Abstract: Embodiments are directed to generating and training a distributed machine learning model using data received from a plurality of third parties using a distributed ledger system, such as a blockchain. As each third party submits data suitable for model training, the data submissions are recorded onto the distributed ledger. By traversing the ledger, the learning platform identifies what data has been submitted and by which parties, and trains a model using the submitted data. Each party is also able to remove their data from the learning platform, which is also reflected in the distributed ledger. The distributed ledger thus maintains a record of which parties submitted data, and which parties removed their data from the learning platform, allowing for different third parties to contribute data for model training, while retaining control over their submitted data by being able to remove their data from the learning platform.

Abstract: Embodiments are directed to generating and training a distributed machine learning model using data received from a plurality of third parties using a distributed ledger system, such as a blockchain. As each third party submits data suitable for model training, the data submissions are recorded onto the distributed ledger. By traversing the ledger, the learning platform identifies what data has been submitted and by which parties, and trains a model using the submitted data. Each party is also able to remove their data from the learning platform, which is also reflected in the distributed ledger. The distributed ledger thus maintains a record of which parties submitted data, and which parties removed their data from the learning platform, allowing for different third parties to contribute data for model training, while retaining control over their submitted data by being able to remove their data from the learning platform.

Abstract: Embodiments are directed to the tracking of data in a generative adversarial network (GAN) model using a distributed ledger system, such as a blockchain. A learning platform implementing a classification model receives, from a third party, a set of data examples generated by a generator model. The set of data examples are processed by the classification model, which outputs a prediction for each data example indicating whether each data example is true or false. The distributed ledger keeps a record of data examples submitted to the learning platform, as well as of predictions determined by the classification model on the learning platform. The learning platform analyzes the records of the distributed ledger, and pairs the records corresponding to the submitted data examples and the generated predictions determined by the classification model, and determines if the predictions were correct. The classification model may then be updated based upon the prediction results.

Abstract: Embodiments are directed to generating and training a distributed machine learning model using data received from a plurality of third parties using a distributed ledger system, such as a blockchain. As each third party submits data suitable for model training, the data submissions are recorded onto the distributed ledger. By traversing the ledger, the learning platform identifies what data has been submitted and by which parties, and trains a model using the submitted data. Each party is also able to remove their data from the learning platform, which is also reflected in the distributed ledger. The distributed ledger thus maintains a record of which parties submitted data, and which parties removed their data from the learning platform, allowing for different third parties to contribute data for model training, while retaining control over their submitted data by being able to remove their data from the learning platform.

Abstract: Embodiments are directed to generating and training a distributed machine learning model using data received from a plurality of third parties using a distributed ledger system, such as a blockchain. As each third party submits data suitable for model training, the data submissions are recorded onto the distributed ledger. By traversing the ledger, the learning platform identifies what data has been submitted and by which parties, and trains a model using the submitted data. Each party is also able to remove their data from the learning platform, which is also reflected in the distributed ledger. The distributed ledger thus maintains a record of which parties submitted data, and which parties removed their data from the learning platform, allowing for different third parties to contribute data for model training, while retaining control over their submitted data by being able to remove their data from the learning platform.

Abstract: Embodiments are directed to the tracking of data in a generative adversarial network (GAN) model using a distributed ledger system, such as a blockchain. A learning platform implementing a classification model receives, from a third party, a set of data examples generated by a generator model. The set of data examples are processed by the classification model, which outputs a prediction for each data example indicating whether each data example is true or false. The distributed ledger keeps a record of data examples submitted to the learning platform, as well as of predictions determined by the classification model on the learning platform. The learning platform analyzes the records of the distributed ledger, and pairs the records corresponding to the submitted data examples and the generated predictions determined by the classification model, and determines if the predictions were correct. The classification model may then be updated based upon the prediction results.

Abstract: A computing system remotely trains a public ensemble model of an artificial intelligence model management system. The system receives, by the model management system, an encrypted representation of a private data value from a client system. The encrypted representation includes annotation information provided by the client system. The system determines, using the encrypted representation and the annotation information, a data value cluster that corresponds to the private data value. Data value clusters are generated using encrypted representations of a private data values provided by client systems. The system obtains, based on the assigned data value cluster, an encrypted representation of a model. The model is trained remotely by the client system using the private data value. The system adds the encrypted representation of the model to the public ensemble model. The public ensemble model is generated using a plurality of encrypted representations of models remotely trained by the client systems.

Abstract: Embodiments are directed to generating and training a distributed machine learning model using data received from a plurality of third parties using a distributed ledger system, such as a blockchain. As each third party submits data suitable for model training, the data submissions are recorded onto the distributed ledger. By traversing the ledger, the learning platform identifies what data has been submitted and by which parties, and trains a model using the submitted data. Each party is also able to remove their data from the learning platform, which is also reflected in the distributed ledger. The distributed ledger thus maintains a record of which parties submitted data, and which parties removed their data from the learning platform, allowing for different third parties to contribute data for model training, while retaining control over their submitted data by being able to remove their data from the learning platform.

Abstract: Embodiments are directed to the tracking of data in a generative adversarial network (GAN) model using a distributed ledger system, such as a blockchain. A learning platform implementing a classification model receives, from a third party, a set of data examples generated by a generator model. The set of data examples are processed by the classification model, which outputs a prediction for each data example indicating whether each data example is true or false. The distributed ledger keeps a record of data examples submitted to the learning platform, as well as of predictions determined by the classification model on the learning platform. The learning platform analyzes the records of the distributed ledger, and pairs the records corresponding to the submitted data examples and the generated predictions determined by the classification model, and determines if the predictions were correct. The classification model may then be updated based upon the prediction results.

Abstract: An electronic documents verification system (and method) detects related contracts, and analyzes contents in the related contracts including a primary contract and associated amendments from raw input data. One embodiment of a disclosed configuration includes a system (and method) for identifying clauses used in the related contracts. The system (and method) extracts features including key references or descriptions within each contract. Additionally, the system (and method) groups the related contracts, and establishes linkages of the related contracts based on the extracted features. Furthermore, the system (and method) analyzes contents in the related contracts based on advanced policy group including a plurality of policy groups.

Abstract: Embodiments relate to a system and a method for identifying, from contractual documents, (i) standard exact clauses matching clause examples and (ii) non-standard clauses semantically related to but not matching the clause examples. A standard feature data set comprising standard exact clauses matching clause examples is obtained. In addition, a mirror feature data set comprising semantically related clauses of the clause examples is obtained using semantic language analysis, where the mirror feature data set encompasses the standard feature data set. Non-standard clauses are obtained by extracting a difference between the mirror feature data set and the standard exact feature data set.

Abstract: An electronic documents verification system (and method) detects related contracts, and analyzes contents in the related contracts including a primary contract and associated amendments from raw input data. One embodiment of a disclosed configuration includes a system (and method) for identifying clauses used in the related contracts. The system (and method) extracts features including key references or descriptions within each contract. Additionally, the system (and method) groups the related contracts, and establishes linkages of the related contracts based on the extracted features. Furthermore, the system (and method) analyzes contents in the related contracts based on advanced policy group including a plurality of policy groups.

Abstract: An electronic documents verification system (and method) detects related contracts, and analyzes contents in the related contracts including a primary contract and associated amendments from raw input data. One embodiment of a disclosed configuration includes a system (and method) for identifying clauses used in the related contracts. The system (and method) extracts features including key references or descriptions within each contract. Additionally, the system (and method) groups the related contracts, and establishes linkages of the related contracts based on the extracted features. Furthermore, the system (and method) analyzes contents in the related contracts based on advanced policy group including a plurality of policy groups.

Abstract: Embodiments relate to a system and a method for identifying, from contractual documents, (i) standard exact clauses matching clause examples and (ii) non-standard clauses semantically related to but not matching the clause examples. A standard feature data set comprising standard exact clauses matching clause examples is obtained. In addition, a mirror feature data set comprising semantically related clauses of the clause examples is obtained using semantic language analysis, where the mirror feature data set encompasses the standard feature data set. Non-standard clauses are obtained by extracting a difference between the mirror feature data set and the standard exact feature data set.

Abstract: Embodiments relate to a system and a method for identifying, from contractual documents, (i) standard exact clauses matching clause examples and (ii) non-standard clauses semantically related to but not matching the clause examples. A standard feature data set comprising standard exact clauses matching clause examples is obtained. In addition, a mirror feature data set comprising semantically related clauses of the clause examples is obtained using semantic language analysis, where the mirror feature data set encompasses the standard feature data set. Non-standard clauses are obtained by extracting a difference between the mirror feature data set and the standard exact feature data set.

Abstract: Embodiments relate to a system and a method for identifying, from contractual documents, (i) standard exact clauses matching clause examples and (ii) non-standard clauses semantically related to but not matching the clause examples. A standard feature data set comprising standard exact clauses matching clause examples is obtained. In addition, a mirror feature data set comprising semantically related clauses of the clause examples is obtained using semantic language analysis, where the mirror feature data set encompasses the standard feature data set. Non-standard clauses are obtained by extracting a difference between the mirror feature data set and the standard exact feature data set.

Abstract: A non-standard and standard clause detection system imports raw input data or contractual documents, and extracts non-standard and standard clauses that are semantically linked. One embodiment of a disclosed configuration is a system and a method for identifying non-standard and standard clauses in contractual documents. The system and the method comprise of generating a primary policy and a secondary policy, obtaining a first feature data set by applying the primary policy to a semantic language evaluator, and obtaining a second feature data set by applying the secondary policy to the semantic language evaluator. The first feature data set obtained is the aggregation of the standard clauses used in the document. Furthermore, the second feature data set encompasses the first feature data set, thus the difference between the first feature data set and the second feature data set is the aggregation of the non-standard clauses.

Abstract: An electronic documents verification system (and method) detects related contracts, and analyzes contents in the related contracts including a primary contract and associated amendments from raw input data. One embodiment of a disclosed configuration includes a system (and method) for identifying clauses used in the related contracts. The system (and method) extracts features including key references or descriptions within each contract. Additionally, the system (and method) groups the related contracts, and establishes linkages of the related contracts based on the extracted features. Furthermore, the system (and method) analyzes contents in the related contracts based on advanced policy group including a plurality of policy groups.

Abstract: A linked addendum detection system identifies and links related contractual documents including a master agreement (MA) and associated amendments from a large volume of data. The system extracts primary features including key references or descriptions within each contractual document. In addition, the system also includes grouping to detect the related contractual documents. Moreover, the system sorts the MA and the associated amendments according to the modification time (MTIME), and further sequentially establishes linkages from one contractual document to the next document in order. Furthermore, the system prepares and provides output result for display to an end user.

Abstract: Embodiments relate to a system and a method for identifying, from contractual documents, (i) standard exact clauses matching clause examples and (ii) non-standard clauses semantically related to but not matching the clause examples. A standard feature data set comprising standard exact clauses matching clause examples is obtained. In addition, a mirror feature data set comprising semantically related clauses of the clause examples is obtained using semantic language analysis, where the mirror feature data set encompasses the standard feature data set. Non-standard clauses are obtained by extracting a difference between the mirror feature data set and the standard exact feature data set.