Product management system, product management method, and product management program

Abstract

This invention achieves highly convenient product management. A product management system 100 comprises: a prediction unit 140 that, on the basis of post-first-step images 101 of each of a plurality of products, including a first product, captured after a first step from among a plurality of steps in the design and manufacturing of a product including clothing, footwear, an accessory, or an object used along a part of the body, generates prediction change information 103 relating to a change after the first step for each of the plurality of products; an image acquisition unit 150 that acquires a post-change image 104 capturing the first product which has changed after the first step; and an identification unit 160 that, on the basis of the prediction change information 103 and the post-change image 104, identifies from among product management information items for managing the plurality of products a first product management information item for managing the first product.

Description

Product management system, product management method, and product management program

[0001] The present disclosure relates to a product management system, a product management method, and a product management program.

[0002] Conventionally, systems for managing products during the design and manufacturing process have been known.

[0003] For example, in a method described in U.S. Patent No. 6,277,645, a computer receives imaging information representing a collection of video segments relating to individual portions of a manufacturing process to form a product. The synchronized collection of video segments is then analyzed to generate a set of observations. The set of observations and assembly rules corresponding to the manufacturing process are then used to generate a set of assertions. The computer then uses the set of observations and the set of assertions to generate a digital trace record for the product.

[0004] JP 2020-42814 A

[0005] The method described in Patent Document 1 is based on the premise that the manufacturing program and assembly process are videotaped. However, for example, the video data may be large in volume, which may require time to process. Furthermore, the manufacturing program and assembly process may include processes that cannot be videotaped. Therefore, the convenience of this method is limited, at least for the reasons mentioned above.

[0006] Therefore, an object of the present disclosure is to realize highly convenient product management.

[0007] A product management system according to one aspect of the present disclosure includes a prediction unit that generates predicted change information regarding changes in each of a plurality of products after undergoing a first process based on a post-first process image of each of a plurality of products including a first product, the post-first process image being taken after the first process has been performed among a plurality of processes in the design and manufacture of a product including any of clothing, footwear, accessories, and items used to fit a part of the body; an image acquisition unit that acquires a post-change image of the first product that has changed after undergoing the first process; and an identification unit that identifies first product management information that manages the first product from among the product management information that manages a plurality of products based on the predicted change information and the post-change image.

[0008] A product management system according to one aspect of the present disclosure includes a prediction unit that generates predicted change information regarding changes based on a post-change image of a first product that has changed after undergoing a first process among multiple processes in the design and manufacture of a product including any of clothing, footwear, accessories, and items used to fit a part of the body; product management information for managing multiple products, the product management information including product identification information that identifies each of the multiple products including the first product and a post-first process image of each of the multiple products that has been taken after each of the multiple products has undergone the first process, and an identification unit that identifies first product management information that manages the first product among the product management information based on the predicted change information.

[0009] A product management method according to one aspect of the present disclosure involves a computer generating predicted change information regarding changes in each of a plurality of products after undergoing a first process based on a post-first process image of each of a plurality of products, including a first product, taken after undergoing a first process among a plurality of processes in the design and manufacture of a product including any of clothing, footwear, accessories, and items used to fit a part of the body; obtaining a post-change image of the first product that has changed after undergoing the first process; and identifying first product management information for managing the first product from among the product management information for managing a plurality of products based on the predicted change information and the post-change image.

[0010] According to the system of this aspect, highly convenient product management can be realized.

[0011] FIG. 1 is a diagram illustrating an overview of processing in a product management system 100 according to an embodiment of the present disclosure. FIG. 2 is a diagram illustrating a configuration of a product management system 100 according to an embodiment of the present disclosure. FIG. 3 is a diagram illustrating an example of product management information stored in a storage unit 110. FIG. 4 is a diagram illustrating an example of identification processing by an identification unit 160. FIG. 5 is a flowchart illustrating an example of processing in a first example of the product management system 100. FIG. 6 is a flowchart illustrating an example of processing in a first example of the product management system 100. FIG. 7 is a diagram illustrating an example of the hardware configuration of a computer 800. FIG. 8 is a diagram illustrating an overview of the product management system 100.

[0012] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Preferred embodiments of the present disclosure will now be described with reference to the accompanying drawings, in which: Fig. 1 is a diagram showing an overview of processing in a product management system 100 according to an embodiment of the present disclosure;

[0013] The product management system 100 is an information processing system implemented by a product management program. The product management system 100 is an information processing system that manages products (hereinafter referred to as "products") including any of clothing, footwear, accessories, and items used to fit a part of the body (e.g., insoles). Based on an image of a product that has changed through design, manufacturing, or use, the product management system 100 identifies product management information corresponding to the product shown in the image.

[0014] First, the product management system 100 manages product management information (e.g., first product management information, second product management information) for each of a plurality of products (e.g., a first product, a second product). Each piece of product management information is associated with, for example, an image of the product taken after the first process of a plurality of processes in design and manufacturing (post-first-process image 101).

[0015] Next, the product management system 100 uses the prediction model 102 to generate predicted change information (for example, predicted change information 103) regarding changes in each of the multiple products after the first process.

[0016] The product management system 100 acquires a post-change image 104 obtained by capturing an image of the first product that has changed after undergoing the first process. Then, the product management system 100 uses an evaluation model 105 to identify, from among the multiple pieces of product management information, first product management information that manages the first product, based on the predicted change information and the post-change image.

[0017] That is, the product management system 100 identifies the first product management information of the first product based on the first post-process image 101 captured after the first process, in other words, predicted change information 103 predicted from the image of the product before the change, and the post-change image 104 of the first product that has changed after the first post-process image is captured.

[0018] In this embodiment, the product includes any of clothing, footwear, accessories, and items that are used to fit a part of the body (for example, insoles) that are currently being designed or manufactured.

[0019] 2 is a diagram showing the configuration of product management system 100 according to an embodiment of the present disclosure. Product management system 100 is communicably connected to design and manufacturing related devices 200 and store devices 300 via a network such as the Internet. Details of product management system 100 will be described later.

[0020] The design and manufacturing related device 200 is a device related to the design or manufacturing of a product. The design and manufacturing related device 200 may be, for example, a plurality of devices corresponding to a plurality of processes in the design and manufacturing of a product.

[0021] The design and manufacturing related device 200 can photograph the product at a predetermined timing in each process and output the photograph to the product management system 100. The predetermined timing in each process may be, for example, before or after the execution of each process. This allows the product management system 100 to manage images of the product in each process in the design and manufacturing related device 200. Note that in this embodiment, an example will be described in which images are photographed after the execution of each process.

[0022] The design and manufacturing related device 200 may be an imaging device provided in the device itself that performs the processing in each process, or may be an imaging device installed in or near the device that performs the processing.

[0023] The in-store device 300 is a device installed in a store. The store may be, for example, a store where products can be purchased, or a store that accepts repairs and provides after-sales service for purchased products.

[0024] The in-store device 300 is equipped with, for example, a photographing device that photographs the product used by the user, and can output the photographed image to the product management system 100 .

[0025] Although FIG. 2 shows one design / manufacturing related device 200 and one store device 300, there may be a plurality of design / manufacturing related devices 200 and a plurality of store devices 300.

[0026] Next, details of the product management system 100 will be described. The product management system 100 includes a storage unit 110, a product management unit 120, a user information acquisition unit 130, a prediction unit 140, an image acquisition unit 150, an identification unit 160, and an association unit 170. Each unit shown in Fig. 2 can be realized, for example, by using a storage area or by a processor executing a program stored in the storage area.

[0027] The storage unit 110 stores information processed in the product management system 100. The storage unit 110 can store, for example, product management information, user information, predicted change information, and post-change images, which will be described later.

[0028] The product management unit 120 manages product management information for managing products. For example, the product management unit 120 creates, retrieves, and updates product management information for each of a plurality of products. The product management unit 120 stores the product management information in the storage unit 110.

[0029] Product management information manages information about each product, such as information about the product's design, manufacturing, sales, and usage. Product management information is, for example, information that manages product meta-information. Product management information manages information about the product for each design and manufacturing process, and for each specified timing during use, for example.

[0030] A product manager can access information about the product by referencing the product management information. Here, the product manager may include, for example, a product designer or design department, a manufacturer or manufacturing department, or a seller or sales department. The product manager may also be, for example, a product manufacturer. This allows the product manager to easily grasp information about the product during the product design, manufacturing, sales, and post-sale (i.e., while the product is being used by a user).

[0031] The product management information is information that manages, for example, a product management code that identifies a product and information about the product in association with each other. The product management code may be, for example, a product number.

[0032] The information about the product may include, for example, information about the product modeling process using additive modeling (or three-dimensional lamination). The information about the modeling process may include, for example, information about the modeling date and time, materials used, modeling conditions, modeling location, device serial number, and information about the modeler. Here, the materials used are information about the materials used to model the footwear or insole using additive modeling, and may include, for example, information about the type, weight, and material lot of the material. The modeling conditions may include information about the additive modeling method (modeling device) used to model the footwear or insole, the production lot, and the like.

[0033] Furthermore, the information about the product may include, for example, information about a processing step for the product. The information about the processing step may include, for example, information about the date and time of the processing, the processing time, the processing conditions, and the person in charge of the processing. Here, the processing conditions may include, for example, information about the equipment, tools, heating temperature and time, and coating material used in the processing, or information about the environment and product condition obtained during manufacturing.

[0034] Furthermore, the information about the product may include, for example, information about a quality check process for a product that has undergone a predetermined process, such as the date and time of the process, information about the person in charge, the quality inspection results, the weight, and other information about product characteristics.

[0035] The information about the product may be information automatically acquired from the equipment that performed the process, or may be information input by the worker.

[0036] The specification information includes information about product specifications, such as the shape or material, type, purpose, or last (shoe mold) of the product. Here, the type may be, for example, information about insoles, shoes, or sandals. The purpose may be, for example, information about a specific sport or activity, such as running, walking, soccer, or tennis. The last may be, for example, information about the last on which the product was designed, and in particular, for example, information about whether an existing product last was used or whether a last that fits the user's foot shape was used.

[0037] The product management information may include design information, manufacturing information, product shipping and storage information, sales history information, customer information (e.g., footprints, physical information, sports history, etc.), usage history information (e.g., usage history information registered via an app), and failure and repair history information. For example, a user may visit a store that has a 3D scanner and scan their footprint, or scan their footprint at home using a smartphone. Here, customer information including the user's name, address, contact information, age, gender, and purchase history, as well as the user's footprint, the scan date and time, and the scan data are recorded.

[0038] The product management information includes product management information for each product. That is, the product management information includes, for example, first product management information for a first product. Furthermore, a post-first-process image captured after a first process among multiple processes in the design and manufacture of the product is managed in association with product identification information.

[0039] Here, the product may be, for example, a product manufactured by additive manufacturing. In this case, the multiple steps in manufacturing the product may include a manufacturing step, a support member removal step, a surplus material separation step, a cleaning step, a drying step, an ultraviolet irradiation step, a heat curing step, polishing, and coating.

[0040] 3 is a diagram showing an example of product management information stored in the storage unit 110. The product management information stored in the storage unit 110 includes, for example, product identification information, specification information, design information, manufacturing information, sales information, and usage information.

[0041] The user information acquisition unit 130 acquires user information related to the user of the first product and stores it in the storage unit 110. Here, the user may be any user of the first product, and may not be the owner of the first product. If there are multiple users of the first product, the user may be any one of the multiple users.

[0042] The user information includes, for example, at least one of usage status information indicating the usage status of the first product by the user and physical information regarding the user's body.

[0043] The usage status information may be any information relating to usage status that affects changes in the product due to use. For example, it may include information regarding the frequency of use of the first product by the user, usage time, usage intensity, usage distance, and road surface conditions (e.g., concrete, dirt, grass, tartan, etc.) at the time of use. Regarding usage time, for example, the start date and time of use, the usage history date and time, and the usage end date and time may be recorded. The use start date and time and the usage end date and time may be input by the user via, for example, a dedicated application installed on a smartphone, or may be obtained and recorded by other means. The usage history date and time is a history of use of customer service, etc., and may be input by the customer service or the user along with the content of the use. If the product is a shoe or insole, the usage status information may include, for example, information regarding the frequency, duration, and intensity of walking or running.

[0044] The physical information may be any information about the body that affects changes in the product due to use. For example, it may include information about the user's height, weight, sex, the size or shape of the part of the body that corresponds to the product, and movement tendencies. Information about movement tendencies may include, for example, information about walking or running tendencies (e.g., pace, stride width, pronation angle and type, ground contact type, pressure on the foot, etc.). If the product is a shoe or insole, the size or shape of the part of the body that corresponds to the product may include, for example, foot size, arch size, width or height, and foot shape.

[0045] The user information acquisition unit 130 may acquire user information, for example, from a user device used by the user. Alternatively, the user information may be acquired from a system that executes an application used by the user. Here, the application used by the user may be, for example, an app that manages product usage records, particularly a running record app. Alternatively, the user information acquisition unit 130 may acquire user information from the user device or the system that executes the application used by the user via the store device 300.

[0046] The prediction unit 140 generates predicted change information regarding changes in each of the multiple products after the first process, based on images of each of the multiple products including the first product taken after the first process of the multiple processes in product design and manufacturing, and stores the predicted change information 103 in the memory unit 110.

[0047] The change after the first step includes, for example, a change that may occur due to a second step that is performed after the first step, or a change that may occur due to use by a user.

[0048] The prediction unit 140 can generate predicted change information for each of the multiple products by, for example, inputting the first post-process images of each of the multiple products into the prediction model 102 .

[0049] Here, the predicted change information may be a predicted image of each of the multiple products, which predicts changes that may occur after the first step. Furthermore, the predicted change information may be a predicted image feature included in each of the multiple products, which predicts changes that may occur after the first step. Note that the image feature may be information that indicates the features of the image. The image feature is not particularly limited, but may be, for example, an A-KAZE (Accelerated-KAZE) feature, an edge, a contour, or a contour shape feature detected by a Sobel filter, a Haar-Like feature, a SIFT (Scale-Invariant Feature Transform) feature, a HOG (Histograms of Oriented Gradients) feature, or a feature extracted from the intermediate layer of a CNN (Convolutional Neural Network).

[0050] The prediction model 102 may be any model that receives an image as input and outputs a predicted image or predicted image features. The prediction model 102 is not particularly limited, but may be, for example, a machine learning model, a generative model such as an autoencoder, a variational autoencoder (VAE), a generative adversarial network (GAN) model, a flow-based generative model, or a diffusion model, or a regression model such as a neural network, a random forest, a support vector machine, or ElasticNet regression.

[0051] Furthermore, the prediction model 102 may be, for example, a machine learning model trained using a first-step image for learning and a correct post-change image for learning, or a machine learning model trained using image features of a first-step image for learning and image features of a correct post-change image for learning.

[0052] In a first embodiment of the present disclosure, the prediction unit 140 is a second step among multiple steps in the design and manufacturing of a product, and can generate predicted change information regarding changes through the second step after the first step.

[0053] Here, the change occurring through the second process after the first process includes, for example, changes in shape and color that may occur due to processing steps in design and manufacturing.

[0054] The prediction unit 140 may generate predicted change information regarding a change that will occur through a process (e.g., a second process) subsequent to the first process. In other words, in this case, the predicted change information indicates a predicted image or predicted image feature that predicts a change in the product that may occur as a result of processing in the second process. In this case, the second process may be a process that is performed immediately after the first process. Note that, in this embodiment, "immediately after" may refer to a case where there is no or a small time interval, or, even if there is a large time interval, may refer to a case where events (e.g., processes) occur consecutively without any other events (e.g., other processes) occurring in between.

[0055] The prediction unit 140 may also generate predicted change information regarding changes in each of the multiple products after each of the multiple processes subsequent to the first process. The multiple processes subsequent to the first process include, for example, a second process and a third process. In other words, in this case, each piece of predicted change information indicates a predicted image or predicted image feature that predicts a change in the product that may occur as a result of processing in the multiple processes subsequent to the first process. This allows the identification unit 160, described later, to identify the first product management information with high accuracy based on the predicted change information predicted for each product and each process.

[0056] Furthermore, the prediction unit 140 may update the predicted change information further based on a post-change image (e.g., a post-second-process image) associated by the association unit 170, which will be described later. That is, in this case, the prediction unit 140 may input the associated post-change image (e.g., a post-second-process image) to the prediction model 102 to generate predicted change information. This allows the prediction unit 140 to sequentially update the predicted change information in accordance with the association process by the association unit 170, which will be described later, and thereby generate highly accurate predicted change information.

[0057] In a second example of the present disclosure, the prediction unit 140 can generate predicted change information regarding changes due to the use of each of the multiple products after the first step. In this case, the prediction unit 140 generates predicted change information that predicts changes that may occur due to the use of each of the multiple products by users after the products are sold, for example.

[0058] Here, changes that may occur due to use by a user include, for example, the adhesion of dirt, the occurrence of distortion due to use (loss of thickness, wear, deformation of shape), and the formation of scratches.

[0059] At this time, the prediction unit 140 can generate predicted change information regarding changes after use by the user based on the user information as well. This allows the prediction unit 140 to generate predicted change information that predicts changes that may occur due to use by the user with high accuracy based on the user status information and physical information. At this time, the prediction model 102 may be a model that receives user information as input.

[0060] Furthermore, the prediction unit 140 can generate predicted change information corresponding to each of a plurality of predicted usage patterns, including a plurality of predicted usage patterns by a user. Here, the plurality of usage patterns may be, for example, patterns set by an administrator of the product management system 100. In this way, even if the prediction unit 140 cannot refer to user information, it can generate predicted change information regarding changes after usage by a user with high accuracy based on the expected usage patterns.

[0061] Furthermore, the prediction unit 140 can generate predicted change information based on images of each of a plurality of products at the end of product manufacturing (for example, at the time of product shipment or sale). This allows the prediction unit 140 to generate predicted change information with high accuracy based on images immediately before use by a user.

[0062] The prediction unit 140 can also generate predicted change information based on at least one of the product's shape and material. In this regard, the product's shape and material can affect the changes in the product that occur with use. For example, if the product is made of a material that is prone to deterioration, the changes in the product that occur with use will be significant. This allows the prediction unit 140 to evaluate, for example, the product's susceptibility to deterioration based on the product's shape and material, and generate highly accurate predicted change information. In this case, the prediction model 102 may be a model that accepts information on the product's shape and material as input.

[0063] The prediction unit 140 can also extract a product management code from the storage unit 110 and generate predicted change information based on the product management code. Here, the product management code is associated with information indicating the shape and material of the product in the product management system 100, for example.

[0064] Furthermore, the prediction unit 140 can generate predicted change information regarding the change based on a post-change image captured of a first product that has changed after undergoing a first step among multiple steps in the design and manufacturing of a product including any of clothing, footwear, accessories, and items used by fitting to a part of the body. That is, the prediction unit 140 can predict a predicted image or predicted image feature values ​​before the change based on the post-change image that has changed after undergoing the first step.

[0065] The image acquisition unit 150 acquires a post-change image obtained by capturing an image of the product that has changed after undergoing the first process, and stores the post-change image in the storage unit 110. The image acquisition unit 150 acquires, for example, a post-change image obtained by capturing an image of the first product that has changed after undergoing the first process.

[0066] Here, the post-change image may include a post-second-process image of the first product captured after the change through the second process after the first process. Furthermore, the post-change image may include a post-third-process image of the first product captured after the change through the third process after the second process. Note that the second and third processes may be processes that are performed at least after the first and second processes, respectively, and are not necessarily processes that are performed immediately after the first and second processes, respectively.

[0067] The post-change image also includes a post-change image after the first product has been changed by the user's use of the first product. In this way, the post-change image is an image of the first product after the first product has been changed.

[0068] The image acquisition unit 150 can acquire the post-change image from the design and manufacturing related device 200 or the store device 300 .

[0069] Specifically, for example, the design and manufacturing related device 200 takes an image of the product after each design and manufacturing process is performed, and outputs the image to the product management system 100. The image acquisition unit 150 acquires the image.

[0070] The in-store device 300 also takes an image of the product that has changed due to use by the user, and outputs the image to the product management system 100. The image acquisition unit 150 acquires the image.

[0071] The image acquisition unit 150 may also acquire the post-change image from, for example, a user device. This allows the user to receive services using the product management system 100 through the user device without visiting the store.

[0072] The identifying unit 160 identifies first product management information that manages a first product from among the product management information that manages a plurality of products, based on the predicted change information and the post-change image.

[0073] The identifying unit 160 identifies which of the product management information of the multiple products corresponds to the post-change product indicated by the post-change image. In other words, the identifying unit 160 identifies the first product management information including the first post-process image by identifying the first post-process image corresponding to the post-change image, for example.

[0074] When the predicted change information is a predicted image, the identification unit 160 can identify the first product management information based on the image feature values ​​indicated by the predicted image and the image feature values ​​indicated by the post-change image. When the predicted change information is a predicted image feature value, the identification unit 160 can identify the first product management information based on the predicted image feature values ​​and the image feature values ​​indicated by the post-change image.

[0075] The identification process by the identification unit 160 is not particularly limited, but may be, for example, an identification process using an evaluation model 105 that inputs two image features and outputs the degree of match between the two image features. In this case, specifically, the identification unit 160 inputs the image features or predicted image features indicated by the predicted image and the image features or predicted image features indicated by each of the predicted images of multiple products into the evaluation model, and obtains the degree of match between each of the image features indicated by the post-change image. The identification unit 160 then identifies the product corresponding to the image features or predicted image features indicated by the predicted image with the highest degree of match, and identifies the product management information for the identified product. This allows the identification unit 160 to perform matching taking into account changes in the product during the manufacturing process and use, thereby enabling high-accuracy identification of the first product management information.

[0076] Furthermore, the identification process by the identification unit 160 may be, for example, a process of calculating the distance (Euclidean distance, Mahalanobis distance, Minkowski distance, etc.) between the predicted image feature vector and the image feature vector of the changed image, a process of calculating the cosine similarity between the predicted image feature vector and the image feature vector of the changed image, or a process of comparing the similarity of bag-of-feature features obtained by histogramming image features.

[0077] 4 is a diagram showing an example of the identification process performed by the identification unit 160. For example, the identification unit 160 inputs predicted change information 103 predicted from the first post-process image 101 included in the first product management information and the changed image 104 into the evaluation model 105 to obtain a degree of match. The identification unit 160 similarly processes other product management information and calculates the degree of match for each. Then, the identification unit 160 identifies, for example, the image feature indicated by the predicted image with the highest degree of match or the product corresponding to the predicted image feature, and identifies the product management information for the identified product.

[0078] Furthermore, the identification unit 160 can identify the first product management information based on the predicted change information and the post-change image (e.g., the image after the third process) that are further updated based on the post-change image (e.g., the image after the second process) associated by the association unit 170, which will be described later. This allows the identification unit 160 to identify the first product management information with high accuracy.

[0079] Furthermore, the identification unit 160 can identify the first product management information based on the image capture time of the post-change image. In this case, the evaluation model may be a model that further inputs the image capture time and outputs the degree of match. This allows the identification unit 160 to identify the first product management information with high accuracy, and also eliminates the need to consider predicted change information for products whose image capture times are clearly inappropriate, thereby narrowing down the number of pieces of predicted change information for products and improving the calculation processing speed.

[0080] In this regard, if the identification unit 160 identifies the first product management information without considering the time when the post-change image was captured, the relationship between the time from when the identified first product management information was captured to the time of the identification process and the time from when the post-change image was captured to the time of the identification process may not be reasonable. Examples of such cases include a case where the identification unit 160 identifies first product management information for which the first process was completed 30 minutes ago, even though it usually takes one day to complete the first and second processes. Another example is a case where the identification unit 160 identifies first product management information sold several days ago based on a post-change image that has undergone deterioration that typically occurs over several years of use. In other words, by identifying the first product management information based additionally on the time when the post-change image was captured, the identification unit 160 can identify the first product management information with high accuracy.

[0081] Furthermore, the identification unit 160 can identify the first product management information further based on user information. In this case, the evaluation model may be a model that further inputs user information and outputs a degree of match. This increases the prediction accuracy of the predicted post-change image information, allowing the identification unit 160 to identify the first product management information with high accuracy and also improves the speed of the identification process.

[0082] In this regard, if the identification unit 160 identifies the first product management information without taking user information into consideration, the relationship between the expected change due to use based on the period since the first product corresponding to the identified first product management information was sold and the degree of change in the post-change image may not be reasonable. For example, such a case may occur when the period since the first product corresponding to the identified first product management information was sold is only a few days, but the degree of change in the post-change image is typically equivalent to a change over several years. In other words, by identifying the first product management information based additionally on user information, the identification unit 160 can identify the first product management information with high accuracy.

[0083] Furthermore, the identification unit 160 can identify the first product management information based on a reference portion included in the product and referenced when the identification unit 160 identifies the product.

[0084] Here, the reference portion may be, for example, a predetermined pattern, mark, or code.

[0085] The reference portion may also be manufactured by additive manufacturing so as to be included in the product, so as to be connected to the product, and / or so as to be embedded inside the product, thereby enabling the identification unit 160 to identify the product management information of the first product with high accuracy.

[0086] Furthermore, the identification unit 160 can identify first product management information that manages a first product from the product management information, based on the product management information that manages a plurality of products, where product identification information that identifies each of the plurality of products including the first product is associated with a first post-process image of each of the plurality of products that has been captured after each of the plurality of products has undergone a first process, and the predicted change information. Specifically, the identification unit 160 identifies the first post-process image that corresponds to the post-change image based on the predicted change information and the first post-process image of each of the plurality of products included in the product management information, thereby identifying the first product management information that includes the first post-process image.

[0087] The associating unit 170 associates the post-change image with the first product management information. This allows the associating unit 170 to update the first product management information. This also allows the prediction unit 140 to use the associated post-change image when generating predicted change information for the first product, allowing the prediction unit 140 to generate predicted change information with high accuracy.

[0088] The associating unit 170 also associates information about the performed first process with the first product management information. Here, the information about the performed first process may be, for example, information about the date of performance of the first process and the performance content of the first process.

[0089] Furthermore, the associating unit 170 can associate the user information with the first product management information, and the identifying unit 160 can perform the identification process based on the user information included in the first product management information. This allows the identifying unit 160 to identify the first product management information with high accuracy.

[0090] Next, a first example of the product management system 100 will be described. In the first example, the prediction unit 140 generates predicted change information regarding a change that occurs through a second process that follows the first process, which is a second process among multiple processes in the design and manufacture of a product. Here, an example will be described in which the first process is a molding process and the second process is a support member removal process, but the first and second processes are not particularly limited.

[0091] 5 is a flowchart showing an example of processing in the first embodiment of the product management system 100. Note that the order of processing shown in FIG. 5 is an example, and the order of processing may be changed as appropriate.

[0092] First, a first product is formed by a first process performed by the design and manufacturing related device 200 (S501). The image acquisition unit 150 acquires a post-first-process image of the first product after the formation process (S502). Here, the design and manufacturing related device 200 photographs the product, for example, at the end of each process.

[0093] The product management unit 120 associates the first post-process image of the first product with the first product management information, and updates the first product management information. Similar processing is performed for products other than the first product, and the first post-process image of each of the multiple products is associated with the respective product management information (S503).

[0094] Next, the prediction unit 140 generates predicted change information for each of the multiple products based on the post-first-process image of each of the multiple products (S504). At this time, the prediction unit 140 may generate predicted change information for after the process immediately after the first process is performed, or may generate predicted change information for after each of the multiple processes after the first process is performed. Here, the prediction unit 140 generates at least predicted change information for after the second process after the first process is performed.

[0095] The image acquisition unit 150 acquires a post-transformation image of any product included in the plurality of products, for example, the first product, after the second process is performed, i.e., a post-transformation image of the first product after the support member removal process (S505).

[0096] The identification unit 160 identifies the product management information of the product indicated by the post-change image based on the post-change image and the predicted change information (S506). In this case, the identification unit 160 identifies the product indicated by the post-change image as a first product based on the predicted change information, and further identifies first product management information of the identified first product.

[0097] Then, the association unit 170 associates the post-change image with the identified first product management information, and updates the first product management information (S507).

[0098] When the design and manufacturing related device 200 processes the third process, the product management system 100 performs a similar process to identify the product management information of the product that has undergone the third process, and associates the post-change image that has undergone the third process with the identified product management information.

[0099] In this way, product management system 100 can appropriately manage each product by capturing and processing images, thereby achieving so-called appropriate traceability. Specifically, product management system 100 can link information obtained during the design and manufacturing processes (e.g., oven temperature, UV irradiation intensity, worker working hours) with product management information and record it in real time.

[0100] This allows product managers to visualize the manufacturing conditions for each product and use them for quality control and anomaly detection. Product managers can also easily understand which steps in the design and manufacturing processes have been completed, improving work efficiency in the design and manufacturing processes and improving product quality.

[0101] Furthermore, by providing product management information during the design and manufacturing process to product purchasers (e.g., prospective users), the product purchasers can check the manufacturing status of the products they have ordered in real time, giving them peace of mind regarding the quality.

[0102] It should be noted that the processing by the product management system 100 does not have to be performed in all steps in the design and manufacturing of the product. The steps in which the processing by the product management system 100 is performed can be selected as appropriate.

[0103] Next, a description will be given of a second embodiment of the product management system 100. In the second embodiment, the prediction unit 140 generates predicted change information regarding changes due to the use of each of a plurality of products after the first process.

[0104] Fig. 6 is a flowchart showing an example of processing in the second embodiment in the product management system 100. Note that the order of processing shown in Fig. 6 is an example, and the order of processing may be changed as appropriate.

[0105] The product management unit 120 manages, for example, product management information obtained through the processing in Example 1. For example, the product management unit 120 manages product management information for each product sold to a user. In this case, the product management information manages, for example, an image of the product at the time of completion of manufacturing as an image after the first process.

[0106] A user of a first product visits a store to request the identification of product management information for the first product, for example, in order to determine whether the first product is defective or to purchase a new product that is designed and manufactured using the same process as the first product.

[0107] A store staff member uses the store device 300 to photograph the first product used by the user and acquires the post-change image. The image acquisition unit 150 acquires the post-change image (S601). Note that at this time, the user may photograph the first product using a user device (e.g., an information terminal) used by the user without visiting the store, and provide the image of the first product to the product management system 100 directly or indirectly via the store device 300.

[0108] The prediction unit 140 generates predicted change information regarding changes due to use of each of the multiple products based on the post-first-process images of each of the multiple products (S602).

[0109] The identification unit 160 identifies the product management information of the product indicated by the post-change image based on the post-change image and the predicted change information. In this case, the identification unit 160 identifies the product indicated by the post-change image as a first product based on the predicted change information, and further identifies first product management information of the identified first product (S603).

[0110] Then, the associating unit 170 associates the post-change image with the identified first product management information, and updates the first product management information (S604).

[0111] In this way, the product management system 100 can, for example, identify product management information corresponding to a product used by a user by capturing and processing images. As a result, when a defect is found in a product used by a user, the product management information for the defective product can be identified to determine the cause of the defect. Specifically, the product manager can determine whether the defect is in the product's shape (design model), the manufacturing process, or the materials. Furthermore, if a user wishes to repurchase an identical product, identifying the product management information for the used product allows the product manager to understand the details of the design and manufacturing processes for the used product and easily remanufacture the identical product.

[0112] Next, a third embodiment of the product management system 100 will be described. In the third embodiment, the prediction unit 140 generates predicted change information regarding the change based on a post-change image captured of the first product that has changed after undergoing the first process, and the identification unit 160 identifies first product management information for managing the first product based on the product management information and the predicted change information. Here, an example will be described in which the change after undergoing the first process is a change due to use by a user, but the change after undergoing the first process is not limited to this and may also be, for example, a change due to a second process performed after the first process in product design and manufacturing.

[0113] 7 is a flowchart showing an example of processing in the third embodiment in the product management system 100. Note that the order of processing shown in FIG. 7 is an example, and the order of processing may be changed as appropriate.

[0114] The product management unit 120 manages, for example, product management information obtained through the processing in Example 1. For example, the product management unit 120 manages product management information for each product sold to a user. In this case, the product management information manages, for example, an image of the product at the time of completion of manufacturing as an image after the first process.

[0115] A user of a first product visits a store to request the identification of product management information for the first product, for example, in order to determine whether the first product is defective or to purchase a new product that is designed and manufactured using the same process as the first product.

[0116] A store staff member uses the store device 300 to photograph the first product used by the user and acquires the post-change image. The image acquisition unit 150 acquires the post-change image (S701). Note that at this time, the user may photograph the first product using a user device (e.g., an information terminal) used by the user without visiting the store, and provide the image of the first product to the product management system 100 directly or indirectly via the store device 300.

[0117] The prediction unit 140 generates predicted change information regarding the change based on the post-change image (702). At this time, the prediction unit 140 generates a predicted image or predicted image features of the first product before use by the user, i.e., at the time of sale, for example.

[0118] The identification unit 160 identifies the product management information of the product indicated by the post-change image based on the first post-process image of each of the multiple products included in the product management information and the predicted change information. In this case, the identification unit 160 identifies the product indicated by the post-change image as the first product based on the predicted change information, and further identifies the first product management information of the identified first product (S703).

[0119] Then, the associating unit 170 associates the post-change image with the identified first product management information, and updates the first product management information (S704).

[0120] In this way, the product management system 100 can identify, for example, product management information corresponding to a product used by a user by capturing and processing images. In this regard, the third embodiment is useful, for example, when the change indicated by the post-change image is a change in the product's original state due to the addition of dirt or scratches. In such cases, when the prediction unit 140 generates pre-change predicted change information from the post-change image, it can easily generate predicted change information by performing image processing to remove the scratches or dirt. However, because there are many possible combinations of the degree of scratches or dirt and the locations where they are attached, when the prediction unit 140 generates post-change predicted change information from the post-first-process image, it is necessary to generate multiple pieces of predicted change information, which may increase the load of the prediction process. Therefore, the third embodiment is useful, for example, when the change indicated by the post-change image is minor.

[0121] Next, an example of a hardware configuration in which the product management system 100 is realized by a computer 800 will be described with reference to Fig. 8. Fig. 8 is a diagram showing an example of the hardware configuration of the computer 800.

[0122] As shown in FIG. 8, a computer 800 includes, for example, a processor 801, a memory 802, a storage device 803, an input I / F unit 804, a data I / F unit 805, a communication I / F unit 806, and a display device 807.

[0123] Computer 800 may be, for example, a server computer, a personal computer (e.g., desktop, laptop, tablet, etc.), a media computing platform (e.g., cable, satellite set-top box, digital video recorder, etc.), a handheld computing device (e.g., PDA, email client, etc.), or some other type of computer or communications platform.

[0124] The processor 801 is a control unit that controls various processes in the computer 800 by executing programs stored in the memory 802 .

[0125] The memory 802 is a storage medium such as a RAM (Random Access Memory), etc. The memory 802 temporarily stores the program code of the program executed by the processor 801 and data required when the program is executed.

[0126] The storage device 803 is a non-volatile storage medium such as a hard disk drive (HDD), flash memory, etc. The storage device 803 stores an operating system and various programs for realizing the above-mentioned components.

[0127] The input I / F unit 804 is a device for receiving input from a user. The input I / F unit 804 is, for example, a keyboard, a mouse, a touch panel, various sensors, a wearable device, etc. The input I / F unit 804 may be connected to the computer 800 via an interface such as a USB (Universal Serial Bus).

[0128] The data I / F unit 805 is a device for inputting data from outside the computer 800. The data I / F unit 805 is, for example, a drive device for reading data stored in various storage media. The data I / F unit 805 may be provided outside the computer 800. When the data I / F unit 805 is provided outside the computer 800, the data I / F unit 805 is connected to the computer 800 via an interface such as a USB.

[0129] The communication I / F unit 806 is a device for performing data communication via a network such as the Internet, either wired or wirelessly, with a device external to the computer 800. The communication I / F unit 806 may be provided outside the computer 800. When the communication I / F unit 806 is provided outside the computer 800, the communication I / F unit 806 is connected to the computer 800 via an interface such as a USB.

[0130] The display device 807 is a device for displaying various types of information. The display device 807 is, for example, a liquid crystal display, an organic EL (Electro-Luminescence) display, a display of a wearable device, or the like. The display device 807 may be provided outside the computer 800. When the display device 807 is provided outside the computer 800, the display device 807 is connected to the computer 800 via, for example, a display cable. Furthermore, when a touch panel is adopted as the input I / F unit 804, the display device 807 may be configured as an integral part of the input I / F unit 804.

[0131] [Effects of the Present Embodiment] One embodiment of the present disclosure has been described above. According to one embodiment of the present disclosure, the product management system 100 can perform product management based on predicted change information predicted from images, thereby realizing highly convenient product management. Furthermore, the product management system 100 can perform product management based on image data, which has a smaller data volume than video data, thereby realizing product management at a high processing speed.

[0132] FIG. 9 is a diagram showing an overview of the product management system 100. The product management system 100 can integrate and manage information on manufacturing, sales, use, repairs, etc. This allows manufacturers to ensure process transparency and use various data for development and business creation. In addition, users can enjoy more personalized product purchasing, after-sales service, and other services.

[0133] Furthermore, since the product management system 100 according to the embodiment of the present disclosure can manage product manufacturers, etc., it is possible to take measures against counterfeit products, for example, by identifying counterfeit products, etc. For example, the administrator of the product management system 100 can identify, as counterfeit products, products that do not have identification information attached to genuine products, products that have identification information different from that of genuine products, or products that cannot be associated with product management information that manages genuine products.

[0134] [Aspects] (1) A product management system according to one aspect of the present disclosure includes: a prediction unit that generates predicted change information regarding changes in each of a plurality of products, including a first product, after a first process among a plurality of processes in the design and manufacture of any of clothing, footwear, accessories, and items used to fit a part of the body, based on post-first-process images of each of the plurality of products, including a first product, captured after the first process; an image acquisition unit that acquires post-change images of the first product after the first process; and an identification unit that identifies first product management information for managing the first product among the product management information for managing the plurality of products based on the predicted change information and the post-change images. The product management system described in (1) above can achieve highly convenient product management. (2) In the product management system described in (1) above, the prediction unit may generate predicted change information regarding changes in each of the plurality of products after each of a plurality of processes subsequent to the first process. According to the product management system (2) above, the product management system 100 can identify the first product management information with high accuracy based on predicted change information predicted for each process. (3) In the product management systems described in (1) and (2) above, the image acquisition unit may acquire a post-second process image of the first product captured after a second process subsequent to the first process and a post-third process image of the first product captured after a third process subsequent to the second process, and when the post-second process image is associated with the first product management information, the prediction unit may update the predicted change information further based on the post-second process image, and the identification unit may identify the first product management information based on the updated predicted change information and the post-third process image. According to the product management system (3) above, the product management system 100 can, so to speak, sequentially update the predicted change information in accordance with the association process, thereby generating predicted change information with high accuracy and identifying the first product management information. (4) In the product management system described in (1) to (3) above, the prediction unit may generate the predicted change information regarding changes due to use of each of the multiple products after the first step.According to the product management system of (4) above, the product management system 100 can identify first product management information corresponding to a product that has changed due to use by a user, thereby achieving highly convenient product management. (5) In the product management system described in (4) above, the system may further include a user information acquisition unit that acquires user information about a user of the first product, and the prediction unit may generate the predicted change information further based on the user information. According to the product management system of (5) above, the product management system 100 can generate predicted change information with high accuracy and identify the first product management information. (6) In the product management system described in (5) above, the user information may include at least one of usage status information indicating the user's use of the first product and physical information about the user's body. According to the product management system of (6) above, the product management system 100 can generate predicted change information with high accuracy and identify the first product management information. (7) In the product management system described in (1) to (6) above, the system may further include an association unit that associates information regarding the implementation details of the first process and user information regarding the user of the first product with the first product management information. According to the product management system described in (7) above, the product management system 100 can update the first product management information to manage at least one of information regarding the implementation details of the first process and user information, thereby achieving highly convenient product management. (8) In the product management system described in (4) to (7) above, the system may further include a user information acquisition unit that acquires user information regarding the user of the first product, and the identification unit may identify the first product management information further based on the user information. According to the product management system described in (8) above, the product management system 100 can identify the first product management information with high accuracy. (9) In the product management system described in (4) to (8) above, the prediction unit may generate the predicted change information corresponding to multiple predicted usage patterns by the user of the first product.According to the product management system (9) above, the product management system 100 can generate predicted change information with high accuracy and identify the first product management information. (10) In the product management systems described in (4) to (9) above, the prediction unit may generate the predicted change information based on images of each of the plurality of products at the end of production. According to the product management system (10) above, the product management system 100 can generate predicted change information with high accuracy and identify the first product management information. (11) In the product management systems described in (1) to (10) above, the prediction unit may generate the predicted change information further based on at least one of the shape, material, and product management code of the first product. According to the product management system (11) above, the product management system 100 can generate predicted change information with high accuracy and identify the first product management information. (12) In the product management systems described in (1) to (11) above, the identification unit may identify the first product management information further based on the capture time of the post-change image. According to the product management system of (13) above, the product management system 100 can identify the first product management information with high accuracy. (13) In the product management systems described in (1) to (12) above, the prediction unit may generate, as the predicted change information, predicted images of each of the multiple products after the change, and the identification unit may identify the first product management information based on image features indicated by the predicted images and image features indicated by the changed images. According to the product management system of (13) above, the product management system 100 can generate predicted images as the predicted change information, and can identify the first product management information with high accuracy. (14) In the product management systems described in (1) to (13) above, the prediction unit may generate, as the predicted change information, predicted image features included in the predicted images of each of the multiple products after the change, and the identification unit may identify the first product management information based on the predicted image features and image features indicated by the changed images.According to the product management system of (14) above, the product management system 100 can generate image features as predicted change information, reducing the amount of data processing and identifying the first product management information with high accuracy. (15) In the product management systems of (1) to (14) above, the first product may include a reference portion referenced when the identification unit identifies the first product management information, and the identification unit may identify the first product management information further based on the reference portion. According to the product management system of (15) above, the product management system 100 can identify the first product management information with high accuracy. (16) In the product management systems of (1) to (15) above, the manufacturing may include manufacturing by an additive manufacturing method, the first process may include a process of forming the product by the additive manufacturing method, and the change after the first process may include a change after the product formed by the additive manufacturing method has undergone at least one of a support member removal process, an excess material separation process, a cleaning process, a drying process, an ultraviolet irradiation process, and a curing process. According to the product management system of (16) above, the product management system 100 can identify, with high accuracy, first product management information for products manufactured by additive modeling. (17) A product management system according to another aspect of the present disclosure includes: a prediction unit that generates predicted change information regarding a change based on a post-change image of a first product that has changed after undergoing a first step among multiple steps in the design and manufacture of a product, including clothing, footwear, accessories, and items used to fit a part of the body; and an identification unit that identifies, from the product management information for managing a plurality of products, first product management information for managing the first product based on the predicted change information, product management information for managing a plurality of products, the product management information including product identification information that identifies each of the plurality of products including the first product and a post-first-step image of each of the plurality of products that has been captured after undergoing the first step. The product management system of (17) above can achieve highly convenient product management.(18) In another aspect of the present disclosure, a product management method includes a computer generating predicted change information regarding changes in each of a plurality of products, including a first product, after a first process among a plurality of processes in the design and manufacture of a product, including clothing, footwear, accessories, and items worn around a body part, based on post-first-process images of each of the plurality of products, including a first product, captured after the first process; acquiring post-change images of the first product after the first process; and identifying first product management information for managing the first product among the product management information for managing the plurality of products based on the predicted change information and the post-change images. The product management method of (17) above enables highly convenient product management. (19) In the product management system described in (1) to (15) above, the association unit associates information obtained in the process where the image was acquired with the identified first product management information. Specifically, the association unit can associate information regarding the first process that was performed with the first product management information, and can also associate user information, usage status information, and product status with the first product management information.

[0135] It should be noted that the present embodiment is provided to facilitate understanding of the present disclosure and is not intended to limit the present disclosure. The present disclosure may be modified or improved without departing from the spirit thereof, and equivalents thereof are also included in the present disclosure.

[0136] In addition, in this disclosure, a "unit" does not simply mean a physical means, but also includes cases where the functions of the "unit" are realized by software. Furthermore, the functions of one "unit" or device may be realized by two or more physical means, devices, or software, and the functions of two or more "units" or devices may be realized by one physical means, device, or software.

[0137] The present disclosure is useful for systems that manage information on products including clothing, footwear, accessories, and items that are worn along a part of the body.

[0138] 100 Product management system, 101 Image after first process, 102 Prediction model, 103 Predicted change information, 104 Changed image, 105 Evaluation model, 110 Storage unit, 120 Product management unit, 130 User information acquisition unit, 140 Prediction unit, 150 Image acquisition unit, 160 Identification unit, 170 Correlation unit, 200 Design and manufacturing related device, 300 Store device

Claims

1. A product management system comprising: a prediction unit that generates predicted change information regarding changes in each of a plurality of products, including a first product, after the first process, based on a post-first process image of each of the plurality of products, including a first product, taken after the first process of a plurality of processes in the design and manufacture of a product including any of clothing, footwear, accessories, and items used by fitting to a part of the body; an image acquisition unit that acquires a post-change image of the first product that has changed after the first process; and an identification unit that identifies first product management information that manages the first product from among the product management information that manages the plurality of products, based on the predicted change information and the post-change image.

2. A product management system as described in claim 1, wherein the prediction unit generates predicted change information regarding each of the changes in each of the plurality of products after each of the plurality of processes subsequent to the first process.

3. A product management system as described in claim 1 or 2, wherein the image acquisition unit acquires a post-second process image of the first product taken after the second process that follows the first process, and a post-third process image of the first product taken after the third process that follows the second process; the prediction unit, when the post-second process image is associated with the first product management information, updates the predicted change information further based on the post-second process image; and the identification unit identifies the first product management information based on the updated predicted change information and the post-third process image.

4. A product management system as described in claim 1 or 2, wherein the prediction unit generates the predicted change information regarding changes due to use of each of the plurality of products after the first step.

5. A product management system as described in claim 4, further comprising a user information acquisition unit that acquires user information regarding users of the first product, wherein the prediction unit generates the predicted change information further based on the user information.

6. A product management system as described in claim 5, wherein the user information includes at least one of usage status information indicating the usage status of the first product by the user and physical information regarding the user's body.

7. A product management system as described in claim 1 or 2, further comprising a matching unit that matches at least one of information regarding the implementation content of the first process and user information regarding the user of the first product with the first product management information.

8. A product management system as described in claim 4, further comprising a user information acquisition unit that acquires user information regarding a user of the first product, wherein the identification unit identifies the first product management information further based on the user information.

9. The product management system according to claim 4, wherein the prediction unit generates the predicted change information corresponding to a plurality of predicted patterns of use by users of the first product.

10. The product management system according to claim 4, wherein the prediction unit generates the predicted change information based on images of each of the plurality of products at the end of the production.

11. A product management system according to claim 1 or 2, wherein the prediction unit generates the predicted change information further based on at least one of the shape, material, and product management code of the first product.

12. A product management system as described in claim 1 or 2, wherein the identification unit identifies the first product management information further based on the time when the post-change image was captured.

13. A product management system as described in claim 1 or 2, wherein the prediction unit generates a predicted image of each of the multiple products after the change as the predicted change information, and the identification unit identifies the first product management information based on image features indicated by the predicted image and image features indicated by the post-change image.

14. A product management system as described in claim 1 or 2, wherein the prediction unit generates predicted image features that each predicted image of the plurality of products after the change has as the predicted change information, and the identification unit identifies the first product management information based on the predicted image features and image features that each post-change image has.

15. A product management system as described in claim 1 or 2, wherein the first product includes a reference portion that is referenced when identified by the identification unit, and the identification unit identifies the first product management information further based on the reference portion.

16. The product management system of claim 3, wherein the manufacturing includes manufacturing by additive manufacturing, the first process includes a process of forming the product by additive manufacturing, and the changes that occur after the first process include changes that occur after the product formed by additive manufacturing has undergone at least one of a process of removing a support member, a process of separating excess material, a cleaning process, a drying process, an ultraviolet irradiation process, and a curing process.

17. A product management system comprising: a prediction unit that generates predicted change information regarding a change based on a post-change image of a first product that has changed after undergoing a first process among multiple processes in the design and manufacture of a product, including clothing, footwear, accessories, and items used to fit a part of the body; product management information that manages multiple products, the product management information associating product identification information that identifies each of multiple products including the first product with a post-first process image of each of the multiple products that has been taken after each of the multiple products has undergone the first process; and an identification unit that identifies first product management information that manages the first product among the product management information based on the predicted change information.

18. A product management method in which a computer generates predicted change information regarding changes in each of a plurality of products, including a first product, after the first process, based on a post-first process image of each of the plurality of products, including a first product, taken after the first process of a plurality of processes in the design and manufacture of products including any of clothing, footwear, accessories, and items used by fitting to a part of the body; obtains a post-change image of the first product that has changed after the first process; and identifies first product management information for managing the first product from among the product management information for managing the plurality of products, based on the predicted change information and the post-change image.

Images