Regina Barzilay

20100153318, Jun 17, 2010

Nov 19, 2009

12/592128

Harr Chen - Cambridge MA, US
Jacob Richard Eisenstein - Pittsburgh PA, US
Regina Barzilay - Cambridge MA, US

Massachusetts Institute of Technology - Cambridge MA

G06F 15/18
G06N 5/02

706 12, 706 46

Some embodiments are directed to identifying semantic properties of documents using free-text annotations associated with the documents. Semantic properties of documents may be identified by using a model that is trained on a corpus of training documents where one or more of the training documents may include free-text annotations. In some embodiments, the model may identify semantic topics expressed only in free-text annotations or only in the body of a document. The model may applied to identify semantic topics associated with a work document or to summarize the semantic topics present in a plurality of work documents.

20110246881, Oct 6, 2011

Oct 1, 2010

12/896404

Nathaniel Kushman - Boston MA, US
Regina Barzilay - Cambridge MA, US
Dina Katabi - Cambridge MA, US
Martin C. Rinard - Arlington MA, US

MASSACHUSETTS INSTITUTE OF TECHNOLOGY - Cambridge MA

G06F 3/01

715708

A system and method that enables a plurality of lay users to collaborate on automating computer tasks is disclosed. In one embodiment, the system automatically performs these tasks, rather than just documenting how to perform them. The system allows a database of solutions to be built for every important computer task. A key characteristic of this system is that users contribute to this database by simply performing the task. The system records the graphical user interface (GUI) actions as the user performs the task. It aggregates GUI traces from multiple users into a canonical sequence of GUI actions parameterized by user-environment that will successfully accomplish the task on a variety of different configurations. A classifier is used to predict which steps are likely to be misinterpreted and requests human intervention to properly perform them. This process can be done iteratively until the translation is believed to be correct.

20200027528, Jan 23, 2020

Aug 29, 2018

16/116805

- Cambridge MA, US
Klavs F. Jensen - Lexington MA, US
Regina Barzilay - Cambridge MA, US
Tommi S. Jaakkola - Burlington MA, US
Wengong Jin - Arlington MA, US

Massachusetts Institute of Technology - Cambridge MA

G16C 20/10
G16C 20/50
G16C 20/20

Techniques for predicting a chemical reaction that includes a set of input molecules. The techniques may include obtaining input molecule information identifying the set of input molecules and predicting at least one chemical reaction that include a transformation between the set of input molecules and a set of output molecules by modifying at least one reaction center of the set of input molecules. The predicting of the at least one chemical reaction may be performed at least in part by using the input molecule information and at least one statistical model relating properties of atoms outside a region of a molecule to reactivity of the molecule at the region to identify the at least one reaction center. The techniques further include outputting information indicating the set of output molecules.