Refinitiv US Organization LLC

Thomson Reuters Enterprise Centre Gmbh

Financial & Risk Organisation Limited

Tim Nugent

Jochen Lothar Leidner

Frank Schilder

Dezhao Song

Thomas W Vacek

Sam Chadwick

Conner Cowling

Ronald Teo

Sameena Shah

Armineh Nourbakhsh

John F Duprey

Natraj Raman

Lucas Carstens

Fabio Petroni

Matt Harding

Zarko Panic

Georgios Gkotsis

John Duprey

Jochen Leidner

The present disclosure relates to systems and methods for analyzing and extracting data related to a structured proceeding, and for identifying, based on the analysis, at least one outcome associated with the structured proceeding. Embodiments provide for receiving data associated with a structured proceeding involving at least one party, the data including at least one docket entry, and analyzing, by an outcome location detector, the data to identify one or more docket entries in the at least one docket entry that are likely to include evidence of an outcome. Embodiments further include analyzing, by an outcome detector, the one or more docket entries determined to be likely to include evidence of an outcome to determine outcomes. The outcomes include at least one of a final outcome and at least one party outcome. The final outcome is associated with the structured proceeding overall, and the at least one party outcome is associated with a party of the at least one party that may have been terminated early.

Systems and methods for determining structured proceeding outcomes

Computer-readable media, systems and methods may improve automatic summarization of transcripts of financial earnings calls. For example, a system may generate segments, such as by disambiguating sentences, from a transcript to be summarized. The system may use an estimator that assesses whether or not the segment should be included in the summary. Different types of estimators may be used. For example, the estimator may be rule-based, trained based on machine-learning techniques, or trained on based on machine-learning with language modeling using natural language processing to fine-tune language models specific to financial earnings calls.

Automatic summarization of financial earnings call transcripts

Computer-readable media, systems and methods may improve classification of content based on a machine-learning natural language processing (ML-NLP) classifier. The system may train a general language model based on a general corpus, further train the general language model based on a domain-specific corpus to generate a domain-specific language model, and conduct supervised machine-learning based on the domain-specific language using topic-specific corpus labeled as relating to topics of interest to generate the ML-NLP classifier. Accordingly, the ML-NLP classifier may be trained on a general corpus, further trained on a domain-specific corpus, and fine-tuned on a topic-specific corpus. In this manner, domain-specific content may be classified into topics of interest. The ML-NLP classifier may classify content into the topics of interest.

Machine-learning natural language processing classifier for content classification

An embodiment of a method of providing financial data to a financial instrument smart contract in a distributed ledger system includes: receiving, by an oracle smart contract in the distributed ledger system, a transaction from the financial instrument smart contract, the transaction including registration data having an identification of financial data to be delivered and an identification of a schedule on which to deliver the financial data; generating a transaction including the financial data; and transmitting the generated transaction to at least one distributed node of the distributed ledger system according to the requested schedule.

Systems and methods for providing financial data to financial instruments in a distributed ledger system

A method of remedying erroneous transactions in a distributed ledger system may include: determining, by a court system associated with the distributed ledger system, a judgment that a transferring, by a contract of the distributed ledger system during a conducting of a financial transaction, of a quantity of tokens of the distributed ledger system, from a first account corresponding to a first counterparty to a second account corresponding to a second counterparty, is erroneous; and in response to determining the judgment, executing, by a court contract of the distributed ledger system, a transfer function, configured to be responsive to the court contract, to transfer at least a portion of the quantity of tokens from the second account to the first account.

Systems and methods for smart contract intervention

A method of providing cross-media event linking may include: receiving, at a first input of an event coreferencing system, a stream of social media postings, and at a second input, a stream of news articles; generating, by the event coreferencing system, a first set of event representations representing events referenced by the social media postings, and a second set of event representations representing events referenced by the news articles; determining, by the event coreferencing system, that at least one of the social media postings references a same event referenced by at least one of the news articles, the determining including determining at least one similarity using data of at least one of the first set of event representations corresponding to the at least one of the social media postings and data of at least one of the second set of event representations corresponding to the at least one of the news articles; and transmitting, by an output of the event resolution system to the user system, an alert including at least one coreferenced event representation representing the event referenced by the at least one of the social media postings and the at least one of the news articles.

Systems and methods for cross-media event detection and coreferencing

Intervention in the operation of a smart contract in a distributed ledger system may include: launching execution of the smart contract; upon launching execution of the smart contract, executing a judgment checking function to determine whether a judgment against the smart contract exists in a judgment database; in response to a judgment against the smart contract existing in the judgment database, intervening in the operation of the smart contract to prevent the execution of the smart contract; and in response to no judgment against the smart contract existing in the judgment database, continuing execution of the smart contract.

Systems and methods for interception of smart contracts

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London, United Kingdom

Tim Nugent