Stony Brook, NY, United States of America

Ke Hu

This inventor holds 8 USPTO granted patents and 8 published patent applications and 4 EPO patents. Top assignee: Google Inc.. Active years: 2022-2026.

IDiyas Innovation Intelligence. (2026). Inventor Profile: Ke Hu. Retrieved from https://idiyas.com/inventor/ke-hu-yjqfbfq5

Data source: USPTO (United States Patent and Trademark Office) public patent records. Weekly synchronization. How IDiyas builds this profile

USPTO Granted Patents = 8 

% Patents Active = 100.0

 

Average Co-Inventor Count = 5.2

ph-index = 2

Forward Citations = 9(Granted Patents)


Location History:

  • Stony Brook, NY (US) (2022 - 2024)
  • Mountain View, CA (US) (2023 - 2024)

Company Filing History:


Years Active: 2022-2026

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8 patents (USPTO):Explore Patents

Title: The Innovative Contributions of Ke Hu

Introduction

Ke Hu is a prominent inventor based in Stony Brook, NY, known for his significant contributions to the field of technology. With a total of seven patents to his name, he has made remarkable advancements in the area of speech recognition and deliberation models. His work is characterized by a blend of innovative methodologies and practical applications.

Latest Patents

Among his latest patents, Ke Hu has developed a method for deliberation by text-only and semi-supervised training. This method involves receiving training data that includes unspoken textual utterances, which are not paired with any corresponding spoken utterance of non-synthetic speech. The process trains a deliberation model that consists of a text encoder and a deliberation decoder on these unspoken textual utterances. Additionally, the model receives first-pass hypotheses and non-causal acoustic embeddings, generated by a recurrent neural network-transducer (RNN-T) decoder. The method further encodes the first-pass hypotheses and generates second-pass hypotheses using the deliberation decoder.

Another notable patent is the transducer-based streaming deliberation for cascaded encoders. This method includes receiving a sequence of acoustic frames and generating a first higher-order feature representation for each corresponding acoustic frame. It also involves generating a first pass speech recognition hypothesis and a text encoding for that hypothesis. The method continues with generating a second higher-order feature representation and a second pass speech recognition hypothesis, enhancing the accuracy and

Profile summary based on public USPTO records.
Data Sources: USPTO Patent Grant XML, Patent Center, EPO & CIPO • Normalized by IDiyas Innovation Graph. Methodology & provenance architecturePlease report any incorrect information to [email protected]
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