System and method for object placement model generation and intelligent process automation

Abstract

A method may include receiving, from a user device, a first selection, wherein the first selection includes an end-user channel location. A method may include receiving, from the user device, a second selection, wherein the second selection includes a menu element. A method may include receiving, from the user device, a third selection, wherein the third selection includes an object placement model generation element. A method may include generating, using an artificial intelligence (AI) model, a suggestion associated with the end-user channel location based at least on the first selection, the second selection, or the third selection. A method may display the suggestion on the user device.

Description

SYSTEM AND METHOD FOR OBJECT PLACEMENT MODEL GENERATION AND INTELLIGENT PROCESS AUTOMATIONCROSS-REFERENCE TO RELATED APPLICATION(S)

[0001] This patent application claims the benefit of priority to U.S. Provisional Patent Application No. 63 / 637,611 , filed on April 23, 2024, the entirety of which is incorporated herein by reference.TECHNICAL FIELD

[0002] The present disclosure relates to systems and methods for generating analytics associated with a product using an Al model.BACKGROUND

[0003] A product manufacturer may manufacture a product and distribute the product to be sold at a retail location. The retail location may include a display area where the product is displayed so that consumers may view and purchase the product. The product manufacturer might desire to ensure that the display area is located in a particular area of the retail location, that the product is located in a particular area of the display area, that a particular amount of the product is maintained in the display area, that the product is being sold at a particular price, that a particular promotion is being displayed in association with the product, that particular other products are located adjacent to the product, etc. Given that the product is sold at a large number of retail locations, the product manufacturer might not be capable of monitoring compliance with the foregoing or ascertaining the efficacy of alternative arrangements.SUMMARY

[0004] In some aspects, the techniques described herein relate to a method including: receiving, from a user device, a first selection, wherein the first selection includes a retail store; receiving, from the user device, a second selection, wherein the second selection includes a menu element; receiving, from the user device, a third selection, wherein the third selection includes a generate planogram element; generating, using an artificial intelligence (Al) model, a suggestion associated with the retail store based at least on the first, the second, or the third user selections; and displaying the generated suggestion on the user device.

[0005] In some aspects, the techniques described herein relate to a method, wherein the menu element further includes: at least one of a add shelves, a new shelves, or a SKU item.

[0006] In some aspects, the techniques described herein relate to a method, further including: a user input specifying at least one of a shelf or a SKU item to prioritize before generating the suggestion associated with the retail store.

[0007] In some aspects, the techniques described herein relate to a method, wherein displaying the generated suggestion on the user device further includes: displaying a user interface element for the user to accept or reject the generated suggestion.

[0008] In some aspects, the techniques described herein relate to a method, further including: in response to the user accepting the generated suggestion, display a save user interface object; and in response to the user rejecting the generated suggestions, display the generate planogram element.

[0009] In some aspects, the techniques described herein relate to a system including: a memory configured to store instructions; and one or more processor configured to execute the instructions to perform operations including: receiving, from a user device, a first selection, wherein the first selection includes a retail store; receiving, from the user device, a second selection, wherein the second selection includes a menu element; receiving, from the user device, a third selection, wherein the third selection includes a generate planogram element; generating, using an artificial intelligence (Al) model, a suggestion associated with the retail store based at least on the first, the second, or the third user selections; and displaying the generated suggestion on the user device.

[0010] In some aspects, the techniques described herein relate to a system, wherein the menu element further includes: at least one of a add shelves, a new shelves, or a SKU item.

[0011] In some aspects, the techniques described herein relate to a system, further including: a user input specifying at least one of a shelf or a SKU item to prioritize before generating the suggestion associated with the retail store.

[0012] In some aspects, the techniques described herein relate to a system, wherein displaying the generated suggestion on the user device further includes: displaying a user interface element for the user to accept or reject the generated suggestion.

[0013] In some aspects, the techniques described herein relate to a system, further including: in response to the user accepting the generated suggestion, display a save user interface object; and in response to the user rejecting the generated suggestions, display the generate planogram element.

[0014] In some aspects, the techniques described herein relate to a non- transitory computer-readable medium storing instructions that, when executed by one or more processors, cause the one or more processors to perform operations including: receiving, from a user device, a first selection, wherein the first selection includes a retail store; receiving, from the user device, a second selection, wherein the second selection includes a menu element; receiving, from the user device, a third selection, wherein the third selection includes a generate planogram element; generating, using an artificial intelligence (Al) model, a suggestion associated with the retail store based at least on the first, the second, or the third user selections; and displaying the generated suggestion on the user device.

[0015] In some aspects, the techniques described herein relate to a non- transitory computer-readable medium, wherein the menu element further includes: at least one of a add shelves, a new shelves, or a SKU item.

[0016] In some aspects, the techniques described herein relate to a non- transitory computer-readable medium, further including: a user input specifying at least one of a shelf or a SKU item to prioritize before generating the suggestion associated with the retail store.

[0017] In some aspects, the techniques described herein relate to a non- transitory computer-readable medium, wherein displaying the generated suggestion on the user device further includes: displaying a user interface element for the user to accept or reject the generated suggestion.

[0018] In some aspects, the techniques described herein relate to a non- transitory computer-readable medium, further including: in response to the user accepting the generated suggestion, display a save user interface object; and in response to the user rejecting the generated suggestions, display the generate planogram element.

[0019] In some aspects, the techniques described herein relate to a method including: receiving, from a user device, a first selection, wherein the first selection includes an end-user channel location; receiving, from the user device, a second selection, wherein the second selection includes a menu element; receiving, from theuser device, a third selection, wherein the third selection includes an object placement model generation element; generating, using an artificial intelligence (Al) model, a suggestion associated with the end-user channel location based at least on the first selection, the second selection, or the third selection; and displaying the suggestion on the user device.

[0020] In some aspects, the techniques described herein relate to a method, wherein the menu element includes: at least one of an add shelves element, a new shelves element, or an object identifier element.

[0021] In some aspects, the techniques described herein relate to a method, further including: a user input specifying at least one of a shelf or an object identifier to prioritize before generating the suggestion associated with the end-user channel location.

[0022] In some aspects, the techniques described herein relate to a method, wherein displaying the suggestion on the user device further includes: displaying a user interface element for a user to accept or reject the suggestion.

[0023] In some aspects, the techniques described herein relate to a method, further including: in response to the user accepting the suggestion, display a save user interface object; and in response to the user rejecting the suggestion, display the object placement model generation element.

[0024] In some aspects, the techniques described herein relate to a method, wherein the suggestion includes an image or video.

[0025] In some aspects, the techniques described herein relate to a method, further including: generating one or more simulations for the end-user channel location based on at least a portion of the suggestion, wherein each of the one or more simulations provides an object placement configuration to be considered by a user.

[0026] In some aspects, the techniques described herein relate to a system including: a memory configured to store instructions; and one or more processors configured to execute the instructions to perform operations including: receiving, from a user device, a first selection, wherein the first selection includes an end-user channel location; receiving, from the user device, a second selection, wherein the second selection includes a menu element; receiving, from the user device, a third selection, wherein the third selection includes an object placement model generation element; generating, using an artificial intelligence (Al) model, a suggestionassociated with the end-user channel location based at least on the first selection, the second selection, or the third selection; and displaying the suggestion on the user device.

[0027] In some aspects, the techniques described herein relate to a system, wherein the menu element further includes: at least one of an add shelves element, a new shelves element, or an object identifier element.

[0028] In some aspects, the techniques described herein relate to a system, further including: a user input specifying at least one of a shelf or an object identifier to prioritize before generating the suggestion associated with the end-user channel location.

[0029] In some aspects, the techniques described herein relate to a system, wherein displaying the suggestion on the user device further includes: displaying a user interface element for a user to accept or reject the suggestion.

[0030] In some aspects, the techniques described herein relate to a system, further including: in response to the user accepting the suggestion, display a save user interface object; and in response to the user rejecting the suggestion, display the object placement model generation element.

[0031] In some aspects, the techniques described herein relate to a system, wherein the suggestion includes an image or video.

[0032] In some aspects, the techniques described herein relate to a system, further including: generating one or more simulations for the end-user channel location based on at least a portion of the suggestion, wherein each of the one or more simulations provides an object placement configuration to be considered by a user.

[0033] In some aspects, the techniques described herein relate to a non- transitory computer-readable medium storing instructions that, when executed by one or more processors, cause the one or more processors to perform operations including: receiving, from a user device, a first selection, wherein the first selection includes an end-user channel location; receiving, from the user device, a second selection, wherein the second selection includes a menu element; receiving, from the user device, a third selection, wherein the third selection includes an object placement model generation element; generating, using an artificial intelligence (Al) model, a suggestion associated with the end-user channel location based at least onthe first selection, the second selection, or the third selection; and displaying the suggestion on the user device.

[0034] In some aspects, the techniques described herein relate to a non- transitory computer-readable medium, wherein the menu element further includes: at least one of an add shelves element, a new shelves element, or an object identifier element.

[0035] In some aspects, the techniques described herein relate to a non- transitory computer-readable medium, further including: a user input specifying at least one of a shelf or an object identifier to prioritize before generating the suggestion associated with the end-user channel location.

[0036] In some aspects, the techniques described herein relate to a non- transitory computer-readable medium, wherein displaying the suggestion on the user device further includes: displaying a user interface element for a user to accept or reject the suggestion.

[0037] In some aspects, the techniques described herein relate to a non- transitory computer-readable medium, further including: in response to the user accepting the suggestion, display a save user interface object; and in response to the user rejecting the suggestion, display the object placement model generation element.

[0038] In some aspects, the techniques described herein relate to a non- transitory computer-readable medium, wherein the suggestion includes an image or video.

[0039] It may be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention, as claimed.BRIEF DESCRIPTION OF THE DRAWINGS

[0040] FIG. 1 depicts an exemplary diagram of an example system for generating analytics associated with a product using an Al model, according to one or more embodiments.

[0041] FIG. 2 depicts an exemplary diagram of example components of one or more devices of FIG. 1 , according to one or more embodiments.

[0042] FIG. 3 depicts an exemplary flowchart of an example process for generating analytics associated with a product using an Al model, according to one or more embodiments.

[0043] FIG. 4 depicts an exemplary diagram of acquiring an image of a product located in a display area of a retail location, according to one or more embodiments.

[0044] FIG. 5 depicts an exemplary diagram of generating, using an Al model, analytics associated with a product, according to one or more embodiments.

[0045] FIG. 6 depicts an exemplary process flow diagram for an intelligent retail experience or “IRX”, according to one or more embodiments.

[0046] FIGs. 7A-7B depict exemplary architecture diagrams for an intelligent retail experience, according to one or more embodiments.

[0047] FIG. 8 depicts an exemplary diagram of a process for training, deploying, and monitoring an Al model for generating analytics associated with a product, according to one or more embodiments.

[0048] FIG. 9 depicts an exemplary process for generating simulated store configurations, according to one or more embodiments.

[0049] FIG. 10 illustrates an implementation of a computer system that executes techniques presented herein.DETAILED DESCRIPTION

[0050] As addressed above, a product manufacturer might desire to ensure that a display area for a product is located in a particular area of the retail location, that a product is located in a particular area of the display area, that a particular amount of the product is maintained in the display area, that the product is being sold at a particular price, that a particular promotion is being displayed in association with the product, that particular other products are located adjacent to the product, etc. Given that the product is sold at a large number of retail locations, the product manufacturer might not be capable of monitoring compliance with the foregoing, or ascertaining the efficacy of alternative arrangements.

[0051] Intelligent Retail Experience (IRX) is an accelerator that may be used for varied use cases across process automation and content generation to create, augment, and version track capabilities utilizing Generative Al (GenAI). The accelerator may be used to improve planogram (also referred to herein as “object placement model” or “object placement diagram”) creations and create immersive retail simulation design with GenAI, utilizing real-time data insights and leveraging expert knowledge enhancing IRX for business associates and retail customers and partners.

[0052] IRX creates an in-store, visually optimized planogram that may allow users to experiment with different combinations of business priorities and SKUs, along with real-time insights to find and implement an optimized planogram. IRX is a GenAI-enabled solution and may include external market data to assist the category managers collaborate and design planograms based on different sets of priorities (e.g., market share, margin share etc.). IRX may enable users to visualize the retail store planogram in a virtual 3D space through computing devices (e.g., laptops, tablets, portable display assistants (PDA), etc.) and VR devices. IRX may enable collaboration and interaction between users. Intended potential users could be category managers and retail store managers. The locations will be virtual, but the intended users could be from major retail stores chains (e.g., Walmart, Costco, Target, etc.). IRX may allow users the ability to quickly design in-store planograms in virtual spaces and iterate over those designs before deploying a finalized design in the physical store.

[0053] Collaboration, planning, and guidance (CPG) enterprises have a limited capability of creating optimized store planograms. They are often done manually and with outdated insights with a high processing time. Companies such as these often rely on black box or out-of-the-box solutions that recommend planograms that may not be optimized for nuanced business priorities. Also, in such cases, there is a reliance on specialists to run and maintain these softwares who are often external retailers who may have competing interests. This is also a time-consuming process with no access to real-time insights as well as a lack of running several scenarios and choosing the best for a particular set of business priorities. The recommendations are typically static and do not lend themselves to a good understanding due to the black box nature.

[0054] To solve the deficiencies, as described above, a simulator engine may simulate several competing scenarios with different business priorities to help recommend the optimum scenario. This may be run and maintained by in-house associates who have a good understanding of this white-box solution. The solution leverages GenAI large language models (LLM) generalization capability to incorporate planogram design principles, user / business priority inputs, real-time insights, and planogram template dimension information to allow dynamic planogram creation efficiently and is responsive to changing business parameters. The accelerator may include both 2D and / or 3D environments (e.g., leveraging unityengine for VR immersive simulation retail experience) that may significantly improve the efficiency of various planogram creations and convert them to store layout simulation. The simulation environment may allow multi-user mode, which further improves collaborations among internal business users, and potentially with external retailer customers / partners.

[0055] Some embodiments of the present disclosure provide a platform for generating, using an Al model, analytics associated with a product. For example, the platform may receive an image of a product located in a display area of a retail location, and generate analytics associated with the product using an Al model and the image. Further, the platform may display the analytics and / or display a recommendation regarding the product.

[0056] Some embodiments of the present disclosure provide a technical improvement in the technical field of planogram compliance and inventory management by utilizing big data and Al techniques to provide the generation of real-time analytics and recommendations based on images of products. Accordingly, in this way, some embodiments of the present disclosure improve the functioning of the user devices associated with planogram compliance and inventory management by providing real-time analytics and recommendations for display.

[0057] FIG. 1 depicts an exemplary diagram of an example system 100 for generating analytics associated with a product using an Al model. As shown in FIG. 1 , the system 100 may include a user device 110, a platform 120, an Al model 130, an imaging device 140, a database 150, and a network 160.

[0058] The user device 110 may be configured to acquire an image of a product located in a display area of a retail location, and / or display analytics generated by the Al model 130. For example, the user device 110 may be a smartphone, a tablet computer, a laptop computer, a desktop computer, a wearable device, or the like.

[0059] The platform 120 may be configured to receive an image of a product (also referred to herein as an “object”) located in a display area of a retail location, and generate, using the Al model 130, analytics associated with the product based on the image of the product. For example, the platform 120 may be a server, a cloud server, a virtual machine, or the like.

[0060] The Al model 130 may be configured to generate analytics associated with a product based on an image of the product located in a display area of a retaillocation. For example, the Al model 130 may be a generative Al model, an artificial neural network (ANN), a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), a support vector machine (SVM), a Bayesian network, a graphical probabilistic model, a K-nearest neighbor classifier, a decision forests, a maximum margin method, or the like.

[0061] The imaging device 140 may be configured to acquire an image of a product located in a display area of a retail location. For example, the imaging device 140 may be a user device, a camera, an Internet of Things (loT) sensor, or the like. According to an embodiment, the imaging device 140 may be fixed in a particular location in the retail location. Alternatively, the imaging device 140 may be attached to a vehicle that is configured to move throughout the retail location. Alternatively, the imaging device 140 may be carried by a user.

[0062] The database 150 may be configured to store information associated with a product. For example, the database 150 may be a hierarchical database, a network database, a relational database, or the like. The network 160 may be configured to permit communication between the user device 110, the platform 120, the imaging device 140, and / or the database 150. For example, the network 160 may be a cellular network (e.g., a fifth generation (5G) network, a long-term evolution (LTE) network, a third generation (3G) network, a code division multiple access (CDMA) network, etc.), a public land mobile network (PLMN), a local area network (LAN), a wide area network (WAN), a metropolitan area network (MAN), a telephone network (e.g., the Public Switched Telephone Network (PSTN)), a private network, an ad hoc network, an intranet, the Internet, a fiber optic-based network, or the like, and / or a combination of these or other types of networks.

[0063] The number and arrangement of the devices of the system 100 shown in FIG. 1 are provided as an example. In practice, the system 100 may include additional devices, fewer devices, different devices, or differently arranged devices than those shown in FIG. 1 . Additionally, or alternatively, a set of devices (e.g., one or more devices) of the system 100 may perform one or more functions described as being performed by another set of devices of the system 100.

[0064] FIG. 2 depicts an exemplary diagram of example components of one or more devices of FIG. 1 . The device 200 may correspond to the user device 110, the platform 120, the imaging device 140, and / or the database 150. As shown in FIG. 2, the device 200 may include a bus 210, a processor 220, a memory 230, a storagecomponent 240, an input component 250, an output component 260, and a communication interface 270.

[0065] The bus 210 includes a component that permits communication among the components of the device 200. The processor 220 may be implemented in hardware, firmware, or a combination of hardware and software. The processor 220 may be a central processing unit (CPU), a graphics processing unit (GPU), an accelerated processing unit (APU), a microprocessor, a microcontroller, a digital signal processor (DSP), a field-programmable gate array (FPGA), an applicationspecific integrated circuit (ASIC), or another type of processing component.

[0066] The processor 220 may include one or more processors capable of being programmed to perform a function. The memory 230 may include a random access memory (RAM), a read only memory (ROM), and / or another type of dynamic or static storage device (e.g., a flash memory, a magnetic memory, and / or an optical memory) that stores information and / or instructions for use by the processor 220.

[0067] The storage component 240 may store information and / or software related to the operation and use of the device 200. For example, the storage component 240 may include a hard disk (e.g., a magnetic disk, an optical disk, a magneto-optic disk, and / or a solid state disk), a compact disc (CD), a digital versatile disc (DVD), a floppy disk, a cartridge, a magnetic tape, and / or another type of non- transitory computer-readable medium, along with a corresponding drive.

[0068] The input component 250 may include a component that permits the device 200 to receive information, such as via user input (e.g., a touch screen display, a keyboard, a keypad, a mouse, a button, a switch, and / or a microphone for receiving the reference sound input). Additionally, or alternatively, the input component 250 may include a sensor for sensing information (e.g., a global positioning system (GPS) component, an accelerometer, a gyroscope, and / or an actuator). The output component 260 may include a component that provides output information from the device 200 (e.g., a display, a speaker for outputting sound at the output sound level, and / or one or more light-emitting diodes (LEDs)).

[0069] The communication interface 270 may include a transceiver-like component (e.g., a transceiver and / or a separate receiver and transmitter) that enables the device 200 to communicate with other devices, such as via a wired connection, a wireless connection, or a combination of wired and wireless connections. The communication interface 270 may permit the device 200 to receiveinformation from another device and / or provide information to another device. For example, the communication interface 270 may include an Ethernet interface, an optical interface, a coaxial interface, an infrared interface, a radio frequency (RF) interface, a universal serial bus (USB) interface, a Wi-Fi interface, a cellular network interface, or the like.

[0070] The device 200 may perform one or more processes described herein. The device 200 may perform these processes based on the processor 220 executing software instructions stored by a non-transitory computer-readable medium, such as the memory 230 and / or the storage component 240. A computer-readable medium may be defined herein as a non-transitory memory device. A memory device may include memory space within a single physical storage device or memory space spread across multiple physical storage devices.

[0071] The software instructions may be read into the memory 230 and / or the storage component 240 from another computer-readable medium or from another device via the communication interface 270. When executed, the software instructions stored in the memory 230 and / or the storage component 240 may cause the processor 220 to perform one or more processes described herein. Additionally, or alternatively, hardwired circuitry may be used in place of or in combination with software instructions to perform one or more processes described herein. Thus, implementations described herein are not limited to any specific combination of hardware circuitry and software.

[0072] The number and arrangement of the components shown in FIG. 2 are provided as an example. In practice, the device 200 may include additional components, fewer components, different components, or differently arranged components than those shown in FIG. 2. Additionally, or alternatively, a set of components (e.g., one or more components) of the device 200 may perform one or more functions described as being performed by another set of components of the device 200.

[0073] FIG. 3 depicts an exemplary flowchart of an example process 300 for generating analytics associated with a product using an Al model. According to an embodiment, the platform 120 may be configured to perform the operations of the process 300. Additionally, or alternatively, the user device 110, the imaging device 140, and / or the database 150 may be configured to perform one or more operations of the process 300.

[0074] As shown in FIG. 3, the process 300 may include receiving an image of a product located in a display area of a retail location (operation 310). The retail location may be any location where the product is displayed, sold, advertised, provided, or the like. For example, the retail location may be a store, a venue, a restaurant, a hotel, an airport, a transportation station, a mall, or the like. According to an embodiment, the retail location may be associated with products provided by different manufacturers. Alternatively, the retail location may be associated with products provided by a single manufacturer.

[0075] The display area may be any area in which the product is displayed, sold, advertised, provided, or the like. For example, the display area may be an aisle, a shelf, a refrigerator, a kiosk, a counter, a display, or the like. The product may be any product that can be displayed, sold, advertised, provided, or the like. For example, the product may be a confectionary, such as a cake, a cookie, a pie, a candy, a mint, a chewing gum, a gelatin, ice cream, sorbet, jam, jelly, chocolate, fudge, fondant, liquorice, taffy, or the like. Alternatively, the product may be food, such as bread, cereal, pizza, meat, chicken, or the like. Alternatively, the product may be a beverage, such as water, juice, alcohol, coffee, or the like. Alternatively, the product may be a non-food item, such as a book, a video game, an electronic device, or the like. It should be understood that the product may be any product that can be displayed.

[0076] The image may include an image of the product. For example, the image may depict one or more sides of the product. The image may include a name of the product. For example, the image may include a label, a title, a designator, or the like, of the product. The image may include a design of the product. For example, the image may include a cover of the product, an illustration on the product, or the like. The image may include a price associated with the product. The image may include a promotion associated with the product. The image may include a product identifier (e.g., a stock keeping unit (SKU), a universal product code (UPC), a price look up (PLU) code, or the like) of the product. The image may include a machine- readable code associated with the product. The image may include a set of products adjacent to the product. For example, the set of products may all be a same underlying type of product. Alternatively, the set of products may be different types of products. The image may include a display condition of the display area. Forexample, the display condition may identify if the display area is clean, dirty, worn- out, broken, damaged, adequately lit, or the like.

[0077] According to an embodiment, the platform 120 may receive an image of a product located in a display area of a retail location from the user device 110. For example, the user device 110 may acquire an image of the product based on a user input, and the user device 110 may transmit the image to the platform 120 based on acquiring the image. Alternatively, the platform 120 may receive an image of a product located in a display area of a retail location from the imaging device 140. For example, the imaging device 140 may acquire an image of the product, and the imaging device 140 may transmit the image to the platform 120. The imaging device 140 may acquire the image of the product based on a predetermined time frame (e.g., every hour, every day, every week, or the like), based on a request from another device, based on an occurrence of a particular event, based on a number of sales of the product as determined based on information received from a point-of- sale (POS) device, or the like. Although FIG. 3 depicts particular operations and a particular order of operations, it should be understood that other embodiments may include different operations, a different order of operations, or the like.

[0078] FIG. 4 depicts an exemplary diagram 400 of acquiring an image of a product located in a display area of a retail location. As shown in FIG. 4, a display area 410 may include various products on display, such as the product 420. Further, the display area 410 may include a label 430 that identifies a product identifier of the product 420 and a price of the product 420. A user may use the user device 110 to capture an image 440 of the display area 410.

[0079] As further shown in FIG. 3, the process 300 may include generating, using an artificial intelligence (Al) model, analytics associated with the product based on the image of the product located in the display area of the retail location (operation 320), displaying the analytics (operation 330), and displaying a recommendation regarding the product (operation 340).

[0080] The analytics may be any type of analytics associated with the product. For example, the analytics may include planogram compliance that identifies a compliance of the product with a planogram. Additionally, or alternatively, the analytics may include pricing compliance that identifies a compliance of a price of the product with pricing information of the product. Additionally, or alternatively, the analytics may include promotion compliance that identifies a compliance of theproduct with promotion information of the product. Additionally, or alternatively, the analytics may include inventory analytics related to inventory of the product. Additionally, or alternatively, the analytics may include planogram variance that identifies variances in planograms associated with the product. Additionally, or alternatively, the analytics may include supplier relationship management (SRM) insights that identify insights for entities (e.g., manufacturers, vendors, suppliers, or the like) associated with the product. Additionally, or alternatively, the analytics may include planogram performance that identifies performance metrics associated with different planograms associated with the product. Additionally, or alternatively, the analytics may include analytics associated with other products associated with other entities. Additionally, or alternatively, the analytics may include on-shelf availability (OSA) insights that identify insights for providing the availability of the product.

[0081] According to an embodiment, the platform 120 may input the image of the product into the Al model 130. The Al model 130 may be configured to perform one or more image processing techniques in association with the image. For example, the Al model 130 may perform object detection, segmentation, classification, captioning, entity resolution, registration, edge detection, pattern recognition, optical character recognition, gauging, or the like. Additionally, or alternatively, the platform 120 may input external data into the Al model 130. For example, the platform 120 may receive external data from the database 150, and input the external data into the Al model 130. The external data may include data associated with the product (e.g., images of the product, a description of the product, a product identifier product, or the like), a planogram associated with the product, sales data associated with the product, pricing data of the product, promotion data of the product, a category of the product, third-party equivalents of the product, competitor data of the product, or the like.

[0082] The platform 120 may generate the analytics based on an output of the Al model 130. According to an embodiment, the platform 120 may generate the analytics in substantially real-time. As used herein, “substantially real-time” may refer to a threshold amount of time that is measured from a time point such as the acquiring of the image, the inputting of the image and / or the external data into the Al model 130, an instruction to generate the analytics, or the like. As examples, “substantially real-time” may be one second, thirty seconds, one minute, or the like.

[0083] FIG. 5 depicts an exemplary diagram 500 of generating, using an Al model, analytics associated with a product. As shown in FIG. 5, the Al model 130 may receive an image 510 of a product and external data 520 associated with the product. Further, the Al model 130 may generate analytics 530 based on the image 510 of the product and the external data 520 associated with the product. For example, the analytics 530 may include planogram compliance 530-1 , pricing compliance 530-2, promotion compliance 530-3, inventory 530-4, planogram variance 530-5, SRM insights 530-6, planogram performance 530-7, other product(s) analytics 530-8. . . and OSA insights 530-n.

[0084] According to an embodiment, the analytics may be associated with planogram compliance that identifies a compliance of the product with a planogram. The platform 120 may receive the image of the product, and information identifying a planogram associated with the product. The platform 120 may compare the image of the product with the information identifying the planogram and determine a compliance of the product with the planogram. The platform 120 may display analytics that indicate a level of compliance with a planogram, such as “100%,” “90%, ” “complaint,” “non-compliant,” etc. Additionally, or alternatively, the platform 120 may display analytics indicating particular non-compliance, such as an incorrect location of the product, an incorrect quantity of the product, an incorrect arrangement of the product, an incorrect arrange of an adjacent product, an incorrect display area of the product, or the like. The platform 120 may display a recommendation for rectifying non-compliance of the product relative to the planogram.

[0085] According to an embodiment, the analytics may be associated with pricing compliance that identifies a compliance of a price of the product with pricing information of the product. The platform 120 may receive the image of the product, and information identifying pricing associated with the product. The platform 120 may compare the image of the product with the information identifying the pricing and determine a compliance of the product with the pricing. The platform 120 may display analytics indicating particular non-compliance with the pricing information, such as an incorrect price of the product. The platform 120 may display a recommendation for rectifying non-compliance of the product relative to the pricing information.

[0086] According to an embodiment, the analytics may be associated with promotion compliance that identifies a compliance of the product with promotioninformation of the product. The platform 120 may receive the image of the product, and information identifying a promotion associated with the product. The platform 120 may compare the image of the product with the information identifying the promotion and determine a compliance of the product with the promotion. The platform 120 may display analytics indicating particular non-compliance, such as an incorrect price of the product, an incorrect promotion, an absence of a promotion, incorrect promotion conditions, incorrect promotion benefits, or the like. The platform 120 may display a recommendation for rectifying non-compliance of the product relative to the promotion.

[0087] FIG. 6 depicts an exemplary process flow diagram of IRX, according to one or more embodiments. The process 600 may start with the user accessing the desired store through user device 110 (e.g., the user navigates into the store 602). Selecting a desired store (also referred to herein as a “retail store” or an “end-user channel location”) may be a first selection the user makes. The store may be displayed as a 2D or 3D representation depending on user device 110 (e.g., laptop may present 2D representation, VR headset may display store as 3D representation). Once the store is displayed via the user device 110, the user may then add shelves to the store space. The shelves may be added using a series of drop menus within a user interface displayed on user device 110 (e.g., the user uses shelf menu to add shelves 604). The menu may include items such as an add shelves element, a new shelves element, or a SKU item element. Selecting a menu item may be a second selection the user makes. The user may make selections using an input device associated with user device 110 (e.g., mouse, touchscreen, pen, etc.) (e.g., the user selects one of the shelves with mouse 606). The user may then select one of the shelves, which may provide additional prompts to the user (e.g., select SKU item). The user may then select a SKU item from a series of menus (e.g., the user selects SKU items from a SKU menu 608). This process 600 may be repeated for additional shelves and / or SKU items. Once the shelves are completed, the user may select a user interface object to generate a planogram (e.g., “Generate Planogram”) 610. Selecting a generate planogram element 610 may be a third selection the user makes.

[0088] The user device 110 may then send, for example through Windows App, a request to the IRX GenAI API for position advice 612 (e.g., generate planogram). The user may optionally inform the IRX GenAI of the shelf and SKUitem(s) to prioritize using a chat user interface 614 displayed in user device 110. Thus, a user input may be used to specify if a shelf or a SKU item should be prioritized prior to generating the suggestion. The IRX GenAI may then respond with position information based on the information received by the Windows App and / or the user through the chat user interface (e.g., user device 110). That is, IRX GenAI API responds with positioning information (text-output) based on user’s shelf & SKUs optimization priority inputs 616. The response may be in the form of text, graphic, or a combination thereof. The Windows App may then use the IRX GenAI response to generate a 2D / 3D model of the SKU item(s) in the selected location on the one or more shelves 618. The user may then evaluate the positioning information and determine if the suggestions are acceptable 620. If the suggestions are acceptable, the information may be saved by selecting a user interface object 622 (e.g., “Save Planogram”). If the suggestions are not acceptable, the user may then modify the suggestion by selecting a user interface object 624 (e.g., “Generate Planogram”). The suggestions associated with the retail store selected may be based on the first selection (e.g., the retail store), the second selection (e.g., the menu element), and / or the third selection (e.g., the generate planogram element). At this point, the process may repeat some or all of the steps until a response provided by the process is acceptable by the user.

[0089] FIGs. 7A-7B depict exemplary architecture diagrams, according to one or more embodiments. The architectures as described in FIGs. 7A-7B may be used in conjunction with the process flow 600 as described in FIG. 6. FIG. 7A depicts an exemplary architecture diagram 700 that may be used with user device 110 (e.g., computer, laptop, tablet, PDA, etc.). The architecture 700 may include an online interface of an organizational network (e.g., Windows Laptop Mars Network) 702 and IRX GenAI tools 704. The Windows laptop Mars Network 702 may include one or more applications (e.g., Windows applications) 706. The Windows applications 706 may include a 3D retail store 708, 3D shelves 710, 3D SKU Items 712, shelf placement 714, SKU placement 716, and GenAI user interface 718. The 3D retail store 708 may include information relating to one or more retail store configurations and layouts (e.g., location and placement of aisle, departments, products, etc.). The 3D retail store 708 information may be stored in a database and may be updated and / or accessed based on user requests. The 3D shelves 710 may include information related to the type (e.g., color, material, dimensions, etc.) that arelocated in the particular retail store selected. The 3D SKU items 712 may be associated with one or more products, each product having a specific SKU number associated therewith. Each SKU item 712 may be related to a specific business entity and may include additional business information (e.g., products sold, regions sold, quantity in stock, market share, etc.). Shelf placement 714 may include additional information (e.g., number of shelves, location of each shelf, etc.) related to the 3D shelves 710 and the 3D retail store 708. The SKU placement 716 may include information relating to the location of the SKU number and corresponding information (e.g., location on shelf, information provided, etc.). The GenAI user interface 718 may include one or more user interface objects. The one or more user interface objects may allow the user to select, generate, and / or manipulate the 3D retail store 708, 3D shelves 710, 3D SKU items 712, and more. The GenAI user interface 718 may include one or more prompts (e.g., text, audio, video, etc.) for use by the user during user navigation of a 3D retail store 708. The IRX GenAI tool 704 may include one or more GenAI APIs 720 to assist in creating, generating, and gathering information from advanced artificial intelligence based on the information received from the system and / or user.

[0090] For example, in connection with FIG. 6 described above, a user may access a 3D retail store 708 from a computing device (e.g., laptop). The system may provide a user interface displaying the selected or requested retail store. The user may then select a location within the store to place a set of shelves or rearrange an existing shelf. The user may then make one or more selections and modifications of the shelves. In one scenario, the user may identify an existing shelf and request a suggested arrangement of products based on new business information relating to that particular product. In another scenario, the user may generate and place a set of shelves and request a suggestion of products and / or placement of those products on the new shelves.

[0091] The system may utilize the 3D shelves 710, SKU items 712 and placements when determining a suggestion for the user. The IRX GenAI API 720 may then take the information from the user to determine one or more suggestions to the user about the retail store, the product, the location and number of products to be placed on each shelf within the retail store based on the business information received from the user or associated with the retail store (e.g., associated information attached to retail store at initial selection). Up determination of one ormore suggestions, the GenAI tool 704 may send the information back to the user for review (e.g., text, audio, video, graphics, etc.). The user may then accept or modify the suggestions. If accepting, the user may save the suggestions for future use. If the user rejects the suggestion, the system may then provide additional prompts to the user for modifying the suggestions. This process may be repeated as needed.

[0092] FIG. 7B depicts an exemplary architecture diagram that may be used with user device 110 as described in FIG. 7A. Portions of the architecture 750 as described in FIG. 7A, may not be described here for brevity. The architecture 750 may include the Windows Laptop Mars Network and corresponding Windows Applications as described above. The architecture may include a VR headset 752 (in the Mars Network) used in conjunction with user device 110. The VR headset 752 may be accessed via a laptop with installable software 760. The VR headset 752 may display the user interface as described above but in 3D, fully immersing the user in the selected retail store. The architecture 750 may include a python server 754 to assist in creating, generating, and gathering information between the organizational network 756 and the IRX GenAI tools 758. The architecture 750 may include an IRX GenAI tool 758 as similar described above.

[0093] The VR headset 752 may interact with an IRX API proxy 762 within python server 754. Specifically, the VR headset 752 may send a planogram request to the API proxy 762 and the API proxy 762 may send a planogram response to the VR headset 752. The Mars Network 756 may send a planogram request to the GenAI tools 758 that includes GenAI API 764. The GenAI tools 758 may send a planogram response to the Mars Network 756.

[0094] For example, a user with a VR headset 752 may request a 3D retail store 708 from the user interface. The 3D retail store 708 may be retrieved and generated for the user to be fully immersed within the retail store. The user may then, through the user interface, make one or more selections regarding the shelves. The user may request to rearrange an existing shelf or add a new shelf to the retail store. If the user requests the rearrange or modify an existing shelf, the user interface will display the shelves and products the user for manipulation. The user can make one or more selections of products, locations, SKU items, etc. to be included in the analysis. The system may then analyze the products and shelves selected and display a suggestion to the user. The suggestion may include an image, a video, a text, and / or a sound and / or the suggestion may be representedgraphically (e.g., an image or video), textually, and / or audially. The user may then accept or reject the suggestions as described above.

[0095] FIG. 8 is a diagram of a process 800 fortraining, deploying, and monitoring an Al model for generating analytics associated with a product.

[0096] The platform 120 may generate, store, train, and / or use the Al model 130. According to an embodiment, the platform 120 may include the Al model 130 and / or instructions associated with the Al model 130. For example, the platform 120 may include instructions for generating the Al model 130, training the Al model 130, using the Al model 130, etc. According to another embodiment, a system or device other than the platform 120 may be used to generate and / or train the Al model 130. For example, a system or device may include instructions for generating the Al model 130, and / or instructions for training the Al model 130. The system or device may provide a resulting trained Al model 130 to the platform 120 or the user device 110 for use.

[0097] As shown in FIG. 8, according to an embodiment, the process 800 may include a training phase 802, a deployment phase 808, and a monitoring phase 814. In the training phase 802, at operation 806, the process 800 may include receiving and processing training data 804 to generate a trained Al model 130 for generating analytics associated with a product. The training data 804 may include images of the product, images of the other products, images of planograms, images of labels, images of pricing information, images of promotion information, external data, or the like.

[0098] Generally, the Al model 130 may include a set of variables (e.g., nodes, neurons, filters, or the like) that are tuned (e.g., weighted, biased, or the like) to different values via the application of the training data 804. According to an embodiment, the training process at operation 806 may employ supervised, unsupervised, semi-supervised, and / or reinforcement learning processes to train the Al model 130. According to an embodiment, a portion of the training data 804 may be withheld during training and / or used to validate the trained Al model 130.

[0099] For supervised learning processes, the training data 804 may include labels or scores that may facilitate the training process by providing a ground truth. Training may proceed by feeding a training dataset from the training data 804 into the Al model 130. The Al model 130 may have variables set at initialized values (e.g., at random, based on Gaussian noise, based on pre-trained values, or the like).The Al model 130 may output analytics associated with a product. The output may be compared with the corresponding label or score (e.g., the ground truth) indicating the data quality, which may then be back-propagated through the Al model 130 to adjust the values of the variables. This process may be repeated for a plurality of samples at least until a determined loss or error is below a predefined threshold. According to an embodiment, some of the training data 804 may be withheld and used to further validate or test the trained Al model 130.

[0100] For unsupervised learning processes, the training data 804 may not include pre-assigned labels or scores to aid the learning process. Instead, unsupervised learning processes may include clustering, classification, or the like, to identify naturally occurring patterns in the training data 804. As an example, the training data 804 may be clustered into groups based on identified similarities and / or patterns. K-means clustering or K-Nearest Neighbors may also be used, which may be supervised or unsupervised. Combinations of K-Nearest Neighbors and an unsupervised cluster technique may also be used. For semi-supervised learning, a combination of training data 804 with pre-assigned labels or scores and training data 804 without pre-assigned labels or scores may be used to train the Al model 130.

[0101] When reinforcement learning is employed, an agent (e.g., an algorithm) may be trained to make a decision regarding the data quality from the training data 804 through trial and error. For example, based on making a decision, the agent may then receive feedback (e.g., a positive reward if the prediction was above a predetermined threshold), adjust its next decision to maximize the reward, and repeat until a loss function is optimized.

[0102] After being trained, the trained Al model 130 may be stored and subsequently applied by the platform 120 during the deployment phase 808. For example, during the deployment phase 808, the trained Al model 130 executed by the platform 120 may receive input data 810. The input data 810 may include an image of a product and / or external data. The Al model 130 may provide as output data 812 analytics associated with a product and / or a recommendation associated with the product.

[0103] The monitoring data 816 may include data that identifies analytics associated with a product and / or recommendations that are displayed. During process 818, the monitoring data 816 may be analyzed along with the predicted output data 812 and input data 810 to determine an accuracy of the trained Al model130. According to an embodiment, based on the analysis, the process 800 may return to the training phase 802, where at operation 806 values of one or more variables of the model may be adjusted to improve the accuracy of the Al model 130.

[0104] The example process 800 described above is provided merely as an example, and may include additional, fewer, different, or differently arranged aspects than depicted in FIG. 8.

[0105] FIG. 8 describes the training, deployment, and monitoring associated with a trained Al model 130 for generating analytics associated with a product. According to an embodiment, one or more other trained Al models may be applied, such as a trained Al model 130, for performing image processing, performing recommendation generation, or the like. Each of the trained Al models may include similar training, deployment, and / or monitoring phases as described above for the trained Al model 130 in FIG. 8, however the particular types of training data, input data, output data, and monitoring data may be different.

[0106] FIG. 9 depicts an exemplary process for generating simulated store configurations, according to one or more embodiments. Process 900 may provide a store design configuration 902 with new products for a target audience 904. The store design configuration 902 may be generated via planogram compliance and placement. Process 900 illustrates a user 906 determining the store design configuration 902 based on analytics 908. The user 906 may be provided one or more simulations 910 of the best configurations for the store based on the data and analytics.

[0107] FIG. 10 illustrates an implementation of a computer system that executes techniques presented herein. The computer system 1000 includes a set of instructions that are executed to cause the computer system 1000 to perform any one or more of the methods or computer based functions disclosed herein. The computer system 1000 operates as a standalone device or is connected, e.g., using a network, to other computer systems or peripheral devices.

[0108] Unless specifically stated otherwise, as apparent from the following discussions, it is appreciated that throughout the specification, discussions utilizing terms such as "processing," "computing," "calculating," “determining”, analyzing” or the like, refer to the action and / or processes of a computer or computing system, or similar electronic computing device, that manipulate and / or transform datarepresented as physical, such as electronic, quantities into other data similarly represented as physical quantities.

[0109] In a similar manner, the term "processor" refers to any device or portion of a device that processes electronic data, e.g., from registers and / or memory to transform that electronic data into other electronic data that, e.g., is stored in registers and / or memory. A “computer,” a “computing machine,” a "computing platform," a “computing device,” or a “server” includes one or more processors.

[0110] In a networked deployment, the computer system 1000 operates in the capacity of a server or as a client user computer in a server-client user network environment, or as a peer computer system in a peer-to-peer (or distributed) network environment. The computer system 1000 is also implemented as or incorporated into various devices, such as a personal computer (PC), a tablet PC, a set-top box (STB), a personal digital assistant (PDA), a mobile device, a palmtop computer, a laptop computer, a desktop computer, a communications device, a wireless telephone, a land-line telephone, a control system, a camera, a scanner, a facsimile machine, a printer, a pager, a personal trusted device, a web appliance, a network router, switch or bridge, or any other machine capable of executing a set of instructions (sequential or otherwise) that specify actions to be taken by that machine. In a particular implementation, the computer system 1000 is implemented using electronic devices that provide voice, video, or data communication. Further, while the computer system 1000 is illustrated as a single system, the term “system” shall also be taken to include any collection of systems or sub-systems that individually or jointly execute a set, or multiple sets, of instructions to perform one or more computer functions.

[0111] As illustrated in FIG. 10, the computer system 1000 includes a processor 1002, e.g., a central processing unit (CPU), a graphics processing unit (GPU), or both. The processor 1002 is a component in a variety of systems. For example, the processor 1002 is part of a standard personal computer or a workstation. The processor 1002 is one or more processors, digital signal processors, application specific integrated circuits, field programmable gate arrays, servers, networks, digital circuits, analog circuits, combinations thereof, or other now known or later developed devices for analyzing and processing data. The processor1002 implements a software program, such as code generated manually (i.e., programmed).

[0112] The computer system 1000 includes a memory 1004 that communicates via bus 1008. Memory 1004 is a main memory, a static memory, or a dynamic memory. Memory 1004 includes, but is not limited to, computer-readable storage media such as various types of volatile and non-volatile storage media, including but not limited to random access memory, read-only memory, programmable read-only memory, electrically programmable read-only memory, electrically erasable read-only memory, flash memory, magnetic tape or disk, optical media and the like. In one implementation, the memory 1004 includes a cache or random-access memory for the processor 1002. In alternative implementations, the memory 1004 is separate from the processor 1002, such as a cache memory of a processor, the system memory, or other memory. Memory 1004 is an external storage device or database for storing data. Examples include a hard drive, compact disc (“CD”), digital video disc (“DVD”), memory card, memory stick, floppy disc, universal serial bus (“USB”) memory device, or any other device operative to store data. The memory 1004 is operable to store instructions executable by the processor 1002. The functions, acts, or tasks illustrated in the figures or described herein are performed by processor 1002 executing the instructions stored in memory 1004. The functions, acts, or tasks are independent of the particular type of instruction set, storage media, processor, or processing strategy and are performed by software, hardware, integrated circuits, firmware, micro-code, and the like, operating alone or in combination. Likewise, processing strategies include multiprocessing, multitasking, parallel processing, and the like.

[0113] As shown, the computer system 1000 further includes a display 1010, such as a liquid crystal display (LCD), an organic light emitting diode (OLED), a flat panel display, a solid-state display, a cathode ray tube (CRT), a projector, a printer or other now known or later developed display device for outputting determined information. The display 1010 acts as an interface for the user to see the functioning of the processor 1002, or specifically as an interface with the software stored in the memory 1004 or in the drive unit 1006.

[0114] Additionally or alternatively, the computer system 1000 includes an input / output device 1012 configured to allow a user to interact with any of the components of the computer system 1000. The input / output device 1012 is a numberpad, a keyboard, a cursor control device, such as a mouse, a joystick, touch screen display, remote control, or any other device operative to interact with the computer system 1000.

[0115] The computer system 1000 also includes the drive unit 1006 implemented as a disk or optical drive. The drive unit 1006 includes a computer- readable medium 1022 in which one or more sets of instructions 1024, e.g. software, is embedded. Further, the sets of instructions 1024 embodies one or more of the methods or logic as described herein. Instructions 1024 resides completely or partially within memory 1004 and / or within processor 1002 during execution by the computer system 1000. The memory 1004 and the processor 1002 also include computer-readable media as discussed above.

[0116] In some systems, computer-readable medium 1022 includes the set of instructions 1024 or receives and executes the set of instructions 1024 responsive to a propagated signal so that a device connected to network 1030 communicates voice, video, audio, images, or any other data over network 1030. Further, the sets of instructions 1024 are transmitted or received over the network 1030 via the communication port or interface 1020, and / or using the bus 1008. The communication port or interface 1020 is a part of the processor 1002 or is a separate component. The communication port or interface 1020 is created in software or is a physical connection in hardware. The communication port or interface 1020 is configured to connect with the network 1030, external media, display 1010, or any other components in the computer system 1000, or combinations thereof. The connection with network 1030 is a physical connection, such as a wired Ethernet connection, or is established wirelessly as discussed below. Likewise, the additional connections with other components of the computer system 1000 are physical connections or are established wirelessly. Network 1030 alternatively be directly connected to the bus 1008.

[0117] While the computer-readable medium 1022 is shown to be a single medium, the term "computer-readable medium" includes a single medium or multiple media, such as a centralized or distributed database, and / or associated caches and servers that store one or more sets of instructions. The term "computer-readable medium" also includes any medium that is capable of storing, encoding, or carrying a set of instructions for execution by a processor or that causes a computer system toperform any one or more of the methods or operations disclosed herein. The computer-readable medium 1022 is non-transitory, and may be tangible.

[0118] The computer-readable medium 1022 includes a solid-state memory such as a memory card or other package that houses one or more non-volatile readonly memories. The computer-readable medium 1022 is a random-access memory or other volatile re-writable memory. Additionally or alternatively, the computer- readable medium 1022 includes a magneto-optical or optical medium, such as a disk or tapes or other storage device to capture carrier wave signals such as a signal communicated over a transmission medium. A digital file attachment to an e-mail or other self-contained information archive or set of archives is considered a distribution medium that is a tangible storage medium. Accordingly, the disclosure is considered to include any one or more of a computer-readable medium or a distribution medium and other equivalents and successor media, in which data or instructions are stored.

[0119] In an alternative implementation, dedicated hardware implementations, such as application specific integrated circuits, programmable logic arrays, and other hardware devices, is constructed to implement one or more of the methods described herein. Applications that include the apparatus and systems of various implementations broadly include a variety of electronic and computer systems. One or more implementations described herein implement functions using two or more specific interconnected hardware modules or devices with related control and data signals that are communicated between and through the modules, or as portions of an application-specific integrated circuit. Accordingly, the present system encompasses software, firmware, and hardware implementations.

[0120] Computer system 1000 is connected to network 1030. Network 1030 defines one or more networks including wired or wireless networks. The wireless network is a cellular telephone network, an 802.10, 802.16, 802.20, or WiMAX network. Further, such networks include a public network, such as the Internet, a private network, such as an intranet, or combinations thereof, and utilizes a variety of networking protocols now available or later developed including, but not limited to TCP / IP based networking protocols. Network 1030 includes wide area networks (WAN), such as the Internet, local area networks (LAN), campus area networks, metropolitan area networks, a direct connection such as through a Universal Serial Bus (USB) port, or any other networks that allows for data communication. Network 1030 is configured to couple one computing device to another computing device toenable communication of data between the devices. Network 1030 is generally enabled to employ any form of machine-readable media for communicating information from one device to another. Network 1030 includes communication methods by which information travels between computing devices. Network 1030 is divided into sub-networks. The sub-networks allow access to all of the other components connected thereto or the sub-networks restrict access between the components. Network 1030 is regarded as a public or private network connection and includes, for example, a virtual private network or an encryption or other security mechanism employed over the public Internet, or the like.

[0121] In accordance with various implementations of the present disclosure, the methods described herein are implemented by software programs executable by a computer system. Further, in an example, non-limited implementation, implementations can include distributed processing, component / object distributed processing, and parallel processing. Alternatively, virtual computer system processing can be constructed to implement one or more of the methods or functionality as described herein.

[0122] Although the present specification describes components and functions that are implemented in particular implementations with reference to particular standards and protocols, the disclosure is not limited to such standards and protocols. For example, standards for Internet and other packet switched network transmission (e.g., TCP / IP, LIDP / IP, HTML, HTTP) represent examples of the state of the art. Such standards are periodically superseded by faster or more efficient equivalents having essentially the same functions. Accordingly, replacement standards and protocols having the same or similar functions as those disclosed herein are considered equivalents thereof.

[0123] It will be understood that the steps of methods discussed are performed in one embodiment by an appropriate processor (or processors) of a processing (i.e., computer) system executing instructions (computer-readable code) stored in storage. It will also be understood that the disclosure is not limited to any particular implementation or programming technique and that the disclosure is implemented using any appropriate techniques for implementing the functionality described herein. The disclosure is not limited to any particular programming language or operating system.

[0124] It should be appreciated that in the above description of example embodiments of the present disclosure, various features are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure and aiding in the understanding of one or more of the various inventive aspects. This method of the present disclosure, however, is not to be interpreted as reflecting an intention that the claimed embodiments require more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the Detailed Description are hereby expressly incorporated into this Detailed Description, with each claim standing on its own as a separate embodiment of the present disclosure.

[0125] Furthermore, while some embodiments described herein include some but not other features included in other embodiments, combinations of features of different embodiments are meant to be within the scope of the present disclosure, and form different embodiments, as would be understood by those skilled in the art. For example, in the following claims, any of the claimed embodiments can be used in any combination.

[0126] Furthermore, some of the embodiments are described herein as a method or combination of elements of a method that can be implemented by a processor of a computer system or by other means of carrying out the function. Thus, a processor with the necessary instructions for carrying out such a method or element of a method forms a means for carrying out the method or element of a method. Furthermore, an element described herein of an apparatus embodiment is an example of a means for carrying out the function performed by the element for the purpose of carrying out the present disclosure.

[0127] In the description provided herein, numerous specific details are set forth. However, it is understood that embodiments of the present disclosure are practiced without these specific details. In other instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.

[0128] Thus, while there has been described what are believed to be the preferred embodiments of the present disclosure, those skilled in the art will recognize that other and further modifications are made thereto without departing from the spirit of the present disclosure, and it is intended to claim all such changesand modifications as falling within the scope of the present disclosure. For example, any formulas given above are merely representative of procedures that may be used. Functionality may be added or deleted from the block diagrams and operations may be interchanged among functional blocks. Steps may be added or deleted to methods described within the scope of the present disclosure.

[0129] The above disclosed subject matter is to be considered illustrative, and not restrictive, and the appended claims are intended to cover all such modifications, enhancements, and other implementations, which fall within the true spirit and scope of the present disclosure. Thus, to the maximum extent allowed by law, the scope of the present disclosure is to be determined by the broadest permissible interpretation of the following claims and their equivalents, and shall not be restricted or limited by the foregoing detailed description. While various implementations of the disclosure have been described, it will be apparent to those of ordinary skill in the art that many more implementations and implementations are possible within the scope of the disclosure. Accordingly, the disclosure is not to be restricted except in light of the attached claims and their equivalents.

Claims

CLAIMSWhat is claimed is:1 . A method comprising: receiving, from a user device, a first selection, wherein the first selection includes an end-user channel location: receiving, from the user device, a second selection, wherein the second selection includes a menu element; receiving, from the user device, a third selection, wherein the third selection includes an object placement model generation element; generating, using an artificial intelligence (Al) model, a suggestion associated with the end-user channel location based at least on the first selection, the second selection, or the third selection; and displaying the suggestion on the user device.

2. The method of claim 1 , wherein the menu element includes: at least one of an add shelves element, a new shelves element, or an object identifier element.

3. The method of claim 1 , further comprising: a user input specifying at least one of a shelf or an object identifier to prioritize before generating the suggestion associated with the end-user channel location.

4. The method of claim 1 , wherein displaying the suggestion on the user device further includes: displaying a user interface element for a user to accept or reject the suggestion.

5. The method of claim 4, further comprising: in response to the user accepting the suggestion, display a save user interface object; and in response to the user rejecting the suggestion, display the object placement model generation element.

6. The method of claim 1 , wherein the suggestion includes an image or video.

7. The method of claim 1 , further comprising: generating one or more simulations for the end-user channel location based on at least a portion of the suggestion, wherein each of the one or more simulations provides an object placement configuration to be considered by a user.

8. A system comprising: a memory configured to store instructions; and one or more processors configured to execute the instructions to perform operations comprising: receiving, from a user device, a first selection, wherein the first selection includes an end-user channel location; receiving, from the user device, a second selection, wherein the second selection includes a menu element; receiving, from the user device, a third selection, wherein the third selection includes an object placement model generation element; generating, using an artificial intelligence (Al) model, a suggestion associated with the end-user channel location based at least on the first selection, the second selection, or the third selection; and displaying the suggestion on the user device.

9. The system of claim 8, wherein the menu element further includes: at least one of an add shelves element, a new shelves element, or an object identifier element.

10. The system of claim 8, further including: a user input specifying at least one of a shelf or an object identifier to prioritize before generating the suggestion associated with the end-user channel location.11 . The system of claim 8, wherein displaying the suggestion on the user device further includes:displaying a user interface element for a user to accept or reject the suggestion.

12. The system of claim 11 , further including: in response to the user accepting the suggestion, display a save user interface object; and in response to the user rejecting the suggestion, display the object placement model generation element.

13. The system of claim 8, wherein the suggestion includes an image or video.

14. The system of claim 8, further comprising: generating one or more simulations for the end-user channel location based on at least a portion of the suggestion, wherein each of the one or more simulations provides an object placement configuration to be considered by a user.

15. A non-transitory computer-readable medium storing instructions that, when executed by one or more processors, cause the one or more processors to perform operations comprising: receiving, from a user device, a first selection, wherein the first selection includes an end-user channel location; receiving, from the user device, a second selection, wherein the second selection includes a menu element; receiving, from the user device, a third selection, wherein the third selection includes an object placement model generation element; generating, using an artificial intelligence (Al) model, a suggestion associated with the end-user channel location based at least on the first selection, the second selection, or the third selection; and displaying the suggestion on the user device.

16. The non-transitory computer-readable medium of claim 15, wherein the menu element further includes:at least one of an add shelves element, a new shelves element, or an object identifier element.

17. The non-transitory computer-readable medium of claim 15, further including: a user input specifying at least one of a shelf or an object identifier to prioritize before generating the suggestion associated with the end-user channel location.

18. The non-transitory computer-readable medium of claim 15, wherein displaying the suggestion on the user device further includes: displaying a user interface element for a user to accept or reject the suggestion.

19. The non-transitory computer-readable medium of claim 18, further including: in response to the user accepting the suggestion, display a save user interface object; and in response to the user rejecting the suggestion, display the object placement model generation element.

20. The non-transitory computer-readable medium of claim 15, wherein the suggestion includes an image or video.

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