AI-powered intelligent PDF editor with query-based editing of multiple documents

Abstract

An AI-powered PDF editing system consisting of a natural language command engine for interpreting user input, a PDF parsing module for creating a structural representation of a document, a domain entity recognition module, and an execution framework for applying editing operations to one or more PDF documents according to a task description derived from the inputs.

Description

Application area of ​​the invention

[0001] The invention relates to computer-implemented document processing systems that integrate natural language interfaces with PDF parsing and machine learning to automate the editing, annotation, extraction and summarization of one or more PDF documents. Background of the invention

[0002] PDFs contain complex layouts with mixed text, images, tables, and forms, making reliable editing and structured extraction difficult without specialized tools. Current workflows analyze PDFs using layout analysis, OCR, and table recognition, and then structure them. NLP components then perform entity recognition and task execution. However, common tools operate in isolation, require scripting, or lack robust orchestration of multiple documents and the ability to infer user intent from natural language. Recent research in document editing explores multimodal inference to map user requests to precise editing commands.However, there is a need for an integrated system that combines prompt-based intent detection with deterministic PDF structure editing and consistent batch processing across numerous files - including preview and reversible results. Summary of the invention

[0003] The invention relates to a system comprising: a natural language processing engine for interpreting user input; a PDF parsing module that creates a structural model of documents, including reading order, layout blocks, tables, and form fields; an entity recognition module that recognizes and identifies domain entities; and a multi-document processing framework that applies consistent editing plans to selected PDFs. A user interface allows input via chat or form, previewing of changes and corrections, and export of the results to PDF, DOCX, or structured formats.

[0004] In various implementations, the system translates input prompts into an execution graph of atomic operations (e.g., redacting entities, replacing text in headers, normalizing table columns), validates target areas using multimodal data processing, and performs edits transactionally with rollback. Batch pipelines track the status of each document and ensure idempotent execution to achieve reproducible results at scale. Detailed description

[0005] A natural language processing engine analyzes user intent using classification and sequence-to-sequence models to generate a normalized task description. Ambiguities are resolved through interactive disambiguation and command reformulation, transforming open-ended prompts into concrete operations with parameters and target ranges. Named entity recognition and rule-based matching identify personal names, organizations, dates, amounts, and user-defined labels to control redaction or metadata extraction.

[0006] The PDF parsing module performs file decoding, layout analysis, OCR for scanned content, table structure recognition, and reading order reconstruction to generate a document object model with coordinates and Z-order. Each element can be precisely edited, for example, by replacing, inserting, deleting, adding annotations, replacing images, and normalizing tables. The module supports previewing changes at the page and element level.

[0007] A command scheduler compiles the task specification into an execution graph of atomic actions with dependencies. The scheduler enforces validation rules (e.g., confirming the existence of entity scopes, verifying the number of columns in tables) and generates a test run preview. After validation, a transaction executor applies the changes, managing undo logs and versioned output. The system exports modified documents and, optionally, audit trails that capture commands and checksums.

[0008] For processing multiple documents, a batch orchestrator distributes execution across the documents, normalizes layout variations using learned templates, and applies consistency conditions (e.g., identical headers and footers within a corpus). It supports throttling, retries, and the continuation of checkpoints, thus ensuring idempotence for large jobs.

[0009] The user interface offers a chat function for prompts and a structured area for parameters. Users can review inline diffs, redaction masks, and extraction tables, accept or refine results, and download output in PDF, DOCX, CSV, or JSON formats. An authorization system governs access to sensitive operations such as redacting personal data; activity logs ensure compliance with legal regulations.

[0010] Machine learning models improve through feedback: Accepted changes and corrections are used to fine-tune intent analysis and entity recognition, with privacy-compliant training and optional local deployment. The system supports plug-ins for domain ontologies and validation rules, as well as connectors for ECM systems.

[0011] Performance and scalability are achieved through streaming parsers and vectorized NLP pipelines; CPU-based extraction can process multiple pages per second, with GPU acceleration available for OCR and layout models. Caches store parsed structures for fast post-processing, and a scheduler optimizes parallel processing for large corpora.

[0012] Security and governance include encryption of data at rest and in transit, role-based access control, audit logs, and data retention policies. The deliverables may include provenance metadata and redaction certificates for legal workflows.

[0013] In the system, a user gives an instruction such as "Redact all social security numbers and update the company name in these 200 PDFs to 'Acme GmbH'". The system analyzes the intent, displays a preview of the redaction masks and replacements, and applies the changes transactionally to the entire dataset. The updated files and an audit report are then exported.

Claims

[1] An AI-powered PDF editing system consisting of a natural language command engine for interpreting user input, a PDF parsing module for creating a structural representation of a document, a domain entity recognition module, and an execution framework for applying editing operations to one or more PDF documents according to a task description derived from the inputs. [2] System according to claim 1, wherein the execution framework comprises a batch orchestrator configured to apply consistent operations to a large number of PDF documents, including validation, preview and rollback to ensure reproducible results. [3] System according to claim 1, wherein the natural language command engine performs command reformulation to resolve ambiguities and generate parameterized editing commands that are bound to specific document areas using multimodal anchoring. [4] System according to claim 1, wherein the PDF parsing module performs layout analysis, OCR, table structure recognition and reading order reconstruction to enable precise redaction, insertion, replacement, annotation and table normalization, and the user interface provides previews and exports in PDF, DOCX, CSV or JSON formats.