The Patent Trial and Appeal Board (PTAB) API is a RESTful API with an easy to use search interface. You can easily browse USPTO PTAB public documents, search for specific content, and request a bulk download of PTAB content. The PTAB API synchronizes close to real time with the PTAB E2E (End-to-End) system making the latest public America Invents Action (AIA) Trial information and documents available. PTAB API v2 has text search capabilities for decision documents.

The PatentsView API is intended to inspire the exploration and enhanced understanding of US intellectual property (IP) and innovation systems. The database driving the API is regularly updated and integrates the best available tools for inventor disambiguation and data quality control. We hope researchers and developers alike will explore the API to discover people and companies and to visualize trends and patterns across the US innovation landscape.

The Trademark Image Search API (beta) allows searching Trademark Images using various attributes like feature, accuracy, metricspaces and candidates. This API also allows searching Trademark documents using various metadata attributes like Specimen Description, Filing Description, Filing Basis, Owner's Name, Street Address, etc.

The Trademark Status and Document Retrieval (TSDR) API allows you to make programmatic requests for trademark case status, documents, and images. Beginning on October 2, 2020, you will need an API key to download bulk data from TSDR using the TSDR API. Using API keys will help optimize usage of our TSDR system, which will help enable users like yourself to better access the information you need from TSDR. The below resources will provide you with more information on using the TSDR API.

This dataset includes the position data of a two-dimensional gantry system experiment in which the G-code commands for the gantry were transmitted through a wireless communications link. The testbed is composed of four main components related to the operation of the gantry system. These components are the gantry system, the Wi-Fi network, the RF channel emulator, and the supervisory computer. In the experimental study, we run a scenario in which the gantry tool moves sequentially between four positions and has a preset dwell at each of the positions.

Materials discovery and development necessarily begins with the preparation and identification of product phase(s). Crystalline compounds can be identified by their characteristic diffraction patterns using X-rays, neutrons, and or electrons. An estimated 20,000 X-ray diffractometers and a comparable number of electron microscopes are used daily in materials research and development laboratories for this purpose.

There are two objectives of this shape retrieval contest:

a) To evaluate partial similarity between query and target objects and retrieve complete 3D models that are relevant to a partial query object.

b) To retrieve 3D models those are relevant to a query depth map. This task corresponds to a real life scenario where the query is a 3D range scan of an object acquired from an arbitrary view direction. The algorithm should retrieve the relevant 3D objects from a database.

Task description

The objectives of this shape retrieval contest are to evaluate the performance of 3D shape retrieval approaches on a new generic 3D shape benchmark.

Task description: In response to a given set of queries, the task is to evaluate similarity scores with the target models and return an ordered ranked list along with the similarity scores for each query.

The objective of this track is to evaluate the performance of 3D shape retrieval approaches on a Generic 3D shape benchmark based on the Google 3D Warehouse.

The objective of this shape retrieval contest is to retrieve 3D models those are relevant to a query range scan. This task corresponds to a real life scenario where the query is a 3D range scan of an object acquired from an arbitrary view direction. The algorithm should retrieve the relevant 3D objects from a database.

Task description: In response to a given set of queries, the task is to evaluate similarity scores with the target models and return an ordered ranked list along with the similarity scores for each query. The set of query consists of range images.