Footwear component design device and footwear component design method

Abstract

To provide technique for achieving both of high design flexibility and design adequacy of a footwear component.SOLUTION: A measurement data acquisition unit 212 in a footwear component design device 250 acquires measurement data of a three-dimensional coordinate group representing anatomical features corresponding to a three-dimensional homologous model, of a foot shape including at least the sole and the side surface of a subject's foot. A model determination unit 222 evaluates similarity between coordinates of the three-dimensional coordinate group in the measurement data and the three-dimensional coordinate group in the plurality of three-dimensional homologous models and determines a three-dimensional homologous model 104 similar to the foot shape of the subject from the plurality of three-dimensional homologous models 104. A basic design determining unit 232 determines basic design data corresponding to the determined three-dimensional homologous model 104 from the plurality of pieces of basic design data. A design processing unit 234 generates footwear component design data corresponding to the foot shape of the subject on the basis of the determined basic design data.SELECTED DRAWING: Figure 2

Description

[Technical field]

[0001] The present disclosure relates to a footwear component design device and a footwear component design method, and in particular to a technique for designing an insole. [Background technology]

[0002] It is preferable to offer a wide variety of sizes for shoes and insoles to accommodate a variety of foot shapes and sizes. However, expanding the size range entails increased manufacturing costs and the risk of having a large inventory, so in reality, most shoe manufacturers offer sizes centered on those that are in high demand.

[0003] There is a known technology for selecting a type of shoe that fits a foot shape based on three-dimensional data obtained by measuring the foot shape three-dimensionally (see, for example, Patent Document 1). In Patent Document 1, two-dimensional shape feature data is generated separately for the toe, middle, and heel parts from the three-dimensional data of the foot shape. Similarly, feature data for each part is generated for the shoe last, and the collected feature data for a large number of shoe lasts is clustered, and a code is assigned to each cluster and stored. The relationship between the feature data of the foot shape and the feature data of the shoe last that fits it is learned using a neural network, and a rule is set for determining the feature data of the shoe last from the feature data of the foot shape.

[0004] Meanwhile, in recent years, in response to diversifying tastes, there has been an increase in products that can be customized or personalized by consumers (see, for example, Patent Documents 2 to 4). In addition, a method for manufacturing products with a high degree of design freedom using additive manufacturing, also known as 3D printing, is known (see, for example, Patent Documents 5 to 7). [Prior art documents] [Patent documents]

[0005] [Patent Document 1] JP 2000-90272 A [Patent Document 2] Patent Publication No. 2022-60213 [Patent Document 3] JP 2023-71642 A [Patent Document 4] Special Publication No. 2023-523141 [Patent Document 5] Special Publication No. 2018-501865 [Patent Document 6] Special Publication No. 2017-522917 [Patent Document 7] JP 2020-163154 A Summary of the Invention [Problem to be solved by the invention]

[0006] Even if additive manufacturing increases the degree of freedom in product design, some designs may result in shapes that are difficult to manufacture or shapes that are not normal for products. To avoid such designs, it is essential to check the validity of the design in advance, but there is also a risk that the design freedom will be narrowed in an effort to ensure the validity of the design. There is also a risk that suitability for customization and personalization will be compromised.

[0007] The present disclosure has been made in consideration of these circumstances, and has as its purpose to provide a technology that achieves both high design freedom and design validity for footwear components. [Means for solving the problem]

[0008] In order to solve the above problems, a footwear component design device according to one embodiment of the present disclosure includes a model memory unit that pre-stores a group of three-dimensional coordinates indicating anatomical features of the foot as a plurality of three-dimensional homologous models for a plurality of foot shapes; a design memory unit that stores a plurality of basic design data as three-dimensional design data used for additive manufacturing of footwear components corresponding to each of the foot shapes of the plurality of three-dimensional homologous models; a measurement data acquisition unit that acquires measurement data of a group of three-dimensional coordinates indicating anatomical features corresponding to the three-dimensional homologous model in a foot shape including at least the sole and lateral surfaces of a subject's foot; a model determination unit that determines a three-dimensional homologous model that is similar to the subject's foot shape from the plurality of three-dimensional homologous models by evaluating the similarity between coordinates between the three-dimensional coordinate group in the measurement data and the three-dimensional coordinate groups in the plurality of three-dimensional homologous models; a basic design determination unit that determines basic design data corresponding to the determined three-dimensional homologous model from the plurality of basic design data; and a design processing unit that generates footwear component design data corresponding to the subject's foot shape based on the determined basic design data.

[0009] Another aspect of the present disclosure is a footwear component design method, which includes the steps of: reading out a group of three-dimensional coordinates indicating anatomical features of a foot, a plurality of three-dimensional homologous models corresponding to a plurality of foot shapes, acquiring measurement data of the group of three-dimensional coordinates indicating anatomical features corresponding to the three-dimensional homologous models of a foot shape including at least the sole and lateral surface of a subject's foot, determining a three-dimensional homologous model similar to the subject's foot shape from the plurality of three-dimensional homologous models by evaluating the similarity between the three-dimensional coordinates in the measurement data and the three-dimensional coordinates in the plurality of three-dimensional homologous models, determining basic design data corresponding to the determined three-dimensional homologous model from a plurality of basic design data which are three-dimensional design data used for additive manufacturing of footwear components corresponding to each foot shape of the plurality of three-dimensional homologous models, and generating footwear component design data corresponding to the subject's foot shape based on the determined basic design data.

[0010] In addition, any combination of the above components, or mutual substitution of the components or expressions of the present disclosure among methods, devices, programs, temporary or non-temporary recording media having programs recorded thereon, systems, etc., are also valid aspects of the present disclosure. Effect of the Invention

[0011] According to the present disclosure, it is possible to achieve both high design freedom and design validity for footwear components. [Brief description of the drawings]

[0012] [Figure 1] FIG. 1 is a diagram showing a basic configuration of a footwear component design system. [Diagram 2] FIG. 2 is a functional block diagram showing each configuration of the footwear part design device. [Diagram 3] FIG. 1 is a diagram showing an example of a three-dimensional homology model. [Figure 4] FIG. 2 is a top perspective view showing the appearance of an insole manufactured by a three-dimensional additive manufacturing method. [Diagram 5] FIG. 2 is a bottom perspective view showing the appearance of an insole manufactured by a three-dimensional additive manufacturing method. [Figure 6] FIG. 13 is a diagram showing the insole without the three-dimensional decoration. [Figure 7] This is a diagram showing an insole with three-dimensional text decoration added to the side. [Figure 8] This is a diagram showing an insole with three-dimensionally decorated marks on the sides and bottom. [Figure 9] FIG. 13 is a diagram showing a schematic slice cross-sectional image in the insole design data. [Figure 10] FIG. 2 is a functional block diagram showing each component of the shape analysis device. [Figure 11] This figure shows the relationship between the distribution of foot length and circumference by gender and the Japanese JIS standard. [Figure 12] FIG. 1 is a diagram showing the difference between a general cluster analysis method and a new cluster analysis method. [Figure 13] FIG. 13 is a diagram illustrating a method of combining different cluster analyses in upper and lower hierarchical layers. [Figure 14] FIG. 1 is a diagram showing Euclidean distance and cosine similarity between points. [Figure 15] FIG. 13 is a diagram showing the relationship between corresponding points in two foot models. [Figure 16] FIG. 13 is a diagram showing a comparison between foot print models using Euclidean distance and cosine similarity. [Figure 17] FIG. 13 is a diagram showing the difference in classification results between cluster analysis based on cosine similarity or Euclidean distance. [Figure 18] FIG. 13 is a diagram showing classification results obtained by cluster analysis based on cosine similarity. [Figure 19] FIG. 13 shows regression lines of foot length and foot circumference for the top three clusters, and the results of principal component analysis of the top three clusters. [Figure 20] FIG. 13 is a diagram showing the results of principal component analysis in clusters. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0013] Hereinafter, the present disclosure will be described based on preferred embodiments with reference to the drawings. In the embodiments and modified examples, the same or equivalent components are given the same reference numerals, and redundant descriptions are omitted as appropriate.

[0014] In this embodiment, footwear or footwear parts customized for each wearer or prospective purchaser are provided. For example, not only are footwear or footwear parts designed to be optimal for the wearer's foot size and foot shape, but the preferences of the prospective purchaser are also reflected in the design and decoration. This makes it possible to design unique footwear or footwear parts that reflect the individual differences and preferences of each wearer or prospective purchaser.

[0015] The term "footwear" as used herein includes shoes such as sports shoes that cover the entire foot, shoes such as sandals that do not cover the entire foot, socks, sock shoes that are integrated with socks, tights, etc. Furthermore, "footwear" includes not only cases where the entire footwear can be customized, but also cases where footwear parts that are part of the footwear can be customized. The footwear part in this embodiment is an inner sole (also generally called an "insole") that is inserted inside the footwear and placed on the midsole. Hereinafter, in this embodiment, an example of designing an inner sole as a footwear part will be described. However, as a modified example, the present invention may be applied to an example of designing footwear parts other than an inner sole, such as a sole or an upper, or may be applied to an example of designing the entire footwear such as sandals.

[0016] In this embodiment, unlike conventional manufacturing methods that mass-produce and sell footwear or footwear parts of the same specifications, a three-dimensional additive manufacturing method (also called "additive manufacturing" or "3D printing") is adopted. The insole, which is a footwear part in this embodiment, is formed with a three-dimensional lattice structure.

[0017] Here, when designing insoles customized for each buyer, it takes a lot of time to design from scratch each time an order is placed. In addition, when creating a design each time an order is placed, it is difficult to confirm in advance whether the manufacturing process will be appropriate for actual production and whether the design is manufacturable. Thus, when customizing footwear, it is necessary to ensure both a high degree of design freedom and design validity.

[0018] Therefore, in this embodiment, a large amount of basic design data, which is a basic design that is premised on customization, is prepared. Then, the design validity of the basic design data within the customizable range is confirmed in advance, thereby achieving both design freedom and design validity.

[0019] A large amount of basic design data is designed as follows. That is, the three-dimensional shapes of the feet of a large number of subjects are measured and stored as foot shape data, which is then classified in advance into a number of clusters using a cluster analysis technique described later. Furthermore, shoe lasts and insoles are designed based on the classified clusters, providing shape variations of footwear products based on standards different from conventional standards, thereby providing footwear or footwear parts that fit the feet of many people. Also, demand forecasts are made for each shape variation according to the distribution of foot shapes.

[0020] The three-dimensional shape of the foot of a person who wishes to purchase an insole is measured to obtain measurement data of the foot shape. The measurement data of the foot shape of the person is three-dimensional point cloud data indicating anatomical features corresponding to a three-dimensional homologous model of the foot shape including at least the sole and side of the person. The three-dimensional homologous model is a model that defines a coordinate group of the anatomical features of an average foot in three-dimensional space, and a predetermined number of contour points, for example 295 points, are defined.

[0021] The degree of similarity is evaluated between the measurement data of the subject's foot shape and a number of foot shape models classified by cluster analysis, and the design data corresponding to the three-dimensional homologous model that resembles the subject's foot shape is used as basic design data. Footwear part design data is generated by adding individual modifications specified by the subject to this basic design data. Individual modifications include modifications according to the prospective purchaser's preferences regarding arch height, and modifications that add decorations that represent freely entered text, patterns, and other designs. An insole with a three-dimensional lattice structure is manufactured by a three-dimensional additive manufacturing method based on the generated footwear part design data.

[0022] Below, we will first explain the footwear component design system 200 operated by a footwear manufacturer, and then explain the shape analysis system that classifies a large number of foot shape models or basic design data by cluster analysis.

[0023] 1 shows the basic configuration of a footwear component design system 200. The footwear component design system 200 includes measuring devices 15, such as a three-dimensional foot shape measuring device 18 and an information terminal 16, and a footwear component design device 250. When the three-dimensional foot shape measuring device 18 is used as the measuring device 15, the foot shape of the subject 10 is scanned by the three-dimensional foot shape measuring device 18. When the information terminal 16 is used as the measuring device 15, an image of the foot of the subject 10 is taken by the camera function of the information terminal 16.

[0024] There are two occasions for acquiring foot shape measurement data: a person considering purchasing an insole is the measurement subject 10, and a large number of subjects for the purpose of acquiring foot shape data are the measurement subjects 10. A person considering purchasing an insole includes, for example, a person considering purchasing an insole at a footwear store, a person wanting to purchase an insole through online sales, etc. A large number of subjects are subjects for the purpose of acquiring measurement data of foot shapes of various sizes or shapes regardless of gender or age at the stage of generating basic design data for footwear or footwear parts.

[0025] When the three-dimensional foot shape measuring device 18 is used as the measuring device 15, the three-dimensional foot shape measuring device 18 acquires three-dimensional point cloud data related to the foot shape of the subject 10 by laser measurement. The "foot shape" here is a three-dimensional model that virtually reproduces the three-dimensional shape of the foot of the subject 10. The three-dimensional foot shape measuring device 18 transmits the three-dimensional point cloud data of the foot shape to the footwear component design device 250. Note that the "footwear component design device" in the claims may refer to the entire footwear component design system 200 or may refer to the footwear component design device 250. In addition, in this embodiment, since the footwear component design device 250 is realized in a form that includes many of the characteristic functions included in the "footwear component design device" in the claims, the footwear component design device 250 essentially corresponds to the "footwear component design device".

[0026] When the information terminal 16 is used as the measuring device 15, the subject 10 photographs his / her own foot with a camera built into the information terminal 16, such as his / her mobile phone terminal, to obtain an image. For example, in the case of a method of photographing the entire foot from directly above the foot, even if the heel is sufficiently captured, the ankle is also captured, making it difficult to capture the contour shape of the entire foot in one image. Therefore, multiple images are taken from multiple angles so that an image is obtained that at least captures the contours of the toes and heel, and the contours of both the left and right sides from the forefoot to the midfoot. The subject 10 may place his / her foot on a dedicated measurement mat 12 and photograph his / her own foot using the camera function and foot shape acquisition application of the information terminal 16. The information terminal 16 transmits multiple images of the foot to the footwear part design device 250.

[0027] If the information terminal 16 has a three-dimensional scanner function using technology such as LiDAR (Light Detection And Ranging), it may scan the surroundings of the foot to obtain three-dimensional point cloud data of the foot shape. Even if the information terminal 16 does not have a three-dimensional scanner function using LiDAR or the like, it may obtain three-dimensional point cloud data of the foot shape by image synthesis processing such as photogrammetry. In these cases, the information terminal 16 transmits the three-dimensional point cloud data of the foot shape to the footwear part design device 250.

[0028] The footwear component design device 250 is a server computer connected to a plurality of three-dimensional foot shape measuring devices 18 and information terminals 16 via a network line such as the Internet or a LAN (Local Area Network) and a communication means such as wireless communication. The footwear component design device 250 may be configured as a single server computer or may be configured as a combination of a plurality of server computers.

[0029] FIG. 2 is a functional block diagram showing each configuration of the footwear component design device 250. The footwear component design device 250 can be realized by various hardware configurations and software configurations. FIG. 2 illustrates functional blocks of the footwear component design device 250 that are realized by cooperation of various hardware configurations and software configurations. Therefore, it is understood by those skilled in the art that these functional blocks can be realized in various forms by hardware only, software only, or a combination thereof. The footwear component design device 250 is configured by a combination of hardware such as a microprocessor such as a CPU (Central Processing Unit) or a GPU (Graphics Processing Unit), a memory, a display, and a communication module. The footwear component design device 250 may be a server computer built and managed by a footwear manufacturer or a footwear retailer.

[0030] Programs having the following functions run on the footwear component design device 250. The functions of the footwear component design device 250 include a communication unit 210, a measurement data acquisition unit 212, a model conversion unit 214, an individual information acquisition unit 216, a footwear information acquisition unit 218, a model storage unit 220, a model determination unit 222, a design storage unit 230, a basic design determination unit 232, a design processing unit 234, and an output unit 236. The communication unit 210 of the footwear component design device 250 is connected to the three-dimensional foot shape measuring device 18 and the information terminal 16 via a network.

[0031] The measurement data acquisition unit 212 acquires three-dimensional point cloud data of the foot shape of the subject 10 from the three-dimensional foot shape measuring device 18 via the communication unit 210, or acquires photographed images of the foot of the subject 10 from the information terminal 16.

[0032] When acquiring three-dimensional point cloud data from the three-dimensional foot shape measuring device 18, the measurement data acquisition unit 212 acquires measurement data of a three-dimensional coordinate group by converting the three-dimensional point cloud data into three-dimensional point cloud data that indicates anatomical features corresponding to a three-dimensional homologous model. The conversion of the three-dimensional point cloud data is executed by the model conversion unit 214.

[0033] When acquiring a plurality of foot images of the subject 10 from the information terminal 16, the measurement data acquisition unit 212 acquires measurement data of a three-dimensional coordinate group by generating three-dimensional point cloud data indicating anatomical features corresponding to a three-dimensional homologous model based on the plurality of foot images. The generation of the three-dimensional point cloud data based on the plurality of foot images and the conversion to three-dimensional point cloud data indicating anatomical features corresponding to a three-dimensional homologous model are performed by the model conversion unit 214.

[0034] In this way, even if the measurement data is acquired using various types of foot shape measuring devices as the measuring device 15, the measurement data acquisition unit 212 converts the measurement data into three-dimensional point cloud data corresponding to a three-dimensional homologous model by the model conversion unit 214. This allows the measurement data to be generalized into a data format that is easy to use. Meanwhile, in a modified example, the measurement data acquired by the measurement data acquisition unit 212 may be converted in advance by the three-dimensional foot shape measuring device 18 or the information terminal 16 into three-dimensional point cloud data corresponding to a three-dimensional homologous model of the foot shape.

[0035] The model storage unit 220 stores in advance a group of three-dimensional coordinates indicating the anatomical features of the foot as a plurality of three-dimensional models for a plurality of foot shapes. The three-dimensional model of the foot shape stored in the model storage unit 220 is, for example, a three-dimensional homologous model in which a group of coordinates of points indicating the anatomical features of an average foot in a three-dimensional space is defined. The three-dimensional homologous model of the foot shape is a model in which a plurality of foot shapes having various sizes and shapes are expressed as polyhedrons that are anatomically associated by the same topological geometric structure with the same number of points. The points indicating the anatomical features of the average foot are obtained in advance based on foot samples obtained from a large number of subjects. The three-dimensional model in the modified example may be, for example, a three-dimensional shape model in a spherical coordinate system, a model in which a template foot shape generated by a three-dimensional CAD is deformed based on anatomical landmarks, or various other models created in accordance with the same anatomical criteria.

[0036] FIG. 3 shows an example of a three-dimensional homologous model. The three-dimensional homologous model 104 of the foot shape is composed of a group of points indicating the anatomical features of the foot. The three-dimensional homologous model 104 does not necessarily faithfully reproduce the foot shape of the subject 10, especially the shape around the toes, but is constructed in an intentionally deformed shape so as to be easily compared with other foot shapes or three-dimensional homologous models. In other words, the three-dimensional homologous model 104 does not have a perfect similarity to the actual foot shape or foot contour of the subject 10, and has a shape in which convex hull processing has been applied to the contour of the toes in particular. In addition, by detecting a plurality of characteristic points included in the foot contour of the three-dimensional homologous model 104 and using each point as a reference, the length of each part, such as the foot length, foot circumference, arch circumference, heel circumference, and heel width, can be measured. The method of measuring the length of each part is known, so the description will be omitted.

[0037] Returning to FIG. 2, the model storage unit 220 stores the multiple three-dimensional homology models 104 in a form in which the upper layer is divided into multiple clusters by cluster analysis based on cosine similarity. The cluster analysis based on cosine similarity is a cluster analysis based on the cosine similarity between position vectors from a predetermined reference point to each point of the three-dimensional homology model 104. The model storage unit 220 also stores the multiple three-dimensional homology models 104 in a form in which the lower layer is further divided into multiple clusters by a cluster analysis different from that of the upper layer. The cluster analysis of the lower layer is a cluster analysis based on a principal component analysis in which the dimensions are reduced to the second principal component or less, excluding the first principal component, when a principal component analysis is performed for each cluster in the upper layer.

[0038] The model determination unit 222 evaluates the similarity between the three-dimensional coordinate group in the measurement data acquired by the measurement data acquisition unit 212 and the three-dimensional coordinate group in the multiple three-dimensional homologous models 104. The model determination unit 222 determines the three-dimensional homologous model 104 similar to the footprint of the measurement subject 10 from the multiple three-dimensional homologous models 104 in the model storage unit 220 based on the similarity between the coordinates. More specifically, the model determination unit 222 first acquires position vectors from a predetermined reference point to each point in the multiple three-dimensional homologous models 104 stored in the model storage unit 220. Next, the model determination unit 222 acquires position vectors from a predetermined reference point to each point of the three-dimensional homologous model in the measurement data. Then, the model determination unit 222 determines the three-dimensional homologous model 104 similar to the footprint of the measurement subject 10 from the multiple three-dimensional homologous models 104 in the model storage unit 220 based on the distance between the points of the corresponding three-dimensional homologous models or the similarity of the position vectors.

[0039] The distance between the points is, for example, Euclidean distance, and the similarity of the position vector is, for example, cosine similarity. The model determination unit 222 adds up the selection result of the three-dimensional homology model similar based on the Euclidean distance and the selection result of the three-dimensional homology model similar based on the cosine similarity, and determines the three-dimensional homology model most similar to the footprint of the measurement subject 10 from among them. It is assumed that about 6000 three-dimensional foot-print models are stored in the model storage unit 220. In that case, even if the model determination unit 222 calculates the similarity between all the three-dimensional homology models and determines the most similar three-dimensional homology model, it can be determined in about 10 seconds of calculation, so that it is possible to extract the footprint in a very short time. The Euclidean distance and the cosine similarity will be described later.

[0040] The model determination unit 222 scales the determined three-dimensional homologous model to various sizes according to the foot length of the subject 10, and calculates the similarity between each of the scaled three-dimensional homologous models and the three-dimensional homologous model of the foot shape of the subject 10. Based on each similarity, the model determination unit 222 determines the scale of the three-dimensional homologous model with the highest similarity.

[0041] The footwear information acquisition unit 218 acquires footwear information indicating the type of shoes specified by the subject. More specifically, the person wanting to purchase an insole inputs information such as the product model and size of the shoes into which the insole will be inserted as footwear information, and the footwear information acquisition unit 218 acquires the footwear information. The information acquired by the footwear information acquisition unit 218 is sent to the basic design determination unit 232. The information such as the product model and size of the shoes is referred to in the selection of basic design data, which will be described later.

[0042] In a modified example, if the insole purchaser does not input information such as the product model and size of the shoe, basic design data of a standard design may be selected regardless of the product model and size of the shoe. Alternatively, on the assumption that the insole purchaser does not input information such as the product model and size of the shoe, basic design data of a standard design may be selected regardless of the product model and size of the shoe.

[0043] The individual information acquisition unit 216 acquires individual information indicating individual modifications designated according to the desires of the prospective purchaser as modifications to the basic design of the insole. The "individual modifications" referred to here are modifications of functions and designs according to the desires of the prospective purchaser for the purpose of customization. For example, individual modifications include modifications related to preferences such as the height and hardness of the arch support, and modifications related to decorative preferences.

[0044] For example, assume that the arch height ratio in the basic design data is a predetermined value of 60-70%. The prospective purchaser can select the arch height ratio as "high", "standard", or "low" according to his / her preference. When the individual information acquisition unit 216 acquires individual information indicating the arch height ratio as "high", the basic design data is modified so that the arch height ratio becomes a predetermined value of 70-80%. When the individual information acquisition unit 216 acquires individual information indicating the arch height ratio as "low", the basic design data is modified so that the arch height ratio becomes a predetermined value of 50-60%. Note that the numerical range of the arch height ratio is merely an example, and other numerical ranges may be used.

[0045] A prospective purchaser can select the hardness of the insole 300 from among "hard," "standard," and "soft" according to his / her preference. When the individual information acquisition unit 216 acquires individual information indicating "hard" or "soft" for the hardness, the partial density of the three-dimensional lattice structure is changed, or the material of the insole 300 is changed. In addition to the hardness, the material of the insole 300 may be selectable according to preference, and the surface treatment method such as embossing, gloss, and matte may be selectable according to preference. Also, the type and mode of the reinforcing member attached to the insole 300 may be selectable according to preference.

[0046] As a modification related to the decorative preferences of the prospective purchaser, a character, a symbol, a mark, or a pattern can be specified, and a decoration that is a three-dimensional representation of at least one of the specified characters, symbols, marks, or patterns (hereinafter referred to as "three-dimensional decoration") can be added to at least one of the side, bottom, or top of the insole 300. The prospective purchaser can input any text up to a predetermined maximum number of characters, and the input text is converted into design data for the three-dimensional decoration and synthesized into the basic design data. The individual information acquisition unit 216 stores a list of terms that are inappropriate from the viewpoint of law or public order and morals as prohibited terms. If the text input by the prospective purchaser includes a prohibited term, conversion to three-dimensional decoration is restricted. Note that discriminatory terms, prohibited terms, inappropriate terms, and terms that evoke them are generated one after another. In order to automatically eliminate these, not only a glossary stored in the form of a list in advance can be referenced, but also a determination can be made via a learning model, a generative artificial intelligence (AI), an application programming interface (API), or the like that is updated from time to time. In addition to text, the prospective purchaser may select other elements such as a single alphabet character, a symbol, a mark, or a pattern to be added as a three-dimensional decoration. As a modified example, the surface color or type of surface pattern of the insole 300 may be selected as a decoration other than the three-dimensional decoration. Also, the prospective purchaser may input a handwritten drawing or a simple drawing created by the prospective purchaser as an original drawing of the three-dimensional decoration and add it as a three-dimensional decoration. A randomly generated pattern or character string may be set and added as a three-dimensional decoration. Information indicating the date when the design of the insole 300 was created, information indicating the location, and a two-dimensional code or barcode indicating these information may be set and added as a three-dimensional decoration. Such decorative content may be specified by the prospective purchaser by having someone else select it through a predetermined online service. The manufacturing number or individual identification number of the insole 300 may be added as a three-dimensional decoration. Also, the feasibility of implementing the three-dimensional decoration may be determined by taking into account factors that affect the manufacturability during additive manufacturing. Furthermore, the shape of the three-dimensional decoration may be automatically corrected so that it is manufacturable.

[0047] The three-dimensional decoration of the text may be specified as desired in terms of font type, size, character thickness, underline, character enclosure, three-dimensional shape height, etc. In the present embodiment, the three-dimensional decoration is configured as a convex three-dimensional shape, but in a modified example, it may be configured as a concave three-dimensional shape, or the potential purchaser may be able to select whether to configure it as a convex or concave shape according to preference.

[0048] The potential purchaser can specify the location of the three-dimensional decoration. The location of the three-dimensional decoration is at least one of the side, top, and bottom of the periphery of the insole 300. Information including the text of the three-dimensional decoration, i.e., the specification of the text, mark, pattern, etc., and individual information indicating the location of the three-dimensional decoration, is sent to the design processing unit 234. The three-dimensional decoration and its location will be described later.

[0049] The basic design determination unit 232 determines, from the multiple pieces of basic design data, basic design data corresponding to the three-dimensional homologous model 104 determined by the model determination unit 222. When footwear information is received from the footwear information acquisition unit 218, the basic design determination unit 232 determines the basic design data by the model determination unit 222 based on the three-dimensional homologous model 104 and the footwear information.

[0050] The design storage unit 230 stores a plurality of basic design data as three-dimensional design data used for additive manufacturing of an insole corresponding to each foot shape of the plurality of three-dimensional homologous models 104 stored in the model storage unit 220. The plurality of three-dimensional homologous models 104 are classified into a plurality of clusters, and the basic design data corresponding to these three-dimensional homologous models 104 are also substantially classified into a plurality of clusters. The plurality of basic design data stored in the design storage unit 230 may correspond one-to-one to the plurality of three-dimensional homologous models 104 classified into the plurality of clusters in the model storage unit 220, or may correspond in any one of many-to-one, one-to-many, and many-to-many ways.

[0051] In the case of many-to-one correspondence, variations of basic design data are prepared so that one basic design data corresponds to a plurality of three-dimensional homologous models 104. For example, depending on the insole product model, it may be desirable to reduce the number of insole variations relative to the number of foot shape variations, and in such a case, many-to-one correspondence may be used. In the case of one-to-many correspondence, variations of basic design data are prepared so that a plurality of basic design data corresponds to one three-dimensional homologous model 104. For example, a plurality of types of insole product models may be prepared for one foot shape. Alternatively, in the case where the bottom shape of the insole differs in accordance with the shoe mold shape of the shoe product model into which the insole is inserted, a plurality of basic design data corresponding to the shoe product model may be prepared for one foot shape. In addition, one basic design data may correspond to a plurality of three-dimensional homologous models 104, and at the same time, such basic design data may be prepared for each insole product model or each shoe product model.

[0052] The multiple basic design data stored in the design storage unit 230 are all designs that have been confirmed in advance to be valid, that is, to be capable of being manufactured by additive manufacturing. In addition, it has been confirmed in advance that the design remains valid even if individual deformations are made to the basic design data within the range that can be specified as individual information.

[0053] The design processing unit 234 generates insole design data as footwear part design data corresponding to the foot shape of the subject 10 based on the basic design data determined by the basic design determination unit 232. When receiving individual information from the individual information acquisition unit 216, the design processing unit 234 generates insole design data as footwear part design data obtained by adding individual deformation to the basic design based on the basic design data and the individual information. For example, when receiving designation information regarding an arch height ratio as the individual information, the design processing unit 234 generates insole design data by deforming the top surface shape of the basic design data including the arch shape so as to obtain the designated arch height ratio. For example, when receiving designation information regarding hardness as the individual information, the design processing unit 234 generates insole design data by deforming a part of the top surface shape of the basic design data so as to obtain the designated hardness. For example, when receiving information including text as the individual information, the design processing unit 234 generates insole design data by adding three-dimensional decoration modeled after the designated text to the basic design based on the basic design data and the individual information.

[0054] In the present embodiment, an example has been described in which the basic design data is individually modified according to the arch height ratio designated by the individual information. In a modified example, a plurality of basic design data each having a different arch height ratio may be prepared in advance as design variations based on one basic design data, and the design validity of each may be confirmed in advance.

[0055] As a result, it is possible to ensure both the design freedom of the insole and the design validity of the insole. In addition, since insole design data based on designs other than the basic design data stored in the design storage unit 230 and the designation of three-dimensional decoration within a range that has not been confirmed in advance are excluded in advance, it is possible to prevent designs that have not been confirmed for design validity from being made. In particular, it is possible to prevent manufacturing failures and the occurrence of defective products caused by making unexpected insole designs based on extremely non-standard foot shapes.

[0056] The output unit 236 transmits the insole design data generated by the design processing unit 234 to the additive manufacturing device 260, thereby executing additive manufacturing of the insole 300 based on the insole design data. In addition, the output unit 236 may display the insole design data on a display screen of the footwear component design device 250, or may transmit the insole design data to the information terminal 16 via the communication unit 210.

[0057] FIG. 4 is an upper perspective view showing the appearance of an insole manufactured by three-dimensional additive manufacturing. FIG. 5 is a lower perspective view showing the appearance of an insole manufactured by three-dimensional additive manufacturing. The left side of FIG. 4 is the toe side 308, and the right side is the heel side 309. The right side of FIG. 5 is the toe side 308, and the left side is the heel side 309. The insole 300 is manufactured by a three-dimensional additive manufacturing method using a photolithography method. The material of the insole 300 is a material that can be molded by a three-dimensional additive manufacturing method using a photolithography method, and is preferably, for example, a resin material or a rubber material that is suitable for maintaining flexibility, elasticity, and durability. When the insole 300 is made of resin, it can be, for example, a polyolefin resin, an ethylene-vinyl acetate copolymer (EVA), a polyamide-based thermoplastic elastomer (TPA, TPAE), a thermoplastic polyurethane (TPU), or a polyester-based thermoplastic elastomer (TPEE). When the insole 300 is made of rubber, it may be made of butadiene rubber (BR), for example. As a modified example, the insole 300 may be constructed by manufacturing mainly the upper layer of the insole 300 from resin or the like by additive manufacturing, mass-producing the lower layer from another lightweight material by a manufacturing method such as injection molding using a mold, and bonding the upper layer and the lower layer together.

[0058] The base layer appearing on the underside 302 and side 303 of the insole 300 is configured in the form of a three-dimensional mesh structure in which a unit structure of a three-dimensional lattice structure is repeatedly arranged. This unit structure is repeatedly arranged in three orthogonal axial directions, i.e., the front-rear direction, the left-right direction, and the up-down direction. The side 303 is hardly provided on the toe side 308, and is provided in the form of a peripheral wall in which the edge of the underside 302 rises slightly obliquely in a mortar shape from the midfoot to the heel side 309, forming the side 303. The side 303 has a larger area on the inner foot side 306 than on the outer foot side 307 because it is provided with height as an arch support. The heel side 309 of the side 303 also has an area, but it is curved greatly to follow the contour of the heel, so it is more difficult to add three-dimensional decoration such as text compared to the inner foot side 306 and the outer foot side 307.

[0059] A surface layer portion appearing on the upper surface 301 of the insole 300 is provided with a large number of holes. The curved shape of the upper surface 301 of the insole 300 is designed to correspond to the shape of the foot so as to fit the shape of the sole of the subject 10. The curved shape of the lower surface of the insole 300 is designed to correspond to the shoe designated by the subject 10 so as to fit the shape of the midsole and the shape of the upper inside of the shoe into which the insole 300 is inserted. For example, a design corresponding to the shoe type of the shoe designated by the subject 10 in the footwear information may be determined, a design corresponding to the designated shoe type, or a design corresponding to the designated foot length size or foot width type of the shoe may be determined. In a modified example, the lower surface shape of the insole 300 may be designed to be a standard shape regardless of whether the shoe is designated in the footwear information. In addition, a material suitable for the type of shoe designated in the footwear information may be selected as the material of the insole 300.

[0060] Fig. 6 is a side view showing the insole 300 without three-dimensional decoration. Fig. 7 is a side view showing the insole 300 with three-dimensional text decoration added to the side. The text decoration 310 is mainly a decoration that models text or a character string three-dimensionally. Fig. 8 is a bottom perspective view showing the insole 300 with three-dimensional mark decoration added to the side and bottom. The mark decorations 311, 312 may be decorations that model three-dimensional marks such as the logo mark or symbol mark of a footwear manufacturer.

[0061] The three-dimensional decoration is arranged on at least one of the side surface 303, the upper surface 301, and the lower surface 302 around the insole 300. However, since the insole 300 is shaped like a sheet, the places where the three-dimensional decoration can be arranged are limited. For example, the edge of the side surface 303 around the insole 300 is slightly raised in a mortar shape from the midfoot to the heel side 309, and has a relatively high and thick shape, so that an installation area can be secured on a part of the side surface 303. More specifically, the inner foot side 306, the outer foot side 307, and the heel side 309 on the side surface 303, which can secure an installation area, can be selected. On the lower surface 302 of the insole 300, the heel part or the center part can be selected. The upper surface 301 of the insole 300 can also be selected, but since it is a place that the sole of the wearer's foot directly touches, it is possible to avoid the part that affects the kicking out or to limit the thickness of the three-dimensional decoration. Alternatively, if the product specifications require fabric to be attached to the upper surface 301 of the insole 300, the decoration may be arranged by printing text, marks, or other decoration on the fabric.

[0062] FIG. 9 is a schematic diagram showing a sliced ​​cross-sectional image in the insole design data. The sliced ​​cross-sectional image 320 in the insole design data is data of each cross-section obtained by horizontally slicing the three-dimensional lattice structure across multiple layers when the insole 300 is placed horizontally. For example, when the DLP (Digital Light Processing) method is adopted as the photo-lithography method, a cross-sectional pattern as shown in FIG. 9 is exposed and laminated for each layer. Each cross-section is formed as a monochrome binary image as shown in FIG. 9, and ultraviolet rays are irradiated onto the irradiated portion 321 represented by white dots to laminate the resin, and ultraviolet rays are not irradiated onto the non-irradiated portion 322 represented by black dots. In this way, the insole 300 is laminated by repeating the planar irradiation of the irradiated portion 321 based on the insole design data of each layer.

[0063] Since the slice cross-sectional image 320 is thus formed as a monochrome binary image, a monochrome binary image of the cross-section of only the three-dimensional decorative portion may be generated, and then the monochrome binary image of the basic design portion may be overlaid and synthesized to generate the slice cross-sectional image 320 of the entire layer. Alternatively, the slice cross-sectional image 320 of each layer may be generated after generating insole design data as an overall three-dimensional model including the three-dimensional decorative portion by adding partial numerical changes to the basic design data as a three-dimensional model in accordance with the shape of the three-dimensional decoration.

[0064] Hereinafter, a description will be given of a shape analysis device 50 that classifies a plurality of three-dimensional homology models 104 stored in the model storage unit 220 by cluster analysis.

[0065] FIG. 10 is a functional block diagram showing each component of the shape analyzer 50. The shape analyzer 50 can be realized by various hardware and software configurations. FIG. 10 shows functional blocks of the shape analyzer 50 that are realized by cooperation of various hardware and software configurations. Therefore, it is understood by those skilled in the art that these functional blocks can be realized in various forms by hardware only, software only, or a combination thereof. The shape analyzer 50 is composed of a combination of hardware such as a microprocessor, a memory, a display, and a communication module. The shape analyzer 50 may be a server computer built and managed by a shoe manufacturer. However, the shape analyzer 50 runs a program having the following functions. The shape analyzer 50 includes, as its functions, a communication unit 19, a measurement data acquisition unit 20, a model generation unit 21, a model storage unit 22, an output unit 26, an analysis processing unit 30, and a statistical processing unit 35. The communication unit 19 of the shape analyzer 50 and the three-dimensional foot shape measuring device 18 are connected via a network. The foot shape of the subject 10 is scanned by the three-dimensional foot shape measuring device 18. For example, in the shoe design stage, a large number of subjects 10 are selected as the subject 10 for the purpose of acquiring foot shape data of various sizes or shapes, regardless of gender or age.

[0066] The model storage unit 22 stores in advance a group of three-dimensional coordinates indicating anatomical features of at least a part of the body as a plurality of three-dimensional models for a plurality of body shapes. The body shape here mainly refers to the foot shape. The three-dimensional model of the foot shape stored in the model storage unit 22 is, for example, a three-dimensional homologous model in which a group of coordinates of points indicating the anatomical features of an average foot in a three-dimensional space is defined. The three-dimensional homologous model of the foot shape is a model in which a plurality of foot shapes having various sizes and shapes are expressed as polyhedrons that are anatomically associated by the same topological geometric structure with the same number of points. The points indicating the anatomical features of the average foot are obtained in advance based on foot samples obtained from a large number of subjects. The three-dimensional model in the modified example may be, for example, a three-dimensional shape model in a spherical coordinate system, or a model in which a template foot shape generated by a three-dimensional CAD is deformed based on anatomical landmarks, or various other models created in accordance with the same anatomical standard.

[0067] The measurement data acquisition unit 20 acquires, as measurement data, a group of three-dimensional coordinates indicating anatomical features corresponding to a three-dimensional homologous model of the foot shape of the measurement subject 10 from the three-dimensional foot shape measuring device 18 via the communication unit 19. The measurement data acquisition unit 20 may acquire measurement data from an external terminal 17 other than the three-dimensional foot shape measuring device 18 via the communication unit 19. The model generation unit 21 generates a three-dimensional homologous model of the foot shape based on the measurement data acquired by the measurement data acquisition unit 20, and stores it in the model storage unit 22 as a new three-dimensional homologous model. In this case, even if measurement data is acquired using various types of foot shape measuring devices as the three-dimensional foot shape measuring device 18 or the external terminal 17, the model generation unit 21 converts it into a three-dimensional homologous model. This allows the measurement data to be generalized into a data format that is easy to use. On the other hand, in a modified example, the measurement data acquired by the measurement data acquisition unit 20 may be converted into a three-dimensional homologous model of the foot shape in advance by the three-dimensional foot shape measuring device 18 or the external terminal 17. In this case, the model generating unit 21 does not need to generate a three-dimensional homologous model of the foot shape based on the measurement data, and the measurement data may be directly stored in the model storage unit 22 as a new three-dimensional homologous model.

[0068] The analysis processing unit 30 has a cluster analysis processing unit 31 and a principal component analysis processing unit 32. The cluster analysis processing unit 31 executes cluster analysis of upper and lower hierarchical levels, which will be described later, on the multiple three-dimensional homologous models stored in the model storage unit 22. The cluster analysis processing unit 31 implements a method that combines different cluster analyses in the upper and lower hierarchical levels. The principal component analysis processing unit 32 executes principal component analysis, which will be described later, on the multiple three-dimensional homologous models of foot shapes stored in the model storage unit 22. After executing the cluster analysis of the upper hierarchical level, the cluster analysis processing unit 31 executes cluster analysis of the lower hierarchical level based on the results of the principal component analysis executed by the principal component analysis processing unit 32.

[0069] Information on each cluster as a classification result obtained by executing the cluster analysis by the cluster analysis processing unit 31 and the principal component analysis by the principal component analysis processing unit 32 is output to the outside by the output unit 26. Based on the classification result by the cluster analysis, the analysis processing unit 30 divides the three-dimensional homology model stored in the model storage unit 22 into a plurality of clusters and stores them.

[0070] The output unit 26 may display the classification results on a display screen of the shape analysis device 50 or the external terminal 17, or may transmit the classification results to the external terminal 17 via the communication unit 19. The output unit 26 outputs a group of three-dimensional coordinates of the three-dimensional homologous model for each cluster. Information on the classification results output by the output unit 26 may be used for designing a shoe last or an insole.

[0071] The statistical processing unit 35 executes a demand prediction of shoe lasts corresponding to each foot shape model based on the distribution of foot shape models for each cluster. The result of the demand prediction is output to the external terminal 17 by the output unit 26. The statistical processing unit 35 manages a database of shoe lasts, footwear or insoles corresponding to the foot shape models for each cluster stored in the model storage unit 22 according to the result of the demand prediction.

[0072] Figure 11 shows the distribution of foot length and circumference by gender and its relationship to the Japanese JIS standard. Shoe sizes in Japan are classified and standardized by the Japanese Industrial Standards (JIS), which is a standard, according to foot length and circumference / width. This standard takes into account differences in physique and specifies different sizes for men, women, and children.

[0073] Figure 11(a) shows the JIS standard for women and the distribution of a sample of many adult women. Figure 11(b) shows the JIS standard for men and the distribution of a sample of many adult men. The JIS standard for women specifies a foot length range of 19.5 cm to 27.0 cm, while the JIS standard for men specifies a foot length range of 20.0 cm to 30.0 cm. Foot circumference / foot width is divided into shoe widths A to F for women and shoe widths A to G for men according to foot length. However, different ranges are specified, such as shoe width A for women being 183 mm for foot circumference and 76 mm for foot width, and shoe width A for men with a foot length of 20.0 cm being 189 mm for foot circumference and 79 mm for foot width.

[0074] The multiple diagonal lines in Figures 11(a) and 11(b) indicate the relationship between foot length [mm] and foot circumference [mm] for each foot length size according to the JIS standard. The multiple diagonal lines indicate women's shoe widths A to F from the bottom in Figure 11(a), and men's shoe widths A to G from the bottom in Figure 11(b). The difference in foot circumference between shoe widths is approximately 6 mm.

[0075] The distribution of female samples shown in Figure 11(a) is mostly within the range of foot length and foot circumference specified by the JIS standard, but there are also samples that fall outside the range. The distribution of male samples shown in Figure 11(b) is mostly within the range of foot length and foot circumference specified by the JIS standard, but like women, there are also samples that fall outside the range.

[0076] The bold diagonal lines in Figure 11(a) and Figure 11(b) indicate the sample averages for men and women. The foot circumference varies by about ±3 to 4 widths above and below the average line for each foot length size. In other words, even if the relationship between foot length and foot circumference as shown in the figure is determined and provided as a guideline, the actual variation or scatter of foot circumference is large. Moreover, while conventional shoe standards only determine the relationship between foot length and foot circumference, the size and shape of the foot are also determined by other parameters, so even if only the foot length and foot circumference are matched, the target shoe does not necessarily fit the target's foot. In order to solve this situation, we decided to classify a group of three-dimensionally measured foot shape models by cluster analysis using the method described below, and design size variations based on the classification.

[0077] FIG. 12 shows the difference between the general cluster analysis method and the new cluster analysis method. When using the general cluster analysis method to cluster the foot shape data group X, it is possible to perform cluster analysis based on Euclidean distance. However, in this method, the clusters are classified in a manner that is significantly influenced by foot length size. In addition, if it is desired to classify not only foot length size but also differences in foot shape, it is possible to divide by foot length and perform cluster analysis for each foot length size. However, even in the case of this method, it is significantly influenced by foot length size, and it is expected that it will be difficult to distinguish from the conventional grading method that classifies only by multiple foot circumferences for each foot length size. In addition, there is a risk that the number of clusters classified will differ for each size.

[0078] Therefore, the cluster analysis processing unit 31 in this embodiment performs cluster analysis across all foot length sizes as shown in the figure. The foot shape data group X is configured in such a way that a large cluster W separated by similar shapes (i.e., similar shapes) across foot length sizes is multiplied by a small cluster H separated by detailed features. The following figure illustrates the hierarchical state of such clusters.

[0079] Fig. 13 shows a method of combining different cluster analyses in upper and lower hierarchical layers. First, clusters 1 to n obtained by dividing a footprint data group X by cluster analysis based on cosine similarity constitute the first hierarchical layer 110, which is the upper hierarchical layer. Clusters (1-1, 1-2, . . . 1-m, 2-1, 2-2, . . . 2-m, . . . , n-1, n-2, . . . nm) divided finely by cluster analysis based on principal component analysis in which the dimensions of clusters 1 to n are reduced to the second principal component or less excluding the first principal component when principal component analysis is performed constitute the second hierarchical layer 112, which is the lower hierarchical layer. The cluster analysis methods for the upper and lower hierarchical layers will be described later.

[0080] FIG. 14 shows the Euclidean distance and cosine similarity between points. An example will be described in which four points, a first point 120, a second point 121, a third point 122, and a fourth point 123, exist in a vector space. A common method for measuring the closeness between two points is a method of judging the distance by looking at a distance called Euclidean distance. The Euclidean distance is the linear distance between two points, and the smaller the value, the more similar the two points are. On the other hand, in this embodiment, a method of judging the closeness by looking at a similarity called cosine similarity is mainly adopted. The cosine similarity is the cosine value (cosθ) of the angle θ formed by the position vectors from the origin of the two points, and the smaller the angle θ, the closer the cosine value is to 1, and the closer the cosine value is to 1, the more similar the two points are. In general, the cosine similarity is used in situations where the similarity between sentences is measured, and is rarely used in situations where the similarity between shapes is measured.

[0081] Since the Euclidean distance (distance 124) from the first point 120 to the second point 121 is shorter than the Euclidean distance (distance 125) from the first point 120 to the third point 122, when the Euclidean distance is used as the standard, the second point 121 is closer to the first point 120 than the third point 122.

[0082] On the other hand, the angle θ between the position vectors of the first point 120 and the second point 121 from the origin 1 The angle θ between the position vectors from the origin of the first point 120 and the third point 122 is 2is smaller and has a cosine value closer to 1, so that when the cosine similarity is used as a standard, the third point 122 is closer to the first point 120 than the second point 121.

[0083] Furthermore, the first point 120 and the fourth point 123 have almost the same position vector from the origin, the angle θ is almost zero, and the cosine similarity is almost 1. Therefore, the fourth point 123 is farther away than the second point 121 when the Euclidean distance is used as the standard, but is extremely close to the first point 120 when the cosine similarity is used as the standard.

[0084] FIG. 15 shows the relationship between corresponding points in two footprint models. The footprint model is composed of a group of points, and each point indicates an anatomical feature in the footprint. Since each footprint model includes points that indicate the same anatomical feature, the similarity between the entire footprint models is determined based on the similarity of the positional relationship between the points. In comparison between a relatively small footprint model 130 and a relatively large footprint model 131, the cosine similarity is calculated based on the angle formed by the position vectors from the origin between corresponding points among the many point groups included in each footprint model. In the side view of FIG. 15(a), points 132 and 133, points 134 and 135, and points 136 and 137 correspond to each other, and in the top view of FIG. 15(b), points 138 and 139, points 140 and 141, and points 142 and 143 correspond to each other, respectively. In Fig. 15(a), the angle between the position vectors of points 132 and 133, the angle between the position vectors of points 134 and 135, and the angle between the position vectors of points 136 and 137 are all almost zero. In Fig. 15(b), the angle between the position vectors of points 138 and 139, the angle between the position vectors of points 140 and 141, and the angle between the position vectors of points 142 and 143 are all almost zero. Therefore, it can be said that foot print model 130 and foot print model 131 are almost similar in shape although they are different in size.

[0085] Figure 16 shows a comparison of Euclidean distance and cosine similarity when comparing foot models. The closer the foot length between the two foot models, the smaller the Euclidean distance value, and the more likely they are to be judged as "similar." Conversely, the larger the Euclidean distance between the two foot models, the more likely they are to be judged as "dissimilar." The closer the foot models are to similar shapes, the closer the cosine similarity is to 1, and the more likely they are to be judged as "similar." Conversely, the closer the cosine similarity is to 0, the more likely they are to be judged as "dissimilar."

[0086] In the example of Figure 16(a), the two footprint models almost overlap. The Euclidean distance is relatively small, and the cosine similarity is approximately 1. Therefore, it is easy to determine that they are "similar" using either the Euclidean distance or the cosine similarity as the criterion.

[0087] On the other hand, in the example of FIG. 16(b), the two footprint models are clearly different in size. The Euclidean distance is relatively large, and it is easy to judge that they are "not similar" when based on Euclidean distance. On the other hand, the cosine similarity is almost 1, which is almost the same as the example of FIG. 16(a), and it is easy to judge that they are "similar" when based on cosine similarity. In other words, when comparing two footprint models, even if there is a large difference when based on Euclidean distance, there may be almost no difference when based on cosine similarity, which shows that cosine similarity is not affected by size. In the example of FIG. 16(b), the two footprint models are different in size, but are similar enough to be said to be almost similar in shape.

[0088] Figure 17 shows the difference in classification results between cluster analysis based on cosine similarity and Euclidean distance. As classification results obtained by actual cluster analysis of a group of footprint data, the right shows a hierarchical cluster based on Euclidean distance, and the left shows a hierarchical cluster based on cosine similarity.

[0089] In the cluster analysis based on Euclidean distance, in the subdivided clusters at the bottom level, the foot length size gradually increases from left to right, such as decreasing toward the left and increasing toward the right. This also shows that the influence of foot length size is dominant in the cluster analysis based on Euclidean distance. In addition, regarding gender, there is a gradation in which the number of males gradually increases from left to right, such as more females toward the left and more males toward the right. This is because women tend to have smaller foot lengths than men, and it can be seen that the cluster analysis based on Euclidean distance is also influenced by gender.

[0090] On the other hand, the cluster analysis based on cosine similarity shows that the foot lengths are randomly mixed from left to right in the lowest cluster, and foot length size is hardly affected.Similarly, gender is randomly mixed and gender is hardly affected.

[0091] Figure 18 shows the classification results of cluster analysis based on cosine similarity. The footprint data samples used for the cluster analysis were from approximately 5,000 adult men and women. A hierarchical clustering method based on the cosine similarity between each point in each footprint data was used.

[0092] The top three clusters (first cluster 150, second cluster 151, third cluster 152) are considered to be the upper hierarchy, and in each of first cluster 150, second cluster 151, and third cluster 152, the foot shape based on the coordinates of each point obtained by normalizing the foot length size is considered to be the average foot.

[0093] By superimposing the foot shape models of the average feet of each cluster, the characteristics of each cluster and the parts where such characteristics are likely to appear can be found from the variation in partial shapes. For example, in the height direction of the instep, the instep of the first cluster 150 is the lowest, the instep of the second cluster 151 is intermediate, and the instep of the third cluster 152 is the highest. For example, in the angle of the first toe, the angle of the first cluster 150 is the most protruding toward the inner foot, the angle of the second cluster 151 is intermediate, and the angle of the third cluster 152 is slightly inclined toward the second toe. For example, in the inclination around the ankle, the inclination of the first cluster 150 is the largest toward the inner foot, the inclination of the second cluster 151 is intermediate, and the inclination of the third cluster 152 is the smallest. For example, in the inclination around the ankle, the inclination of the first cluster 150 is the largest toward the inner foot, the inclination of the second cluster 151 is intermediate, and the inclination of the third cluster 152 is the smallest.

[0094] FIG. 19 shows the regression lines of foot length and foot circumference for the top three clusters, and the results of the principal component analysis for the top three clusters. First, FIG. 19 shows the regression lines showing the relationship between foot length and foot circumference for each of the first cluster 150, the second cluster 151, and the third cluster 152, superimposed on a graph of the JIS standard similar to FIG. 11. According to this, it can be seen that the regression lines are roughly parallel to each other, but their slopes are significantly different from the slope of the regression line showing the relationship between foot length and foot circumference in the JIS standard. Therefore, by designing shoe last variations according to the regression lines of each cluster, it becomes possible to design variations that are different from the conventional grading for men and women according to the JIS standard, that is, variations that do not distinguish between men and women and are divided by the shape characteristics of the foot shape other than the foot length size.

[0095] The principal component analysis processing unit 32 performs principal component analysis on the foot shape data for each cluster, calculates data by adding the first principal component PC1, which is the variance, to the average foot, which is the center of gravity for each cluster, and plots the relationship between foot length and foot circumference in the data on the graph in FIG. 19. Although each cluster is primarily clustered based on cosine similarity and classified in a manner that allows the influence of foot length size to be ignored, the data itself contains information on foot length. Therefore, when the principal component analysis processing unit 32 performs principal component analysis, the first principal component PC1 extracts a principal component that is largely influenced by foot length. However, since the average foot is divided into clusters with different shapes based on cosine similarity as a large classification in the upper hierarchy, it is easy to classify the lower hierarchy by cluster based on principal components. In this regard, as plotted by open circles in the graph, the plot showing the relationship between foot length and foot circumference in the average foot + first principal component PC1 for each cluster roughly follows the regression line for each cluster, so the results of the cluster analysis and the results of the principal component analysis are not contradictory.

[0096] FIG. 20 shows the results of principal component analysis on clusters. When the principal component analysis processing unit 32 executes principal component analysis on a cluster, it becomes clear which parameters have a large influence on the variance of the data held by that cluster. In FIG. 20, the first principal component PC1 is set on the vertical axis in the upper part, the second principal component PC2 is set on the vertical axis in the middle part, and the third principal component PC3 is set on the vertical axis in the lower part. On the left side, foot length is set on the horizontal axis, and on the right side, foot circumference is set on the horizontal axis. In each graph, the group of plots surrounded on the left side represents females, and the group of plots surrounded on the right side represents males.

[0097] As shown in the two graphs on the left and right in the upper row, the first principal component PC1 is not only strongly correlated with foot length, but also strongly correlated with foot circumference. On the other hand, as shown in the graphs in the middle and lower rows, the regression line of the second principal component PC2 and below is almost horizontal, and it can be seen that they are almost not affected by foot length or foot circumference. In this embodiment, the characteristics of the results of such principal component analysis are utilized to exclude the first principal component PC1 that is affected by foot length and foot circumference, and only the second principal component PC2 and below that are not affected by foot length and foot circumference are used for sub-clustering. The utilization of such characteristics is considered to be advantageous in realizing the construction of clustered variations focusing on differences in detailed shapes, which is different from the conventional grading by size.

[0098] As shown in Fig. 13, in the first hierarchical layer 110, which is a higher hierarchical layer, the cluster analysis processing unit 31 performs cluster analysis based on cosine similarity to separate the clusters. In the second hierarchical layer 112, which is a lower hierarchical layer, the cluster analysis processing unit 31 performs cluster analysis based on principal component analysis in which the dimensions of each of the clusters separated in the first hierarchical layer 110 are reduced to the second principal component or less, so that the clusters are separated into many smaller clusters. The principal component analysis in this case is a principal component analysis in which the dimensions are reduced to the second principal component or less, excluding the first principal component, in the principal component analysis result by the principal component analysis processing unit 32. In the cluster analysis in the second hierarchical layer 112, hierarchical clustering based on the Ward method using Euclidean distance as a criterion is used, based on the second principal component PC2 and the principal components with a cumulative contribution rate of up to 99%.

[0099] The group of foot models divided into many clusters according to the detailed features can be used not only for designing variations of shoe lasts and footwear, but also for designing insoles. For example, the number of variations to be absorbed in the design of shoe lasts and footwear may be reduced by absorbing variations in the design of insoles regarding the detailed shapes that are not dependent on the foot length or foot circumference. In other words, the variations of shoe lasts and footwear may be combined with the variations of insoles to design the overall variations and cover the entire group of foot models.

[0100] In addition, each foot model may be assigned to a size range according to the JIS standard based on which of the shoe lasts in the size range designed according to the JIS standard it fits among the numerous foot models included in the foot model group.

[0101] Since the number of people of the footprint data included in each cluster is different, the ratio of the number of people of each cluster to the entire footprint data group, which is the population, is different. On the other hand, when a database holds a footprint data group that has been collected and accumulated for various purposes other than the footprint data group used in the cluster analysis in the first embodiment, the distribution of each foot length size in the population can be calculated based on the data. Therefore, the statistical processing unit 35 can quantify the ratio of each combination of cluster and foot length by multiplying the ratio of the number of people of each cluster by the distribution of each foot length size. The ratio of each combination of cluster and foot length when the foot length size is divided into a range of 22 cm to 31 cm in 1 cm increments is quantified.

[0102] When the statistical processing unit 35 designs variations of combination patterns of sizes and shapes of footwear products and insoles based on the ratio of each combination of clusters and foot lengths, it can predict demand as initial stock according to the ratio of each combination of clusters and foot lengths. In this case, it is possible to exclude combination patterns of clusters and foot lengths for which demand is predicted to be extremely low from the variations of footwear products from the beginning. The statistical processing unit 35 can reflect the demand prediction not only in the demand prediction as initial stock, but also in the production plan when the stock is reduced due to actual sales. The statistical processing unit 35 may output information regarding the ratio of each combination of clusters and foot lengths to the outside via the output unit 26.

[0103] The present disclosure has been described above based on the embodiments. These embodiments are merely examples, and it will be understood by those skilled in the art that various modifications are possible in the combination of each component and each processing process, and that such modifications are also within the scope of the present disclosure. Furthermore, the above-described embodiments can be generalized to obtain the following aspects.

[0104] In the above embodiment, an example in which the footwear component design system 200 is applied to customization of an insole and design of the insole has been described. In a modified example, the system may be applied to customization and design of shoes such as sandals whose entirety, including the upper, can be manufactured by additive manufacturing. Alternatively, the system may be applied to customization and design of some footwear components such as the sole and upper of a shoe. Alternatively, the system may be applied to customization and design of products other than footwear and footwear components, such as seamless clothing items such as gloves, hats, and clothing accessories, accessories such as glasses, rings, necklaces, and earrings, accessories such as headphones, hearing aids, and ear monitors, computer mice, mouthpieces, and bedding such as pillows.

[0105] [Aspect 1] a model storage unit that stores in advance a group of three-dimensional coordinates indicating anatomical features of the foot as a plurality of three-dimensional homologous models corresponding to a plurality of foot shapes; a design storage unit configured to store a plurality of basic design data as three-dimensional design data used in additive manufacturing of footwear parts corresponding to each foot shape of the plurality of three-dimensional homologous models; a measurement data acquisition unit that acquires measurement data of a group of three-dimensional coordinates that indicate anatomical features corresponding to the three-dimensional homologous model of a foot shape including at least the sole and sides of the subject's foot; a model determination unit that determines a three-dimensional homologous model that is similar to the foot shape of the subject from the plurality of three-dimensional homologous models by evaluating a similarity between coordinates between a group of three-dimensional coordinates in the measurement data and a group of three-dimensional coordinates in the plurality of three-dimensional homologous models; a basic design determination unit that determines basic design data corresponding to the determined three-dimensional homologous model from the plurality of basic design data; a design processing unit that generates footwear part design data corresponding to the foot shape of the subject based on the determined basic design data; A footwear part design device comprising:

[0106] As a result, the footwear component design device of aspect 1 can design footwear or footwear components that are optimal for the foot size and foot shape of each wearer or prospective purchaser. Since basic design data is determined from multiple three-dimensional homologous models prepared in advance, design validity can be guaranteed. Therefore, it is possible to achieve both high design freedom and design validity for footwear or footwear components.

[0107] [Aspect 2] The footwear component design device described in aspect 1, characterized in that the model determination unit acquires position vectors from a predetermined reference point to each point in the multiple three-dimensional homology models, acquires position vectors from the predetermined reference point to each point of the three-dimensional homology models in the measurement data, and determines a three-dimensional homology model that is similar to the subject's body shape from the multiple three-dimensional homology models based on the distance between points of corresponding three-dimensional homology models or the similarity of the position vectors.

[0108] This allows the footwear component design device of aspect 2 to offer a wider range of sizes that fit individual feet in a more detailed manner than the conventional size range that was standardized based only on foot length and foot width, making it possible to design footwear or footwear components that are optimal for the foot size and foot shape of each wearer or prospective purchaser.

[0109] [Aspect 3] The footwear part design device according to aspect 1 or 2, wherein the model memory unit stores the multiple three-dimensional homology models in a form divided into multiple clusters by cluster analysis based on the cosine similarity between position vectors from a predetermined reference point to each point of the multiple three-dimensional homology models.

[0110] This allows the footwear component design device of aspect 3 to classify based on similarity that is less affected by size, enabling a range of sizes that precisely fit the feet of each individual, regardless of foot size, and thus allowing the design of footwear or footwear components that are optimal for the foot size and foot shape of each wearer or prospective purchaser.

[0111] Aspect 4 The footwear component design device according to aspect 3, wherein the model storage unit stores the plurality of three-dimensional homology models in a form in which the upper hierarchy is divided into a plurality of clusters by cluster analysis based on cosine similarity, and the lower hierarchy is further divided into a plurality of clusters by cluster analysis based on principal component analysis in which dimensionality is reduced to the second principal component or less excluding the first principal component when principal component analysis is performed for each cluster in the upper hierarchy.

[0112] This allows the footwear component design device of aspect 4 to classify based on factors that are less affected by gender and size, and also enables a range of sizes to be developed that precisely fit the feet of each individual, regardless of gender or size. This makes it possible to design footwear or footwear components that are optimal for the foot size and foot shape of each wearer or prospective purchaser.

[0113] Aspect 5 an individual information acquiring unit that acquires individual information indicating an individual modification designated according to a desire of the subject as a modification to a basic design of the footwear part, The footwear part design device of any one of aspects 1 to 4, characterized in that the design processing unit generates the footwear part design data by adding the individual deformation to the basic design based on the basic design data and the individual information.

[0114] As a result, in the footwear component design device of aspect 5, not only can footwear or footwear components optimal for the foot size and foot shape of each wearer or prospective purchaser be designed, but the preferences of the prospective purchaser can also be reflected in the design and decoration. In this way, it is possible to design a unique footwear or footwear component that reflects individual differences and preferences through customization. Since basic design data is determined from a plurality of three-dimensional homologous models prepared in advance, it is possible to ensure design validity. Therefore, it is possible to achieve both high design freedom and design validity for footwear or footwear components.

[0115] Aspect 6 the individual information acquisition unit acquires, as the individual information, information including text designated according to a desire of the subject as a decorative shape to be added to an outer surface of the footwear part; The footwear part design device of aspect 5 is characterized in that the design processing unit generates the footwear part design data by adding a decorative shape modeled on the specified text to the basic design based on the basic design data and the individual information.

[0116] As a result, in the footwear component design device of aspect 6, not only can footwear or footwear components optimal for the foot size and foot shape of each wearer or prospective purchaser be designed, but the preferences of the prospective purchaser can also be reflected in the design and decoration. In this way, it is possible to design unique footwear or footwear components that reflect individual differences and preferences in a customized manner. Since basic design data is determined from multiple three-dimensional homologous models prepared in advance, it is possible to ensure design validity. Therefore, it is possible to achieve both high design freedom and design validity for footwear or footwear components.

[0117] Aspect 7 A footwear information acquisition unit that acquires footwear information indicating a type of footwear specified by the subject, the plurality of basic design data are designed to correspond to at least any one of shoe last shapes among a plurality of types of footwear, A footwear part design device described in any one of aspects 1 to 6, characterized in that the basic design determination unit determines the basic design data based on a three-dimensional homologous model similar to the subject's foot shape and the footwear information.

[0118] As a result, the footwear component design device of aspect 7 can not only design footwear components that are optimal for the foot size and foot shape of each wearer or prospective purchaser, but can also design footwear components of a size and shape that are suitable for the footwear preferences of each wearer or prospective purchaser.

[0119] Aspect 8 8. The footwear part design device according to any one of aspects 1 to 7, wherein the footwear part is an insole of footwear.

[0120] As a result, in the footwear component design device of aspect 8, not only can an insole optimal for the foot size and foot shape of each wearer or prospective purchaser be designed, but the preferences of the prospective purchaser can also be reflected in the design and decoration. In this way, a unique insole that reflects individual differences and preferences can be customized and designed. Since basic design data is determined from multiple three-dimensional homologous models prepared in advance, design validity can be guaranteed. Therefore, it is possible to achieve both high design freedom and design validity for the insole.

[0121] Aspect 9 retrieving a plurality of three-dimensional coordinates representing anatomical features of the foot into a plurality of three-dimensional homologous models for a plurality of foot shapes; acquiring measurement data of a group of three-dimensional coordinates indicating anatomical features corresponding to the three-dimensional homologous model of a foot shape including at least the sole and lateral surfaces of the subject's foot; determining a three-dimensional homologous model that is similar to the foot shape of the subject from among the plurality of three-dimensional homologous models by evaluating the similarity between the three-dimensional coordinate group in the measurement data and the three-dimensional coordinate group in the plurality of three-dimensional homologous models; determining basic design data corresponding to the determined three-dimensional homologous model from a plurality of basic design data, which is three-dimensional design data used for additive manufacturing of footwear parts corresponding to each foot shape of the plurality of three-dimensional homologous models; generating footwear part design data corresponding to the foot shape of the subject based on the determined basic design data; A footwear component design method comprising:

[0122] As a result, in the footwear component design method of aspect 9, it is possible to design footwear or footwear components that are optimal for the foot size and foot shape of each wearer or prospective purchaser. Since basic design data is determined from multiple three-dimensional homologous models prepared in advance, it is possible to ensure design validity. Therefore, it is possible to achieve both high design freedom and design validity for footwear or footwear components. [Explanation of symbols]

[0123] 104 three-dimensional homologous model, 212 measurement data acquisition unit, 216 individual information acquisition unit, 218 footwear information acquisition unit, 220 model memory unit, 222 model determination unit, 230 design memory unit, 232 basic design determination unit, 234 design processing unit, 250 footwear part design device, 300 insole, 303 side surface.

Claims

1. a model storage unit that stores in advance a group of three-dimensional coordinates indicating anatomical features of the foot as a plurality of three-dimensional homologous models corresponding to a plurality of foot shapes; a design storage unit configured to store a plurality of basic design data as three-dimensional design data used in additive manufacturing of footwear parts corresponding to each foot shape of the plurality of three-dimensional homologous models; a measurement data acquisition unit that acquires measurement data of a group of three-dimensional coordinates that indicate anatomical features corresponding to the three-dimensional homologous model of a foot shape including at least the sole and sides of the subject's foot; a model determination unit that determines a three-dimensional homologous model that is similar to the foot shape of the subject from the plurality of three-dimensional homologous models by evaluating a similarity between coordinates between a group of three-dimensional coordinates in the measurement data and a group of three-dimensional coordinates in the plurality of three-dimensional homologous models; a basic design determination unit that determines basic design data corresponding to the determined three-dimensional homologous model from the plurality of basic design data; a design processing unit that generates footwear part design data corresponding to the foot shape of the subject based on the determined basic design data; A footwear part design device comprising:

2. The footwear component design device according to claim 1, characterized in that the model determination unit obtains position vectors from a predetermined reference point to each point in the multiple three-dimensional homology models, obtains position vectors from the predetermined reference point to each point of the three-dimensional homology models in the measurement data, and determines a three-dimensional homology model that is similar to the subject's body shape from the multiple three-dimensional homology models based on the distance between points of corresponding three-dimensional homology models or the similarity of the position vectors.

3. The footwear component design device according to claim 1, characterized in that the model memory unit stores the multiple three-dimensional homologous models in a form divided into multiple clusters by cluster analysis based on the cosine similarity between position vectors from a predetermined reference point to each point of the three-dimensional homologous models.

4. The footwear part design device according to claim 3, characterized in that the model storage unit stores the plurality of three-dimensional homology models in a form in which an upper hierarchy is divided into a plurality of clusters by cluster analysis based on cosine similarity, and a lower hierarchy is further divided into a plurality of clusters by cluster analysis based on principal component analysis in which dimensions are reduced to the second principal component or less excluding the first principal component when principal component analysis is performed for each cluster in the upper hierarchy.

5. an individual information acquiring unit that acquires individual information indicating an individual modification designated according to a desire of the subject as a modification to a basic design of the footwear part, The footwear part design device according to claim 1 , wherein the design processing unit generates the footwear part design data by adding the individual modification to the basic design based on the basic design data and the individual information.

6. the individual information acquisition unit acquires, as the individual information, information including text designated according to a desire of the subject as a decorative shape to be added to an outer surface of the footwear part; The footwear part design device according to claim 5, characterized in that the design processing unit generates the footwear part design data by adding a decorative shape modeled on the specified text to the basic design based on the basic design data and the individual information.

7. A footwear information acquisition unit that acquires footwear information indicating a type of footwear specified by the subject, the plurality of basic design data are designed to correspond to at least any one of shoe last shapes among a plurality of types of footwear, The footwear component design device according to claim 1 , characterized in that the basic design determination unit determines the basic design data based on a three-dimensional homologous model similar to the foot shape of the subject and the footwear information.

8. The footwear part designing device according to claim 1 , wherein the footwear part is an insole of the footwear.

9. retrieving a plurality of three-dimensional coordinates representing anatomical features of the foot into a plurality of three-dimensional homologous models for a plurality of foot shapes; acquiring measurement data of a group of three-dimensional coordinates indicating anatomical features corresponding to the three-dimensional homologous model of a foot shape including at least the sole and lateral surfaces of the subject's foot; determining a three-dimensional homologous model that is similar to the foot shape of the subject from among the plurality of three-dimensional homologous models by evaluating the similarity between the three-dimensional coordinate group in the measurement data and the three-dimensional coordinate group in the plurality of three-dimensional homologous models; determining basic design data corresponding to the determined three-dimensional homologous model from a plurality of basic design data, which is three-dimensional design data used for additive manufacturing of footwear parts corresponding to each foot shape of the plurality of three-dimensional homologous models; generating footwear part design data corresponding to the foot shape of the subject based on the determined basic design data; A footwear component design method comprising:

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