JP2024036299A - Information processing device, information processing method, and program - Google Patents

Abstract

[Problem] To improve the convenience of proposing clothing that fits better. A user instruction acquisition unit acquires a user's instruction to a store terminal. The user master body generation unit 55 generates a custom master body by customizing the matching master body based on the user's instructions, and stores the custom master body in the user master body DB 62. When a predetermined trigger is generated after a predetermined time has elapsed since the custom master body was last stored in the user master body DB 62, the user master body update unit 57 updates the user master body in consideration of changes in the human body due to the elapse of the predetermined time. The custom master body CMB1 is updated and stored in the user master body DB62. [Selection diagram] Figure 4

Description

The present invention relates to an information processing device, an information processing method, and a program.

2. Description of the Related Art Traditionally, items worn by people, such as clothing and shoes, have been manufactured to fit human body models with unique dimensions. Such a human body model is a human body model made of three-dimensional data generated based on measurement data of a predetermined user, and is also called a master body. Grading and the like are performed based on this master body, and a plurality of types of master bodies having different sizes are generated.

JP2012-033128A

However, depending on the user, an item produced to fit a master body generated using a conventional method may not fit the user's body to any of the existing sizes. Specifically, for example, when a user tries on a long-sleeved shirt to purchase, the sleeve length fits a size L, but the neckline does not fit a size L, and instead fits a size M. There are cases where it happens. In such a case, the user has no choice but to give up on purchasing a long-sleeved shirt, and the long-sleeved shirt provider loses a business opportunity even though there are users who are about to purchase a long-sleeved shirt. An undesirable situation will occur.
The reason why this situation is undesirable for both the user and the provider is that in the conventional master body generation process, the master body is generated based only on the measurement data of a given user, as described above. It is from. That is, the master body is generated without taking into account the body shape distribution of many people in the market.
For this reason, there is a need for a simple method for efficiently generating multiple types of master bodies that have a wide range of body shape distributions for many people in the market.
Furthermore, a person's body shape changes, and there is a need to update the master body.

The present invention was made in view of this situation, and an object of the present invention is to improve convenience in proposing clothing that fits better.

To achieve the above object, an information processing device according to one embodiment of the present invention includes:
A plurality of types of statistical bodies obtained by clustering three-dimensional scan data of a plurality of people's bodies into a plurality of groups using a predetermined method are obtained from a first database stored for each of the plurality of groups. statistical body acquisition means for acquiring a predetermined type of statistical body as a user base body based on user body data indicating characteristics;
a user master body generating means for generating a user master body by customizing the user base body based on the user's instructions, and storing the generated user master body in a second database;
When a predetermined trigger occurs after a predetermined time has elapsed since the user master body was last stored in the second database, the user master body is updated in consideration of changes in the human body due to the elapse of the predetermined time. user master body updating means for storing the information in the second database;
Equipped with.

An information processing method and program according to one embodiment of the present invention are a processing method and program corresponding to the above-described information processing apparatus according to one embodiment of the present invention.

According to the present invention, it is possible to suggest clothes that better fit the target user.

1 is an image diagram showing an outline of a service realized using an information processing system including a server according to an embodiment of an information processing apparatus of the present invention. FIG. 1 is a diagram illustrating an example of the configuration of an information processing system including a server according to an embodiment of an information processing apparatus of the present invention. 3 is a block diagram showing the hardware configuration of a server in the information processing system of FIG. 2. FIG. 3 is a diagram showing an example of a functional configuration of the server in FIG. 2. FIG. FIG. 2 is a diagram schematically showing the flow of the service shown in FIG. 1. FIG. FIG. 2 is a diagram illustrating an example of generation of the matching master body of FIG. 1; FIG. 2 is a diagram showing an example of a time-series change in a custom master body based on the time-series change model of FIG. 1; FIG. 2 is a diagram illustrating an example of a UI for matching the matching master body of FIG. 1 and generating a custom master body. FIG. 2 is a diagram illustrating an example of a UI for matching the matching master body of FIG. 1 and generating a custom master body. FIG. 2 is a diagram illustrating an example of the production cost of a pattern for the clothing shown in FIG. 1; FIG. 2 is a diagram illustrating an example of the calculation cost of an image presented to a user for the clothing of FIG. 1; 2 is a diagram illustrating an example of inputting customization data in FIG. 1. FIG. It is a diagram showing an example of a service using three-dimensional scan data and a custom master body of this service. FIG. 2 is a diagram illustrating an example of the use of this service for product design and production planning using a custom master body. FIG. 2 is a diagram illustrating an example of the use of this service for product design and production planning using a custom master body.

Embodiments of the present invention will be described below with reference to the drawings. First, an overview of a service to which an information processing system according to an embodiment of the present invention is applied (hereinafter referred to as "this service") will be described with reference to FIG. 1.
FIG. 1 is an image diagram showing an overview of services realized using an information processing system according to an embodiment of the present invention.

This service generates a master body for each item (e.g., T-shirt, long-sleeved shirt, etc.) from 3D scan data of the bodies of many people (hereinafter referred to as "multiple person sample data"), and applies this service. This is a service that provides clothing that better fits a target user by providing the user's statistical body.

That is, this service is a service that generates a master body.
Specifically, a statistical body is created by performing clustering from sample data of multiple people into clusters into which each of the multiple people is classified.
In the example of FIG. 1, each of the multiple people is clustered into three clusters CS, CM, and CL based on the scan data of the multiple people. Then, a paper pattern is generated for each cluster. That is, in the example of FIG. 1, types KS, KM, and KL are generated as the respective types of three clusters CS, CM, and CL. This paper pattern is used when generating a fitting paper pattern, which will be described later.
Additionally, a statistical master body is generated for each cluster. In the example of FIG. 1, a matching master body MMB, which will be described later, is illustrated as a statistical master body of the cluster CM.

Here, the scan data of a large number of people includes scan data of a certain person among the large number of people at multiple points in time (eg, past and present). This service generates a time-series change model based on scan data at multiple points in time. A time-series change model is a model that estimates how a person's body shape changes over time.
Note that the time-series change model may be generated using not only scan data of a certain person at multiple points in time, but also statistical processing of scan data of people with similar characteristics. Specifically, for example, a time-series change model for a man who does not have an athletic body type is generated by statistically processing changes in his body shape from the age of 30 to 40 for a man who does not have an athletic body type.

As described above, after the statistical master body and time-series change model are generated in advance, the user U can receive services such as ordering clothing that fits the master body that reflects his or her own body shape.
That is, the user U provides matching data DM regarding his/her body type. Specifically, for example, user U provides data such as his/her height, weight, age, etc. Thereby, matching is performed to determine which of the above-mentioned clusters the user U belongs to.
As a result, user U is matched with the matching master body associated with the cluster to which user U belongs.
In the example of FIG. 1, the matching master body MMB associated with the cluster CM is matched to the user U.
That is, the "matching master body" is a master body indicating a three-dimensional body shape that is associated with a cluster that has been clustered in advance.

Next, the user U provides customization data DC regarding his/her body type. Specifically, for example, the user U provides information corresponding to the fact that his/her shoulder width is wider than that of the matching master body MMB as the customization data DC.
In this way, the user provides information on the results of comparing various items (for example, shoulder width, chest circumference, etc.) with the matching master body MMB as custom data DC.
Note that the customization data DC may be provided not only in the form of a comparison result with the matching master body MMB, but also in the form of a comparison result with a separately prepared photograph or an actual size value.
Based on the customization data DC provided by the user U in this manner, a customized custom master body is generated.
In the example of FIG. 1, a custom master body CMB1 is generated as a result of user U's customization in 2022.
The custom master body CMB1 is a master body that is customized to the body shape of the user U based on the customization data DC provided by the user U from the statistically generated matching master body MMB.

Next, a fitting pattern K1 is created for the custom master body CMB1.
That is, as described above, the type KM is created in association with the cluster CM to which the user U belongs. The clothing created based on the type KM associated with the cluster CM basically fits the body shape of the matching master body MMB associated with the cluster CM.
However, as described above, since the custom master body CMB1 indicating the body shape of the user U is customized from the matching master body MMB, it is conceivable that clothing created based on the model KM will not necessarily fit.
Therefore, in this service, by attaching clothing created based on the model KM in a virtual three-dimensional space to the custom master body CMB1 customized to the body shape of the user U, the user U You can simulate what it will look like when you wear it.
The user U can check the simulated appearance when wearing the item (for example, the silhouette, the amount of fabric, etc.) and edit the fitting pattern K1 to suit his or her preference.
By providing this fitting pattern K1 to the clothing manufacturer, the user U can obtain clothing that suits him/her.

User U can obtain clothing that suits him/her by ordering clothing using fitting pattern K1, but as described above, user U's body shape may change from day to day.
That is, the user U cannot constantly use the fitting pattern K1. The same applies to the customized master body CMB1 as of 2022.

Therefore, in this service, a custom master body CMB2 after a predetermined period (here, the next year, 2023) is generated from the customized master body CMB1 as of 2022 using a time-series change model.
Then, the user U also generates a fitting pattern K2 using the custom master body CMB2 generated using the time-series change model.
As a result, after a predetermined period of time has elapsed, the user U can use the custom master body CMB2 that suits his/her body type without having to perform personal matching or customization to create a custom master body. Then, the user U can obtain clothing that suits his/her body type.
Although not shown, after the custom master body CMB2 is generated using the time-series change model, the user U can further customize it to suit himself.
By adopting such a mechanism, this service allows user U to easily use his or her own custom master body and obtain clothing that suits his or her body type.

Next, the configuration of an information processing system including the information processing apparatus of the present invention that realizes the provision of the above-mentioned services will be described.

FIG. 2 is a diagram illustrating an example of the configuration of an information processing system including a server according to an embodiment of the information processing apparatus of the present invention.

The information processing system shown in FIG. 2 is configured to include a server 1 and a store terminal 2. The information processing system shown in FIG.
The server 1 and the store terminal 2 are each connected to each other via a network N such as the Internet.

The server 1 is an information processing device that executes processing such as classification into a plurality of clusters, generation of master bodies and custom master bodies, and paper patterns based on scan data of multiple people.
The store terminal 2 is a terminal placed in a store, into which a user U (or a store clerk, not shown) inputs various information, and is used to generate a custom master body CMB1 and a pattern K1.

FIG. 3 is a block diagram showing the hardware configuration of the server 1 in the information processing system of FIG. 2. As shown in FIG.

The server 1 includes a CPU (Central Processing Unit) 11, a ROM (Read Only Memory) 12, a RAM (Random Access Memory) 13, a bus 14, an input/output interface 15, an input section 16, and an output section 17. , a storage section 18, a communication section 19, and a drive 20.

The CPU 11 executes various processes according to programs recorded in the ROM 12 or programs loaded into the RAM 13 from the storage section 18 .
The RAM 13 also appropriately stores data necessary for the CPU 11 to execute various processes.

The CPU 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input/output interface 15 is also connected to this bus 14 . An input section 16 , an output section 17 , a storage section 18 , a communication section 19 , and a drive 20 are connected to the input/output interface 15 .

The input unit 16 includes a keyboard, a mouse, etc., and inputs various information.
The output unit 17 is composed of a display, a speaker, etc., and outputs various information as images and sounds.

The storage unit 18 is composed of a hard disk, a DRAM (Dynamic Random Access Memory), etc., and stores various data.
The communication unit 19 communicates with other devices (store terminal 2 in the example of FIG. 2) via a network N including the Internet.

A removable medium 30 made of a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is appropriately installed in the drive 20. The program read from the removable medium 30 by the drive 20 is installed in the storage unit 18 as necessary.
Further, the removable medium 30 can also store various data stored in the storage section 18 in the same manner as the storage section 18.

Although not shown, the store terminal 2 in the information processing system of FIG. 2 can also have the same configuration as that of FIG. 3.
FIG. 4 is a diagram illustrating an example of the functional configuration of the server in FIG. 2.

As shown in FIG. 4, the CPU 11 of the server 1 includes a statistical body generation section 51, a user body data acquisition section 52, a statistical body acquisition section 53, a user instruction acquisition section 54, and a user master body generation section. 55, a user pattern generating section 56, a user master body updating section 57, a user pattern updating section 58, and an image generating section 59 function.
Note that one area of the storage unit 18 is provided with a three-dimensional scan data DB 60, a statistical body DB 61, and a user master body DB 62.

The statistical body generation unit 51 generates a statistical master body for each item from the multiple person sample data stored in the three-dimensional scan data DB 60.
The statistical body generation section 51 includes a clustering section and a time series model generation section.
The clustering unit performs clustering based on the multiple person sample data. The statistical body generation unit 51 generates and manages statistical master bodies corresponding to each of the clusters CS, CM, and CL.
The time series model generation unit generates a time series change model from the multiple person sample data stored in the three-dimensional scan data DB 60.
The statistical master body and the time-series change model are stored and managed in the statistical body DB 61.
Further, for each of a plurality of groups (for each cluster CS, CM, CL), a pattern of a predetermined item made from a standard statistical body is stored in the statistical body DB 61 as a standard pattern of the group.

The user body data acquisition unit 52 acquires user body data from the store terminal 2. The user body data is data regarding the user's body shape and the like. The user master body includes, for example, the matching data DM shown in FIG.

The statistical body acquisition unit 53 extracts, from the statistical body DB 61, a statistical master body that is associated with a cluster to which the user's body belongs, from among the statistical master bodies, based on user body data indicating the user's body characteristics. as the matching master body.

The user instruction acquisition unit 54 acquires a user's instruction to the store terminal. The user's instructions include an instruction to specify the custom data DC. Specifically, for example, an instruction to adjust the dimensions of a predetermined portion of the matching master body MMB is acquired as a user instruction.

The user master body generation unit 55 customizes the matching master body based on the user's instructions, thereby generating a custom master body CMB1 (referred to as a user master body as appropriate) and storing it in the user master body DB 62.

Every time the custom master body CMB1 is stored in the user master body DB 62, the user pattern generation unit 56 creates a statistical body that serves as a reference for the group to which the matching master body MMB belonged and a reference for a group created from it. Based on the paper pattern and the user master body, a paper pattern of a predetermined item to be applied to the user is generated as a user pattern.

When a predetermined trigger occurs after a predetermined period of time has passed since the custom master body CMB1 was last stored in the user master body DB 62, the user master body update unit 57 takes into account changes in the human body due to the elapse of the predetermined time. The custom master body CMB1 is updated and stored in the user master body DB62 as a custom master body CMB2.

The image generation unit 59 generates an image showing a predetermined item object made from the user pattern paper being attached to the user master body in the virtual space, and presents it to the user.
Here, the image generation unit 59 can also generate an image showing a state in which an object reflecting the characteristics of a predetermined item in the real world is attached to the user master body, and present the image to the user.

The user pattern updating unit 58 updates the user pattern based on instructions from the user to whom the image is presented.

FIG. 5 is a diagram schematically showing the flow of this service in FIG. 1.
As shown in FIG. 5, the user U can easily create a 3D body by operating the terminal.
That is, as explained using FIG. 1, the user U can create a custom master body CMB1 without performing a three-dimensional scan of his/her own body shape using the store terminal 2 (or his own smartphone, etc.). can be obtained.

With this service, as shown in FIG. 5, it is possible to automatically generate 3D data indicating the size of the item from existing measurement information.
That is, in the explanation of FIG. 1, a paper pattern is created and associated with each cluster, but the server 1 creates this paper pattern as 3D data indicating the size of the item from existing measurement information, etc. I can do it.
Thereby, the user U can intuitively check the length and fit using the custom master body CMB1 of his/her body type.

FIG. 6 is a diagram illustrating an example of generation of the matching master body of FIG. 1.
As shown in FIG. 6, first, a large amount of highly accurate scan data (scan data from multiple people) collected using a body scanner is analyzed using AI. That is, as described above in the explanation of FIG. 1, the matching master body is generated by AI processing (statistical processing).
At this time, clustering is performed. In the example shown in the upper right corner of FIG. 6, clusters for each height for a certain bust size are generated. Furthermore, in the example shown in the lower right corner of FIG. 6, clusters for each bust size at a certain height are generated.
A matching master body is generated for each cluster.
In this way, clustering and matching master bodies are generated using various items (shoulder width, height, bust, etc.).

FIG. 7 is a diagram showing an example of a UI that reflects time-series changes in the custom master body based on the time-series change model of FIG. 1.
On the left side of the UI in FIG. 7, a custom master body at a certain point in time (for example, custom master body CMB1) is displayed. On the right side of the UI in FIG. 7, a custom master body (for example, custom master body CMB2) after a predetermined period of time is displayed.
At the right end of the UI in FIG. 7, each item of body characteristics is displayed as a slider.
The server 1 can automatically calculate time-series changes based on attributes (for example, gender and age) of the user U, and can reflect them in updating the custom master body CMB.
Then, the user U can further customize based on the automatically calculated time series changes. Specifically, for example, suppose that, according to the server 1, it is calculated that the chest circumference of the user U is 105 cm based on the attributes of the user U, but the chest circumference of the user U is actually 110 cm. In that case, the user U can operate the UI shown in FIG. 7 to further customize and generate a custom master body CMB2 from the automatically calculated time series changes.

FIG. 8 is a diagram illustrating an example of a UI for matching the matching master body in FIG. 1 and generating a custom master body.
FIG. 9 is a diagram illustrating an example of a UI for matching the matching master body in FIG. 1 and generating a custom master body.
First, the server 1 presents a three-dimensional model of a standard body type (matching master data) based on height, weight, own attributes (gender, age), etc. (matching data DM) input by the user U.
The user inputs custom data DC for his/her body type by operating the UI shown in FIGS. 8(B) and 9(A).
Thereby, the user U can obtain a custom master body CMB1 customized based on the user's own customization data DC, as shown in FIG. 9(B).

FIG. 10 is a diagram illustrating an example of the production cost of a pattern for the clothing shown in FIG. 1.
The table shown in FIG. 10 employs items such as genre, feature, master 3D, individual SKU of brand, and 3D data that requires additional creation as columns.
That is, the genre is the type of clothing. Further, the feature is the classification of clothing in that genre. Further, the master 3D is information that specifies a three-dimensional model (or pattern) associated with clothing of the genre and characteristics. Further, the individual SKU of a brand is the brand and its individual SKU that are required when actually ordering clothing of that genre and characteristics. In addition, 3D data that requires additional creation is information that indicates 3D data that requires additional creation for the clothing model used in the virtual space when fitting clothing of the genre and characteristics to a custom master body in the virtual space. It is.
That is, as shown in FIG. 10, the server 1 separately manages 3D data that needs to be created additionally to the basic master 3D depending on each genre and each feature of clothing, and creates additional data as appropriate. This makes it possible to reduce the cost of generating image data of fitting results to be presented to the user U.

FIG. 11 is a diagram illustrating an example of the calculation cost of an image presented to a user for the clothing of FIG. 1.
In the table shown in FIG. 11, items such as genre, feature, material, and required accuracy of physical calculation are adopted as columns.
That is, the genre is the type of clothing. Further, the feature is the classification of clothing in that genre. Further, the material is the type of material assumed for the clothing of the genre and characteristic. In addition, the required accuracy for physics students is an index that indicates the required accuracy of physics calculations for the genre, characteristics, and materials.
That is, the elongation, distortion, and fit of clothing differ depending on each category of clothing, each feature, and each combination of materials. Therefore, as shown in FIG. 11, the server 1 reduces the cost of generating image data of the fitting results to be presented to the user U based on the required accuracy in the physical system. This allows the user U to more easily check the image of the fitting result, and in turn, allows the user to make fine adjustments to the fitting.

FIG. 12 is a diagram illustrating an example of inputting the customization data in FIG. 1.
It is difficult for the user to grasp the differences between the matching master body MMB and the matching master body MMB shown in FIG. 12(A) and the amount of the differences. Therefore, in this service, as shown in Figure 12 (B), the custom master body CMB and your own photo are displayed side by side or superimposed, so that you can understand the differences between the custom master body CMB and the custom master body CMB. You can input the amount.
Note that the server 1 can also accept the photo of the user U shown in FIG. 12(B), calculate the differences from the custom master body CMB and the amount thereof by image analysis, and acquire the results as custom data DC.

FIG. 13 is a diagram showing an example of a service using three-dimensional scan data and a custom master body of this service.
As shown in FIG. 13(A), conventional body data has been nothing more than measurements and landmark-based body shape analysis.
However, with this service, as shown in Figure 13 (B), it is possible to collect 3D scan data and custom master body CMB information, so it is possible to analyze the distribution of 3D shapes, quantify the whole body shape, and create a database. It becomes possible. This allows clothing manufacturers and others to provide feedback on product design and production planning, as well as marketing and service provision from a new perspective.

FIG. 14 is a diagram showing an example of application of this service to product design and production planning using a custom master body.
In addition, since this service collects 3D scan data and custom master body information, it is possible to better monitor changes over time and changes in the shape of the custom master body CMB1 due to user U's input of customization data DC. This can be carried out suitably.
That is, when the shape of a certain part increases or decreases, it is analyzed which parts interact with each other and how. As a result, even complex measurements such as volume and surface area that are difficult to measure manually can be easily quantified.
This makes it possible to quantitatively construct grading rules, which has been difficult in the past.
In addition, since changes due to aging (chronological changes) are analyzed, it is possible to create products tailored to age.

FIG. 15 is a diagram showing an example of application of this service to product design and production planning using a custom master body.
Additionally, this service collects three-dimensional scan data and custom master body information, making it possible to optimize inventory as shown in FIG. 15. That is, let us assume that the LL size of a certain product A is not selling well as a stand. In this case, the first hypothesis may be that the absolute number of users whose body types are targeted by the LL size is small. For example, a second hypothesis may be that there is a user who falls within the LL size range, but other parts of the user fall outside of the expected silhouette and the silhouette does not form the intended silhouette.
Verification of such a hypothesis has been difficult with conventional measurements.
However, by using this service's 3D scan data and custom master body information, it is possible to verify such hypotheses (especially the second hypothesis).

Although one embodiment of the present invention has been described above, the present invention is not limited to the above-described embodiment, and modifications, improvements, etc. within the range that can achieve the purpose of the present invention are included in the present invention. It is.

Furthermore, for example, the provider of this service may weight the evaluation of outliers from the statistical body "width" by body part, such as imposing a high penalty on the buttocks because the buttocks tend to feel tight. You may. This realizes matching that takes into account the "tightness" and "looseness" that the user feels when wearing the item.

Further, in the embodiment described above, an example was described in which clustering is performed based on scan data of a large number of people, but the present invention is not particularly limited to this.
In other words, the provider of this service can, for example, not only perform clustering based on typical parameters such as height and chest circumference, but also use proprietary indicators to classify people into abstract clusters such as sportsman type and metabolic type. A similar mechanism may be adopted.

Further, for example, in the embodiment described above, various information regarding the scanned human body can be associated with the acquired scan data of multiple human samples. Specifically, for example, it is possible to link objective information about the scanned human body, such as gender, age, and weight, as well as subjective information about the scanned human body, such as items that are often worn or items that are of no interest. . Furthermore, it is possible to link all kinds of information, such as address, occupation, and other personal information about the scanned human body, to the scan data of a large sample of people. Furthermore, the scan data of these many people and the various information linked thereto can be utilized as big data.

For example, in the embodiment described above, four types of statistical bodies are created by clustering, but the present invention is not limited to this. That is, the number of types of statistical bodies created by clustering may be four or more or four or less.

For example, in the embodiment described above, a T-shirt and a long-sleeved shirt are cited as specific examples of the item, but the item is not limited to these. The item may be any clothing that can be worn by the user, such as a ladies' jacket, a raglan sleeve shirt, a men's jacket, a men's vest, a skirt, ladies' pants, and men's pants.
Furthermore, items may include not only clothing but any article that can be worn by a user. Specifically, for example, shoes such as sports shoes and high heels may be included.
In such a case, the provider of this service may, for example, collect information such as "foot circumference,""footwidth,""footlength,""heelshape," and "toe height" as data items acquired from the 3D scan data. ”, “toe shape”, etc. can be adopted.

Furthermore, for example, as a specific example of the priority when shoes are adopted as an item, the following example is assumed.
That is, when "bumps" is adopted as an item, for example, "foot circumference" and "heel shape" may be given high priority. Further, in consideration of design, the priority of "toe shape" may be set high.
Further, for example, when "heeled shoes" are adopted as an item, high priority may be set for "foot circumference,""heelshape," and "toe shape," for example. Further, in consideration of design, for example, "toe height" may be given a high priority.
For example, when "dress shoes" are adopted as an item, the priorities of "foot circumference" and "toe height" may be set high, for example. Further, in consideration of design, for example, "heel shape" may be given a high priority.
For example, when "sneakers" are adopted as an item, the priority of "foot width" may be set high, for example. Further, in consideration of design, for example, a high priority may be set for "toe shape".
For example, when "competition shoes" are adopted as an item, high priority may be set for "foot circumference,""heelshape,""toeshape," and "foot width," for example. Further, in consideration of design, for example, "toe height" may be given a high priority.
Further, for example, when "boots" are adopted as an item, the priority of "foot circumference" may be set high, for example. Further, in consideration of design, for example, "toe height" may be given a high priority.

Further, for example, in the embodiment described above, the priority of dimension items is listed as a plurality of parameters related to items, but the priority is not limited thereto.
Specifically, for example, the provider of this service may use parameters related to functionality such as "ease of wearing,""ease of putting on,""ease of taking off," and "ease of walking," as well as "beauty of appearance," Parameters related to design such as "color" may be freely adopted.
Furthermore, for example, the provider of this service may adopt parameters related to the constituent elements of the item, such as "parts for the purpose of adjusting strings, zippers, etc." and "parts with elasticity such as rubber."
For example, the provider of this service may adopt parameters related to the gender of the user who wears the item, such as ``Is the item worn by men?'' ``Is the item worn by women?'' ``Is the item worn by both men and women?'' You may.

Further, for example, the provider of this service may employ the user's subjective expectation value regarding the brand as a parameter. Specifically, for example, if a user is expecting a sports brand to provide running shoes, by creating a statistical body with multiple types of patterns based on the distribution of items required for running, It becomes possible to provide items more in line with user needs.
Further, for example, if the user is expecting pumps for women, the provider of this service may place particular importance on the item "pain". In such a case, the weights and priority items to be considered for the above-mentioned parameters (particularly parameters related to functionality) may be changed.
Specifically, for example, if the item is high heels and the width of the toe height of the statistical body is distributed between 3 and 5 cm, for example, the width of the toe height of the statistical body is distributed between 3 and 6 cm. You can set it to widen it (set it to increase the maximum value).
By doing this, the provider of this service can improve the design of the high heels without the user's toes coming into contact with the inside of the high heels (because the height of the toes of high heels is never less than 3 cm). (increase the product lineup).

Moreover, each hardware configuration shown in FIG. 3 is merely an example for achieving the object of the present invention, and is not particularly limited.

Further, the functional block diagrams shown in FIGS. 5 and 10 are merely examples, and are not particularly limited. In other words, it is sufficient that the information processing system is equipped with a function that can execute the above-mentioned series of processes as a whole, and what kind of functional blocks are used to realize this function is determined by the examples shown in FIGS. 5 and 10 in particular. but not limited to.

Further, the location of the functional blocks is not limited to the location shown in FIG. 4, and may be any location. For example, at least a part of the functional blocks on the server 1 side may be provided on the store terminal 2 side, or vice versa.
One functional block may be composed of a single piece of hardware, or may be composed of a single piece of software.

When the processing of each functional block is executed by software, a program constituting the software is installed in a computer or the like from a network or a recording medium.
The computer may be a computer built into dedicated hardware. Further, the computer may be a computer that can execute various functions by installing various programs, such as a server, a general-purpose smartphone, or a personal computer.

Recording media containing such programs are not only comprised of removable media that is distributed separately from the device itself in order to provide the program to each user, but also are pre-installed in the device body and delivered to each user. Consists of provided recording media, etc.

Note that in this specification, the step of writing a program to be recorded on a recording medium is not only a process that is performed chronologically in accordance with the order, but also a process that is not necessarily performed chronologically but in parallel or individually. It also includes the processing to be executed.

In summary, the information processing apparatus to which the present invention is applied only needs to have the following configuration, and can take various embodiments.
That is, the information processing device to which the present invention is applied is
A plurality of types of statistical bodies obtained by clustering three-dimensional scan data of a plurality of people's bodies into a plurality of groups using a predetermined method are obtained from a first database stored for each of the plurality of groups. statistical body acquisition means for acquiring a predetermined type of statistical body as a user base body based on user body data indicating characteristics;
a user master body generating means for generating a user master body by customizing the user base body based on the user's instructions, and storing the generated user master body in a second database;
When a predetermined trigger occurs after a predetermined time has elapsed since the user master body was last stored in the second database, the user master body is updated in consideration of changes in the human body due to the elapse of the predetermined time. user master body updating means for storing the information in the second database;
It is sufficient to have the following.
Thereby, it is possible to improve convenience when suggesting clothing that fits better.

For each of the plurality of groups, a pattern of a predetermined item made from a statistical body serving as a reference is stored in the first database as a group reference pattern,
Every time the user master body is stored in the second database, the reference statistical body of the group to which the user base body belonged, the group reference pattern created therefrom, and the user master body user pattern generating means for generating a pattern of the predetermined item to be applied to the user as a user pattern based on the user's pattern;
It is possible to further include the following.

an image generating means for generating, in a virtual space, an image showing a state in which the object of the predetermined item made from the user pattern is attached to the user master body, and presenting the image to the user;
user pattern updating means for updating the user pattern based on instructions from the user to whom the image is presented;
It is possible to further include the following.

The image generating means may further generate the image showing the object, which reflects the characteristics of the predetermined item in the real world, being attached to the user master body, and present the image to the user. .

1: Server 2: Store terminal 11: CPU
12:ROM
13:RAM
14: Bus 15: Input/output interface 16: Input section 17: Output section 18: Storage section 19: Communication section 20: Drive 30: Removable media 51: Statistical body generation section 51
52: User body data acquisition unit 52
53: Statistical body acquisition unit 53
54: User instruction acquisition unit 54
55: User master body generation unit 55
56: User pattern generation unit 56
57: User master body update section 57
58: User pattern update section 58
59: Image generation unit 59
60: 3D scan data DB
61: Statistical body DB
62: User master body DB

Claims (6)

A plurality of types of statistical bodies obtained by clustering three-dimensional scan data of a plurality of people's bodies into a plurality of groups using a predetermined method are obtained from a first database stored for each of the plurality of groups. statistical body acquisition means for acquiring a predetermined type of statistical body as a user base body based on user body data indicating characteristics;
a user master body generating means for generating a user master body by customizing the user base body based on the user's instructions, and storing the generated user master body in a second database;
When a predetermined trigger occurs after a predetermined time has elapsed since the user master body was last stored in the second database, the user master body is updated in consideration of changes in the human body due to the elapse of the predetermined time. user master body updating means for storing the information in the second database;
An information processing device comprising: For each of the plurality of groups, a pattern of a predetermined item made from a statistical body serving as a reference is stored in the first database as a group reference pattern,
Every time the user master body is stored in the second database, the reference statistical body of the group to which the user base body belonged, the group reference pattern created therefrom, and the user master body user pattern generating means for generating a pattern of the predetermined item to be applied to the user as a user pattern based on the user's pattern;
The information processing device according to claim 1, further comprising: an image generating means for generating, in a virtual space, an image showing the predetermined item object made from the user pattern attached to the user master body and presenting it to the user;
user pattern updating means for updating the user pattern based on instructions from the user to whom the image is presented;
The information processing device according to claim 2, further comprising:. The image generating means further generates the image showing the object, which reflects the characteristics of the predetermined item in the real world, being attached to the user master body, and presents the image to the user.
The information processing device according to claim 3. In an information processing method executed by an information processing device,
A plurality of types of statistical bodies obtained by clustering three-dimensional scan data of a plurality of people's bodies into a plurality of groups using a predetermined method are obtained from a first database stored for each of the plurality of groups. a statistical body obtaining step of obtaining a predetermined type of statistical body as a user base body based on user body data indicating characteristics;
a user master body generation step of generating a user master body by customizing the user base body based on the user's instructions, and storing the user master body in a second database;
When a predetermined trigger occurs after a predetermined time has elapsed since the user master body was last stored in the second database, the user master body is updated in consideration of changes in the human body due to the elapse of the predetermined time. updating the user master body in the second database;
Information processing methods including. to the computer,
A plurality of types of statistical bodies obtained by clustering three-dimensional scan data of a plurality of people's bodies into a plurality of groups using a predetermined method are obtained from a first database stored for each of the plurality of groups. a statistical body obtaining step of obtaining a predetermined type of statistical body as a user base body based on user body data indicating characteristics;
a user master body generation step of generating a user master body by customizing the user base body based on the user's instructions, and storing the user master body in a second database;
When a predetermined trigger occurs after a predetermined time has elapsed since the user master body was last stored in the second database, the user master body is updated in consideration of changes in the human body due to the elapse of the predetermined time. updating the user master body in the second database;
A program that executes control processing including.

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