Abstract: Systems and methods used in a cognitive assistant for detecting human emotions from speech audio signals is described. The system obtains audio signals from an audio receiver and extracts human speech samples. Subsequently, it runs a machine learning based classifier to analyze the human speech signal and classify the emotion observed in it. The user is then notified, based on their preferences, with a summary of the emotion detected. Notifications can also be sent to other systems that have been configured to receive them. Optionally, the system may include the ability to store the speech sample and emotion classification detected for future analysis. The system's machine learning classifier is periodically re-trained based on labelled audio speech data and updated.

Abstract: Systems and Methods for assisting the hearing-impaired are described. The methods rely on obtaining audio signals from the ambient environment of a hearing-impaired person. The audio signals are analyzed by a machine learning model that can classify audio signals into audio categories (e.g. Emergency, Animal Sounds) and audio types (e.g. Ambulance Siren, Dog Barking) and notify the user leveraging a mobile or wearable device. The user can configure notification preferences and view historical logs. The machine learning classifier is periodically trained externally based on labelled audio samples. Additional system features include an audio amplification option and a speech to text option for transcribing human speech to text output.

Abstract: Systems and Methods for assisting the hearing-impaired are described. The methods rely on obtaining audio signals from the ambient environment of a hearing-impaired person. The audio signals are analyzed by a machine learning model that can classify audio signals into audio categories (e.g. Emergency, Animal Sounds) and audio types (e.g. Ambulance Siren, Dog Barking) and notify the user leveraging a mobile or wearable device. The user can configure notification preferences and view historical logs. The machine learning classifier is periodically trained externally based on labelled audio samples. Additional system features include an audio amplification option and a speech to text option for transcribing human speech to text output.

Abstract: A truck box assembly including a sidewall, a pair of doors, and a support assembly is provided. The sidewall may define an opening to provide clearance for a wheel house. Each of the pair of doors extends from one side of the opening. The support assembly may be mounted to a truck box and support the sidewall as the sidewall moves between retracted and expanded positions. Each of the doors may be moveable between an open position and a closed position filling the opening in the expanded position. The support assembly may include a mount post secured to the truck box at a standard interface plate (SIP) bracket and a horizontal member extending from the mount post and secured to the truck box at a stake pocket hole.

Abstract: A truck box assembly including a sidewall, a pair of doors, and a support assembly is provided. The sidewall may define an opening to provide clearance for a wheel house. Each of the pair of doors extends from one side of the opening. The support assembly may be mounted to a truck box and support the sidewall as the sidewall moves between retracted and expanded positions. Each of the doors may be moveable between an open position and a closed position filling the opening in the expanded position. The support assembly may include a mount post secured to the truck box at a standard interface plate (SIP) bracket and a horizontal member extending from the mount post and secured to the truck box at a stake pocket hole.