Patent Examination Data System lets customers retrieve and download multiple records of USPTO patent application or patent filing status at no cost. PEDS contains the bibliographic, published document and patent term extension data tabs in Public PAIR from 1981 to present. There is also some data dating back to 1935. Customers can download the entire dataset containing the data for all indexed documents. There are over 13 million records in PEDS. The data can be accessed by anyone using the web interface or the provided Application Programming Interface (API).

Contains the bibliographic text (front page) of each patent grant issued weekly (Tuesdays) from January 6, 1976 to present (excludes images/drawings). Subset of the Patent Grant Full Text Data.

Contains the full text of each patent grant issued weekly (Tuesdays) from January 6, 1976 to present (excludes images/drawings). Subset of the Patent Grant Full Text Data with Embedded TIFF Images.

Contains the full text, images/drawings, and complex work units (tables, mathematical expressions, chemical structures, and genetic sequence data) of each patent grant issued weekly (Tuesdays) from January 2, 2001 to present.

The 24 Hour Box zipfile contains images of daily Trademark applications in either JPG format (black and white, grayscale, or color) or TIFF format (black and white) processed through the Trademark Image Capture and Retrieval System (TICRS) and viewable using any standard image viewer. JPG images are accompanied by an XML file that contains text information about the application. TIFF images contain the application number embedded in the image (stamped prior to scan).

Pending and registered trademark text data (no images) to include word mark, serial number, registration number, filing date, registration date, goods and services, classification number(s), status code(s), and design search code(s).

Assignment (ownership) text data (no images).

Trademark Trial and Appeal Board (TTAB) text data (no images).

This software is a Python module for estimating uncertainty in predictions of machine learning models. It is a Python package that calculates uncertainties in machine learning models using bootstrapping and residual bootstrapping. It is intended to interface with scikit-learn but any Python package that uses a similar interface should work.

This dataset was an initial test harness infrastructure test for the TrojAI program. It should not be used for research. Please use the more refined datasets generated for the other rounds. The data being generated and disseminated is training, validation, and test data used to construct trojan detection software solutions. This data, generated at NIST, consists of human level AIs trained to perform a variety of tasks (image classification, natural language processing, etc.).