首页|Patent Issued for Real-time content integration based on machine learned selecti ons (USPTO 12003577)
Patent Issued for Real-time content integration based on machine learned selecti ons (USPTO 12003577)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-From Alexandria, Virginia, NewsRx jour nalists report that a patent by the inventors Brewer, Jason (Marina del Rey, CA, US), Farnham, Rodrigo B. (Los Angeles, CA, US), Lue, David B. (Santa Monica, CA , US), Stucky-Mack, Nicholas J. (Los Angeles, CA, US), filed on January 19, 2023 , was published online on June 4, 2024. The patent's assignee for patent number 12003577 is Snap Inc. (Santa Monica, Cal ifornia, United States). News editors obtained the following quote from the background information suppli ed by the inventors: "Users can execute applications on their mobile client devi ces to receive posts and collections of content published by other users. For ex ample, a user may browse content within an application and select a content item (e.g., slideshow, article) for viewing. When the content is requested, the serv er handling the request must assemble the content, some of which may be provided by third parties, on-the-fly and send the assembled content to the user before the user notices a delay. The limited amount of time and limited network bandwid th constrain how content is selected for display." As a supplement to the background information on this patent, NewsRx corresponde nts also obtained the inventors' summary information for this patent: "The descr iption that follows includes systems, methods, techniques, instruction sequences , and computing machine program products that embody illustrative embodiments of the disclosure. In the following description, for the purposes of explanation, numerous specific details are set forth in order to provide an understanding of various embodiments of the inventive subject matter. It will be evident, however , to those skilled in the art, that embodiments of the inventive subject matter may be practiced without these specific details. In general, well-known instruct ion instances, protocols, structures, and techniques are not necessarily shown i n detail.
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