component 204 can preprocess the pressure distribution image of the test shoe before passing the data to either the statistical system 208 or the machine learning system 210. For example, the test shoe may be of a size for which the authenticity determination system 116 does not have authenticity determination pressure distribution data. In these cases, the image analysis component 204 can "resize" the image to a size for which the authenticity determination system 116 has authenticity determination pressure distribution data. In another embodiment, the image analysis component 204 may utilize available algorithms to enlarge or reduce existing authenticity determined pressure image data, such as nearest neighbor or bilinear interpolation algorithms.

[0028] In several embodiments using statistical analysis, a pressure distribution image of the test shoe is provided to the statistical system 208. The statistical system 208 uses statistical analysis (e.g., a statistical image model) to determine whether the test shoe is genuine or not. Thus, the statistical system 206 comprises an evaluation component 212, a pressure data storage device 214, and a threshold component 216.

[0029] The evaluation component 212 is configured to apply pressure distribution images to a statistical model. In a particular embodiment, the statistical model is a statistical color model trained to detect specific colors (and shades of color) and their locations within the pressure distribution image. The detected color / location dataset can then be compared to a corresponding dataset of real shoes. The corresponding dataset can be retrieved from the pressure data storage device 214. The evaluation component 212 then compares the multiple datasets. When performing this comparison, the evaluation component 212 may determine the percentage of multiple pressure measurements (color / location data) that fall within a threshold (e.g., within 95%) of the corresponding pressure measurements from the pressure data storage device 214. This percentage can be the authenticity score. In another embodiment, the pedobarographic statistical parametric mapping (pSPM) method can be used to compare the multiple datasets.

[0030] In some embodiments, the comparison takes into account the age of the test shoe. The age of the test shoe may be important because the sole material changes and deforms at different rates with use. In these embodiments, the pressure data storage device 214 contains multiple different datasets of pressure distributions (or position / pressure / time vectors) of the same brand / model for multiple different years.

[0031] For pressure distribution behavior, an additional parameter of time is considered. Therefore, the dataset consists of color and position at a specific time. When performing the comparison, the evaluation component 212 may determine the proportion of multiple pressure measurements (color / position data) in each time frame that are within a threshold (e.g., within 95%) of the corresponding pressure measurements from the pressure data storage device 214. The mean or median of these proportions can then be determined to derive an authenticity score.

[0032] The threshold component 216 determines whether a test shoe is genuine or not based on an authenticity score determined from an evaluation performed by the evaluation component 212. In several exemplary embodiments, the authenticity score includes the percentage of multiple locations where pressure (e.g., color) is within the corresponding value or color threshold for genuine shoes. For example, if multiple pressure measurements at 90% or more of multiple locations (e.g., an authenticity threshold of 90% or 0.9) are within the corresponding pressure threshold in the database, the threshold component 216 labels the test shoe as genuine. In some cases, the authenticity threshold is the same for all types of shoes of a particular brand or category. In other cases, the authenticity threshold differs based on brand, model, and / or category. In some cases, the threshold component 216 may be part of the evaluation component 212.

[0033] In several embodiments using machine learning analysis, a pressure distribution image is provided to a machine learning system 210. The machine learning system 210 is configured to train one or more machine learning (ML) truth / false models to determine the probability that the pressure distribution image of the test footwear is authentic. The machine learning system 210 also refines the ML truth / false models by retraining them with updated training data. During inference or runtime, the machine learning system 210 uses the trained ML truth / false models to determine the probability that the test footwear is authentic.

[0034] To enable these multiple operations, the machine learning system 210 includes a training component 218, an evaluation component 220, and a threshold component 222, all of which are configured to communicate with each other (e.g., via a bus, shared memory, or switch). Figure 2 shows the training component 218 and evaluation component 220 being implemented within the machine learning system 210, but alternatively, the training component 218 may be separate from the evaluation component 220 in a different system or server.

[0035] In some embodiments, the training component 218 trains an authenticity model using pressure distribution images of both genuine and counterfeit shoes as training data. In some cases, pairs of position / color data (or position / color / time vectors of a video) for both genuine and counterfeit shoes are extracted from the corresponding pressure distribution images and used as training data for training the authenticity model. Machine learning can be performed using artificial intelligence such as neural networks. For example, a neural network can be trained by providing a set of pressure distribution images (or multiple pairs of position / pressure data) for genuine footwear and a set of pressure distribution images for the corresponding counterfeit footwear. Multiple weights can then be adjusted as appropriate to obtain the desired result. Training the authenticity model may include training on the probability that the footwear is genuine (e.g., an authenticity score). For consistency, the preset amount of force applied to each shoe by the pressure measuring machine (as well as the size and shape of the artificial foot) should be the same for genuine footwear, counterfeit footwear, and footwear being authenticated.

[0036] In some cases, additional data can be included in the training data to improve accuracy. This additional data may include the year and place of manufacture of a real shoe. This can be identified from a label found inside the shoe (e.g., an image of the label). For example, the quality and properties of the material used in the sole can change over many years, and the material or manufacturing may differ slightly between different factories / locations. Furthermore, materials degrade based on use / aging. Therefore, the training data could include pressure distribution data for different ages (e.g., year of manufacture) of real footwear of the same type / model.

[0037] Any number of authenticity models can be trained. For example, a separate model can be trained for each brand and model of authentic footwear (e.g., Gucci New Ace GG Supreme Trainer). Alternatively, the authenticity models may be trained for specific brands (e.g., Louis Vuitton, Gucci, Prada, Nike) and / or footwear categories (e.g., high-tops, high heels).

[0038] Over time, the training data may be updated to refine the truth / false determination model. For example, over the years, multiple pressure measurements may become larger at some locations on the sole or vary for different authentic footwear. In addition, authentic footwear is constantly being produced (e.g., the same model but with different materials, the same model and materials but manufactured in different locations, the same model but with different sole technology). The training data may be updated to reflect these changes and the corresponding retrained ML truth / false determination model.

[0039] During runtime or inference time, the evaluation component 220 of the machine learning system 210 is configured to determine the probability that the test footwear is genuine. Thus, the pressure distribution image is applied by the evaluation component 220 to an appropriate ML truth / false model. In some cases, the evaluation component 220 formats the pressure distribution image into one or more input vectors of multiple pairs of color and location data (e.g., extracting multiple pairs of color / location data as multiple features). That is, corresponding colors can be identified at multiple different locations in the pressure distribution image. The input vectors are then applied by the evaluation component 220 to the corresponding ML truth / false model. The ML truth / false model then provides a result which is the probability (or proportion) that the test footwear is genuine.

[0040] The probability output by the evaluation component 220 (e.g., authenticity score) is then compared to the authenticity threshold by the threshold component 222. In some cases, the authenticity threshold is the same for all types of footwear of a particular brand or category. In other cases, the authenticity threshold differs based on the brand, model, and / or category. For example, the authenticity threshold could be 0.8 or 80% for designer sneakers. Thus, a probability of 0.79 output by the evaluation component 220 would label the test footwear as counterfeit or not genuine, while a probability of 0.82 would label the test footwear as genuine.

[0041] Figure 4 is a flowchart illustrating the operation of an exemplary method 400 for authenticating footwear according to an exemplary embodiment. Several operations in method 400 can be performed by the authenticity determination system 116 using the components described above with respect to Figure 2. Thus, method 400 is described as an example with reference to the authenticity determination system 116. However, it should be understood that at least some of the operations of method 400 can be deployed on various other hardware configurations or performed by similar components located elsewhere in the network environment 100. Thus, method 400 is not intended to be limited to the authenticity determination system 116.

[0042] In operation 402, the authenticity determination system 116 (e.g., communication component 202) receives a request to determine the authenticity of footwear. The request may be received from a seller, a buyer, or an intermediary who determines the authenticity of footwear on behalf of the seller or buyer. In some cases, the request includes a pressure distribution image of the footwear to be determined (e.g., a request from an intermediary who has a pressure measuring device). Additionally or alternatively, the request may include one or more of the following associated with the footwear: a serial number, the year of purchase or manufacture, or the brand / model of the footwear. In some cases, the request does not include a pressure distribution image but includes a reference to a pressure distribution image that is uploaded to the image data storage device 206.

[0043] In operation 404, the image analysis component 204 accesses a previously uploaded pressure distribution image of the footwear. In some cases, the pressure distribution image may be received from an entity different from the user who submitted the request. For example, the footwear in question may be a return from a buyer to a seller and delivered to an intermediary who performs authenticity testing. In this case, the intermediary may have a pressure measuring machine that generates a pressure distribution image and uploads that pressure distribution image to the image data storage device 206. Here, the authenticity testing request may be submitted by an individual associated with the seller or the intermediary. A similar scenario may occur where the intermediary receives the footwear from the seller before delivering it to the buyer and performs authenticity testing on behalf of the buyer (e.g., the request is from the buyer or the intermediary). Furthermore, the seller may want to pre-test the authenticity of the footwear before offering it for sale (e.g., the request is from the seller or the intermediary). In several embodiments where the request includes a pressure distribution image, operation 404 is skipped (e.g., operation 404 is optional or not required).

[0044] In operation 406, the image analysis component 204 preprocesses the pressure distribution image of the test footwear. For example, the test footwear may be of a size for which the authenticity determination system 116 does not have pressure distribution data for authenticity determination. In these cases, the image analysis component 204 "resizes" the image to a size for which the authenticity determination system 116 has pressure distribution data for authenticity determination. In several embodiments where resizing is not required, operation 406 is optional or not required.

[0045] In several embodiments using statistical analysis, method 400 proceeds to operation 408 in which statistical image analysis is performed. Operation 408 is described in more detail with reference to Figure 5 below. In several embodiments using machine learning analysis, method 400 proceeds to operation 410 in which the machine learning analysis is performed. Operation 410 is described in more detail with reference to Figure 6 below.

[0046] In operation 412, an indicator of the authenticity of the footwear is provided. In some cases, this indicator may be displayed on the user interface of the client device 106 via the client application 108. In other cases, electronic communication (e.g., email, text message) can be generated and sent to the client device 106. If the goods being authenticated are listed (or will be listed) for sale using the network system 102, a badge indicating the authenticity of the footwear may be graphically included in the display of the footwear listing.

[0047] Figure 5 is a flowchart illustrating several operations (e.g., operation 408) of Method 500 for pressure distribution image analysis according to an exemplary embodiment. The operations in Method 500 may be performed by the truth / false determination system 116 using the components described above with respect to Figure 2. Therefore, Method 500 is described as an example with reference to the truth / false determination system 116. However, it should be understood that at least some of the operations of Method 500 may be deployed on various other hardware configurations or performed by similar components located elsewhere within the network environment 100. Therefore, Method 500 is not intended to be limited to the truth / false determination system 116.

[0048] In operation 502, the evaluation component 212 applies the pressure distribution image to a statistical model. In certain embodiments, the statistical model comprises a statistical image model trained to identify multiple colors (e.g., multiple color shading) at multiple different locations on the pressure distribution image. In some cases, the statistical image model can generate a color / location dataset.

[0049] In operation 504, the evaluation component 212 compares a color / location dataset from the pressure distribution image of the footwear to be authenticated with a corresponding color / location dataset accessed from the pressure data storage device 214. The evaluation component 212 accesses the corresponding color / location dataset of the authentic footwear using identifiers / information associated with that footwear (e.g., received with the request). In some cases, the pressure data storage device 214 contains multiple different pressure distributions for multiple different years of the same brand / model. Therefore, information associated with the footwear may include the year of purchase or manufacture of the footwear, and / or place of manufacture.

[0050] The evaluation component 212 compares the colors (representing pressure measurements) at multiple locations along the sole to a dataset of corresponding colors / locations for the soles of real footwear. When performing this comparison, the evaluation component 212 may determine whether each color / location measurement falls within a comparison threshold (e.g., 95%) for the corresponding color / location measurement of real footwear.

[0051] In operation 506, the threshold component 216 calculates an authenticity score based on its comparison. The authenticity score may include the percentage of multiple locations where the pressure measurements of the test footwear are within the comparison threshold of the corresponding pressure measurements of the genuine footwear. For example, if 9 out of 10 locations are within the threshold of the corresponding pressure measurements of the genuine footwear, the authenticity score is 0.9 or 90%.

[0052] In operation 508, it is determined whether the authenticity score meets (e.g., meets or exceeds) the authenticity threshold. For example, if the pressure measurements at 90% of multiple locations (e.g., 90% or an authenticity threshold of 0.9) are within the corresponding pressure threshold for genuine footwear, the threshold component 216 labels the test footwear as genuine in operation 510. If the authenticity score does not meet the authenticity threshold, the footwear is labeled as counterfeit in operation 512.

[0053] In some cases, the authenticity threshold is the same for all types of footwear of a particular brand or category. In other cases, the authenticity threshold differs based on the brand, model, and / or category. The authenticity threshold can be a default threshold, configurable by the operator of the authenticity determination system 116, or machine-learned. For example, training a machine learning model may involve training one or more models to determine the authenticity threshold.

[0054] Figure 6 is a flowchart illustrating the operation (e.g., operation 410) of Method 600 for performing machine learning pressure distribution image analysis according to an exemplary embodiment. Several operations in Method 600 may be performed by the truth / false system 116 using the components described above with respect to Figure 2. Thus, Method 600 is described as an example with reference to the truth / false system 116. However, it should be understood that at least some of the operations of Method 600 may be deployed on various other hardware configurations or performed by similar components located elsewhere in the network environment 100. Thus, Method 600 is not intended to be limited to the truth / false system 116.

[0055] In operation 602, the ML authenticity model corresponding to the footwear to be authenticated is identified by the evaluation component 220. In some cases, separate machine learning models are trained for each brand and model of genuine footwear. Alternatively, the machine learning models may be trained for specific brands (e.g., Louis Vuitton, Gucci, Prada) or product categories (e.g., designer sneakers, designer high heels). In these cases, the corresponding machine learning models are identified. Therefore, operation 602 may be optional if only a single ML authenticity model is trained.

[0056] In operation 604, the pressure distribution image of the test footwear is applied to the corresponding machine learning model by the evaluation component 220. In some cases, the entire pressure distribution image is applied to the ML truth / false determination model. In other cases, the evaluation component 220 can extract multiple features from the pressure distribution image. For example, the multiple features may include multiple colors at multiple different locations on the pressure distribution image. The multiple features can then be formatted into one or more input vectors applied to the ML truth / false determination model by the evaluation component 220. In some cases, additional information may be formatted into one or more of the multiple input vectors. The additional information may include, for example, the year (year of manufacture, year of purchase, etc.) and place (e.g., where it was manufactured) associated with the footwear.

[0057] In operation 606, a truth value or probability score is obtained from the ML truth value model. The probability score indicates the probability that the test footwear is genuine (for example, that the pressure distribution image of the test footwear matches the corresponding pressure distribution image of a genuine footwear).

[0058] Next, in operation 608, the authenticity score is compared to an authenticity threshold by the threshold component 222. In some cases, the authenticity threshold is the same for all types of footwear of a particular brand or category. In other cases, the authenticity threshold differs based on the brand, model, and / or category. For example, the authenticity threshold could be 0.8 or 80% for designer sneakers. The authenticity threshold can be a default threshold, configurable by the operator of the authenticity determination system 116, or machine-learned.

[0059] In operation 610, the threshold component 222 determines whether the authenticity score meets (e.g., satisfies or exceeds) the authenticity threshold. For example, an authenticity probability of 0.79 output by the evaluation component 220 does not meet the authenticity threshold of 0.8. If the authenticity score or probability meets the authenticity threshold, the threshold component 222 labels the footwear as genuine in operation 612. If the authenticity score or probability does not meet the authenticity threshold, the footwear is labeled as counterfeit in operation 614.

[0060] Some counterfeiters may replace a genuine shoe with a counterfeit shoe (for example, a genuine left shoe being replaced with a counterfeit right shoe). Therefore, before labeling a pair as genuine, it is possible to perform an analysis on both the left and right shoes in the pair.

[0061] While several exemplary embodiments have been described above using pressure distribution images, several alternative embodiments may use pressure distribution data such as pressure distribution tables or matrices that do not include graphics. In these embodiments, a table or matrix of pressure measurements at multiple different locations (and time in the case of a video) can be obtained from a pressure measuring machine. Alternatively, the pressure distribution images can be preprocessed (e.g., by the image analysis component 204) to generate a table or matrix. The table or matrix can then be sent to a statistical system 208 or a machine learning system 210 for analysis. The statistical system 208 can compare multiple values ​​in the table or matrix with corresponding tables or matrices of real values. Alternatively, the machine learning system can apply the multiple values ​​in the table or matrix to a corresponding ML model.

[0062] Figure 7 shows several components of a machine 700 capable of reading multiple instructions from a mechanical storage medium (e.g., a mechanical storage device, a non-temporary mechanical storage medium, a computer storage medium, or any preferred combination thereof) in several exemplary embodiments, and executing any one or more of the methods described herein. Specifically, Figure 7 shows a schematic diagram of a machine 700 in an exemplary form of a computer device (e.g., a computer), in which multiple instructions 724 (e.g., software, a program, an application, an applet, an app, or other executable code) causing the machine 700 to execute any one or more of the methods described herein can be executed in whole or in part.

[0063] For example, multiple instructions 724 can cause the machine 700 to execute the flowcharts shown in Figures 4 to 6. In a particular embodiment, multiple instructions 724 can transform the machine 700 into a specific machine (e.g., a specially configured machine) programmed to perform the described and illustrated functions in the described manner.

[0064] In alternative embodiments, machine 700 may operate as a standalone device or be connected to other machines (e.g., networked). In a networked configuration, machine 700 may operate as a server machine or client machine in a server-client network environment, or as a peer machine in a peer-to-peer (or distributed) network environment. Machine 700 may be a server computer, a client computer, a personal computer (PC), a tablet computer, a laptop computer, a netbook, a set-top box (STB), a personal digital assistant (PDA), a mobile phone, a smartphone, a web appliance, a network router, a network switch, a network bridge, or any machine capable of executing (sequentially or otherwise) multiple instructions 724 that specify multiple operations to be performed by that machine. Furthermore, although only a single machine is shown, the term “machine” should also be interpreted to include a collection of machines that individually or collectively execute multiple instructions 724 to perform any one or more of the multiple methods described herein.

[0065] The machine 700 includes a processor 702 (e.g., a central processing unit (CPU), a graphics processing unit (GPU), a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a radio frequency integrated circuit (RFIC), or any suitable combination thereof), main memory 704, and static memory 706, which are configured to communicate with each other via a bus 708. The processor 702 may include microcircuits that can be configured to be temporarily or permanently configured by some or all of a plurality of instructions 724, so that the processor 702 can be configured to perform any one or more of the plurality of methods described herein, in whole or in part. For example, one or more microcircuits of a set of the processor 702 may be configured to perform one or more components described herein.

[0066] The machine 700 may further include a graphics display 710 (e.g., a plasma display panel (PDP), a light-emitting diode (LED) display, a liquid crystal display (LCD), a projector, or a cathode ray tube (CRT), or any other display capable of displaying graphics or moving images). The machine 700 may also include an input device 712 (e.g., a keyboard), a cursor control device 714 (e.g., a mouse, touchpad, trackball, joystick, motion sensor, or other pointing device), a storage unit 716, a signal generation device 718 (e.g., a sound card, amplifier, speaker, headphone jack, or any suitable combination thereof), and a network interface device 720.

[0067] The storage unit 716 includes a mechanical storage medium 722 (e.g., a tangible mechanical storage medium) storing a plurality of instructions 724 (e.g., software) that embody any one or more of the methods or functions described herein. The plurality of instructions 724 may also be entirely or at least partially present in the main memory 704, in the processor 702 (e.g., in the processor's cache memory), or both, before or during the execution of those instructions by the machine 700. Thus, the main memory 704 and the processor 702 can be considered as mechanical storage media (e.g., tangible and non-temporary mechanical storage media). The plurality of instructions 724 may be transmitted over the network 726 via the network interface device 720.

[0068] In some exemplary embodiments, the machine 700 may be a portable computing device and may have one or more additional input components (e.g., sensors or gauges). Some examples of such input components include image input components (e.g., one or more cameras), audio input components (e.g., microphones), direction input components (e.g., compasses), position input components (e.g., GPS (global positioning system) receivers), orientation components (e.g., gyroscopes), motion detection components (e.g., one or more accelerometers), altitude detection components (e.g., altimeters), and gas detection components (e.g., gas sensors). Multiple inputs collected by any one or more of these input components may be accessible and available for use by any of the components described herein.

[0069] Executable instructions and machine storage media Various memories (e.g., 704, 706, and / or memories of one or more processors 702) and / or storage units 716 may store one or more sets of instructions and data structures (e.g., software) 724 that embody or utilize any one or more of the methods or functions described herein. When these instructions are executed by one or more processors 702, they cause various operations to be performed for carrying out the disclosed embodiments.

[0070] As used herein, the terms “machine storage medium,” “device storage medium,” and “computer storage medium” (collectively referred to as “machine storage medium 722”) have the same meaning and may be used interchangeably in this disclosure. These terms refer to one or more storage devices and / or media that store multiple executable instructions and / or data (e.g., centralized or distributed databases, and / or associated caches and servers), as well as cloud-based storage systems or storage networks that include multiple storage devices or devices. Accordingly, these terms shall be construed as including, but not limited to, solid-state memory, including memory inside or outside a processor, as well as optical and magnetic media. Specific examples of machine storage medium, computer storage medium, and / or device storage medium 722 include, for example, non-volatile memory including semiconductor memory devices such as EPROM (erasable programmable read-only memory), EEPROM (electrically erasable programmable read-only memory), FPGAs, and flash memory devices, magnetic disks such as internal hard disks and removable disks, magneto-optical disks, and CD-ROM and DVD-ROM disks. The terms one or more “mechanical storage media,” one or more “computer storage media,” and one or more “device storage media” 722 specifically exclude carrier waves, modulated data signals, and other such media, at least some of which are covered under the term “signaling media” as set forth below. In this context, mechanical storage media are non-transient.

[0071] signal medium The terms “signaling medium” or “transmission medium” shall be interpreted to include any form, such as modulated data signals, carrier waves, etc. The term “modulated data signal” means a signal in which one or more of its properties are set or modified in order to encode the information contained in the signal.

[0072] Computer-readable media The terms “machine-readable medium,” “computer-readable medium,” and “device-readable medium” mean the same thing and may be used interchangeably in this disclosure. These terms are defined to include both machine storage media and signaling media. Therefore, these terms include both storage devices / mediums and carrier / modulated data signals.

[0073] Multiple instructions 724 may further be transmitted or received over a communication network 726 using a transmission medium via a network interface device 720 and utilizing one of several well-known transfer protocols (e.g., HTTP). Multiple examples of the communication network 726 include local area networks (LANs), wide area networks (WANs), the Internet, mobile phone networks, POTS (plain old telephone service) networks, and wireless data networks (e.g., Wi-Fi, LTE, and WiMAX networks). The term “transmission medium” shall be interpreted as including any intangible medium capable of storing, encoding, or carrying multiple instructions 724 for execution by machine 700, and including digital or analog communication signals or other intangible mediums for enabling communication of such software.

[0074] Throughout this specification, multiple instances may implement a component, operation, or structure described as a single instance. While individual operations of one or more methods are illustrated and described as separate operations, one or more of these operations may occur simultaneously, and it is not required that the operations occur in the order in which they are illustrated. Multiple structures and functions presented as separate components in exemplary configurations may be implemented as a combined structure or component. Similarly, structures and functions presented as a single component may be implemented as multiple separate components. These and other variations, modifications, additions, and improvements are included within the scope of the subject matter of this specification.

[0075] A “component” refers to a device, physical entity, or logic whose boundaries are defined by other technologies, such as function or subroutine calls, branching points, application programming interface APIs, or partitioning or modularization of specific processing or control functions. Multiple components can be combined through interfaces with other components to perform machine processing. A component is a packaged, functional hardware unit designed to be used together with other components, and is often part of a program that performs a specific function among several related functions. Multiple components can constitute either a software component (e.g., code embodied in a machine-readable medium) or a hardware component.

[0076] A “hardware component” is a tangible unit capable of performing a specific operation and can be configured or arranged in a specific physical manner. In various exemplary embodiments, one or more computer systems (e.g., a standalone computer system, a client computer system, or a server computer system) or one or more hardware components of a computer system (e.g., a processor or a group of processors) may be configured by software (e.g., an application or application portion) as hardware components that operate to perform a specific operation as described herein.

[0077] In some embodiments, hardware components may be implemented mechanically, electronically, or in any suitable combination thereof. For example, a hardware component may comprise a dedicated circuit or logic permanently configured to perform a specific operation. For example, a hardware component may be a special-purpose processor such as an FPGA (field programmable gate array) or ASIC. A hardware component may comprise programmable logic or circuit configured by software to perform a specific operation temporarily. For example, a hardware component may comprise software contained within a general-purpose processor or other programmable processor. When configured by such software, the hardware component is no longer a general-purpose processor, as it becomes a specific machine (or a specific component of a machine) independently tuned to perform the configured function. It will be understood that the decision to implement a hardware component mechanically, in a dedicated and permanently configured circuit, or in a temporarily configured circuit (e.g., configured by software) may be made based on cost and time considerations.

[0078] Therefore, the term “hardware component” should be understood to encompass tangible entities that are physically constructed, permanently configured (e.g., hardwired), or temporarily configured (e.g., programmed) in order to operate in a particular way or to perform a particular operation as described herein. Considering multiple examples of hardware components being temporarily configured (e.g., programmed), each of the multiple hardware components does not need to be configured or instantiated in any single instance. For example, if a hardware component consists of general-purpose processors configured by software to become special-purpose processors, the general-purpose processors may be configured as different special-purpose processors (e.g., with different hardware components) at different times. Thus, the software may, for example, configure the processor to constitute a particular hardware component in one instance at a time, while composing a different hardware component in a different instance at a different time.

[0079] Hardware components can provide information to other hardware components and receive information from other hardware components. Therefore, the described hardware components can be considered to be communicatively coupled. When multiple hardware components exist simultaneously, communication can be achieved by signal transmission between or within two or more of the hardware components (e.g., via appropriate circuits and buses). In multiple examples where multiple hardware components are configured or instantiated at different times, communication between such hardware components may be achieved, for example, through the storage and retrieval of information in memory structures accessed by the multiple hardware components. For example, one hardware component can not only perform an operation but also store the output of that operation in a communicatively coupled memory device. Another hardware component can then retrieve and process the stored output by subsequently accessing the memory device. Multiple hardware components can also initiate communication with input or output devices, or they can operate on resources (e.g., collections of information).

[0080] Various operations of the exemplary methods described herein may be performed, at least partially, by one or more processors that are temporarily (e.g., by software) or permanently configured to perform the operations in question. Whether temporarily or permanently configured, such processors may constitute a processor implementation component that operates to perform one or more operations or functions described herein. As used herein, “processor implementation component” means a hardware component implemented using one or more processors.

[0081] Similarly, some of the methods described herein may be at least partially processor-implemented, and a processor is an example of hardware. For example, at least some of the operations of a method may be performed by one or more processors or processor implementation components. Furthermore, one or more processors may operate to support the execution of the operations in a “cloud computing” environment or as “software as a service” (SaaS). For example, at least some of the operations may be performed by a group of computers (as an example of a machine containing multiple processors), and these operations may be accessible over a network (e.g., the Internet) and over one or more suitable interfaces (e.g., an API (application program interface)).

[0082] The specific performance characteristics of multiple operations may reside not only within a single machine, but may also be distributed among one or more processors deployed across multiple machines. In some exemplary embodiments, one or more processors or processor implementation components may be located in a single geographical location (e.g., a home environment, an office environment, or a server farm). In other exemplary embodiments, one or more processors or processor implementation components may be distributed across multiple geographical locations.

[0083] Multiple examples Example 1 is a method for authenticating footwear using pressure distribution image analysis. The method comprises: receiving a request to authenticate footwear; accessing a pressure distribution image of the footwear, the pressure distribution image comprising a pressure distribution represented by a plurality of colors generated based on a predetermined force applied to the footwear; analyzing the pressure distribution image, the analysis comprising comparing the pressure distribution image with a pressure distribution image of a real footwear; determining, based on the analysis, whether the pressure distribution image falls within an authenticity threshold of the real pressure distribution image; and, based on the pressure distribution image falling within the authenticity threshold, presenting an index of the authenticity of the footwear.

[0084] In Example 2, the subject of Example 1 may optionally include, analyzing the pressure distribution image, applying the pressure distribution image for the footwear to a machine learning model trained on training data that includes data derived from the pressure distribution image of the actual footwear.

[0085] In Example 3, the subject of Example 1 or Example 2 may optionally further include training the machine learning model using the training data derived from the pressure distribution images of real and counterfeit footwear.

[0086] In Example 4, the subject of any of Examples 1-3 may optionally include training the machine learning model for each of the real footwear over a year, and performing the analysis based in part on the year of the product.

[0087] In Example 5, any subject from Examples 1 to 4 may optionally include the pressure distribution image including a pressure distribution heatmap or pedovarographic measurement image generated based on the force applied to the footwear.

[0088] In Example 6, the subject of any of Examples 1 to 5 may optionally include a video of the dynamic pressure distribution during the process performed using the footwear by a pressure measuring machine.

[0089] In Example 7, the subject of any of Examples 1 to 6 may optionally include the following: the requirement includes the brand, model, and size of the footwear to be authenticated, and the method includes determining whether the footwear to be authenticated is the same size as the genuine footwear, and, based on the footwear being of a different size, resizing the pressure distribution image of the footwear to correspond to the size of the genuine footwear.

[0090] In Example 8, the subject of any of Examples 1 to 7 may optionally include that accessing the pressure distribution image of the footwear involves generating the pressure distribution image of the footwear using a pressure measuring machine having an artificial foot equipped with multiple sensors and applying the preset force to the footwear.

[0091] In Example 9, the subject of any of Examples 1-8 may optionally include, in providing the indicator of authenticity, graphic displaying a badge indicating that the footwear is genuine in the list.

[0092] Example 10 is a system for determining the authenticity of footwear using pressure distribution image analysis. The system comprises one or more processors and a memory for storing a plurality of instructions, the plurality of instructions, when executed by the one or more processors, cause the one or more processors to perform a plurality of operations, the plurality of operations comprising: receiving a request to determine the authenticity of footwear; accessing a pressure distribution image of the footwear, the pressure distribution image comprising a pressure distribution represented by a plurality of colors generated based on a predetermined force applied to the footwear; analyzing the pressure distribution image, the analysis comprising comparing the pressure distribution image with a pressure distribution image of a real footwear; determining, based on the analysis, whether the pressure distribution image is within an authenticity threshold of the real pressure distribution image; and, based on whether the pressure distribution image is within the authenticity threshold, presenting an index of the authenticity of the footwear.

[0093] In Example 11, the subject of Example 10 may optionally include, in analyzing the pressure distribution image, applying the pressure distribution image for the footwear to a machine learning model trained on training data that includes data derived from the pressure distribution image of the actual footwear.

[0094] In Example 12, the subject of Example 10 or Example 11 may optionally further include the method training the machine learning model using the training data derived from the pressure distribution images of real and counterfeit footwear.

[0095] In Example 13, the subject of any of Examples 10-12 may optionally include training the machine learning model for each of the real footwear over a year, and performing the analysis based in part on the year of the product.

[0096] In Example 14, any subject from Examples 10 to 13 may optionally include the pressure distribution image including a pressure distribution heatmap or pedovarographic measurement image generated based on the force applied to the footwear.

[0097] In Example 15, any subject from Examples 10 to 14 may optionally include a video of the dynamic pressure distribution during the process performed using the footwear by a pressure measuring machine.

[0098] In Example 16, the subject of any of Examples 10 to 15 may optionally include the following: the requirement includes the brand, model, and size of the footwear to be authenticated, and the method includes determining whether the footwear to be authenticated is the same size as the genuine footwear, and, based on the footwear being of a different size, resizing the pressure distribution image of the footwear to correspond to the size of the genuine footwear.

[0099] In Example 17, any subject from Examples 10 to 16 may optionally include generating the pressure distribution image of the footwear using a pressure measuring machine having an artificial foot equipped with multiple sensors and applying the preset force to the footwear.

[0100] In Example 18, any subject from Examples 10 to 17 may optionally include, for the presentation of the indicator of authenticity, the graphic display of a badge indicating that the footwear is genuine in the list.

[0101] Example 19 is a computer storage medium that, when executed by one or more processors of a machine, includes a plurality of instructions causing the machine to perform a plurality of operations for authenticating footwear using pressure distribution image analysis. The plurality of operations include: receiving a request for authenticating footwear; accessing a pressure distribution image of the footwear, the pressure distribution image including a pressure distribution represented by a plurality of colors generated based on a set force applied to the footwear; analyzing the pressure distribution image, the analysis including comparing the pressure distribution image with a pressure distribution image of a genuine footwear; determining, based on the analysis, whether the pressure distribution image is within an authenticity threshold of the genuine pressure distribution image; and, based on whether the pressure distribution image is within the authenticity threshold, presenting an index of the authenticity of the footwear.

[0102] In Example 20, the subject of Example 19 may optionally include that accessing the pressure distribution image of the footwear involves generating the pressure distribution image for the footwear using a pressure measuring machine having an artificial foot that applies a preset pressure to the footwear.

[0103] Some parts of this specification are presented in terms of algorithms or symbolic representations of multiple operations on data stored as bits or binary digital signals in machine memory (e.g., computer memory). These algorithms or symbolic representations are examples of techniques used by those skilled in the field of data processing to communicate the content of their work to others skilled in the field. As used herein, “algorithm” is a self-consistent sequence of multiple actions or similar operations to achieve a desired result. In this context, algorithms and operations include the physical operation of physical quantities. Usually, but not always, such quantities take the form of electrical, magnetic, or optical signals that can be stored, accessed, transferred, combined, compared, or otherwise operated (operated) by a machine. It is sometimes convenient, primarily for reasons of general use, to refer to such signals using words such as “data,” “content,” “bit,” “value,” “element,” “symbol,” “character,” “term,” “number,” and “digit.” However, these terms are merely convenient labels and should be associated with the appropriate physical quantities.

[0104] Unless otherwise specified, descriptions herein using terms such as “processing,” “computing,” “calculating,” “determining,” “presenting,” and “displaying” refer to the operation or process of a machine (e.g., a computer) that manipulates or transforms data represented as physical (e.g., electronic, magnetic, or optical) quantities in one or more memories (e.g., volatile memory, non-volatile memory, or any preferred combination thereof), registers, or other machine components that receive, store, transmit, or display information. Furthermore, unless otherwise specified, the articles “a” or “an” are used herein to include one or more cases, as is common in multiple patent documents. Finally, where used herein, the conjunction “or” refers to a non-exclusive “or” unless otherwise specified.

[0105] While the outline of this subject matter has been described with reference to specific examples, various modifications and changes can be made to these examples without departing from the broader scope of the multiple examples of the invention. For example, the various examples or their features may be mixed and adapted or optional by those skilled in the art. Such examples of the subject matter may, for convenience only, be referred to as “inventions” individually or collectively in this specification, but where two or more are actually disclosed, the scope of this application is not intended to be arbitrarily limited to any single invention or concept.

[0106] The various examples provided herein are considered to be described in sufficient detail to enable those skilled in the art to implement the disclosed teachings. Other examples may be used and derived therefrom so that structural and logical substitutions and modifications may be made without departing from the scope of this disclosure. Accordingly, “modes for carrying out the invention” should not be interpreted in a restrictive sense, and the scope of the various examples is defined only by the appended claims, along with the entire scope of equivalents to which such claims are entitled.

[0107] Furthermore, multiple examples may be provided for multiple resources, multiple operations, or multiple structures described herein as a single example. In addition, the boundaries between various resources, operations, modules, engines, and data stores are arbitrary to some extent, and certain operations are shown in the context of a particular exemplary configuration. Other assignments of functions are conceivable and may be included within the scope of various examples of the invention. In general, multiple structures and functions presented as separate resources in an exemplary configuration may be implemented as a combined set of structures or resources. Similarly, multiple structures and functionalities presented as a single resource may be implemented as separate resources. These and other variations, modifications, additions, and improvements fall within the scope of the examples of the invention represented by the appended claims. Accordingly, this specification and the drawings should be considered illustrative rather than restrictive.

Claims

1. It is a method, Receiving requests to verify the authenticity of footwear, Accessing a pressure distribution image of the sole of the footwear, wherein the pressure distribution image includes a pressure distribution represented by a plurality of colors generated based on a predetermined force applied to the footwear; The analysis involves using an image analysis system to analyze the pressure distribution image, and this analysis includes comparing the pressure distribution image with a pressure distribution image of the sole of the actual footwear. Based on the above analysis, it is determined whether the pressure distribution image is within the authenticity threshold of the real pressure distribution image, A method comprising: presenting an indicator of the authenticity of the footwear based on the fact that the pressure distribution image is within the authenticity threshold.

2. Analyzing the aforementioned pressure distribution image means The method according to claim 1, comprising applying the pressure distribution image of the sole of the footwear to a machine learning model trained with training data including data derived from the pressure distribution image of the sole of the actual footwear.

3. The method according to claim 2, further comprising training the machine learning model using training data derived from the pressure distribution images of the soles of genuine and counterfeit footwear.

4. Training the aforementioned machine learning model includes training it for each of the aforementioned real footwear for a year, The method according to claim 3, wherein the analysis is performed based on the year of the footwear.

5. The method according to claim 1, wherein the pressure distribution image includes a pressure distribution heatmap or pedovarographic measurement image generated based on the force applied to the footwear.

6. The method according to claim 1, wherein the pressure distribution image includes a video of the dynamic pressure distribution during a process performed using the footwear, measured by a pressure measuring machine.

7. The aforementioned requirements include the brand, model, and size of the footwear to be determined to be true or false. The determination of whether the footwear to be authenticated is the same size as the genuine footwear, The method according to claim 1, further comprising resizing the pressure distribution image of the sole of the footwear to correspond to the actual size of the footwear, based on the fact that the footwear is of a different size.

8. Accessing the pressure distribution image of the sole of the footwear means The method according to claim 1, comprising generating a pressure distribution image of the sole of the footwear using a pressure measuring machine having an artificial foot equipped with multiple sensors and applying the preset force to the footwear.

9. Having the aforementioned indicator of authenticity present is, The method according to claim 1, further comprising graphically displaying a badge indicating that the footwear is authentic in the list of footwear.

10. It is a system, One or more processors, A memory for storing multiple instructions, wherein when the multiple instructions are executed by one or more processors, the one or more processors cause the other to perform multiple operations. The aforementioned multiple operations are, Receiving requests to verify the authenticity of footwear, Accessing a pressure distribution image of the sole of the footwear, wherein the pressure distribution image includes a pressure distribution represented by a plurality of colors generated based on a predetermined force applied to the footwear; The analysis involves analyzing the pressure distribution image, which includes comparing the pressure distribution image with a pressure distribution image of the sole of the actual footwear. Based on the above analysis, it is determined whether the pressure distribution image is within the authenticity threshold of the real pressure distribution image, A system comprising: presenting an indicator of the authenticity of the footwear based on the fact that the pressure distribution image is within the authenticity threshold.

11. The system according to claim 10, wherein analyzing the pressure distribution image includes applying the pressure distribution image for the sole of the footwear to a machine learning model trained with training data including data derived from the pressure distribution image for the sole of the actual footwear.

12. The aforementioned multiple operations are, The system according to claim 11, further comprising training the machine learning model using training data derived from the pressure distribution images of the soles of genuine and counterfeit footwear.

13. Training the aforementioned machine learning model includes training it for each of the aforementioned real footwear for a year, The analysis described above is based on the year of the footwear, according to the system of claim 12.

14. The system according to claim 10, wherein the pressure distribution image includes a pressure distribution heatmap or pedovarographic measurement image generated based on the force applied to the footwear.

15. The system according to claim 10, wherein the pressure distribution image includes a video of the dynamic pressure distribution during a process performed using the footwear by a pressure measuring machine.

16. The aforementioned requirements include the brand, model, and size of the footwear to be determined to be true or false. The aforementioned multiple operations are, The determination of whether the footwear to be authenticated is the same size as the genuine footwear, The system according to claim 10, further comprising resizing the pressure distribution image of the sole of the footwear to correspond to the actual size of the footwear, based on the fact that the footwear is of a different size.

17. Accessing the pressure distribution image of the sole of the footwear means The system according to claim 10, comprising generating a pressure distribution image of the sole of the footwear using a pressure measuring machine having an artificial foot equipped with multiple sensors and having an artificial foot that applies the preset force to the footwear.

18. Having the aforementioned indicator of authenticity present is, The system according to claim 10, further comprising graphically displaying a badge indicating that the footwear is authentic in the list of footwear.

19. A machine storage medium containing multiple instructions, wherein, when the multiple instructions are executed by one or more processors of the machine, the machine is made to perform multiple operations. The aforementioned multiple operations are, Receiving requests to verify the authenticity of footwear, Accessing a pressure distribution image of the sole of the footwear, wherein the pressure distribution image includes a pressure distribution represented by a plurality of colors generated based on a predetermined force applied to the footwear; The analysis involves analyzing the pressure distribution image, which includes comparing the pressure distribution image with a pressure distribution image of the sole of the actual footwear. Based on the above analysis, it is determined whether the pressure distribution image is within the authenticity threshold of the real pressure distribution image, A mechanical storage medium comprising: displaying an indicator of the authenticity of the footwear based on the fact that the pressure distribution image is within the authenticity threshold.

20. Accessing the pressure distribution image for the sole of the footwear means, The mechanical storage medium according to claim 19, comprising generating a pressure distribution image of the sole of the footwear using a pressure measuring machine having an artificial foot that applies a preset pressure to the footwear.

Images