Systems, methods, and storage media for extracting patent document templates from a patent corpus are disclosed. Exemplary implementations may: obtain a patent corpus; receive one or more parameters; determine one or more subsets of the patent corpus by filtering the patent corpus based on the one or more parameters; identify one or more document clusters within individual ones of the one or more subsets of the patent corpus; obtain a patent document template corresponding to the first document cluster; and/or perform other operations.

Systems, methods, and storage media for extracting patent document templates from a patent corpus are disclosed. Exemplary implementations may: obtain a patent corpus; receive one or more parameters; determine one or more subsets of the patent corpus by filtering the patent corpus based on the one or more parameters; identify one or more document clusters within individual ones of the one or more subsets of the patent corpus; obtain a patent document template corresponding to the first document cluster; and/or perform other operations.

A system and method for deconstructing a document is described herein, where the method is an improvement over existing document deconstruction techniques. These improvements increase speed and accuracy by rapidly identifying the source in a document by splitting the document into a plurality of sections and performing a perceptual image hashing on each section. Then a hamming distance is used to compare the hash for each section with the hashes of known documents to identify the source who sent the document.

A system and method for deconstructing a document is described herein, where the method is an improvement over existing document deconstruction techniques. These improvements increase speed and accuracy by rapidly identifying the source in a document by splitting the document into a plurality of sections and performing a perceptual image hashing on each section. Then a hamming distance is used to compare the hash for each section with the hashes of known documents to identify the source who sent the document.

Systems, apparatuses, methods, and computer program products are disclosed for screening data instances based on a target text of a target corpus. A screening device analyzes a target corpus to generate at least two term dictionaries for the target corpus. The screening apparatus, based on a frequency of a term in the target corpus, determines a term weight for the term; for each data instance, determines term scores for the data instance and the target text based on the term weights; filters the data instances based on the term scores, to generate a short list of data instances; determines term similarity scores between each data instance of the short list and target text based on the term weights; and provides a data instance determined to likely correspond to the target text and an indication of the corresponding term similarity score(s). A term is a word or an n-gram.

A blank template form generation method and system may employ synthetically generated blank template forms, differing from each other in one or more respects, to train a neural network to recognize relevant differences between otherwise similar forms, including types and locations of keywords and potential locations of values corresponding to the keywords. In an embodiment, filled or partly filled forms as well as blank template forms may be used later in training. Forms are input in pairs to identify differences between the two. Depending on the differences, weights of a neural network may be adjusted. After training, when a form is input into the system, whether the form is filled or blank, a blank template may be generated for future use.

A blank template form generation method and system may employ synthetically generated blank template forms, differing from each other in one or more respects, to train a neural network to recognize relevant differences between otherwise similar forms, including types and locations of keywords and potential locations of values corresponding to the keywords. In an embodiment, filled or partly filled forms as well as blank template forms may be used later in training. Forms are input in pairs to identify differences between the two. Depending on the differences, weights of a neural network may be adjusted. After training, when a form is input into the system, whether the form is filled or blank, a blank template may be generated for future use.

Datasets are available from different dataset servers and often lack well-defined metadata. Thus, comparing datasets is difficult. Additionally, there might be different versions of the same dataset which makes the search even more difficult. Using systems and methods described herein, quality scores, dataset versioning, topic identification, and semantic relatedness metadata is stored about datasets stored on dataset servers. A user interface is provided to allow a user to search for datasets by specifying search criteria (e.g., a topic and a minimum quality score) and to be informed of responsive datasets. The user interface may further inform the user of the quality scores of the responsive datasets, the versions of the responsive datasets, or other metadata. From the search results, the user may select and download one or more of the responsive datasets.

Decentralized computer systems and methods are disclosed for hyperledger-based payment transactions, alerts, and dispute settlement, using smart contracts. One method includes: receiving transaction information comprising transaction data of transaction attributes for a transaction for a good or service originating at a merchant; storing the transaction data of the transaction attributes for the transaction, in a smart contract generated or updated by a smart contract application; deploying the Smart contract into one or more blockchain networks, wherein at least one of the one or more blockchain networks; performing one or more iterations of enabling, using an inter ledger protocol, to transfer at least some of the transaction data of the transaction attributes of the transaction to another of the one or more blockchain networks, wherein each of the one or more blockchain networks serves one or more business functions of the transaction.

Decentralized computer systems and methods are disclosed for hyperledger-based payment transactions, alerts, and dispute settlement, using smart contracts. One method includes: receiving transaction information comprising transaction data of transaction attributes for a transaction for a good or service originating at a merchant; storing the transaction data of the transaction attributes for the transaction, in a smart contract generated or updated by a smart contract application; deploying the Smart contract into one or more blockchain networks, wherein at least one of the one or more blockchain networks; performing one or more iterations of enabling, using an inter ledger protocol, to transfer at least some of the transaction data of the transaction attributes of the transaction to another of the one or more blockchain networks, wherein each of the one or more blockchain networks serves one or more business functions of the transaction.