DESC:[0037] For the purpose of promoting an understanding of the principles of the disclosure, reference will now be made to embodiments illustrated in the accompanying figures. It will nevertheless be understood that no limitation of the scope of the disclosure is thereby intended.
[0038] In the present disclosure, the term "exemplary" is used to mean serving as an example, instance, or illustration.
[0039] The terms "comprise", "comprising", or variations thereof are intended to cover non-exclusive inclusion.
SYSTEM ARCHITECTURE
[0040] Referring to FIG. 1, an exemplary distributed system architecture implementing the present invention is illustrated. The system comprises an OpenRTB ad exchange listener module, a geo-matching polygon context engine, a machine learning inference engine, a bid optimization engine, and a bid response transmission module.
[0041] The OpenRTB ad exchange listener module receives real-time bid requests from an advertising exchange. Each bid request contains geo-coordinate information, including latitude and longitude associated with the requesting device.
[0042] The listener module may be implemented as one or more network-facing computing instances configured to accept bid requests conforming to OpenRTB messaging requirements and to make the request payload available to downstream processing components. The listener module cooperates with the remaining modules of the system so that request handling, geo-spatial evaluation, inference, and response generation occur within a common real-time processing pipeline.
[0043] The geo-matching polygon context engine is operatively coupled to a repository of geo-spatial polygon definitions representing target regions. The engine is configured to receive the geo-coordinate information extracted from a bid request, to evaluate the geo-coordinate information relative to the stored polygon definitions, and to derive a polygon identifier for use by downstream components of the distributed system.
[0044] The machine learning inference engine is configured to receive input features, including polygon identifier, contextual attributes, and historical performance metrics. In response to the received input features, the machine learning inference engine computes a predicted polygon performance value representing an estimated value of the bid opportunity.
[0045] The bid optimization engine is configured to calculate a final bid amount based on the predicted value generated by the machine learning inference engine. The bid response transmission module is configured to transmit an optimized bid response back to the advertising exchange within the RTB response window.
[0046] In an exemplary distributed implementation, the foregoing modules may operate across multiple computing nodes while functionally cooperating as a single DSP architecture. The distributed arrangement assists in handling high request volume while maintaining the sequence of operations required for polygon-level processing and machine learning inference in real time.
REAL-TIME BID PROCESSING FLOW
[0047] Referring to FIG. 2, the system performs a sequence of operations for processing each bid request. Upon receiving a bid request, the system extracts geo-coordinates from the request payload.
[0048] The extracted geo-coordinates are processed by the geo-matching polygon context engine to determine whether the coordinates fall within predefined geo-spatial polygon regions. The processing may include using the geographic coordinates as input to polygon lookup logic and to point-in-polygon evaluation logic so as to determine region-level relevance with higher resolution than coarse geo-targeting techniques.
[0049] The system performs a point-in-polygon computation to determine polygon membership. Once polygon membership is determined, a polygon identifier (Polygon ID) is attached to the bid request context.
[0050] The attachment of the polygon identifier enriches the bid request context with geo-spatial information that is directly usable by the machine learning inference engine. By deriving the polygon identifier before inference, the system ensures that polygon-level context participates in the predictive determination of bid value.
[0051] After polygon-level context has been associated with the request, the system invokes the machine learning inference engine configured to evaluate the potential value of the bid request. The output of the inference engine is then provided to the bid optimization engine for calculation of a final bid amount.
[0052] The optimized bid response is transmitted back to the advertising exchange within the RTB response window. By performing geo-coordinate extraction, polygon lookup, polygon membership determination, inference, bid optimization, and response transmission in a coordinated sequence, the system maintains real-time operability under the timing requirements of OpenRTB processing.
GEO-MATCHING POLYGON CONTEXT
[0053] Referring to FIG. 3, the geo-matching polygon context module maintains a repository of geo-spatial polygon definitions representing target regions. The target regions may correspond to geographic areas of interest that are relevant to bidding decisions in the OpenRTB Demand-Side Platform environment.
[0054] The module performs efficient polygon lookup operations using spatial indexing techniques. The use of spatial indexing permits the system to narrow the set of polygon definitions to be evaluated for a given request and thereby improves the practical feasibility of polygon-level processing within a strict RTB response interval.
[0055] The geo-matching polygon context module is configured to associate the incoming geo-coordinates with a matching region and to assign the corresponding polygon identifier to the request context. The polygon identifier associated with the matching region is attached to the bid request and forwarded to the machine learning inference engine.
[0056] In an exemplary arrangement, the polygon identifier functions as a compact geo-spatial representation of the request location relative to the predefined polygon regions. This allows downstream components to use the geo-spatial result without repeatedly performing the same polygon-resolution operation after the context has already been determined for the request.
[0057] The geo-matching polygon context engine therefore provides a technical mechanism by which precise polygon-level geographic context is introduced into the real-time bid decision process while remaining compatible with distributed request handling and low-latency operation.
POLYGON COMPUTATION MECHANISM
[0058] Referring to FIG. 4, the polygon computation mechanism performs point-in-polygon evaluation using geo-coordinate inputs. The computation determines whether the extracted latitude and longitude coordinates lie within a defined polygon boundary.
[0059] The point-in-polygon evaluation may be performed after identifying one or more candidate polygon definitions through the spatial indexing techniques of the geo-matching polygon context engine. The combination of indexed lookup and polygon boundary evaluation enables the system to identify a relevant polygon region with increased computational efficiency in comparison with indiscriminate full-set boundary evaluation.
[0060] Once the geographic point is determined to lie within a polygon boundary, the system identifies the associated polygon region and uses the corresponding polygon identifier as contextual input for subsequent bid optimization processes. If the geo-coordinate information does not correspond to a predefined polygon region, the system may continue processing without a matched polygon identifier or may use an alternate request context while preserving the operation of the remainder of the RTB pipeline.
[0061] The polygon computation mechanism is therefore not merely an abstract mathematical operation in isolation. Rather, it is performed as part of a concrete distributed request-processing architecture in which the computation directly controls context propagation to downstream inference and bid response modules.
REAL-TIME ML INFERENCE FLOW
[0062] Referring to FIG. 5 and FIG. 4 collectively, the machine learning inference engine is configured to evaluate the potential value of a bid request after polygon-level context has been derived for that request. The machine learning model receives input features including polygon identifier, contextual attributes, and historical performance metrics.
[0063] The polygon identifier serves as a geo-spatial feature representing the relationship between the requesting device location and a predefined target region. The contextual attributes may include request context associated with the bid opportunity, and the historical performance metrics may reflect prior performance information relevant to estimating the potential value of the current request.
[0064] The inference engine computes a predicted polygon performance value (PPV) representing the estimated value of the bid opportunity. The PPV is supplied to the bid optimization engine for use in determining the final bid response.
[0065] In a distributed implementation, the machine learning inference engine may be deployed across distributed machine learning inference nodes while still functioning as part of the common DSP architecture. Distribution of the inference workload assists the system in accommodating high request throughput while continuing to perform polygon-level inference within strict timing constraints.
[0066] The system may additionally regulate machine learning inference invocation in a latency-aware manner. In such implementations, inference execution is dynamically controlled in view of real-time processing conditions so that the DSP can reduce latency variance and optimize CPU utilization while preserving the ability to generate bid responses for suitable requests.
BID OPTIMIZATION ENGINE
[0067] Referring to FIG. 5, the bid optimization engine calculates a final bid amount based on the predicted value generated by the machine learning inference engine. The bid optimization engine thus converts the predicted polygon performance value into a bid value suitable for use in the RTB response.
[0068] Because the bid optimization engine operates on the output of the machine learning inference engine after polygon-level context has already been determined, the final bid amount reflects both the geo-spatial context of the request and the estimated value of the opportunity derived from the predictive model.
[0069] Once the final bid amount has been determined, the system prepares an optimized bid response for transmission to the advertising exchange. The response transmission is performed within the RTB response window so that the generated bid can participate in the real-time bidding process.
[0070] In some implementations, the system selectively controls whether the response is transmitted. Such control is consistent with the latency mitigation architecture described herein and assists in reducing unnecessary bid responses and network transmission when the system determines that a response should not be sent.
FEEDBACK AND MODEL RETRAINING LOOP
[0071] Referring to FIG. 6, an exemplary feedback and model retraining loop may be associated with the machine learning inference process. Historical performance metrics used by the machine learning inference engine may be updated over time based on observed operational outcomes associated with prior bid processing activity.
[0072] In an exemplary implementation, information associated with previously processed bid opportunities may be fed back to maintain or refresh the historical performance metrics used as input features by the machine learning model. The feedback loop thus supports continued use of relevant performance information in the distributed OpenRTB Demand-Side Platform architecture.
[0073] The feedback and model retraining loop may therefore operate as an auxiliary process coupled to, but distinct from, the real-time bid path. By separating such updating activity from the immediate response generation path, the system preserves the ability to process current bid requests within strict RTB timing requirements while still maintaining historical performance information used by the inference engine.
LATENCY MITIGATION MECHANISM
[0074] Referring to FIG. 7, the system may include a latency mitigation mechanism configured to support operation of the distributed OpenRTB Demand-Side Platform under strict response deadlines. As described in the abstract of the provisional specification, the architecture may comprise a geo-spatial indexing engine, a latency-aware inference invocation controller, distributed machine learning inference nodes, and a selective bid transmission controller.
[0075] The latency mitigation mechanism dynamically regulates machine learning execution, polygon resolution evaluation, and network transmission decisions in order to reduce latency variance, optimize CPU utilization, minimize unnecessary bid responses, and improve distributed system efficiency in sub-100 millisecond real-time bidding environments.
[0076] In an exemplary implementation, the latency-aware inference invocation controller cooperates with the request-processing flow so that machine learning inference is invoked in a controlled manner during bid processing. Likewise, the selective bid transmission controller cooperates with the response path so that transmission of a bid response is governed in accordance with the latency-aware operation of the system.
[0077] The latency mitigation mechanism is thus integrated with the concrete modules of the distributed DSP architecture and contributes directly to improved real-time operation of the system. The mechanism is not a mere business preference or abstract rule, but a technical control arrangement affecting processor usage, geo-spatial computation timing, inference execution, and network packet transmission within the real-time bidding environment.
TECHNICAL ADVANTAGES
[0078] The disclosed system provides several technical advantages, including efficient polygon-level geo-spatial targeting within RTB constraints, reduced latency variance in real-time bidding systems, optimized machine learning inference invocation, improved distributed compute utilization, and reduction in unnecessary bid responses and network transmission.
[0079] By deriving a polygon identifier from geo-coordinate information and using that polygon identifier as contextual input to the machine learning inference engine, the system achieves high-resolution geo-matching without requiring the entire bid decision pipeline to rely solely on coarse geographic rules.
[0080] By combining geo-spatial indexing, point-in-polygon evaluation, distributed inference, bid optimization, and controlled response transmission in a coordinated architecture, the invention provides a distributed computing system capable of performing high-resolution geo-matching and machine learning inference while maintaining deterministic response timing within real-time bidding environments.
,CLAIMS:1. A distributed system for real-time bidding in an OpenRTB Demand-Side Platform, the system comprising: an OpenRTB ad exchange listener module configured to receive, from an advertising exchange, real-time bid requests associated with geo-coordinate information of a requesting device; a geo-matching polygon context engine configured to process the geo-coordinate information relative to predefined geo-spatial polygon regions and to derive a polygon identifier for a bid request context based on polygon membership determined from the geo-coordinate information; a machine learning inference engine configured to receive input features including at least the polygon identifier and to compute a predicted value metric representing an estimated value of a bid opportunity; a bid optimization engine configured to determine a bid value based on the predicted value metric; and a bid response transmission module configured to transmit a bid response to the advertising exchange within a real-time bidding response window.
2. The distributed system as claimed in claim 1, wherein the geo-matching polygon context engine maintains a repository of geo-spatial polygon definitions representing target regions and performs polygon lookup operations using spatial indexing techniques to reduce a number of polygon definitions subjected to polygon-membership evaluation for an incoming real-time bid request.
3. The distributed system as claimed in claim 1, wherein the geo-matching polygon context engine is configured to perform point-in-polygon computation using extracted latitude and longitude coordinates to determine whether the geo-coordinate information lies within a defined polygon boundary, and to attach, to the bid request context, the polygon identifier associated with a matching region.
4. The distributed system as claimed in claim 1, wherein the machine learning inference engine is deployed across distributed machine learning inference nodes and is selectively invocable in a latency-aware manner by a latency-aware inference invocation controller configured to regulate invocation of the machine learning inference engine under latency constraints of a real-time bidding environment.
5. The distributed system as claimed in claim 1, further comprising a selective bid transmission controller configured to govern whether the bid response is transmitted by the bid response transmission module, wherein the system is further configured to dynamically regulate machine learning execution, polygon resolution evaluation, and network transmission decisions during processing of the real-time bid requests.

6. A computer-implemented method for real-time polygon-level machine learning inference in an OpenRTB Demand-Side Platform, the method comprising: receiving, by an OpenRTB ad exchange listener module, a real-time bid request from an advertising exchange; extracting geo-coordinates from a request payload of the real-time bid request; processing, by a geo-matching polygon context engine, the geo-coordinates relative to predefined geo-spatial polygon regions to derive a polygon identifier for a bid request context based on polygon membership; providing the polygon identifier as an input feature to a machine learning inference engine; computing, by the machine learning inference engine, a predicted value metric representing an estimated value of a bid opportunity; determining, by a bid optimization engine, a bid value based on the predicted value metric; and transmitting, by a bid response transmission module, a bid response to the advertising exchange within a real-time bidding response window.
7. The computer-implemented method as claimed in claim 6, wherein processing the geo-coordinates comprises performing polygon lookup using spatial indexing techniques, performing point-in-polygon computation to determine whether extracted latitude and longitude coordinates lie within a defined polygon boundary, identifying a matching polygon region, and attaching to the bid request context a polygon identifier associated with the matching polygon region.
8. The computer-implemented method as claimed in claim 6, further comprising selectively invoking machine learning inference in a latency-aware manner, selectively controlling transmission of the bid response, updating historical performance metrics through a feedback loop associated with prior bid processing activity, and performing model retraining separately from a real-time bid path.
9. A non-transitory computer-readable medium storing instructions that, when executed by one or more processors of a distributed real-time bidding system, cause the one or more processors to perform operations comprising: receiving a real-time bid request from an advertising exchange; extracting geo-coordinates from the real-time bid request; processing the geo-coordinates relative to predefined geo-spatial polygon regions to derive a polygon identifier for a bid request context based on polygon membership determined from the geo-coordinates; using the polygon identifier as an input feature for machine learning inference to compute a predicted value metric representing an estimated value of a bid opportunity; determining a bid value based on the predicted value metric; and transmitting a bid response within a real-time bidding response window.
10. The non-transitory computer-readable medium as claimed in claim 9, wherein the operations further comprise performing polygon lookup using spatial indexing techniques, performing point-in-polygon evaluation to determine polygon membership, selectively invoking machine learning inference in a latency-aware manner, selectively controlling response transmission, updating historical performance metrics through a feedback loop associated with prior bid processing activity, and performing the operations across distributed machine learning inference nodes within the OpenRTB Demand-Side Platform.