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Jinn or djinn are supernatural creatures in Islamic mythology as well as pre-Islamic Arabian mythology. They are mentioned frequently in the Quran and other Islamic texts and inhabit an unseen world called Djinnestan, another universe beyond the known universe. The Quran says that the jinn are made of a smokeless and "scorching fire", but are also physical in nature, being able to interact in a tactile manner with people and objects and likewise be acted upon. The jinn, humans and angels make up the three known sapient creations of God. Like human beings, the jinn can be good, evil, or neutrally benevolent and hence have free will like humans and unlike angels. The shaytan jinn are the analogue of demons in Christian tradition, but the jinn are not angels and the Quran draws a clear distinction between the two creations. The Quran states in surat Al-Kahf , Ayah 50, that Iblis is one of the jinn. Wikipedia.

Disclosed are systems, apparatuses, circuits, methods and computer executable code sets for generating and providing content recommendations to match the tastes and preferences of a group of users. a Recommendation Engine is used for generating two or more individual content recommendation sets for each of the members in the user group. A Recommendation Aggregation Module is used for adding and combining the individual content recommendation sets into an aggregated recommendation set. a Recommendation Selection Module is used for selecting at least a subset of the content items in the aggregated recommendation set for inclusion in a content recommendation result set. A Profile Engine is used for building individual group users profiles from which a merged group profile is constructed, or for building a single joint group profile based on inputs from multiple group users.

Disclosed are systems, apparatuses, circuits and methods for extrapolating meaning from vocalized speech or otherwise obtained text. Speech of a speaking user is sampled and digitized, the digitized speech is converted into a text stream, the text stream derived from speech or otherwise obtained is analyzed syntactically and semantically, a knowledgebase in the specific context domain of the text stream is utilized to construct one or more semantic/syntactic domain specific query analysis constrains/rule-sets, and a Domain Specific Knowledgebase Query (DSKQ) or set of queries is built at least partially based on the domain specific query analysis constrains/rule-sets.

Disclosed are systems, apparatuses, circuits, methods and computer executable code sets for generating and providing hybrid content recommendations. One or more recommendation engines are collaboratively arranged based on the conditions of a recommendation request. The collaborative recommendation engine arrangement is used for generating a set of content recommendations for the request.

News Article | December 1, 2014
Site: www.cedmagazine.com

Walmart’s Vudu service has finally introduced Jinni’s search and discovery system into its user interface for all Vudu users. Jinni announced that Vudu was a customer at the beginning of this year, along with Time Warner Cable, Bouygues Telecom, C More Entertainment (formerly Canal+), Prisa, and SingTel. Jinni takes what it calls a semantic approach to categorizing content. The result, the company claims, is the ability to provide mood-based search and taste-based recommendations, leading to a higher level of personalization. Jinni, like many other search and discovery specialists, goes beyond those obvious categories; it says it characterizes content by mood, style, plot, setting and more. Netflix, for example, has created highly sophisticated algorithms for its search and discovery process, categorizing movies not only by actors, directors, and genres, but also by any number of other characteristics. But Netflix viewers are still generally limited to searching by top level categories such as title, actor, genre. Jinni also differentiates itself from Netflix by pointing out that its categorization process is automated while Netflix uses a manual tagging process. The challenge appears to be finding clients who agree that giving viewers more search options is a useful thing to do. Vudu is adding features that other services, such as Hulu, introduced some time ago, such as creating a page that displays all the shows any particular viewer is still in the middle of watching, and automatically queueing up the next installment of episodic content. Vudu, with Jinni, is going further than many other video services by letting viewers select sub-genres (action comedies, buddy comedies, etc.), or all movies by plot (heros, romance, time travel, etc.), or mood, or years (the ‘30s, the ‘70s, etc.) Yosi Glick, Jinni Co-Founder & CEO added, “We are proud to have a customer like VUDU adopt our solution using our full discovery feature set. This is further validation of our semantic approach we have built over the years which has now become the dominant trend by the leading players.”

News Article | April 13, 2015
Site: www.lightreading.com

There aren't many solo players left delivering TV content recommendation engines, so maybe it's no surprise that one of the big ones is now looking to expand into new markets. Jinni announced this week that it's getting into the ad tech business. The move by Jinni Media Ltd. doesn't deviate very far from the company's technology roots. Jinni said it will use the same Entertainment Genome powering its recommendation engine to help TV and movie studios target advertising on the web and mobile platforms. The proprietary semantic technology matches content inventory with individual user tastes. Repurposed for the ad business, the technology will provide a Demand Side Platform (DSP) for entertainment advertisers that want to carry their promotions over to an online environment. In addition to TV and movie studios, Jinni's new solution is designed for programmers looking to promote content in video-on-demand libraries. Jinni said the product has already been tested in commercial pilots with major Hollywood studios, and that the results showed significant improvement over traditional demographic-based ad targeting. “When it comes to entertainment," declared Jinni Co-Founder and CEO Yosi Glick, "the truth is that totally different demographics often share the same tastes in movies and TV shows. Therefore, online advertising of movies and TV shows needs a fresh approach. With our proprietary moviegoer and TV viewer database and our mature semantic technology to understand user tastes, we can now provide entertainment brands with a much more efficient way to buy their media and target their relevant audiences.” In the service provider world, Jinni already has an impressive line-up of customers. Big names from the list include AT&T Inc. (NYSE: T), Comcast Corp. (Nasdaq: CMCSA, CMCSK), Microsoft Corp. (Nasdaq: MSFT) and Time Warner Cable Inc. (NYSE: TWC). (See AT&T Jumps On Board With Jinni.)

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