Nippon Telegraph and Telephone had 261 patents in artificial intelligence during Q1 2024. The patents filed by Nippon Telegraph and Telephone Corp in Q1 2024 include technologies related to speaker diarization models, processing methods for inference in edge and server devices, transmission systems for calculating jitter patterns, information processing devices for generating tendency information based on user preferences, and learning apparatus for training models using data from different domains. GlobalData’s report on Nippon Telegraph and Telephone gives a 360-degree view of the company including its patenting strategy. Buy the report here.

Nippon Telegraph and Telephone grant share with artificial intelligence as a theme is 48% in Q1 2024. Grant share is based on the ratio of number of grants to total number of patents.

Recent Patents

Application: Speaker diarization method, speaker diarization device, and speaker diarization program (Patent ID: US20240105182A1)

The patent filed by Nippon Telegraph and Telephone Corp. describes a speaker diarization method and apparatus that involves dividing a sequence of acoustic features for each frame of an acoustic signal into segments of a predetermined length, generating an array with these segments arranged in a specific manner, and using this array to learn and generate a model for estimating speaker labels for each frame. The model utilizes speaker vectors as input and outputs speaker labels, with the possibility of incorporating recurring neural networks for processing the array in different directions.

Furthermore, the method and apparatus also include estimating speaker labels for each frame of the acoustic signal using the generated model, potentially utilizing a moving average of multiple frames for this estimation. The sequence of acoustic features is represented in a two-dimensional format, which is then converted into a three-dimensional acoustic feature array for processing. The patent also covers the implementation of this speaker diarization system in a computer-readable non-transitory recording medium, allowing for the execution of operations related to dividing, generating arrays, and learning models for speaker label estimation based on the acoustic features and vectors associated with the input frames.

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