System and method for matching advertisements to multimedia content elements

Abstract
A system and method for matching an advertisement item to a multimedia content element. The method comprises: extracting at least one multimedia content element from a personalized multimedia content channel, the personalized multimedia content channel having at least one user, wherein multimedia content elements in the personalized multimedia content channel are customized for each user; generating at least one signature of the at least one multimedia content element; searching for at least one advertisement item respective of the at least one generated signature; and causing a display of the at least one advertisement item within a display area of a user node associated with a user of the personalized multimedia content channel.
Description
TECHNICAL FIELD

The present disclosure relates generally to the analysis of multimedia content, and more specifically to a system for matching a relevant advertisement to the analyzed multimedia content to be displayed as part of a web-page on a user's display.


BACKGROUND

The Internet, also referred to as the worldwide web (WWW), has become a mass media where the content presentation is largely supported by paid advertisements that are added to the web-page content. Typically, advertisements are displayed using portions of code written in, for example, hyper-text mark-up language (HTML) or JavaScript that is inserted into, or otherwise called up by documents also written in HTML and which are sent to a user node for display. A web-page typically contains text and multimedia elements that are intended for display on the user's display device.


One of the most common types of advertisements on the Internet is in a form of a banner advertisement. Banner advertisements are generally images or animations that are displayed within a web-page. Other advertisements are simply inserted at various locations within the display area of the document. A typical web-page displayed today is cluttered with many advertisements, which frequently are irrelevant to the content being displayed, and as a result the user's attention is not given to them. Consequently, the advertising price of potentially valuable display area is low because its respective effectiveness is low.


It would therefore be advantageous to provide a solution that would overcome the deficiencies of the prior art by matching an advertisement to an already existing image.


SUMMARY

A summary of several example embodiments of the disclosure follows. This summary is provided for the convenience of the reader to provide a basic understanding of such embodiments and does not wholly define the breadth of the disclosure. This summary is not an extensive overview of all contemplated embodiments, and is intended to neither identify key or critical elements of all embodiments nor to delineate the scope of any or all aspects. Its sole purpose is to present some concepts of one or more embodiments in a simplified form as a prelude to the more detailed description that is presented later. For convenience, the term “some embodiments” may be used herein to refer to a single embodiment or multiple embodiments of the disclosure.


The disclosed embodiments include a method for matching an advertisement item to a multimedia content element. The method comprises extracting at least one multimedia content element from a personalized multimedia content channel, the personalized multimedia content channel having at least one user, wherein multimedia content elements in the personalized multimedia content channel are customized for each user; generating at least one signature of the at least one multimedia content element; searching for at least one advertisement item respective of the at least one generated signature; and causing a display of the at least one advertisement item within a display area of a user node associated with a user of the personalized multimedia content channel.


The disclosed embodiments also include a system for matching advertisement items to multimedia content elements. The system comprises a processing unit; and a memory, the memory containing instructions that, when executed by the processing unit, configure the system to: extract at least one multimedia content element from a personalized multimedia content channel, the personalized multimedia content channel having at least one user, wherein multimedia content elements in the personalized multimedia content channel are customized for each user; generate at least one signature of the at least one multimedia content element; search for at least one advertisement item respective of the at least one generated signature; and cause a display of the at least one advertisement item within a display area of a user node associated with a user of the personalized multimedia content channel.





BRIEF DESCRIPTION OF THE DRAWINGS

The subject matter disclosed herein is particularly pointed out and distinctly claimed in the claims at the conclusion of the specification. The foregoing and other objects, features, and advantages of the disclosed embodiments will be apparent from the following detailed description taken in conjunction with the accompanying drawings.



FIG. 1 is a schematic block diagram of a system for processing multimedia content displayed on a web-page according to an embodiment.



FIG. 2 is a flowchart describing the process of matching an advertisement to multimedia content displayed on a web-page according to an embodiment.



FIG. 3 is a block diagram depicting the basic flow of information in the signature generator system.



FIG. 4 is a diagram showing the flow of patches generation, response vector generation, and signature generation in a large-scale speech-to-text system.





DETAILED DESCRIPTION

It is important to note that the embodiments disclosed herein are only examples of the many advantageous uses of the innovative teachings herein. In general, statements made in the specification of the present application do not necessarily limit any of the various claimed embodiments. Moreover, some statements may apply to some inventive features but not to others. In general, unless otherwise indicated, singular elements may be in plural and vice versa with no loss of generality. In the drawings, like numerals refer to like parts through several views.


Certain exemplary embodiments disclosed herein allow matching at least an appropriate advertisement that is relevant to a multimedia content displayed on a web-page, and analyzing the multimedia content displayed on the web-page accordingly. Based on the analysis results, for one or more multimedia content elements included the web-page, one or more matching signatures are generated. The signatures are utilized to search for appropriate advertisement(s) to be displayed in the web-page. In one embodiment, in addition to the signatures, the advertisements can be searched and used in the web-page, based on extracted taxonomies, context, and/or the user preferences.



FIG. 1 shows an exemplary and non-limiting schematic diagram of a system 100 for providing advertisements for matching multimedia content displayed in a web-page in accordance one embodiment. A network 110 is used to communicate between different parts of the system. The network 110 may be the Internet, the world-wide-web (WWW), a local area network (LAN), a wide area network (WAN), a metro area network (MAN), and other networks capable of enabling communication between the elements of the system 100.


Further connected to the network 110 are one or more client applications, such as web browsers (WB) 120-1 through 120-n (collectively referred hereinafter as web browsers 120 or individually as a web browser 120, merely for simplicity purposes). A web browser 120 is executed over a computing device including, for example, personal computers (PCs), personal digital assistants (PDAs), mobile phones, a tablet computer, and other kinds of wired and mobile appliances, equipped with browsing, viewing, listening, filtering, and managing capabilities etc., that are enabled as further discussed herein below.


A server 130 is further connected to the network 110 and may provide to a web browser 120 web-pages containing multimedia content, or references therein, such that upon request by a web browser, such multimedia content is provided to the web browser 120. The system 100 also includes a signature generator system (SGS) 140. In one embodiment, the SGS 140 is connected to the server 130. The server 130 is enabled to receive and serve multimedia content and causes the SGS 140 to generate a signature respective of the multimedia content. The process for generating the signatures for multimedia content, is explained in more detail herein below with respect to FIGS. 3 and 4. The various elements of the system 100 as depicted in FIG. 1 are also described in the above-referenced U.S. Pat. No. 8,959,037 to Raichelgauz, et al., which is assigned to common assignee, and is incorporated hereby by reference for all that it contains. It should be noted that each of the server 130 and the SGS 140, typically comprises a processing unit, such as processor (not shown) that is coupled to a memory. The memory contains instructions that can be executed by the processing unit. The server 130 also includes an interface (not shown) to the network 110.


A plurality of publisher servers 150-1 through 150-m are also connected to the network 110, each of which is configured to generate and send online advertisements to the server 130. The publisher servers 150-1 through 150-m typically receive the advertised content from advertising agencies that set the advertising campaign. In one embodiment, the advertisements may be stored in a data warehouse 160 which is connected to the server 130 (either directly or through the network 110) for further use.


The system 100 may be configured to generate customized channels of multimedia content. Accordingly, a web browser 120 or a client channel manager application (not shown), available on either the server 130, on the web browser 120, or as an independent or plug-in application, may enable a user to create customized channels of multimedia content. Such customized channels of multimedia content are personalized content channels that are generated in response to selections made by a user of the web browser 120 or the client channel manager application. The system 100, and in particular the server 130 in conjunction with the SGS 140, determines which multimedia content is more suitable to be viewed, played or otherwise utilized by the user with respect to a given channel, based on the signatures of selected multimedia content. These channels may optionally be shared with other users, used and/or further developed cooperatively, and/or sold to other users or providers, and so on. The process for defining, generating and customizing the channels of multimedia content are described in greater detail in the above-referenced U.S. Pat. No. 8,959,037.


According to the embodiments disclosed herein, a user visits a web-page using a web-browser 120. When the web-page is uploaded on the user's web-browser 120, a request is sent to the server 130 to analyze the multimedia content contained in the web-page. The request to analyze the multimedia content can be generated and sent by a script executed in the web-page, an agent installed in the web-browser, or by one of the publisher servers 150 when requested to upload on or more advertisements to the web-page. The request to analyze the multimedia content may include a URL of the web-page or a copy of the web-page. In one embodiment, the request may include multimedia content elements extracted from the web-page. A multimedia content element may include, for example, an image, a graphic, a video stream, a video clip, an audio stream, an audio clip, a video frame, a photograph, and an image of signals (e.g., spectrograms, phasograms, scalograms, etc.), and/or combinations thereof and portions thereof.


The server 130 analyzes the multimedia content elements in the web-page to detect one or matching advertisements for the multimedia content elements. It should be noted that the server 130 may analyze all or a sub-set of the multimedia content elements contained in the web-page. It should be further noted that the number of matching advertisements that are provided in response for the analysis can be determined based on the number of advertisement banners that can be displayed on the web-page or pre-configured by a campaign manager. The SGS 140 generates for each multimedia content element provided by the server 130 at least one signature. The generated signature(s) may be robust to noise and distribution as discussed below. Then, using the generated signature(s) the server 130 searches the data warehouse 160 for a matching advertisement. For example, if the signature of an image indicates a “sea shore” then an advertisement for a swimsuit can be a potential matching advertisement.


It should be noted that using signatures for searching of advertisements ensures more accurate reorganization of multimedia content than, for example, when using metadata. For instance, in order to provide a matching advertisement for a sport car it may be desirable to locate a car of a particular model. However, in most cases the model of the car would not be part of the metadata associated with the multimedia content (image). Moreover, the car shown in an image may be at angles different from the angles of a specific photograph of the car that is available as a search item. The signature generated for that image would enable accurate recognition of the model of the car because the signatures generated for the multimedia content elements, according to the disclosed embodiments, allow for recognition and classification of multimedia elements, such as, content-tracking, video filtering, multimedia taxonomy generation, video fingerprinting, speech-to-text, audio classification, element recognition, video/image search and any other application requiring content-based signatures generation and matching for large content volumes such as, web and other large-scale databases.


In one embodiment, the signatures generated for more than one multimedia content element are clustered. The clustered signatures are used to search for a matching advertisement. The one or more selected matching advertisements are retrieved from the data warehouse 160 and uploaded to the web-page on the web browser 120 by means of one of the publisher servers 150.



FIG. 2 depicts an exemplary and non-limiting flowchart 200 describing the process of matching an advertisement to multimedia content displayed on a web-page according to an embodiment. At S205, the method starts when a web-page is uploaded to one of the web-browsers (e.g., web-browser 120-1). In S210, a request to match at least one multimedia content element contained in the uploaded web-page to an appropriate advertisement item is received. The request can be received from a publisher server (e.g., a server 150-1), a script running on the uploaded web-page, or an agent (e.g., an add-on) installed in the web-browser. S210 can also include extracting the multimedia content elements for a signature that should be generated.


In S220, a signature to the multimedia content element is generated. The signature for the multimedia content element generated by a signature generator is described below. In S230, an advertisement item is matched to the multimedia content element respective of its generated signature. In one embodiment, the matching process includes searching for at least one advertisement item respective of the signature of the multimedia content and a display of the at least one advertisement item within the display area of the web-page. In one embodiment, the matching of an advertisement to a multimedia content element can be performed by the computational cores that are part of a large scale matching discussed in detail below.


In S240, upon a user's gesture, the advertisement item is uploaded to the web-page and displayed therein. The user's gesture may be: a scroll on the multimedia content element, a press on the multimedia content element, and/or a response to the multimedia content. This ensures that the user attention is given to the advertised content. In S250 it is checked whether there are additional requests to analyze multimedia content elements, and if so, execution continues with S210; otherwise, execution terminates.


As a non-limiting example, a user uploads a web-page that contains an image of a sea shore. The image is then analyzed and a signature is generated respective thereto. Respective of the image signature, an advertisement item (e.g., a banner) is matched to the image, for example, a swimsuit advertisement. Upon detection of a user's gesture, for example, a mouse scrolling over the sea shore image, the swimsuit ad is displayed.


The web-page may contain a number of multimedia content elements; however, in some instances only a few advertisement items may be displayed in the web-page. Accordingly, in one embodiment, the signatures generated for the multimedia content elements are clustered and the cluster of signatures is matched to one or more advertisement items.



FIGS. 3 and 4 illustrate the generation of signatures for the multimedia content elements by the SGS 140 according to one embodiment. An exemplary high-level description of the process for large scale matching is depicted in FIG. 3. In this example, the matching is for a video content.


Video content segments 2 from a Master database (DB) 6 and a Target DB 1 are processed in parallel by a large number of independent computational Cores 3 that constitute an architecture for generating the Signatures (hereinafter the “Architecture”). Further details on the computational Cores generation are provided below. The independent Cores 3 generate a database of Robust Signatures and Signatures 4 for Target content-segments 5 and a database of Robust Signatures and Signatures 7 for Master content-segments 8. An exemplary and non-limiting process of signature generation for an audio component is shown in detail in FIG. 4. Finally, Target Robust Signatures and/or Signatures are effectively matched, by a matching algorithm 9, to Master Robust Signatures and/or Signatures database to find all matches between the two databases.


To demonstrate an example of signature generation process, it is assumed, merely for the sake of simplicity and without limitation on the generality of the disclosed embodiments, that the signatures are based on a single frame, leading to certain simplification of the computational cores generation. The Matching System is extensible for signatures generation capturing the dynamics in-between the frames.


The Signatures' generation process will now be described with reference to FIG. 4. The first step in the process of signatures generation from a given speech-segment is to breakdown the speech-segment to K patches 14 of random length P and random position within the speech segment 12. The breakdown is performed by the patch generator component 21. The value of the number of patches K, random length P and random position parameters is determined based on optimization, considering the tradeoff between accuracy rate and the number of fast matches required in the flow process of the server 130 and SGS 140. Thereafter, all the K patches are injected in parallel into all computational Cores 3 to generate K response vectors 22, which are fed into a signature generator system 23 to produce a database of Robust Signatures and Signatures 4.


In order to generate Robust Signatures, i.e., Signatures that are robust to additive noise L (where L is an integer equal to or greater than 1) by the Computational Cores 3 a frame ‘i’ is injected into all the Cores 3. Then, Cores 3 generate two binary response vectors: {right arrow over (S)} which is a Signature vector, and {right arrow over (RS)} which is a Robust Signature vector.


For generation of signatures robust to additive noise, such as White-Gaussian-Noise, scratch, etc., but not robust to distortions, such as crop, shift and rotation, etc., a core Ci={ni} (1≦i≦L) may consist of a single leaky integrate-to-threshold unit (LTU) node or more nodes. The node ni equations are:







V
i

=



j




w
ij



k
j









n
i=custom character(Vi−Thx)


where, custom character is a Heaviside step function; wij is a coupling node unit (CNU) between node i and image component j (for example, grayscale value of a certain pixel j); kj is an image component T (for example, grayscale value of a certain pixel j); Thx is a constant Threshold value, where x is ‘j’ for Signature and ‘RS’ for Robust Signature; and Vi is a Coupling Node Value.


The Threshold values Thx are set differently for Signature generation and for Robust Signature generation. For example, for a certain distribution of Vi values (for the set of nodes), the thresholds for Signature (ThS) and Robust Signature (ThRS) are set apart, after optimization, according to at least one or more of the following criteria:

1: For: Vi>ThRS
1−p(V>ThS)−1−(1−ε)l<<1


i.e., given that l nodes (cores) constitute a Robust Signature of a certain image I, the probability that not all of these I nodes will belong to the Signature of same, but noisy image, Ĩ is sufficiently low (according to a system's specified accuracy).

2: p(Vi>ThRS)≈l/L

i.e., approximately l out of the total L nodes can be found to generate a Robust Signature according to the above definition.


3: Both Robust Signature and Signature are generated for certain frame i.


It should be understood that the generation of a signature is unidirectional, and typically yields lossless compression, where the characteristics of the compressed data are maintained but the uncompressed data cannot be reconstructed. Therefore, a signature can be used for the purpose of comparison to another signature without the need of comparison to the original data. The detailed description of the Signature generation is discussed in more detail in the above-referenced U.S. Pat. No. 8,326,775, of which this patent application is a continuation-in-part, and is hereby incorporated by reference.


A Computational Core generation is a process of definition, selection, and tuning of the parameters of the cores for a certain realization in a specific system and application. The process is based on several design considerations, such as:


(a) The Cores should be designed so as to obtain maximal independence, i.e., the projection from a signal space should generate a maximal pair-wise distance between any two cores' projections into a high-dimensional space.


(b) The Cores should be optimally designed for the type of signals, i.e., the Cores should be maximally sensitive to the spatio-temporal structure of the injected signal, for example, and in particular, sensitive to local correlations in time and space. Thus, in some cases a core represents a dynamic system, such as in state space, phase space, edge of chaos, etc., which is uniquely used herein to exploit their maximal computational power.


(c) The Cores should be optimally designed with regard to invariance to a set of signal distortions, of interest in relevant applications.


The various embodiments disclosed herein can be implemented as hardware, firmware, software, or any combination thereof. Moreover, the software is preferably implemented as an application program tangibly embodied on a program storage unit or computer readable medium consisting of parts, or of certain devices and/or a combination of devices. The application program may be uploaded to, and executed by, a machine comprising any suitable architecture. Preferably, the machine is implemented on a computer platform having hardware such as one or more central processing units (“CPUs”), a memory, and input/output interfaces. The computer platform may also include an operating system and microinstruction code. The various processes and functions described herein may be either part of the microinstruction code or part of the application program, or any combination thereof, which may be executed by a CPU, whether or not such a computer or processor is explicitly shown. In addition, various other peripheral units may be connected to the computer platform such as an additional data storage unit and a printing unit. Furthermore, a non-transitory computer readable medium is any computer readable medium except for a transitory propagating signal.


All examples and conditional language recited herein are intended for pedagogical purposes to aid the reader in understanding the principles of the disclosed embodiment and the concepts contributed by the inventor to furthering the art, and are to be construed as being without limitation to such specifically recited examples and conditions. Moreover, all statements herein reciting principles, aspects, and embodiments of the disclosed embodiments, as well as specific examples thereof, are intended to encompass both structural and functional equivalents thereof. Additionally, it is intended that such equivalents include both currently known equivalents as well as equivalents developed in the future, i.e., any elements developed that perform the same function, regardless of structure.

Claims
  • 1. A method implemented by a computing system for matching an advertisement item to a multimedia content element, comprising: extracting at least one multimedia content element from a personalized multimedia content channel, the personalized multimedia content channel having at least one user, wherein multimedia content elements in the personalized multimedia content channel are customized for each user;generating at least one signature for the at least one multimedia content element, wherein the generating further comprises processing a plurality of content segments of the multimedia content element in parallel by a plurality of independent computational cores and wherein the content segments are further broken into patches having random lengths and are located at random positions within the multimedia content element, each patch being injected into a respective one of the plurality of independent computational cores;searching for at least one advertisement item based on the at least one generated signature; andcausing a display of the at least one advertisement item within a display area of a user node associated with a user of the personalized multimedia content channel.
  • 2. The method of claim 1, wherein the at least one advertisement item is displayed based on the user node when a gesture of a user is detected by the user node.
  • 3. The method of claim 1, further comprising: clustering the at least one generated signature for each of the at least one multimedia content element, wherein the at least one advertisement item is searched for based on the clustered signatures.
  • 4. The method of claim 1, wherein the multimedia content element is at least one of: an image, graphics, a video stream, a video clip, an audio stream, an audio clip, a video frame, a photograph, and images of signals.
  • 5. The method of claim 1, further comprising: pushing multimedia content elements to the personalized multimedia content channel based on preferences of the associated user.
  • 6. The method of claim 1, further comprising: removing multimedia content elements from the personalized multimedia content channel based on preferences of the associated user.
  • 7. A non-transitory computer readable medium having stored thereon instructions for causing one or more processing units to execute the method according to claim 1.
  • 8. A system for matching advertisement items to multimedia content elements, comprising: a processing unit; anda memory, the memory containing instructions that, when executed by the processing unit, configure the system to:extract at least one multimedia content element from a personalized multimedia content channel, the personalized multimedia content channel having at least one user, wherein multimedia content elements in the personalized multimedia content channel are customized for each user;generate at least one signature of the at least one multimedia content element, wherein to generate the at least one signature a plurality of content segments of the multimedia content element are processed in parallel by a plurality of independent computational cores, and wherein the content segments are further broken into patches having random lengths and are located at random positions within the multimedia content element, each patch being injected into a respective one of the plurality of independent computational cores;search for at least one advertisement item based on the at least one generated signature; andcause a display of the at least one advertisement item within a display area of a user node associated with a user of the personalized multimedia content channel.
  • 9. The system of claim 8, wherein the at least one advertisement item is displayed based on the user node when a gesture of a user is detected by the user node.
  • 10. The system of claim 8, wherein the system is further configured to: cluster the at least one generated signature for each of the at least one multimedia content element, wherein the at least one advertisement item is searched for based on the clustered signatures.
  • 11. The system of claim 8, wherein the multimedia content element is at least one of: an image, graphics, a video stream, a video clip, an audio stream, an audio clip, a video frame, a photograph, and images of signals.
  • 12. The system of claim 8, wherein the system is further configured to: push multimedia content elements to the personalized multimedia content channel based on preferences of the associated user.
  • 13. The system of claim 8, wherein the system is further configured to: remove multimedia content elements from the personalized multimedia content channel based on preferences of the associated user.
  • 14. A method for matching an advertisement item to a multimedia content element in a network-based system, comprising: receiving in the network-based system from a client of at least one user an indication of at least one multimedia content element from a personalized multimedia content channel, the personalized multimedia content channel being at least for the at least one user, wherein multimedia content elements in the personalized multimedia content channel are customized for each user;generating in the network-based system at least one signature for the at least one multimedia content element, wherein the generating further comprises processing a plurality of content segments of the multimedia content element in parallel by a plurality of independent computational cores and wherein the content segments are further broken into patches having random lengths and are located at random positions within the multimedia content element, each patch being injected into a respective one of the plurality of independent computational cores;searching in the network-based system for at least one advertisement item based on the at least one generated signature; andtransmitting from the network-based system toward the client the at least one advertisement item, the advertising item being suitable for display within an area of the client.
  • 15. The method of claim 1, wherein at least one of the at least one signature for the at least one multimedia content element signature is a robust signature.
  • 16. The system of claim 8, wherein at least one of the at least one signature for the at least one multimedia content element signature is a robust signature.
Priority Claims (3)
Number Date Country Kind
171577 Oct 2005 IL national
173409 Jan 2006 IL national
185414 Aug 2007 IL national
CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No. 13/624,397 filed on Sep. 21, 2012, now allowed. The Ser. No. 13/624,397 Application is a continuation-in-part (CIP) of: (a) U.S. patent application Ser. No. 13/344,400 filed on Jan. 5, 2012, now U.S. Pat. No. 8,959,037, which is a continuation of U.S. patent application Ser. No. 12/434,221, filed May 1, 2009, now U.S. Pat. No. 8,112,376; (b) U.S. patent application Ser. No. 12/195,863, filed Aug. 21, 2008, now U.S. Pat. No. 8,326,775, which claims priority under 35 USC 119 from Israeli Application No. 185414 filed on Aug. 21, 2007, and which is also a continuation-in-part of the below referenced U.S. patent application Ser. No. 12/084,150; and (c) U.S. patent application Ser. No. 12/084,150 filed on Apr. 25, 2008, now U.S. Pat. No. 8,655,801, which is the National Stage of International Application No. PCT/IL2006/001235, filed on Oct. 26, 2006, which claims foreign priority from Israeli Application No. 171577 filed on Oct. 26, 2005, and Israeli Application No. 173409 filed on Jan. 29, 2006. All of the applications referenced above are herein incorporated by reference for all that they contain.

US Referenced Citations (285)
Number Name Date Kind
4733353 Jaswa Mar 1988 A
4932645 Schorey et al. Jun 1990 A
4972363 Nguyen et al. Nov 1990 A
5307451 Clark Apr 1994 A
5568181 Greenwood et al. Oct 1996 A
5806061 Chaudhuri et al. Sep 1998 A
5852435 Vigneaux et al. Dec 1998 A
5870754 Dimitrova et al. Feb 1999 A
5873080 Coden et al. Feb 1999 A
5887193 Takahashi et al. Mar 1999 A
5978754 Kumano Nov 1999 A
6052481 Grajski et al. Apr 2000 A
6076088 Paik et al. Jun 2000 A
6122628 Castelli et al. Sep 2000 A
6128651 Cezar Oct 2000 A
6137911 Zhilyaev Oct 2000 A
6144767 Bottou et al. Nov 2000 A
6147636 Gershenson Nov 2000 A
6243375 Speicher Jun 2001 B1
6243713 Nelson et al. Jun 2001 B1
6329986 Cheng Dec 2001 B1
6381656 Shankman Apr 2002 B1
6411229 Kobayashi Jun 2002 B2
6422617 Fukumoto et al. Jul 2002 B1
6523046 Liu et al. Feb 2003 B2
6524861 Anderson Feb 2003 B1
6550018 Abonamah et al. Apr 2003 B1
6594699 Sahai et al. Jul 2003 B1
6611628 Sekiguchi et al. Aug 2003 B1
6618711 Ananth Sep 2003 B1
6643620 Contolini et al. Nov 2003 B1
6643643 Lee et al. Nov 2003 B1
6665657 Dibachi Dec 2003 B1
6704725 Lee Mar 2004 B1
6732149 Kephart May 2004 B1
6751363 Natsev et al. Jun 2004 B1
6751613 Lee et al. Jun 2004 B1
6754435 Kim Jun 2004 B2
6763519 McColl et al. Jul 2004 B1
6774917 Foote et al. Aug 2004 B1
6795818 Lee Sep 2004 B1
6804356 Krishnamachari Oct 2004 B1
6819797 Smith et al. Nov 2004 B1
6845374 Oliver et al. Jan 2005 B1
6901207 Watkins May 2005 B1
6938025 Lulich et al. Aug 2005 B1
7006689 Kasutani Feb 2006 B2
7013051 Sekiguchi et al. Mar 2006 B2
7020654 Najmi Mar 2006 B1
7043473 Rassool et al. May 2006 B1
7047033 Wyler May 2006 B2
7199798 Echigo et al. Apr 2007 B1
7260564 Lynn et al. Aug 2007 B1
7277928 Lennon Oct 2007 B2
7302117 Sekiguchi et al. Nov 2007 B2
7313805 Rosin et al. Dec 2007 B1
7340458 Vaithilingam et al. Mar 2008 B2
7353224 Chen et al. Apr 2008 B2
7376672 Weare May 2008 B2
7376722 Sim et al. May 2008 B1
7433895 Li et al. Oct 2008 B2
7464086 Black et al. Dec 2008 B2
7526607 Singh et al. Apr 2009 B1
7536417 Walsh et al. May 2009 B2
7574668 Nunez et al. Aug 2009 B2
7577656 Kawai et al. Aug 2009 B2
7657100 Gokturk et al. Feb 2010 B2
7660468 Gokturk et al. Feb 2010 B2
7660737 Lim et al. Feb 2010 B1
7694318 Eldering et al. Apr 2010 B2
7697791 Chan et al. Apr 2010 B1
7769221 Shakes et al. Aug 2010 B1
7788132 Desikan et al. Aug 2010 B2
7836054 Kawai et al. Nov 2010 B2
7860895 Scofield et al. Dec 2010 B1
7904503 De Mar 2011 B2
7920894 Wyler Apr 2011 B2
7921107 Chang et al. Apr 2011 B2
7974994 Li et al. Jul 2011 B2
7987194 Walker et al. Jul 2011 B1
7987217 Long et al. Jul 2011 B2
7991715 Schiff et al. Aug 2011 B2
8000655 Wang et al. Aug 2011 B2
8036893 Reich Oct 2011 B2
8098934 Vincent et al. Jan 2012 B2
8112376 Raichelgauz et al. Feb 2012 B2
8266185 Raichelgauz et al. Sep 2012 B2
8312031 Raichelgauz et al. Nov 2012 B2
8315442 Gokturk et al. Nov 2012 B2
8316005 Moore Nov 2012 B2
8326775 Raichelgauz et al. Dec 2012 B2
8345982 Gokturk et al. Jan 2013 B2
8548828 Longmire Oct 2013 B1
8655801 Raichelgauz et al. Feb 2014 B2
8677377 Cheyer et al. Mar 2014 B2
8682667 Haughay Mar 2014 B2
8688446 Yanagihara Apr 2014 B2
8706503 Cheyer et al. Apr 2014 B2
8775442 Moore et al. Jul 2014 B2
8799195 Raichelgauz et al. Aug 2014 B2
8799196 Raichelquaz et al. Aug 2014 B2
8818916 Raichelgauz et al. Aug 2014 B2
8868619 Raichelgauz et al. Oct 2014 B2
8880539 Raichelgauz et al. Nov 2014 B2
8880566 Raichelgauz et al. Nov 2014 B2
8886648 Procopio et al. Nov 2014 B1
8898568 Bull et al. Nov 2014 B2
8922414 Raichelgauz et al. Dec 2014 B2
8959037 Raichelgauz et al. Feb 2015 B2
8990125 Raichelgauz et al. Mar 2015 B2
9009086 Raichelgauz et al. Apr 2015 B2
9031999 Raichelgauz et al. May 2015 B2
9087049 Raichelgauz et al. Jul 2015 B2
9104747 Raichelgauz et al. Aug 2015 B2
9191626 Raichelgauz et al. Nov 2015 B2
9197244 Raichelgauz et al. Nov 2015 B2
9218606 Raichelgauz et al. Dec 2015 B2
9235557 Raichelgauz et al. Jan 2016 B2
9256668 Raichelgauz et al. Feb 2016 B2
9330189 Raichelgauz et al. May 2016 B2
9438270 Raichelgauz et al. Sep 2016 B2
20010019633 Tenze et al. Sep 2001 A1
20010056427 Yoon et al. Dec 2001 A1
20020019881 Bokhari et al. Feb 2002 A1
20020038299 Zernik et al. Mar 2002 A1
20020059580 Kalker et al. May 2002 A1
20020087530 Smith et al. Jul 2002 A1
20020099870 Miller et al. Jul 2002 A1
20020107827 Benitez-Jimenez et al. Aug 2002 A1
20020123928 Eldering Sep 2002 A1
20020126872 Brunk et al. Sep 2002 A1
20020129296 Kwiat et al. Sep 2002 A1
20020143976 Barker et al. Oct 2002 A1
20020152267 Lennon Oct 2002 A1
20020157116 Jasinschi Oct 2002 A1
20020159640 Vaithilingam et al. Oct 2002 A1
20020161739 Oh Oct 2002 A1
20020163532 Thomas et al. Nov 2002 A1
20020174095 Lulich et al. Nov 2002 A1
20020178410 Haitsma et al. Nov 2002 A1
20030028660 Igawa et al. Feb 2003 A1
20030041047 Chang et al. Feb 2003 A1
20030050815 Seigel et al. Mar 2003 A1
20030078766 Appelt et al. Apr 2003 A1
20030086627 Berriss et al. May 2003 A1
20030126147 Essafi et al. Jul 2003 A1
20030182567 Barton et al. Sep 2003 A1
20030191764 Richards Oct 2003 A1
20030200217 Ackerman Oct 2003 A1
20030217335 Chung et al. Nov 2003 A1
20040003394 Ramaswamy Jan 2004 A1
20040025180 Begeja et al. Feb 2004 A1
20040068510 Hayes et al. Apr 2004 A1
20040107181 Rodden Jun 2004 A1
20040111465 Chuang et al. Jun 2004 A1
20040117367 Smith et al. Jun 2004 A1
20040128142 Whitham Jul 2004 A1
20040128511 Sun et al. Jul 2004 A1
20040133927 Sternberg et al. Jul 2004 A1
20040153426 Nugent Aug 2004 A1
20040215663 Liu et al. Oct 2004 A1
20040249779 Nauck et al. Dec 2004 A1
20040260688 Gross Dec 2004 A1
20040267774 Lin et al. Dec 2004 A1
20050131884 Gross et al. Jun 2005 A1
20050144455 Haitsma Jun 2005 A1
20050177372 Wang et al. Aug 2005 A1
20050238238 Xu et al. Oct 2005 A1
20050245241 Durand et al. Nov 2005 A1
20050281439 Lange Dec 2005 A1
20060004745 Kuhn et al. Jan 2006 A1
20060013451 Haitsma Jan 2006 A1
20060020860 Tardif et al. Jan 2006 A1
20060020958 Allamanche et al. Jan 2006 A1
20060026203 Tan et al. Feb 2006 A1
20060031216 Semple et al. Feb 2006 A1
20060041596 Stirbu et al. Feb 2006 A1
20060048191 Xiong Mar 2006 A1
20060064037 Shalon et al. Mar 2006 A1
20060112035 Cecchi et al. May 2006 A1
20060129822 Snijder et al. Jun 2006 A1
20060143674 Jones et al. Jun 2006 A1
20060153296 Deng Jul 2006 A1
20060159442 Kim et al. Jul 2006 A1
20060173688 Whitham Aug 2006 A1
20060184638 Chua et al. Aug 2006 A1
20060204035 Guo et al. Sep 2006 A1
20060217818 Fujiwara Sep 2006 A1
20060224529 Kermani Oct 2006 A1
20060236343 Chang Oct 2006 A1
20060242139 Butterfield et al. Oct 2006 A1
20060242554 Gerace et al. Oct 2006 A1
20060247983 Dalli Nov 2006 A1
20060248558 Barton Nov 2006 A1
20060253423 McLane et al. Nov 2006 A1
20070019864 Koyama et al. Jan 2007 A1
20070042757 Jung et al. Feb 2007 A1
20070061302 Ramer et al. Mar 2007 A1
20070067304 Ives Mar 2007 A1
20070067682 Fang Mar 2007 A1
20070071330 Oostveen et al. Mar 2007 A1
20070074147 Wold Mar 2007 A1
20070091106 Moroney Apr 2007 A1
20070130159 Gulli et al. Jun 2007 A1
20070168413 Barletta et al. Jul 2007 A1
20070195987 Rhoads Aug 2007 A1
20070220573 Chiussi et al. Sep 2007 A1
20070244902 Seide et al. Oct 2007 A1
20070253594 Lu et al. Nov 2007 A1
20070255785 Hayashi et al. Nov 2007 A1
20070294295 Finkelstein et al. Dec 2007 A1
20080040277 DeWitt Feb 2008 A1
20080046406 Seide et al. Feb 2008 A1
20080049629 Morrill Feb 2008 A1
20080072256 Boicey et al. Mar 2008 A1
20080091527 Silverbrook et al. Apr 2008 A1
20080152231 Gokturk et al. Jun 2008 A1
20080163288 Ghosal et al. Jul 2008 A1
20080165861 Wen et al. Jul 2008 A1
20080201299 Lehikoinen et al. Aug 2008 A1
20080201314 Smith et al. Aug 2008 A1
20080204706 Magne et al. Aug 2008 A1
20080253737 Kimura et al. Oct 2008 A1
20080270373 Oostveen et al. Oct 2008 A1
20080313140 Pereira et al. Dec 2008 A1
20090013414 Washington et al. Jan 2009 A1
20090022472 Bronstein et al. Jan 2009 A1
20090089587 Brunk et al. Apr 2009 A1
20090119157 Dulepet May 2009 A1
20090125529 Vydiswaran et al. May 2009 A1
20090125544 Brindley May 2009 A1
20090148045 Lee et al. Jun 2009 A1
20090157575 Schobben et al. Jun 2009 A1
20090172030 Schiff et al. Jul 2009 A1
20090175538 Bronstein et al. Jul 2009 A1
20090204511 Tsang Aug 2009 A1
20090216639 Kapczynski et al. Aug 2009 A1
20090245573 Saptharishi et al. Oct 2009 A1
20090245603 Koruga et al. Oct 2009 A1
20090253583 Yoganathan Oct 2009 A1
20090277322 Cai et al. Nov 2009 A1
20100023400 DeWitt Jan 2010 A1
20100042646 Raichelgauz et al. Feb 2010 A1
20100082684 Churchill et al. Apr 2010 A1
20100088321 Solomon et al. Apr 2010 A1
20100104184 Bronstein et al. Apr 2010 A1
20100106857 Wyler Apr 2010 A1
20100125569 Nair et al. May 2010 A1
20100162405 Cook et al. Jun 2010 A1
20100173269 Puri et al. Jul 2010 A1
20100191567 Lee et al. Jul 2010 A1
20100268524 Nath et al. Oct 2010 A1
20100306193 Pereira et al. Dec 2010 A1
20100318493 Wessling Dec 2010 A1
20100322522 Wang et al. Dec 2010 A1
20110035289 King et al. Feb 2011 A1
20110052063 McAuley et al. Mar 2011 A1
20110055585 Lee Mar 2011 A1
20110106782 Ke et al. May 2011 A1
20110145068 King et al. Jun 2011 A1
20110202848 Ismalon Aug 2011 A1
20110208822 Rathod Aug 2011 A1
20110246566 Kashef et al. Oct 2011 A1
20110251896 Impollonia et al. Oct 2011 A1
20110313856 Cohen et al. Dec 2011 A1
20120082362 Diem et al. Apr 2012 A1
20120131454 Shah May 2012 A1
20120150890 Jeong et al. Jun 2012 A1
20120167133 Carroll et al. Jun 2012 A1
20120197857 Huang et al. Aug 2012 A1
20120330869 Durham Dec 2012 A1
20130031489 Gubin et al. Jan 2013 A1
20130067035 Amanat et al. Mar 2013 A1
20130086499 Dyor et al. Apr 2013 A1
20130089248 Remiszewski et al. Apr 2013 A1
20130104251 Moore et al. Apr 2013 A1
20130159298 Mason et al. Jun 2013 A1
20130173635 Sanjeev Jul 2013 A1
20130325550 Varghese et al. Dec 2013 A1
20130332951 Gharaat et al. Dec 2013 A1
20140019264 Wachman et al. Jan 2014 A1
20140147829 Jerauld May 2014 A1
20140188786 Raichelgauz et al. Jul 2014 A1
20140310825 Raichelgauz et al. Oct 2014 A1
20150289022 Gross Oct 2015 A1
Foreign Referenced Citations (4)
Number Date Country
0231764 Apr 2002 WO
03005242 Jan 2003 WO
2004019527 Mar 2004 WO
2007049282 May 2007 WO
Non-Patent Literature Citations (71)
Entry
Li, et al., “Matching Commercial Clips from TV Streams Using a Unique, Robust and Compact Signature,” Proceedings of the Digital Imaging Computing: Techniques and Applications, Feb. 2005, vol. 0-7695-2467, Australia.
Lin, et al., “Robust Digital Signature for Multimedia Authentication: A Summary”, IEEE Circuits and Systems Magazine, 4th Quarter 2003, pp. 23-26.
May et al., “The Transputer”, Springer-Verlag, Berlin Heidelberg, 1989, teaches multiprocessing system.
Vailaya, et al., “Content-Based Hierarchical Classification of Vacation Images,” I.E.E.E.: Multimedia Computing and Systems, vol. 1, 1999, East Lansing, MI, pp. 518-523.
Vallet, et al., “Personalized Content Retrieval in Context Using Ontological Knowledge,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 17, No. 3, Mar. 2007, pp. 336-346.
Whitby-Strevens, “The Transputer”, 1985 IEEE, Bristol, UK.
Yanai, “Generic Image Classification Using Visual Knowledge on the Web,” MM'03, Nov. 2-8, 2003, Tokyo, Japan, pp. 167-176.
Wang et al. “A Signature for Content-based Image Retrieval Using a Geometrical Transform”, ACM 1998, pp. 229-234.
Liu, et al., “Instant Mobile Video Search With Layered Audio-Video Indexing and Progressive Transmission”, Multimedia, IEEE Transactions on Year: 2014, vol. 16, Issue: 8, pp. 2242-2255, DOI: 10.1109/TMM.2014.2359332 IEEE Journals & Magazines.
Mladenovic, et al., “Electronic Tour Guide for Android Mobile Platform with Multimedia Travel Book”, Telecommunications Forum (TELFOR), 2012 20th Year: 2012, pp. 1460-1463, DOI: 10.1109/TELFOR.2012.6419494 IEEE Conference Publications.
Park, et al., “Compact Video Signatures for Near-Duplicate Detection on Mobile Devices”, Consumer Electronics (ISCE 2014), The 18th IEEE International Symposium on Year: 2014, pp. 1-2, DOI: 10.1109/ISCE.2014.6884293 IEEE Conference Publications.
Zang, et al., “A New Multimedia Message Customizing Framework for Mobile Devices”, Multimedia and Expo, 2007 IEEE International Conference on Year: 2007, pp. 1043-1046, DOI: 10.1109/ICME.2007.4284832 IEEE Conference Publications.
Clement, et al. “Speaker Diarization of Heterogeneous Web Video Files: A Preliminary Study”, Acoustics, Speech and Signal Processing (ICASSP), 2011, IEEE International Conference on Year: 2011, pp. 4432-4435, DOI: 10.1109/ICASSP.2011.5947337 IEEE Conference Publications, France.
Gong, et al., “A Knowledge-based Mediator for Dynamic Integration of Heterogeneous Multimedia Information Sources”, Video and Speech Processing, 2004, Proceedings of 2004 International Symposium on Year: 2004, pp. 467-470, DOI: 10.1109/ISIMP.2004.1434102 IEEE Conference Publications, Hong Kong.
Lin, et al., “Summarization of Large Scale Social Network Activity”, Acoustics, Speech and Signal Processing, 2009, ICASSP 2009, IEEE International Conference on Year 2009, pp. 3481-3484, DOI: 10.1109/ICASSP.2009.4960375, IEEE Conference Publications, Arizona.
Nouza, et al., “Large-scale Processing, Indexing and Search System for Czech Audio-Visual Heritage Archives”, Multimedia Signal Processing (MMSP), 2012, pp. 337-342, IEEE 14th Intl. Workshop, DOI: 10.1109/MMSP.2012.6343465, Czech Republic.
Chuan-Yu Cho, et al., “Efficient Motion-Vector-Based Video Search Using Query by Clip”, 2004, IEEE, Taiwan, pp. 1-4.
Gomes et al., “Audio Watermaking and Fingerprinting: For Which Applications?” University of Rene Descartes, Paris, France, 2003.
Ihab Al Kabary, et al., “SportSense: Using Motion Queries to Find Scenes in Sports Videos”, Oct. 2013, ACM, Switzerland, pp. 1-3.
Jianping Fan et al., “Concept-Oriented Indexing of Video Databases: Towards Semantic Sensitive Retrieval and Browsing”, IEEE, vol. 13, No. 7, Jul. 2004, pp. 1-19.
Nam, et al., “Audio Visual Content-Based Violent Scene Characterization”, Department of Electrical and Computer Engineering, Minneapolis, MN, 1998, pp. 353-357.
Shih-Fu Chang, et al., “VideoQ: A Fully Automated Video Retrieval System Using Motion Sketches”, 1998, IEEE, , New York, pp. 1-2.
Wei-Te Li et al., “Exploring Visual and Motion Saliency for Automatic Video Object Extraction”, IEEE, vol. 22, No. 7, Jul. 2013, pp. 1-11.
Zhu et al., Technology-Assisted Dietary Assessment. Computational Imaging VI, edited by Charles A. Bouman, Eric L. Miller, Ilya Pollak, Proc. of SPIE-IS&T Electronic Imaging, SPIE vol. 6814, 681411, Copyright 2008 SPIE-IS&T. pp. 1-10.
Brecheisen, et al., “Hierarchical Genre Classification for Large Music Collections”, ICME 2006, pp. 1385-1388.
Boari et al, “Adaptive Routing for Dynamic Applications in Massively Parallel Architectures”, 1995 IEEE, Spring 1995.
Burgsteiner et al.: “Movement Prediction From Real-World Images Using a Liquid State Machine”, Innovations in Applied Artificial Intelligence Lecture Notes in Computer Science, Lecture Notes in Artificial Intelligence, LNCS, Springer-Verlag, BE, vol. 3533, Jun. 2005, pp. 121-130.
Cernansky et al., “Feed-forward Echo State Networks”; Proceedings of International Joint Conference on Neural Networks, Montreal, Canada, Jul. 31-Aug. 4, 2005; Entire Document.
Cococcioni, et al, “Automatic Diagnosis of Defects of Rolling Element Bearings Based on Computational Intelligence Techniques”, University of Pisa, Pisa, Italy, 2009.
Emami, et al, “Role of Spatiotemporal Oriented Energy Features for Robust Visual Tracking in Video Surveillance, University of Queensland”, St. Lucia, Australia, 2012.
Fathy et al., “A Parallel Design and Implementation for Backpropagation Neural Network Using NIMD Architecture”, 8th Mediterranean Electrotechnical Corsfe rersce, 19'96. MELECON '96, Date of Conference: May 13-16, 1996, vol. 3, pp. 1472-1475.
Foote, Jonathan, et al. “Content-Based Retrieval of Music and Audio”, 1997 Institute of Systems Science, National University of Singapore, Singapore (Abstract).
Freisleben et al., “Recognition of Fractal Images Using a Neural Network”, Lecture Notes in Computer Science, 1993, vol. 6861, 1993, pp. 631-637.
Garcia, “Solving the Weighted Region Least Cost Path Problem Using Transputers”, Naval Postgraduate School, Monterey, California, Dec. 1989.
Guo et al, “AdOn: An Intelligent Overlay Video Advertising System”, SIGIR, Boston, Massachusetts, Jul. 19-23, 2009.
Howlett et al., “A Multi-Computer Neural Network Architecture in a Virtual Sensor System Application”, International Journal of Knowledge-based Intelligent Engineering Systems, 4 (2). pp. 86-93, 133N 1327-2314; first submitted Nov. 30, 1999; revised version submitted Mar. 10, 2000.
International Search Authority: “Written Opinion of the International Searching Authority” (PCT Rule 43bis.1) including International Search Report for International Patent Application No. PCT/US2008/073852; Date of Mailing: Jan. 28, 2009.
International Search Authority: International Preliminary Report on Patentability (Chapter I of the Patent Cooperation Treaty) including “Written Opinion of the International Searching Authority” (PCT Rule 43bis. 1) for the corresponding International Patent Application No. PCT/IL2006/001235; Date of Issuance: Jul. 28, 2009.
International Search Report for the corresponding International Patent Application PCT/IL2006/001235; Date of Mailing: Nov. 2, 2008.
IPO Examination Report under Section 18(3) for corresponding UK application No. GB1001219.3, dated May 30, 2012.
IPO Examination Report under Section 18(3) for corresponding UK application No. GB1001219.3, dated Sep. 12, 2011; Entire Document.
Iwamoto, K.; Kasutani, E.; Yamada, A.: “Image Signature Robust to Caption Superimposition for Video Sequence Identification”; 2006 IEEE International Conference on Image Processing; pp. 3185-3188, Oct. 8-11, 2006; doi: 10.1109/ICIP.2006.313046.
Jaeger, H.: “The “echo state” approach to analysing and training recurrent neural networks”, GMD Report, No. 148, 2001, pp. 1-43, XP002466251 German National Research Center for Information Technology.
Lin, C.; Chang, S.: “Generating Robust Digital Signature for Image/Video Authentication”, Multimedia and Security Workshop at ACM Mutlimedia '98; Bristol, U.K., Sep. 1998; pp. 49-54.
Lyon, Richard F.; “Computational Models of Neural Auditory Processing”; IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP '84, Date of Conference: Mar. 1984, vol. 9, pp. 41-44.
Maass, W. et al.: “Computational Models for Generic Cortical Microcircuits”, Institute for Theoretical Computer Science, Technische Universitaet Graz, Graz, Austria, published Jun. 10, 2003.
Mandhaoui, et al, “Emotional Speech Characterization Based on Multi-Features Fusion for Face-to-Face Interaction”, Universite Pierre et Marie Curie, Paris, France, 2009.
Marti, et al, “Real Time Speaker Localization and Detection System for Camera Steering in Multiparticipant Videoconferencing Environments”, Universidad Politecnica de Valencia, Spain, 2011.
Mei, et al., “Contextual In-Image Advertising”, Microsoft Research Asia, pp. 439-448, 2008.
Mei, et al., “VideoSense—Towards Effective Online Video Advertising”, Microsoft Research Asia, pp. 1075-1084, 2007.
Morad, T.Y. et al.: “Performance, Power Efficiency and Scalability of Asymmetric Cluster Chip Multiprocessors”, Computer Architecture Letters, vol. 4, Jul. 4, 2005 (Jul. 4, 2005), pp. 1-4, XP002466254.
Nagy et al, “A Transputer, Based, Flexible, Real-Time Control System for Robotic Manipulators”, UKACC International Conference on CONTROL '96, Sep. 2-5, 1996, Conference 1996, Conference Publication No. 427, IEE 1996.
Natsclager, T. et al.: “The “liquid computer”: A novel strategy for real-time computing on time series”, Special Issue on Foundations of Information Processing of Telematik, vol. 8, No. 1, 2002, pp. 39-43, XP002466253.
Ortiz-Boyer et al., “CIXL2: A Crossover Operator for Evolutionary Algorithms Based on Population Features”, Journal of Artificial Intelligence Research 24 (2005), pp. 1-48 Submitted Nov. 2004; published Jul. 2005.
Raichelgauz, I. et al.: “Co-evolutionary Learning in Liquid Architectures”, Lecture Notes in Computer Science, [Online] vol. 3512, Jun. 21, 2005 (Jun. 21, 2005), pp. 241-248, XP019010280 Springer Berlin / Heidelberg ISSN: 1611-3349 ISBN: 978-3-540-26208-4.
Ribert et al. “An Incremental Hierarchical Clustering”, Visicon Interface 1999, pp. 586-591.
Scheper et al, “Nonlinear dynamics in neural computation”, ESANN'2006 proceedings—European Symposium on Artificial Neural Networks, Bruges (Belgium), Apr. 26-28, 2006, d-side publi, ISBN 2-930307-06-4.
Semizarov et al. “Specificity of Short Interfering RNA Determined through Gene Expression Signatures”, PNAS, 2003, pp. 6347-6352.
Theodoropoulos et al, “Simulating Asynchronous Architectures on Transputer Networks”, Proceedings of the Fourth Euromicro Workshop on Parallel and Distributed Processing, 1996. PDP '96.
Verstraeten et al., “Isolated word recognition with the Liquid State Machine: a case study”; Department of Electronics and Information Systems, Ghent University, Sint-Pietersnieuwstraat 41, 9000 Gent, Belgium, Available online Jul. 14, 2005; Entire Document.
Verstraeten et al.: “Isolated word recognition with the Liquid State Machine: a case study”, Information Processing Letters, Amsterdam, NL, vol. 95, No. 6, Sep. 30, 2005 (Sep. 30, 2005), pp. 521-528, XP005028093 ISSN: 0020-0190.
Ware et al., “Locating and Identifying Components in a Robot's Workspace using a Hybrid Computer Architecture”; Proceedings of the 1995 IEEE International Symposium on Intelligent Control, Aug. 27-29, 1995, pp. 139-144.
Xian-Sheng Hua et al.: “Robust Video Signature Based on Ordinal Measure” In: 2004 International Conference on Image Processing, ICIP '04; Microsoft Research Asia, Beijing, China; published Oct. 24-27, 2004, pp. 685-688.
Zeevi, Y. et al.: “Natural Signal Classification by Neural Cliques and Phase-Locked Attractors”, IEEE World Congress on Computational Intelligence, IJCNN2006, Vancouver, Canada, Jul. 2006 (Jul. 2006), XP002466252.
Zhou et al., “Ensembling neural networks: Many could be better than all”; National Laboratory for Novel Software Technology, Nanjing Unviersirty, Hankou Road 22, Nanjing 210093, PR China; Received Nov. 16, 2001, Available online Mar. 12, 2002; Entire Document.
Zhou et al., “Medical Diagnosis With C4.5 Rule Preceded by Artificial Neural Network Ensemble”; IEEE Transactions on Information Technology in Biomedicine, vol. 7, Issue: 1, pp. 37-42, Date of Publication: Mar. 2003.
Lau, et al, “Semantic Web Service Adaptation Model for a Pervasive Learning Scenario”, 2008 IEEE Conference on Innovative Technologies in Intelligent Systems and Industrial Applications Year: 2008, pp. 98-103, DOI: 10.1109/CITISIA.2008.4607342 IEEE Conference Publications.
McNamara, et al., “Diversity Decay in Opportunistic Content Sharing Systems”, 2011 IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks Year: 2011, pp. 1-3, DOI: 10.1109/WoWMoM2011.5986211 IEEE Conference Publications.
Santos, et al., “SCORM-MPEG: an Ontology of Interoperable Metadata for Multimedia and e-Learning”, 2015 23rd International Conference on Software, Telecommunications and Computer Networks (SoftCOM) Year 2015, pp. 224-228, DOI: 10.1109/SOFTCOM.2015.7314122 IEEE Conference Publications.
Wilk, et aL, “The Potential of Social-Aware Multimedia Prefetching on Mobile Devices”, 2015 International Conference and Workshops on Networked Systems (NetSys) Year: 2015, pp. 1-5, DOI: 10.1109/NetSys.2015.7089081 IEEE Conference Publications.
Odinaev, et al., “Cliques in Neural Ensembles as Perception Carriers”, Technion—Israel Institute of Technology, 2006 International Joint Conference on Neural Networks, Canada, 2006, pp. 285-292.
Related Publications (1)
Number Date Country
20160005085 A1 Jan 2016 US
Continuations (2)
Number Date Country
Parent 13624397 Sep 2012 US
Child 14856981 US
Parent 12434221 May 2009 US
Child 13344400 US
Continuation in Parts (4)
Number Date Country
Parent 13344400 Jan 2012 US
Child 13624397 US
Parent 12195863 Aug 2008 US
Child 13624397 US
Parent 12084150 Apr 2009 US
Child 12195863 US
Parent 12084150 US
Child 13624397 US