Prior Art Search Acceleration

Large language model (LLM)-based information retrieval for large datasets using indices to improve query response times and scalability. The method involves creating an index of the input text files containing vector embeddings of the text in voronoi cells. When a query comes in, the query embedding is compared to the vectors in the voronoi cell with the closest match to generate a response without needing to search the full text. This allows using a subset of embeddings for querying instead of the entire text. The indices can also be merged and partitioned to further reduce the search space.

Source

Searching across multiple ontologic knowledge graphs using an overlay graph to improve efficiency and accuracy. The method involves generating overlay graphs that map entities and relations from multiple source graphs. When a search request comes in, an appropriate overlay graph is selected based on the entity and relation. The search is then executed on the overlay graph, which translates the request into queries for the source graphs. Results are received from the sources and used to respond to the search.

Source

Dynamic indexing and searching technique to improve search efficiency and reduce search times. The search space is split into a primary subset and a secondary subset. When a query is received, only the primary subset is initially searched. If the results are insufficient, a supplemental search is performed on the secondary subset using the same query. This asynchronous secondary search occurs after the primary search and presents additional results. Items from the secondary search can also be moved to the primary subset for future queries. This reduces search times by prioritizing the most relevant subset for initial searches.

Source

Method for searching for relevant patent documents using machine learning and AI techniques to improve the effectiveness and efficiency of patent searching. The method involves parsing claim elements, selecting keywords, identifying synonyms, forming search strings, ranking documents, determining citations, and mapping elements. This allows targeted searching of databases using user-defined elements instead of keywords, improving recall and reducing false positives compared to keyword-based searching.

Source

Patent document retrieval method that uses metadata and semantic conversion to improve efficiency and accuracy compared to keyword search. The method involves selecting a retrieval method: 1) similarity search using metadata fields like invention title, patent type, etc., 2) keyword search in metadata, or 3) semantic search using a summary field. For method 1, similarity to metadata fields filters doc IDs. For method 2, keywords filter doc IDs from metadata. For method 3, semantic similarity to summary field filters top 5 doc IDs.

Source

Patent management system that provides tools to help in decision making at each stage of the patenting process. The system allows quick claim scope determination, prior art analysis, portfolio management, and other tasks. It uses mapping, mining, and analytics techniques to organize, search, and visualize patent concepts. The system also provides features like expanded search results, claim similarity indexing, and highlighting key terms across claims. It aims to enable efficient patent research, comparison, and analysis.

Source

Assisting patent retrieval using user selection instructions to iteratively refine and expand search criteria. The method involves selecting a patent document, extracting classification data and keywords, generating new search conditions based on the selected patent's classification and keywords, then searching for more documents matching those conditions. This iterative refinement allows refining and expanding the search beyond the initial search criteria.

Source

A hybrid AI system for patent claims analysis that combines machine learning and user input to accurately search and analyze patent documents. The system allows users to initiate searches based on reference documents, narrow the search focus, and select sections. It uses ML to transform the document and claims into embeddings representing their meaning. Element-by-element searches find matches based on these embeddings. Confidence scores rank the results. The system provides feedback to improve future searches.

Source

Semi-automatic patent claims analysis that improves the speed and accuracy of a computer or server when performing a patent search, particularly for infringement analysis. The analysis involves using preconditioning rules to determine relevant portions of a reference document, a query is submitted to find a set of patents that are similar to the reference document, a set of matching patents from the query are received and claim construction rules are used to determine how a meaning of claim elements of the patents will be ascertained in further analysis, Patent infringement rules and an artificial intelligence (AI) system are used to search the relevant portions of the reference document with the claim constructed claim elements of the patents returned from the search, the set of patents are ranked based on assigned confidence scores indicating a degree to which respective claim elements match the relevant portions of the reference document, and the results are presented to a client device.

Source

A method for assisting users in searching patents. The method involves enabling users to select and search within patent documents based on key criteria like classification codes and related tags. The system retrieves patent documents matching the initial search criteria. Users can then select one or more patents of interest and extract additional criteria like classification codes from them. The system generates a refined search using the extracted criteria to find more relevant patents. This iterative process allows users to navigate and refine patent searches using criteria specific to patents.

Source

Patent search system that helps users find relevant patents by assisting with search criteria selection. The system receives an initial search criteria, then when a user selects a patent, it extracts keywords from that patent. The system generates a refined search with those keywords. This iterative process of selecting patents and extracting keywords allows focusing the search.

Source

Generating search strings from seed documents or collection of seed documents. The search strings are generated based on the content of the seed documents, the number of claims analyzed, the number of associated documents, and the number of search strings generated from the search strings.

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

A platform for conducting patent searches and analyzing patent value using a combination of AI, machine learning, and blockchain technology. The platform leverages natural language processing to improve patent search relevance by analyzing similarities between claim limitations and prior art specifications. It also uses AI algorithms to rank prior art based on relevance to specific claim elements. The platform further provides tools for highlighting, weighting, and customizing search parameters.

Source

Searching, filtering, and analyzing large numbers of patent-related documents to find documents of interest to a user. It provides user interface tools for filtering and analyzing patent-related documents to find documents of interest. It also allows searching for specific patent drawings.

Source

Data-driven method to efficiently screen a large number of patent documents for relevancy to a target subject matter using a triage approach. The method involves mapping key concepts from claims of the identified patent pool to quickly screen and rule out irrelevant patents. The concepts marked as "definitely not in target subject matter" are used to eliminate patents without manually reviewing each one.

Source

A method and device for searching similar patents using a claim tree and word sets to improve accuracy and efficiency compared to manual searching. The method involves generating a claim tree diagram from the patent claims, with each node representing a claim. Multiple word sets are generated from the patent text. Search formulas are created for each word set and used to search the patent database. The search results are mapped back to the claim tree nodes. Similarity between each patent and the original patent is calculated using a weighted model. The patent with highest similarity per node is retained. This set of similar patents is returned. The claim tree and word set approach helps find key retrieval information and improves patent search accuracy.

Source

Patent search system that improves efficiency by using data analysis to identify key patents in a technology field. The system has a patent database, data processing unit, and screening management unit. The processing unit analyzes patent data to find important metrics like citation counts and inventor affiliations. The screening management unit uses these metrics to automatically identify key patents in a search result, instead of manually reviewing each patent. This reduces time and improves efficiency compared to traditional patent searching.

Source

Computer systems and methods for searching, filtering, and analyzing large numbers of patent-related documents to find documents of interest to a user. The system utilizes a comprehensive patent-based parts index derived from the US patent corpus to identify and interrelate part expressions (i.e., part names) in patents. This allows users to search for drawings containing specific parts, search for patents with similar drawings to a selected drawing, and determine non-literal support for claim terms in patent specifications using conceptually related variants.

Source

Optimizing patent literature searches by taking a user's search formula and expanding it to a higher-quality search by adding relevant search terms and operators. The system receives a user's search formula, classifies it based on operators, adds additional relevant search terms to each group, combines the groups with operators to create a final optimized search formula, and provides that to the user. This expands and improves the user's initial search.

Source