Various systems and methods are provided that identify prior art patent references for a subject patent application. For example, the system preprocesses a corpus of patent references to identify keywords that are present in each of the patent references, n-grams present in the corpus, and a weighting associated with the identified n-grams. To identify prior art patent references, the system requests a user to provide a patent application. The system extracts n-grams found in the provided patent application and orders the n-grams based on the assigned n-gram weights. The system compares the top Y-rated n-grams with the identified keywords and retrieves patent references that include a keyword that matches one of the top Y-rated n-grams. The system re-ranks the retrieved patent references using, for example, artificial intelligence. The top Z-ranked retrieved patent references are transmitted to a user device for display in a user interface.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer-implemented method for identifying relevant documents using artificial intelligence comprising: retrieving, from a document database, a plurality of prior art documents; for individual patent documents in the plurality of prior art documents, processing the respective prior art document to identify one or more keywords associated with the respective prior art document; processing the prior art documents in the plurality of prior art documents to identify a plurality of n-grams and a number of occurrences of each n-gram; analyzing the plurality of n-grams to identify a weight associated with each n-gram in the plurality of n-grams, wherein the weight associated with each n-gram in the plurality of n-grams is based on a number of occurrences of the respective n-gram; receiving, from a user, a first patent document; processing a claims section of the first prior art document to identify a second plurality of n-grams; ranking each n-gram in the second plurality of n-grams based on the identified weights associated with each n-gram in the plurality of n-grams; identifying a first set of n-grams in the second plurality of n-grams that are ranked above a threshold value; comparing the first set of n-grams with the one or more keywords; identifying a first set of prior art documents in the plurality of prior art documents associated with a keyword in the one or more keywords that matches an n-gram in the first set of n-grams based on the comparison; for each prior art document in the first set of prior art documents, determining a similarity between the first patent document and the respective prior art document in the first set of prior art documents; scoring the first set of prior art documents based on the determined similarities; ranking the first set of prior art documents based on the scoring; and transmitting search result data to a user device for display in a user interface, wherein the search result data comprises prior art documents in the first set of prior art documents that are ranked above a second threshold value. 2. The computer-implemented method of claim 1, wherein determining a similarity between the first patent document and the respective prior art document in the first set of prior art documents further comprises determining the similarity between the first patent document and the respective prior art document in the first set of prior art documents using one of a vector comparison, a comparison of patent fields, or artificial intelligence. 3. The computer-implemented method of claim 2, wherein the patent fields comprises at least one of a title field, an inventor field, an assignee field, a patent number field, a patent date field, a priority date field, a classification code field, an art unit field, a sub-art unit field, an examiner name field, an application number field, an application filing date field, or an other publications field. 4. The computer-implemented method of claim 1, wherein the prior art documents in the plurality of prior art documents comprise at least one of a patent, a patent publication, or an academic paper. 5. The computer-implemented method of claim 1, wherein determining a similarity between the first patent document and the respective prior art document in the first set of prior art documents further comprises determining the similarity between the first patent document and the respective prior art document in the first set of prior art documents using a neural network. 6. The computer-implemented method of claim 1, wherein the vector comparison comprises cosine similarity. 7. A document identification system for identifying a prior art reference using artificial intelligence comprising: one or more computer processors; and a computer readable storage medium storing program instructions configured for execution by the one or more computer processors in order to cause the computing system to: retrieve, from a document database, a plurality of prior art documents; process the prior art documents in the plurality of prior art documents to identify a plurality of n-grams and a weight associated with each n-gram in the plurality of n-grams; receive, from a user, a first patent document; process a claims section of the first patent document to identify a second plurality of n-grams; rank each n-gram in the second plurality of n-grams based on the identified weights associated with each n-gram in the plurality of n-grams; identify a first set of n-grams in the second plurality of n-grams that are ranked above a threshold value; identify a first set of prior art documents in the plurality of prior art documents based on the first set of n-grams in the second plurality of n-grams; rank the first set of prior art documents based on a similarity between the first prior art document and the respective prior art document in the first set of prior art documents; and display, in a user interface, prior art documents in the first set of prior art documents that are ranked above a second threshold value. 8. The document identification system of claim 7, wherein the prior art documents in the plurality of prior art documents comprise at least one of a patent, a patent publication, or an academic paper. 9. The document identification system of claim 7, wherein the computer readable storage medium further stores program instructions that cause the computing system to rank the first set of prior art documents using one of a vector comparison, a comparison of patent fields, or artificial intelligence. 10. The document identification system of claim 7, wherein the computer readable storage medium further stores program instructions that cause the computing system to retrieve a blacklist that lists a third plurality of n-grams and not weight n-grams in the third plurality of n-grams. 11. The document identification system of claim 7, wherein the computer readable storage medium further stores program instructions that cause the computing system to rank the first set of prior art documents based on the similarity and based on user feedback using machine learning techniques. 12. Non-transitory, computer-readable storage media comprising computer-executable instructions for identifying a prior art reference using artificial intelligence, wherein the computer-executable instructions, when executed by a computer system, cause the computer system to: retrieve, from a document database, a plurality of prior art documents; process the prior art documents in the plurality of prior art documents to identify a plurality of n-grams and a weight associated with each n-gram in the plurality of n-grams; receive, from a user, a first patent document; process a claims section of the first patent document to identify a second plurality of n-grams; rank each n-gram in the second plurality of n-grams based on the identified weights associated with each n-gram in the plurality of n-grams; identify a first set of n-grams in the second plurality of n-grams that are ranked above a threshold value; identify a first set of prior art documents in the plurality of prior art documents based on the first set of n-grams in the second plurality of n-grams; rank the first set of prior art documents based on a similarity between the first prior art document and the respective prior art document in the first set of prior art documents; and display, in a user interface, prior art documents in the first set of prior art documents that are ranked above a second threshold value. 13. The non-transitory, computer-readable storage media of claim 12, wherein the prior art documents in the plurality of prior art documents comprise at least one of a patent, a patent publication, or an academic paper. 14. The non-transitory, computer-readable storage media of claim 12, wherein the computer-executable instructions, when executed, further cause the computer system to rank the first set of prior art documents using one of a vector comparison, a comparison of patent fields, or artificial intelligence. 15. The non-transitory, computer-readable storage media of claim 12, wherein the computer-executable instructions, when executed, further cause the computer system to retrieve a blacklist that lists a third plurality of n-grams and not weight n-grams in the third plurality of n-grams. 16. The non-transitory, computer-readable storage media of claim 12, wherein the computer-executable instructions, when executed, further cause the computer system to rank the first set of prior art documents based on the similarity and based on user feedback using machine learning techniques.