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Description

Patentfield serves as a comprehensive platform for patent exploration and analysis, integrating sophisticated search capabilities, data visualization, and artificial intelligence features. It goes beyond mere patent searches by offering AI-driven semantic search and classification tools that expedite the patent screening process. The platform's AI has been trained on over 10 million patent documents, allowing it to comprehend word meanings and identify related patents effectively. By generating a similarity score that ranks results in descending order, users can swiftly access patent literature relevant to their specific technological inquiries without the need for pre-existing training datasets. Additionally, the similar image search functionality enables users to upload image data to find patents and design publications with comparable visuals, specifically focusing on the illustrations presented within those documents. Users can also input multiple drawings, facilitating searches that draw on a combination of various perspectives, such as six distinct views for design patents or a mix of exterior and internal images for regular patents, enhancing the overall search experience. This multifaceted approach to patent searching streamlines the process, making it more efficient and user-friendly.

Description

Word2Vec is a technique developed by Google researchers that employs a neural network to create word embeddings. This method converts words into continuous vector forms within a multi-dimensional space, effectively capturing semantic relationships derived from context. It primarily operates through two architectures: Skip-gram, which forecasts surrounding words based on a given target word, and Continuous Bag-of-Words (CBOW), which predicts a target word from its context. By utilizing extensive text corpora for training, Word2Vec produces embeddings that position similar words in proximity, facilitating various tasks such as determining semantic similarity, solving analogies, and clustering text. This model significantly contributed to the field of natural language processing by introducing innovative training strategies like hierarchical softmax and negative sampling. Although more advanced embedding models, including BERT and Transformer-based approaches, have since outperformed Word2Vec in terms of complexity and efficacy, it continues to serve as a crucial foundational technique in natural language processing and machine learning research. Its influence on the development of subsequent models cannot be overstated, as it laid the groundwork for understanding word relationships in deeper ways.

API Access

Has API No 

API Access

Has API No 

Screenshots View All

Screenshots View All

No images available

Integrations

Gensim No 
Microsoft Excel Yes 

Integrations

Gensim Yes 
Microsoft Excel No 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Pricing Details

Free
Open source
Free Trial No 
Free Version Yes 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Deployment

Web-Based No 
On-Premises Yes 
iPhone App No 
iPad App No 
Android App No 
Windows Yes 
Mac Yes 
Linux Yes 
Chromebook No 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support No 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Vendor Details

Company Name

Patentfield

Country

United States

Website

en.patentfield.com

Vendor Details

Company Name

Google

Founded

1998

Country

United States

Website

code.google.com/archive/p/word2vec/

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Product Features

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Alternatives

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