Method for modeling position of item by analyzing plurality of images

Abstract

A method by which a computing device models a position of an item by analyzing a plurality of images, according to one embodiment of the present disclosure, is disclosed. The method comprises the steps of: acquiring a plurality of images related to a store; utilizing a neural network model so as to generate a semantic map on the basis of the plurality of images; acquiring customer analysis information; utilizing a position prediction model so as to acquire position coordinates of an item on the basis of the customer analysis information; and utilizing a generation model so as to generate, on the basis of the position coordinates and the semantic map, an image in which the position of the item in the store is modeled.

Description

A method for modeling the location of items by analyzing multiple images.

[0001] The present disclosure relates to a method for modeling the location of an item within a store on an image, and more particularly, to a method for modeling the location of an item within a store on an image by analyzing a plurality of images.

[0002] Store analytics, including customer behavior, preferences, and the identification of abnormal behavior, can be used to inform decision-making in store operations and management. Therefore, store analytics can enhance business competitiveness and improve customer experience. In particular, store analytics utilizing artificial intelligence can provide more efficient and accurate results than traditional analytics methods.

[0003] Specifically, face detection and re-identification technologies can be used to collect customer data and analyze customer characteristics. Furthermore, AI cameras can be used to collect customer behavioral data and analyze customer movements. Furthermore, customer movement analysis can be used to determine customer interests. These analytical results can be used to identify decision-making factors or obstacles in a customer's journey from the moment they enter a store to their purchase decision.

[0004] Recently, research is actively underway to provide personalized AI services that identify consumer needs and provide tailored recommendation services.

[0005] Korean Patent No. 10-2585358 (registration date: September 26, 2023) discloses a method for analyzing the behavioral patterns of customers in an unmanned store using an artificial intelligence-based image analysis model.

[0006] The present disclosure relates to a method for modeling the location of an item within a store on an image by utilizing multiple images obtained by photographing the store and customer analysis information.

[0007] Meanwhile, the technical task to be achieved by the present disclosure is not limited to the technical task mentioned above, and may include various technical tasks within a scope obvious to a person skilled in the art from the contents described below.

[0008] To solve the above-described problem, a method for modeling the location of an item by analyzing a plurality of images, performed by a computing device, is disclosed. The method may include the steps of: acquiring a plurality of images related to a store; generating a semantic map based on the plurality of images using a neural network model; acquiring customer analysis information; acquiring location coordinates of the item based on the customer analysis information using a location prediction model; and generating an image in which the location of the item within the store is modeled based on the location coordinates and the semantic map using a generation model.

[0009] In one embodiment, the step of obtaining the customer analysis information may include a step of obtaining customer statistical information based on first data obtained from a first sensor, and a step of obtaining customer characteristic information based on second data obtained from a second sensor.

[0010] In one embodiment, the step of obtaining the customer statistical information may include a step of obtaining the customer's movement information by analyzing the first data obtained from the depth sensor.

[0011] In one embodiment, the step of obtaining the customer characteristic information may include a step of obtaining personal information of the customer by analyzing the second data obtained from the camera sensor.

[0012] In one embodiment, the step of obtaining the location coordinates of the item includes the step of obtaining the location coordinates of the item based on the customer statistical information, the customer characteristic information, and additional information by utilizing the location prediction model, wherein the additional information includes at least one of item information and grid information of the store, and the item information may include information on the size of the space occupied by the item within the store and analysis information on the target customer base for the item.

[0013] In one embodiment, the step of generating the semantic map based on the plurality of images may include the steps of extracting egocentric features of the plurality of images, converting the egocentric features into an allocentric feature map, and predicting an allocentric meaning based on the allocentric feature map and generating the semantic map.

[0014] In one embodiment, the step of converting the egocentric feature into the allocentric feature map may include the step of projecting the egocentric feature onto the allocentric memory map based on a camera internal parameter matrix and depth information of each pixel of the image.

[0015] In one embodiment, the step of converting the egocentric feature into the allocentric feature map may include the step of bidirectionally projecting the egocentric feature and fusing the bidirectionally projected allocentric memory features to generate an allocentric representation.

[0016] In one embodiment, the step of generating an image in which the location of the item within the store is modeled may include a step of obtaining an image embedding vector for the location coordinates by utilizing a first neural network model based on a multi-modal basis.

[0017] In one embodiment, the step of generating an image in which the location of the item within the store is modeled may further include a step of utilizing a second neural network model to input the image embedding vector and generating an image in which the location of the item within the store is modeled based on the semantic map.

[0018] A computer program stored in a computer-readable storage medium is disclosed for solving the above-described problem. When the computer program is executed by one or more processors, the one or more processors perform operations for analyzing a plurality of images to model the location of an item, wherein the operations may include: acquiring a plurality of images related to a store; generating a semantic map based on the plurality of images using a neural network model; acquiring customer analysis information; acquiring location coordinates of an item based on the customer analysis information using a location prediction model; and generating an image in which the location of the item within the store is modeled based on the location coordinates and the semantic map using a generation model.

[0019] In addition, a computing device is disclosed for solving the above-described problem. The computing device includes at least one processor and a memory, and the at least one processor may be configured to acquire a plurality of images related to a store, generate a semantic map based on the plurality of images using a neural network model, acquire customer analysis information, acquire location coordinates of an item based on the customer analysis information using a location prediction model, and generate an image in which the location of the item within the store is modeled based on the location coordinates and the semantic map using a generation model.

[0020] The present disclosure has the effect of configuring a store structure optimized for purchase and visually presenting it by modeling the location of items within the store on images using multiple images acquired by photographing the store and customer analysis information.

[0021] Additionally, it is effective in analyzing products that are popular with customers and maximizing sales by changing the placement of said products.

[0022] Meanwhile, the effects of the present disclosure are not limited to the effects mentioned above, and various effects may be included within a range apparent to those skilled in the art from the contents described below.

[0023] FIG. 1 is a block diagram of a computing device performing operations according to one embodiment of the present disclosure.

[0024] FIG. 2 is a schematic diagram illustrating a neural network according to one embodiment of the present disclosure.

[0025] FIG. 3 is a flowchart illustrating a method for modeling the location of an item by analyzing multiple images according to one embodiment of the present disclosure.

[0026] FIG. 4 is a conceptual diagram illustrating a semantic map according to one embodiment of the present disclosure.

[0027] FIG. 5 is a schematic diagram illustrating a neural network model for generating a semantic map according to one embodiment of the present disclosure.

[0028] FIG. 6 is a schematic diagram illustrating a location modeling system that generates an image in which the location of an item is modeled according to one embodiment of the present disclosure.

[0029] FIG. 7 is a conceptual diagram illustrating an image in which an item location is modeled according to one embodiment of the present disclosure.

[0030] FIG. 8 is a simplified, general schematic diagram of an exemplary computing environment in which embodiments of the present disclosure may be implemented.

[0031] Various embodiments are now described with reference to the drawings. In this disclosure, various descriptions are provided to facilitate understanding of the present disclosure. However, it will be apparent that these embodiments can be practiced without these specific descriptions.

[0032] The terms "component," "module," "system," and the like, as used herein, refer to computer-related entities, hardware, firmware, software, a combination of software and hardware, or an execution of software. For example, a component may be, but is not limited to, a procedure running on a processor, a processor, an object, a thread of execution, a program, and / or a computer. For example, both an application running on a computing device and the computing device may be a component. One or more components may reside within a processor and / or a thread of execution. A component may be localized within a single computer. A component may be distributed between two or more computers. Furthermore, these components may execute from various computer-readable media having various data structures stored therein. Components may communicate via local and / or remote processes, for example, by signals comprising one or more data packets (e.g., data from one component interacting with another component in a local system, a distributed system, and / or data transmitted to another system via a network such as the Internet via signals).

[0033] Furthermore, the term "or" is intended to mean an inclusive "or" rather than an exclusive "or." That is, unless otherwise specified or clear from context, "X utilizes A or B" is intended to mean one of its natural inclusive permutations. That is, if X utilizes A; X utilizes B; or X utilizes both A and B, "X utilizes A or B" can apply to any of these cases.

[0034] Additionally, the terms "comprises" and / or "comprising" should be understood to imply the presence of the features and / or components. However, it should be understood that the terms "comprises" and / or "comprising" do not exclude the presence or addition of one or more other features, components, and / or groups thereof. Furthermore, unless otherwise specified or clear from context to refer to the singular form, the singular in the present disclosure and claims should generally be construed to mean "one or more."

[0035] And, the term "at least one of A or B" should be interpreted to mean "if it includes only A", "if it includes only B", or "if it is combined in the composition of A and B".

[0036] Those skilled in the art should further appreciate that the various illustrative logical blocks, configurations, modules, circuits, means, logics, and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both. To clearly illustrate the interchangeability of hardware and software, various illustrative components, blocks, configurations, means, logics, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application. However, such implementation decisions should not be interpreted as causing a departure from the scope of the present disclosure.

[0037] The description of the disclosed embodiments is provided to enable a person skilled in the art to make or use the present disclosure. Various modifications to these embodiments will be apparent to those skilled in the art. The general principles defined herein may be applied to other embodiments without departing from the scope of the present disclosure. Thus, the present disclosure is not limited to the embodiments set forth herein. The present disclosure is to be construed in the widest scope consistent with the principles and novel features disclosed herein.

[0038]

[0039] FIG. 1 is a block diagram of a computing device performing operations according to one embodiment of the present disclosure.

[0040] The configuration of the computing device (100) illustrated in FIG. 1 is merely a simplified example. In one embodiment of the present disclosure, the computing device (100) may include other configurations for performing the computing environment of the computing device (100), and only some of the disclosed configurations may constitute the computing device (100).

[0041] A computing device (100) may include a processor (110), memory (130), and network unit (150).

[0042] The processor (110) may be configured with one or more cores, and may include a processor for data analysis and deep learning, such as a central processing unit (CPU), a general purpose graphics processing unit (GPGPU), and a tensor processing unit (TPU) of a computing device. The processor (110) may read a computer program stored in the memory (130) and perform data processing for machine learning according to an embodiment of the present disclosure. According to an embodiment of the present disclosure, the processor (110) may perform operations for learning a neural network model. The processor (110) may perform calculations for learning a neural network model, such as processing input data for learning in deep learning (DL), extracting features from input data, calculating errors, and updating weights of a neural network model using backpropagation. At least one of the CPU, GPGPU, and TPU of the processor (110) may process learning of the neural network model. For example, a CPU and a GPGPU can work together to train a neural network model and classify data using the neural network model. Furthermore, in one embodiment of the present disclosure, processors of multiple computing devices can be used together to train a neural network model and classify data using the neural network model. Furthermore, a computer program executed on a computing device according to one embodiment of the present disclosure may be a CPU, GPGPU, or TPU executable program.

[0043]

[0044] A computing device (100) according to one embodiment of the present disclosure may refer to a location modeling system. The location modeling system may refer to a system that predicts the optimal location of an item optimized for purchase within the entire store space and models the location of the item within the store on an image based on the predicted location coordinates. In this specification, "item" may refer to a product or a product area that is to be stocked or placed in the store. In this specification, a location optimized for purchase may refer to an optimal location for selling or promoting a specific product or service.

[0045] A location modeling system can utilize a neural network model to generate a top-view map based on a front-view image sequence. The top-view map may refer to an allocentric semantic map incrementally generated from egocentric observations.

[0046] A location modeling system can analyze customer characteristics using cameras and track customer movements using depth sensors. The location modeling system can obtain customer analysis data, including customer characteristics and movement information.

[0047] The location modeling system can utilize a location prediction model to obtain the location coordinates of an item based on the customer analysis information. The location coordinates of the item may refer to the location coordinates of the space within the store where the item is to be located.

[0048] The location modeling system can generate an image in which the location of the item within the store is modeled based on the location coordinates and the semantic map by utilizing a generative model.

[0049] Accordingly, the location modeling system according to embodiments of the present disclosure has the effect of configuring a store structure optimized for purchase and visually presenting it by modeling the location of items using a plurality of images acquired by photographing a store and customer analysis information.

[0050] Additionally, it is effective in analyzing products that are popular with customers and maximizing sales by changing the placement of said products.

[0051]

[0052] According to one embodiment of the present disclosure, the memory (130) can store any form of information generated or determined by the processor (110) and any form of information received by the network unit (150).

[0053] According to one embodiment of the present disclosure, the memory (130) may include at least one type of storage medium among a flash memory type, a hard disk type, a multimedia card micro type, a card type memory (e.g., SD or XD memory, etc.), a random access memory (RAM), a static random access memory (SRAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a programmable read-only memory (PROM), a magnetic memory, a magnetic disk, and an optical disk. The computing device (100) may also operate in relation to web storage that performs the storage function of the memory (130) on the internet. The description of the above-described memory is merely an example, and the present disclosure is not limited thereto.

[0054] The network unit (150) according to one embodiment of the present disclosure can use various wired communication systems such as a public switched telephone network (PSTN), xDSL (x Digital Subscriber Line), RADSL (Rate Adaptive DSL), MDSL (Multi Rate DSL), VDSL (Very High Speed ​​DSL), UADSL (Universal Asymmetric DSL), HDSL (High Bit Rate DSL), and a local area network (LAN).

[0055] In addition, the proposed network unit (150) according to one embodiment of the present disclosure can use various wireless communication systems such as CDMA (Code Division Multi Access), TDMA (Time Division Multi Access), FDMA (Frequency Division Multi Access), OFDMA (Orthogonal Frequency Division Multi Access), SC-FDMA (Single Carrier-FDMA) and other systems.

[0056] In one embodiment, the network unit (150) may be configured regardless of the communication mode, such as wired or wireless, and may be configured as various communication networks, such as a personal area network (PAN) and a wide area network (WAN). In addition, the network may be the well-known World Wide Web (WWW), and may also utilize a wireless transmission technology used for short-distance communication, such as infrared (IrDA) or Bluetooth. The technologies described in the present disclosure may be used not only in the networks mentioned above but also in other networks.

[0057]

[0058] FIG. 2 is a schematic diagram illustrating a neural network according to one embodiment of the present disclosure.

[0059] Throughout this disclosure, the terms "neural network," "neural network model," and "neural network" may be used interchangeably. A neural network may be comprised of a set of interconnected computational units, generally referred to as nodes. These nodes may also be referred to as neurons. A neural network comprises at least one node. The nodes (or neurons) comprising the neural network may be interconnected by one or more links.

[0060] At this time, within the neural network model, one or more nodes connected through links can relatively form a relationship between input nodes and output nodes. The concept of input nodes and output nodes is relative, and any node in an output node relationship with respect to one node can also be in an input node relationship with respect to another node, and vice versa. As described above, the relationship between input nodes and output nodes can be created based on links. One or more output nodes can be connected to one input node through links, and vice versa.

[0061] In a relationship between input nodes and output nodes connected through a single link, the data of the output node can have its value determined based on the data input to the input node. Here, the link interconnecting the input nodes and the output nodes can have a weight (in this case, parameters and weights can be used with the same meaning throughout the present disclosure). The weight can be variable and can be varied by a user or an algorithm so that the neural network model can perform a desired function. For example, when one or more input nodes are interconnected to one output node through respective links, the output node can determine the output node value based on the values ​​input to the input nodes connected to the output node and the weights set for the links corresponding to the respective input nodes.

[0062] As described above, a neural network is a network in which one or more nodes are interconnected through one or more links, forming input and output node relationships within the network. The characteristics of a neural network can be determined based on the number of nodes and links within the network, the relationships between the nodes and links, and the weights assigned to each link. For example, if two neural networks have the same number of nodes and links but different weight values ​​for the links, the two neural networks can be perceived as different from each other.

[0063] A neural network can be composed of a set of one or more nodes. A subset of the nodes comprising the neural network can form a layer. Some of the nodes comprising the neural network can form a layer based on their distances from the initial input node. For example, a set of nodes that are n distances from the initial input node can form layer n. The distance from the initial input node can be defined by the minimum number of links that must be traversed to reach the node from the initial input node. However, this definition of a layer is arbitrary for illustrative purposes, and the layer order within a neural network can be defined in a different way than described above. For example, a layer of nodes can be defined by its distance from the final output node.

[0064] An initial input node may refer to one or more nodes within a neural network into which data is directly input without going through links with other nodes. Alternatively, within a neural network, it may refer to nodes that do not have other input nodes connected by links in the relationship between nodes based on links. Similarly, a final output node may refer to one or more nodes within a neural network that do not have output nodes in their relationship with other nodes. Furthermore, a hidden node may refer to nodes that constitute a neural network other than the initial input node and the final output node.

[0065] A neural network according to one embodiment of the present disclosure may be a neural network in which the number of nodes in an input layer may be the same as the number of nodes in an output layer, and the number of nodes decreases and then increases as it progresses from the input layer to the hidden layer. In addition, a neural network according to another embodiment of the present disclosure may be a neural network in which the number of nodes in an input layer may be less than the number of nodes in an output layer, and the number of nodes decreases as it progresses from the input layer to the hidden layer. In addition, a neural network according to another embodiment of the present disclosure may be a neural network in which the number of nodes in an input layer may be greater than the number of nodes in an output layer, and the number of nodes increases as it progresses from the input layer to the hidden layer. A neural network according to another embodiment of the present disclosure may be a neural network in a combined form of the neural networks described above.

[0066] A deep neural network (DNN) can refer to a neural network that includes multiple hidden layers in addition to input and output layers. Using a deep neural network, one can identify latent structures in data. That is, one can identify latent structures in photos, text, videos, voices, and music (e.g., what objects are in a photo, what the content and emotion of a text are, what the content and emotion of a voice are, etc.). A deep neural network can include a convolutional neural network (CNN), a recurrent neural network (RNN), an autoencoder, a restricted Boltzmann machine (RBM), a deep belief network (DBN), a Q network, a U network, a Siamese network, a generative adversarial network (GAN), and the like. The description of the above-described deep neural network is merely an example, and the present disclosure is not limited thereto.

[0067] In one embodiment of the present disclosure, the network function may include an autoencoder. An autoencoder may be a type of artificial neural network that outputs output data similar to input data. The autoencoder may include at least one hidden layer, and an odd number of hidden layers may be arranged between input and output layers. The number of nodes in each layer may be reduced from the number of nodes in the input layer to an intermediate layer called a bottleneck layer (encoding), and then expanded symmetrically from the bottleneck layer to the output layer (symmetrical to the input layer). The autoencoder may perform nonlinear dimensionality reduction. The number of input layers and output layers may correspond to the dimensionality after preprocessing of the input data. In the autoencoder structure, the number of nodes in the hidden layer included in the encoder may have a structure in which the number of nodes decreases as it moves away from the input layer. The number of nodes in the bottleneck layer (the layer with the fewest nodes between the encoder and decoder) may be kept above a certain number (e.g., more than half of the input layer), as too few nodes may not transmit enough information.

[0068] A neural network model including a neural network can be trained using at least one of supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning. Training a neural network model can be a process of applying knowledge to the neural network model to perform a specific action.

[0069] Neural network models can be trained to minimize output errors. Training involves repeatedly inputting training data into the neural network model, calculating the neural network model output and target error for the training data, and backpropagating the neural network model error from the output layer to the input layer to update the weights of each node in the neural network model to reduce the error. In supervised learning, training data with the correct answer labeled for each training data is used (i.e., labeled training data). In unsupervised learning, the correct answer may not be labeled for each training data. For example, in the case of supervised learning for data classification, the training data may be data in which each training data category is labeled. Labeled training data is input to the neural network model, and the error can be calculated by comparing the output (category) of the neural network model with the labels of the training data. As another example, in unsupervised learning for data classification, the error can be calculated by comparing the input training data with the neural network model output. The calculated error is backpropagated in the neural network model in the backward direction (i.e., from the output layer to the input layer), and the connection weights of each node in each layer of the neural network model can be updated according to the backpropagation. The amount of change in the connection weights of each node to be updated can be determined by the learning rate. The calculation of the neural network model for the input data and the backpropagation of the error can constitute an epoch. The learning rate can be applied differently depending on the number of iterations of the epoch of the neural network model. For example, a high learning rate can be used in the early stage of training a neural network model so that the neural network model quickly achieves a certain level of performance, thereby increasing efficiency, and a low learning rate can be used in the later stage of training to increase accuracy.

[0070] In neural network model training, the training data can typically be a subset of the actual data (i.e., the data to be processed using the trained neural network model). Therefore, there may be epochs where the error on the training data decreases but the error on the actual data increases. Overfitting is a phenomenon where the error on the actual data increases due to excessive training on the training data. For example, a neural network model trained on yellow cats may fail to recognize cats of any color other than yellow, which could be a form of overfitting. Overfitting can increase the error in machine learning algorithms. Various optimization methods can be used to prevent overfitting. These methods include increasing the training data, regularization, dropout (inactivating some nodes in the network during the learning process), and batch normalization.

[0071]

[0072] FIG. 3 is a flowchart illustrating a method for modeling the location of an item by analyzing multiple images according to one embodiment of the present disclosure.

[0073] Referring to Figure 3, the location modeling system can acquire multiple images related to the store (S110). The multiple images may represent a front-view image sequence observed along a specific path within the store. For example, the multiple images may represent images captured from a frontal view of the store from different viewpoints or locations using multiple cameras.

[0074] The location modeling system can generate a semantic map based on the plurality of images by utilizing a neural network model (S120).

[0075] FIG. 4 is a conceptual diagram illustrating a semantic map according to one embodiment of the present disclosure.

[0076] Referring to FIG. 4, the plurality of images (210-1 to 210-4; 210) may refer to egocentric images that capture the environment from the observer's perspective. As described above, the plurality of images (210) may refer to frontal images that capture the environment when looking straight at the store from different perspectives or locations. The semantic map (220) may refer to an allocentric image captured from an external perspective. For example, the semantic map may refer to a top-view map that is gradually generated from an egocentric observation. As illustrated in FIG. 4, the semantic map may refer to an allocentric semantic map from a bird's eye view (230) of an actual environment.

[0077] FIG. 5 is a schematic diagram illustrating a neural network model for generating a semantic map according to one embodiment of the present disclosure.

[0078] Referring to FIG. 5, the position modeling system can utilize an encoder (310) to extract egocentric features of the plurality of images (EI). For example, the encoder may be a transformer-based backbone model and may include a Vision Transformer (ViT). The egocentric features may include contextual features and long-range dependencies.

[0079] The position modeling system can utilize the projection module (320) to transform egocentric features into an allocentric feature map. For example, the position modeling system can utilize the projection module to project egocentric features onto an allocentric memory map to generate an allocentric representation.

[0080] To project egocentric features onto an allocentric memory map, the position modeling system can first transform 2D image coordinates into 3D world coordinates. Specifically, the position modeling system can calculate the camera intrinsic parameter matrix (K) and the depth (d) of each pixel in the image. u , v ), the pixel coordinates (u, v) of the image coordinate system can be transformed into the camera coordinate system (x, y, z). The position modeling system can transform the camera coordinates into world coordinates (X, Y, Z) using a rotation matrix and a translation matrix. The position modeling system can calculate the projective index (i, j) by dividing the world coordinates X and Z of each point by the resolution of the corresponding data set. The position modeling system can utilize the projective index (i, j) to project egocentric features into an allocentric memory map.

[0081] In some embodiments, the location modeling system may update and accumulate incoming observations from two directions via a bidirectional GRU to enhance long-range content dependency and fully aggregate incoming information. For example, a higher-order GRU unit may process an allocentric memory tensor in the forward direction from Mt-1 to Mt features, and a lower-order GRU unit may process an allocentric memory tensor in the backward direction from Mt to Mt-1 features. The location modeling system may utilize a convolutional layer to fuse two projected allocentric memory features. Here, Mt may represent the spatial memory of the current time step, and Mt-1 may represent the spatial memory of the previous time step.

[0082] The location modeling system can effectively avoid misclassification of occluded objects by accumulating observations bidirectionally and in parallel at each time step. Consequently, the location modeling system can generate a meaningful allocentric representation, i.e., an allocentric feature map, by fusing bidirectionally projected features.

[0083] The location modeling system can utilize a decoder (330) to predict allocentric meaning based on an allocentric feature map and generate a semantic map (ASM). For example, the decoder may be a CNN-based decoder.

[0084]

[0085] Referring back to FIG. 3, the location modeling system can obtain customer analysis information (S130). The customer analysis information may include customer statistical information and customer characteristic information. For example, the customer statistical information may include visit statistics, visit area information, and customer movement information. The visit statistics may include floating population, visit conversion rate, visitors, and dwell time. The visit area information may include the number of visits, engagement conversion rate, dwell time, and purchase estimates. The customer movement information may include a journey flow map, heat map, and visitor behavior analysis. The customer characteristic information may include customer personal information such as gender, age group, hairstyle, body type, and clothing.

[0086] The location modeling system can obtain customer demographic information based on first data acquired from a first sensor. For example, the location modeling system can obtain customer movement information by analyzing the first data acquired from a depth sensor. The depth sensor may include a Time-of-Flight (ToF) sensor, an infrared sensor, radar, or the like. Customer demographic information can be obtained using a depth sensor, which makes it difficult to identify the customer's personal information during data collection.

[0087] The location modeling system can obtain customer characteristic information based on second data acquired from a second sensor. For example, the location modeling system can analyze the second data acquired from a camera sensor to obtain personal information about the customer. According to embodiments, multiple cameras can be used to minimize blind spots and obtain accurate customer characteristic information.

[0088] The location modeling system can utilize a multi-modal model that uses a camera to analyze customer characteristics, as it requires image data, and a depth sensor to identify customer movements.

[0089]

[0090] The location modeling system can utilize a location prediction model to obtain the location coordinates of an item based on the customer analysis information (S140). The location coordinates of the item may refer to the location coordinates of the space within the store where the item is to be located.

[0091] According to embodiments, the location modeling system may further obtain the location coordinates of an item based on additional information including at least one of item information and store grid information. The item information may include information on the size of the space occupied by the item within the store, analysis information on the target customer base for the item, etc. The store grid information may refer to location information assigned to each grid cell formed by dividing the space within the store into a grid shape. The analysis information on the target customer base may include the age of the target customer base, the gender of the target customer base, etc.

[0092] The above location prediction model may include a regression model, a time series prediction model, a deep learning-based model, etc. The location prediction model may obtain the location coordinates of an item based on customer analysis information, item information, store grid information, etc.

[0093] For example, if a store wants to secure a children's collection area, the location modeling system can predict the optimal location of the children's collection area within the entire store based on the movement information of women in their 30s and 40s, the target customer base for the children's collection.

[0094] Specifically, the location prediction model can determine the movement information of the target customer base for the infant transfer based on analysis information of the target customer base for the item (e.g., gender, age, etc. of the target customer base) and customer analysis information (e.g., customer movement information and personal information of the customer). The location prediction model can search for the location of the infant collection area along the movement information of the target customer base in the entire space of the store. At this time, the location prediction model can obtain the location coordinates of the infant collection area optimized for purchase based on information on the size of the space occupied by the infant collection area and grid information of the store.

[0095] For example, if there are a first empty space and a second empty space during the movement path of the target customer base, the location prediction model may compare the first empty space and the second empty space with the size of the space occupied by the infant / toddler collection area. If the first empty space is smaller than the size of the space occupied by the infant / toddler collection area and the second empty space is larger than the size of the space occupied by the infant / toddler collection area, the location prediction model may output the location coordinates of the second empty space based on the grid information of the store. However, if both the first empty space and the second empty space are larger than the size of the space occupied by the infant / toddler collection area, the location prediction model may output the location coordinates of either the first empty space or the second empty space by further considering accessibility, etc.

[0096]

[0097] The location modeling system can generate an image in which the location of the item within the store is modeled based on the location coordinates and the semantic map by utilizing a generative model (S150).

[0098] FIG. 6 is a schematic diagram illustrating a location modeling system that generates an image in which the location of an item is modeled according to one embodiment of the present disclosure.

[0099] Referring to FIG. 6, the location modeling system (400) may include a location prediction model (410) and a generation module (420). The generation module (420) may include a multi-modal based first neural network model (430), a second neural network model (440), and a decoder (450). The location prediction model (410) may output location coordinates (PC) of an item based on customer analysis information, as described with reference to FIG. 4.

[0100] The location modeling system can obtain an image embedding vector (EV1) for the location coordinates by utilizing a multi-modal based first neural network model (430). The first neural network model (430) can receive location coordinates (PC) as input and output an image embedding vector (EV1) having a feature most highly correlated with the location coordinates (PC).

[0101] A first neural network model (430) based on multi-modal can be trained to predict NxN possible (coordinate information, image information) pairs when a configuration of N (coordinate information, image information) pairs is given. The multi-modal module can output an embedding vector by passing the N pieces of coordinate information and the N pieces of image information through an encoder, respectively. The multi-modal module can calculate the cosine similarity between the coordinate information embedding vector and the image embedding vector. The multi-modal module can learn a multi-modal embedding space by training the image encoder and the coordinate information encoder together so as to maximize the cosine similarity of the actual pair and minimize the cosine similarity of the remaining incorrect pairs.

[0102] The location modeling system can utilize a second neural network model to input the image embedding vector (EV1) and generate an image (MI) in which the location of the item within the store is modeled based on the semantic map (ASM). The second neural network model can be a UNet-based generative model (440) that generates an image information tensor (EV2) from the image embedding vector (EV1). A decoder (450) can output an image (MI) in which the location of the item within the store is modeled from the image information tensor (EV2).

[0103]

[0104] FIG. 7 is a conceptual diagram illustrating an image in which an item location is modeled according to one embodiment of the present disclosure.

[0105] In the example of securing a children's collection area within the store mentioned above, Fig. 7 shows a map of the entire store, the bold line shows the movement path of the target customer base for the children's collection, and point XX shows the starting point.

[0106] The location modeling system can locate the children's collection area along the target customer's movement path throughout the store. The location modeling system can model the children's collection area at point YY, which is highly accessible to the target customer, among the empty spaces along the target customer's movement path. Therefore, it effectively creates a store structure optimized for purchases and presents it visually.

[0107] According to embodiments, when a new toy is released and the store wants to promote the new toy in the children's corner, the location modeling system can model the display location of the new toy for promotion. The location modeling system can predict the display location of the new toy throughout the store based on the movement path information of children aged 4 to 5, who are the target customer base of the new toy. In this case, the location modeling system can predict not only the display location of the new toy, but also the display height of the new toy by taking into account the height of the target customer base. The location modeling system can model the display area of ​​the new toy at point YY, which is highly accessible to the target customer base, among empty spaces existing in the movement path of the target customer base, and display the display location.

[0108] According to embodiments, a location modeling system can analyze products popular with customers and change the layout of such products. The location modeling system can analyze product-specific sales and profits, customer feedback, and previous purchase history to determine popular products and change the layout of the determined popular products. For example, the location modeling system can change the layout of such popular products to areas with high customer traffic. The location modeling system predicts the optimal location of such popular products within the entire store based on overall customer movement information and generates images modeling the locations of such popular products within the store, thereby effectively creating a store structure that maximizes sales.

[0109]

[0110] According to one embodiment of the present disclosure, a computer-readable medium storing a data structure is disclosed.

[0111] A data structure can refer to the organization, management, and storage of data to enable efficient access and modification. A data structure can refer to the organization of data to solve a specific problem (e.g., data retrieval, data storage, or data modification in the shortest possible time). A data structure can also be defined as the physical or logical relationships between data elements designed to support specific data processing functions. The logical relationships between data elements can include connections between user-defined data elements. The physical relationships between data elements can include actual relationships between data elements physically stored on a computer-readable storage medium (e.g., persistent storage). Specifically, a data structure can include a collection of data, relationships between data, and functions or commands applicable to the data. An effectively designed data structure allows a computing device to perform operations while minimizing the use of its resources. Specifically, a computing device can improve the efficiency of operations, reading, inserting, deleting, comparing, exchanging, and searching through an effectively designed data structure.

[0112] Data structures can be categorized as linear or nonlinear, depending on their form. A linear data structure can be a structure in which only one data item is linked to the next. Linear data structures can include lists, stacks, queues, and deques. A list can refer to a series of data sets with an internal order. Lists can also include linked lists. A linked list is a data structure in which data is linked in a single line, each item having a pointer. In a linked list, a pointer can contain information about the next or previous item. Linked lists can be expressed as singly linked lists, doubly linked lists, or circular linked lists, depending on their form. A stack can be a data listing structure with limited data access. A stack can be a linear data structure in which data operations (e.g., insertion or deletion) can only be performed at one end of the data structure. Data stored in a stack can be a Last-in-First-out (LIFO) data structure. A queue is a data structure with limited access to data. Unlike a stack, it can be a first-in, first-out (FIFO) data structure, with later data being retrieved later. A deck can be a data structure that can process data at both ends.

[0113] A nonlinear data structure can be a structure in which multiple data are connected behind a single data. Nonlinear data structures can include graph data structures. A graph data structure can be defined by vertices and edges, and an edge can include a line connecting two different vertices. A graph data structure can include a tree data structure. A tree data structure can be a data structure in which there is only one path connecting two different vertices among multiple vertices included in the tree. In other words, it can be a data structure that does not form a loop in a graph data structure.

[0114] A data structure may include a neural network model. The data structure including the neural network model may be stored on a computer-readable medium. The data structure including the neural network model may include preprocessed data for processing by the neural network model, data input to the neural network model, weights of the neural network model, hyperparameters of the neural network model, data obtained from the neural network model, activation functions associated with each node or layer of the neural network model, loss functions for learning the neural network model, etc. The data structure including the neural network model may include any of the components among the components disclosed above. That is, the data structure including the neural network model may be configured to include all or any combination of the following: preprocessed data for processing by the neural network model, data input to the neural network model, weights of the neural network model, hyperparameters of the neural network model, data obtained from the neural network model, activation functions associated with each node or layer of the neural network model, loss functions for learning the neural network model, etc. In addition to the above-described components, the data structure including the neural network model may include any other information that determines the characteristics of the neural network model. Additionally, the data structure may include any form of data used or generated in the computational process of the neural network model, and is not limited to the aforementioned. The computer-readable medium may include a computer-readable recording medium and / or a computer-readable transmission medium. The neural network model may be composed of a set of interconnected computational units, which may generally be referred to as nodes. These nodes may also be referred to as neurons. The neural network model is composed of at least one node.

[0115] The data structure may include data input to a neural network model. The data structure including the data input to the neural network model may be stored on a computer-readable medium. The data input to the neural network model may include training data input during the training process of the neural network model and / or input data input to the neural network model after training has been completed. The data input to the neural network model may include data that has undergone preprocessing and / or data that is the target of preprocessing. Preprocessing may include a data processing process for inputting data to the neural network model. Accordingly, the data structure may include data that is the target of preprocessing and data generated by the preprocessing. The above-described data structure is merely an example, and the present disclosure is not limited thereto.

[0116] The data structure may include weights of a neural network model. (In the present disclosure, the terms "weight" and "parameter" may be used interchangeably.) The data structure including the weights of the neural network model may be stored in a computer-readable medium. The neural network model may include a plurality of weights. The weights may be variable and may be varied by a user or an algorithm so that the neural network model can perform a desired function. For example, when one or more input nodes are interconnected to one output node by respective links, the output node may determine a data value output from the output node based on values ​​input to the input nodes connected to the output node and weights set for links corresponding to each input node. The above-described data structure is merely an example, and the present disclosure is not limited thereto.

[0117] By way of example and not limitation, the weights may include weights that vary during the training process of the neural network model and / or weights that have completed training of the neural network model. The weights that vary during the training process of the neural network model may include weights at the start of an epoch and / or weights that vary during the epoch. The weights that have completed training of the neural network model may include weights that have completed an epoch. Accordingly, a data structure including the weights of the neural network model may include a data structure including weights that vary during the training process of the neural network model and / or weights that have completed training of the neural network model. Therefore, the above-described weights and / or combinations of each weight are included in the data structure including the weights of the neural network model. The above-described data structures are merely examples and the present disclosure is not limited thereto.

[0118] A data structure including the weights of a neural network model can be stored in a computer-readable storage medium (e.g., memory, hard disk) after going through a serialization process. Serialization can be a process of converting a data structure into a form that can be stored on the same or a different computing device and later reconstructed and used. A computing device can serialize the data structure to transmit and receive data over a network. The data structure including the weights of a serialized neural network model can be reconstructed on the same or a different computing device through deserialization. The data structure including the weights of a neural network model is not limited to serialization. Furthermore, the data structure including the weights of a neural network model can include a data structure that increases computational efficiency while minimizing the use of computing device resources (e.g., a B-Tree, a Trie, an m-way search tree, an AVL tree, a Red-Black Tree in nonlinear data structures). The foregoing is merely an example, and the present disclosure is not limited thereto.

[0119] The data structure may include hyperparameters of a neural network model. Furthermore, the data structure including the hyperparameters of the neural network model may be stored on a computer-readable medium. The hyperparameters may be variables that can be varied by the user. The hyperparameters may include, for example, a learning rate, a cost function, a loss function, the number of epoch iterations, weight initialization (e.g., setting a range of weight values ​​to be subject to weight initialization), and the number of hidden units (e.g., the number of hidden layers, the number of nodes in the hidden layer). The above-described data structure is merely an example, and the present disclosure is not limited thereto.

[0120]

[0121] FIG. 8 is a simplified, general schematic diagram of an exemplary computing environment in which embodiments of the present disclosure may be implemented.

[0122] Although the present disclosure has been described above as being generally implemented by a computing device, those skilled in the art will appreciate that the present disclosure may also be implemented in combination with computer-executable instructions and / or other program modules that may be executed on one or more computers and / or as a combination of hardware and software.

[0123] Generally, program modules include routines, programs, components, data structures, and the like that perform particular tasks or implement particular abstract data types. Furthermore, those skilled in the art will appreciate that the methods of the present disclosure can be implemented with other computer system configurations, including single-processor or multiprocessor computer systems, minicomputers, mainframe computers, as well as personal computers, handheld computing devices, microprocessor-based or programmable consumer electronics, and the like, each of which may be operatively connected to one or more associated devices.

[0124] The described embodiments of the present disclosure can be practiced in a distributed computing environment, where certain tasks are performed by remote processing devices that are connected through a communications network. In a distributed computing environment, program modules may be located in both local and remote memory storage devices.

[0125] Computers typically include a variety of computer-readable media. Computer-readable media can be any media that can be accessed by a computer, and includes both volatile and nonvolatile media, transitory and non-transitory media, removable and non-removable media. By way of example, and not limitation, computer-readable media can include computer-readable storage media and computer-readable transmission media. Computer-readable storage media includes both volatile and nonvolatile media, transitory and non-transitory media, removable and non-removable media implemented in any method or technology for storing information such as computer-readable instructions, data structures, program modules, or other data. Computer-readable storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital video disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium that can be accessed by a computer and used to store the desired information.

[0126] Computer-readable transmission media typically includes any information delivery media that embodies computer-readable instructions, data structures, program modules, or other data in a modulated data signal, such as a carrier wave or other transport mechanism. The term modulated data signal means a signal that has one or more of its characteristics set or changed so as to encode information in the signal. By way of example, and not limitation, computer-readable transmission media includes wired media, such as a wired network or direct-wired connection, and wireless media, such as acoustic, RF, infrared, or other wireless media. Combinations of any of the above are also intended to be included within the scope of computer-readable transmission media.

[0127] An exemplary environment for implementing various aspects of the present disclosure is illustrated, including a computer (1102), which includes a processing unit (1104), system memory (1106), and a system bus (1108). The system bus (1108) connects system components, including but not limited to the system memory (1106), to the processing unit (1104). The processing unit (1104) may be any of a variety of commercially available processors. Dual processors and other multiprocessor architectures may also be utilized as the processing unit (1104).

[0128] The system bus (1108) may be any of several types of bus structures that may be additionally interconnected to a memory bus, a peripheral bus, and a local bus using any of a variety of commercial bus architectures. The system memory (1106) includes read-only memory (ROM) (1110) and random access memory (RAM) (1112). A basic input / output system (BIOS) is stored in non-volatile memory (1110), such as ROM, EPROM, or EEPROM, and includes basic routines that help transfer information between components within the computer (1102), such as during start-up. The RAM (1112) may include high-speed RAM, such as static RAM, for caching data.

[0129] The computer (1102) includes an internal hard disk drive (HDD) (1114) (e.g., EIDE, SATA) - which may be configured for external use within a suitable chassis (not shown), a magnetic floppy disk drive (FDD) (1116) (e.g., for reading from or writing to a removable diskette (1118)), and an optical disk drive (1120) (e.g., for reading from or writing to a CD-ROM disk (1122) or other high-capacity optical media such as a DVD). The hard disk drive (1114), the magnetic disk drive (1116), and the optical disk drive (1120) may be connected to the system bus (1108) by a hard disk drive interface (1124), a magnetic disk drive interface (1126), and an optical drive interface (1128), respectively. The interface (1124) for implementing an external drive includes at least one or both of Universal Serial Bus (USB) and IEEE 1394 interface technologies.

[0130] These drives and their associated computer-readable media provide non-volatile storage of data, data structures, computer-executable instructions, and the like. In the case of the computer (1102), the drives and media correspond to storing any data in a suitable digital format. While the description of computer-readable media above refers to HDDs, removable magnetic disks, and removable optical media such as CDs or DVDs, those of ordinary skill in the art will appreciate that other types of computer-readable media, such as zip drives, magnetic cassettes, flash memory cards, cartridges, and the like, may also be used in the exemplary operating environment, and that any such media may contain computer-executable instructions for performing the methods of the present disclosure.

[0131] A number of program modules, including an operating system (1130), one or more application programs (1132), other program modules (1134), and program data (1136), may be stored in the drive and RAM (1112). All or portions of the operating system, applications, modules, and / or data may be cached in RAM (1112). It will be appreciated that the present disclosure may be implemented in various commercially available operating systems or combinations of operating systems.

[0132] A user may enter commands and information into the computer (1102) via one or more wired / wireless input devices, such as a keyboard (1138) and a pointing device such as a mouse (1140). Other input devices (not shown) may include a microphone, an IR remote control, a joystick, a game pad, a stylus pen, a touch screen, and the like. These and other input devices are often connected to the processing unit (1104) via an input device interface (1142) that is connected to the system bus (1108), but may be connected by other interfaces such as a parallel port, an IEEE 1394 serial port, a game port, a USB port, an IR interface, and the like.

[0133] A monitor (1144) or other type of display device is also connected to the system bus (1108) via an interface, such as a video adapter (1146). In addition to the monitor (1144), the computer typically includes other peripheral output devices (not shown), such as speakers, a printer, and so on.

[0134] The computer (1102) may operate in a networked environment using logical connections to one or more remote computers, such as remote computer(s) (1148), via wired and / or wireless communications. The remote computer(s) (1148) may be a workstation, a computing device computer, a router, a personal computer, a portable computer, a microprocessor-based entertainment device, a peer device, or other conventional network node, and generally include many or all of the components described for the computer (1102), although for simplicity, only the memory storage device (1150) is shown. The logical connections shown include wired / wireless connections to a local area network (LAN) (1152) and / or a larger network, such as a wide area network (WAN) (1154). Such LAN and WAN networking environments are common in offices and companies and facilitate enterprise-wide computer networks, such as intranets, all of which may be connected to a worldwide computer network, such as the Internet.

[0135] When used in a LAN networking environment, the computer (1102) is connected to a local network (1152) via a wired and / or wireless communication network interface or adapter (1156). The adapter (1156) may facilitate wired or wireless communications to the LAN (1152), which may include a wireless access point installed therein for communicating with the wireless adapter (1156). When used in a WAN networking environment, the computer (1102) may include a modem (1158), be connected to a communications computing device on the WAN (1154), or have other means of establishing communications over the WAN (1154), such as via the Internet. The modem (1158), which may be internal or external and wired or wireless, is connected to the system bus (1108) via a serial port interface (1142). In a networked environment, program modules or portions thereof described for the computer (1102) may be stored in a remote memory / storage device (1150). It will be appreciated that the network connections depicted are exemplary and other means of establishing a communications link between the computers may be used.

[0136] The computer (1102) operates to communicate with any wireless device or object that is arranged and operates via wireless communication, such as a printer, a scanner, a desktop and / or portable computer, a portable data assistant (PDA), a communication satellite, any equipment or location associated with a radio-detectable tag, and a telephone. This includes at least Wi-Fi and Bluetooth wireless technologies. Accordingly, the communication may be a predefined structure as in a conventional network, or may simply be an ad hoc communication between at least two devices.

[0137] Wi-Fi (Wireless Fidelity) enables connections to the Internet and other devices without wires. Wi-Fi is a wireless technology that allows devices, such as computers, to send and receive data anywhere within the coverage area of ​​a base station, both indoors and outdoors, similar to cell phones. Wi-Fi networks use wireless technologies called IEEE 802.11 (a, b, g, etc.) to provide secure, reliable, and high-speed wireless connections. Wi-Fi can be used to connect computers to each other, to the Internet, and to wired networks (using IEEE 802.3 or Ethernet). Wi-Fi networks can operate in the unlicensed 2.4 and 5 GHz radio bands, at data rates of, for example, 11 Mbps (802.11a) or 54 Mbps (802.11b), or in products that include both bands (dual-band).

[0138] Those skilled in the art will appreciate that information and signals may be represented using any of a variety of different technologies and techniques. For example, the data, instructions, commands, information, signals, bits, symbols, and chips referenced in the above description may be represented by voltages, currents, electromagnetic waves, magnetic fields or particles, optical fields or particles, or any combination thereof.

[0139] Those skilled in the art will appreciate that the various illustrative logical blocks, modules, processors, means, circuits, and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, various forms of programs or design code (referred to herein, for convenience, as software), or a combination of both. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. Those skilled in the art may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present disclosure.

[0140] The various embodiments presented herein can be implemented as a method, apparatus, or article of manufacture using standard programming and / or engineering techniques. The term article of manufacture includes a computer program, carrier, or media accessible from any computer-readable storage device. For example, computer-readable storage media include, but are not limited to, magnetic storage devices (e.g., hard disks, floppy disks, magnetic strips, etc.), optical disks (e.g., CDs, DVDs, etc.), smart cards, and flash memory devices (e.g., EEPROMs, cards, sticks, key drives, etc.). Furthermore, various storage media presented herein include one or more devices and / or other machine-readable media for storing information.

[0141] It should be understood that the specific order or hierarchy of steps in the presented processes is merely an example of exemplary approaches. It should be understood that the specific order or hierarchy of steps in the processes may be rearranged within the scope of the present disclosure based on design priorities. The appended method claims provide elements of various steps in a sample order, but are not intended to be limited to the specific order or hierarchy presented.

[0142] The description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present disclosure. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other embodiments without departing from the scope of the present disclosure. Therefore, the present disclosure is not intended to be limited to the embodiments disclosed herein, but is to be construed in the broadest scope consistent with the principles and novel features disclosed herein.

[0143] As described above, the relevant contents have been described in the best form for carrying out the invention.

Claims

1. A method of modeling the location of an item by analyzing multiple images performed by a computing device, A step of acquiring multiple images related to a store; A step of generating a semantic map based on the plurality of images by utilizing a neural network model; Steps to obtain customer analysis information; A step of obtaining the location coordinates of an item based on the customer analysis information by utilizing a location prediction model; and A step of generating an image in which the location of the item within the store is modeled based on the location coordinates and the semantic map by utilizing a generative model. Including, method.

2. In paragraph 1, The steps for obtaining the above customer analysis information are: A step of obtaining customer statistical information based on first data obtained from a first sensor; and Step of obtaining customer characteristic information based on second data obtained from a second sensor Including, method.

3. In paragraph 2, The steps for obtaining the above customer statistics information are: A step of obtaining customer movement information by analyzing the first data acquired from the depth sensor, method.

4. In paragraph 3, The step of obtaining the above customer characteristic information is: A step of obtaining personal information of the customer by analyzing the second data acquired from the camera sensor, method.

5. In paragraph 2, The step of obtaining the location coordinates of the above item is: A step of obtaining location coordinates of the item based on the customer statistical information, the customer characteristic information, and additional information by utilizing the above location prediction model. Including, The above additional information includes at least one of item information and store grid information, The above item information includes information on the size of the space occupied by the item within the store and analysis information on the target customer base for the item. The grid information of the above store is the location information assigned to each grid cell formed by dividing the space within the store into a grid shape. method.

6. In paragraph 1, The step of generating the semantic map based on the above multiple images is: A step of extracting egocentric features of the above multiple images; A step of converting the above egocentric features into environment-centric (allocentric) feature maps; and A step of predicting environment-centric meaning based on the above environment-centric feature map and generating the above semantic map. Including, method.

7. In paragraph 6, The step of converting the above ego-centric features into the above environment-centric feature map is: A step of projecting the egocentric features onto an environment-centric memory map based on the camera internal parameter matrix and the depth information of each pixel of the image. Including, method.

8. In paragraph 7, The step of converting the above ego-centric features into the above environment-centric feature map is: A step of bidirectionally projecting the above egocentric features and fusing the bidirectionally projected environment-centric memory features to create an environment-centric representation. Including more, method.

9. A computer program stored in a computer-readable storage medium, wherein the computer program, when executed by one or more processors, causes the one or more processors to perform operations of analyzing a plurality of images to model a location of an item, the operations comprising: An action to obtain multiple images related to a store; An operation of generating a semantic map based on the plurality of images by utilizing a neural network model; Actions to obtain customer analytics information; An operation of obtaining the location coordinates of an item based on the customer analysis information by utilizing a location prediction model; and An operation of generating an image in which the location of the item within the store is modeled based on the location coordinates and the semantic map by utilizing a generative model. Including, A computer program stored on a computer-readable storage medium.

10. In paragraph 9, The action of obtaining the above customer analysis information is as follows: An operation of obtaining customer statistical information based on first data obtained from a first sensor; and An operation for obtaining customer characteristic information based on second data obtained from a second sensor. Including, A computer program stored on a computer-readable storage medium.

11. In Article 10, The action of obtaining the location coordinates of the above item is: An operation of obtaining location coordinates of the item based on the customer statistical information, the customer characteristic information, and additional information by utilizing the above location prediction model. Including, The above additional information includes at least one of item information and store grid information, The above item information includes information on the size of the space occupied by the item within the store and analysis information on the target customer base for the item. The grid information of the above store is the location information assigned to each grid cell formed by dividing the space within the store into a grid shape. A computer program stored on a computer-readable storage medium.

12. In paragraph 9, The operation of generating the semantic map based on the above multiple images is as follows: An operation of extracting egocentric features of the above multiple images; The operation of transforming the above egocentric features into environment-centric (allocentric) feature maps; and An operation of predicting environment-centric meaning based on the above environment-centric feature map and generating the above semantic map. Including, A computer program stored on a computer-readable storage medium.

13. As a computing device, at least one processor; and Memory Including, At least one processor of the above, Obtain multiple images related to the store, Using a neural network model, a semantic map is generated based on the multiple images. Obtain customer analytics information, By utilizing the location prediction model, the location coordinates of the item are obtained based on the customer analysis information, and By utilizing the generative model, an image is generated in which the location of the item within the store is modeled based on the location coordinates and the semantic map. Computing device.

14. In paragraph 13, Obtain customer statistical information based on the first data acquired from the first sensor, and It is further configured to acquire customer characteristic information based on second data acquired from the second sensor. Computing device.

15. In paragraph 14, By utilizing the above location prediction model, it is further configured to obtain the location coordinates of the item based on the customer statistical information, the customer characteristic information, and additional information, The above additional information includes at least one of item information and store grid information, The above item information includes information on the size of the space occupied by the item within the store and analysis information on the target customer base for the item. The grid information of the above store is the location information assigned to each grid cell formed by dividing the space within the store into a grid shape. Computing device.

Images