Gumgum, Inc.

University of Southern California

Gumgum Sports Inc.

Brian Jongmin Kim

Jeffrey Benjamin Katz

Cambron Neil Carter

Ellis Fan-chuin Meng

Lawrence Yu

Alexander Baldwin

Systems and methods are provided for analyzing images or video using computer vision. Data comprising real time or near real time information or historical information is retrieved that is associated with a sporting event at a physical location. A time segment is identified of a display device at the physical location for acquisition. The display device is configurable to present visual sponsorship data during the time segment for an assigned sponsor. It is determined that one or more rules are satisfied by the data. An indication is transmitted that the first rule is satisfied to a computing device of a sponsor. A bid or valuation is generated based at least on the first rule being satisfied. A request to acquire the time segment is received from the computing device of the sponsor, and the display device at the physical location is caused to present visual sponsorship data for the sponsor during the time segment.

Automated media analysis for sponsor valuation

Systems and methods are provided for determining a media cost equivalent to attribute to the appearance of a sponsor's logo or other object in a media item, such as an image or video. This determination may be based on factors such as clarity, prominence, visibility, size, or placement of a sponsor logo or other object related to the sponsor within at least one media item. One or more machine learning models may be utilized to automatically identify objects within, and otherwise analyze, image data in order to determine sponsor value.

A multi-sensor system may include a catheter that has lumen, is flexible, is made of a polymer, and has a circular cross section that has an outer diameter of no more than 0.5 cm; and one or more sensors that sense multiple characteristics of material flowing within the lumen, including at least two of the following: flow rate, pressure, and composition of the material. A multi-sensor system may include a catheter that has lumen, is flexible, is made of a polymer, and has a circular cross section that has an outer diameter of no more than 0.5 cm; and one or more sensors that sense multiple characteristics of material flowing within the lumen, including at least two of the following: flow rate, pressure, and composition of the material.

Multi-sensor platform for diagnosing catheter status

Automated control of display devices

Systems and methods are described for training machine learning models to detect objects in image or video data. A system may select a first sample set of frames from one or more video files. Indications of a location of an object of interest in each of at least two sample frames may be received, then the system may determine the location of the object of interest across a number of intermediary frames using a tracker. Annotation data may be stored identifying the objects of interest in the sample frames, and the annotation data may be used in training a machine learning model to identify the object of interest in subsequently provided image or video data.

Training machine learning models to detect objects in video data

Systems and methods are described for identifying at least one sports team depicted in media content, such as image or video data. Features of the media content may be provided as input to a first set of classification models that are each trained to identify at least one type of scene associated with one or more sports, then features of the media content may be provided to a second set of classification models trained to identify at least one object associated with one or more sports. Once a sport depicted in the media content is determined based on the first and second set of classification models, the system may determine a team depicted in the media content based at least in part by comparing aspects of the media content to stored data associated with various teams that play the identified sport.

Machine learning models for identifying sports teams depicted in image or video data

Systems and methods are provided for analyzing images or video using computer vision. Media items, including images or videos, are retrieved from one or more media channels, such as social media network services, streaming media networks, or broadcast networks. A media item is identified that depicts a object associated with a sponsor. A media cost equivalent is determined that is associated with the media item as distributed by the media channels. Using computer vision, a percentage is determined for the media cost equivalent. The percentage is determined based at least in part on at least one of size, clarity, duration or position of the sponsor object or another object in the media item. A sponsor valuation is calculated based at least in part on the media cost equivalent adjusted by the percentage.

Automated analysis of image or video data and sponsor valuation

Systems and methods are described for augmenting video data based on automated identification of one or more objects depicted in the video data. One or more classification models may identify an object of interest in video data. An aggregated duration count may be maintained that reflects a length of time that the object of interest has been depicted in the video data. This duration or additional metric data derived in part from the duration may be displayed in association with display of the video data and continuously updated during playback of the video data.

Augmenting video data to present real-time sponsor metrics

Systems and methods are described for using video fingerprinting to detect depiction of one or more objects of interest in video data. An object of interest may first be identified in one or more frames of video data. A system may then create a digital video fingerprint representing the one or more frames in which the object is depicted. A potentially large amount of subsequent video data may then be received or retrieved, and the system may determine that the object appears in a portion of the subsequent video data based at least in part by identifying that the digital video fingerprint at least substantially matches a portion of the subsequent video data.

Identifying visual objects depicted in video data using video fingerprinting

A self-monitoring catheter system may detect an obstruction during use of a catheter. The system may include a catheter with an interior lumen that has one or more ports into the lumen; a first and a second electrode; and an impedance measurement instrument that measures changes in the impedance between the first and the second electrodes after the first electrode is exposed to fluid within the catheter lumen, thereby signaling an obstruction during use of the catheter.

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Los Angeles, CA, United States of America

Brian Jongmin Kim