Yehud, Israel

Zeev Rannon

This inventor holds 1 USPTO granted patent and 2 published patent applications. Top assignee: Gong I.o Ltd.. Active years: 2026.

IDiyas Innovation Intelligence. (2026). Inventor Profile: Zeev Rannon. Retrieved from https://idiyas.com/inventor/zeev-rannon

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

Calculating rank
USPTO Granted Patents = 1 

Average Co-Inventor Count = 1.0

ph-index = 1


Company Filing History:


Years Active: 2026

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1 patent (USPTO):Explore Patents

Title: Zeev Rannon and Audio-Processing Innovation.

Introduction

Zeev Rannon is an inventor associated with Yehud, Israel, and Gong I.o Ltd. His work includes a patent application describing techniques for improving audio processing by combining clipping engines with acoustic models.

Latest Patents

Rannon is listed with 1 patent. The patent, “Techniques for Improved Audio Processing Using Combinations of Clipping Engines and Acoustic Models,” describes a system and method for training an acoustic model over multiple iterations. In each iteration, the acoustic model generates predictions from features extracted from training audio, while a language model generates predictions using sample data that demonstrates a range of key terms. The system uses both sets of predictions to divide the training audio into clips, then tunes the acoustic model with a machine-learning algorithm using those clips.

Career Highlights

Rannon’s listed company affiliation is Gong I.o Ltd. The patent’s focus on combining acoustic and language-model predictions reflects an approach to refining audio-processing systems through iterative training.

Collaborations

Rannon’s listed coworker is Eduard Golstein. The available information identifies their professional association but does not specify further details about their collaboration.

Conclusion

Zeev Rannon’s listed patent presents a method for improving audio processing by using language-model and acoustic-model predictions to guide training-data clipping and model tuning.

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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