Directional pathfinding-based machine learning data processing with adaptive feedback-controlled computational resource allocation

Abstract

A system and method for processing data using directional pathfinding algorithms with adaptive feedback-controlled computational resource allocation. The system includes a directional pathfinding module that calculates data value density metrics and generates optimized processing trajectories using heuristic search algorithms. A layered processing architecture processes data through hierarchical layers analogous to geological formations, with deeper layers analyzed only when data meets confidence thresholds. An adaptive feedback learning system receives inference confidence scores and generates real-time trajectory adjustment signals, similar to Measurements-While-Drilling techniques in directional drilling. A computational resource allocation unit dynamically distributes processing resources based on data value density. The invention achieves computational cost reductions of approximately 50% compared to conventional batch processing while maintaining equivalent output quality, providing an adaptive, resource-efficient AI processing framework applicable across multiple industries from edge computing to high-performance data centers.

Description

Attorney Docket No. 21681-168179-PCT Customer No. 42798SYSTEM AND METHOD FOR DIRECTIONAL PATHFINDING-BASED MACHINE LEARNING DATA PROCESSING WITH ADAPTIVE FEEDBACK-CONTROLLED COMPUTATIONAL RESOURCE ALLOCATIONFIELD

[0001] The present disclosure relates generally to artificial intelligence and machine learning systems, and more particularly to systems and methods for processing data using directional pathfinding algorithms with adaptive feedback-controlled computational resource allocation. The invention provides a novel Al architecture that dynamically navigates and processes data using an optimized directional pathfinding approach inspired by techniques used in directional drilling for oil and gas exploration.DEFINITIONS

[0002] For purposes of this disclosure, the following terms shall have the meanings set forth below:

[0003] "Adaptive Feedback Learning System" or "AFLS" refers to a computational module that receives inference confidence scores from processing layers and generates real-time trajectory adjustment signals to modify the pathfinding module's data navigation strategy, similar to Measurements-While-Drilling (MWD) techniques in directional drilling that provide real-time geological feedback.

[0004] "Computational Resource Allocation Unit" or "CRAU" refers to a hardware or firmware component that dynamically distributes GPU cores, CPU threads, and memory bandwidth among processing tasks based on calculated data value density metrics, significantly reducing energy usage compared to static allocation methods.Attorney Docket No. 21681-168179-PCT Customer No. 42798

[0005] "Data Value Density" or "DVD" refers to a quantitative metric, expressed as a floatingpoint value between 0.0 and 1.0, representing the predicted informational yield per computational operation fora given data region, calculated as a ratio of weighted relevance scores to required computational resources.

[0006] "Directional Pathfinding Module" refers to a processing component implementing heuristic search algorithms (including but not limited to A*, Monte Carlo Tree Search, or reinforcement learning-based steering) to determine optimal data processing trajectories through multi-dimensional data spaces.

[0007] "Layered Data Processing" or "LDP" refers to a hierarchical processing architecture comprising multiple analysis layers of increasing computational complexity, analogous to geological formations or geophysical stratification, where data progresses to deeper layers only upon meeting confidence thresholds.

[0008] "Processing Trajectory" refers to a sequence of computational operations defined by an ordered set of data regions and associated processing parameters, dynamically adjusted based on intermediate results and feedback signals.

[0009] "Real-Time Adjustment Signal" refers to a control signal generated by the AFLS containing trajectory modification parameters transmitted to the pathfinding module with latency of less than 100 milliseconds from confidence score receipt.BACKGROUND

[0010] Current Al and ML models process data using batch-based or brute-force methodologies, often leading to unnecessary computation, increased costs, and inefficiencies. Conventional deep learning architectures, such as transformers, convolutional neural networks (CNNs), and reinforcement learning models, rely on exhaustive search or static hierarchical layers that do not dynamically adjust computation based on real-time feedback.Attorney Docket No. 21681-168179-PCT Customer No. 42798

[0011] Conventional artificial intelligence and machine learning systems process data using batch-based or brute-force methodologies that treat all data regions with equal computational priority. These approaches result in unnecessary computation, increased costs, and significant inefficiencies. Conventional deep learning architectures, such as transformers, convolutional neural networks (CNNs), and reinforcement learning models, rely on exhaustive search or static hierarchical layers that do not dynamically adjust computation based on real-time feedback.

[0012] These conventional approaches result in significant computational inefficiencies. Studies have shown that up to 60% of computational resources in standard inference pipelines are expended on low-value data regions that contribute minimally to final outputs. This inefficiency translates directly to increased energy consumption, extended processing times, and elevated operational costs.

[0013] In the field of directional drilling for oil and gas exploration, techniques have been developed that enable efficient resource targeting by dynamically adjusting well trajectories based on real-time geological feedback. These techniques optimize the path to valuable resources while minimizing unnecessary drilling through low-value geological formations.However, such directional, feedback-controlled optimization approaches have not been systematically applied to Al systems, where optimizing computational paths based on relevance could significantly reduce resource consumption while improving performance. Accordingly, there is a desire and need in the art for such systems.SUMMARY

[0014] The present disclosure recognizes that similar principles of adaptive, feedback-controlled pathfinding can be applied to data processing, enabling Al systems to navigate data spaces with the same efficiency that directional drilling achieves in geological formations. ThisAttorney Docket No. 21681-168179-PCT Customer No. 42798 approach provides an adaptive, resource-efficient Al processing framework that mirrors path optimization techniques in the context of machine learning.

[0015] In accordance with one aspect of the present disclosure, a computer-implemented system for processing data comprises: a directional pathfinding module configured to calculate data value density metrics for a plurality of data regions and generate processing trajectories based on heuristic search algorithms; a layered processing architecture comprising at least a surface analysis layer, a mid-level processing layer, and a deep processing layer, each layer having progressively higher computational complexity; an adaptive feedback learning system configured to receive confidence scores from the processing layers and generate real-time adjustment signals; and a computational resource allocation unit configured to dynamically distribute processing resources based on calculated data value density.

[0016] In accordance with another aspect, a method for processing data using directional pathfinding comprises: receiving input data; calculating data value density for regions of the input data; generating an initial processing trajectory using a heuristic search algorithm; processing data through hierarchical layers based on confidence thresholds; receiving feedback signals indicating processing confidence; dynamically adjusting the processing trajectory based on feedback; and allocating computational resources proportionally to data value density.

[0017] Systems and methods according to the present disclosure achieve computational cost reductions of approximately 45-55% compared to conventional batch processing approaches while maintaining equivalent or superior output quality. Instead of traditional exhaustive computation, the system prioritizes and adjusts its focus in real-time, leading to faster insights, lower computational costs, and improved adaptability.

[0018] Directional Pathfinding for Data NavigationAttorney Docket No. 21681-168179-PCT Customer No. 42798

[0019] Al models adopt an optimized pathfinding algorithm (e.g., A*, Monte Carlo Tree Search, or reinforcement learning-based steering) to selectively process high-value data regions.

[0020] Layered Data Processing Inspired by Geophysical Stratification

[0021] Information is processed in hierarchical layers, analogous to geological formations, with deeper layers analyzed only if necessary.

[0022] Reduces unnecessary computational effort while maintaining insight depth.

[0023] Adaptive Feedback Learning System (AFLS)

[0024] Uses real-time feedback to dynamically adjust its data-processing trajectory, similar to Measurements-While-Drilling (MWD) in directional drilling.

[0025] Incorporates reinforcement learning and Bayesian optimization to iteratively refine computations.

[0026] Computational Resource Optimization

[0027] Al dynamically allocates GPU / CPU power based on predicted data value density, significantly reducing energy usage.

[0028] Real-Time Al Adjustment Mechanism

[0029] Allows Al models to modify their processing behavior dynamically rather than relying on static inference models.

[0030] Scalable and Industry-Agnostic Architecture

[0031] Designed for applications in predictive analytics, natural language processing

[0032] (NLP), optimization problems, and real-time decision-making.

[0033] Capable of scaling efficiently from edge computing (low-power Al) to high-performance data centers.

[0034] According to one approach, a computer-implemented system for processing data using directional pathfinding is provided having a directional pathfinding module configured to:Attorney Docket No. 21681-168179-PCT Customer No. 42798 calculate data value density metrics for a plurality of data regions based on predicted relevance scores and computational requirements; generate processing trajectories using at least one heuristic search algorithm selected from A*, Monte Carlo Tree Search, and reinforcement learning-based steering; a layered processing architecture comprising: a surface analysis layer operating at O(n) complexity; a mid-level processing layer operating at O(n log n) complexity; a deep processing layer operating at O(n2) or greater complexity; wherein data progresses between layers based on confidence thresholds; an adaptive feedback learning system configured to: receive inference confidence scores from the processing layers; generate realtime trajectory adjustment signals with latency less than 100 milliseconds; a computational resource allocation unit configured to dynamically distribute GPU cores, CPU threads, and memory bandwidth based on calculated data value density.

[0035] In this approach, the data value density calculator computes: DVD(r) = (Zi relevance_scorei x confidence_weighti) I (FLOPS(r) x mem_overhead(r)). Tthe layered processing architecture processes information in hierarchical layers analogous to geological formations, with deeper layers analyzed only when data meets confidence thresholds. The adaptive feedback learning system operates similarly to Measurements-While-Drilling (MWD) techniques in directional drilling by providing real-time feedback for trajectory adjustment. The computational resource allocation unit implements proportional allocation according to:GPU_alloc(r) = min(GPU_total, floor(GPU_total x DVD(r) x scaling_factor)).

[0036] In another approach, a method for processing data using directional pathfinding is provided having the steps of: receiving input data; conducting an initial low-resource scan to identify high-value information clusters; calculating data value density for regions of the input data; generating an initial processing trajectory using a heuristic search algorithm; processing data through hierarchical layers comprising surface analysis, mid-level processing, and deep processing based on confidence thresholds; receiving feedback signals indicating processingAttorney Docket No. 21681-168179-PCT Customer No. 42798 confidence; dynamically adjusting the processing trajectory based on feedback using reinforcement learning and Bayesian optimization; allocating computational resources proportionally to data value density.

[0037] In this approach, dynamically adjusting the processing trajectory comprises re-routing processing paths for increased efficiency based on intermediate results. The hierarchical layers filter low-value data while providing full analytical depth to high-value data. The further comprises training the system using reinforcement learning with reward functions that optimize for efficiency and accuracy in navigating data. The system achieves computational cost reductions of approximately 45-55% compared to conventional batch processing while maintaining equivalent output quality.

[0038] In another approach, a non-transitory computer-readable medium storing instructions that, when executed by a processor, cause the processor to: implement a directional pathfinding module that calculates data value density and generates optimized processing trajectories; process data through a layered architecture analogous to geophysical stratification, with surface, mid-level, and deep processing layers; operate an adaptive feedback learning system that continuously adjusts processing paths in real-time based on confidence scores; and dynamically allocate computational resources based on predicted data value density to reduce energy usage.

[0039] In this approach, the adaptive feedback learning system enables continual improvement without manual retraining. The instructions further cause the processor to support deployment in edge computing, cloud processing, and hybrid configurations. The instructions further cause the processor to modify processing behavior dynamically rather than relying on static inference models.

[0040] According to another approach, an artificial intelligence processing system is provided having means for dynamically navigating data space using directional pathfinding to selectivelyAttorney Docket No. 21681-168179-PCT Customer No. 42798 process high-value data regions; means for processing information in hierarchical formations analogous to geological formations, selectively analyzing data regions based on value; means for continuously adjusting processing paths in real-time based on inference feedback, optimizing efficiency and accuracy; and means for dynamically distributing computational resources based on real-time data value estimation.

[0041] In another approach, a computer implemented system comprising a non generic, specially configured artificial intelligence processing architecture includes: a directional pathfinding module implemented as a hardware configured processing unit comprising: - a trajectory generator that is specifically programmed to modify hardware level execution ordering of data processing tasks; - a data value density (DVD) calculator configured to compute DVD values using predicted relevance scores and computational requirements; and - a path cost evaluator configured to generate hardware enforceable processing trajectories using at least one heuristic search algorithm selected from A*, Monte Carlo Tree Search, and reinforcement learning based steering; a layered processing architecture implemented as a pipeline of physically distinct processing stages, the architecture comprising: - a surface analysis layer operating at O(n) complexity; - a mid level processing layer operating at O(n log n) complexity; - a deep processing layer operating at O(n2) or greater complexity; wherein each layer is executed on a dedicated set of processing cores and data progresses between layers only when confidence thresholds are met, thereby reconfiguring the underlying hardware utilization in real time; an adaptive feedback learning system (AFLS) comprising a hardware implemented control loop configured to: - receive inference confidence scores from the processing layers; -generate, with latency less than 100 milliseconds, hardware level trajectory adjustment signals that modify allocation of GPU cores, CPU threads, and memory channels; a computational resource allocation unit (CRAU) configured as a non conventional hardware controller that dynamically distributes GPU cores, CPU threads, and memory bandwidth based on calculatedAttorney Docket No. 21681-168179-PCT Customer No. 42798 data value density, wherein the CRAU adjusts hardware resources during execution without halting or restarting any processing task, thereby improving computer resource efficiency in a manner not achievable by conventional static allocation systems.

[0042] A method for processing data using directional pathfinding on a specially-configured Al processing architecture is also provided, the method comprising:- receiving input data;- conducting an initial low-resource scan using dedicated low-power cores to identify high-value information clusters;- calculating data value density;- generating an initial processing trajectory using a heuristic search algorithm;- processing data through hierarchical layers comprising surface analysis, mid-level processing, and deep processing based on confidence thresholds,wherein the hierarchical layers are executed on physically distinct processing units whose resource assignments are dynamically reconfigured in real time;- receiving feedback signals indicating processing confidence;- dynamically adjusting the processing trajectory using reinforcement learning and Bayesian optimization to modify hardware-level execution order and resource allocation;- allocating computational resources proportionally to data value density using anon-conventional, real-time hardware controller that reallocates GPU cores, CPU threads, and memory channels during ongoing computation.In another approach, a non-transitory computer-readable medium storing instructions that, when executed by a specially configured Al hardware architecture, cause the architecture in one provided approach to:- implement a directional pathfinding module that calculates data value density and generates optimized, hardware-enforced processing trajectories;Attorney Docket No. 21681-168179-PCT Customer No. 42798 - process data through a layered architecture with distinct hardware execution blocks corresponding to surface, mid-level, and deep analysis layers;- operate an adaptive feedback learning system that adjusts processing paths in real time using trajectory signals that directly reconfigure hardware processing pipelines; and- dynamically allocate computational resources based on predicted data value density via a real-time hardware controller not found in conventional computer architectures.

[0043] Other technical advantages will be readily apparent to one skilled in the art from the figures, descriptions and claims included herein. Moreover, while specific advantages have been enumerated above, various embodiments may include all, some, or none of the enumerated advantages.BRIEF DESCRIPTION OF THE DRAWINGS

[0044] The present invention relates to artificial intelligence (Al) and machine learning (ML), specifically to a novel system and method for Al-based data processing that utilizes the principles of directional drilling to optimize resource allocation, computational efficiency, and adaptive learning.

[0045] FIG. 1 is a block diagram illustrating the overall system architecture of a directional Al processing system according to an embodiment, showing the data flow from input to insights, including pathfinding, layered processing, adaptive feedback, and computational optimization.

[0046] FIG. 2 is a bar chart comparing computational cost and processing time between traditional Al approaches and the directional Al system, demonstrating approximately 50% reduction while maintaining high accuracy.

[0047] FIG. 3 is a flowchart illustrating the adaptive feedback learning system operation according to an embodiment, showing a real-time feedback loop that continuously adjusts the Al's processing paths based on received insights.Attorney Docket No. 21681-168179-PCT Customer No. 42798

[0048] FIG. 4 is a block diagram illustrating deployment architectures for the directional Al system including edge computing, cloud processing, and hybrid configurations, showing the scalable deployment strategy from low-power Al to high-performance data centers.

[0049] FIG. 5 is a detailed flowchart illustrating the pathfinding algorithm operation including path cost evaluation and trajectory optimization according to an embodiment.

[0050] FIG. 6 is a block diagram illustrating the layered data processing architecture showing the surface, mid-level, and deep processing layers with associated confidence thresholds, analogous to geophysical stratification.

[0051] FIG. 7 is a diagram illustrating the computational resource allocation unit operation including resource monitoring and distribution control according to an embodiment.

[0052] Corresponding reference characters may indicate corresponding components throughout the several views of the drawings. Skilled artisans will appreciate that elements in the figures are illustrated for simplicity and clarity and have not necessarily been drawn to scale. For example, the dimensions of some of the elements in the figures may be exaggerated relative to other elements to help to improve understanding of various embodiments of the present invention. Also, common but well-understood elements that are useful or necessary in a commercially feasible embodiment are often not depicted in order to facilitate a less obstructed view of these various embodiments of the present embodiments.

[0053] While the features described herein may be susceptible to various modifications and alternative forms, specific embodiments thereof are shown by way of example in the drawings and are herein described in detail. It should be understood, however, that the drawings and detailed description thereto are not intended to be limiting to the particular form disclosed, but on the contrary, the intention is to cover all modifications, equivalents and alternatives falling within the spirit and scope of the subject matter as defined by the appended claims.Attorney Docket No. 21681-168179-PCT Customer No. 42798 DETAILED DESCRIPTION

[0054] Directional drilling techniques, used in the oil and gas industry, enable efficient resource targeting by adjusting well trajectories dynamically based on real-time geological feedback. This approach has not been applied to Al systems, where optimizing computational paths based on relevance could significantly reduce resource consumption while improving performance.

[0055] The present invention introduces a Directional Al System that mirrors the path optimization techniques of directional drilling in the context of machine learning, providing an adaptive, resource-efficient Al processing framework.

[0056] The following detailed description refers to the accompanying drawings that illustrate embodiments of the present disclosure. Other embodiments are possible, and modifications may be made to the embodiments within the spirit and scope of the present disclosure.Therefore, the detailed description is not meant to limit the scope of the claims.System Architecture Overview

[0057] Referring to FIG. 1, a directional Al processing system 100 according to an embodiment includes a pathfinding module 110, a layered processing architecture 120, an adaptive feedback learning system (AFLS) 130, and a computational resource allocation unit (CRAU) 140. The Directional Al System consists of an Al inference engine equipped with these interconnected components that work together to optimize data processing efficiency. Input data 102 is received by the system and processed to generate insights 104 through dynamic navigation of the data space.

[0058] The pathfinding module 110 includes a trajectory generator 112 implementing one or more heuristic search algorithms selected from A*, Monte Carlo Tree Search (MCTS), and reinforcement learning-based steering mechanisms. A data value density (DVD) calculator 114Attorney Docket No. 21681-168179-PCT Customer No. 42798 computes the predicted informational yield for candidate data regions. A path cost evaluator 116 determines optimal processing sequences based on DVD values and computational requirements. The Al model is trained using reinforcement learning, where reward functions optimize for efficiency and accuracy in navigating data.

[0059] The data value density calculator 114 operates according to the following formula:DVD(r) = (Zi relevance_scorei x confidence_weighti) I (FLOPS(r) x mem_overhead(r)) where r represents a data region, relevance_scorei represents the predicted relevance of feature i within region r, confidence_weighti represents a learned weighting factor for feature i, FLOPS(r) represents the estimated floating-point operations required to process region r, and mem_overhead(r) represents the memory bandwidth requirements for region r. This method ensures that computational resources are focused only on relevant data, significantly reducing unnecessary calculations.Layered Data Processing Architecture

[0060] The layered processing architecture 120 comprises three hierarchical layers designed to minimize computational expenditure while maintaining insight quality. Information is processed in these hierarchical layers, analogous to geological formations, with deeper layers analyzed only if necessary. This approach reduces unnecessary computational effort while maintaining insight depth. The system processes data similar to geophysical stratification in directional drilling:

[0061] Surface Analysis Layer 122 (Layer 1): This layer performs broad classification and filtering operations at O(n) computational complexity. Data elements not meeting a first confidence threshold Ti are filtered before progression to deeper layers. Low-value data is ignored while high-value data receives appropriate analytical depth.Attorney Docket No. 21681-168179-PCT Customer No. 42798

[0062] Mid-Level Processing Layer 124 (Layer 2): This layer identifies core patterns and relationships at O(n log n) complexity. Elements meeting threshold Ti but not threshold T2receive mid-level analysis without incurring deep processing costs.

[0063] Deep Processing Layer 126 (Layer 3): This layer extracts refined insights at O(n2) or greater complexity. Only data elements exceeding threshold T2progress to this computationally intensive layer, ensuring full analytical depth only where necessary and avoiding exhaustive computation on low-value data.Adaptive Feedback Learning System

[0064] Referring to FIG. 3, the adaptive feedback learning system 130 operates as a closed-loop control system with components including a confidence score receiver 132, a performance evaluator 134, and a trajectory modifier 136. The AFLS uses real-time feedback to dynamically adjust its data-processing trajectory, similar to Measurements-While-Drilling (MWD) in directional drilling that provides real-time geological feedback.

[0065] The confidence score receiver 132 collects inference confidence values from each processing layer. The performance evaluator 134 compares actual processing outcomes against predicted values to identify trajectory optimization opportunities. Feedback sensors measure inference confidence scores, and the Al modifies its pathfinding approach in response to performance data. The system incorporates reinforcement learning and Bayesian optimization to iteratively refine computations.

[0066] The trajectory modifier 136 generates real-time adjustment signals with latency of less than 100 milliseconds, enabling dynamic trajectory modification during active processing. The adaptive nature of this system ensures continual improvement without manual retraining.Pathfinding Algorithm for Optimized Data NavigationAttorney Docket No. 21681-168179-PCT Customer No. 42798

[0067] Referring to FIG. 5, the pathfinding algorithm implemented by module 110 follows an iterative optimization process. The Al system utilizes heuristic search and reinforcement learning algorithms to determine the most efficient computational trajectory for data analysis. The pathfinding process follows these steps:

[0068] Initial Data Exploration: The system first conducts a low-resource scan to identify high-value information clusters. This initial exploration uses minimal computational resources to map the data landscape.

[0069] Path Selection: A reinforcement learning model selects the optimal path for detailed data processing based on calculated DVD values and predicted outcomes.

[0070] Adaptive Refinement: As results are generated, the model re-routes processing paths for increased efficiency based on feedback from the AFLS. This continuous refinement ensures optimal resource utilization throughout the processing cycle.Computational Resource Allocation

[0071] Referring to FIG. 7, the CRAU 140 dynamically allocates GPU / CPU power based on predicted data value density, significantly reducing energy usage compared to static allocation methods. The CRAU includes a resource monitor 142 that tracks available computational capacity, a distribution controller 144 that allocates resources according to DVD-based priorities, and an efficiency tracker 146 that monitors utilization metrics.

[0072] The distribution controller 144 implements a proportional allocation algorithm:GPU_alloc(r) = min(GPU_total, floor(GPU_total x DVD(r) x scaling_factor))This ensures that high-value data regions receive proportionally greater computational resources while maintaining system stability through the minimum function constraint.Real-Time Al Adjustment MechanismAttorney Docket No. 21681-168179-PCT Customer No. 42798

[0073] The system allows Al models to modify their processing behavior dynamically rather than relying on static inference models. This real-time adjustment capability distinguishes the directional Al approach from conventional batch processing systems.Deployment Architectures

[0074] Referring to FIG. 4, the directional Al system supports multiple deployment configurations including edge computing for low-power Al with local processing and minimal latency, cloud processing for high-performance scalable resources, and hybrid configurations that combine edge and cloud capabilities with adaptive routing for optimal balance. The scalable and industry-agnostic architecture is designed for applications in predictive analytics, natural language processing (NLP), optimization problems, and real-time decision-making. The system is capable of scaling efficiently from edge computing to high-performance data centers.Performance Comparison

[0075] As demonstrated in FIG. 2, experimental results confirm that Directional Al reduces computational costs by approximately 50% while maintaining high accuracy compared to traditional Al systems. The directional Al approach achieves processing time reductions of 48% and computational cost reductions of 45% relative to conventional batch processing methods, while maintaining equivalent output quality metrics.

[0076] One embodiment shows a directional Al processing system. The Directional Al System may have of an Al inference engine equipped with:A pathfinding module that dynamically selects data regions to analyze.A layered processing model that mimics geophysical formations for data prioritization. An adaptive feedback loop that adjusts processing paths in real-time based on received outputs.Attorney Docket No. 21681-168179-PCT Customer No. 42798

[0077] The Al model is trained using reinforcement learning, where reward functions optimize for efficiency and accuracy in navigating data.

[0078] Pathfinding Algorithm for Optimized Data Navigation

[0079] The Al system utilizes heuristic search and reinforcement learning algorithms to determine the most efficient computational trajectory for data analysis. The pathfinding process follows these steps:Initial Data Exploration - The system first conducts a low-resource scan to identify high- value information clusters.Path Selection - A reinforcement learning model selects the optimal path for detailed data processing.Adaptive Refinement - As results are generated, the model re-routes processing paths for increased efficiency.

[0080] This method ensures that computational resources are focused only on relevant data, significantly reducing unnecessary calculations.

[0081] Layered Data Processing (LDP) Model

[0082] The Al system processes data in hierarchical layers, similar to geophysical stratification in directional drilling:Layer I (Surface Analysis): Broad classification and filtering.Layer 2 (Mid-Level Processing): Identifies core patterns and relationships.Layer 3 (Deep Processing): Extracts refined insights only where necessary, avoiding exhaustive computation.Attorney Docket No. 21681-168179-PCT Customer No. 42798

[0083] This model ensures that lower-value data is ignored, while high-value data receives full analytical depth.

[0084] Adaptive Feedback Learning System (AFLS). Here the Al includes real-time feedback loops to refine its learning trajectory dynamically:Feedback sensors measure inference confidence scores.The Al modifies its pathfinding approach in response to performance data.Uses Bayesian optimization and reinforcement learning rewards to adjust its decisionmaking.

[0085] The adaptive nature of this system ensures continual improvement without manual retraining.

[0086] Further, the terms and phrases used herein are not intended to be limiting; but rather, to provide an understandable description of the invention. While the embodiments have been described in conjunction with specific embodiments, it is evident that many alternatives, modifications and variations will be apparent to those skilled in the art in light of the foregoing description. Accordingly, the present embodiments attempt to embrace all such alternatives, modifications and variations that fall within the spirit and scope of the appended claims.Throughout this specification and the drawings and figures associated with this specification, numerical labels of previously shown or discussed features may be reused in another drawing figure to indicate similar features. While the specification concludes with claims defining the features of the invention that are regarded as novel, it is believed that the invention will be better understood from a consideration of the description in conjunction with the drawing figures, in which like reference numerals are carried forward.

Claims

Attorney Docket No. 21681-168179-PCT Customer No. 42798CLAIMSCLAIMSWhat is claimed is:

1. A computer-implemented system comprising a non-generic, specially-configured artificial intelligence processing architecture, the system including:a directional pathfinding module implemented as a hardware-configured processing unit comprising:- a trajectory generator that is specifically programmed to modify hardware-level execution ordering of data-processing tasks;- a data value density (DVD) calculator configured to compute DVD values using predicted relevance scores and computational requirements; and- a path cost evaluator configured to generate hardware-enforceable processing trajectories using at least one heuristic search algorithm selected from A*, Monte Carlo Tree Search, and reinforcement-learning-based steering;a layered processing architecture implemented as a pipeline of physically distinct processing stages, the architecture comprising:- a surface analysis layer operating at O(n) complexity;- a mid-level processing layer operating at O(n log n) complexity;- a deep processing layer operating at O(n2) or greater complexity;wherein each layer is executed on a dedicated set of processing cores and data progresses between layers only when confidence thresholds are met, thereby reconfiguring the underlying hardware utilization in real time;an adaptive feedback learning system (AFLS) comprising a hardware-implemented control loop configured to:- receive inference confidence scores from the processing layers;- generate, with latency less than 100 milliseconds, hardware-level trajectory adjustment signals that modify allocation of GPU cores, CPU threads, and memory channels;Attorney Docket No. 21681-168179-PCT Customer No. 42798 a computational resource allocation unit (CRAU) configured as a non-conventional hardware controller that dynamically distributes GPU cores, CPU threads, and memory bandwidth based on calculated data value density,wherein the CRAU adjusts hardware resources during execution without halting or restarting any processing task, thereby improving computer resource efficiency in a manner not achievable by conventional static allocation systems.

2. The system of claim 1, wherein the data value density calculator computes: DVD(r) = (Zi relevance_scorei x confidence_weighti) I (FLOPS(r) x mem_overhead(r)).

3. The system of claim 1, wherein the layered processing architecture processes information in hierarchical layers analogous to geological formations, with deeper layers analyzed only when data meets confidence thresholds.

4. The system of claim 1, wherein the adaptive feedback learning system operates similarly to Measurements-While-Drilling (MWD) techniques in directional drilling by providing real-time feedback for trajectory adjustment.

5. The system of claim 1, wherein the computational resource allocation unit implements proportional allocation according to: GPU_alloc(r) = min(GPU_total, floor(GPU_total x DVD(r) x scaling_factor)).

6. A method for processing data using directional pathfinding on a specially-configured Al processing architecture, the method comprising:- receiving input data;- conducting an initial low-resource scan using dedicated low-power cores to identify high-value information clusters;- calculating data value density;Attorney Docket No. 21681-168179-PCT Customer No. 42798 - generating an initial processing trajectory using a heuristic search algorithm;- processing data through hierarchical layers comprising surface analysis, mid-level processing, and deep processing based on confidence thresholds,wherein the hierarchical layers are executed on physically distinct processing units whose resource assignments are dynamically reconfigured in real time;- receiving feedback signals indicating processing confidence;- dynamically adjusting the processing trajectory using reinforcement learning and Bayesian optimization to modify hardware-level execution order and resource allocation;- allocating computational resources proportionally to data value density using anon-conventional, real-time hardware controller that reallocates GPU cores, CPU threads, and memory channels during ongoing computation.

7. The method of claim 6, wherein dynamically adjusting the processing trajectory comprises re-routing processing paths for increased efficiency based on intermediate results.

8. The method of claim 6, wherein the hierarchical layers filter low-value data while providing full analytical depth to high-value data.

9. The method of claim 6, further comprising training the system using reinforcement learning with reward functions that optimize for efficiency and accuracy in navigating data.

10. The method of claim 6, wherein the system achieves computational cost reductions of approximately 45-55% compared to conventional batch processing while maintaining equivalent output quality.

11. A non-transitory computer-readable medium storing instructions that, when executed by a specially configured Al hardware architecture, cause the architecture to:Attorney Docket No. 21681-168179-PCT Customer No. 42798- implement a directional pathfinding module that calculates data value density and generates optimized, hardware-enforced processing trajectories;- process data through a layered architecture with distinct hardware execution blocks corresponding to surface, mid-level, and deep analysis layers;- operate an adaptive feedback learning system that adjusts processing paths in real time using trajectory signals that directly reconfigure hardware processing pipelines;- dynamically allocate computational resources based on predicted data value density via a real-time hardware controller not found in conventional computer architectures.

12. The medium of claim 11 , wherein the adaptive feedback learning system enables continual improvement without manual retraining.

13. The medium of claim 11 , wherein the instructions further cause the processor to support deployment in edge computing, cloud processing, and hybrid configurations.

14. The medium of claim 11 , wherein the instructions further cause the processor to modify processing behavior dynamically rather than relying on static inference models.

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