Dynamic pairwise document evaluation system and method using adaptive comparative document scores

Abstract

A computer-implemented document evaluation system employs dual metrics combining an Intrinsic Document Quality Score (IDQS) and Comparative Document Score (CDS) for dynamic, context-aware evaluation across domains. The system addresses the cold start problem by immediately generating IDQS for new documents using Large Language Model evaluation of structured summaries that exclude author and source information to reduce bias. Documents are continuously re-ranked through pairwise IDQS comparisons, with CDS scores updated using zero-sum scoring. The microservices architecture with distributed processing enables scalable handling of large volumes. A matching algorithm selects document pairs for comparison based on similarity metrics using feature engineering. The approach improves computational efficiency by comparing IDQS values rather than full documents while maintaining content quality focus. The system provides immediate quality assessment and evolving comparative evaluation, offering comprehensive document ranking adaptable to different fields and document types.

Description

Title: DYNAMIC PAIRWISE DOCUMENT EVALUATION SYSTEM AND METHOD USING ADAPTIVE COMPARATIVE DOCUMENT SCORESDYNAMIC PAIRWISE DOCUMENT EVALUATION SYSTEM AND METHOD USING ADAPTIVE COMPARATIVE DOCUMENT SCORESCROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the benefit under 35 U.S.C. §119(e) of U.S. Provisional Patent Application No. 63 / 699,157, filed September 25, 2024, the entire contents of which are incorporated herein by reference.BACKGROUND

[0002] The field of content creation and dissemination is constantly evolving, with millions of new documents published annually across various domains. Efficiently identifying and prioritizing high-quality, relevant content is crucial for advancing knowledge and fostering innovation. However, traditional methods of document evaluation, such as view counts, likes, or domain-specific metrics such as citation counts for academic papers, have several limitations.

[0003] First, many evaluation metrics require a significant amount of time to accumulate, creating a cold start problem for newly published documents. For example, in academic research, it takes two to five years on average for a paper to accumulate enough citations for meaningful evaluation. Second, content from well-known authors or prestigious institutions may receive disproportionate attention due to reputation rather than intrinsic quality. In academic publishing, papers from renowned authorsor institutions may receive up to thirty percent more citations. Third, quantitative metrics do not necessarily reflect the quality or relevance of a document’s content in relation to other works in the field. Studies in academic publishing show that up to fifty percent of citations are perfunctory and do not indicate true influence. Fourth, many metrics can be artificially inflated through various means. For instance, in academia, some fields report self-citation rates as high as twenty percent.

[0004] These limitations create a need for a more dynamic, fair, and context-aware system for evaluating and ranking documents. Existing solutions have attempted to address these issues through various means. Alternative metrics measure online attention and engagement with content, including social media mentions and downloads. However, these metrics can be easily gamed and may not reflect true quality or impact. Expert review, while valuable, is time-consuming, subjective, and difficult to scale for large volumes of documents. Machine learning-based approaches aim to predict document impact based on various features but often struggle with the cold start problem and may perpetuate existing biases in the training data.

[0005] Current machine learning approaches face several technical challenges, including data sparsity, temporal dynamics, domain-specific variations, and bias mitigation.

[0006] To address these challenges, we introduce the Dynamic Pairwise Document Evaluation System (DPDES), a system and method for dynamically evaluating documents using comparative analysis. The DPDES establishes two distinct yet complementary metrics: the Intrinsic Document Quality Score (IDQS) and the Comparative Document Score (CDS).

[0007] The Intrinsic Document Quality Score (IDQS) provides an immediate,content-based evaluation of each document upon ingestion. This score is generated based on structured summaries of the document, excluding author and source information to reduce bias. The IDQS serves as an initial quality assessment and addresses the cold start problem by providing an instant metric for new documents.

[0008] The Comparative Document Score (CDS) is a dynamic score that evolves through pairwise comparisons with other documents in the system. It uses the IDQS as a starting point and provides a context-aware evaluation of a document’s quality and relevance relative to other documents. While the IDQS remains static for a given document, the CDS adapts over time as more comparisons are made and new documents are added to the system.

[0009] Together, these scores offer a comprehensive approach to document evaluation that combines immediacy with nuanced, comparative assessment. This dualmetric system provides several key advantages. The IDQS provides an immediate quality assessment upon document ingestion, reducing the meaningful time to evaluation from years to seconds upon publication. By basing the IDQS on content alone and excluding author and source information, the system significantly reduces biases related to author reputation, institutional affiliation, or language proficiency. The CDS, derived through pairwise comparisons, ensures that documents are evaluated against some set of arbitrary criteria in the context of other documents in the system, providing a more nuanced and focused assessment of quality and relevance. By comparing IDQS values for initial pairwise comparisons rather than full documents or summaries, the DPDES significantly improves computational efficiency. This allows for rapid processing and comparative evaluation of large volumes of documents. The system's design allows for continuous incorporation and evaluation of new documents, making it suitable for fields with high publication volumes. As new documents areadded, their IDQS is immediately calculated, and they are gradually integrated into the comparative framework through the CDS. The configurable criteria for IDQS generation allow the system to be tailored to different domains and document types, while the evolving nature of the CDS ensures that the evaluation remains relevant as the document ecosystem changes over time.

[0010] By providing these new metrics and evaluation methods, the DPDES enables users to make more informed decisions about which documents to prioritize, especially in fields with high publication volumes or when assessing newly published works. The combination of IDQS and CDS provides both an immediate quality indicator and an evolving, context-aware evaluation, offering users a more nuanced and comprehensive understanding of a document’s value.

[0011] Through this approach, the DPDES provides a more dynamic, fair, and real-time evaluation of document quality and relevance, while effectively addressing the challenges faced by traditional evaluation methods and existing machine learning approaches.SUMMARY OF THE INVENTION

[0012] The following, referred to as the Dynamic Pairwise Document Evaluation System (DPDES), is a methodical approach to ranking and evaluating documents across various domains. It employs a unique combination of techniques including an Intrinsic Document Quality Score (IDQS) mechanism for initial document evaluation, a Comparative Document Score (CDS) mechanism for ongoing document ranking, a document matching algorithm for selecting appropriate comparisons, a machine learning driven evaluation process for determining document quality, and adistributed microservices architecture for scalability and real-time processing.

[0013] Key features of the DPDES include immediate quality assessment where newly ingested documents are immediately evaluated to generate an Intrinsic Document Quality Score (IDQS), addressing the cold start problem common in traditional evaluation systems. The system provides dynamic ranking where documents arc continuously evaluated and re-ranked based on pairwise comparisons of their IDQS, with their Comparative Document Scores updated accordingly. Bias reduction is achieved as the system focuses on content-based comparison using structured summaries that exclude author and source information. This approach, combined with other data science and natural language processing techniques, significantly reduces potential biases related to creator reputation, institutional affiliation, or language proficiency. The microservices architecture, combined with the use of IDQS for comparisons, allows for efficient processing of large volumes of documents. This approach significantly improves inference speeds and reduces computational requirements, making the system highly scalable and cost-effective for large-scale document evaluation. The system can be applied to various fields and document types, incorporating domain-specific evaluation criteria through customizable machine learning models. By comparing IDQS values rather than full documents or summaries, the system significantly reduces computational overhead while maintaining a focus on intrinsic document quality.

[0014] The DPDES consists of several interconnected components. The Document Ingestion Service handles the intake of new documents and updates to existing ones, publishing a message to the queue for processing and generating the Intrinsic Document Quality Score. The Matching Service selects appropriate documents for comparison based on similarity metrics and other relevant factors. The Comparison and Evaluation Service generates IDQS values by evaluating input documents andimplements the Comparative Document Score mechanism by comparing IDQS values. The Initial Document Processor manages the initial batch processing of documents when the system is first set up or when a large number of documents need to be processed at once, utilizing parallel processing techniques for efficiency.

[0015] DPDES represents a significant advancement in the field of document evaluation, offering a. more dynamic, fair, and efficient method for ranking and discovering valuable contributions across various domains. The system’s ability to provide immediate quality assessment through IDQS, handle large volumes of documents, and offer real-time ranking updates addresses the scalability and bias challenges faced by traditional evaluation methods.DETAILED DESCRIPTIONSystem Architecture

[0016] The DPDES system employs a microservices architecture to ensure scalability, flexibility, and maintainability. The system comprises several key components.

[0017] The Document Ingestion Service handles the intake and initial processing of documents. The Matching Service selects appropriate documents for comparison using advanced algorithms. The Evaluation and Comparison Service conducts document evaluations with comparisons and updates Comparative Document Score and Intrinsic Document Quality Score. The Initial Document Processor manages batch processing for system setup and large-scale updates. The Distributed Message Queue facilitates asynchronous communication between microservices. The Database stores document metadata, scores, and comparison results.

[0018] These components interact through a combination of RESTful APIs and message queues, allowing for asynchronous processing and real-time updates. The microservices architecture provides several technical advantages. Each service can be scaled independently based on demand. Issues in one service do not directly affect others. Different microservices can use optimal technologies for their specific tasks. Microservices can be updated or replaced individually without system-wide downtime.

[0019] Communication between microservices is primarily handled through RESTful APIs for synchronous operations and distributed message queues for asynchronous tasks. This approach ensures loose coupling between components and enables efficient, real-time data processing.

[0020] The use of distributed messaging queue for message queuing provides distributed high-speed, in-memory data storage and retrieval, crucial for maintaining system responsiveness under high load. A database with document-oriented structure allows for flexible schema design and efficient querying of document metadata and score data.

[0021] The DPDES system addresses the cold start problem by immediately incorporating new documents into the comparison process, leveraging the Initial Document Processor for efficient batch handling. The architecture supports real-time ranking updates through the continuous operation of the Document Comparison Service and the use of Redis for rapid data propagation.

[0022] Deployment of the DPDES system utilizes containerization orchestration technologies. This approach ensures consistent environments across development and production, facilitates easy scaling and updates, and provides robust fault toleranceand service discovery mechanisms.Document Ingestion Process

[0023] The document ingestion process is handled by the Document Ingestion Service, which is responsible for receiving new document submissions through a RESTful API, validating and sanitizing input data, assigning initial default scores to new documents, storing document metadata in the MongoDB database, and queuing new documents for processing by the Matching Service.

[0024] The API endpoints for document submission are implemented using FastAPI, a modern, fast (high-performance) web framework for building APIs with Python. The primary endpoint, ' / ingest_document_batch’, accepts POST requests with a JSON payload containing an array of existing document ids.

[0025] Input validation and sanitization are crucial for maintaining data integrity and system security. The system employs Pydantic models to define the expected structure of input data and perform automatic validation. This includes type checking, format validation such as ensuring valid DOIs, and sanitization of text fields to prevent potential security vulnerabilities such as cross-site scripting (XSS) attacks.

[0026] Upon successful validation, new documents are assigned an initial Comparative Document Score (CDS) of 1500. This starting value ensures that new documents have a fair chance of being matched and compared, addressing the cold start problem inherent in many ranking systems. The initial score may be adjusted based on factors such as the document venue’s prestige or the authors’ historical performance, if such data is available.

[0027] Document metadata is stored in a data store using a carefully designed datamodel. The key fields include id (a unique identifier), title, comparative document score, idqs_score, and comparison_count. Indexes are created on frequently queried fields such as comparative_document_score and id to optimize query performance.

[0028] After storing the document metadata, the system uses distributed message service to queue the new document for processing by the Matching Service. This is done by pushing the document’s_ id to a message queue list named ’new_documents_queue’. This asynchronous approach allows the Document Ingestion Service to quickly respond to API requests while offloading the computationally intensive matching process to a separate service.

[0029] For handling updates to existing documents, the system first checks if a document with the given id already has a comparative_document_score. If found, it updates the existing record with any new information provided, ensuring that the comparative document— score and comparison count fields are preserved. The updated document is then re-queued for matching to ensure that its ranking remains current.

[0030] To handle large volumes of new documents efficiently, the system includes a batch processing capability. When a batch of documents is submitted, the system processes them in parallel, utilizing Python’s asyncio library in conjunction with FastAPI to handle multiple documents simultaneously without blocking. This approach significantly improves throughput when ingesting large datasets, such as during initial system setup or when incorporating a backlog of documents from a new source.Document Preparation Process

[0031] The system leverages structured summary data generated by a Transformerbased Large Language Model (LLM) when performing inference on document content. The contents of a structured summary may be generated by directing the LLM to output a summary given a source document.

[0032] An example of a structured summary schema for a specific embodiment concerning scientific source documents in the field of machine learning may include the following fields. Keywords as a list of keywords associated with the document. Methodology as an object containing name (name of the primary methodology used), type (type of methodology such as Fine-tuning or Ensemble), description (brief description of the methodology), base model (base model used if applicable), and domains (list of domains the methodology is applied to). Models as a list of models used or relevant to the document, which may include ARIMA and variants, Vector Autoregression (VAR), Vector Error Correction Model (VECM), GARCH and variants (including MGARCH), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Convolutional Neural Network (CNN), Transformer-based models, Mamba (State Space Model), S4 (Structured State Space Sequence Model), Random Forest, Quantile Forest, Gradient Boosting models (XGBoost, LightGBM, CatBoost), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Naive Bayes, Logistic Regression, Linear Regression and variants, Quantile Regression, Decision Trees, Hidden Markov Models, Kalman Filter, Bayesian Networks, Artificial Neural Networks (ANN), Recurrent Neural Networks (RNN), Deep Belief Networks, Reinforcement Learning models, Genetic Algorithms, Particle Swarm Optimization, Fuzzy Logic Systems, Ensemble Methods, Dimensionality Reduction Techniques (PCA, ICA, t- SNE, UMAP), State Space Models, Exponential Smoothing, Prophet, DeepAR, Tem-poral Fusion Transformers, N-BEATS, WaveNet, Copula Models, Regime-Switching Models, Jump Diffusion Models, Stochastic Volatility Models, Factor Models, Auto- ARIMA, TBATS, Gaussian Process Regression, Autoencoder-based models, Graph Neural Networks (GNN), Attention Free Networks (AFN), Mixture of Experts (MoE), Diffusion Models, Variational Autoencoders (VAE), Generative Adversarial Networks (GAN), Self-Supervised Learning models, Few-Shot Learning models, Zero-Shot Learning models, Multi-Task Learning models, Transfer Learning approaches, Federated Learning models, Quantum Machine Learning models, Neuro-Symbolic Al, Capsule Networks, Memory Networks, Neural Turing Machines, Differentiable Neural Computers, Metric Learning models, Online Learning algorithms, Anomaly Detection models, Time Series Classification models, Hierarchical Time Series models, Quantilebased models (including Quantile Regression and Quantile Forest), and Large Language Model (LLM).

[0033] The structured summary may also include evaluation_metrics as a list of evaluation metrics used or relevant to the document, which may include Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), R-squared (R2), Area Under the ROC Curve (AUG), Accuracy, Precision, Recall, Fl Score, Hit Rate, Confusion Matrix, Brier Score, Log Loss, Spearman’s Rank Correlation Coefficient, Kendall's Tau, Mean Absolute Scaled Error (MASE), Thcil’s U statistic, Information Ratio, Sharpe Ratio, Sortino Ratio, Maximum Drawdown, Value at Risk (VaR), Conditional Value at Risk (CVaR), Jensen’s Alpha, Treynor Ratio, Calmar Ratio, Omega Ratio, Tail Ratio, Upside Potential Ratio, Downside Deviation, Tracking Error, Information Coefficient (IC), Probabilistic Sharpe Ratio (PSR), Maximum Adverse Excursion (MAE), and Quantile Loss.

[0034] Additional structured fields include objective— functions as a list of objectivefunctions used in the model training or optimization process which may include Empirical Risk Minimization (ERM), Cross-entropy loss, Mean Squared Error (MSE), Maximum Likelihood Estimation (MLE), Kullback-Leibler Divergence, Hinge Loss, Huber Loss, Negative Log-Likelihood, Information Gain, and Gini Impurity. Problem types as types of problems addressed in the document, such as Regression, Classification, Time Series Forecasting, Clustering, Dimensionality Reduction, Anomaly Detection, Reinforcement Learning, Natural Language Processing, Computer Vision, Optimization, Ranking, Density Estimation, Generative Modeling, Causal Inference, Multi-task Learning, Transfer Learning, Few-shot Learning, Zero-shot Learning, Imbalanced Learning, Online Learning, Active Learning, Semi-supervised Learning, Unsupervised Learning, Structured Prediction, Sequence-to-Sequence Learning, Graph Learning, Recommender Systems, Survival Analysis, Portfolio Optimization, Risk Management, Pairs Trading, Market Making, and Algorithmic Execution.

[0035] The structured summary also contains reproducibility as an object with score (reproducibility score on a scale of one to five) and notes (notes on reproducibility factors). Data_availability as an object with status (available, partially available, or not available) and notes (notes on data availability). Potential applications as a list of potential applications for the document’s findings, which may include domainspecific chatbots, specialized information extraction systems, task-specific decision support systems, financial sentiment analysis, market trend prediction, algorithmic trading strategy development, risk management and assessment, and portfolio optimization. Limitations as a list of limitations or shortcomings of the document. Future work as suggestions for future research or improvements. Relevance to x as an object with score (relevance score on a scale of one to ten) and notes (notes on relevance to arbitrary benchmark). Implementation_complexity as an object withscore (complexity score on a scale of one to ten) and notes (notes on implementation complexity).

[0036] This methodology helps to minimize latent sources of bias in the source document by selectively controlling for various issues, such as non-native authorship and biases associated with author or publication reputation. While bias-mitigation and efficiency gains are observed with using structured summary data, for processing, the system is agnostic to the original format of the documents (full PDFs or structured summaries). This process occurs before the ingestion process and comparison processes. The use of structured summaries significantly improves inference speeds, efficiency, and cost-effectiveness when evaluating large volumes of source documents. This improvement in efficiency allows the system to process and compare a much larger number of documents in a given time frame compared to systems that work with full text documents. The reduced computational requirements lead to lower operational costs, making the system more scalable and economically viable for large-scale document evaluation.Matching Algorithm

[0037] The Matching Service implements an algorithm for selecting appropriate documents for comparison. Key features of this algorithm include similarity-based matching using feature engineering techniques to analyze and compare document content and use of a DocumentSimilarityService class to calculate similarities between documents.

[0038] The algorithm employs a combination of multiple features for similarity calculation. Text features (metadata) using TF-IDF vectorization, categorical features (category, subcategory, publication week-year) using one-hot encoding, list fields(models, keywords, evaluation metrics, problem types, applications, methodology domains) using multi-label binarization, and numerical scores (relevance, complexity, standardized metrics).

[0039] The system applies feature weighting, dimensionality reduction using Trun- catcdSVD, and utilization of a k-Nearest Neighbors model with Manhattan distance for finding similar documents. Document processing occurs through retrieval from a. distributed queue (’new_documents_queue’) and generation of matches with similar documents that have not been compared before. The number of matches per document is limited (default is fifty, configurable via environment variable) to ensure fair distribution of comparisons. Documents that have already been compared with the current document are excluded to prevent redundant comparisons.

[0040] The algorithm generates pairs of document IDs for comparison, considering similarity scores and previous comparison history. It uses a data store to store document metadata and comparison history, facilitating efficient querying and updating of document information. The system employs distributed messaging service for queueing documents to be processed and storing generated matches, enabling asynchronous processing and efficient data propagation.

[0041] The design supports horizontal scalability, allowing multiple instances to process the document queue concurrently, with load balancing achieved through a distributed message queue list data structure. Implementation includes retry logic for database operations to handle temporary failures, enhancing system robustness. Environment variables are used for configuration, facilitating deployment across different environments and easy adjustment of system parameters. Comprehensive logging tracks the processing of documents and any errors encountered, aiding in system monitoring and debugging.

[0042] This matching algorithm ensures efficient, fair, and scalable pairing of documents for comparison, considering various aspects of each document to find meaningful matches while maintaining system performance and reliability.Document Evaluation and Comparison Process

[0043] The Document Evaluation and Comparison Service is responsible for assessing document quality and conducting pairwise comparisons. This process involves two main stages: Evaluation and Comparison.Evaluation Process (IDQS Generation)

[0044] The evaluation process begins with document retrieval where the system retrieves documents from the queue for evaluation. An Intrinsic Document Quality Score (IDQS) is generated for each document through a generative Transformer-based Large Language Model (LLM) evaluation using predefined criteria.

[0045] The LLM assesses multiple aspects of the document, including novelty and originality of the content, methodological rigor and soundness (where applicable), clarity and coherence of presentation, and potential impact and significance in the field. For each criterion a Criterion Score is generated with an associated confidence level. Predefined weights associated with each criterion are factored against output criterion scores and confidence ratios output by the LLM.

[0046] The final evaluation score, IDQS, can be calculated using the following equation:

[0047] Where n is the number of criteria, W_i is the predefined weight for criterion i, S_ i is the output score for criterion i provided by the LLM, and C_i is the confidence ratio for criterion i output by the LLM. This equation sums the products of the predefined weights, output scores, and confidence ratios for all criteria to compute the final evaluation score.

[0048] Bias mitigation is achieved through anonymization where author names and institutional affiliations are removed before evaluation, and isolated context where documents are evaluated individually, without the presence of competing documents. The generated IDQS is stored in the database along with the document metadata.Comparison Process

[0049] The comparison process begins with pair retrieval where the system retrieves matched document pairs from the queue. The pre-calculated IDQS values for both documents are compared to determine the superior document. The calculated score changes are applied to both documents involved in the comparison.

[0050] The Comparative Document Score (CDS) Update mechanism can be described as follows. For expected score calculation, for documents A and B with current scores CDS_A and CDS B respectively, the expected score for document A is calculated as:

[0051] The expected score for document B is the complement: EB= 1 — EA(3)

[0052] The actual outcome S_A for document A is defined as 1 if A wins, 0.5 if draw, and 0 if B wins. Similarly, for document B, S_B equals 1 minus S_A.

[0053] The change in score for document A is calculated as:ΔCDSA= K(SA- EA) (4)

[0054] Where K is a factor determining the maximum change in score (in this case,K equals 32). Similarly, for document B:ΔCDSB= K(SB- EB) (5)

[0055] The new Comparative Document Scores are then calculated as:

[0056] This mechanism ensures that the sum of the score changes is always zero:ΔCDSA+ ΔCDSB= 0 (8)

[0057] This zero-sum property maintains the relative nature of the ComparativeDocument Scores within the system.

[0058] The new CDS values are stored in the database, along with an incremented comparison count for each document. A comprehensive record of each comparisonis stored, including unique comparison ID, IDs of the compared documents, precomparison CDS and IDQS values, comparison outcome, calculated score changes, post-comparison CDS values, and updated confidence intervals.

[0059] The score update process is implemented as an atomic operation to ensure data consistency. A database transaction is initiated encompassing both documents involved in the comparison. New scores are calculated for both documents using the formulas and modifications described above. The ’comparative_document_score’ field is updated for both document records in the database. The ’comparison count’ field is incremented for both documents. The transaction is committed, ensuring atomicity of the update operation.

[0060] To facilitate system auditing, performance analysis, and potential score adjustments, all score changes are logged in a separate ’score changes’ collection. Each log entry includes detailed information about the comparison and resulting score changes, enabling comprehensive analysis of the system’s performance over time as well as scoring replays to ensure consistency and robustness.

[0061] This comprehensive CDS update mechanism ensures that document rankings are continuously refined based on pairwise comparisons, providing a dynamic and adaptive evaluation of document quality and relevance. This advanced Comparative Document Score mechanism forms the core of the DPDES’s ability to provide dynamic, fair, and meaningful rankings of documents, addressing the limitations of traditional static metrics and enabling real-time evaluation of document relevance and quality.Quality Assurance

[0062] Each document undergoes multiple comparisons with different opponents to establish a robust ranking. The service is designed to be stateless and horizontally scalable, allowing for concurrent processing of evaluations and comparisons.Scalability and Performance Considerations

[0063] The Dynamic Pairwise Document Evaluation System (DPDES) is designed with scalability and high performance as core principles, enabling it to handle large volumes of documents and provide real-time scoring updates. This section details the architectural and technical considerations implemented to achieve these goals.

[0064] The system employs a microservices architecture, which provides several benefits for scalability and maintainability. Each service (Document Ingestion, Matching, Comparison, and others) can be scaled independently based on its specific load and resource requirements. Different microservices can use technologies best suited for their specific tasks, allowing for optimized performance. Issues in one service do not directly affect the others, improving overall system resilience. Microservices can be updated or replaced individually without affecting the entire system.

[0065] The DPDES utilizes containerization and orchestration technologies to ensure efficient deployment and management. Each microservice is packaged as a Docker container, ensuring consistency across development, testing, and production environments. The system uses Kubernetes for container orchestration, providing automated deployment and scaling of containers, load balancing across service instances, self-healing capabilities (automatic restarts, replacements, and rescheduling), and rolling updates and rollbacks for zero-downtime deployments.

[0066] To handle the large volume of document data and frequent updates, the system implements several database scaling strategies. Database sharding distributes the data and query load across multiple servers. Read-heavy operations are directed to read replicas, reducing load on the primary database. Carefully designed indexes optimize query performance for common operations. Data is partitioned based on access patterns, improving query efficiency.

[0067] Efficient load balancing and request routing are crucial for maintaining high performance. An Ingress Controller manages external access to the microservices, providing load balancing and SSL termination. LoadBalancer implements intelligent loadbalancing across deployed microservices.

[0068] To handle time-consuming tasks without blocking user interactions, the system uses a Publish / Subscribe mechanism for real-time event broadcasting across ini- croservices. Long-running tasks such as document ingestion and comparisons arc processed asynchronously using message queues. Microservices communicate via events, allowing for loose coupling and improved scalability.

[0069] Various techniques are employed to optimize system performance. Operations like bulk document ingestion or score updates are processed in batches for efficiency. Data is loaded on-demand to reduce initial load times and resource usage. Data is compressed during transmission and storage to reduce bandwidth and storage requirements. Database queries are optimized using techniques like projection, limit, and aggregation pipelines.

[0070] Comprehensive monitoring and auto-scaling ensure the system maintains high performance under varying loads. Monitoring tools are used for real-time monitoring of system metrics and alerting. The Horizontal Pod Autoscaler automaticallyadjusts the number of pods based on CPU utilization or custom metrics. Database connection pooling efficiently manages database connections to prevent overload.

[0071] These scalability and performance considerations enable the DPDES to efficiently process and score large volumes of documents, providing a robust and responsive platform for real-time document evaluation across various domains and applications.BRIEF DESCRIPTION OF THE DRAWINGS

[0072] The DPDES will be better understood from the following detailed description, taken in conjunction with the accompanying drawings, wherein like reference numerals refer to like parts, and in which:

[0073] Figure 1 is a high-level system architecture diagram of the Document Processing and Dynamic Evaluation System (DPDES), illustrating the main components and their interactions. This diagram depicts the microservices architecture, including the Document Ingestion Service, Matching Service, Pairwise Document Evaluation and Comparison Service, and Initial Document Processor. It also shows the Message Queue for inter-service communication and a Database for persistent storage. The diagram illustrates the flow of documents and data between these components, highlighting the system’s modular and scalable design.

[0074] Figure 2 is a flowchart depicting the document processing workflow within the DPDES, from ingestion to ranking update. This diagram shows the step-by- step process of how a document is received, validated, summarized, and scored. It illustrates the initial quality assessment, the assignment of comparative scores, andthe iterative matching and comparison process. The flowchart demonstrates the system’s ability to continually refine document rankings through multiple comparisons. The decision step "More Matches?” is determined by the output of the Matching microservice which determines all of the documents selected for comparison.

[0075] Figure 3 is a diagram illustrating the Comparative Document Score (CDS) update process during a document comparison. This graph demonstrates the algorithm and logic used to calculate and apply score changes based on the outcome of a document comparison. It shows how the system determines a winner based on Intrinsic Document Quality Scores (IDQS), handles draws, and updates the CDS accordingly, ensuring a fair and dynamic ranking system.

[0076] Figure 4 is a detailed diagram of the document matching algorithm (Select Opponent step in Figure 2), showcasing the process of selecting appropriate documents for comparison. This graph illustrates the steps involved in retrieving unmatched documents, extracting features, calculating similarity scores, and applying selection criteria. It also demonstrates the system’s ability to adapt its search criteria if no suitable matches are found initially, ensuring comprehensive comparisons across the document set. When suitable matches are found based on the similarity measures, they are selected for comparison as illustrated in the diagram.

[0077] Figure 5 is a scalability diagram illustrating the system’s ability to handle large volumes of documents through distributed processing and load balancing techniques. This graph showcases various scalability features including container orchestration, load balancing, distributed caching, and database clustering. It demonstrates how the DPDES system maintains performance and reliability as the number of documents and comparisons increases, through both horizontal and vertical scaling strategies.

[0078] Figure 6 is a sequence diagram detailing the document evaluation and comparison process within the DPDES. This diagram illustrates the interactions between the Document Ingestion Service, Evaluation and Comparison Service, Data Storage Service, and Messaging Service. It shows the flow of a new document through the system, including queuing, data retrieval, IDQS generation through LLM evaluation, CDS calculation through pairwise comparison, and result storage. This diagram highlights the system’s efficient use of microservices and asynchronous processing.

[0079] Figure 7 is a system deployment diagram showcasing the DPDES’s implementation within a Kubernetes cluster. This graph illustrates how external clients interact with the system through an Ingress point, and how various microservices (Document Ingestion, Comparison and Evaluation, Matching, LLM) are deployed within the cluster. It also shows the use of MongoDB for persistent storage and Redis for caching and inter-service communication, demonstrating the system’s robust and scalable cloud-native architecture.BRIEF DESCRIPTION OF THE DRAWINGS

[0081] The drawings are schematic and not to scale. They illustrate example architectures, data flows, and deployments for explanatory purposes only. References to particular technologies or products (e.g., Redis, MongoDB, Kubernetes) are illustrative and non-limiting; any functionally equivalent cache, message queue, database, model host, or orchestration platform may be substituted. Functions may be combined, split, reordered, or omitted in alternative implementations without departing from the scope of the claims. For a more complete understanding of the invention, reference is made to the following description and accompanying drawings, in which:

[0082] FIG. 1 is an illustrative high-level system architecture diagram of the Document Processing and Dynamic Evaluation System (DPDES), showing example components and interactions;

[0083] FIG. 2 is an illustrative flowchart depicting an example document processing workflow within the DPDES, from ingestion to ranking update;

[0084] FIG. 3 is an illustrative diagram of a Comparative Document Score (CDS) update process during a document comparison;

[0085] FIG. 4 is an illustrative diagram of an example document matching algorithm for selecting comparison counterparts;

[0086] FIG. 5 is an illustrative scalability diagram showing example distributed processing and load balancing techniques;

[0087] FIG. 6 is an illustrative sequence diagram of an example document evaluation and comparison process within the DPDES; and

[0088] FIG. 7 is an illustrative system deployment diagram showing one example DPDES implementation within a container orchestration platform.

Claims

CLAIMSWhat is claimed:

1. A computer-implemented document evaluation and ranking method comprising: ingesting a plurality of electronic documents into a computer system; using a large language model (LLM) to perform an evaluation of each of the documents and to assign a separate initial document quality score (IDQS) to each of the documents based on the evaluation; assigning an initial comparative document score (CDS) to each of the documents; performing pairwise comparisons of the documents, including selecting, based on a similarity criterion, pairs of the documents to compare with each other, and, for each selected pair of the documents, comparing the IDQS values of the documents in the pair to determine a comparison outcome comprising a first-document win, a second- document win, or a draw, and determining an adjustment to the CDS value of each document in the pair based on the comparison outcome; and ranking the plurality of documents based on their respective CDS values.

2. The method of claim 1, further comprising: repeating the comparing and ranking steps with different pairs of the documents to dynamically adjust an overall ranking of the documents.

3. The method of claim 1, further comprising: generating a structured summary of each of the documents, wherein the IDQS of each document is generated based on the structured summary of the document.

4. The method of claim 1, wherein the LLM assigns an IDQS to each document based on a plurality of criteria, including: novelty and originality of the content, methodological rigor and soundness of the content, clarity and coherence of presentation of the content, and potential impact and significance in the field.

5. The method of claim 1, wherein determining the comparison outcome further comprises, responsive to a difference between the IDQS values for the pair being less than a threshold invoking a pairwise evaluation model that consumesrepresentations of the two documents and outputs a pairwise preference score, and using the pairwise preference score to select the comparison outcome.

6. The method of claim 5, wherein the pairwise evaluation model comprises a large language model configured to compare structured summaries of the documents that exclude author identity and source metadata, and to produce at least one of: (i) a probability P(A > B), (ii) a confidence value, or (iii) a draw probability.

7. The method of claim 5, wherein the thresholdis adaptive based on at least one of: a comparison count for each document, an estimated uncertainty or confidence interval for each document’s CDS value, or a recency of prior comparisons.

8. The method of claim 5, wherein determining the adjustment to the CDS values comprises scaling a base adjustment by a function of the pairwise preference score or an associated confidence, to increase adjustments for high-confidence outcomes and reduce adjustments for low-confidence outcomes.

9. The method of claim 1, wherein ties between the IDQS values are resolved by applying the pairwise evaluation model to the documents or to their structured summaries and selecting the outcome based on the model’s pairwise preference score.

10. A computer system for dynamic document evaluation and ranking, comprising: one or more processors; and one or more non-transitory memory devicesstoring instructions that, when executed by the one or more processors, cause the system to implement: (i) a document ingestion service configured to receive electronic documents and persist metadata for each document; (ii) an evaluation service configured to generate, for each document, an intrinsic document quality score (IDQS) by applying a large language model (LLM) to a content representation that excludes author identity and source metadata; (iii) a comparison service configured to maintain, for each document, a comparative document score (CDS) and to update the CDS by processing pairwise comparisons whose outcomes are determined from the respective IDQS values; (iv) a matching service configured to select pairs of documents for comparison based at least on similarity between documents and on prior comparison history; and (v) a data store and transactional update mechanism configured to atomically record, for each comparison, pre-comparison CDS values, a comparison outcome, corresponding CDS adjustments, and post-comparison CDS values; wherein the evaluation service produces an IDQS that remains static for a given version of a document, and the comparison service updates the CDS over time as additional comparisons are processed to yield a dynamic ranking.

11. A non-transitory computer-readable medium storing instructions that, when executed by one or more processors, cause the one or more processors to perform operations comprising: receiving a plurality of electronic documents; for each document, generating an intrinsic document quality score (IDQS) by evaluating the document against a plurality of criteria with a large language model (LLM) and combining criterion scores using predetermined weights and confidence factors; initializing a comparative document score (CDS) for each document; selecting pairs of documents for comparison using a similarity-based policy; for each selected pair, determining an outcome using the respectiveIDQS values and adjusting the documents’ CDS values using a zero-sum logistic update that applies an expected-score function and a scaling parameter; persisting the adjusted CDS values with an incremented comparison count; and outputting a ranking of the plurality of documents ordered by respective CDS values, wherein the IDQS for each document is unchanged by the comparisons and the CDS is revised over time as additional pairwise outcomes are applied.

12. A computer-implemented method for matching documents for comparative evaluation, comprising: maintaining, for each document in a corpus, a comparison history identifying documents previously compared with that document; constructing, for each document, a feature vector that includes at least one of: term-frequency features from metadata, one-hot or multi-label encodings for categorical and list fields, and normalized numerical fields; applying dimensionality reduction to the feature vectors to obtain reduced-dimension embeddings; for a target document, retrieving candidate counterparts using a nearest-neighbors search over the embeddings with a distance metric; filtering the candidates to exclude documents present in the comparison history of the target document; limiting a number of selected counterparts for the target document to a configurable maximum to provide fair distribution of comparisons across the corpus; when an insufficient number of counterparts remain after filtering, broadening selection using relaxed similarity thresholds; and enqueuing non-duplicate document pairs for comparative scoring; wherein the matching ensures that each enqueued pair has not been previously compared and balances similarity-based relevance with coverage of documents across the corpus.

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