Tel Aviv, Israel
Tel Aviv, Israel

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.

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Disclosed are systems, methods, devices, circuits, and associated computer executable code for taste profiling of internet or network users. A User Events Analysis Server filters out vast amounts of irrelevant data, hard to isolate in conventional methods, and extracts valuable data from web-browsing or networking events. A User Taste Profiling Server automatically generates domain specific (e.g. media content) semantic taste profiles for users associated with the filtered and extracted web-browsing or networking events. Among other applications, such taste profiles may facilitate effective targeting of advertising campaigns in the given content domain.

Jinni | Date: 2010-08-18

A method includes operating a content crawler over a machine communication network to form a set of input content; operating a mapper on a machine memory comprising the input content to form non-transitory machine logic comprising genes for the content, wherein operating the mapper comprises the application of previously mapped genes for the input content to the mapping of subsequent genes for the input content; and applying the genes to a machine memory storing a database of content from which user recommendations are formed.

Jinni said AT&T is using its recommendation engine to offer U-verse TV video-on-demand subscribers a personalized program guide. The deal, announced Wednesday, is one of Jinni’s biggest since it was founded in 2008. The Tel Aviv-based company also licenses its semantic discovery product to Comcast, VUDU and Microsoft for use on its Xbox gaming consoles. International customers include Belgium’s Belgacom, Spain’s Prisa TV and SingTel in Singapore. Jinni’s recommendation engine is unique in the way it indexes movies and TV shows with  “descriptive gene tags” such as “clever heist,” “surprising twist” and “offbeat.” When combined with anonymous data that details the viewing habits of U-verse subscribers, it could allow AT&T to recommend that subscribers who it believes are in the mood for "rough criminal heroes" watch CBS's Hawaii Five-0, 22 Bullets or Gangster Squad. Jinni said AT&T is the largest customer of Ericsson’s Mediaroom platform to deploy its recommendation engine in the United States. In October 2012 – nearly a year before Microsoft sold Mediaroom to Ericsson – Jinni rival ThinkAnalytics said that Microsoft had added its recommendation engine to its Mediaroom IPTV partner program. AT&T is using Jinni on every set-top deployed in U-verse homes, Jinni said. “Working with AT&T U-Verse is a major milestone toward realizing our vision of making the video discovery experience an intuitive, enjoyable part of watching TV,” CEO Yosi Glick said in Tuesday’s announcement. More

News Article | December 1, 2014

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

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.)

News Article | February 27, 2015

I live in Boston, so you'll excuse me if I've been a bit surly lately. Every parking spot is marked with some jamoke's busted-up lawn chair, giant icicles are tumbling from rooftops like thunderbolts thrown by Zeus, and the mass transit system is pokier than a kiddie train at an amusement park. Except far less reliable. Anyway, here are some apps to help get you through the rest of the winter. Leave me alone. Try Jetsetter (iOS, Web). This last-minute travel-deals site is chock-full of discounted hotel rooms at fancy places around the world. The deals run from so-so to stellar, and include some exclusives that can top out at 60% off regular prices. The iOS app leverages your camera to scan your credit card so you can book a trip in less than a minute. Dangerous. Make sure to load up the app (Android, iOS). Believe it or believe it, getting 700 feet of snow dumped on top of the Northeast has made for some pretty great skiing out here. has the lowdown on about 5,000 resorts around the world, and can pinpoint the closest one to you. You can see how many runs are open (hint: all of them, if you're in the Northeast), view zoomable trail maps, look up ticket prices, and even view live video feeds from various resorts. Quantify your feelings with Moodlytics (Android, iOS). My mood has been totally fine, so I don't need something that can track more than 40 variations, complete with charts and calendars and suggestions for how to improve said mood. I don't need to set goals in order to reach an optimum number for my own personal Happiness Index. I don't need to export the data to a PDF file so I can share it with my therapist. I'm fine! Work out at home with Virtual Trainer Bodyweight (Android, iOS). As its name suggests, you use your own body weight to perform exercises from the comfort of your own home. The app features instructional videos for 46 exercises that you can cobble together into a custom workout routine. You'll earn points for each exercise you complete, and you can target specific muscle groups. Take Jinni (iOS, Web) for a spin. You tell it what you like, and it'll recommend stuff to watch on TV and on Amazon, Netflix, Hulu Plus, and iTunes. Jinni's content recommendation engine runs deeper than most, tracking content based on things like mood, plot, place, style, type of humor, story tempo, levity, and a whole bunch of other markers. Take some time to train it, and you'll be pleasantly surprised by its recommendations.

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.

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, methods and computer executable code sets for assessing the relevance and impact of a secondary content, such as an advertisement, presented within primary content. A first assessment module is used for assessing a taste profile based relevance and/or impact of the primary content, and/or the secondary content, on the target viewer. A second assessment module is used for assessing a correlation level between the primary content and/or segments thereof, and the secondary content and/or segments thereof.

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.

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