Today Thomson Reuters announced the launch of HighQ Contract Analysis. The produce speeds contract review and analysis with machine learning technology. to answer the specific questions legal professionals want to address – in an easy-to-read report.
According to the press release, HighQ Contract Analysis leverages machine learning and pre-trained models “to help attorneys increase efficiency, reduce risk, and accelerate the contract-review process for transaction due diligence, compliance review and contract investigation.”
“AI-powered applications require three key ingredients – data, subject matter expertise and technology – and HighQ Contract Analysis builds upon Thomson Reuters decades-long leadership in AI-driven products for legal professionals,” said Andy Martens, head of Research Products at Thomson Reuters. “HighQ Contract Analysis begins with the deep knowledge of Practical Law editors who use their expertise to develop proprietary contract review templates specific to legal domains, and then leverages the work of AI experts at Thomson Reuters Labs to train and validate its machine-learning models. The result is a highly tailored, guided review that saves our customers’ time and costs, and improves the accuracy and insights of the contract review process.”
The introduction of HighQ Contract Analysis comes during the first-ever Thomson Reuters Legal SYNERGY conference, which has been promoted as “a premiere event for legal professionals that includes product sessions, continuing legal education and networking with peers.”
Looking Under the Hood
HighQ Contract Analysis is built to tackle specific transactional areas. At launch the product addresses real estate leases and sales and services agreements. In the near future it will be extended to cover new topics including intellectual property agreements and employment agreements. Each topic is built out with the involvement of Practical Law attorney editors who develop a list of key questions attorneys might pose during a contract review process. HighQ Contract Analysis find answers to specific questions such as, “What are the landlord’s maintenance obligations?” or “Is there a mutual right to break?”
An attorney starts the process by uploading a document into the HighQ AI Hub which classifies the contract, and identifies essential facts like parties, deal value, language and jurisdiction. The new HighQ pre-trained domain models then “automatically extract and retrieve defined terms and definitions from within the agreement, divide the document into text snippets, evaluate every snippet against the review questions, and returns text that meets the criteria relevant to answering each question.”
A Guided Review interface enables a reviewer to assess those answers, comment, annotate and assign risks in the document. Users can analyze contracts in bulk as well as review a single document. HighQ Contract Analysis also allows users to compare contracts to an identified company standard or Practical Law standard documents, enabling reviewers to quickly identify non-standard terms, deviations and additional risks.
“A typical use case would be for a buyer assessing a purchase of an office block, based in part on a review of all the contracts associated with the properties being purchased,” said Rawia Ashraf, vice president of Legal Practice and Productivity at Thomson Reuters. “The buyer needs to identify key risks, such as how much income is generated by these properties, what properties are likely to be vacant and who is liable for things such as insurance and repair. This is fast, easy work for HighQ Contract Analysis.”
Sophisticated AI and integration
HighQ Contract Analysis provides an integrated experience, enabling customers to view and edit machine-learning extraction results, annotate documents and collaborate with teams – all within HighQ. Users can leverage HighQ collaboration, workflow and visualization tools to conduct further analysis and generate reports on top of the extracted data.
What’s Up Next? Later this year, a new AI Model Trainer will be released. This tool will provide an easy-to-use, end-to-end process to manage, re-train and evaluate the machine-learning data models to refine their analysis of a user’s own contracts. Longer term, users will be able to define their own models, managing the questions and facts to match their, and their client’s, expectations.
Here is a link to the full press release.