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Description

Dolcera's premier patent analytics platform, PCS, boasts a vast database of over 110 million patents from around the globe, which is refreshed daily to ensure timely and detailed responses to all patent inquiries. This innovative tool, designed by experts at Stanford University, leverages artificial intelligence to facilitate intelligent searching and analytics. By utilizing a sophisticated machine learning algorithm, PCS delves into various data sources beyond traditional patent literature, allowing for the creation of comprehensive and meaningful categories. Users are empowered to pose the correct inquiries, with the system’s use of synonyms, semantics, and various word forms enabling quick and efficient search formulation without the necessity of poring over scientific documents. High-quality analytics are provided by linking patents to their ultimate owners, ensuring accuracy in results. Furthermore, PCS features a cluster cloud, CPC taxonomy, and assignee normalization that are recognized for their exceptional precision. The platform offers a streamlined search experience, eliminating clutter and making it accessible to users without any prior search experience or expertise. In this way, PCS not only enhances the efficiency of patent searching but also significantly uplifts the overall user experience.

Description

GloVe, which stands for Global Vectors for Word Representation, is an unsupervised learning method introduced by the Stanford NLP Group aimed at creating vector representations for words. By examining the global co-occurrence statistics of words in a specific corpus, it generates word embeddings that form vector spaces where geometric relationships indicate semantic similarities and distinctions between words. One of GloVe's key strengths lies in its capability to identify linear substructures in the word vector space, allowing for vector arithmetic that effectively communicates relationships. The training process utilizes the non-zero entries of a global word-word co-occurrence matrix, which tracks the frequency with which pairs of words are found together in a given text. This technique makes effective use of statistical data by concentrating on significant co-occurrences, ultimately resulting in rich and meaningful word representations. Additionally, pre-trained word vectors can be accessed for a range of corpora, such as the 2014 edition of Wikipedia, enhancing the model's utility and applicability across different contexts. This adaptability makes GloVe a valuable tool for various natural language processing tasks.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

Payitiv CRM

Integrations

Payitiv CRM

Pricing Details

Free
Free Trial
Free Version

Pricing Details

Free
Free Trial
Free Version

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Vendor Details

Company Name

Dolcera

Country

United States

Website

www.dolcera.com/web/pcs/

Vendor Details

Company Name

Stanford NLP

Country

United States

Website

nlp.stanford.edu/projects/glove/

Product Features

Product Features

Alternatives

Alternatives

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