Dynamic media routing via individually sparsified grid performance ratings

Abstract

Apparatus and related methods relate to selective computation of attributes for real-time space spot prioritization. In an illustrative embodiment, a system may retrieve space data structures representing physical spaces. The space data structures may, for example, include hierarchical grid structures. Real-time environmental data may be received from a distributed sensor network. Advertisement placement criteria may, for example, be generated from a media placement package. Hard filters may be applied to exclude grid units that do not meet criteria. A Grid Performance Rating may, for example, be computed for remaining grid units. Grid units with high ratings may be aggregated to form space spots. A Spot Performance Rating may, for example, be computed for each space spot. Control signals may be generated to distribute media to selected space spots. Various embodiments may advantageously enable fast, low-cost real-time distribution of advertisements to physical locations.

Description

Dynamic Media Routing via Individually Sparsified Grid Performance RatingsCROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the benefit of US Provisional Application No. 63 / 906,133, titled " Dynamic Media Routing via Individually Sparsified Grid Performance Ratings," filed by Khai Gan Chuah on Oct. 27, 2025. This application also claims the benefit of US Provisional Application No. 63 / 714,877, titled "020 Content Management and Collaborative Promotion System I," filed by Khai Gan Chuah on Nov. 1 , 2024. This application also claims the benefit of US Provisional Application No. 63 / 768,195, titled "020 Content Management and Collaborative Promotion System II," filed by Khai Gan Chuah on Mar. 7, 2025. The entire contents of each of the foregoing applications are incorporated herein by reference. Unless expressly stated, changes in terminology from priority application (s) to this application are made without prejudice or disclaimer of subject matter. Changes from the priority application(s) (e.g., provisional applications(s)) are intended to be broadening and / or additive unless expressly stated otherwise. Replacement of alternative terms with a single representative term, for example, are inclusive unless otherwise defined. Various embodiments may also be found in previous disclosure(s) incorporated by reference. Embodiments of similar languages in this application are not modifications or disclaimer of the embodiments disclosed in previous incorporated disclosures unless otherwise stated.

[0002] This application may share inventor(s) and / or subject matter with one or more of the following patent applications, each naming inventor(s) including Khai Gan CHUAH: US 15 / 065,857 filed Mar 10, 2016, US 16 / 224,518 filed Dec 18, 2018, and US 17 / 302,281 filed Apr 29, 2021 , all titled "Offline to online management system"; and US no. 17 / 453,843 filed Nov 06, 2021 , CA no. 3229579 filed Jun 24, 2022, CN no. 202280056998 filed Jun 24, 2022, EP no. 22748196 filed Jun 24, 2022, JP no. 2024510627 filed Jun 24, 2022, KR no. 20247008536 filed Jun 24, 2022, US no. 18 / 685,481 filed Jun 24, 2022, and WO no. PCT / US2022 / 073163 filed Jun 24, 2022, all titled "Automatic retail display management". The entire contents of each of the foregoing applications and their priority applications, if any, are incorporated herein by reference.BACKGROUND

[0003] Media distribution and advertisement systems are integral to modern marketing strategies, providing platforms for delivering content to targeted audiences across various physical and digital spaces. These systems may, for example, utilize various technologies in an attempt to optimize the placement and / or timing of advertisements. In some instances, media distribution systems may incorporate data analytics to assess audience engagement and adjust content delivery.

[0004] In various applications, media distribution and advertisement systems may be employed in retail environments, transportation hubs, and public spaces to engage consumers and promote products and services. In some examples, retail settings may utilize digital signage to display promotional content that aligns with current store offerings and customer preferences. Transportation hubs, such as airports and train stations, may employ dynamic advertising systems to target travelers with relevant information and offers. Public spaces, including city centers and event venues, may use large-scale displays to capture the attention of passersby and enhance brand visibility.

[0005] In some examples, shopping malls may incorporate interactive kiosks to provide personalized advertisements to shoppers. Stadiums and sports arenas may, for example, implement LED screens to showcase advertisements during events. In some examples, amusement parks may utilize projection mapping to create immersive advertising experiences. Hospitals and healthcare facilities may, for example, employ digital displays to inform patients and visitors about health-related productsand services. In some embodiments, educational institutions may use digital boards to promote academic programs and events.

[0006] Methods and / or systems in media distribution and advertisement may involve static displays and fixed scheduling. Some systems may rely on manual processes for content placement and scheduling. In some examples, media distribution may involve a bidding process for advertisement slots. Manual selection of advertisement content may be utilized in certain systems. Some methods may include predetermined scheduling based on historical data. In various systems, media distribution may be executed through direct negotiations with media outlets.TECHNICAL FIELD

[0007] Apparatus and methods generally relate to computation technology and / or data structures such as, for example, related to media distribution.SUMMARY

[0008] Apparatus and related methods relate to selective computation of attributes for real-time space spot prioritization. In an illustrative embodiment, a system may retrieve space data structures representing physical spaces. The space data structures may, for example, include hierarchical grid structures. Real-time environmental data may be received from a distributed sensor network. Advertisement placement criteria may, for example, be generated from a media placement package. Hard filters may be applied to exclude grid units that do not meet criteria. A Grid Performance Rating may, for example, be computed for remaining grid units. Grid units with high ratings may be aggregated to form space spots. A Spot Performance Rating may, for example, be computed for each space spot. Control signals may be generated to distribute media to selected space spots. Various embodiments may advantageously enable fast, low-cost real-time distribution of advertisements to physical locations.BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Various embodiments of the present embodiments are described with reference to the following FIGURES.

[0010] Fig. 1 depicts an example online to offline (020) network and content management system in an illustrative use-case scenario

[0011] Fig. 2 depicts a block diagram of an example 020 system.

[0012] Fig. 3 depicts an example method of generating an example space data structure. Fig. 4 depicts an example method of generating a space spot data structure. Fig. 5 depicts an example method of generating a space spot grid template(s). Fig. 6 depicts an example method of automatic analysis (e.g., visual analysis) of a physical space. Fig. 7 depicts an example method of meshing (e.g., automatic grid unit generation).

[0013] Fig. 8 depicts an example vision model. Fig. 9 depicts an example visual analysis model. Fig. 10 depicts an example weather integration model. Fig. 11 depicts an example geographic model. Fig. 12 depicts an example ensemble Al system. Fig. 13 depicts an example GPR engine. Fig. 14 depicts an example data preprocessing module. Fig. 15 depicts an example multi-objective adaptation engine. Fig. 16 depicts an example 020 routing engine. Fig. 17 depicts an example environmental recognition module. Fig. 18 depicts an example grid adaptation module. Fig. 19 depicts an example template generation module. Fig. 20 depicts an example temporal model.

[0014] Fig. 21 depicts a block diagram of an illustrative co-sharing engine.

[0015] Fig. 22 depicts a block diagram of an illustrative dynamic reconfiguration module.

[0016] Fig. 23 depicts a block diagram of an example 020 system architecture.

[0017] Fig. 24 depicts an example attribute array data structure visualization. Fig. 25 depicts an example GPR data structure visualization. Fig. 26 depicts an example SPR data structure visualization. Fig. 27 depicts an example SSGT data structure visualization. Fig. 28 depicts an example MPP data structure visualization.

[0018] Fig. 29A depicts example grid overlays on a floor plan of a restaurant. Fig. 29B depicts example grid overlays on a floor plan of a retail store. Fig. 29C depicts example grid overlays on a floor plan of a transit station. Fig. 29D depicts example grid overlays on a wall of a restaurant.

[0019] Like reference numerals refer to like parts throughout the various views unless otherwise specified. Embodiments and portions of embodiments illustrated and described herein are non-limiting and non-exhaustive.DETAILED DESCRIPTION

[0020] In order to assist rapid comprehension, this document introduces an offline to online (020) system configured, for example, to perform media generation, configuration, placement, routing, and / or distribution, in Figs. 1-2. Methods related to configuration and / or operation of the 020 system and / or its components are described with respect to Figs. 3-7. The discussion turns to example component modules, engines, and / or modules of the 020 system with reference to Figs. 8-23. Then, example data structures and / or visualizations are disclosed with respect to Figs. 24-29D. Finally, various additional embodiments and / or features are discussed related to 020 and / or media generation, configuration, placement, routing, and / or distribution systems and / or methods.

[0021] Fig. 1 shows a network 100 that includes an 020 engine 102 interfacing with various physical spaces 104. The network 100 may, for example, facilitate the delivery of media to these physical spaces 104. The physical spaces 104 encompass a restaurant 106. In some examples, the physical spaces 104 may include an apartment building 108. A park 110 may be included in some embodiments. Some embodiments may include a gas station 112. A retail store 114 may be part of the physical spaces 104 in some examples. In some embodiments, a kiosk 116 may be included. Each of these spaces may be connected in a media distribution network 100 through the 020 engine 102.

[0022] A media originator 118 is operably connected to the 020 engine 102. A device of a media originator 118 may, for example, operate (e.g., in communication with the 020 engine 102) to generate a media placement package 120. The 020 engine 102 may operate on the media placement package 120 to generate and / or distribute media to selected space spots in the network of physical spaces.

[0023] The 020 engine 102 includes an 020 routing engine 122. The 020 routing engine 122 may, for example, manage routing (e.g., implementation of triggers, rules, and / or templates) to distribute content (e.g., physical, digital) to selected physical spaces. The 020 engine 102 may, for example, automatically generate and / or distribute content to selected physical spaces based on a specific media placement package 120. For example, the 020 engine 102 may automatically compute a highest value configuration and / or placement for a specific media placement package 120 (MPP), such as within constraints of the media placement package 120. As an illustrative example, a media originator 118 may define an offering (e.g., service, product, brand), goals (e.g., increase sales by Q3, alignment with brand standards), content (e.g., specific images, brand assets), and / or budget (e.g., maximum of $14k / month). These media placement criteria (MPC) may, for example, be packaged in an MPP (e.g., in an interactive query, via an API push from a marketing firm). The 020 engine 102 may, for example,automatically generate media, and / or select one or more highest computed value configuration and / or placement for the media based on the received MPP. The 020 engine 102 may, for example, automatically update, regenerate, reconfigure, and / or relocate the media and / or placement, such as based on changing environmental conditions (e.g., associated with the physical spaces).

[0024] In this example, each physical space 104 is subdivided into grids 124. Grids 124 are divided into individual grid unit 126.The grids 124 are shown in various configurations across the different physical spaces. For example, the restaurant 106 has a grid 124 placed near tables. The apartment building 108 has grid 124 placed above an entrance. The park 110 has grid 124 placed on a sign near a bench. The gas station 112 has a digital display subdivided in a rectangular grid 124, and a passive (e.g., poster) display subdivided into a radial grid 124. The retail store 114 is shown with a grid in the window on the street. The kiosk 116 has a triangular grid (e.g., passive such as an adhesive display) disposed next to a screen. In some examples, the kiosk 116 screen may be available as a grid 124 (e.g., when not in use).

[0025] In this example, sensors 128 are positioned within at least some of the physical spaces 104 in the network 100 For example, a light bulb integrated sensor 128 may monitor occupancy at a table near the grid 124. The 020 routing engine 122 may adjust media configuration and / or placement in response to the occupancy sensor. A sensor 128 at the apartment building 108 may be a motion detector, audio sensor, and / or camera. For example, the 020 routing engine 122 may adjust media displayed in response to periodic and / or real-time environmental factors (e.g., traffic conditions and / or demographics). Some embodiments may, for example, include sensors 128 in the park 110 as shown. Valuation of the grid units 126 near the park bench may, for example, vary based on presence detection by the sensor 128. Sensor 128 at kiosk 116 may, for example, detect weather conditions at an outdoor kiosk and / or lighting conditions at an indoor kiosk. These sensors 128 may, for example, collect data relevant to the 020 engine 102. The data collected by the sensors 128 may, for example, facilitate realtime configuration and / or routing of media, such as by the 020 routing engine 122.

[0026] For example, the 020 engine 102 may operate on an attribute array 130. The attribute array 130 may be generated for each grid unit 126. An attribute array 130. The attribute array 130 may be populated with attributes 132 (e.g., specific to the grid unit 126, such as individually and / or in common with the grid 124, the physical space as a whole, and / or multiple physical spaces). The 020 engine 102 may operate on the attribute array 130 to compute a MPP-specific value for each grid unit. Based on those values, the 020 engine 102 may aggregate grid units into a selected 'space spot' for placement of media. Accordingly, the 020 engine 102 may, for example, objectively infer a most valuable space spot as function of the media placement package 120 and the particular attributes 132 (e.g., at a given time). The 020 routing engine 122 may, for example, re-route the media to a different space spot and / or reconfigure the space spot with different grid units 126, such as environmental conditions change (e.g., purchasing trends, weather, demographics, day / night).

[0027] Across an extended network 100 of potentially thousands of physical spaces and potentially millions of grid units 126, each with multiple attributes 132, and potentially tens of thousands or more media originator 118 (e.g., some with multiple media placement package 120) competing for placement, the 020 engine 102 may experience unsustainable computational demands to compute MPP-specific performance ratings for each grid unit for each MPP, especially if sub-day, sub-hour, or sub-minute frequencies are targeted.

[0028] Accordingly, various embodiments may advantageously provide technical solutions enabling sustainable computation of individualized, real-time, and / or automatic grid performance ratings across even vast networks of physical spaces and mediaplacements. For example, MPPs may be associated with selected attributes 134 within attribute arrays 130. The selected attributes 134 may, for example, represent a sparsified set of attributes relevant to inferring grid performance ratings (GPR) for grid units for the linked MPP. Space spots may be selected based on aggregated space performance ratings (SPR), such as computed from MPP-specific GPR for selected grid units. Multiple levels of filtering may, for example, drastically reduce a number of grid units to be evaluated. Technical solutions provided by the 020 engine 102 may, for example, advantageously address technical problems related to computationally-efficiently automatically routing media to various physical spaces 104 with individualized and granular performance rating inferences.

[0029] Fig. 2 depicts a block diagram of an example 020 engine 102 system. In this example, the 020 engine 102 includes a processor 200. Various embodiments may include one or more processors. A processor may, by way of example and not limitation, include multiple (e.g. , sub) processors. The processor 200 may include, for example, one or more microprocessors. The processor 200 is operably coupled to memory 202. The memory 202 may, for example, include one or more physical modules For example, the memory 202 may include random access memory. The memory 202 may be configured, by way of example and not limitation, to hold program(s) of instruction and / or operating data during and / or around execution by the processor 200.

[0030] The processor 200 is operably coupled to a communication module 204. Various embodiments may include one or more communication modules. The communication module 204 may, for example, enable data exchange with external devices. For example, the communication module 204 facilitates interactions with user devices and sensors.

[0031] The 020 engine 102 further includes storage 206. Various embodiments may include one or more storage modules. For example, a storage module may include multiple (e.g., sub) storage modules. The storage 206 may, for example, include multiple storage devices. For example, the storage 206 may include hard disk storage. The storage 206 may, for example, include solid state storage. A storage module may, for example, be configured to store one or more programs of instruction. A program of instruction may, for example, be configured to cause operations to be performed when executed by the processor 200. For example, program(s) of instruction may be loaded (e.g., temporarily) from the storage module 206 into the memory 202, such as, for example, in preparation for and / or during execution by the processor 200. As shown, in this example, the storage 206 includes modules / engines 208. The modules / engines 208 may, for example, be configured to perform specific computational tasks. For example, the modules / engines 208 may operate on signals from the communication module 204. In some embodiments, the modules / engines 208 may include an 020 routing engine 122. In various examples, the modules / engines 208 may include an grid adaptation module 1800. Some examples may incorporate a data preprocessing module 1400 within the modules / engines 208. In certain embodiments, the modules / engines 208 may include a environmental recognition module 1700. The modules / engines 208 may, for example, include an ensemble Al system 1200. Some implementations may include a multi-objective adaptation engine 1502 within the modules / engines 208. In certain examples, the modules / engines 208 may include a content distribution engine.

[0032] As depicted, the storage 206 includes one or more models 210. Models 210 may support data processing and decision-making. For example, the models 210 may be generated in response to incoming signals and / or historical data. . The models 210 may, for example, include a visual analysis model 900. In some examples, a weather integration model 1000 may be included in the models 210. A geographic model 1100 may, in some embodiments, be included within the models 210. Some examples of models 210 may include a temporal model 2302. Ensemble models (e.g., of the ensemble Al system 1200)may, for example, be part of the models 210. Various models 210 may, for example, support data processing and decisionmaking in different contexts.

[0033] Internal data 212 is also located within the storage 206. The internal data 212 may, for example, include operational data relevant to the engine's functions. For example, internal data 212 may assist in analyzing performance metrics. Internal data may include, for example, one or more stores of data structures disclosed herein. Internal data may include, for example, intermediate data structures (e.g., during building and / or update). In some embodiments, intermediate data structures may be transferred to the internal data 212 from the memory 202, for example. Internal data may include, by way of example and not limitation, lookup tables and / or databases. Internal data may, for example, include historical data.

[0034] The 020 engine 102 may interface, as depicted, with one or more external components. In this example, a display device 216 may present visual content, thus facilitating user interaction.

[0035] The user device 218 may, for example, facilitate further user interaction with the system in the role of an advertiser. Some embodiments may include the user acting as a media originator, for example. In various examples, the user device 218 may support interaction by a designer. The user device 218 may, for example, enable a physical space operator to interact with the system. Online shoppers may, in some examples, utilize the user device 218 for interaction Consumers at physical spaces may also, for example, benefit from interaction through the user device 218. The user device 218 may additionally support interaction from an 020 manager, for example.

[0036] The 020 engine 102 may, as depicted, be operably coupled to one or more sensors 128. Sensors may, for example, be located in physical spaces. Sensors may, for example, be provided via an external service (e.g., a weather service, airport information). A sensor 128 may, for example, collect environmental data from a temperature sensor. Temperature sensors may advantageously provide data related to thermal conditions. In some examples, the sensor 128 may include a humidity sensor. Humidity sensors may, for example, offer data on moisture levels. Some embodiments may utilize a light sensor in the sensor 128. Light sensors may facilitate the detection of ambient light conditions.

[0037] In other embodiments, the sensor 128 may comprise a proximity sensor. Proximity sensors may allow for detection of nearby objects. Vision sensors, such as a camera, may be included in some examples of the sensor 128. Vision sensors may provide capabilities for visual data capture. Various embodiments may incorporate a motion detector within the sensor 128. Motion detectors may enable sensing of movement.

[0038] In certain examples, the sensor 128 may include a distance sensor. Distance sensors may facilitate the measurement of space between objects. A 3D sensor may be utilized in some implementations of the sensor 128. 3D sensors may provide advantages in identifying spatial environments.

[0039] As shown, external data stores 224 are accessible. External data stores 224 may, for example, store one or more types of data structures disclosed herein. For example, external data stores 224 may be incorporated into other devices. External data stores may, for example, be accessed via APIs and / or other communication connections. For example, the 020 engine 102 may be connected to a retail point of sale system, for example. The 020 engine 102 may be connected to ecommerce engines. The 020 engine 102 may, for example, be connected to market data and / or analytics. The 020 engine 102 may, for example, be connected to price information. In some embodiments, such as shown, external data may include historical data 222. Historical data 222 may, for example, advantageously guide predictive analytics and / or system adaptations.

[0040] Fig. 3 depicts an example space data structure generation method 300. Space data structures may, for example, advantageously contribute to solving a technical problem of efficient and coordinated representation in computing environments of physical advertising spaces. The method may, for example, advantageously provide a novel systematic approach to transforming physical environments into structured digital representations suitable for automated analysis and / or adaptation.

[0041] This example process commences at step 302. At step 304, the identification of a physical space 104 is received. The identification received may include coordinate data, address information, and / or facility identifiers that designate (e.g., uniquely) the physical location to be analyzed. This initial input may, for example, establish a scope and / or boundaries for subsequent processing operations. In some embodiments, the step 304 may include, for example, part or all of example space identification engine operation method 600.

[0042] In some embodiments, the physical space identification may be performed automatically (e.g , in response to input provided by a user device) For example, a user may take a picture of a physical space. The 020 engine 102 may, for example, automatically analyze the image and / or other data from the user device (e.g., geolocation data) to determine a location of the physical space. In some embodiments, the 020 engine 102 may generate an identifier (e.g., a new unique identifier of a new physical space).

[0043] Step 306 retrieves the geometric representation of the physical space. This may, for example, include transformation from physical reality to digital representation. For example, Step 306 may include operation of a vision model 800 configured to process visual inputs such as images 810, 3D models 812, and / or video 814, such as, for example, through an image processing module 816 and / or object detection module 818. In various embodiments, Step 306 may include operation of a visual analysis model 900, which may employ a visual impact assessment engine 908 configured to evaluate grid space visual characteristics 902 and / or utilize a content placement zone identifier 910, such as to determine advertising surface locations. The vision model 800 may, for example, employ computer vision techniques including convolutional neural networks (e.g., for feature extraction), object detection algorithms such as YOLO and / or R-CNN (e.g., for identifying advertising surfaces), and / or semantic segmentation models (e.g., for spatial analysis). The visual analysis model 900 may incorporate deep learning architectures trained on historical performance data 906, such as to assess visual characteristics and / or predict advertising effectiveness based on spatial configurations.

[0044] Step 308 generates N proposed advertising locations and sets i equal to 1 , initializing an iterative counter for evaluating multiple potential advertising positions within the physical space. For example, Step 308 may include operation of a geographic model 1100. The geographic model 1100 may, for example, employ a spatial analysis engine 1110, such as operating on GIS data 1102 and / or geolocation coordinates 1104. The geographic model 1100 may include traffic pattern recognition 1112, which may analyze traffic patterns 1106. The geographic model 1100 may include a proximity analyzer 1116 configured, for example, to evaluate spatial relation data 1108. The geographic model 1100 may utilize machine learning algorithms such as random forests and / or gradient boosting machines trained on historical traffic patterns and / or demographic data to identify high-value locations. In some embodiments, the geographic model 1100 may employ spatial clustering algorithms such as DBSCAN and / or K-means, such as to group potential advertising locations based on geographic proximity and similar characteristics. The model may, for example, incorporate regression analysis, such as to predict foot traffic density based on time-of-day patterns, seasonal variations, and / or proximity to points of interest.

[0045] At decision step 310, a determination is made regarding whether the physical space is an advertising location. For example, Step 310 may include determining whether an advertising location is valid, applying validation criteria to assess technical feasibility, regulatory compliance, and / or physical suitability. This determination may involve, for example, checking against filters for mandatory requirements and / or evaluating accessibility (e.g., for installation and / or maintenance). The step 310 may include receiving input from a user. If the location is determined to be valid, the process proceeds to step 314.

[0046] In step 314, grid units 126 are generated. For example, step 314 may be performed according to an example grid generation method 700. Step 314 may, for example, include subdividing the advertising location into one or more layers of spatial units.

[0047] Following grid unit generation, at step 214, an SDS is linked to grids 124 and the physical space 104. During step 214, data structure relationships may, for example, be established. These relationships may, for example, advantageously enable reliable hierarchical navigation and / or efficient attribute retrieval. This linking operation may, for example, create referential integrity between the space data structure and constituent grid components. Linking may, for example, facilitate subsequent computational operations.

[0048] Step 316 involves generating one or more space data structures (SDS). Step 316 may, for example, include instantiating primary data objects that will contain various spatial, temporal, and / or performance-related information for the physical space As shown in this example, step 318 generates static attributes 132. Static attributes 132 may, by way of example and not limitation, include geolocation data, structural properties, dimensions, orientation, and / or permanent environmental context. The generation of static attributes may involve querying external data stores 224 for building information, accessing property databases, and / or extracting dimensional data from the geometric representation obtained in step 306.

[0049] Step 320 generates (e.g., concurrently) periodic attributes 132. Periodic attributes 132 may, by way of example and not limitation, include traffic patterns, demographic data, historical performance metrics, scheduled events, and / or other data that exhibit regular temporal variation (e.g., expected to be updated on a periodic basis). The generation of periodic attributes may, for example, leverage historical data 222, such as to establish baseline patterns. Generation of periodic attributes may, for example, employ time-series analysis models, such as for forecasting periodic trends. In some embodiments, a temporal model 2302 may be operated. By way of example and not limitation, the temporal model 2302 may be applied to analyze historical time-series data and / or identify recurring patterns in foot traffic, demographic presence, and / or other environmental conditions that may, for example, influence advertising effectiveness on a periodic basis.

[0050] Step 324 initializes (e.g., in parallel) real-time attributes 132. Real-time attributes 132 may, for example, establish data fields related to dynamic environmental conditions. Such dynamic conditions may, for example, include current weather, lighting levels, occupancy, noise levels, and / or instantaneous traffic flow. The initialization may set default values and / or establish data streams from sensors 128 in the distributed sensor network. The step 324 may, for example, prepare the system to receive and / or process continuous environmental updates. The real-time attributes may be configured to accept inputs from various sensor types. Examples may, for example, include temperature sensors, traffic counting systems, timestamp data, light level sensors, and / or other sensors 1410. Real-time data (and / or other data) may, for example, be processed through a data preprocessing module 1400.

[0051] Step 326 updates the SDS with the generated attributes. Step 326 may, for example, populate the attribute arrays 130 with the static, periodic, and / or real-time attribute values computed in the preceding steps. This update operation may, for example, advantageously create a comprehensive digital representation of the physical advertising space. This digital representation may, for example, advantageously integrate permanent characteristics, temporal patterns, and / or current conditions into a unified data structure suitable for dynamic, time-efficient, and / or resource efficient computational analysis.

[0052] In step 226, the updated SDS is stored. For example, the space data structures 1500 may be stored in a data store system (e.g., internal data 212, external data stores 224). Storing the SDS may, for example, persist space data structure, such as for subsequent retrieval and / or processing operations. The storage operation may involve writing to relational databases, document stores, and / or distributed storage systems, depending on the system architecture and / or scalability requirements.

[0053] Decision step 330 determines if i is greater than N, evaluating whether all proposed advertising locations have been processed. If I is not greater than N, indicating that additional locations remain to be evaluated, step 312 increments i, advancing the iterator to the next proposed location, and the process repeats (starting with step 310 in this example).

[0054] If i is greater than N, indicating that all proposed advertising locations have been processed and validated, the method concludes at step 332. For example, the space data structure generation method 300 may advantageously generate space data structures for all viable advertising locations within the physical space. The resulting space data structures 1500 may, for example, advantageously provide a foundation for subsequent computational operations including grid performance rating calculation, space spot formation, and / or media placement adaptation and / or routing.

[0055] The method may, for example, advantageously reduce computational complexity such as by selectively generating and storing only relevant attribute data for validated locations, avoiding unnecessary processing of unsuitable advertising positions. The hierarchical organization of space data structures may, for example, advantageously enable efficient querying and / or analysis at varying levels of spatial granularity, supporting both broad space-level assessments and detailed grid-unit- level optimization. The integration of multiple specialized models including vision models 800, visual analysis models 900, and geographic models 1100 may, for example, advantageously provide a multi-dimensional analysis that captures visual, spatial, and / or geographic factors influencing advertising effectiveness, which may advantageously enable novel automatic valuation and / or resource allocation procedures in physical space management.

[0056] Fig. 4 depicts an example space spot generation method 400. The method may, for example, advantageously generate space spot data structures 2310. Such embodiments may, for example, advantageously solve technical problems related to dynamically identifying and / or configuring 'optimal' advertising zones within physical spaces through selective computational processing. The space spot generation method 400 method may, for example, advantageously provide a systematic approach to transforming hierarchical grid structures into actionable space spots, such as by evaluating efficiently and / or automatically computed grid unit performance against advertiser-specific criteria while reducing computational resource consumption (e.g., using attribute sparsification).

[0057] The depicted example process begins at step 402. At step 404, media placement criteria (MPC) (e.g., media placement criteria 804) are received from an originating device of a media originator 118. For example, the 020 engine 102 may receive one or more media placement packages 120. The media placement criteria may include, for example, target display parameters. Such parameters may, for example, specify desired performance targets, audience demographics,geographic constraints, temporal requirements, and / or budget limitations. The criteria may, for example, include media format specifications. Such specification may, for example, define minimum size requirements, resolution standards, orientation preferences, and / or technical compatibility requirements (e.g., for display devices). The media placement criteria may, for example, advantageously establish an automatic evaluation framework against which grid units 126 will be assessed, which may advantageously enable the system to automatically identify spaces that 'best' match advertiser objectives.

[0058] Corresponding space data structures (e.g., space data structures 1500) are retrieved at step 406 (e.g., from one or more data store system). Each retrieved space data structure may, for example, represent a physical space 104 or portion thereof, such as a restaurant 106, retail store 114, gas station 112, park 110, apartment building 108, or kiosk 116. The space data structure may associate the space with a hierarchical grid structure and / or associated attribute arrays 130. The retrieval operation may, for example, load the corresponding hierarchical grids 124 that subdivide physical spaces into (e.g., progressively smaller) grid units 126. This step may, for example, advantageously establish a spatial framework for subsequent analysis. The space data structures may, for example, contain and / or link to attribute arrays 130 for the corresponding grid units.

[0059] The attribute arrays 130 are updated at step 408. For example, the attribute array 130 may be updated in response to current environmental conditions and / or temporal changes.

[0060] Update operations may, for example, include receiving real-time environmental data from one or more distributed sensor networks. The real-time environmental data may include, by way of example and not limitation, weather conditions from a weather integration model 1000 processing current weather conditions 1004 and weather forecast data 1006, lighting levels from light level sensors, occupancy measurements from traffic counting systems, and / or other environmental parameters from sensors 1410. The system may, for example, compute updated real-time attributes, such as by processing this sensor data through a data preprocessing module 1400.

[0061] In various embodiments, the update operation may refresh periodic attributes (e.g., if temporal conditions warrant recalculation) which may, for example, include incorporating updated traffic patterns 1106, demographic shifts, and / or historical performance data 906. In some embodiments, the step 408 may include checking whether updates are required (e.g., moving on if no update is needed such as if new data has not been received and / or a refresh time since last update has not been reached).

[0062] In some examples, the step 408 may include linking the SDS to the MPC (e.g., temporarily, permanently).

[0063] Step 410 involves applying hard filters. The hard filters may, for example, be generated from the media placement criteria 804. For example, the hard filters may advantageously exclude grid units 126 that fail to meet mandatory criteria associated with the media placement criteria. These hard filters may, for example, implement binary exclusion rules that immediately disqualify grid units. Hard filters may, by way of example and not limitation, include legal restrictions, such as prohibitions against alcohol advertising near schools. Hard filters may, for example, include technical incompatibilities, such as requirements for digital display capabilities in locations equipped only with static structures. Hard filters may, for example, include format constraints, such as minimum size requirements that exceed available space dimensions. The hard filtering operation(s) may, by way of example and not limitation, advantageously significantly reduce computational load, such as by eliminating unsuitable candidates before resource-intensive performance calculations are performed. For example, the step 410 may advantageously address technical challenge of processing large numbers of grid units within real-time constraints ina dynamic (e.g., constantly shifting placement and / or automatically managed) media routing network across a distributed (e.g., global) network of physical spaces.

[0064] The method advances at step 412 with a determination of whether remaining grid units exist after the hard filtering operation. If no grid units remain, indicating that no locations within the retrieved space data structures satisfy the mandatory criteria, the process proceeds directly to step 438 to end without generating viable space spots. This early termination mechanism may, for example, advantageously prevent unnecessary computational processing when fundamental requirements cannot be satisfied, which may advantageously conserve system resources for more promising placement opportunities. In some embodiments, the step 438 may include returning an indication to adjust media placement criteria 804. For example, the step 438 may include indicating to a media originator 118 which criteria may open more opportunities (e.g., an indication that X adjustment requirement Y is associated with Z potential placement opportunities). This indication may, for example, be generated based on results of step 410, for example.

[0065] When grid units remain after hard filtering, step 414 sparsities attributes based on the media placement criteria. The sparsification step may, for example, provide a computationally efficient approach to grid performance evaluation. In various embodiments, the sparsification operation(s) may apply a trained model(s), such as the sparsification module 1308 (e.g., of the GPR model 1300), to select a subset of relevant attributes from the complete attribute arrays 130 For example, sparsification may include selection of a subset of attributes in the attribute array 130. The selection may include application of an attribute relevance identifier 1304, such as to determine which attributes from the more comprehensive attribute arrays significantly influence performance for the specific media placement criteria received. A weight calculation engine 1306 may, for example, determine weightings for the identified relevant attributes based on the media placement criteria. This may include, for example, applying adaptation algorithms to compute coefficients that reflect the relative importance of each attribute in predicting placement success. The sparsification module 1308 may, for example, filter the attribute arrays to retain only the relevant subset. The resulting sparsified attributes may, for example, advantageously dramatically reduce the dimensionality and / or computational resource demands of subsequent computational operations, such as by eliminating attributes that do not materially affect the evaluation for the current placement criteria and / or enabling simpler computations (e.g., dot products). In some embodiments, a sparsification result may be stored in association with the media placement package 120 and / or the space data structures 1500.

[0066] At step 416, a grid performance rating (GPR) is computed from the sparsified attributes for each remaining grid unit. The computation selectively processes only the subset of relevant attributes identified in step 414 to produce a performance score (e.g., single / unified score, MPC-specific score) per grid unit. For example, the step 414 may include performing weighted summation and / or more complex mathematical operations (e.g., combining normalized attribute values with their corresponding weights) to produce a single performance score per grid unit. This selective processing approach may, for example, advantageously solve a technical problem in automatic but custom routing networks of computational scalability for advertiser-specific performance ratings, such as by reducing operations from processing all attributes in the attribute arrays 130 to processing only a small subset relevant to current media placement criteria. Various embodiments may, for example, advantageously achieve one or more orders of magnitude increase in computational efficiency while maintaining or even increasing prediction accuracy.

[0067] In some embodiments, the GPR computation may, for example, incorporate outputs from multiple specialized models operating within an ensemble Al system 1200. For example, a multiple model system (e.g., such as in an ensemble or other model aggregation architecture) may advantageously combine visual analysis output 1202, weather integration output 1204, geographic model output 1206, and / or temporal model output 1208. By way of example and not limitation, a voting mechanism 1210 and / or vector summation engine 1212 may advantageously synthesize multi-dimensional assessments into unified performance ratings.

[0068] Step 418 checks if grid units exceed the GPR threshold. This step may, for example, evaluate whether any of the computed performance ratings surpass a minimum acceptable value that indicates sufficient advertising potential to warrant inclusion in space spot formation. The GPR threshold may, for example, be specified in the media placement criteria 804. The GPR threshold(s) may, for example, be generated by the 020 engine 102 based on the media placement criteria 804. For example, the 020 engine 102 may calculate a GPR threshold historically associated with performance returns specified by the media originator 118.

[0069] If the threshold is not exceeded by any grid units, indicating that no locations within the current space data structures demonstrate adequate performance for the media placement criteria, the process ends at step 438 without generating space spots. This threshold-based filtering may, for example, advantageously provides an additional control mechanism that permits only predicted high-performing locations proceed to more resource-intensive stages.

[0070] When grid units exceed the GPR threshold, the process proceeds to step 420, where selected SDS with the highest GPR is chosen. This step may, for example, advantageously identify one or more space data structures containing the grid unit(s) that achieve the maximum performance rating for the media placement criteria. This selection may, for example, advantageously establish a starting point for space spot formation by focusing on a most promising location(s) within the available physical spaces.

[0071] At step 322, a space spot data structure (SSDS) is generated. This step may, for example, include instantiating a data object (e.g., new data structure) that will contain information about the aggregated advertising zone being formed. The space spot data structure may, for example, serve as a container linking multiple grid units 126 into a cohesive advertising space and / or for storing performance metrics and / or configuration specifications that may advantageously guide media deployment.

[0072] Step 422 aggregates grid units from the selected SDS based on physical space characteristics. This step may, for example, implement dynamic aggregation logic that identifies adjacent units (e.g., high-performing) and clusters them together to form contiguous advertising zones. The aggregation process may, for example, evaluate spatial adjacency (e.g., through coordinate analysis). The process may, for example, assess visual coherence (e.g., generating aggregated units that present a unified advertising surface). The process may, for example, consider structural boundaries (e.g., such as walls or pillars that may define natural clustering limits). The clustering operation may, for example, employ spatial algorithms such as, by way of example and not limitation: connected component analysis, region growing, and / or graph-based clustering. The spatial algorithms may, for example, advantageously identify target groupings of grid units 126 that maximize combined predicted performance (e.g., aggregate GPR) while maintaining physical contiguity and / or aesthetic integrity.

[0073] In step 424, it is determined if aggregated grid units meet the MPC. This step may, for example, specifically evaluate whether the aggregate size of the clustered units satisfies minimum format specifications defined in the media placementcriteria. This determination may, for example, assess the total area, dimensions, and / or configuration of the aggregated grid units against requirements such as minimum display size, aspect ratio constraints, and / or technical compatibility across the aggregated zone. In some embodiments, the step 424 may include applying a model (e.g., a generative model(s)) to the proposed space spot (the aggregated grid units) to generate an inference whether it meets aesthetic criteria of the MPC. If the aggregated grid units do not meet the media placement criteria (e.g., indicating that the current cluster is too small, improperly configured, and / or otherwise inadequate for the advertiser requirements), the process proceeds to step 426 to assess whether additional SDS should be aggregated. If additional space data structures are available that could supplement the current cluster, the process returns to step 322 to generate an updated space spot data structure incorporating grid units from multiple physical spaces and / or different regions within the same space. This iterative refinement may, for example, advantageously enable the system to automatically construct space spots of appropriate scale and configuration, such as even combining resources across spatial boundaries.

[0074] When aggregated units meet the MPC (e.g., inferred to satisfy all dimensional, technical, and / or format requirements specified by the media originator), step 328 involves updating the SSDS linking SDS of aggregated units. This step may, for example, advantageously establish data structure relationships that connect the space spot to (e.g., all) constituent grid units and / or their parent space data structures. This linking operation may, for example, advantageously create referential integrity, which may enable efficient traversal of the data hierarchy and / or facilitate subsequent operations such as attribute aggregation, performance calculation, and / or configuration specification.

[0075] At step 428, a spot performance rating (SPR) is computed for each SSDS. The SPR may, by way of example and not limitation, include multi-objective 'optimization' algorithms, such as implemented in a multi-objective adaptation engine 1502. The SPR computation may, for example, aggregate GPRS 1504 of constituent grid units, incorporating the individual performance ratings into a comprehensive space-level metric. The multi-objective adaptation engine 1502 may, for example, employ an objective function definition 1514 that balances multiple competing factors such as space efficiency 1506 (e.g., measuring utilization of available area), revenue potential 1508, (e.g., estimating commercial value such as based on traffic and / or visibility), aesthetic constraints 1510 (e.g., assessing visual coherence and / or brand alignment), and / or operational constraints 1512, (e.g., accounting for installation, maintenance, and / or power requirements). A constraint satisfaction module 1516 may, for example, enforce mandatory requirements. An optimization engine 1518 may identify configurations that increase overall value across multiple objectives. An aggregation module 1520 may, for example, synthesize factors with contextual considerations. The final SPR may, for example, provide a holistic assessment of space spot quality that may advantageously guide prioritization and / or selection decisions.

[0076] Step 430 ranks SSDS by their SPR. This step may, for example, advantageously order generated space spot data structures according to their computed performance ratings to automatically create a prioritized list of advertising opportunities. This ranking operation may, for example, enable the 020 routing engine 122 to automatically identify the most predicted valuable placements, and make (e.g., automatic) allocation decisions (e.g., when demand exceeds available premium locations).

[0077] At step 432, a space spot grid template (SSGT) is generated for each space spot. Step 432 may, for example, advantageously create detailed specifications that define how aggregated grid units are configured (e.g., physically) for media display. The template generation process may, for example, invoke a template generation module 1902. The templategeneration module 1902 may, for example, employ a template synthesis engine 1906 such as to create layout configurations, a configuration generator 1908 such as to specify technical display parameters such as resolution, orientation, brightness, and / or refresh rates, and / or a multi-option generator 1910 such as to produce multiple alternative configurations (e.g. , offering different trade-offs such as between visual impact and space efficiency). The generated SSGT may, for example, define boundaries of the space spot, specify layout configurations for content placement zones 918 within the aggregated area, set technical parameters for display configuration, and / or set scheduling information related to content delivery. The SSGT may, for example, advantageously serves as a comprehensive blueprint data structure that translates abstract space spot concepts into actionable deployment instructions, such as for automatic content distribution systems.

[0078] In step 434, it is determined whether the SSDS is accepted. The step 434 may include, for example, evaluating the space spot against final approval criteria. Such criteria may include, for example, advertiser confirmation, budget verification, availability validation, and / or automated quality checks In some embodiments, step 434 may include manual review (e.g., by media originator 118). If accepted, indicating that the space spot meets all requirements and is approved for media placement, step 436 updates the selected SSDS. For example, the SSDS may be updated to include (e.g., link, embedding) the SSGT(s), approval status, and / or other deployment parameters. The step 436 may, for example, complete the iterative process for the current space spot. The system may, for example, continue processing additional space spots or, as shown, conclude at step 438 the generation method.

[0079] This method may, for example, advantageously address technical challenges of space spot generation (e.g., 'real- time' such as continuously shifting space spots) in environments with numerous potential advertising locations, such as by employing selective computation through attribute sparsification in step 414. Sparsification may, for example, advantageously reduce processing requirements while maintaining or improving evaluation accuracy. The dynamic aggregation approach may, for example, advantageously enable flexible, automatic space spot formation that adapts to varying media format requirements and / or physical space configurations. Such embodiments may, for example, enable computer systems to determine a 'maximized' utilization of available advertising surfaces. The multi-objective adaptation may, for example, advantageously provide automated performance assessment balancing competing factors, which may enable, for example, automatic prioritization of space spots. Integration of multiple specialized models may, for example, leverage diverse analytical perspectives, which may, for example, advantageously produce robust performance predictions and / or efficient space spot configurations. Hierarchical data structure linking may, for example, facilitate efficient data management and / or enable rapid reconfiguration in response to changing, by way of example and not limitation: media placement criteria, available media packages, available physical spaces, and / or environmental conditions. Such embodiments may, for example, advantageously enable the 020 engine 102 to perform automatic dynamic adaptation to real-time circumstances.

[0080] The novel method(s) disclosed may, for example, advantageously specifically address technical constraints of computer systems (e.g., even general-purpose computer systems), effectively transforming them into special-purpose automatic media routing engines using new, technology-specific processes.

[0081] Fig. 5 depicts an example SSGT generation method 500. The method may, for example, advantageously address technical problems related to translating space spot configurations into actionable display specifications, such as through systematic layout adaptation and / or technical parameter definition. The method may, for example, provide a structuredapproach to creating deployment data structures that balance, for example, visual impact, space efficiency, and / or technical feasibility while, for example, accommodating temporal variability and / or real-time adaptation requirements.

[0082] The depicted example process begins at step 502. At step 504, SSDS and MPC are received (e.g., provided by a device, retrieved from a data store(s)) as inputs to the template generation process. At step 506, space spot boundaries are defined. The step 506 may, for example, include extracting coordinates of constituent grid units 126 within the space spot data structure. The boundaries may, for example, determine an outer perimeter encompassing the aggregated physical advertising zone. The step 506 may, for example, include calculating total area. The boundary definition process may analyze spatial relationships between grid units (e.g., identifying contiguous regions, assessing irregular geometries such as resulting from adaptive aggregation, and / or establishing reference coordinate systems such as for positioning content elements within the defined space). The step 506 may, for example, advantageously create spatial specifications that provide a foundation for layout configuration and / or content placement

[0083] Grid space visual characteristics are evaluated at step 508. For example, step 508 may include operation of a visual analysis model 900 The visual analysis model 900 may, for example, employ a visual impact assessment engine 908, such as to analyze grid space visual characteristics 902. Characteristics may, for example, include viewing angles (e.g., from high- traffic positions), visibility under different lighting conditions (e.g., captured by light level sensors), aesthetic coherence (e.g., with surrounding architectural elements and / or the media being placed), and / or obstruction patterns (e.g., detected by an environmental recognition module 1700). The evaluation may, for example, incorporate historical performance data 906. Historical data may, for example, correlate visual characteristics with advertising effectiveness, such as to predict impact potential for the current space spot configuration. The visual analysis model 900 may, by way of example and not limitation, utilize computer vision algorithms, such as for identifying visual boundaries, predicting attention capture, and / or assessing aesthetic quality and / or brand alignment (e.g., via scene understanding models trained on advertising effectiveness datasets).

[0084] Identification of content placement zones occurs at step 510. Step 510 may, for example, include operation of a content placement zone identifier 910 (e.g., within the visual analysis model 900). The identifier may, for example, analyze the defined space spot boundaries from step 506 and / or the visual characteristics from step 508 to determine suggested (e.g., computed 'optimal') content placement zones 918 within the advertising space. The identification process may, for example, segment the space into multiple zones. Zones may include, for example, primary zones (e.g., for main messaging), secondary zones (e.g., for supporting content), and / or branding zones (e.g., for logo or identity elements). The content placement zone identifier 910 may, for example, employ spatial 'optimization' (computational) algorithms configured to balance attributes such as, by way of example and not limitation, visibility, compositional elements according to design principles such as rule of thirds or golden ratio, and / or visual hierarchy (e.g., guiding viewer attention through intentional sequencing of content elements). The identified zones may, for example, provide structured frameworks for content arrangement that enhance advertising effectiveness through strategic spatial organization.

[0085] At step 512, a visual impact score is assessed. Step 512 may include operation of a visual impact assessment engine 908. The engine may, for example, quantify predicted effectiveness of potential template configurations. The assessment may, for example, aggregate factors including, by way of example and not limitation: visibility scores such as based on viewing angles and / or distances, attention capture probability, aesthetic quality ratings such as from trained models, and / or brand alignment metrics (e.g., evaluating consistency with advertiser identity). The visual impact scores 920 may, for example,provide quantitative metrics for comparing alternative template configurations and / or selecting (e.g., automatically) arrangements that are computed to increase (e.g., 'maximize') advertising effectiveness. The scoring process may, for example, employ multi-criteria decision analysis techniques such as weighted summation and / or analytical hierarchy process to combine diverse factors into unified performance ratings.

[0086] In this example, high visual impact SSGTs are generated at step 514. Steps 514-518 may include, for example, operation of system 1900, such as including template generation module 1902. SSGTs generated at step 514 may, for example, be generated using attributes correlated to high visual impact performance. For example, the SSGT generation may emphasize prominent content placement in high-visibility zones, utilize bold layouts with strong visual hierarchy, and / or allocate space to maximize attention capture (e.g., even if space efficiency is somewhat reduced). The template synthesis engine 1906 may employ generative design algorithms that explore layout variations adapting for high visual impact scores 920 while satisfying boundary and technical constraints.

[0087] In this example, high space efficiency SSGTs are produced at step 516. These SSGTs may, for example, be generated using attributes weighted towards efficient utilization of available advertising area These templates may, for example, be generated prioritizing high coverage of the space spot area, reducing unused or negative space, and / or packing content elements more densely to extract higher usage from the allocated physical space. The space efficiency optimization may, by way of example and not limitation, employ bin packing algorithms, area utilization metrics, and / or layout compression techniques configured to maintain readability while increasing content density. The SSGTs generated in step 516 may be generated favoring reduced space usage at the expense of visual impact.

[0088] The depicted embodiments generate intermediate SSGTs at step 518. These SSGTs may, for example, be generated as intermediate options (e.g., progressive options) between the high visual impact SSGTs and the high space efficiency SSGTs. that balance, for example, visual impact and space efficiency according to trade-off parameters (e.g., configurable and / or automatically generated). A balancing analyzer 912 within the visual analysis model 900 may, for example, perform value-space balance analysis to identify configurations that offer computed favorable compromises between competing objectives. The intermediate templates may, for example, advantageously provide options for advertisers who seek balanced solutions rather than extreme optimization along single dimensions. The multi-option generator 1910 within the template generation module 1902 may, for example, create these variations by systematically adjusting trade-off weights and regenerating layouts across the efficiency-impact spectrum.

[0089] Although visual impact and space efficiency are shown in this example as a dimensional spectrum, other dimensions may be used in addition to or alternatively visual impact and / or space efficiency. For example, brand conformance and / or aesthetic matching may be used. Performance rating and / or cost may be used. Installation and / or maintenance related metrics may be used. Various embodiments may advantageously generate options balancing mutliple parameters.

[0090] Initialization of an iteration variable 'I' occurs at step 520, setting i equal to 1 in this example. The iterator may be used, as shown, during processing multiple template options generated in steps 514, 516, and 518. The initialization begins an iterative refinement loop in this example generating detailed configuration parameters for each generated template option.

[0091] At step 522, the ith SSGT is selected from the set of template options produced. At step 524, a layout configuration is generated for the selected template, such as through operation of a layout engine 1806 and configuration generator 1908. The layout configuration may, for example, define content placement zones 918 within the space spot boundaries, which mayinclude specifying coordinates, dimensions, and / or positioning for each content element. A configuration generator 1908 may, for example, set content dimensions (e.g. , providing compliance with media format specifications from the media placement criteria). The step 524 may include determining visual hierarchy (e.g., establishing sequence and / or prominence of content elements). The configuration generator 1908 may, for example, generate spatial relationships between components (e.g., spacing, alignment, grouping). The layout configuration may incorporate responsive design principles that enable content adaptation to varying display conditions.

[0092] Technical display parameters are set at step 526. The display parameters may, for example, establish space-specific requirements for implementing the template. These may, for example, include device specific parameters (e.g., for display on active devices). The parameters may, for example, include physical parameters such as for manufacturing and / or configuration of physical (e.g., passive) displays. Physical parameters may, for example, include dimensions and / or materials. In some examples, a configuration generator 1908 may specify resolution requirements matching device display capabilities and / or content quality standards, orientation preferences (e.g., indicating portrait, landscape, and / or angled positioning appropriate for the space spot geometry and / or viewing angles), display type compatibility (e.g., designating suitable technologies such as LED, LCD, projection, static printing), and / or brightness-contrast specifications (e.g., configured for visibility under lighting conditions, such as analyzed by a lighting condition analyzer 1710 in the environmental recognition module 1700). Technical parameters may, by way of example and not limitation, include refresh rates (e g., for dynamic content), color depth requirements, and / or aspect ratio constraints, which may, for example, be configured to facilitate target content rendering across the space spot.

[0093] At step 528, a determination is made if the configuration is temporally variable. For example, the the media placement criteria and / or space spot characteristics may be analyzed, such as to assess whether dynamic scheduling and / or real-time adaptation may correspond to advertising effectiveness. Temporal variability may, for example, be indicated by fluctuating traffic patterns 1106 (e.g., identified by the geographic model 1100), variable environmental conditions (e.g., changing lighting and / or weather such as detected by sensors 128), advertiser specifications (e.g., time-targeted messaging) in the media placement criteria, and / or space spot physical structure (e.g., a passive pole that can only have a poster replaced once a month vs a cloud-connected LED display in an event facility). If temporally variable conditions are detected (e.g., indicating that the template should adapt its parameters in response to temporal changes), temporal adjustment rules are generated at step 530.

[0094] Step 530 may, for example, include analysis from a temporal model 2302. The model(s) may, for example, identify time-based patterns and / or schedules. The temporal adjustment rules may, for example, define triggers, such as for dynamic adjustment specifying conditions. Example conditions that activate reconfiguration may include traffic density thresholds such as from traffic counting systems, lighting level changes such as from light level sensors, and / or time-of-day transitions. The rules may, for example, specify sensor data inputs from the distributed sensor network. Examples include, by way of example and not limitation, pedestrian traffic such as from sensors 128, lighting conditions, weather data such as from the weather integration model 1000, and / or other environmental parameters on which adaptation decisions rely. Thresholds for reconfiguration may, for example, be set (e.g., by temporal model 2302), such as to determine a magnitude of environmental change required to trigger template adjustments. Such thresholds may, for example, be configured to balance responsiveness against excessive reconfiguration that could, for example, disrupt content delivery and / or overload the 020 engine 102. Thetemporal adjustment rules may, for example, define display duration variations, content rotation patterns, and / or scheduling information configured, for example, to increase advertising exposure across different temporal contexts. These rules may, for example, advantageously enable the template to maintain effectiveness as environmental conditions change throughout operating periods.

[0095] Integration instructions are generated at step 532. These instructions may, for example, facilitate deployment of the template through content distribution systems and physical displays (e.g., devices). A configuration generator 1908 may, for example, create content distribution system commands. Commands may, for example, specify data transfer protocols, content scheduling directives, and / or synchronization signals (e.g., coordinating multi-device deployments). The integration instructions may, for example, include physical display device configuration steps detailing installation procedures, device parameter settings, and / or calibration requirements (e.g., configured to achieve specified technical display parameters). Synchronization requirements may, for example, define timing coordination between multiple displays within the space spot, content refresh intervals, and / or temporal alignment with real-time adjustment triggers. These instructions may, for example, advantageously enable the 020 routing engine 122 to execute template specifications through control signals 2218 to physical display devices and / or content distribution systems (e.g., to passive physical spaces).

[0096] At step 534, it is determined if the MPC is satisfied. The step 534 may include, for example, validating the configured template against media placement criteria requirements The validation process may verify (e.g., automatically), by way of example and not limitation, that layout configurations accommodate media format specifications, technical display parameters meet quality standards, space spot boundaries satisfy minimum size requirements, and / or integration instructions comply with deployment constraints. If the media placement criteria are not satisfied, the process returns to step 524 (e.g., to adjust the layout configuration, such as potentially modifying content placement, dimensions, and / or arrangement such as to achieve compliance).

[0097] Once satisfied at step 534, the SSGT package is updated at step 536 by compiling the generated specifications. These specifications may include, for example, boundary definitions, layout configurations, technical display parameters, temporal scheduling information, and / or integration instructions. For example, a SSGT output(s) 1912 may be generated as a data structure(s). The package may, for example, link the template to the space spot identifier (e.g., establishing referential relationships between the template and its parent space spot data structure). The package may, for example, associate the template with the media placement criteria (e.g., media placement package 120). The link(s) may, for example, associate requirements with template generation. The updated SSGT package may, for example, represent a complete deployment 'blueprint' ready for execution by content distribution systems.

[0098] The loop is iterated at step 540, repeating the process (e.g., returning to step 522 in this example), until the SSGTs (e.g., all template variations produced in steps 514, 516, and 518), at which point the method ends at step 542. This process may, for example, advantageously provide advertisers and / or automated selection systems with multiple validated options representing different attribute balancing strategies.

[0099] The method may, for example, advantageously address technical challenges related to automatic template generation, such as by employing Al-driven visual analysis leveraging, for example, computer vision and / or machine learning to assess advertising effectiveness factors that would be difficult to quantify, and is not currently quantified in manual processes. The generation of multiple template options may, for example, separate automatic processes into phase-adaptedprocessing steps which may, for example, be completed by different engines. The steps may, for example, advantageously enable automatic evaluation and ranking instead of a human process of manual 'intuition' of target balance. The method may, for example, advantageously provide a 020 engine 102 flexibility for different advertiser priorities and / or enable data-driven selection of optimal configurations rather than relying on manually predetermined layouts. Temporal adjustment rules may, for example, advantageously enables templates to maintain effectiveness across varying environmental conditions, such as by adapting display parameters in response to real-time sensor data. Structured data package generation may, for example, advantageously facilitate efficient communication with content distribution systems, which may advantageously enable automated deployment (e.g., through the 020 routing engine 122), such as even without manual intervention.

[0100] Fig. 6 depicts an example space identification engine operation method 600. The method may, for example, provide automatic analysis of physical spaces. Embodiments may, for example, advantageously address technical problems related to identifying and / or evaluating potential advertising surfaces through computer vision and / or Al-driven assessment. The method may, for example, advantageously provides systematic surface detection, environmental analysis, and / or qualitybased filtering, such as to generate proposed advertising locations.

[0101] The process begins at step 602. At step 604, physical space identification is received The identification may include space address, coordinates, and / or space type designation such as restaurant 106, retail store 114, park 110, gas station 112, apartment building 108, or kiosk 116 Step 606 retrieves the physical space environment, accessing existing spatial data or facility information.

[0102] In step 608, visual data availability is determined. If available at step 608, step 610 acquires the visual data. As an example, step 610 may include acquiring images 810 (e.g., from multiple angles), 3D models 812 (e.g., from walkthroughs, scans, and / or 2D-to-3D generation), and / or video 814 footage. Visual data may, for example, be acquired from sensors and / or external data sources (e.g., databases, APIs). If determined unavailable, step 612 requests visual data collection. For example, step 612 may include prompting a user (e.g., physical space operator) for visual data. Step 612 may, for example, generate deployment instructions (e.g., for a visual acquisition drone, for a human operator, for sensors 128).

[0103] Once data is acquired, step 614 applies visual analysis models to the acquired data. Step 614 may include, for example, operation of a vision model 800. The vision model 800 may, for example, employ an image processing module 816 such as for normalization and / or feature extraction, an object detection module 818 such as to detect physical objects (e.g., structural features such as walls, poles, pillars, ceilings, floors; objects such as digital displays, moving surfaces), a surface classification module 820 such as configured to perform type categorization and / or material analysis, and / or a spatial analysis module 822 such as configured for dimension calculation and / or coordinate extraction. A visual analysis model 900 may, for example, process data, such as to evaluate grid space visual characteristics 902.

[0104] Step 616 identifies potential advertising surfaces. For example, the output of step 616 may include identified advertising surfaces 824. Identified advertising surfaces 824 may include associated coordinates, surface types, dimensions, orientation data, and / or material properties. The identification may, for example, leverage computer vision algorithms and / or other models including, for example, in feature detection, object recognition, surface detection, and / or spatial boundary delineation.

[0105] Step 618 assesses whether environmental recognition is to be performed. If yes, step 620 identifies visible environmental factors. For example, step 620 may include operating an environmental recognition module 1700. Theenvironmental recognition module 1700 may, for example, employ a visual obstruction detector 1708 such as for occlusion analysis, a lighting condition analyzer 1710 such as for illumination measurement, and / or a traffic flow pattern recognizer such as for movement tracking. The environmental recognition module 1700 may, for example, operate on visual data 1702 and / or sensor data 1704 (e.g. , from sensors 128).

[0106] Step 622 determines related environmental factors. Related environmental factors may be associated with the physical space but may, for example, not be visually discernible from the acquired visual data. Environmental data may include, for example, proximity to areas of different traffic levels (e.g., calculated by a proximity analyzer 1116), visibility angles, and / or exposure potential.

[0107] Environmental factors may include, for example, physical space type (e.g., law office, retail shop, bus station, electric pole). Environmental factors may, for example, include physical surroundings (e.g., interior I exterior, sun exposure, rain exposure). Environmental factors may include, for example, historical traffic patterns. Environmental factors may include, for example, demographics of people normally at the physical space. Environmental factors may include, for example, sales data correlated to the physical space (e.g., corresponding to historical advertisements and / or goods and / or services normally provided at the physical space). Environmental factors may, for example, include local and / or regional attributes (e.g., holidays, languages, regulations, cultural expectations). Step 620 and / or step 622 may, for example, generate environmental factors 1714. If environmental recognition is not confirmed at step 618, the flow proceeds directly to step 624.

[0108] Step 624 examines whether quality assessment should be performed. If warranted, step 626 determines visual characteristics. Visual characteristics may include, for example, size, orientation, and / or clarity through analysis (e.g., of the identified advertising surfaces 824). Step 628 determines historical performance. For example, historical performance data 906 may be generated and / or retrieved (e.g., accessing stored records correlating similar surfaces with advertising effectiveness). Step 630 calculates a preliminary value score. The score may, for example, be generated by combining visual characteristics, environmental factors 1714, and / or historical performance data 906 through weighted algorithms and / or trained models.

[0109] At step 632, the calculated value score is evaluated against a quality threshold. If the threshold is not met, step 634 excludes the surface from further consideration. If met, step 636 extracts location attributes. Attributes may, for example, include dimensions (e.g., height, width, height, area, volume, weight capacity), orientation (e.g., facing direction, viewing angle), coordinates within the physical space 104, surface type(s), and / or material properties. The extraction may, for example, categorize surfaces as static structure (permanent), dynamic structure (digital and / or moving), and / or hybrid. Grid suggestions (e.g., preliminary) may be generated, such as suggesting grid divisions, coordinate system types, and / or granularity levels.

[0110] Step 638 determines if additional surfaces exist for assessment. If yes, the process repeats (e.g., looping back to step 616 in this example). If no further surfaces are identified, the process concludes at step 640. Step 640 may include outputting a data structure(s) with proposed advertising locations, such as ranked by preliminary value, grouped by location type, and / or tagged with extracted attributes.

[0111] This method advantageously employs Al-driven computer vision such as to automate surface identification, using methods not used by manual inspection. Environmental recognition may, for example, incorporate contextual factors affecting advertising value beyond surface characteristics alone. Quality-based filtering may, for example, reduces computational resource usage, such as by excluding low-value surfaces before resource-intensive grid generation operations.

[0112] Fig. 7 depicts an example grid generation method 700. The method may, for example, advantageously address technical problems related to automatically creating hierarchical spatial representations with variable granularity adapted to space complexity. The method may, for example, advantageously provide systematic grid layer creation, adaptive meshing, and / or overlay management, such as for multiple coordinate systems and / or media types.

[0113] The process initiates at step 702. Step 704 receives physical space data. Physical space data may, for example, be retrieved (e.g., from SDS). Data may, for example, include visual data (images 810, 3D models 812, video 814), space boundaries and / or dimensions, and / or identified advertising surfaces 824, such as from prior analysis. Step 706 identifies space complexity. Complexity may, for example, be analyzed using the physical space data. Step 706 may include Al-driven assessment. Step 706 may, for example, employ a vision model 800, such as with a spatial analysis module 822. The spatial analysis module 822 may, for example, be configured to detect surface characteristics, architectural intricacy, layout variability, and / or other factors that may influence grid density.

[0114] At step 708, a coordinate system is generated. The coordinate system may, for example, be generated based on space geometry (e.g., complexity) and / or advertising surface distribution. By way of example and not limitation, rectangular systems may be selected for regular spaces. Triangular systems may be selected for specialized tessellation. Radial systems may, for example, be selected for circular and / or sector-based layouts. Multiple coordinate systems may be combined (e.g., in one or more data structures), such as to create overlay structures. Overlay structures with multiple coordinate systems may, for example, advantageously accommodate different media types (e.g., SSGT layouts) and / or or spatial zones within the same physical space 104.

[0115] Step 710 creates grid layer i, initially representing the first layer. The first layer may, for example, be a broadest spatial division (GO). The grid layer creation applies the selected coordinate system to subdivide the physical space.

[0116] Step 722 determines if adaptive meshing is to be performed. Step 171 may include manual selection and / or application of administrator-configured parameters. Step 722 may, for example, evaluate whether space complexity varies significantly across regions, warranting variable grid density. If adaptive meshing is not to be performed, step 712 defines uniform cell boundaries with consistent dimensions throughout the grid layer. If adaptive meshing is to be performed, step 724 defines cell boundaries with variable density (e.g., according to space complexity). In step 724 a meshing engine may, for example, allocate finer granularity to intricate and / or higher value regions (e.g., high-traffic zones, complex geometries). Coarser granularity may be allocated to 'simple' regions (e.g., open areas, low-value zones). For example, step 724 may include operation of a grid adaptation module 1800. The grid adaptation module 1800 may, for example, employ a complexity analyzer 1804 and / or granularity adjustment engine 1810.

[0117] Step 714 defines cell coordinates. This may include, for example, calculating center points, comer vertices, and / or representative positions for each grid unit 126 within the current layer. Step 716 assigns labels to cells. For example, step 716 may include generating hierarchical naming. Hierarchical addressing may be generated such that traceability is maintained to parent units across layers (e.g., G1-2.1 as subdivision of GO-2).

[0118] At step 718, the method checks if maximum grid unit area is met. Step 718 may include, for example, evaluating whether current granularity satisfies minimum resolution parameters (e.g., from historical media placement criteria and / or space spot formation needs). If not met (e.g., indicating insufficient detail), step 720 increments grid layer counter i and the process repeats (e.g., returning to step 710 as shown) to create the next layer (e.g., G1 , G2, etc.). The process may, forexample, iteratively subdivide previous layer cells and / or maintain hierarchical naming chains. This process continues until step 718 confirms adequate granularity.

[0119] Once the coordinate system completes generating layers, step 726 generates and / or updates the data structure. This may, for example, include compiling layer definitions, establishing parent-child relationships between grid layers, and / or creating hierarchical structures (e.g. , stored as space data structures).

[0120] Step 728 determines whether additional overlays are to be added. For example, step 728 may include assessing if multiple media types (e.g., static structures, dynamic displays, hybrid configurations) coexist within the physical space and / or multiple coordinate systems are to be available. If additional overlays are to be created, the process repeats (e.g., returning to step 708 as depicted), generating overlay grid layers (e.g., with different coordinate systems and / or configurations). Overlay types may, by way of example and not limitation, include static structure grids for permanent fixtures, dynamic structure grids for digital displays or moving elements, and / or hybrid grids operating in fixed structure with dynamic data mode, dynamic structure with fixed data mode, and / or mixed mode.

[0121] Once overlays are complete, step 730 generates grid units 126. Grid units 126 may, for example, be individual records. Generating grid units may, for example, include assigning unique identifiers with hierarchical naming, storing coordinates and / or boundaries, and / or linking units to parent layers and / or space data structures. The grid units may be configured with real-time adjustment capability. For example, radial grids may dynamically resize sectors, such as based on sensor data from sensors 128. Such embodiments may, for example, advantageously enable adaptive response to traffic patterns 1106 and / or other environmental changes. The method concludes at step 732, outputting grid structures such as, for example, ready for attribute array generation and / or performance evaluation.

[0122] The depicted method advantageously employs adaptive meshing, such as to allocate computational resources efficiently. For example, adaptive meshing may advantageously enable concentrating grid density in 'complex' regions with more detailed analysis, while reducing overhead in 'simple' regions. Hierarchical layering may, for example, advantageously enable multi-scale analysis and / or flexible space spot formation across varying size requirements. Overlay support may, for example, advantageously accommodate diverse media types within unified spatial frameworks, which may, for example, advantageously facilitate coordinated management of heterogeneous advertising modalities.

[0123] Fig. 8 depicts an example vision model 800. The vision model 800 may, for example, advantageously enable automated advertising surface identification, such as through Al-driven visual analysis. The model may, for example, process diverse visual inputs to automatically detect, classify, and / or characterize potential advertising surface within physical spaces.

[0124] The input layer 802 receives one or more inputs. In this example, inputs include visual inputs. Visual inputs, as depicted, include images 810. The images may, for example, be captured (e.g., from one or more angles), such as by sensors 128. The visual inputs may include 3D models 812 (e.g., acquired from walkthroughs and / or laser scans). The visual inputs may, for example, include video 814 (e.g., providing temporal sequences). These inputs may originate from distributed sensor networks monitoring physical spaces 104 such as restaurants 106, retail stores 114, parks 110, gas stations 112, apartment buildings 108, or kiosks 116. The inputs may, for example, be retrieved from external data stores 224. External data stores 224 may, for example, include imaging services such as, for example, maps, street view, and / or satellite imagery. The inputs may, for example, be received from user devices 218.

[0125] The processing layer 806 contains modules for data analysis. In this example, the image processing module 816 may, for example, perform normalization converting inputs to standardized formats (e.g., through a data preprocessing module 1400). The image processing module 816 may, for example, perform feature extraction, such as using convolutional neural networks and / or computer vision techniques, such as to identify edges, textures, and / or structural elements. An object detection module 818 may, for example, execute detection tasks. For example, the object detection module 818 may operate on the visual inputs. The object detection module 818 may, for example, include wall detection, such as using edge detection and / or plane fitting, pole / pillar detection (e.g., such as through cylindrical shape recognition), ceiling detection (e.g., via overhead surface identification), floor detection (e.g., via ground plane estimation), digital display detection (e.g., identifying screens and / or LED panels), and / or moving surface detection (e.g., identifying vehicles and / or dynamic elements. The module may, for example, employ model architectures trained on architectural and / or advertising surface datasets.

[0126] A surface classification module 820 may, for example, perform type categorization. The surface classification module 820 may, for example, distinguish between static structures, dynamic displays, and / or hybrid surfaces. The module may, for example, perform material analysis (e.g., identifying properties such as concrete, glass, metal, and / or digital substrates). The module generated outputs may, for example, configured to influence installation feasibility and / or advertising durability.

[0127] A spatial analysis module 822 may, for example, calculates dimensions (height, width, area) (e.g., via photogrammetry and / or depth estimation). The module may, for example, determine orientations (e.g., facing direction, viewing angles). The module may, for example, perform coordinate extraction establishing positions within the physical space coordinate system. The module may, for example, derive 3D spatial information such as, by way of example and not limitation, via structure-from- motion algorithms, stereo vision, and / or depth sensors.

[0128] The output layer 808 generates identified advertising surfaces 824. The identified advertising surfaces 824 may be represented as data structures containing coordinates within the physical space, surface types (e.g., wall, pole, ceiling, floor, display, moving, obstructions), dimensions (e.g., providing height, width, area (e.g., for space allocation), volume, distances), orientation data (e.g., specifying facing direction, viewing angles, such as for placement strategies), and / or material properties (e.g., indicating substrate characteristics such as affecting installation methods and / or content display capabilities). These outputs may, for example, provide surface characterization. The outputs may, for example, advantageously enable grid generation (e.g., subsequent), such as via grid generation method 700 and / or space data structure creation, such as via space data structure generation method 300.

[0129] The vision model 800 may, for example, advantageously automate surface identification that would otherwise require extensive manual surveying, performing detection across varying architectural styles, structural features, and / or environmental conditions. The vision model 800 may, for example, generate structured outputs, such as compatible with downstream computational processes, such as for grid-based valuation and / or media placement and / or routing.

[0130] Fig. 9 depicts an example visual analysis model 900. The visual analysis model 900 may, for example, address a technical problem of automatically calculating 'optimized' advertising space layouts, such as through Al -driven visual assessment and / or template generation. The model may, for example, advantageously analyze spatial characteristics, such as to produce multiple configuration options balancing multiple constraints.

[0131] The input layer 802 receives grid space visual characteristics 902. The characteristics may, for example, include characteristics generated by the vision model 800. Examples may include, for example, viewing angles, visibility patterns, and / or aesthetic elements from spaces analyzed by the vision model 800.

[0132] input layer 802 includes media placement criteria 804. The media placement criteria 804 (e.g. , as a media placement package 120) may, for example, include targeting parameters, format specifications, and / or budget constraints, such as from the media originator 118. Space spot boundaries 904 may, for example, define aggregated grid units 126, such as for which template configuration is to be performed. Historical performance data 906 may, for example, supply past metrics. Such metrics may, for example, include advertising effectiveness metrics, such as correlating visual configurations with engagement outcomes.

[0133] The processing layer 806 transforms inputs through specialized components. Avisual impact assessment engine 908 may, for example, generate a visual impact score. For example, visual impact assessment engine 908 may assess visibility from traffic positions, such as using attention prediction models. The engine may, for example, assess aesthetic impact, such as through models trained on design principles, quantify factors such as viewing distance, angle favorability, and / or compositional balance.

[0134] A content placement zone identifier 910 may, for example, identify zones available for content placement. The identifier engine may, for example, calculate optimal zones (e.g., according to one or more optimization algorithms), such as within space spot boundaries 904. The identifier may, for example, analyze visibility patterns, generate layout hierarchy (e.g., defining zones such as primary, secondary, and / or branding zones), and / or apply design principles (e.g., rule of thirds, visual flow optimization).

[0135] A balancing analyzer 912 may, for example, operate as a trade-off analyzer. For example, the balancing analyzer 912 may operate a space / value trade-off analyzer. The analyzer may, for example, performs balance calculations of parameters (e.g., value-space). The analyzer may, for example, perform multi-objective adaptation (e.g., optimization algorithms), evaluate trade-offs between parameters (e.g., visual impact maximization and space efficiency in this example), and / or generates algorithmic-optimal configurations representing different priority weightings.

[0136] A template generator 914 may, for example, generate templates (e.g., SSGTs). For example, the template generator 914 may be operated to generate multiple layout options, such as through generative design algorithms, synthesize configurations (e.g., integrating zone placements with technical constraints), and / or produce variations (e.g., spanning a range of 'competing' parameter balance points).

[0137] In this example, the output layer 808 delivers template layout options 916. The template layout options 916 may, for example, include SSGT output(s) 1912 presenting alternative arrangements with different optimization strategies. The output layer 808 may, as depicted, include content placement zones 918. The zones may, for example, specifying coordinates and / or dimensions for each content element (e.g., within a space). The output layer 808 includes in this example visual impact scores 920. The scores may, for example, quantifying predicted effectiveness, such as through weighted metrics of visibility, attention capture, and / or aesthetic quality. The output layer 808 may include, as in this example, recommended configurations 922. The configurations may, for example, be used to generate templates, such as based on media placement criteria 804 analysis and / or historical performance data 906 correlation.

[0138] The visual analysis model 900 may, for example, advantageously employ Al-driven assessment, such as to evaluate complex visual factors beyond manual analysis capabilities. The visual analysis model 900 may, for example, generate multiple template options, which may advantageously enable data-driven selection (e.g., rather than manually intuited layouts). The visual analysis model 900 may, for example, advantageously integrate historical performance correlation, such as to predict effectiveness of proposed configurations. The visual analysis model 900 may, for example, advantageously support a SSGT generation method 500.

[0139] Fig. 10 depicts an example weather integration model 1000. The weather integration model 1000 may, for example, advantageously address a technical problem of dynamically (e.g., automatically) routing media based on automatically calculated advertising effectiveness predictions and attribute weighting responsive to meteorological conditions. The model may, for example, process weather data such as to adjust grid performance ratings and / or enable dynamic reconfiguration based on environmental changes.

[0140] An input layer 802 receives historical weather data 1002. The historical weather data 1002 may, for example, include past temperature, precipitation, wind, and / or lighting conditions, such as correlated with advertising performance, current weather conditions 1004 (e.g., from sensors 128 and / or external weather services providing real-time environmental state), and / or weather forecast data 1006 (e.g., offering predictive meteorological information). Historical performance 1008 (e.g., historical performance data 906) may be integrated, such as to correlate weather patterns with advertising effectiveness outcomes.

[0141] The depicted processing layer 806 employs a data correlation module 1010. The module may, for example, match weather patterns with performance metrics, identify correlations between conditions (e.g., rain, snow, fog) and visibility and / or engagement, and / or establish baseline relationships for prediction.

[0142] One or more machine learning engines 1012 may, for example, implement supervised learning algorithms (e.g., gradient boosting), neural networks (e.g., trained on historical weather-performance datasets), apply regression and / or classification models (e.g., predicting effectiveness under varying conditions), and / or generate weather-condition-specific adjustment factors. The machine learning engine 1012 may, for example, operate on outputs of the data correlation module 1010, such as for training and / or prediction. The machine learning engine 1012 may, for example, output to an impact prediction module 1014.

[0143] The impact prediction module 1014 may, for example, generate impact scores. For example, the module may apply visibility effect calculations. The calculations may, for example, estimate how precipitation, fog, and / or lighting affects advertisement metrics (e.g., as viewability). The module may, for example, predict engagement variations, such as based on weather-induced behavior changes (e.g., indoor vs. outdoor traffic shifts, dwell time modifications). The module may, for example, quantify condition-specific performance degradation and / or enhancement factors.

[0144] The output layer 808 generates weather adjusted outputs 1016. The weather adjusted outputs 1016 may, for example, include weather-adjusted attribute weights modifying the application of attributes (e.g., visibility, traffic under current conditions), predicted effectiveness (e.g., by condition). The weather adjusted outputs 1016 may, for example, include estimated GPR and / or SPR adjustments (e.g., for forecasted weather). The outputs may, for example, include dynamic adjustment recommendations, such as suggested template and / or display parameter changes (e.g., brightness increases in fog, content rotation during rain). The outputs may, for example, include real-time modification signals (e.g., triggeringreconfiguration when weather thresholds are exceeded), and / or weather impact scores (e.g., quantifying the magnitude of weather effects on advertising value).

[0145] The weather integration model 1000 may, for example, advantageously incorporate environmental variability into performance predictions, enabling the system to automatically route media in response to changing conditions. The model may, for example, support dynamic reconfiguration in response to weather changes. The reconfiguration, may, for example, be in response to changes detected by sensors 128. For example, real-time attributes in attribute arrays 130 may be updated. GPR and / or SPR recomputation may be triggered. For example, operations such as described in space spot generation method 400, may, for example, advantageously automatically perform resource allocation under diverse meteorological circumstances.

[0146] Fig. 11 depicts an example geographic model 1100 that addresses the technical problem of spatial analysis and / or traffic pattern assessment, such as for advertising location valuation. The model may, for example, process geographic data, such as to identify high-value zones and / or predict audience exposure

[0147] The input layer 802 receives GIS data 1102. The GIS data 1102 may, for example, provide topographic information, building footprints, and / or infrastructure details. Geolocation coordinates 1104 may, for example, specify precise positions of grid units 126, such as within physical spaces 104, traffic patterns 1106 (e.g., pedestrian, shopper, driver) such as including movement flows, density measurements, and / or temporal variations (e.g., from sensors, historical records). Spatial relation data 1108 may, for example, define distances, such as between grid units and / or points of interest such as transit stations, retail centers, and / or entertainment venues.

[0148] The processing layer 806 may, for example, employ a spatial analysis engine 1110. The engine may, for example, calculate geographic relationships, such as through distance metrics and / or proximity analysis, determine positioning relative to high-traffic corridors, and / or evaluate spatial configurations such as for visibility and / or accessibility. Traffic pattern recognition 1112 may, for example, perform flow analysis, such as identifying primary user routes and / or movement directions, and / or density mapping, such as quantifying occupancy levels across metrics such as spatial zones and / or temporal periods.

[0149] A traffic zone identifier 1114 may, for example, detect hotspots, such as where traffic concentration exceeds thresholds, perform exposure calculations such as estimating advertisement view frequency (e.g., based on traffic volume and / or dwell time), and / or prioritize zones, such as with increased audience reach potential.

[0150] A proximity analyzer 1116 may, for example, evaluate distances, such as to points of interest which may, for example, include transit hubs, commercial districts, and / or attractions associated with generating traffic. The analyzer may, for example, conducts accessibility scoring, such as assessing ease of audience approach and / or viewing opportunity, and / or weight grid units, such as based on favorable positioning relative to high-value destinations.

[0151] An output layer 808 generates geographic analysis results 1118. The results may, by way of example and not limitation, include traffic density predictions, such as forecasting audience volume (e.g., based on temporal patterns and / or spatial characteristics). The results may, for example, include exposure zone identifications, such as linking grid units 126 with visibility and / or traffic conditions levels. Results may, for example, include spatial relationship scores, such as quantifying positional advantages relative to traffic generators. Results may, for example, include geographic suitability ratings. Results may, for example, advantageously provide holistic location quality assessments. These outputs may, for example, update attribute arrays 130, such as with real-time and / or periodic attributes (e.g., traffic data) and / or static attributes (e.g.,geolocation). These updated attribute arrays may, for example, advantageously support GPR and / or SPR computation, such as in space spot generation method 400.

[0152] The geographic model 1100 may, for example, advantageously incorporate spatial context, such as beyond individual grid characteristics. The model may, for example, advantageously enable valuation, such as based on geographic dependent factors such as positional relationships and / or traffic dynamics. The model outputs may, for example, advantageously influence advertising effectiveness in physical spaces such as restaurants 106, retail stores 114, gas stations 112, parks 110, and transit locations.

[0153] Fig. 12 depicts an ensemble Al system 1200. The ensemble Al system 1200 may, for example, address technical problems related to synthesizing diverse analytical perspectives (e.g., from different models, engines, and / or models) into unified performance assessments (e.g., through multi-model consensus mechanisms). The system may, for example, advantageously combine specialized model outputs to produce robust predictions that leverage complementary strengths of different analytical approaches.

[0154] The input layer 802 receives visual analysis output 1202, such as from the visual analysis model 900. The visual analysis output 1202 may, for example, contain visual impact scores 920 and / or content placement recommendations. Weather integration output 1204, such as from the weather integration model 1000, may, for example, provide weather-adjusted attribute weights and / or impact scores. Geographic model output 1206, such as from the geographic model 1100, may include, for example, traffic density predictions and / or spatial relationship scores. Temporal model output 1208, such as from a temporal model 2302, may offer, for example, time-based pattern analysis and / or peak period identification. Media placement criteria 804, such as from the media placement package 120 may, for example, guide the ensemble integration, such as by specifying advertiser priorities and / or requirements.

[0155] The processing layer 806 may, for example, provide one or more synthesis mechanisms. In this example, the processing layer 806 employs a voting mechanism 1210. The voting mechanism 1210 may, for example, facilitate multi-model consensus such as through confidence weighting. For example, each model may contribute predictions. The voting mechanism may weight each input such as, for example, by its historical accuracy and / or reliability for the current context. The voting mechanism 1210 may, for example, implement majority voting. The voting mechanism 1210 may, for example, implement weighted voting. The voting mechanism 1210 may, for example, implement model averaging, such as to aggregate diverse assessments.

[0156] As an illustrative example, the visual analysis model 900 may predict a visual impact score of 0.88 with a confidence of 0.92, the weather integration model 1000 may predict a weather-adjusted score of 0.82 with a confidence of 0.85, the geographic model 1100 may predict a location score of 0.91 with a confidence of 0.89, and the temporal model 2302 may predict a time-based score of 0.79 with a confidence of 0.78. The voting mechanism 1210 may, for example, weight each prediction by its confidence score, yielding a weighted average of (0.88*0.92 + 0.82x0.85 + 0.91 x0.89 + 0.79x0.78) / (0.92 + 0.85 + 0.89 + 0.78) = 0.85.

[0157] A vector summation engine 1212 may, for example, perform weighted averaging of model outputs. The vector summation engine 1212 may, for example, integrate attribute assessments and / or performance predictions, such as through mathematical combination. The engine may, for example, balance contributions such as based on confidence scores and / or domain relevance. As an illustrative example, the visual analysis model 900 may provide a visibility attribute vector [0.90, 0.85,0.88] representing visibility from three viewing angles, the geographic model 1100 may provide a traffic attribute vector [0.87, 0.91 , 0.84] representing traffic density at three time periods, and the temporal model 2302 may provide a seasonality attribute vector [0.82, 0.88, 0.79] representing performance across three seasonal periods. The vector summation engine 1212 may, for example, compute a weighted sum of these vectors based on domain relevance weights (e.g., 0.4 for visual, 0.35 for geographic, 0.25 for temporal), yielding an integrated attribute vector [0.87, 0.88, 0.84],

[0158] The conflict resolution module 1214 may, for example, detect disagreements when models produce contradictory assessments. The conflict resolution module 1214 may, for example, resolve conflicts based on priority rankings derived from model accuracy on similar historical cases. The conflict resolution module 1214 may, for example, apply tie-breaking rules and / or contextual analysis, such as to select preferred predictions when consensus is ambiguous. As an illustrative example, the visual analysis model 900 may predict a high suitability score of 0.91 for a grid unit 126 based on visibility analysis, while the weather integration model 1000 may predict a low suitability score of 0.62 for the same grid unit 126 based on frequent adverse weather conditions (e.g., fog, rain) that reduce visibility. The conflict resolution module 1214 may, for example, detect this disagreement (e.g., a difference exceeding a threshold of 0.20). The conflict resolution module 1214 may, for example, evaluate historical accuracy of each model for similar grid units 126 in comparable weather conditions. If the weather integration model 1000 has demonstrated 87% accuracy in predicting performance degradation due to weather conditions in similar locations, while the visual analysis model 900 has demonstrated 72% accuracy when weather factors are significant, the conflict resolution module 1214 may, for example, assign higher priority to the weather integration model 1000 output, such as yielding a resolved score of 0.68 (weighted toward the weather model prediction).

[0159] A consensus algorithm 1216 may, for example, perform (e.g., final) decision synthesis, such as combining resolved model outputs into unified assessments. The consensus algorithm 1216 may, for example, generate confidence scores quantifying prediction reliability, such as based on inter-model agreement and / or individual model certainties. The consensus algorithm 1216 may, for example, produce integrated recommendations (e.g., leveraging complementary analytical strengths). As an illustrative example, after conflict resolution, the visual analysis model 900 may contribute a score of 0.88 with confidence 0.90, the weather integration model 1000 may contribute a score of 0.82 with confidence 0.87, the geographic model 1100 may contribute a score of 0.90 with confidence 0.91 , and the temporal model 2302 may contribute a score of 0.85 with confidence 0.83. The consensus algorithm 1216 may, for example, compute a final unified score of 0.86, such as through confidence-weighted averaging. The consensus algorithm 1216 may, for example, compute an overall confidence score (e.g., based on inter-model agreement). For example, the algorithm may calculate the standard deviation of the model scores (e.g., 0.033 in this example). The algorithm may map the standard deviation to a confidence metric. The engine may, for example, yield a high confidence of 0.92 (e.g., due to strong inter-model agreement (low variance)).

[0160] The output layer 808 generates ensemble output 1218. The output may, for example, provide unified attribute assessments. The assessments may, for example, synthesize visual, weather, geographic, and / or temporal factors into holistic (e.g., unified) grid unit valuations, and / or combined performance predictions integrating multiple perspectives. The assessments may, for example, advantageously facilitate robust GPR and / or SPR estimates (e.g., with associated confidence metrics). These outputs may, for example, feed into the GPR computation in space spot generation method 400 (e.g., step 416), such as where sparsified attributes are processed to calculate GPR incorporating ensemble-derived assessments. As an illustrative example, the ensemble output 1218 may include a unified GPR of 0.86 for a grid unit 126, with a confidencescore of 0.92, derived from the consensus of visual impact (0.88), weather adjustment (0.82), geographic factors (0.90), and temporal patterns (0.85). The ensemble output 1218 may, for example, include attribute assessments indicating that the grid unit 126 has high visibility (0.90), moderate weather resilience (0.75), high traffic density (0.88), and strong peak period performance (0.87).

[0161] The ensemble Al system 1200 may, for example, advantageously reduce prediction variance, such as by synthesizing across multiple models. The ensemble Al system 1200 may, for example, increase robustness against individual model failures and / or biases through redundancy. The ensemble Al system 1200 may, for example, capture multi-dimensional factors that single models might overlook, thereby advantageously enhancing the accuracy and / or reliability of advertising space valuation in the 020 system. As an illustrative example, if the visual analysis model 900 were to fail and / or produce erroneous output due to poor image quality, the ensemble Al system 1200 may, for example, continue to produce reliable predictions by relying on the weather integration model 1000, the geographic model 1100, and / or the temporal model 2302, with the consensus algorithm 1216 adjusting confidence scores to reflect the reduced input diversity.

[0162] Fig. 13 depicts a GPR model 1300. The GPR model 1300 may, for example, address technical problems related to efficient grid performance rating computation, such as through selective attribute processing and / or weighting adjustment. The model may, for example, identify relevant attributes and / or compute performance scores. The model may, for example, reduce computational overhead.

[0163] The input layer 802 receives historical performance data 906. The performance data may, for example, correlate past advertising placements with effectiveness outcomes. Media placement criteria 804 may, for example, specify current advertiser requirements. Attribute arrays 130 may, for example, contain static, periodic, and / or real-time attributes, such as for grid units 126. Past GPR and / or SPR results (e.g., from previous evaluations) may, for example, advantageously enable temporal learning and / or pattern refinement.

[0164] The processing layer 806 employs a learning module 1302. The learning module 1302 in this example is a supervised learning model. The learning model may, for example, implement machine learning algorithms such as random forests, gradient boosting machines, and / or neural networks. The learning module 1302 may, for example, be trained on historical performance data 906. The supervised learning module 1302 may, for example, recognize patterns correlating attribute combinations with advertising success. The supervised learning module 1302 may, for example, execute pattern recognition (e.g., identifying attribute interactions predictive of performance). The supervised learning module 1302 may, for example, apply training algorithms through cross-validation and / or optimization techniques. The supervised learning module 1302 may, for example, refine predictive accuracy. As an illustrative example, the supervised learning module 1302 may be trained using a gradient boosting machine on 10,000 historical advertising placements. Each historical placement may, for example, include attribute data (e.g., visibility score, traffic density, weather conditions, time of day) and corresponding performance outcomes (e.g., click-through rate, conversion rate). The supervised learning module 1302 may, for example, identify that combinations of high visibility (e.g., score above 0.85) and peak traffic periods (e.g., 17:00-19:00) correlate with advertising success rates exceeding 0.90. The learning module 1302 may, for example, apply cross-validation across multiple data folds. Isem, the learning module 1302 may include and / or be replaced by inference models. For example, some embodiments may use small and / or large language models (e.g., to infer attributes, associations, and / or MPC such as from an MPP).

[0165] The attribute relevance identifier 1304 may, for example, perform feature selection. The attribute relevance identifier 1304 may, for example, use techniques such as recursive feature elimination, mutual information scoring, and / or regularization. The attribute relevance identifier 1304 may, for example, identify the subset of attributes from attribute arrays 130 that significantly influence performance for given media placement criteria 804. The attribute relevance identifier 1304 may, for example, perform relevance scoring. The relevance scoring may, for example, quantify each attribute's predictive importance. The relevance scoring may, for example, enable prioritization of computationally valuable features. The relevance scoring may, for example, eliminate redundant and / or uninformative attributes. As an illustrative example, a complete attribute array 130 for a grid unit 126 may contain 150 attributes including visibility metrics, traffic patterns, weather data, temporal factors, and demographic information. The attribute relevance identifier 1304 may, for example, apply recursive feature elimination to evaluate each attribute's contribution to prediction accuracy. The attribute relevance identifier 1304 may, for example, determine that 18 attributes (e.g., visibility score, peak traffic density, weather condition category, time of day, proximity to high-traffic destinations) account for 92% of predictive power. The attribute relevance identifier 1304 may, for example, assign relevance scores ranging from 0.85 (visibility score) to 0.12 (ambient temperature) The attribute relevance identifier 1304 may, for example, identify 132 attributes with relevance scores below a threshold of 0.15 as candidates for elimination. The attribute relevance identifier 1304 may, for example, determine that three attributes (e.g., wind speed, barometric pressure, day of week) are redundant with other retained attributes (e.g., weather condition category, temporal patterns).

[0166] The weight calculation engine 1306 may, for example, apply criteria-based weighting. The criteria-based weighting may, for example, adjust attribute importance based on advertiser priorities in media placement criteria 804. The weight calculation engine 1306 may, for example, determine coefficients through optimization algorithms. The optimization algorithms may, for example, maximize correlation between weighted attribute combinations and historical performance outcomes. The weight calculation engine 1306 may, for example, produce attribute-specific weights. The attribute-specific weights may, for example, enable selective summation in GPR computation (e.g., as described in space spot generation method 400, such as at step 416). As an illustrative example, media placement criteria 804 for a first advertiser may specify high priority for visibility (weight: 0.7) and moderate priority for traffic density (weight: 0.3). Media placement criteria 804 for a second advertiser may specify balanced priorities for visibility (weight: 0.4), traffic density (weight: 0.3), and demographic alignment (weight: 0.3). The weight calculation engine 1306 may, for example, retrieve historical performance data 906 for similar advertiser profiles. The weight calculation engine 1306 may, for example, apply an optimization algorithm (e.g., gradient descent) to determine coefficients that maximize correlation between weighted attribute combinations and historical conversion rates. For the first advertiser, the weight calculation engine 1306 may, for example, determine optimal coefficients of 0.75 for visibility score, 0.25 for traffic density, and 0.05 for demographic alignment, achieving a correlation of 0.91 with historical performance. For the second advertiser, the weight calculation engine 1306 may, for example, determine optimal coefficients of 0.42 for visibility score, 0.33 for traffic density, and 0.35 for demographic alignment, achieving a correlation of 0.88 with historical performance. The weight calculation engine 1306 may, for example, produce attribute-specific weights enabling weighted summation: GPR = (0.75 x visibility) + (0.25 x traffic) + (0.05 x demographics) for the first advertiser.

[0167] The sparsification module 1308 may, for example, reduce or eliminate irrelevant attributes based on outputs from the attribute relevance identifier 1304. The sparsification module 1308 may, for example, reduce dimensionality from complete attribute arrays 130 to sparse subsets. The sparse subsets may, for example, contain only relevant features. The sparsificationmodule 1308 may, for example, increase computational efficiency. The computational efficiency may, for example, be increased by limiting processing to selected attributes. The sparsification module 1308 may, for example, achieve computational reductions exceeding fifty percent. The sparsification module 1308 may, for example, maintain prediction accuracy. As an illustrative example, a complete attribute array 130 may contain 150 attributes requiring 150 multiplication operations and 149 addition operations per grid unit 126 for GPR computation. The sparsification module 1308 may, for example, eliminate 132 irrelevant attributes identified by the attribute relevance identifier 1304, reducing the attribute array 130 to a sparse subset of 18 attributes. The sparsification module 1308 may, for example, reduce computational requirements to 18 multiplication operations and 17 addition operations per grid unit 126, for example achieving a computational reduction of 88% ((150-18) / 150). For a physical space 104 containing 500 grid units 126, the sparsification module 1308 may, for example, reduce total operations from 74,500 (150x500 multiplications + 149x500 additions) to 8,500 (18x500 multiplications + 17x500 additions). The sparsification module 1308 may, for example, maintain prediction accuracy of 0.87 compared to 0.88 (e.g., achieved using the complete attribute array 130), representing a negligible accuracy reduction of 1.1% while achieving an 88% computational reduction. Moreover, sparsified attribute arrays may advantageously reduce downstream computations and / or operations (e.g , queries, storage operations).

[0168] In some embodiments, a selected attributes 134 array (sparsified attributes) may, for example, be a temporary data structure (e.g., in memory). In some embodiments, an MFC may be correlated with the selected attributes 134 (e.g., in a space data structure). Selected attributes 134 array(s) may, for example, be saved as permanent data structures.

[0169] The output layer 808 generates model output 1310. The model output 1310 may, for example, include the subset of relevant attributes identified for current media placement criteria 804. The model output 1310 may, for example, include weighting coefficients per attribute. The weighting coefficients may, for example, enable weighted summation. The model output 1310 may, for example, include attribute importance rankings. The attribute importance rankings may, for example, prioritize features by predictive value. The model output 1310 may, for example, include confidence scores. The confidence scores may, for example, quantify prediction reliability based on training performance and validation metrics. These outputs may, for example, directly support the sparsification and GPR computation operations in method 400 (e.g., steps 414-416). These outputs may, for example, enable efficient real-time performance evaluation across numerous grid units 126 in physical spaces 104. The outputs may, for example, include weighting coefficients (e.g., per attribute), attribute importance rankings, and / or confidence scores.

[0170] As an illustrative example, the model output 1310 for a specific media placement criteria 804 may include a subset of 18 relevant attributes (e.g., visibility score, peak traffic density, weather condition, time of day, proximity to destinations, demographic alignment, historical performance, seasonal factors, lighting conditions, viewing angles, obstruction presence, surface quality, content compatibility, competitive density, event proximity, accessibility, maintenance status, regulatory compliance). The model output 1310 may, for example, include weighting coefficients: visibility score (0.75), peak traffic density (0.25), weather condition (0.18), time of day (0.15), and so forth. The model output 1310 may, for example, include attribute importance rankings: visibility score (rank 1 , importance 0.85), peak traffic density (rank 2, importance 0.78), weather condition (rank 3, importance 0.72). The model output 1310 may, for example, include a confidence score of 0.91 based on validation performance (e.g., R2= 0.89, RMSE = 0.04) across 2,000 validation samples.

[0171] The GPR model 1300 may, for example, reduce computational requirements. The computational requirements may, for example, be reduced through selective attribute processing. The GPR model 1300 may, for example, adapt weighting dynamically to advertiser-specific criteria. The GPR model 1300 may, for example, avoid applying fixed valuations. The GPR model 1300 may, for example, leverage historical learning. The historical learning may, for example, improve prediction accuracy over time. The prediction accuracy may, for example, improve as more performance data accumulates. As an illustrative example, the GPR model 1300 may initially achieve a prediction accuracy of 0.82 when trained on 5,000 historical placements. As the historical performance data 906 accumulates to 20,000 placements over six months, the GPR model 1300 may, for example, be retrained. The retrained GPR model 1300 may, for example, achieve an improved prediction accuracy of 0.89, representing an 8.5% improvement in predictive performance. The GPR model 1300 may, for example, identify new attribute interactions (e.g., correlation between weather conditions and demographic behavior patterns) that were not statistically significant in the smaller training dataset.

[0172] In various embodiments, sparsification may, for example, provide a computational efficiency technique for selective attribute processing (e.g., during GPR computation). The method may, for example, begin with a complete attribute array 130 for a grid unit 126 containing n total attributes (e.g., 132). The attributes 132 may, for example, include static attributes such as geolocation and / or structural properties. The attributes 132 may, for example, include periodic attributes such as traffic patterns 1106 and / or historical performance data 906. The attributes 132 may, for example, include real-time attributes such as temperature 1402, lighting levels 1408, and / or occupancy data from sensors 128. In various embodiments, n may equal twelve attributes per grid unit 126, though other values are contemplated.

[0173] Upon receiving media placement criteria 804 from a media placement package 120, the system may, for example, apply one or more models to identify relevant attributes. The models may, for example, have been trained on historical performance data correlating attribute patterns with performance. An attribute relevance identifier may, for example, compute relevance scores for each attribute relative to the received media placement criteria. Attributes with relevance scores exceeding a predetermined threshold may, for example, be selected to form subset S of relevant attribute indices.

[0174] In an illustrative example, subset S may include indices {2, 5, 10, 11} corresponding to traffic volume, demographics, surface type, and time-of-day attributes, respectively. This selection may, for example, yield k = 4 relevant attributes where k is substantially less than n = 12, representing a sixty-seven percent reduction in the number of attributes requiring processing. The sparsification module 1308 may, for example, extract only the selected attributes 134 indexed by subset S from the complete attribute array 130. The sparsification module 1308 may, for example, create a sparse attribute array that contains solely the attributes necessary for performance evaluation under the current media placement criteria 804.

[0175] A weight calculation engine may, for example, assign weights to the selected attributes 134, such as based on their importance to the media placement criteria. The weights may, for example, be constrained to sum to unity in some examples. The constraint may, for example, enforce normalized influence across attributes. In the continuing example, traffic volume may receive a weight of 0.45, demographics a weight of 0.35, surface type a weight of 0.12, and time-of-day a weight of 0.08, such as reflecting the relative importance of each factor for the specific advertising placement under consideration. The weight calculation engine 1306 may, for example, determine these weights through constrained optimization algorithms. The constrained optimization algorithms may, for example, maximize correlation between weighted attribute combinations and historical performance outcomes.

[0176] Prior to GPR computation, a data preprocessing module 1400 may, for example, normalize each relevant attribute to a predetermined range (e.g., to a range of zero to one). As an illustrative example: for continuous attributes, normalization may employ linear scaling based on minimum and maximum observed values. For binary attributes, normalization may assign a value of one for matches and zero for non-matches. For categorical attributes, normalization may compute scores relative to maximum possible scores. In the example, traffic volume may normalize to 0.82, demographics to 0.75, surface type to 1 .00 (binary match), and time-of-day alignment to 0.90.

[0177] The GPR may, for example, be computed using only the selected attributes 134 according to the formula: {GPR}(g) = sum_{i \in S} (wj times vj), where g represents the grid unit identifier, wj represents the weight for attribute i, and vj represents the normalized value for attribute i. In the example calculation, {GPR} = (0.45 * 0.82) + (0.35 * 0.75) + (0.12 * 1.00) + (0.08 * 0.90) = 0.369 + 0.263 + 0.120 + 0.072 = 0.824. This selective processing may, for example, achieve computational efficiency by performing only k multiplications and k-1 additions rather than n multiplications and n-1 additions. The computational efficiency may, for example, reduce computational operations by approximately (n-k) / n, which equals sixtyseven percent in the illustrated example, even before downstream and / or update computations are taken into account.

[0178] The computed GPR may, for example, be compared against a threshold value. The comparison may, for example, determine eligibility for space spot formation. Grid units 126 with GPR values exceeding the threshold may, for example, be included in candidate sets, such as for aggregation into space spots. Grid units with insufficient GPR values may, for example, be excluded from further consideration. This filtering operation may, for example, further reduce computational load by limiting subsequent space spot formation processing to high-performing candidates.

[0179] The sparsification method may, for example, provide substantial computational advantages over processing complete attribute arrays 130. Without sparsification, computing GPR for each grid unit 126 may, for example, require n multiplications and n-1 additions, totaling 2n-1 operations per grid unit. For a physical space 104 containing one thousand grid units 126, processing all twelve attributes may, for example, require twenty-three thousand operations. With sparsification reducing the attribute count to k = 4, the same physical space may, for example, require only seven thousand operations, representing a seventy percent reduction in computational load. This efficiency gain may, for example, translate to reduced processing time. This efficiency gain may, for example, translate to lower memory consumption by approximately (n-k) / n percent. This efficiency gain may, for example, translate to reduced power consumption. This efficiency gain may, for example, translate to decreased network bandwidth requirements in distributed sensor networks. The efficiency gain may, for example, enable real-time GPR computation across larger numbers of grid units 126 and physical spaces 104 than would be computationally feasible using complete attribute processing.

[0180] A sparsified attribute data structure may, for example, be implemented as a sparse array containing only selected attributes 134 identified by the attribute relevance identifier 1304. The data structure may, for example, store attribute identifiers corresponding to subset S. The data structure may, for example, store normalized attribute values for each relevant attribute. The data structure may, for example, store corresponding weights from the weight calculation engine 1306. In various embodiments, the sparsified data structure may, for example, be stored in memory 202 during active computation. The sparsified data structure may, for example, be persisted to storage 206 (e.g., as part of internal data 212) for subsequent retrieval. The data structure may, for example, include metadata indicating the media placement criteria 804 that generated the particular sparsification configuration. The metadata may, for example, enable reuse of sparsified arrays, such as whenidentical and / or similar media placement criteria are subsequently received. The sparsified data structure may, for example, advantageously reduce memory footprint compared to complete attribute arrays 130. The sparsified data structure may, for example, maintain sufficient information for accurate GPR computation. The sparsified data structure may, for example, support efficient caching and rapid recalculation when environmental conditions change but, for example, sparsification patterns remain stable.

[0181] Fig. 14 depicts a data preprocessing module 1400. The data preprocessing module 1400 may, for example, advantageously address technical problems related to normalizing heterogeneous sensor data into standardized formats for attribute array computation. The module may, for example, process diverse data streams from distributed sensors 128. The module may, for example, produce consistent (e.g. , cleaned inputs) such as for GPR and / or SPR calculation.

[0182] In this example, the input layer 802 receives temperature 1402 (e.g., in various units such as Celsius, Fahrenheit, Kelvins). Traffic counts 1404 may, for example, be received from different counting methodologies. Timestamp data 1406 may, for example, be received in various time zones. Light levels 1408 may, for example, be received in different illumination units (e g., lux, foot-candles, candelas, Voille). Other sensors 1410 may, for example, provide environmental and / or operational data including humidity, noise levels, and / or occupancy measurements.

[0183] The processing layer 806 employs a unit conversion module 1412. The unit conversion module 1412 may, for example, perform standard unit conversion (temperature to Celsius, illumination to lux) The unit conversion module 1412 may, for example, perform scale normalization. The scale normalization may, for example, enforce uniformity across data inputs. An outlier removal module 1414 may, for example, conduct statistical outlier detection. The statistical outlier detection may, for example, use techniques such as z-score analysis and / or interquartile range filtering. The outlier removal module 1414 may, for example, remove anomalous measurements. The anomalous measurements may, for example, distort attribute calculations. A missing value handler 1416 may, for example, implement imputation algorithms including forward-fill, mean substitution, and / or interpolation. The missing value handler 1416 may, for example, maintain data integrity when sensor readings are absent. A temporal alignment module 1418 may, for example, handle time zone conversion to UTC standard. The temporal alignment module 1418 may, for example, perform interval synchronization. The interval synchronization may, for example, align measurements to consistent temporal intervals (e.g., one-minute bins). The temporal alignment module 1418 may, for example, enforce temporal consistency across sensor streams, such as with varying update frequencies.

[0184] The output layer 808 generates standardized data 1420. The data may, for example, have consistent units and / or formats, cleaned data arrays (e.g., outliers removed, missing values imputed), and / or temporally aligned streams (e.g., synchronized to common intervals). These outputs may, for example, populate attributes (e.g., real-time, periodic, and / or static) in attribute arrays 130. These outputs may, for example, advantageously enable accurate GPR computation (e.g., in method 400). The accurate GPR computation may, for example, be enabled, such as by enforcing data quality and / or consistency across heterogeneous data sources (e.g., sensor networks monitoring physical spaces 104, disparate external data sources).

[0185] The data preprocessing module 1400 may, for example, advantageously enable integration of diverse sensor types and / or other data sources. The integration may, for example, be enabled without requiring uniform instrumentation.

[0186] Fig. 15 depicts a multi-objective adaptation engine 1502. The multi-objective adaptation engine 1502 may, for example, facilitate computing Spot Performance Ratings (SPR). The multi-objective adaptation engine 1502 may, for example,balance competing factors such as through ‘optimization1algorithms. The engine may, for example, synthesize multiple objectives. The engine may, for example, produce comprehensive space spot valuations.

[0187] The input layer 802 receives, in this example, GPR 1504 (e.g. , element reference for GPR values), such as from grid units 126 (e.g., computed in method 400). Media placement criteria 804 may, for example, specify advertiser requirements. Space efficiency 1506 metrics may, for example, measure area utilization ratios. Revenue potential 1508 may, for example, estimate based on factors such as, for example, traffic and visibility (e.g., from the geographic model 1100). Aesthetic constraints 1510 may, for example, define brand alignment and / or visual coherence requirements. Operational constraints 1512 may, for example, include installation, maintenance, and / or power costs. Inputs may, for example, be externally generated. Inputs may, for example, be generated (e.g., inferred from MPP, specified in MPC).

[0188] The processing layer 806 employs, in this example, an objective function definition 1514. The objective function definition 1514 may, for example, specify multi-goal optimization. The multi-goal optimization may, for example, balance performance, efficiency, revenue, aesthetics, and / or costs. A constraint satisfaction module 1516 may, for example, validate feasibility. The feasibility may, for example, be validated such as by enforcing (e.g., all) mandatory requirements from media placement criteria 804. The constraint satisfaction module 1516 may, for example, reject configurations violating size, technical, and / or regulatory constraints. An optimization engine 1518 may, for example, perform optimization and / or weighted summation. The optimization engine 1518 may, for example, identify configurations computed with overall maximum value across competing objectives. The optimization engine 1518 may, for example, conduct trade-off analysis. The trade-off analysis may, for example, balance factors like visual impact versus space efficiency. An aggregation module 1520 may, for example, integrate GPR 1504 from constituent grid units 126, such as with contextual factors. The contextual factors may, for example, include traffic patterns 1106 and / or other environmental factors 1714. The aggregation module 1520 may, for example, compute composite performance metrics.

[0189] The output layer 808 generates adaptation package 1522 (e.g., also referred to as optimization results). The adaptation package 1522 may, for example, contain SPR values (e.g., for each space spot) computed (e.g., via multi-objective formulas). The package may, for example, include computed optimal space spot configurations. The configurations may, for example, specify which grid units 126 to aggregate, and / or trade-off analysis results (e.g., documenting competing parameter balances, such as efficiency-impact for example). The SPR calculation may, for example, follow: {SPR}(s) = sum_{j} (alpha * F J(s)) - C_op (s), where alphaj represents objective weights, FJ(s) represents objective factors (e.g., aggregated GPR, space efficiency, revenue potential, aesthetic score, other factors), and C_op (s) represents operational costs.

[0190] The multi-objective adaptation engine 1502 may, for example, advantageously produce holistic valuations. The holistic valuations may, for example, extend beyond single-metric optimization. The multi-objective adaptation engine 1502 may, for example, enable advertiser-specific prioritization, such as through adjustable objective weights. The multi-objective adaptation engine 1502 may, for example, identify optimal (e.g., Pareto-optimal) configurations. The configurations may, for example, offer favorable multi-dimensional trade-offs. The multi-objective adaptation engine 1502 may, for example, support informed space spot selection and / or ranking, such as in method 400 (e.g., step 428).

[0191] Fig. 16 depicts an 020 routing engine 122. The 020 routing engine 122 may, for example, address technical problems related to distributing media content, such as to selected physical spaces (e.g., through automated routing logic and / or device configuration). The engine may, for example, advantageously match media placement requirements withpreferred (e.g., computed optimal) space spots. The engine may, for example, generate control signals configured to induce physical deployment (e.g., of SSGTs).

[0192] In this example, the input layer 802 receives media placement package 120 (e.g., specifying target criteria and / or format requirements). Space data structures 1500 may, for example, contain hierarchical grid structures for physical spaces 104. Available physical spaces 1600 may, for example, identifying restaurants 106, retail stores 114, gas stations 112, parks 110, and / or other locations. Space spot SPR rankings 1602 may, for example, provide performance-based prioritization metric (e.g., from the multi-objective adaptation engine 1502).

[0193] The processing layer 806 employs, in this example, a routing logic module 1604. The routing logic module 1604 may, for example, determine routing paths and / or prioritization (e.g., computed 'optimal'). The routing paths and / or prioritization may, for example, be determined based on SPR rankings 1602 and / or budget constraints (e.g., from the MPG). A matching algorithm 1606 may, for example, perform criteria-to-space matching. The criteria-to-space matching may, for example, be performed by comparing media placement criteria 804 with space spot characteristics. The matching algorithm 1606 may, for example, compute compatibility scores. The compatibility scores may, for example, assess alignment between advertiser requirements and available locations. A distribution optimizer 1608 may, for example, manage resource allocation across multiple space spots. The distribution optimizer 1608 may, for example, maximize efficiency. The efficiency may, for example, be maximized by balancing coverage, cost, and / or performance. A load balancer 1610 may, for example, distribute media content across selected spaces. The load balancer 1610 may, for example, manage display capacity. The load balancer 1610 may, for example, prevent overutilization and / or underutilization of locations (e.g. premium locations, historically low value locations).

[0194] A control signal generator 1612 may, for example, produce command signals 2108, such as for physical display device configuration. The command signals 2108 may, for example, include resolution settings, brightness adjustments, content scheduling parameters, and synchronization requirements.

[0195] The output layer 808 generates, in this example, routing output 1614 (e.g., containing control signals 2218). The routing output 1614 may, for example, be operable for reconfiguring display parameters of physical display devices. Routing output 1615 may, for example, include space spot assignments (e.g., linking media content to specific space spots in selected physical spaces 104). These outputs may, for example, enable the content distribution system, such as to execute media deployment according to routing decisions. In some examples, routing output may be configured to trigger and / or implement creation, operation, installation, maintenance, removal, and / or delivery of passive physical displays (e.g., posters, stickers, 3D displays) and / or active displays (e.g., digital screens, marquees, etc.).

[0196] The 020 routing engine 122 may, for example, advantageously automate media distribution. The media distribution automation may, for example, advantageously reduce or eliminate manual placement decisions. The 020 routing engine 122 may, for example, enhance resource allocation, such as through algorithmic matching and / or load balancing. The 020 routing engine 122 may, for example, enable dynamic reconfiguration. The dynamic reconfiguration may, for example, be enabled by generating updated control signals 2218. The updated control signals 2218 may, for example, be generated when environmental conditions change. The updated control signals 2218 may, for example, be generated when new media placement packages 120 are received. The 020 routing engine 122 may, for example, support real-time adaptation.

[0197] Fig. 17 depicts an environmental recognition module 1700. The environmental recognition module 1700 may, for example, advantageously address technical problems related to identifying environmental factors (e.g., affecting advertising value / performance) such as through automated analysis of visual, sensor, spatial data and / or other data. The module may, for example, advantageously inform grid unit valuation.

[0198] The input layer 802 receives, in this example, visual data 1702. The data may, for example, including images 810 and video 814 from sensors 128, sensor data 1704 from light level sensors 1408 and / or other sensors 1410 (e.g., measuring noise and / or other environmental conditions). Data may, for example, come from integrated sensors. Data may, for example, come from user devices (e.g., media originator devices, physical space operator devices), such as smartphones, 3D cameras. Data may, for example, come from map feeds, satellite feeds, and / or GIS sources. Spatial analysis data 1706, such as from the geographic model 1100, may advantageously provide context such as about the physical space 104. Grid unit 126 may, for example, provide location specifications.

[0199] The processing layer 806 employs, in this example, a visual obstruction detector 1708. The visual obstruction detector 1708 may, for example, perform occlusion analysis. The occlusion analysis may, for example, identify objects blocking views. The visual obstruction detector 1708 may, for example, perform visibility assessment. The visibility assessment may, for example, quantify sight-line quality. A lighting condition analyzer 1710 may, for example, measure illumination levels from light level sensors 1408. The lighting condition analyzer 1710 may, for example, assign quality scores. The quality scores may, for example, rate lighting favorability for advertisement visibility. A traffic flow pattern recognizer 1712 may, for example, track movement (e.g., pedestrian motion, shopper motion, driver motion), such as from traffic counting systems. The traffic flow pattern recognizer 1712 may, for example, analyze flow directions and / or density patterns.

[0200] A proximity analyzer 1116 may, for example, compute distances, such as to points of interest. The proximity analyzer 1116 may, for example, conduct accessibility scoring. The accessibility scoring may, for example, be based on spatial relation data 1108.

[0201] The output layer 808 generates environmental factors 1714. These factors may, by way of example and not limitation, include visual obstruction identifications, such as specifying blocked zones and / or visibility angles, lighting quality scores (e.g., quantifying illumination favorability), and / or traffic flow patterns (e.g., describing movement densities and / or directions). These outputs may, for example, populate static, periodic, and / or real-time attributes in attribute arrays 130. These outputs may, for example, inform GPR computation, such as in method 400. The GPR computation may, for example, be informed by incorporating environmental context affecting advertising effectiveness.

[0202] The environmental recognition module 1700 may, for example, advantageously automate environmental assessment. The environmental assessment may, for example, otherwise require manual site surveys. The environmental recognition module 1700 may, for example, dynamically update environmental factors. The environmental factors may, for example, be updated as conditions change through real-time sensor data 1704. The environmental recognition module 1700 may, for example, provide context-aware valuation. The context- aware valuation may, for example, incorporate visibility, lighting, and / or traffic factors. The visibility, lighting, and traffic factors may, for example, significantly impact advertising performance in physical spaces 104.

[0203] Fig. 18 depicts a grid adaptation module 1800. The grid adaptation module 1800 may, for example, address technical problems related to automatically determining grid configurations suitable for media performance inference, routing, and / orplacement. The module may, for example, suggest grid divisions, coordinate systems, and / or granularity levels such as, for example, adapted to space complexity.

[0204] The input layer 802 receives, in this example, physical spaces 104 (e.g., providing spatial boundaries and / or dimensions). Identified advertising surfaces 824 may, for example, be received from the vision model 800. Space complexity 1802 may, for example, quantify space and / or surface complexity, architectural intricacy, and / or layout variability. Media placement criteria 804 may, for example, specify format requirement, size constraints, and / or goals.

[0205] The processing layer 806 may, for example, employ a complexity analyzer 1804. The complexity analyzer 1804 may, for example, assess space complexity, such as through geometric analysis and / or surface distribution patterns. The complexity analyzer 1804 may, for example, determine necessary detail levels such as for grid granularity. A grid division calculator 1808 may, for example, compute division schemes balancing coverage uniformity with complexity-adapted density. The grid division calculator 1808 may, for example, define hierarchical layer structures (GO, G1 , G2). The layer structures may, for example, provide progressive subdivision. A granularity adjustment engine 1810 (e.g., also referred to as a granularity ‘optimizer’) may, for example, determine resolution levels (e.g., matching space complexity). The granularity adjustment engine 1810 may, for example, adjust grid density, such as to concentrate detail in more intricate regions while reducing overhead in more simple zones. A coordinate system selector 1812 may, for example, select system types (e.g., rectangular, triangular, radial, cylindrical, trapezoidal, irregular), such as based on space geometry, for example. The coordinate system selector 1812 may, for example, manage overlay configurations, such as for multiple media types. The coordinate system selector 1812 may, for example, advantageously enable simultaneous application of diverse coordinate frameworks.

[0206] The output layer 808 generates grid configuration package 1814. The package may, for example, contain grid division (e.g., suggestions), such as specifying cell boundaries and / or naming (e.g., hierarchical), recommended coordinate system types (e.g., for primary and / or overlay grids), proposed layer structures (e.g., defining granularity progression such as GO— >G1— >G2), and / or grid density recommendations (e.g., indicating adaptive meshing zones). These outputs may, for example, facilitate grid generation, such as in method 700. The outputs may, for example, enable efficient spatial organization adapted to physical space characteristics. The grid configuration package 1814 may, for example, include grid definitions themselves.

[0207] The grid adaptation module 1800 may, for example, advantageously automate grid configuration, reducing or eliminating manual design. The grid adaptation module 1800 may, for example, increase computational efficiency, such as through complexity-adapted granularity. The grid adaptation module 1800 may, for example, support diverse space types, such as from simple retail stores 114 to complex transit stations (e.g., through flexible coordinate system selection).

[0208] Fig. 19 depicts a template generation module 1902. The template generation module 1902 may, for example, address a technical problem of automatically creating (e.g., even without human intervention) advertising templates from aggregated grid clusters. The module may, for example, generate multiple template options balancing competing parameters (e.g., visual impact and space efficiency as in this example).

[0209] The input layer 802 receives grid 124 clusters, such as from aggregated grid units 126. Media placement criteria 804 may, for example, specify format requirements and / or constraints. Visual impact scores 920 may, for example, be received from the visual analysis model 900. Balance outputs 1904 (e.g., from system 1900 and / or other balancing modules) may, for example, guide optimization priorities.

[0210] The processing layer 806 employs in this example a template synthesis engine 1906. The template synthesis engine 1906 may, for example, generate initial layout structures and / or content zone arrangements. A layout engine 1806 may, for example, perform space optimization maximizing area utilization and visual impact through compositional analysis. A configuration generator 1908 may, for example, define technical specifications including resolution, orientation, and / or display parameters, such as generating parameter sets for device configuration.

[0211] A balancing analyzer 912 may, for example, execute balance calculations (e.g., cost-benefit analysis), such as identifying computed 'optimal' (e.g., Pareto-optimal, highest parameter 1 while not violating parameter 2 ... i, and / or other optimization algorithms) configurations across competing objectives. Space / value is shown.

[0212] In some embodiments, the balancing analyzer 912 may, for example, execute value-space balance calculations. For example, the balancing analyzer 912 may evaluate trade-offs between visual impact maximization and space efficiency. In an illustrative example, a first template configuration may allocate 85% of available space to high-visibility content zones, achieving a visual impact score of 0.92 but a space efficiency rating of 0.68. A second template configuration may allocate 95% of available space through denser content packing, achieving a space efficiency rating of 0.88 but a visual impact score of 0.74. The balancing analyzer 912 may, for example, compute a weighted score for each configuration based on media placement criteria 804 priorities. If visual impact is weighted at 0.6 and space efficiency at 0.4, the first configuration may receive a composite score of 0.824 (0.92x0.6 + 0.68x0.4), while the second configuration may receive a score of 0.796 (0.74x0.6 + 0.88x0.4), indicating the first configuration as preferable for the specified priorities.

[0213] In some embodiments, the balancing analyzer 912 may, for example, perform cost-benefit analysis. For example, the balancing analyzer 912 may evaluate operational constraints 1512 against revenue potential 1508. In an illustrative example, a first space spot configuration may require installation of three LED displays with a combined operational cost of $450 per month, generating an estimated revenue potential of $2,800 per month (e.g., based on traffic patterns 1106 and visibility scores). A second space spot configuration may utilize existing static display surfaces with an operational cost of $120 per month, generating an estimated revenue potential of $1 ,600 per month. The balancing analyzer 912 may, for example, compute net value as revenue potential minus operational costs, yielding $350 per month for the first configuration and $1,480 per month for the second configuration. The balancing analyzer 912 may, for example, further compute return on investment ratios, yielding 6.22 for the first configuration ($2,800 / $450) and 13.33 for the second configuration ($1 ,600 / $120). The balancing analyzer 912 may, for example, weight these factors according to advertiser priorities in media placement criteria 804, such as prioritizing absolute net value versus return on investment efficiency.

[0214] In some embodiments, the balancing analyzer 912 may, for example, identify 'optimal' configurations. For example, the balancing analyzer 912 may evaluate multiple variations across competing objectives. These objectives may, for example, include visual impact, space efficiency, installation complexity, and / or maintenance requirements. In an illustrative example, five template configurations may be evaluated: Configuration A (visual impact: 0.95, space efficiency: 0.60, installation complexity: 0.85, maintenance: 0.70), Configuration B (visual impact: 0.88, space efficiency: 0.75, installation complexity: 0.65, maintenance: 0.80), Configuration C (visual impact: 0.82, space efficiency: 0.88, installation complexity: 0.50, maintenance: 0.90), Configuration D (visual impact: 0.78, space efficiency: 0.82, installation complexity: 0.55, maintenance: 0.85), and Configuration E (visual impact: 0.90, space efficiency: 0.70, installation complexity: 0.75, maintenance: 0.75). The balancing analyzer 912 may, for example, identify that Configuration D is dominated by Configuration C (e.g., which achieves superioror equal performance across all objectives), eliminating Configuration D from consideration. The balancing analyzer 912 may, for example, identify Configurations A, B, C, and E as Pareto-optimal, representing different favorable trade-offs that cannot be improved in one objective without degrading another.

[0215] In some embodiments, the balancing analyzer 912 may, for example, perform temporal trade-off analysis. For example, the balancing analyzer 912 may evaluate configurations across different time periods, such as based on temporal model 2302 outputs. In an illustrative example, a first configuration may achieve high visual impact during peak traffic periods (07:00-09:00, 17:00-19:00) with a score of 0.91 , but lower performance during off-peak periods with a score of 0.68 due to lighting conditions analyzed by lighting condition analyzer 1710. A second configuration may achieve more consistent performance across time periods, such as with peak period scores of 0.82 and off-peak scores of 0.79. The balancing analyzer 912 may, for example, weight temporal performance based on advertiser priorities, such as emphasizing peak period effectiveness at 0.7 weight and off-peak at 0.3 weight, such as yielding composite temporal scores of 0.841 for the first configuration (0.91 x0.7 + 0.68x0 3) and 0.811 for the second configuration (0.82x0 7 + 0.79x0.3).

[0216] In some embodiments, the balancing analyzer 912 may, for example, evaluate brand alignment trade-offs. For example, the balancing analyzer 912 may assess aesthetic constraints 1510 against space efficiency 1506. In an illustrative example, a first template may maintain strict brand color palette requirements and compositional guidelines, achieving an aesthetic score of 0.94 but limiting content density to achieve a space efficiency of 0.71 . A second template may relax certain aesthetic constraints to increase content packing, achieving a space efficiency of 0.86 but an aesthetic score of 0.79. The balancing analyzer 912 may, for example, apply weighting based on advertiser brand sensitivity specified in media placement criteria 804. For a luxury brand emphasizing aesthetic coherence with a brand alignment weight of 0.75 and space efficiency weight of 0.25, the first template may receive a composite score of 0.883 (0.94x0.75 + 0.71 x0.25), while the second template may receive a score of 0.808 (0.79x0.75 +0.86x0.25), indicating the first template as preferable despite lower space efficiency.

[0217] In some embodiments, the balancing analyzer 912 may, for example, perform multi-location trade-off analysis. For example, the balancing analyzer 912 may evaluate space spot configurations across multiple physical spaces 104 to balance geographic coverage against per-location performance. In an illustrative example, a first allocation strategy may concentrate resources in three high-performance locations (restaurants 106 in high-traffic areas) with average SPR of 0.87, achieving concentrated impact but limited geographic reach. A second allocation strategy may distribute resources across eight moderate-performance locations (retail stores 114 and gas stations 112) with average SPR of 0.74, achieving broader geographic coverage but lower per-location performance. The balancing analyzer 912 may, for example, compute total expected impressions, yielding 45,000 impressions per day for the concentrated strategy and 52,000 impressions per day for the distributed strategy. The balancing analyzer 912 may, for example, evaluate cost per impression, yielding 0.08USD for the concentrated strategy and 0.11 USD for the distributed strategy. The balancing analyzer 912 may, for example, weight these factors according to campaign objectives in media placement criteria 804, such as prioritizing cost efficiency versus geographic penetration.

[0218] In some embodiments, the balancing analyzer 912 may, for example, evaluate accessibility trade-offs. For example, the balancing analyzer 912 may assess installation complexity against performance potential. In an illustrative example, a first space spot may be located on a high-visibility wall surface requiring specialized mounting equipment and structural modifications, with an installation complexity score of 0.82 (higher values indicating greater complexity) but a predicted GPRof 0.89. A second space spot may utilize an existing display framework with minimal installation requirements, achieving an installation complexity score of 0.35 but a predicted GPR of 0.76. The balancing analyzer 912 may, for example, compute weighted scores based on project timeline constraints and budget limitations in media placement criteria 804. For a rapid deployment scenario weighting installation simplicity at 0.6 and performance at 0.4, the first space spot may receive a composite score of 0.848 (inverse of 0.82 is 0.18, so 0.18x0.6 + 0.89x0.4 = 0.464), while the second space spot may receive a score of 0.694 (inverse of 0.35 is 0.65, so 0.65x0.6 + 0.76x0.4 = 0.694), indicating the second space spot as preferable for time-sensitive deployments.

[0219] In some embodiments, the balancing analyzer 912 may, for example, perform dynamic reconfiguration trade-off analysis. For example, the balancing analyzer 912 may evaluate the benefits of temporal variability against implementation complexity. In an illustrative example, a first template configuration may implement dynamic adjustment rules from temporal model 2302, automatically reconfiguring display parameters in response to traffic density thresholds, lighting level changes, and weather conditions from weather integration model 1000. This dynamic configuration may achieve an average effectiveness score of 0.86 across varying conditions but require integration with real-time sensor data 1704 and control signal generation, resulting in a system complexity score of 0 78. A second template configuration may utilize static parameters optimized for average conditions, achieving an effectiveness score of 0.79 but a system complexity score of 0.32. The balancing analyzer 912 may, for example, weight these factors based on operational preferences in media placement criteria 804, such as emphasizing reliability and simplicity for locations with limited technical support infrastructure versus maximizing adaptive performance for locations with robust monitoring capabilities.

[0220] A multi-option generator 1910 may, for example, create template variations spanning high visual impact, high space efficiency, and / or intermediate balance points.

[0221] The output layer 808 generates SSGT output(s) 1912. The SSGT output(s) 1912 may, for example, contain space spot grid templates with boundary definitions and / or layout specifications. Multiple layout options may, for example, present alternative arrangements with different parameter priorities. Configuration specifications may, for example, detail technical display parameters and / or integration instructions. These outputs may, for example, complete the SSGT generation process in SSGT generation method 500, such as providing comprehensive deployment blueprints.

[0222] The template generation module 1902 may, for example, advantageously automate template design, which may reduce or eliminate manual layout creation; generate diverse options (e.g., advantageously enabling data-driven selection rather than fixed and / or manual configurations); and / or balance competing objectives such as through algorithmic optimization supporting MPC-specific priorities.

[0223] Fig. 20 depicts an example temporal model 2302. The model may, for example, advantageously address a technical problem of automatic time-based pattern analysis, such as for automatically updated attribute array 130 (e.g., enabling MPC- specific automatic dynamic routing of media). The model may, for example, process historical trends, calendar events, and / or seasonal variations, such as to inform temporal attribute weighting.

[0224] The input layer 802 may, for example, receive historical time-series data (e.g., providing past traffic and / or performance trends), calendar events (e.g., indicating significant dates affecting behavior patterns), temporal patterns (e.g., identifying recurring cycles), and / or seasonal trends (e.g., capturing periodic variations).

[0225] The processing layer 806 may, for example, as shown, employ a time-series analysis engine 2002. The engine may, for example, detect trends and / or cyclical patterns, such as through seasonal decomposition. A peak period identifier 2004 may, for example, detect high-traffic windows and / or activity spikes, such as for determining advertisement timing. A seasonality analyzer may, for example, evaluate monthly variations, holiday impacts, and / or weather correlations. An event impact predictor 2008 may, for example, detect special events and / or predict traffic surges. A temporal weighting module 2010 may, for example, apply time-based value adjustments and / or recency scoring, such as to refine attribute importance.

[0226] The output layer 808 may, for example, generate temporal analysis results 2012 including peak period schedules identifying optimal display times, seasonal adjustments modifying attribute weights based on temporal context, and / or timebased weighting factors incorporating recency and / or cyclical patterns. These outputs may, for example, populate periodic attributes in attribute arrays 130 and / or inform GPR computation in method 400, such as by incorporating temporal context affecting advertising effectiveness across different time periods in physical spaces 104.

[0227] The temporal model 2302 may, for example, advantageously enable time-aware valuation adjusting for predictable traffic variations, support dynamic scheduling through peak period identification, and / or incorporate event-driven adaptation for special circumstances affecting advertising performance.

[0228] Fig. 21 depicts a co-sharing engine 2100. The co-sharing engine 2100 may, for example, allocate advertising spaces, such as among multiple advertisers.

[0229] An input layer 802 may, for example, receive multiple data types. Multiple advertiser requirements 2102 may, for example, specify individual advertiser parameters. Grid values 2104 may, for example, include GPR and / or SPR metrics. Geographic locations 2106 may, for example, identify target areas. Budget constraints 2108 may, for example, specify financial limits. Compatibility criteria 2110 may, for example, define compatibility parameters. These inputs may, for example, facilitate multi-advertiser coordination.

[0230] A processing layer 806 may include multiple modules. A matching algorithm 2112 may, for example, perform advertiser-to-space matching and / or multi-party optimization. Space allocation optimization 2114 may, for example, perform territory division, time-slot allocation, and / or resource distribution. Budget-based partitioning 2116 may, for example, perform cost allocation, revenue distribution, and / or fair-share calculation. Compatibility scoring 2118 may, for example, perform brand conflict detection, audience overlap analysis, and / or content compatibility assessment. These modules may, for example, facilitate shared space management.

[0231] An output layer 808 may, for example, generate allocation results. Advertiser pairings / groupings 2120 may, for example, identify compatible advertiser combinations. Space allocation requirements 2122 may, for example, specify space distributions. Shared space configurations 2124 may, for example, define co-sharing parameters. Interaction tracking parameters 2126 may, for example, specify monitoring metrics. These outputs may, for example, facilitate coordinated advertisement deployment.

[0232] Fig. 22 shows a dynamic reconfiguration module 2200. The module may, for example, be structured to manage and / or adjust advertisement spaces through various layers and elements. The dynamic reconfiguration module 2200 begins with an input layer 802. Real-time environmental data 2202 may, for example, provide baseline inputs, such as for adaptive processing. For example, the real-time environmental data 2202 may include real-time data in the attribute arrays 130. In some embodiments, the input layer 802 may receive periodic data, such as from attribute arrays.

[0233] In this example, the processing layer 806 incorporates multiple components, such as a grid configuration package 1814. The grid configuration package 1814 may, for example, include current grid configurations. Performance ratings 2204 (e.g., GPR of affected grid units, SPR of space spots) may, for example, be used, such as to evaluate advertisement space effectiveness. Change thresholds 2206 may, for example, establish limits that, when crossed, may prompt adjustments.

[0234] In the media placement criteria 804, a change detection engine 2208 may, for example, analyze environmental delta calculations and / or detect anomalies. The engine may apply change thresholds to updated attribute arrays (e.g., selected attributes 134), for example. The engine may, for example, operate on selected attributes 134 arrays, such as based on MPCs associated with a specific grid unit(s) and / or space spot(s).

[0235] A reconfiguration decision logic engine 2210 may, for example, assess impacts and / or evaluates decision trees. This process may, for example, advantageously facilitate robust cost-benefit analysis calculations, which may facilitate reconfiguration decisions, for example. The reconfiguration decision logic engine 2210 may, for example, apply SSGT parameters.

[0236] A priority recalculation engine 2212 may, for example, adjust attributes, such as for adjusting GPR and / or SPR. This recalculation may, for example, facilitate ongoing routing. The priority recalculation engine 2212 may, for example, interact with data preprocessing module 1400 and / or multi-objective adaptation engine 1502, such as to recalculate GPR.

[0237] A signal generator 2214 may, for example, generate control signals. For example, the generator may interact with visual analysis model 900 and / or 020 routing engine 122, such as to format and assemble SSGTs and / or implement SSGTs.

[0238] A control trigger 2216 may, for example, trigger processes such as of 020 routing engine 122, for example, such as when detected changes cross thresholds (e.g., change thresholds 2206 such as from SSGTs).

[0239] A feedback loop 2224 may, for example, provide iterative (e.g., real-time adjustments). In this example, the loop returns after priority recalculation to change detection. This loop may, for example, enable ongoing dynamic reprioritization.

[0240] The dynamic reconfiguration module 2200 generates reconfiguration commands 2220 through an output layer 808. The reconfiguration commands may, for example, generate updated performance ratings 2204 (e.g., GPR, SPR). Updated control signals 2218 and / or reconfiguration commands 2220 may, for example, facilitate operational changes. Outputs such as display parameter adjustments 2222 may, for example, adjust media displays (e.g., display device 216). A dynamic reconfiguration module 2200 may, for example, enable a 020 engine 102 (e.g., 020 routing engine 122) to dynamically reroute media across a network of physical spaces 104 in response to changing inputs.

[0241] Fig. 23 depicts a system 1900 providing an illustrative embodiment implementing dynamic media routing via individually sparsified GPR. In this example, various modules, engines, and models are organized into nine interconnected layers with real-time feedback. Other embodiments are contemplated, at least as disclosed with reference to inputs / outputs discussed elsewhere herein.

[0242] Layer 1 in this example is configured to perform data input and acquisition. As shown, imaging and spatial inputs 2300 may be received, which may provide spatial and / or environmental context for analysis. Media placement package 120 (e.g., received from media originator 118) may, for example, specify advertiser requirements and / or goals. Historical performance data 906 may, for example, supply past data (e.g., effectiveness metrics). Real-time environmental data 2202 (e.g., from distributed sensors 128 and / or external data stores 224) may, for example, monitor currentconditions. Temperature 1402, traffic counts 1404, light levels 1408, sales data, and customer interactions are only illustrative examples.

[0243] Layer 2 in this example is configured to perform preprocessing and visual analysis. A data preprocessing module 1400 may, for example, normalize heterogeneous sensor inputs. A vision model 800 may, for example, process data inputs (e.g., visual data files), such as to generate identified advertising surfaces 824. Environmental recognition module 1700 may, for example, assess environmental factors (e.g., obstructions, lighting, traffic, and / or sales such as discussed elsewhere).

[0244] Layer 3 in this example is configured to perform grid structure generation. Grid adaptation module 1800 may assess the space (e.g., based on outputs from layer 2), compute subdivisions, determine coordinate systems and / or overlays, and / or generate grid configuration package 1814.

[0245] Layer 4 in this example is configured to perform attribute array generation. Attribute arrays 130 may, for example, link grid units 126 with static, periodic, and / or real-time attributes. Geographic model 1100 may, for example, provide spatial analysis. Weather integration model 1000 may, for example, generate and / or adjust weather related attributes. Temporal model 2302 may, for example, identify time-based patterns and / or generate / update temporally varying attributes. Visual analysis model 900 may, for example, generate visual impact scores 920 and / or content placement zones 918.

[0246] Layer 5 in this example is configured to perform model integration. Ensemble Al system 1200 may, for example, synthesize multiple model outputs (e.g., from layer 4), producing ensemble output 1218 with unified attribute assessments. In some examples, although attribute array 130 is shown in layer 4, the attribute array 130 may be generated in layer 5. In some examples, a preliminary array may be generated in layer 4 and updated in layer 5 as shown (e.g., consolidated, such as to reduce attribute count and / or reduce dimensionality of individual attributes such as from, for example, arrays into, for example, a single value).

[0247] Layer 6 in this example is configured to perform performance rating computation. Media placement criteria 804 may, for example, be generated (e.g., extracted, inferred) from media placement package 120. Media placement criteria 804 may, for example, guide hard filter application, such as excluding non-compliant grid units 126. GPR model 1300 may, for example, employ various models and / or engines to compute GPR for grid units via selective attribute processing. The GPR may be calculated, for example, via attribute sparsification and weighting, such as to generate a holistic (e.g., individual value) score (e.g., per evaluated grid unit). Higher-performing grid units may, for example, be aggregated into space spots. Multi-objective adaptation engine 1502 may, for example, compute SPR.

[0248] Layer 7 in this example is configured to perform template generation. Template generation module 1902 may, for example, produce SSGT output(s) 1912. The outputs may, for example, include SSGTs with layout specifications, technical parameters, and / or integration instructions. Space spot data structures 2310 may, for example, link SSGTs to MPC-specific- aggregated grid units (e.g., space spot data structures 2310).

[0249] Layer 8 in this example is configured to perform content routing and distribution. 020 routing engine 122 may, for example, select space spots, such as via SPR rankings 1602 and / or produce control signals 2218 to implement SSGTs at associated space spots.

[0250] Layer 9 in this example is configured to perform physical space deployment. Physical displays in spaces such as restaurant 106, retail store 114, gas station 112, park 110, kiosk 116, and / or other spaces 2306 may, for example, be configured to display media from the media originators 118 according to SSGT at selected (e.g., automatically) space spots,according to control signals 2218. Control signals 2218 may, for example, be directly sent to devices at the spaces. Control signals 2218 may, for example, be sent to intermediate destinations (e.g., manufacturing, printing, delivery, installation, operation, and / or maintenance services) such that the media is displayed at the physical spaces.

[0251] This example illustratively depicts an example feedback loop. Sensors 128 - such as in deployed physical spaces, external data stores 224, and / or other sources may, for example, feed real-time environmental data 2202 and / or periodic data back to layer 1. This feedback loop may, for example, facilitate ongoing attribute updates, GPR and / or SPR recalculation, space spot reformation and / or re-selection, dynamic reconfiguration, and / or re-routing, such as in response to changing environmental conditions.

[0252] Various embodiments may for example, advantageously enable selective computation through sparsification (e.g., Layer 6), multi-model synthesis (e.g., Layer 5), dynamic space spot formation (e.g., Layer 6), and / or real-time adaptation.

[0253] Fig. 24 depicts a grid / attribute display 2400, such as of an example attribute array 130 data structure. I n this example, grid units G2-3.1.2 are shown within a context of a hierarchical grid-based system. The structure may, for example, advantageously organize physical space data through layered attribute categorization.

[0254] The highest level in this example represents a physical space designated as "Times_Square_Mall_L2." The 020 engine 102 has subdivide the physical space into grid layers (e g., grid 124), which includes in this example multiple hierarchical layers (GO, G1 , G2). For example, the layers may advantageously provide progressive spatial refinement.

[0255] Within each grid 124 layer, individual grid units 126 represent discrete spatial sections. Attribute arrays 130 organize attributes 132 within each grid unit 126 into one or more categories. In this example, three categories are depicted. Some embodiments may implement, for example, individual arrays linked to attribute data structure (e.g., via metadata, pointers). Some embodiments may, for example, implement subarrays addressable within a larger array. Some embodiments may implement a single array per grid, grid layer or grid unit, for example. Hierarchical addressing may, for example, be determined by address ranges, for example, within the array.

[0256] A static attribute category may include, by way of example and not limitation, geolocation data and / or structural properties. These attributes may, for example, be expected to remain relatively constant over extended periods. Static attributes may, for example, include elevation, dimensions, orientation, surface type, structure, material, and / or location.

[0257] Periodic attributes may, by way of example and not limitation, include traffic patterns 1106 (e.g., temporal density variations), demographics (e.g., audience characteristics), economic data (e.g., seasonally varying sales data), calendar data (e.g., events), and / or historical performance data 906 (e.g., past media placements and / or actual performance). These attributes may, for example, update on regular intervals (hourly, daily, monthly, quarterly, seasonally, annually, multi-annually). Periodic attributes may include, for example, average traffic, peak hours, gender, average / media / max / min eye height, day pattern, historical performance, and / or seasonal data.

[0258] Real-time attributes may, for example, include fluctuating attributes. Examples may include temperature 1402, light levels 1408, weather conditions, instantaneous traffic information, current user engagement (e.g., purchases, QR code engagement, kiosk engagement, attention engagement). These attributes may update continuously, such as from sensors 128 and / or other (e.g., live) data feeds. Real-time attributes may include, for example, current traffic, temperature, humidity, lighting, noise, weather, air quality index, density, time, visibility, current sales data, trending products, and / or proximity alerts.

[0259] In some embodiments, attribute updates and / or downstream computations (e.g., GPR, SPR) may, for example, be performed based on different types of attributes. Static attributes may not be recalculated or may be recalculated (e.g., only) when certain triggers are reached (e.g., an attribute is changed). In some instances, this may result in computational efficiency by reducing unnecessary processing. Periodic attributes may, for example, be recalculated on a pre-determined schedule. For example, a periodic attribute may be associated with an update frequency. Dependent attributes and / or downstream computations may, for example, be updated (e.g., only) on the associated schedule(s). This may, for example, facilitate timely updates while conserving computational resources. Realtime attributes may, for example, be recalculated as changes occur, such as when a threshold is reached. For example, thresholds associated with the dynamic reconfiguration module 2200 (e.g., such as specified in an SSGT) may trigger recalculation of GPR and / or SPR (e.g., adjustment of space spots configuration and / or physical space assignment) when fluctuations in the sparsified real-time attributes reach a certain level. In some embodiments, 'global' and / or physical space-specific thresholds may be set (e g., by the 020 engine 102, by a space operator). Such recalculations may advantageously enhance responsiveness to dynamic conditions, while reducing computational demand. Accordingly, automated real-time responsiveness may be made available on a large scale network (e.g., across a city, state, country, and / or internationally).

[0260] The hierarchical structure may, for example, advantageously facilitate management of advertising spaces, such as through integration of static, periodic, and / or real-time data. The structure may, for example, advantageously support dynamic reconfiguration and / or selective attribute processing. The architecture may, for example, enable adaptation to changing environmental conditions, facilitating GPR computation, such as through sparsification. The attribute array 130 structure may, for example, advantageously reduce computational overhead, such as by organizing attributes categorically. For example, the structure may advantageously provide selective access to relevant subsets during performance rating calculations.

[0261] Fig. 25 depicts an example GPR data structure visualization as a GPR display 2500. The example demonstrates an example GPR calculation (including a sparsification process such as disclosed at least with reference to Fig. 13), for an example grid unit G2-3.1.2.

[0262] In various embodiments, an attribute array 130 may include attributes 132. As shown in this example, attributes 132 may include, for example variables such as geolocation (A1), noise level (A9), among others such as temperature (A3) and traffic levels (A2). These attributes may, for example, form a holistic dataset useful for subsequent analysis.

[0263] Media placement criteria 804 (e.g., generated from and / or contained in a media placement package 120) defines, in this example, parameters like target audience, target display times, and / or format requirements.

[0264] In certain embodiments, a model, such as a GPR model 1300 (e.g., a learning module 1302, an attribute relevance identifier 1304 as shown) may be applied (e.g., to the media placement criteria 804 and the attribute array 130) to determine relevant attributes. In cooperation, for example, with a sparsification module 130, selected attributes 134 (e.g., relevant to the MPC), such as traffic and time-of-day in this example, are selected from the attribute array 130.

[0265] Attribute weights 2502 are associated with (e.g., each of) the selected attributes 134 (e.g., by a weight calculation engine 1306). The weights may, for example, distribute importance of attributes based on MPC, context, and / or historical relevance, by way of example and not limitation.

[0266] A data preprocessing module 1400 may convert and normalize the selected attributes into normalized attributes 2504. The GPR 1504 is computed in this example by multiplying the weights and values of these attributes.

[0267] These examples demonstrate an example structured approach to processing and analyzing data with associated data structures, which may advantageously enhance media placement efficiency through strategic selection and use of relevant attributes.

[0268] Processing only k = 4 selected attributes 134 rather than n = 12 complete attributes 132 reduces operations from 23 to 7 per grid unit 126, achieving 67% reduction in this example. For 1000 grid units, this reduces computations from 23,000 to 7,000 operations, which may advantageously facilitate real-time processing across large physical spaces 104.

[0269] The data structure may, for example, support selective attribute processing by storing and / or processing only selected attributes 134 identified for specific media placement criteria 804, which may advantageously reduce memory consumption and / or enable efficient GPR recalculation (e.g., in response to periodic and / or real-time environmental data 2202 updates).

[0270] Fig. 26 depicts an example SPR display 2600 demonstrating space spot formation and multi-objective adaptation for an illustrative Space Spot #42 in an associated space spot data structure 2310

[0271] In this example, grid units 126 with GPR values exceeding a threshold (e.g., 0.75) are aggregate into a space spot. Space spot display 2602 may, for example, dynamically show a space spot being aggregated (e.g., as shown, with the dashed-line boundary shifting upwards and to the right, and expanding width to encompass 6 grid units). In this example, the following grid units are aggregated into a space spot: G1 -2.1 (GPR=0.78), G1-2.2 (GPR=0.82), G2-2.1 (GPR=0.76), G2-2.2 (GPR=0.81), G3-2.1 (GPR=0.85), G3-2.2 (GPR=0.79). Adjacency constraints may, for example, enforce spatial contiguity in some examples.

[0272] Factors 2604 in this example include five objectified factors. The factors may, for example, be generated by multiobjective adaptation engine 1502. The example factors include:

[0273] Fi: Aggregated GPR = (0.78+0.82+0.76+0.81 +0.85+0.79) / 6 = 0.802

[0274] F2: Space efficiency 1506 = 12.6m2 / 10.5m2= 1 .20

[0275] F3: Revenue potential 1508 = $85 / day normalized to 0.85

[0276] F4: Aesthetic score (e.g., from visual analysis model 900) = 0.88

[0277] F5: Operational constraints 1512 (cost factor) = 0.15

[0278] Weight calculation (e.g., by a weight calculation engine 1306 and / or objective function definition 1514) assigns weights to each grid unit. In this example: Oi=0.40, a2=0.25, a3=0.20, a4=0.10, a5=0.05.

[0279] The SPR 2610 is calculated in this example (e.g., by optimization engine 1518) calculates: SPR = (0.40*0.802) + (0.25x1.20) + (0.20x0.85) + (0.10x0.88) - (0.05x0.15) = 0.321 + 0.300 + 0.170 + 0.088 - 0.008 = 0.871.

[0280] The SPR display 2600 includes, in this example, a space spot commentary 2612. The commentary may, for example, guide a user and / or downstream processing (e.g., a model responsible for selecting a 'best' space spot(s)). In this example, SPR = 0.871 exceeds a premium threshold (0.80), indicating a 'high-value' placement which may, for example, align with the budget constraints 2108 of the MPC.

[0281] The space spot data structure 2310 may, for example, store aggregated GPR values from constituent grid units 126, objective factor values, optimization weights, and / or computed SPR. The space spot data structure 2310 may, for example, advantageously enable efficient ranking (e.g., in space spot generation method 400), support space spot selection (e.g., automatically / machine autonomous), and / or enable subsequent control signal generation (e.g., through control signal generator 1612).

[0282] Fig. 27 depicts a SSGT output(s) 1912 display, such as of an example SSGT data structure. The SSGT data structure may, for example, include multiple (e.g., hierarchical) levels for organizing spatial and operational information related to display of media at one or more selected space spots.

[0283] A boundary definitions 2700 section in this example SSGT includes grid spaces designated as G2.1.1, G2.1.2, and G2.1 .3. These grid spaces may, for example, be aggregated to form a space spot. The total area is 12.6 square meters in this example. The dimensions are shown specified as 4.2 x 3.0 meters. Physical coordinates may, for example, indicate a geographical location. An elevation (10.2 meters in this example) may be included in some examples, as shown.

[0284] The grid structure may include multiple levels and / or sub structures. Level 1 in this example includes , such as GO, G1 , G2, and G3. Level 2 includes grid units 126, such as Unit["1.1"], Unit["1.2"], Unit["2.1"], and Unit["2.2"]. In some examples, as shown, a specific selected grid unit 126 may be designated as G2-3.2.

[0285] This SSGT output(s) 1912 display depicts a visual layout display 2702 of the selected grid units. In this example, a layout specifications 2704 section is included. These specifications may, for example, define content placement zones. A primary zone may occupy 2.52x1.80 meters. A secondary zone may be 1.26 x 0.90 meters A branding zone may be 0.42 x 0.30 meters. Visual hierarchy may include four sections, together with associations with particular zones and / or specifications. These zones may, for example, facilitate structured content presentation.

[0286] Technical parameters 2706 are included in this example. In situations where an active display is targeted (e.g., as in this example), display parameters may be specified. A display type may be LED Digital. A refresh rate may be 60 Hz. Resolution may be defined as 1920 x 1080 pixels. Color depth may target 24-bit RGB. Orientation may be landscape (e.g., with 0 degree rotation). Supported formats may include JPEG and PNG. Brightness level may be adjustable to 50 cd / square meter. Maximum content file size may be 50 MB. These parameters may, for example, facilitate technical compatibility.

[0287] Campaign execution parameters may be defined, as shown, in a temporal scheduling 2708 section. A campaign start may be 2025-10-21 at 00:00:00. A campaign end may be 2025-10-27 at 23:59:59. Display duration may, for example, be 15 seconds per cycle (e.g., minimum). Rotation pattern may, for example, occur every 3 minutes. A passive display (e.g., a poster, a manually configured display such as a 3D product display) may be more often refreshed in months. Peak periods may, for example, be defined from 12:00:00 to 14:00:00 and 18:00:00 to 20:00:00. These scheduling parameters may facilitate temporal coordination. Outside of these selected times, for example, generic brand content may be displayed ("off-peak fallback").

[0288] Content distribution system commands may be included, as shown in implementation rules 2710. Commands may include DECODE, DEPLOY, CREATE, and / or FALLBACK protocols. Synchronization requirements may, for example, specify time synchronization (e.g., with NTP server protocols), as shown. These instructions may facilitate system integration (e.g., by 020 routing engine 122).

[0289] Quality parameters may be defined. Active inspection may not be required in some examples. Focus may be, for example, on prompting advertisement updates before expiry. Other data 2712 may be generated (e.g., by 020 engine 102), such as software version 2.4.1 , system status, SSGT status, and / or other information relevant to media display and / or distribution. Status may be marked as approved, as shown. Editorial value and / or contact information may be provided. These parameters may facilitate quality management, for example.

[0290] Fig. 28 depicts a media placement package 120. In some examples, a MPP may be received from a media originator 118. In some examples, a MPP may, for example, be generated (e.g., de novo, extended, enriched, updated) by a 020 engine102 based on inputs from a media originator 118. The media placement package 120 depicted may, for example, be generated by the 020 engine 102 in response to inputs from a media originator 118.

[0291] In this example, campaign information 2800 is specified. A campaign name and ID may identify the campaign. Product and / or service information may be included. Duration may be 30 days in some examples. Total budget may be 50,000 USD. A daily budget cap may be 2,000 USD. Priority level may be designated as High (P1). These parameters may, for example, facilitate campaign identification and resource allocation (e.g., in subsequent models, such as in system 1900).

[0292] In this example, target audience 2802 parameters are defined. Demographics may specify age (e.g., 25-45 years), attributes (e.g., tech-savvy, professionals). Income range may be 60K-150K. Interests may include smart home technology and loT. Lifestyle may be urban professionals and homeowners. These parameters may facilitate audience targeting.

[0293] In this example, temporal requirements 2804 may specify time-based parameters. Campaign dates and times may be defined. Peak windows may be 07:00-09:00, 12:00-14:00, and 17:00-19:00. Weekday priority may be indicated. Off-peak periods may use reduced rates. These requirements may facilitate temporal adaptation.

[0294] Geographic constraints 2806, in this example, define location parameters. Target cities, for example, include New York City, San Francisco, and Seattle Location types may comprise shopping malls, tech stores, and transit stations. Proximity requirements may be specified. Exclusion zones may be designated for competitor stores. These constraints may, for example, facilitate geographic targeting.

[0295] In this example, media format specifications 2808 may, for example, specify technical format requirements. Digital LED / LCD screens are specified in this example. Minimum resolution may be 1920*1080 pixels. Size range may be 1.5 square meters to 15 square meters. Format may be MP4 (H.264), such as with 15-second spots. Auto-brightness capability may be required. These specifications may, for example, facilitate technical compatibility.

[0296] Performance targets may be defined, as shown in performance requirements 2810. Minimum impressions may be 5,000 per day. Target CPM may be 8-12. Quality thresholds may specify GPR 0.70 and SPR 0.75. Expected CTR may be 0.5% or higher. These requirements may facilitate performance monitoring.

[0297] Budget distribution may be specified, as shown in budget allocation 2812. Geographic split may allocate NYC 50%, San Francisco 30%, and Seattle 20%. Time-based split may allocate Peak 70% and / or Off-peak 30%. Maximum per location may be $150 per day. Reserve budget may be 10%. These allocations may, for example, facilitate financial management. For example, the 020 engine 102 may generate GPR and / or SPR.

[0298] Media assets may be specified, as shown in content assets 2814. Three creative files with CDN URLs are included in this example. Rotation strategy may include Video-1 (60%), Video-2 (30%), and Image-1 (10%). All assets may be in Full HD format. These assets may facilitate content delivery.

[0299] 'Optimization' preferences may be defined as shown in optimization preferences 2816. Primary objective may be to maximize impressions. Weather adaptation may be enabled. Real-time bidding may include a $12 ceiling. A / B testing and auto-adjustment may be supported. These preferences may facilitate dynamic optimization.

[0300] Fig. 29A depicts a display 2906 of example grid overlays on a floor plan of a restaurant 106. The grid 124 may include a rectangular configuration with zone-based divisions. Multiple hierarchical grid layers may, for example, be applied.

[0301] A bar area 601.1 is located at the top. Layer GO may include a 3*3 zone grid 124 for major areas. Layer G1 may include a 5*5 medium grid 124 over a dining area. The dining area grid 5008 includes multiple placement zones for tablesthroughout the main floor area. Layer G2 may include an 8x4 fine grid 124 on a wall display surface. The wall display grid 5009 overlays the wall opposite the entrance.

[0302] Space spot #42 may include 4 aggregated G2 grid units 126 on the wall display. The entrance is positioned at the bottom center. The zone grid 5002 for seating arrangements divides the dining area. A display area 601.3 is located on the wall next to the bar area 601.1. Restrooms are labeled as 601.2. A kitchen has been identified as a no-ad zone.

[0303] Fig. 29B depicts example grid overlays on a floor plan of a retail store 114. The grid 124 includes in this example a hybrid configuration with irregular polygonal zones. Multiple hierarchical grid layers may be applied.

[0304] The entrance 601.5 is positioned at the lower center. Layer GO includes irregular functional zones, such as following store layout. The functional zones layer may, for example, accommodate various operational areas. Layer G1 may include a 10x7 regular grid 124 over a shopping floor. A food table 604.1 is positioned centrally. Layer G2 may include a radial grid 124 around digital display pillars. The pillar radial grid layer may, for example, spatially organize fixtures. Layer G3 may include a 4x11 fine grid 124 on a back wall.

[0305] Space spot #15 may correspond to a wall section. Space spot #23 may correspond to a floor section. Checkout areas 601.6 and 601 7 are aligned adjacent to the entrance 601.5. Walking paths 6043 and storage area 601.8 form irregular polygonal zones. The wall display grid layer 5005 provides designated display regions. A display 602 is positioned along the back wall. An additional display 601.9 is positioned toward the right side. Aisles and digital pillars may be included in some examples.

[0306] Fig. 29C depicts example grid overlays on a floor plan of a transit station. The grid 124 includes a hexagonal configuration with flow-based density mapping. Multiple hierarchical grid layers may be applied.

[0307] Entrance / exit gates are located at the top center. Layer GO may include traffic flow zones (High / Med / Low) with color coding. The hexagonal grid layer 5001 facilitates optimized flow-based density mapping. Layer G1 may include a hexagonal grid 124 for even coverage of a concourse. Layer G2 may include a dense rectangular grid 124 on digital walls. A density heatmap overlay 5206 measures pedestrian flow within the central area.

[0308] Space spot #78 may correspond to a wall section. Space spot #91 may correspond to a hexagonal cluster. The main concourse 602 defined by the traffic zones layer 5203 situates pedestrian traffic centrally. A display wall 602.1 and retail area 602.4 enhance wayfinding. Train platforms 602.6 are located at the bottom. Kiosks 602.2 and density relief zones 602.3 are strategically positioned. Ticketing machines, an info booth, and escalators may be included in some examples.

[0309] Fig. 29D depicts example grid overlays on a wall of a restaurant 106. The visualization shows a visibility zone analysis using a multi-grid overlay. Four layers of analysis may be applied.

[0310] Layer 1 includes visibility zones layer 5300 with color-coded areas based on viewing positions. Zone V1 may show high visibility from Tables 1 and 2. Zone V2 may show moderate visibility from Table 3. Zone V3 may include an entry door view (first impression zone). Zone 01 may be obscured by a decorative plant 603.1. Zone 02 may be blocked by a hanging light fixture. Zone 03 may be blocked by a wall-mounted shelf. A premium zone may show multi-angle visibility from multiple viewpoints.

[0311] Layer 2 includes the performance grid showing computed GPR ratings. A 12x6 rectangular grid 124 overlay may be applied. Premium zone may show GPR = 0.92. High visibility zone may have a GPR = 0.85. Moderate zone may show GPR = 0.72-0.78. Obscured zones may have a GPR = 0.20-0.35.

[0312] Layer 3 includes viewing angles with sightline cones, such as from viewer positions. Tables 1 and 2 may have 45° and 60° viewing cones. A door viewer may have a 50° viewing cone. Dashed sightlines may show direct line-of-sight.

[0313] Layer 4 includes space spot candidates with purple dashed outlines. Space spot #127 may include a premium center area (4x2.5m). Space spot #128 may show good table visibility (3x2.5m). Space spot #129 may include an entry view zone (2.5x3m).

[0314] The restaurant wall may be 12m x 6m with brick texture. Four dining tables are positioned at various positions. An entry door with viewer position is shown. A decorative plant 603.1 creates an obstruction. A hanging light fixture creates an obstruction. A wall-mounted shelf creates an obstruction. A door 603.2 is located along the right side.

[0315] Although various embodiments are shown and described, other embodiments are contemplated.

[0316] For example, some embodiments may be configured for various use cases such as medical, industrial, residential, commercial, retail, foodservice, military. Various embodiments may be configured for hospitals. For example, grid units 126 may correspond to patient room walls, waiting areas, and / or elevator banks. Real-time attributes may include, for example, patient flow rates. Periodic attributes may include, for example, visitation hours.

[0317] Some embodiments may be configured for manufacturing facilities Sensors 128 may, for example, monitor production line status and / or shift changes. Media routing (e.g., the 020 engine 102) may, for example, adjust safety messaging and / or productivity metrics displays.

[0318] Various embodiments may be configured for apartment complexes. Temporal attributes may, for example, reflect resident traffic patterns (e.g., for lobby displays targeting move-in periods and / or amenity promotions).

[0319] Some embodiments may be configured for office buildings. Periodic attributes may, for example, include meeting room schedules and / or employee density. For example, the 020 engine 102 may generate and / or route cafeteria promotions and / or building service advertisements. Various embodiments may be configured for shopping malls. Models may, for example, analyze foot traffic between anchor stores. The 020 engine 102 may, for example, automatically position advertisements at high-conversion zones based on GPR, SPR, and / or MPC. Some embodiments may be configured for restaurants. Visual analysis models 900 may, for example, automatically compute high placement value on walls visible from multiple table positions.

[0320] Various embodiments may be configured for military bases. Hard filters may, for example, exclude grid units 126 in secure areas. Compatibility criteria may, for example, restrict media placement categories. Some embodiments may, for example, be configured for universities. Temporal models 2302, for example, may incorporate class schedules and / or semester cycles (e.g., for textbook and / or housing advertisements).

[0321] Various embodiments may be configured for airports. Weather integration models 1000 may, for example, advantageously enable the 020 engine 102 to automatically adjust travel insurance, local amenities (e.g., hotels, restaurants), and / or destination promotions based on flight delays.

[0322] Some embodiments may be configured for stadiums. Real-time attributes may, for example, include game scores and / or crowd energy levels and / or sentiment. For example, the 020 engine 102 may dynamically route media based on fluctuating MPC-specific GPRs and / or SPRs differentially to different areas of the field (e.g., winning team fan side vs losing team fan side). For example, the system may trigger contextually relevant concession and / or merchandise advertisements.

[0323] Various embodiments may be configured for hotels. Space spots may, for example, be formed in elevator lobbies. Associated SPR calculations may, for example, incorporate guest check-in / check-out patterns.

[0324] Some embodiments may be configured for parks. Environmental recognition modules 1700 may, for example, identify seasonal weather conditions affecting outdoor gear and / or beverage promotion placement.

[0325] Some embodiments may include a hierarchical grid structure, which may be a multi-layered spatial representation system mapping physical advertising environments with varying levels of granularity. This structure may be stored, for example, in a first data structure and may include several components.

[0326] In various embodiments, vertical grid layers may represent a top-down subdivision approach where each successive layer could provide increasingly finer granularity of physical space. For example, Grid 0 (GO) may represent the broadest division with cells labeled sequentially. Each subsequent grid level may add a new sub-index, allowing every grid unit to be traced back to its parent. This could, for instance, enable precise location tracking and performance analysis across complex advertising layouts.

[0327] Overlay grid layers may, in some embodiments, be lateral grid systems coexisting across the same physical location but corresponding to different media types. Unlike vertical layers, overlay layers may represent different advertising modalities that can simultaneously occupy the same space

[0328] Certain embodiments may include various overlay grid types, for instance some embodiments may include static structure grids mapping to permanent structures where advertising content remains unchanged for a specific duration, providing a reliable organizational framework. In various embodiments, dynamic structure grids may be designed for digital screens or rotating displays where content can be updated in near real-time, accommodating rapidly changing advertisements. Some embodiments may incorporate static-dynamic hybrid structure grids, combining features of both static and dynamic grids, potentially operating in distinct modes such as a fixed structure with dynamic data assignments or dynamic structure with fixed data assignments.

[0329] Some embodiments may include a distributed sensor network comprising interconnected data collection devices deployed throughout physical space. These sensors may capture real-time environmental data and include various devices like cameras or motion sensors. In various examples, the network may continuously transmit data to the system, providing updated information about the physical environment.

[0330] In some examples, a data preprocessing module may normalize heterogeneous sensor data into standardized formats and units. This module may apply various transformation algorithms, converting raw sensor inputs into consistent units, removing outliers, and aligning temporal data to common time intervals. This may enable meaningful comparisons and processing.

[0331] Attribute arrays may be multi-dimensional data structures assigned to each grid unit, storing various characteristics influencing advertising value. These arrays may, for example, contain categories such as static, periodic, and real-time attributes.

[0332] In various embodiments, static attributes may include physical location data or structural properties. Periodic attributes may represent regularly changing characteristics, such as foot traffic patterns. Real-time attributes may involve dynamically updating characteristics, like current foot traffic volume, derived from normalized sensor data.

[0333] Some embodiments of advertisement placement criteria may involve specifications received from advertiser devices defining targeting parameters and format requirements, such as target audience demographics or geographic targeting preferences. Format requirements may include advertisement dimensions or campaign objectives.

[0334] Certain embodiments may utilize hard filters, comprising binary exclusion rules applied to grid units immediately disqualifying locations that fail to meet specific criteria. These requirements may include regulatory compliance or technical compatibility.

[0335] In some embodiments, a Grid Performance Rating (GPR) may be a numerical score assigned to each grid unit, quantifying its potential advertising effectiveness. The system may calculate the GPR via computationally efficient processes like selective summing, reducing computational load by processing only attributes relevant to advertisement placement criteria.

[0336] Space spots may be aggregated areas formed by dynamically combining adjacent grid units that exceed a specified GPR threshold. Some embodiments may involve identifying high-performing grid units and clustering them to form cohesive advertising spaces, optimizing effectiveness.

[0337] A Spot Performance Rating (SPR) may, in various embodiments, be a comprehensive score calculated for each space spot using multi-objective optimization algorithms. These algorithms may balance competing factors like advertiser requirements or space efficiency, aggregating GPRs of included grid units.

[0338] The Space Spot Grid Template (SSGT) may be a data-driven specification that defines how selected grid units are grouped for displaying advertisements. This template may, for example, provide boundary definitions or layout specifications.

[0339] In some embodiments, a content distribution system may be responsible for delivering and / or displaying advertisements according to the SSGT specifications. Upon receiving instructions, it may retrieve and format appropriate advertisement content, schedule display timing, and monitor performance. This system may achieve efficiency by selectively processing attributes, enabling real-time valuation.

[0340] In some examples, a system may be configured to perform computational resource allocation in physical space management. A data store system may, for example, be configured to store multiple space data structures. These space data structures may represent various physical spaces, such as a restaurant, an apartment building, a park, a gas station, a retail store, or a kiosk, as shown in FIG. 1 . Each space data structure may include a hierarchical grid structure, which may subdivide the physical space into progressively smaller grid units. This hierarchical grid structure may, for example, facilitate detailed analysis and management of the physical spaces.

[0341] A distributed sensor network may be configured to monitor real-time environmental conditions within the physical spaces. In some examples, sensors may be deployed in locations such as a restaurant or a park to collect data on environmental conditions like temperature, humidity, or occupancy levels. This real-time data may, for example, facilitate dynamic adjustments to resource allocation. In some embodiments, temperature sensors may be deployed within a physical space to monitor ambient temperature levels. The data collected from these sensors may be used to adjust advertisements by promoting products or services that are temperature-sensitive, such as cold beverages during hot weather or heating services during colder conditions. This adjustment may enhance the relevance of advertisements to the current environmental conditions.

[0342] In some embodiments, humidity sensors may, by way of example and not limitation, be utilized to measure the moisture levels in the air. The data from these sensors may be used to modify advertisements to feature products that areaffected by humidity, such as dehumidifiers in high-humidity environments or moisturizing products in low-humidity conditions. This approach may improve the effectiveness of advertisements by aligning them with the environmental needs of the audience.

[0343] In some embodiments, occupancy sensors may, by way of example and not limitation, be employed to detect the number of people present in a given area. The data from these sensors may be used to dynamically adjust the frequency or type of advertisements displayed, such as increasing the display of high-demand products during peak occupancy times or promoting exclusive offers during low-traffic periods. This may optimize advertisement exposure based on real-time audience size.

[0344] In some embodiments, light sensors may be used to assess the lighting conditions within an advertising space. The data from these sensors may be used to adjust the brightness or contrast of digital advertisements to ensure optimal visibility and readability under varying lighting conditions. This adjustment may enhance the visual impact of advertisements and improve audience engagement.

[0345] In some embodiments, motion sensors may be implemented to track movement patterns within a space. The data from these sensors may be used to tailor advertisements based on the flow of pedestrian traffic, such as positioning ads in high-traffic areas or timing ad displays to coincide with peak movement periods. This may increase the likelihood of advertisements being noticed by the target audience

[0346] A processor may be configured to perform several operations. The processor may, for example, retrieve the space data structures from the data store system. This retrieval may allow the processor to access the hierarchical grid structures for further processing.

[0347] The processor may compute updated attribute arrays for each grid unit in response to real-time environmental data from the distributed sensor network. Each attribute array may include static attributes, such as geolocation data of the corresponding physical space. Static attributes may, for example, provide a fixed reference for the location of each grid unit. Periodic attributes may include traffic data associated with the corresponding physical space. This traffic data may, for example, reflect patterns of movement or occupancy over time. Real-time attributes may include local environmental data of the realtime environmental data associated with the corresponding physical space. Real-time attributes may, for example, facilitate immediate adjustments based on current conditions.

[0348] In some embodiments, static attributes may include geolocation data. Structural characteristics may be included in some embodiments. Safety ratings may be part of the static attributes in some examples. Historical significance may be included in some embodiments. Ownership information may be part of the static attributes in some examples. These attributes may provide a stable reference for the location and characteristics of each grid unit. Some embodiments may include periodic attributes such as traffic data. Wi-Fi signal strength may be included in some embodiments. Surrounding business types may be part of the periodic attributes in some examples. Accessibility may be included in some embodiments. Maintenance schedules may be part of the periodic attributes in some examples. These attributes may reflect patterns of movement or occupancy over time, providing insights into the dynamic nature of the environment. Various embodiments may include realtime attributes such as local environmental data. Weather conditions may be included in some embodiments. Pedestrian traffic may be part of the real-time attributes in some examples. Eye tracking data may be included in some embodiments. Time-of-day factors may be part of the real-time attributes in some examples. These attributes may facilitate immediate adjustments based on current conditions, enhancing the responsiveness of the system.

[0349] In some embodiments, sensor data attributes may include temperature readings. Humidity levels may be included in some embodiments. Air quality indices may be part of the sensor data attributes in some examples. Noise levels may be included in some embodiments. Light intensity may be part of the sensor data attributes in some examples. These attributes may be collected from distributed sensors to provide real-time environmental insights. Some embodiments may include computed attributes such as visibility angles. Proximity to high-traffic areas may be included in some embodiments. Potential advertising value may be part of the computed attributes in some examples. Audience demographics may be included in some embodiments. Engagement metrics may be part of the computed attributes in some examples. These attributes may be derived from a combination of sensor data and historical performance data to increase the effectiveness of advertising strategies.

[0350] In some embodiments, static attributes may include zoning regulations. Legal compliance requirements may be included in some embodiments. Cultural significance may be part of the static attributes in some examples. Architectural style may be included in some embodiments. Material composition may be part of the static attributes in some examples. These attributes may facilitate adherence to local regulations and cultural norms for advertising placements. Some embodiments may include periodic attributes such as seasonal traffic patterns. Event schedules may be included in some embodiments. Promotional calendars may be part of the periodic attributes in some examples. Competitor activity may be included in some embodiments. Market trends may be part of the periodic attributes in some examples. These attributes may inform strategic planning and resource allocation for advertising campaigns.

[0351] In some embodiments, real-time attributes may include social media activity. Live event attendance may be included in some embodiments. Emergency alerts may be part of the real-time attributes in some examples. Traffic congestion may be included in some embodiments. Public transportation schedules may be part of the real-time attributes in some examples. These attributes may enable advertisers to respond quickly to changing conditions and increase engagement. Some embodiments may include sensor data attributes such as motion detection. Vibration levels may be included in some embodiments. Electromagnetic interference may be part of the sensor data attributes in some examples. Ultraviolet radiation may be included in some embodiments. Infrared signals may be part of the sensor data attributes in some examples. These attributes may provide additional context for understanding the physical environment and its impact on advertising effectiveness.

[0352] In some embodiments, computed attributes may include cost-benefit analysis. Return on investment projections may be included in some embodiments. Brand alignment scores may be part of the computed attributes in some examples. Competitive positioning may be included in some embodiments. Customer sentiment analysis may be part of the computed attributes in some examples. These attributes may support data-driven decision-making and increase the overall effectiveness of advertising strategies.

[0353] The processor may generate media placement criteria from a media placement package 120 received from an originating device of a media originator 118, as shown in FIG. 1. The media placement criteria may include target display parameters. In some examples, the media placement criteria may include media format specifications. These criteria may, for example, guide the selection of appropriate grid units 126 for media placement. Some embodiments may utilize a multi-layer grid approach to dynamically reconfigure advertising spaces. This approach may, for example, allow a single advertisement tospan multiple grid units 126 in one period. In some examples, the advertisement may subdivide into smaller advertisements in subsequent periods. Such dynamic reconfiguration may advantageously optimize space utilization. In some cases, it may accommodate varying advertising needs over time.

[0354] In some embodiments, media placement criteria may include target audience demographics. These criteria may, for example, facilitate the selection of grid units 126. The selection may, for example, align with the preferences and behaviors of a specific demographic group.

[0355] In some embodiments, the system may utilize Al to generate media placement criteria from a media placement package. The Al may, for example, analyze the package to identify demographic and psychographic attributes relevant to the target audience. This analysis may, for example, facilitate the alignment of media placement with audience preferences and behaviors.

[0356] In some embodiments, the Al may evaluate historical performance data included in the media placement package. This evaluation may, for example, inform the selection of grid units 126. The Al may, for example, identify patterns of successful media placements. These patterns may, for example, be used to predict future performance. Predicting future performance may, for example, increase the effectiveness of media placement strategies.

[0357] In some embodiments, the Al may incorporate environmental factors from the media placement package into the criteria generation process. These factors may include, for example, lighting conditions. Pedestrian traffic patterns may, for example, be included as environmental factors. Visual obstructions may, for example, also be considered as environmental factors. By considering these factors, the Al may, for example, enhance the effectiveness of media placements. The Al may, for example, select grid units 126 that increase visibility and engagement.

[0358] In some embodiments, the Al may integrate commercial factors from the media placement package. These factors may include, for example, pricing potential. Market demand may, for example, be another commercial factor. This integration may, for example, enable the system to prioritize grid units 126 that offer a higher potential return on investment. The Al may, for example, adjust media placement criteria dynamically based on real-time market conditions. Adjusting criteria dynamically may, for example, increase advertising value.

[0359] In some embodiments, the Al system may include a visual analysis model. This model may, for example, be trained using a dataset of images depicting various advertising environments. The training process may involve supervised learning techniques to identify and classify potential advertising spots based on visual characteristics. The visual analysis model may increase the system's ability to assess the suitability of different spaces for advertising with greater accuracy.

[0360] Some embodiments may incorporate a weather integration model. This model may be trained using historical weather data and its impact on advertising effectiveness. The training may utilize machine learning algorithms to predict how different weather conditions may affect the visibility and engagement of advertisements. The weather integration model may enhance the system's capability to adjust advertising strategies dynamically based on real-time weather conditions.

[0361] In some embodiments, the Al system may include a geographic model. This model may be trained using geographic information system (GIS) data to understand spatial relationships and pedestrian traffic patterns. The training process may involve analyzing geographic data to increase the effectiveness of advertisement placement in high-traffic areas. The geographic model may facilitate the system's ability to select grid units that maximize exposure to target audiences.

[0362] Some embodiments may utilize a temporal model. This model may be trained using time-series data to understand temporal patterns in audience behavior. The training may involve identifying peak times for audience engagement and adjusting advertising schedules accordingly. The temporal model may enhance the effectiveness of media placements by aligning them with optimal time periods for audience interaction.

[0363] Various embodiments may incorporate an ensemble Al approach. This approach may involve training multiple specialized models, each focusing on a specific aspect of advertising space evaluation. The ensemble Al may combine outputs from visual, weather, geographic, and temporal models to provide a comprehensive analysis. This approach may increase the robustness and accuracy of the system by leveraging the strengths of each specialized model.

[0364] In some embodiments, media placement criteria may include geographic location specifications. These criteria may, for example, support the placement of advertisements in areas with high foot traffic. In some examples, the placement may occur in proximity to relevant businesses.

[0365] In some embodiments, media placement criteria may include time-of-day parameters. These criteria may, for example, allow advertisements to be displayed during peak hours. The display during peak hours may, for example, occur when the target audience is most likely to be present.

[0366] In some embodiments, media placement criteria may include environmental conditions. These criteria may, for example, facilitate the placement of advertisements in locations with favorable lighting In some examples, the placement may occur in locations with favorable weather conditions to enhance visibility.

[0367] In some embodiments, media placement criteria may include budget constraints. These criteria may, for example, guide the allocation of advertising spaces based on cost-effectiveness. The allocation may, for example, consider available financial resources.

[0368] Some embodiments may employ Al and computer vision technologies to automate grid generation. These technologies may, for example, analyze images or 3D walkthroughs to identify potential advertising spots, such as walls or poles. This automated analysis may enhance the efficiency of identifying high-value advertising locations by leveraging visual characteristics. In some examples, it may consider environmental factors.

[0369] Various embodiments may incorporate a hybrid filtering system to manage advertising content placement. Hard filters may, for example, exclude certain advertisements based on legal or technical constraints. In some examples, soft weightings may adjust valuations based on factors like weather or time of day. This hybrid approach may provide flexibility and compliance with regulatory requirements. It may also optimize advertising value.

[0370] In some embodiments, the system may integrate multi-source data collection to enhance grid valuation. Environmental data, such as weather and pedestrian traffic, may be collected in real-time. In some examples, transaction data may provide insights into consumer behavior. This comprehensive data integration may improve the accuracy of grid valuations. It may support informed decision-making for advertisers.

[0371] Some embodiments may offer smart template generation to optimize advertising layouts. Al may, for example, evaluate grid spaces to create templates that maximize visual impact. In some examples, the templates may enhance advertising value. This template generation may provide advertisers with multiple layout options. It may balance space usage and value to meet diverse advertising objectives.

[0372] The processor may apply hard filters to exclude grid units that fail to meet mandatory criteria associated with the media placement criteria. This filtering process may, for example, facilitate the consideration of suitable grid units for media placement.

[0373] The processor may sparsify attributes of remaining grid units and compute a GPR for the remaining grid units. This may involve applying a model trained on historical performance data of media placements across different physical spaces. The model may identify a subset of relevant attributes from the attribute arrays, as shown in FIG. 1 . A corresponding weighting of at least some of the relevant attributes may be determined based on the media placement criteria. This selective processing may, for example, reduce unnecessary attribute computations while facilitating the computation of the GPR per grid unit specifically for the media placement criteria received.

[0374] The processor may dynamically aggregate grid units with GPR values exceeding a threshold to form space spots having a minimum aggregate size satisfying the media format specifications. This aggregation may, for example, increase the use of available space for media placement.

[0375] The processor may compute a SPR for each space spot based on the GPR values of the grid units making up the space spot. The SPR may, for example, provide an overall performance metric for the space spot.

[0376] The processor may automatically control content distribution systems by generating control signals configured to physically reconfigure display parameters of physical display devices. This reconfiguration may facilitate the distribution of media corresponding to the media placement criteria to selected space spots in selected physical spaces as a function of SPR. This control may, for example, enhance the effectiveness of media distribution.

[0377] The processor may monitor the real-time environmental data from the distributed sensor network corresponding to the physical display devices corresponding to the selected space spots. This monitoring may, for example, allow for ongoing adjustments to media placement based on changing conditions.

[0378] In response to updated real-time environmental data received from the distributed sensor network, the processor may compute only updated real-time attributes of the attribute arrays. The processor may then recompute the GPR and SPR and generate updated control signals. These updates may, for example, facilitate the reconfiguration of the physical display devices in response to changing real-time environmental conditions in at least some of the physical spaces.

[0379] Some embodiments may include a computer-implemented method for reducing computational load in advertisement distribution across geographically distributed spaces. The method may involve retrieving space data structures representing physical spaces, each comprising a hierarchical grid structure with multiple grid layers. Real-time environmental data may be received from a distributed sensor network. Advertisement placement criteria may be received from an advertiser device. For each space data structure, attribute arrays for grid units may be updated, comprising static, periodic, and real-time attributes. Hard filters may be applied to exclude grid units that fail to meet mandatory criteria. A Grid Performance Rating (GPR) may be calculated for remaining grid units by identifying relevant attributes and selectively summing them. Grid units with GPR values exceeding a threshold may be dynamically aggregated to form space spots. A Spot Performance Rating (SPR) may be calculated for each space spot, and an inference of the most valuable space spot for the advertisement may be generated based on the calculated SPRs.

[0380] In some embodiments, the hierarchical grid structure may include radial grid layers configured to subdivide the physical space into sectors based on radial coordinates. These radial grid layers may dynamically adjust sector sizes basedon real-time environmental data, such as pedestrian traffic patterns detected by the distributed sensor network. Al-driven analysis, including computer vision technologies, may optimize advertisement placement by analyzing pedestrian density and flow direction. The attribute arrays may include dynamic attributes related to pedestrian traffic, updated at a frequency determined by the rate of change in traffic patterns.

[0381] Various embodiments may include a system for managing advertising spaces, comprising a multi-layer grid system configured to divide physical advertising spaces into multiple coordinate systems, including rectangular, triangular, and radial systems. A dynamic reconfiguration module may allow flexible advertisement placement by dynamically reconfiguring grid spaces. An attribute vector system may represent valuation factors for each grid space using an array of attributes. An Al- driven analysis module may analyze images and / or 3D walkthroughs, such as to identify potential advertising spots and determine suitability based on visual characteristics. A value determination mechanism may categorize products and services and allocate categories based on grid value

[0382] Some embodiments may include a method for optimizing advertising space allocation, comprising dividing physical advertising spaces into multiple coordinate systems using a multi-layer grid approach. Grid spaces may be dynamically reconfigured to allow flexible advertisement placement. Valuation factors for each grid space may be represented using an attribute vector system. Images or 3D walkthroughs may be analyzed with Al to identify potential advertising spots and determine suitability. Products and services may be categorized, and categories may be allocated based on grid value

[0383] In some embodiments, a system for automated grid generation may include an Al module configured to analyze images or 3D walkthroughs to identify potential advertising spots. A grid optimization module may suggest optimal grid divisions and potential advertising spot configurations. A template generation module may create advertising templates by combining grid clusters. An environmental recognition module may identify factors affecting advertising value. A quality assessment module may evaluate the advertising potential of spaces based on visual analysis and historical performance data.

[0384] In some embodiments, the 020 system may be configured as a Smart Spatial Grid Mapping System, such as for Advertisement Placement. The system and associated methods may, for example, involves capturing physical locations, such as target spaces, using image capture devices, for example, cameras. The captured environment may, for example, be processed and mapped into a grid system. The grid system may, for example, vary in resolution and / or structure (e.g., dependent on specific implementations). Each grid may, for example, include cells representing distinct spatial segments, with properties tailored to the chosen grid model.

[0385] Each grid cell may, for example, be assigned a unique address such as embedding spatial metadata and / or linking to a database (e.g., for storing detailed information about that grid). This address may, for example, serve as a reference point for interacting with the physical environment digitally.

[0386] Artificial intelligence may, for example, initialize each grid cell with data from multiple sources. For example, environmental attributes may include lighting, temperature, and / or spatial orientation. Other sources may include online databases, such as product placement strategies and / or historical performance. Contextual knowledge libraries, like industryspecific heuristics, may be used. These initial values may, for example, be dynamic, adapting with inputs such as human interaction, live image / video feeds, wireless signal data, and / or sales transactions.

[0387] Input data may, by way of example and not limitation, be numerical, textual, and / or categorical. Input data may, for example, assist Al models and systems in generating a detailed contextual profile and / or characteristic tag for each grid cell.Each cell may, for example, effectively become a decision unit, where Al evaluates its profile for the relevant category assignment, aligning with the space's environmental and behavioral characteristics.

[0388] Al analyzes the spatial layout to generate optimized space spots grouped by high-value grid clusters. These spots may be rectangular, for example, 2ft x 4ft, and / or irregular, and may, for example, be selected based on data density and / or quality.

[0389] As an illustrative example, in a retail setting, such as behind a cashier counter, Al may identify space spots, for example, in the upper-left corner. Historical performance and customer behavior may indicate this zone as suitable for promoting beverages, tagging it for advertisers seeking ad spots for soda drinks.

[0390] The 020 platform may, for example, activate processes such as Winner Grid Aggregation, identifying high-performing grid cells to form advertising zones. Automated Template Generation may, for example, use grid clusters (e.g., space spots) to produce multiple space spot templates (e.g., SSGTs), each configured for specific environments.

[0391] These templates may, for example, be automatically selected and / or presented to advertisers via an interactive interface, enabling them to select and / or reserve desirable spots. Data-driven templates may increase effectiveness of ad space for visibility and engagement, advantageously providing, for example, new revenue opportunities such as by monetizing underutilized areas.

[0392] Various embodiments may, for example, relate to systems configured to subdivide physical spaces into grid units. In some examples, a space structure may be divided into smaller segments (e.g., progressively smaller segments). These segments may, for example, be referred to as grid units. The space structure may, for example, include indoor environments. In some embodiments, the space structure may include outdoor environments. The space structure may, for example, include permanent structures such as walls. In some examples, the space structure may include movable structures, such as vehicles.

[0393] Some embodiments may include grid units having various geometric configurations. The grid units may, for example, have rectangular shapes. In some examples, the grid units may have hexagonal shapes. The geometric configuration may, for example, be selected based on environmental conditions. In some embodiments, the geometric configuration may be selected based on mapping requirements. The geometric configuration may, for example, be selected based on application needs.

[0394] Various embodiments may include assigning unique identifiers to grid units. These unique identifiers may, for example, be referred to as Grid_COR. In some examples, the unique identifiers may include grid IDs. The unique identifiers may, for example, include grid addresses. Each grid unit may, for example, be uniquely identifiable through its assigned identifier. The unique identifiers may, for example, facilitate searching of grid units. The unique identifiers may, for example, facilitate locating of grid units.

[0395] Some embodiments may include a processor configured to access a database storing grid unit data. The processor may, for example, rapidly search grid units based on the unique identifiers. In some examples, the processor may assess grid units based on stored attributes. The processor may, for example, assign purposes to grid units based on the assessment. This functionality may, for example, facilitate automated grid unit management.

[0396] In various embodiments, grid units may be designated for applications beyond advertising. The grid units may, for example, be used for attribute labeling. In some examples, the grid units may be used for geospatial tagging. The grid units may, for example, serve as interactive platforms for audience engagement. In some embodiments, grid units may demarcatedanger zones. The grid units may, for example, designate sites for urgent care facilities. In some examples, the grid units may issue hazard warnings. The grid units may, for example, mark flood zones.

[0397] Some embodiments may include a hierarchical grid structure. The hierarchical grid structure may, for example, include multiple vertical layers. In some examples, a first layer may be designated as Grid 0 (GO). The first layer may, for example, represent the broadest division of the space structure. Additional layers may, for example, be designated as G1 , G2, G3, and so forth. Each successive layer may, for example, represent progressively finer subdivisions.

[0398] Various embodiments may include a labeling scheme for grid cells. In some examples, cells in Grid 0 may be labeled sequentially. The labels may, for example, follow a pattern such as G0-0, GO-1 , GO-2, up to GO-n. In some embodiments, cells in Grid 1 may include parent references. For example, a cell labeled G1-0.1 may, for example, indicate the cell is on Grid 1. The cell may, for example, inherit from parent cell G0-0. The cell may, for example, be the second subdivision of G0-0.

[0399] Some embodiments may include hierarchical naming for deeper grid levels In some examples, a cell labeled G2- 0.1.2 may be located on Grid 2. The cell may, for example, have G1-0.1 as its parent. The cell may, for example, be the third subdivision of G1-0 1. This hierarchical naming scheme may, for example, facilitate tracing of lineage for each grid unit. The naming scheme may, for example, simplify location identification within complex layouts.

[0400] In various embodiments, the hierarchical grid system may break space structures into trackable segments. The system may, for example, scale, such as by adding additional layers as needed. In some examples, the system may identify subdivisions (e.g., advantageously supporting performance tracking). The system may, for example, utilize metadata linking, such as to maintain organization. This organization may, for example, facilitate dynamic content updates. The organization may, for example, support data-driven decision-making (e.g., automatic) at varying granularity levels.

[0401] Some embodiments may include alternative grid formats. These alternative formats may, for example, achieve similar subdivision outcomes. In some examples, the core functionality may include logical subdivision of spaces. The core functionality may, for example, include maintaining parent-child references for cells. This consistent approach may, for example, facilitate location identification. The approach may, for example, support content updates across large advertising areas.

[0402] Various embodiments may provide a grid overlay system, such as for managing multiple media formats. In some examples, vertical hierarchical structures may organize advertising spaces into tiered levels. These structures may, for example, manage media assets in a layered fashion. However, vertical structures alone may, for example, have limitations, such as in addressing coexistence of different media formats. In some embodiments, a system may include overlay grids. The overlay grids may, for example, advantageously address limitations of a vertical hierarchy.

[0403] Some embodiments may include an overlay grid system configured to superimpose multiple grid types. The grid types may, for example, occupy the same advertising space simultaneously. In some examples, advertising campaigns may be strategically placed based on content requirements. The placement may, for example, be based on audience traffic patterns. The placement may, for example, be based on environmental conditions. This system may, for example, facilitate selective assignment of advertisement placements with increased accuracy.

[0404] In various embodiments, overlay grids may run laterally across the same physical location. Each overlay grid layer may, for example, be defined by a type of media it accommodates. In some examples, a first overlay grid may correspond to static structures. Static structures may, for example, include billboards. Static structures may, for example, include walls. Insome embodiments, a second overlay grid may correspond to dynamic structures. Dynamic structures may, for example, include digital screens. Dynamic structures may, for example, include rotating displays. Dynamic structures may, for example, include moving vehicles.

[0405] Some embodiments may include hybrid overlay grids. The hybrid overlay grids may, for example, combine static and dynamic elements. In some examples, a hybrid grid may include a partial digital overlay atop a static billboard. In various embodiments, two or more overlay grid types may coexist in the same advertising environment. Each overlay grid may, for example, be governed by unique parameter sets. These parameters may, for example, include dimensions. The parameters may, for example, include content refresh rates. The parameters may, for example, include lighting requirements. The parameters may, for example, include visibility requirements. The parameters may, for example, include durability considerations.

[0406] Various embodiments may include a processor configured to select grid spaces based on multiple factors. In some examples, the processor may consider content requirements The processor may, for example, determine whether advertisement content is static or animated. The processor may, for example, match content requirements to appropriate grid layers. In some embodiments, the processor may consider audience profiles. The processor may, for example, consider traffic patterns. Different grid spots may, for example, encounter varying audience demographics at different times. The processor may, for example, utilize historical data to determine high-impact placements. The processor may, for example, utilize realtime sensor data. The processor may, for example, utilize predictive models.

[0407] Some embodiments may include consideration of environmental factors. Environmental factors may, for example, include weather-resistance requirements. Environmental factors may, for example, include power availability for digital displays. Environmental factors may, for example, include permissible frequency of updates. Environmental factors may, for example, include maintenance requirements.

[0408] In various embodiments, the overlay grid system may provide flexibility. The system may, for example, support permanent campaigns with extended durations. The system may, for example, support rapidly changing dynamic advertisements. In some examples, campaigns may be deployed without physical reconfiguration of advertising structures. The system may, for example, facilitate allocation of advertisements to specific grid spots. This allocation may, for example, increase exposure. This allocation may, for example, increase relevance.

[0409] As an illustrative example, some embodiments may include an indoor shopping mall with multiple overlapping grid types. In some examples, a first grid type may include a static structure grid for wall posters. Wall sections may, for example, be subdivided into rectangular units. These units may, for example, be labeled as SG0, SG1 , SG2. In some embodiments, a second grid type may include a dynamic structure grid for digital panels. Digital panel locations may, for example, be subdivided and labeled as DG0, DG1, DG2. In some examples, a third grid type may include a hybrid grid for interactive displays. Interactive display zones may, for example, be subdivided and labeled as HG0, HG1 , HG2.

[0410] Various embodiments may include a processor configured to analyze overlay grids. The processor may, for example, determine optimal placement for each advertising campaign. In some examples, the processor may consider display parameters across multiple overlay grid types. The processor may, for example, select placements based on campaign objectives. This approach may, for example, provide increased control over advertisement deployment. The approach may, for example, provide adaptability to environmental changes.

[0411] Some embodiments may, for example, be configured to perform dynamic pricing, such as for advertising spaces. In some examples, a system may include a processor configured to calculate pricing based on real-time conditions. The processor may, for example, consider time-of-day factors. The processor may, for example, consider seasonal patterns. The processor may, for example, consider foot traffic data. The processor may, for example, consider weather forecasts.

[0412] Various embodiments may distinguish between static out-of-home (OOH) advertising and digital out-of-home (DOOH) advertising. In some examples, for static OOH, the processor may schedule static (e.g., poster, plaque, 3D display, product display unit) installations, such as to coincide with promotional periods. The processor may, for example, recommend switching to holiday-themed advertisements based on calendar data. The processor may, for example, adjust pricing according to changing daily conditions.

[0413] Some embodiments may include monitoring of digital panel specifications for DOOH. The processor may, for example, monitor resolution parameters. The processor may, for example, monitor location classifications such as prime or secondary. The processor may, for example, monitor environmental lighting conditions. The processor may, for example, combine these parameters with real-time external data This combination may, for example, facilitate calculation of pricing for media slots in digital loops.

[0414] In various embodiments, the processor may analyze time-specific factors. The processor may, for example, analyze morning rush hour patterns. The processor may, for example, analyze lunchtime crowd data. The processor may, for example, analyze evening commuting patterns. The processor may, for example, adjust rates accordingly. In some examples, pricing for a 15-second slot at 8:00 AM may differ from pricing for the same slot at 2:00 PM.

[0415] Some embodiments may include flexible pricing structures. The pricing structures may, for example, include timespecific content scheduling. For static OOH, the processor may, for example, schedule posters to align with calendar events. For DOOH, the processor may, for example, rotate content based on peak engagement times. The pricing may, for example, follow variable rate models. Variable rates may, for example, depend on location popularity. Variable rates may, for example, depend on precise time of display. Variable rates may, for example, depend on day of display. Variable rates may, for example, depend on duration of media content.

[0416] Various embodiments may include adaptive pricing models. The pricing may, for example, fluctuate based on realtime conditions. Real-time conditions may, for example, include local events. Real-time conditions may, for example, include seasonality factors. Real-time conditions may, for example, include weather forecasts. This approach may, for example, function similarly to yield management in hotel and airline industries.

[0417] In some embodiments, advertisers may benefit from cost efficiency. Advertisers may, for example, pay only for specific locations and times. This payment structure may, for example, eliminate unnecessary expenditures. In some examples, advertisers may achieve enhanced targeting. Advertisers may, for example, deliver relevant content to appropriate audiences at optimal times. This targeting may, for example, increase engagement rates. This targeting may, for example, increase conversion rates. In various embodiments, advertisers may benefit from scalability. Campaigns may, for example, be scaled up or down based on performance. Campaigns may, for example, be scaled based on budget constraints.

[0418] Some embodiments may provide benefits for space providers. Space providers may, for example, achieve maximized revenue through dynamic pricing. Advertising spaces may, for example, be priced according to demand. Advertising spaces may, for example, be priced according to value. In some examples, space providers may benefit from flexible utilization.Advertising spaces may, for example, be managed more efficiently. This management may, for example, reduce idle inventory. This management may, for example, increase overall usage rates. In various embodiments, space providers may attract partnerships by offering an adaptable platform. This platform may, for example, foster long-term business relationships.

[0419] Various embodiments may include example scenarios for DOOH media. In some examples, a coffee chain may launch a seasonal beverage campaign. A morning campaign portion may, for example, target locations near office buildings. The morning campaign may, for example, target locations near transit hubs. Content may, for example, feature morning beverages. Timing may, for example, span 6 AM to 10 AM. Duration may, for example, include 30-second loops. Pricing may, for example, include premium rates due to high foot traffic during peak morning hours.

[0420] Some embodiments may include an afternoon campaign portion. The afternoon campaign may, for example, target shopping malls. The afternoon campaign may, for example, target recreational areas. Content may, for example, feature afternoon promotions. Timing may, for example, span 12 PM to 4 PM. Duration may, for example, include 15-second loops. Pricing may, for example, be adjusted based on medium traffic levels.

[0421] In various embodiments, an evening campaign portion may be included. The evening campaign may, for example, target entertainment districts. The evening campaign may, for example, target locations near restaurants. Content may, for example, feature evening beverages. Timing may, for example, span 6 PM to 10 PM. Duration may, for example, include 20- second loops. Pricing may, for example, vary based on event schedules. Pricing may, for example, vary based on local happenings.

[0422] Some embodiments may relate to streamlining connections between space providers and content providers. A system may, for example, include a processor configured to provide detailed analysis of physical spaces. The processor may, for example, provide valuation of physical spaces for advertising purposes. The analysis may, for example, utilize data-driven insights. These insights may, for example, increase placement effectiveness. These insights may, for example, increase advertisement effectiveness.

[0423] Various embodiments may provide benefits for advertisers. Advertisers may, for example, receive accurate valuation of high-impact venues at fair pricing. In some examples, advertisers may access advanced analytics. The analytics may, for example, track performance metrics such as views. The analytics may, for example, track interaction rates. The analytics may, for example, track engagement levels. This data may, for example, support informed decisions when adjusting content. This data may, for example, support informed decisions when adjusting placement to optimize campaigns.

[0424] Some embodiments may provide benefits for space providers. Space providers may, for example, earn passive income with reduced effort. In some examples, the system may oversee technical aspects. Technical aspects may, for example, include hardware installation. Technical aspects may, for example, include scheduling. Technical aspects may, for example, include media deployment. This oversight may, for example, facilitate full utilization of available advertising spots.

[0425] In various embodiments, the system may integrate advertisements into digital platforms. Digital platforms may, for example, include restaurant menu applications. Digital platforms may, for example, include websites. Digital platforms may, for example, include mobile interfaces. This integration may, for example, provide multiple touchpoints for consumer engagement. This integration may, for example, provide expanded reach for advertisers.

[0426] Various embodiments may relate to a system configured, for example, to facilitate collaborative promotional arrangements between multiple entities. For example, the 020 engine may include one or more co-sharing engines (e.g., co-sharing engine 2100). In some examples, the system may include a database configured to store entity profile data. The entity profile data may, for example, include merchant information. In some embodiments, the entity profile data may include sponsor information. The database may, for example, facilitate matching between entities based on stored profile attributes.

[0427] Some embodiments may include a processor operably coupled to the database. The processor may, for example, be configured to receive promotional criteria from a first entity device. The promotional criteria may, for example, specify target products or services. In some examples, the promotional criteria may specify discount parameters. The promotional criteria may, for example, define contribution amounts from participating entities.

[0428] The processor may, in some embodiments, identify candidate second entities from the database. This identification may, for example, be based on compatibility metrics derived from the entity profile data. In some examples, the compatibility metrics may consider geographic location. The compatibility metrics may, for example, consider target audience demographics. In some embodiments, the compatibility metrics may consider historical participation data.

[0429] Various embodiments may include generating a promotion structure data object. The promotion structure data object may, for example, define cost allocation between the first entity and at least one of the candidate second entities. In some examples, the promotion structure data object may specify discount values applicable to products or services. The promotion structure data object may, for example, include temporal parameters for promotional periods. In some embodiments, the promotion structure data object may include geographic parameters

[0430] Some embodiments may include transmitting the promotion structure data object to entity devices. The entity devices may, for example, correspond to selected candidate second entities. In some examples, the transmission may facilitate review of the proposed promotional arrangement. The processor may, for example, receive acceptance signals from the entity devices. These acceptance signals may, for example, indicate agreement to participate in the collaborative promotion.

[0431] The processor may, in various embodiments, update transaction processing rules. These rules may, for example, be stored in the database. In some examples, the updated rules may specify discount application at point of sale. The updated rules may, for example, allocate costs between participating entities according to the promotion structure data object. This allocation may, for example, occur during transaction processing.

[0432] Some embodiments may include monitoring transaction data. The transaction data may, for example, correspond to purchases of products or services subject to the collaborative promotion. In some examples, the processor may compute performance metrics from the transaction data. These performance metrics may, for example, include total transaction volume. The performance metrics may, for example, include redemption rates. In some embodiments, the performance metrics may include cost per participating entity.

[0433] Various embodiments may include generating performance reports. The performance reports may, for example, be transmitted to the entity devices. In some examples, the reports may facilitate assessment of promotional effectiveness. The reports may, for example, include comparative data across different promotional periods. The reports may, for example, include recommendations for future promotional arrangements.

[0434] In some embodiments, the system may include one or more interface module. A web-based interface module may, for example, be configured to generate interactive displays. These displays may, for example, present available promotional opportunities to potential sponsor entities. In some examples, the displays may include filtering options based on product categories. The displays may, for example, include filtering options based on geographic regions.

[0435] Some embodiments may include receiving selection inputs through the interface. The selection inputs may, for example, indicate sponsor interest in specific promotional opportunities. In some examples, the processor may generate proposed contribution amounts based on the selection inputs. These proposed amounts may, for example, be calculated using historical performance data stored in the database. The calculation may, for example, consider expected return on investment metrics.

[0436] Various embodiments may include subscription management functionality. The processor may, for example, be configured to receive subscription registration from sponsor entities. In some examples, the subscription may define recurring contribution amounts. The subscription may, for example, specify categories of products or services for which the sponsor entity agrees to participate. The processor may, for example, automatically allocate subscription funds to matching promotional opportunities. This allocation may, for example, occur based on predefined criteria specified in the subscription.

[0437] Some embodiments may include a mobile application interface. The mobile application interface may, for example, facilitate consumer interaction with promoted products or services. In some examples, the mobile application may present available discounts. The mobile application may, for example, generate redemption codes for applying collaborative discounts. The processor may, for example, receive redemption data from point-of-sale systems. This redemption data may, for example, trigger cost allocation processing between participating entities.

[0438] In various embodiments, the system may include a matching algorithm module. The matching algorithm module may, for example, be configured to compute compatibility scores between merchant entities and potential sponsor entities. In some examples, the compatibility scores may be based on brand alignment metrics. The compatibility scores may, for example, be based on target demographic overlap. The compatibility scores may, for example, be based on geographic proximity. The matching algorithm may, for example, apply machine learning models trained on historical collaboration success data.

[0439] Some embodiments may include dynamic discount adjustment functionality. The processor may, for example, monitor real-time transaction velocity. In some examples, the processor may adjust discount values based on inventory levels. The processor may, for example, adjust discount values based on time-of-day patterns. These adjustments may, for example, be constrained by parameters specified in the promotion structure data object. The adjustments may, for example, be communicated to point-of-sale systems in real-time.

[0440] Various embodiments may include fraud detection processing. The processor may, for example, analyze transaction patterns associated with collaborative promotions. In some examples, the processor may identify anomalous redemption patterns. The processor may, for example, flag transactions exceeding statistical thresholds for review. This fraud detection may, for example, reduce potential abuse of collaborative promotional arrangements.

[0441] In some embodiments, the entity profile data may include operational attributes. These operational attributes may, for example, specify business hours. The operational attributes may, for example, specify product inventory capacity. The operational attributes may, for example, specify seasonal availability patterns. The processor may, for example, utilize these operational attributes when generating promotion structure data objects. This utilization may, for example, increase temporal alignment between participating entities.

[0442] Some embodiments may include multi-tier contribution structures. The promotion structure data object may, for example, define different contribution levels for sponsor entities. In some examples, higher contribution levels may correspond to increased visibility in promotional materials. Higher contribution levels may, for example, correspond to preferentialplacement in mobile application displays. The processor may, for example, manage tier assignments based on contribution amounts and participation history.

[0443] Various embodiments may include cross-promotion functionality. The processor may, for example, identify complementary product or service offerings across different merchant entities. In some examples, the processor may generate bundled promotional opportunities. These bundled opportunities may, for example, span multiple merchant entities. The promotion structure data object may, for example, define cost allocation across multiple merchants and multiple sponsors. This functionality may, for example, facilitate complex multi-party promotional arrangements.

[0444] In some embodiments, the system may include geographic targeting parameters. The processor may, for example, restrict promotional visibility based on consumer location data. In some examples, mobile application users may receive promotional notifications when within a defined radius of participating merchant locations. This geographic targeting may, for example, increase conversion rates by presenting promotions to consumers when physically proximate to redemption locations.

[0445] Some embodiments may include A / B testing functionality. The processor may, for example, generate multiple variant promotion structures with different parameters. In some examples, the processor may randomly assign different consumer segments to different variants. The processor may, for example, compute comparative performance metrics across variants. These metrics may, for example, inform optimization of future promotional arrangements.

[0446] Various embodiments may include automated reconciliation processing. The processor may, for example, periodically compute net cost obligations for each participating entity. In some examples, the processor may generate settlement instructions for financial transfers between entities. The processor may, for example, interface with payment processing systems to execute settlements. This automation may, for example, reduce administrative overhead associated with collaborative promotions.

[0447] In some embodiments, the system may include reputation scoring functionality. The processor may, for example, compute reliability scores for participating entities, such as based on historical performance. In some examples, entities with higher reliability scores may receive preferential matching opportunities. The reliability scores may, for example, reflect fulfillment rates of promotional obligations. The reliability scores may, for example, reflect timeliness of financial settlements.

[0448] Some embodiments may include notification systems. The processor may, for example, generate alerts when promotional opportunities matching entity criteria become available. In some examples, these alerts may be transmitted via email. These alerts may, for example, be transmitted via mobile push notifications. The alerts may, for example, include expiration timeframes for accepting promotional opportunities. This notification functionality may, for example, facilitate timely responses to time-sensitive promotional arrangements.

[0449] Specific example implementations of embodiments disclosed herein, and / or complimentary thereto, are specifically contemplated and incorporated herein. For example, modules, features, components, method, and / or examples disclosed in US Provisional Application No. 63 / 714,877, titled "020 Content Management and Collaborative Promotion System I," filed by Khai Gan Chuah on Nov. 1 , 2024 and / or US Provisional Application No. 63 / 768,195, titled "020 Content Management and Collaborative Promotion System II," filed by Khai Gan Chuah on Mar. 7, 2025, are expressly incorporated. Any individual or combination of feature, component, method, and / or system may, for example, be incorporated into one or more systems, methods, models, engines, and / or modules disclosed herein.

[0450] Although various embodiments are described as ‘optimum’ and / or ‘optimized’ and / or performing ‘optimization,’ it should be understood in the sense of a computational optimum. For example, a model, engine, and / or model may compute an ‘optimum’ solution according to (e.g. , predetermined) logic (e.g., using objective, deterministic, fuzzy, and inference logic). The solution may, for example, not be absolutely optimal. It may, for example, not actually be a maximum. The logic may seek for a local and / or absolute maxima and / or minima (e.g., in a specified, dynamically determined, or predetermined range).

[0451] As an illustrative example, a system for computational resource allocation in physical space management may include a data store system configured to store space data structures representing physical spaces. Each space data structure may include a hierarchical grid structure subdividing the physical space into progressively smaller grid units. The system may include a distributed sensor network configured to monitor real-time environmental conditions within the physical spaces. The system may include a processor configured to retrieve, from the data store system, the space data structures. The processor may be configured to compute updated attribute arrays for each grid unit in response to real-time environmental data from the distributed sensor network.

[0452] Each attribute array may include static attributes including geolocation data of the physical space. Each attribute array may include periodic attributes including traffic data associated with the physical space. Each attribute array may include realtime attributes including local environmental data of the real-time environmental data, associated with the physical space. The processor may be configured to generate media placement criteria from a media placement package from an originating device of a media originator, including target display parameters and media format specifications.

[0453] The processor may be configured to apply hard filters to exclude grid units that fail to meet mandatory criteria associated with the media placement criteria. The processor may be configured to sparsity attributes of remaining grid units and compute a Grid Performance Rating (GPR) for the remaining grid units. The processor may be configured to apply a model trained on historical performance data of media placements across different physical spaces to identify, based on the media placement criteria, a subset of relevant attributes from the attribute arrays. The processor may be configured to identify a corresponding weighting of at least some of the relevant attributes, based on the media placement criteria. The processor may be configured to selectively process the subset of relevant attributes and the corresponding weighting such that the GPR is computed per grid unit and specifically for the media placement criteria received while reducing unnecessary attribute computations. The processor may be configured to dynamically aggregate selected grid units of the grid units with GPR values exceeding a threshold to form space spots having a minimum aggregate size satisfying the media format specifications. The processor may be configured to compute a Spot Performance Rating (SPR) for each space spot based on GPR values of the selected grid units.

[0454] The processor may be configured to automatically control content distribution systems by generating control signals configured to physically reconfigure display parameters of physical display devices such that one or more media package, corresponding to the media placement criteria, is distributed to selected space spots in selected physical spaces as a function of SPR. The processor may be configured to monitor the real-time environmental data, from the distributed sensor network and corresponding to the physical display devices. The physical display devices may correspond to the selected space spots. The processor may be configured to, in response to updated real-time environmental data received from the distributed sensor network, compute selected updated real-time attributes of the attribute arrays, recompute the GPR and SPR, and generateupdated control signals such that the physical display devices are reconfigured in response to changing real-time environmental conditions in at least some of the physical spaces.

[0455] As an illustrative example, a computer-implemented method performing computational resource allocation in physical space management systems may include operations that reduce processing load through selective attribute computation. The operations may include retrieving, from one or more data store systems, space data structures representing physical spaces. Each of the space data structures may include a hierarchical grid structure representing a physical space of the physical spaces. The hierarchical grid structure may include grid layers subdividing the physical space into progressively smaller grid units. The operations may include, in response to real-time environmental data from a distributed sensor network that monitors physical conditions within physical spaces, for each of the space data structures, computing, by the processor, updated attribute arrays for each of the grid units.

[0456] Each attribute array may include static attributes including geolocation data of the physical space. Each attribute array may include periodic attributes including traffic data associated with the physical space. Each attribute array may include realtime attributes including local environmental data of the real-time environmental data, associated with the physical space

[0457] The operations may include generating, by the processor and from a media placement package from an originating device of a media originator, media placement criteria including target display parameters and media format specifications. The operations may include reducing computational resource demands. Reducing computational resource demands may include applying, by the processor, hard filters such that grid units are excluded that fail to meet mandatory criteria associated with the media placement criteria. Reducing computational resource demands may include sparsifying attributes of remaining grid units and computing, by the processor, a Grid Performance Rating (GPR) for the remaining grid units.

[0458] Computing the GPR may include applying a model trained on historical performance data of media placements across different physical spaces to identify, based on the media placement criteria, a subset of relevant attributes from the attribute arrays. Computing the GPR may include identifying a corresponding weighting of at least some of the relevant attributes, based on the media placement criteria. Computing the GPR may include selectively processing the subset of relevant attributes and the corresponding weighting such that the GPR is computed per grid unit and specifically for the media placement criteria received while reducing unnecessary attribute computations that would otherwise consume processing resources. The operations may include dynamically aggregating, by the processor, selected grid units of the grid units with GPR values exceeding a threshold such that space spots are formed having a minimum aggregate size satisfying the media format specifications. The operations may include computing, by the processor, a Spot Performance Rating (SPR) for each space spot based on GPR values of the selected grid units. The operations may include automatically controlling content distribution systems. Automatically controlling content distribution systems may include generating control signals configured to physically reconfigure display parameters of physical display devices such that one or more media package, corresponding to the media placement criteria, is distributed to selected space spots in selected physical spaces of the physical spaces as a function of SPR.

[0459] The operations may include monitoring the real-time environmental data, from the distributed sensor network, corresponding to the physical display devices. The physical display devices may correspond to the selected space spots. The operations may include, in response to updated real-time environmental data received from the distributed sensor network, computing only updated real-time attributes of the attribute arrays, recomputing the GPR and SPR, and generating updatedcontrol signals such that the physical display devices are reconfigured in response to changing real-time environmental in at least some of the physical spaces.

[0460] As an illustrative example, a computer program product for performing computational resource allocation in physical space management systems may include a non-transitory computer-readable medium having instructions stored thereon. When executed by a processor, the instructions may cause the processor to perform operations that reduce processing load through selective attribute computation. The operations may include retrieving, from one or more data store systems, space data structures representing physical spaces. Each of the space data structures may include a hierarchical grid structure representing a physical space of the physical spaces. The hierarchical grid structure may include grid layers subdividing the physical space into progressively smaller grid units. The operations may include, in response to real-time environmental data from a distributed sensor network that monitors physical conditions within physical spaces, for each of the space data structures, computing updated attribute arrays for each of the grid units.

[0461] Each attribute array may include static attributes including geolocation data of the physical space. Each attribute array may include periodic attributes including traffic data associated with the physical space. Each attribute array may include realtime attributes including local environmental data of the real-time environmental data, associated with the physical space. The operations may include generating media placement criteria from a media placement package from an originating device of a media originator, including target display parameters and media format specifications. The operations may include reducing computational resource demands. Reducing computational resource demands may include applying hard filters such that grid units are excluded that fail to meet mandatory criteria associated with the media placement criteria. Reducing computational resource demands may include sparsifying attributes of remaining grid units and computing a Grid Performance Rating (GPR) for the remaining grid units.

[0462] Computing the GPR may include applying a model trained on historical performance data of media placements across different physical spaces to identify, based on the media placement criteria, a subset of relevant attributes from the attribute arrays. Computing the GPR may include identifying a corresponding weighting of at least some of the relevant attributes, based on the media placement criteria. Computing the GPR may include selectively processing the subset of relevant attributes and the corresponding weighting such that the GPR is computed per grid unit and specifically for the media placement criteria received while reducing unnecessary attribute computations that would otherwise consume processing resources.

[0463] The operations may include dynamically aggregating selected grid units of the grid units with GPR values exceeding a threshold such that space spots are formed having a minimum aggregate size satisfying the media format specifications. The operations may include computing a Spot Performance Rating (SPR) for each space spot based on the GPR values of the selected grid units. The operations may include automatically controlling content distribution systems, including generating control signals configured to physically reconfigure display parameters of physical display devices such that one or more media package, corresponding to the media placement criteria, is distributed to selected space spots in selected physical spaces of the physical spaces as a function of SPR.

[0464] The operations may include monitoring the real-time environmental data, from the distributed sensor network, corresponding to the physical display devices. The physical display devices may correspond to the selected space spots. The operations may include, in response to updated real-time environmental data received from the distributed sensor network, computing only updated real-time attributes of the attribute arrays, recomputing the GPR and SPR, and generating updatedcontrol signals such that the physical display devices are reconfigured in response to changing real-time environmental conditions in at least some of the physical spaces.

[0465] The hierarchical grid structure may further include a multi-layer grid system including at least one of rectangular, triangular, or radial coordinate systems for subdividing the physical space.

[0466] The multi-layer grid system may further include vertical grid layers configured to subdivide the physical space into progressively finer granularity. Each vertical grid layer may include grid units with a consistent naming scheme for precise location tracking.

[0467] The hierarchical grid structure may further include overlay grid layers configured to represent different advertising modalities that can occupy a same space simultaneously.

[0468] The distributed sensor network may further include sensors including at least one of a camera, a LiDAR sensor, and / or a thermal sensor configured to capture environmental data

[0469] The real-time attributes may include weather conditions associated with the physical space.

[0470] The processor may be further configured to employ a hybrid filtering system. The hybrid filtering system may include the hard filters, configured to induce binary inclusion or exclusion. The hybrid filtering system may include soft weightings representing continuous factors in the media placement criteria.

[0471] The processor may be further configured to integrate multi-source data collection including environmental data, human inputs, and real-time monitoring data for computing the updated attribute arrays.

[0472] The processor may be further configured to apply an ensemble Al engine with multiple inference models configured to vote on different attributes. The processor may be further configured to compute the GPR by combining voting results including vector summation.

[0473] The ensemble Al engine may further include a visual analysis model configured to process image data from the distributed sensor network. The ensemble Al engine may further include a weather integration model configured to incorporate weather data into the attribute arrays. The ensemble Al engine may further include a geographic model configured to analyze geolocation data for spatial analysis. The ensemble Al engine may further include a temporal model configured to evaluate time-based factors affecting grid performance.

[0474] The processor may be further configured to calculate GPRs via weighted summation of the relevant attributes as modified by the weighting.

[0475] The processor may be further configured to dynamically adjust the GPR based on real-time pedestrian traffic data received from the distributed sensor network.

[0476] The media placement criteria may further include category-specific preferences. The processor may be configured to adjust the GPR based on these preferences.

[0477] The processor may be further configured to apply an adaptive meshing engine to adjust grid density based on complexity of the physical space.

[0478] The processor may be further configured to generate advertising templates by combining grid clusters based on the media placement criteria.

[0479] The processor may be further configured to evaluate grid spaces by analyzing grid units configured within a grid. Such analysis may involve calculating arrangement and positioning parameters to generate advertising templates adjusted to specific viewer engagement metrics and / or spatial configuration feedback.

[0480] The processor may be configured to select a selected template, from generated templates, based on media placement criteria, including target audience demographics and budget constraints.

[0481] The processor may be further configured to apply the selected template in dynamically reconfiguring grid spaces, facilitating flexible advertisement placement in response to changing environmental conditions.

[0482] Application of the selected template may include adjusting grid unit valuations based on real-time data, such as pedestrian traffic patterns and lighting conditions, to optimize advertising effectiveness.

[0483] The processor may be further configured to generate multiple template options with different trade-offs between space usage and value. The processor may be further configured to generate a display of the multiple template options to a media originator. The processor may be further configured to receive from the media originator a selected template of the template options.

[0484] The processor may be configured to apply the selected template by associating it in a data structure with the content distribution system. Media may be displayed according to layout specifications and temporal scheduling information associated with the selected template

[0485] The processor may be further configured to apply environmental recognition techniques to identify visual obstructions affecting the GPR, of the selected grid units.

[0486] The processor may be further configured to apply a co-sharing engine to allocate advertising spaces between multiple advertisers based on grid values and geographic location.

[0487] The co-sharing engine may further include a matching algorithm configured to match compatible advertisers. The cosharing engine may further include space allocation configured to divide advertising spaces based on budget and value requirements. The co-sharing engine may further include interaction tracking configured to track customer engagement with each advertiser's content.

[0488] The processor may be further configured to suggest add-on services based on physical grid attributes and / or environmental conditions.

[0489] The add-on services may include cleaning services. The add-on services may include inspection services. The addon services may include repair services. The add-on services may include decoration services.

[0490] As used in the claims, comprising and consisting are used in the United States open-ended and closed-ended sense, respectively.

[0491] It will be understood that various modifications can be made within the scope of this disclosure. Embodiments depict illustrative combinations of disclosed features, components, and / or steps. Any combination of disclosed features is expressly contemplated unless specifically excluded or required by the context. For example, one or more advantageously results may be achieved if components are removed, added, multiplied, scaled, and / or rearranged, and / or if steps in a method are omitted, added, repeated, and / or performed in a different order. Therefore, other implementations are contemplated within the scope of the following claims.

Claims

Claims1 . A system for computational resource allocation in physical space management, comprising: a data store system configured to store a plurality of space data structures representing a plurality of physical spaces, each space data structure comprising a hierarchical grid structure subdividing the physical space into progressively smaller grid units; a distributed sensor network configured to monitor real-time environmental conditions within the plurality of physical spaces; a processor configured to: retrieve, from the data store system, the plurality of space data structures; compute updated attribute arrays for each grid unit in response to real-time environmental data from the distributed sensor network, each attribute array comprising: static attributes including geolocation data of the physical space; periodic attributes including traffic data associated with the physical space; and real-time attributes including local environmental data of the real-time environmental data, associated with the physical space; generate media placement criteria from a media placement package from an originating device of a media originator, including target display parameters and media format specifications; apply hard filters to exclude grid units that fail to meet mandatory criteria associated with the media placement criteria; sparsify attributes of remaining grid units and compute a Grid Performance Rating, GPR, for the remaining grid units by: applying a model trained on historical performance data of media placements across different physical spaces to identify, based on the media placement criteria: a subset of relevant attributes from the attribute arrays; and a corresponding weighting of at least some of the relevant attributes, based on the media placement criteria; selectively process the subset of relevant attributes and the corresponding weighting such that the GPR is computed per grid unit and specifically for the media placement criteria received while reducing unnecessary attribute computations; dynamically aggregate selected grid units of the grid units with GPR values exceeding a threshold to form space spots having a minimum aggregate size satisfying the media format specifications; compute a Spot Performance Rating, SPR, for each space spot based on GPR values of the selected grid units; automatically control content distribution systems by generating control signals configured to physically reconfigure display parameters of physical display devices such that one or more media package, corresponding to the media placement criteria, is distributed to selected space spots in selected physical spaces as a function of SPR;monitor the real-time environmental data, from the distributed sensor network and corresponding to the physical display devices, wherein the physical display devices correspond to the selected space spots; and in response to updated real-time environmental data received from the distributed sensor network, compute selected updated real-time attributes of the attribute arrays, recompute the GPR and SPR, and generate updated control signals such that the physical display devices are reconfigured in response to changing real-time environmental conditions in at least some of the plurality of physical spaces.

2. A computer-implemented method performing computational resource allocation in physical space management systems, comprising operations that reduce processing load through selective attribute computation, the operations comprising: retrieving, from one or more data store systems, a plurality of space data structures representing a plurality of physical spaces, each of the space data structures comprising a hierarchical grid structure representing a physical space of the plurality of physical spaces, wherein the hierarchical grid structure comprises a plurality of grid layers subdividing the physical space into progressively smaller grid units; in response to real-time environmental data from a distributed sensor network that monitors physical conditions within the plurality of physical spaces, for each of the plurality of space data structures: computing updated attribute arrays for each of the grid units, wherein each attribute array comprises: static attributes comprising geolocation data of the physical space; periodic attributes comprising traffic data associated with the physical space; and real-time attributes including local environmental data of the real-time environmental data, associated with the physical space; and generating from a media placement package from an originating device of a media originator, media placement criteria comprising target display parameters and media format specifications; reducing computational resource demands comprising: applying hard filters such that grid units are excluded that fail to meet mandatory criteria associated with the media placement criteria; sparsifying attributes of remaining grid units and computing a Grid Performance Rating, GPR, for the remaining grid units comprising: applying a model trained on historical performance data of media placements across different physical spaces to identify, based on the media placement criteria: a subset of relevant attributes from the attribute arrays, and a corresponding weighting of at least some of the relevant attributes, based on the media placement criteria; and selectively processing the subset of relevant attributes and the corresponding weighting such that the GPR is computed per grid unit and specifically for the media placement criteria received whilereducing unnecessary attribute computations that would otherwise consume processing resources; dynamically aggregating selected grid units of the grid units with GPR values exceeding a threshold such that space spots are formed having a minimum aggregate size satisfying the media format specifications; computing a Spot Performance Rating, SPR, for each space spot based on GPR values of the selected grid units; automatically controlling content distribution systems, comprising generating control signals configured to physically reconfigure display parameters of physical display devices such that one or more media package, corresponding to the media placement criteria, is distributed to selected space spots in selected physical spaces of the plurality of physical spaces as a function of SPR; monitoring the real-time environmental data, from the distributed sensor network, corresponding to the physical display devices, wherein the physical display devices correspond to the selected space spots; and in response to updated real-time environmental data received from the distributed sensor network, computing only updated real-time attributes of the attribute arrays, recomputing the GPR and SPR, and generating updated control signals such that the physical display devices are reconfigured in response to changing real-time environmental in at least some of the plurality of physical spaces.

3. A computer program product for performing computational resource allocation in physical space management systems, the computer program product comprising a non-transitory computer-readable medium having instructions stored thereon, which when executed by a processor, cause the processor to perform operations that reduce processing load through selective attribute computation, the operations comprising: retrieving, from one or more data store systems, a plurality of space data structures representing a plurality of physical spaces, each of the space data structures comprising a hierarchical grid structure representing a physical space of the plurality of physical spaces, wherein the hierarchical grid structure comprises a plurality of grid layers subdividing the physical space into progressively smaller grid units; in response to real-time environmental data from a distributed sensor network that monitors physical conditions within physical spaces, for each of the space data structures: computing updated attribute arrays for each of the grid units, wherein each attribute array includes: static attributes including geolocation data of the physical space; periodic attributes including traffic data associated with the physical space; and real-time attributes including local environmental data of the real-time environmental data, associated with the physical space; generating media placement criteria from a media placement package from an originating device of a media originator, including target display parameters and media format specifications; reducing computational resource demands comprising:applying hard filters such that grid units are excluded that fail to meet mandatory criteria associated with the media placement criteria; sparsifying attributes of remaining grid units and computing a Grid Performance Rating, GPR, for the remaining grid units comprising: applying a model trained on historical performance data of media placements across different physical spaces to identify, based on the media placement criteria: a subset of relevant attributes from the attribute arrays; and a corresponding weighting of at least some of the relevant attributes, based on the media placement criteria; and selectively processing the subset of relevant attributes and the corresponding weighting such that the GPR is computed per grid unit and specifically for the media placement criteria received while reducing unnecessary attribute computations that would otherwise consume processing resources; dynamically aggregating selected grid units of the grid units with GPR values exceeding a threshold such that space spots are formed having a minimum aggregate size satisfying the media format specifications; computing a Spot Performance Rating, SPR, for each space spot based on the GPR values of the selected grid units; automatically controlling content distribution systems, including generating control signals configured to physically reconfigure display parameters of physical display devices such that one or more media package, corresponding to the media placement criteria, is distributed to selected space spots in selected physical spaces of the physical spaces as a function of SPR; monitoring the real-time environmental data, from the distributed sensor network, corresponding to the physical display devices, wherein the physical display devices correspond to the selected space spots; and in response to updated real-time environmental data received from the distributed sensor network, computing only updated real-time attributes of the attribute arrays, recomputing the GPR and SPR, and generating updated control signals such that the physical display devices are reconfigured in response to changing real-time environmental conditions in at least some of the physical spaces.

4. Any one of claims 1-3, wherein the hierarchical grid structure further comprises a multi-layer grid system including at least one of rectangular, triangular, or radial coordinate systems for subdividing the physical space.

5. Any one of claims 1-4, wherein the multi-layer grid system further comprises vertical grid layers configured to subdivide the physical space into progressively finer granularity, each vertical grid layer comprising a plurality of grid units with a consistent naming scheme for precise location tracking.

6. Any one of claims 1-5, wherein the hierarchical grid structure further comprises overlay grid layers configured to represent different advertising modalities that can occupy a same space simultaneously.

7. Any one of claims 1-6, wherein the distributed sensor network further comprises a plurality of sensors including at least one of a camera, a LIDAR sensor, and / or a thermal sensor configured to capture environmental data.

8. Any one of claims 1-7, wherein the real-time attributes comprise weather conditions associated with the physical space.

9. Any one of claims 1-8, wherein the processor is further configured to employ a hybrid filtering system comprising: the hard filters, configured to induce binary inclusion or exclusion; and soft weightings representing continuous factors in the media placement criteria.

10. Any one of claims 1-9, wherein the processor is further configured to integrate multi-source data collection including environmental data, human inputs, and real-time monitoring data for computing the updated attribute arrays.

11. Any one of claims 1-10, wherein the processor is further configured to: apply an ensemble Al engine with multiple inference models configured to vote on different attributes, compute the GPR by combining voting results comprising vector summation.

12. Claim 11, wherein the ensemble Al engine further comprises: a visual analysis model configured to process image data from the distributed sensor network; a weather integration model configured to incorporate weather data into the attribute arrays; a geographic model configured to analyze geolocation data for spatial analysis; and / or a temporal model configured to evaluate time-based factors affecting grid performance.

13. Any one of claims 1-12, wherein the processor is further configured to calculate GPRs via weighted summation of the relevant attributes as modified by the weighting.

14. Any one of claims 1-13, wherein the processor is further configured to dynamically adjust the GPR based on realtime pedestrian traffic data received from the distributed sensor network.

15. Any one of claims 1-14, wherein the media placement criteria further comprise category-specific preferences, and the processor is configured to adjust the GPR based on these preferences.

16. Any one of claims 1-15, wherein the processor is further configured to apply an adaptive meshing engine to adjust grid density based on complexity of the physical space.

17. Any one of claims 1-16, wherein the processor is further configured to generate advertising templates by combining grid clusters based on the media placement criteria.

18. Any one of claims 1-17, wherein the processor is further configured to evaluate grid spaces by analyzing grid units configured within a grid, wherein such analysis involves calculating arrangement and positioning parameters to generate advertising templates adjusted to specific viewer engagement metrics and / or spatial configuration feedback.

19. Claim 18, wherein the processor is configured to select a selected template, from a plurality of generated templates, based on media placement criteria, including target audience demographics and budget constraints.

20. Any one of claims 1-19, wherein the processor is further configured to apply the selected template in dynamically reconfiguring grid spaces, facilitating flexible advertisement placement in response to changing environmental conditions.

21. Claim 20, wherein application of the selected template includes adjusting grid unit valuations based on real-time data, such as pedestrian traffic patterns and lighting conditions, to optimize advertising effectiveness.

22. Any one of claims 1-21 , wherein the processor is further configured to generate multiple template options with different trade-offs between competing parameters, and generating a display of the multiple template options to a media originator, and receiving from the media originator a selected template of the template options.

23. Claim 22, wherein the processor is configured to apply the selected template by associating it in a data structure with a content distribution system, wherein media is displayed according to layout specifications and temporal scheduling information associated with the selected template.

24. Any one of claims 1-23, wherein the processor is further configured to apply environmental recognition techniques to identify visual obstructions affecting the GPR, of the selected grid units.

25. Any one of claims 1-24, wherein the processor is further configured to apply a co-sharing engine to allocate advertising spaces between multiple advertisers based on grid values and geographic location.

26. Claim 25, wherein the co-sharing engine further comprises: a matching algorithm configured to match compatible advertisers; space allocation configured to divide advertising spaces based on budget and value requirements; and interaction tracking configured to track customer engagement with each advertiser's content.

27. Any one of claims 1-26, wherein the processor is further configured to suggest add-on services based on physical grid attributes and / or environmental conditions.

28. Claim 27, wherein the add-on services comprise: cleaning services; inspection services; repair services; and / or decoration services.

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