Efficient Document Generation in Intellectual Property Systems

A system for generating decentralized applications, such as smart contracts and legal documents, using decentralized identity and data aggregation to improve efficiency and reduce costs in transactions involving assets, services, and contracts. The system employs decentralized identifiers (DIDs) derived from asymmetric key cryptography to identify parties and assets, enabling peer-to-peer transactions and client-side data aggregation of legal, financial, and accounting information. Legal documents are generated using a hybrid approach that combines structured data models with natural language legal prose. The structured data is aggregated from client-side data associated with the decentralized identifiers, ensuring accurate and efficient document creation.

Source

A method for automatically extracting patent document templates from a patent corpus. This involves identifying sets of similar patent documents and extracting the common text shared between them. This approach enables the creation of standardized document templates by analyzing and finding the shared elements across a collection of similar patents.

Source

A method for creating a patent document summary using natural language processing and machine learning techniques. This method analyzes the text of a patent document to generate a concise summary that captures key information. It involves identifying repetitive word sequences in the document, replacing subsequent occurrences of those sequences with shorter substitutes, and generating the summary based on the modified text.

Source

A method for managing the authoring of electronic documents to enable effective monitoring, analysis, and verification. This method involves collecting designated sections of authored content and converting them, along with their correlations and relational attributes, into a structured dataset called RD (relation data). This allows tracking changes, analyzing relationships, and verifying consistency between content sections. The RD is stored in a relational database, while the original electronic document is stored separately.

Source

A system to help draft patent documents that reduce time and errors by providing drafting assistance. The system uses auto-complete lists based on managed elements to speed up drafting and reduce errors by enabling users to select from pre-defined options. It also provides translation services and claims drafting support.

Source

Generating accurate text for patent applications by combining natural language processing with structured templating. A system uses natural language understanding (NLU) to parse input text and extract structured components. It then leverages natural language generation (NLG) to generate precise output text using a templating language enhanced with functions that manipulate the text based on the data model. This allows the system to generate patent application sections by extracting claims structures from existing applications and inserting the parts into a claim model data object.

Source

A system to automatically create patent application templates based on prior art references to reduce the time and expertise needed to draft patent applications. The system parses prior art documents to identify sections related to the invention. It then generates a template patent application by removing claims and sections from the prior art that are not relevant to the invention. The template can be edited by the drafter to insert the novel features of the invention.

Source

Automating the drafting process of documents like patent applications to optimize the placement of supporting text like definitions, notes, and references. The method involves searching for terms in the document, determining where to insert associated definitions, and automatically inserting the definitions inline.

Source

Generating automated responses to patent office correspondence to reduce the time and effort required to respond to office actions during prosecution. The method involves parsing the office action to identify sections and generating response options for each section using techniques like clustering similar responses from a corpus of examples. The options are presented to a user who selects them or enters custom responses.

Source

Automated computer-based patent application drafting system that provides a user interface to input invention features, system components, figures, and acronyms. It uses this data to generate patent application documents like specifications, claims, and figures. The system leverages natural language processing, tree structures, templates, and editing functions to automate and streamline the patent drafting process.

Source

Using AI to automatically generate patent applications from seed sentences, which can reduce the time and effort required compared to manual drafting. The system involves tokenizing seed sentences and converting them into vectors. These vectors are then used as input to a sequence generation model that outputs new vectors. The output vectors are converted back into tokens to form the generated document. By providing seed sentences, the system learns the structure and content of a patent application and can generate new applications with similar content and language.

Source

An automated method for generating patent documents that reduces the time and cost involved in preparing patent applications. The method involves automated extraction of application data from user input and using that data to create patent specification components like diagrams and flowcharts, which are then combined with templates to generate complete patent documents.

Source

Parsing unstructured documents, such as patent documents, to combine figures, figure descriptions, and parts lists into a single output for easier reading and comprehension. The method involves detecting drawing pages, extracting figure descriptions, and combining them with parts lists onto one output page.

Source

System that automatically prepares patent applications by capturing image and text data, processing the data to add numbering and formatting, and generating a final application. The system uses image processing to analyze drawings, add figure numbers and lines, and detect components. It uses text processing to format descriptions and detect missing or mismatched component numbers. The processed image and text data are then typeset into a final patent application document.

Source

Analyzing research literature to provide insights and reports for strategic decision making. It involves indexing patent and research documents, determining topics, finding common phrases, quantifying topic overlap between patents and research, predicting patent classes, and generating customized reports for users based on their roles. The reports help identify emerging research, measure commercialization, and predict exploitable areas.

Source