This inventor holds 1 USPTO granted patent. Top assignee: Adobe, Inc.. Active years: 2026.
Company Filing History:
Years Active: 2026
Title: Daniel McKEE - Innovator in Audio Recommendation Systems
Introduction
Daniel McKEE is an inventor based in Urbana-Champaign, IL (US). He is known for his innovative work in the field of audio recommendation systems, particularly utilizing machine learning techniques. His research focuses on enhancing the way music is recommended for video content, making it more intuitive and user-friendly.
Latest Patent Applications
Daniel McKEE has filed a patent application titled "NATURAL LANGUAGE-GUIDED MUSIC AUDIO RECOMMENDATION FOR VIDEO USING MACHINE LEARNING." This application describes an audio recommendation system that is trained to suggest music audio sequences for pairing with query video sequences using neural networks. The system involves receiving an input that includes a query video sequence and natural language text. It generates a fused visual-text embedding based on a visual embedding and a text embedding corresponding to the input. The system then compares audio embeddings for music audio sequences from a database with the fused visual-text embedding. Ultimately, it determines a recommended music audio sequence based on a similarity metric calculated between an audio embedding for the music audio sequence and the fused visual-text embedding.
Conclusion
Daniel McKEE's contributions to the field of audio recommendation systems highlight the potential of machine learning in enhancing user experiences with multimedia content. His innovative approach aims to bridge the gap between visual and auditory elements in video applications.