The present invention relates generally to the analysis of multimedia content captured by a user device, and more specifically to a system for identifying at least one target area of the multimedia content.
Wearable computing devices are clothing and accessories incorporating computer and advanced electronic technologies. Such wearable computer devices may be watches, bracelets, glasses, pendants, and so on that include one or more sensors in order to capture the signals related to the user activity.
Some wearable computing devices are further equipped with a network interface and a processing unit by which they are able to provide online content respective of the user activity, to the user. Wearable computing devices that are designed to collect signals related to user activity which the user carries in order to ease daily life are expected to become more and more common.
Due to the large amount and variety of signals, the problem with such wearable computing devices is that the task of identifying the exact content in which the user is interested in of the collected signals is quite complex, especially in cases where content related to the user activity changes fast due the dynamic and inconsistent daily activities of users.
It would therefore be advantageous to provide a solution that would overcome the deficiencies of the prior art by identifying the target area of user interest within the collected content.
A summary of several example aspects 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 aspects nor 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.
Certain embodiments disclosed herein include a system and a method for detecting a target area of user interest within a multimedia content element. The method comprises receiving the multimedia content element from a user computing device; partitioning the multimedia content element into a number of partitions, each partition having at least one object therein; generating at least one signature for each of the multimedia content elements, wherein each of the at least one signatures for each partition represents a concept; determining a context of the multimedia content element based on the concepts; and identifying at least one partition of the multimedia content as a target area of user interest based on the context of the multimedia content element.
The system comprises a processor communicatively connected to a network; a memory connected to the processing system, the memory containing instructions that when executed by the processing system, configure the system to: receive the multimedia content element from a user computing device; partition the multimedia content element into a number of partitions, each partition having at least one object therein; generate at least one signature for each of the multimedia content elements, wherein each of the at least one signatures for each partition represents a concept; determine a context of the multimedia content element based on the concepts; and identify at least one partition of the multimedia content as a target area of user interest based on the context of the multimedia content element.
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.
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.
By way of example, the various disclosed embodiments include a system and method that determine a target area of user interest in a multimedia content element based on analysis of the multimedia content element. Accordingly, a multimedia content element is received from a user device. In a preferred embodiment, the user device is a wearable computing device. The multimedia content element is partitioned into a number of partitions wherein each partition includes at least one object. At least one signature is generated for each partition of the multimedia content element.
The signatures are analyzed to identify at least one partition of the multimedia content element as a target area of user interest. As will be discussed below the target area of user interest are determined based on the context of the multimedia content element.
Further connected to the network 110 are one or more computing devices 120-1 through 120-N (collectively referred to hereinafter as computing devices 120 or individually as a computing device 120) that are installed with client applications, such as web browsers (WB) 125-1 through 125-n (collectively referred to hereinafter as web browsers 125 or individually as a web browser 125). In a preferred embodiment, some of the computing devices 120 are wearable computing devices. It should be appreciated that the computing devices 120 also may also include a personal computer (PC), a personal digital assistant (PDA), a mobile phone, a smart phone, 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.
The system 100 also includes a plurality of information sources 150-1 through 150-m (collectively referred to hereinafter as information sources 150 or individually as information sources 150) being connected to the network 110. Each of the information sources 150 may be, for example, a web server, an application server, a publisher server, an ad-serving system, a data repository, a database, and the like. Also connected to the network 110 is a data warehouse 160 that stores multimedia content elements, clusters of multimedia content elements, and the context determined for a web page as identified by its URL. In the embodiment illustrated in
The various embodiments disclosed herein are realized using the context server 130 and a signature generator system (SGS) 140. The SGS 140 may be connected to the context server 130 directly or through the network 110. The context server 130 is configured to receive and serve multimedia content elements and to cause the SGS 140 to generate a signature respective of the multimedia content elements. The process for generating the signatures for multimedia content is explained in more details herein below with respect to
It should be noted that each of the server 130 and the SGS 140 typically comprises a processing system (not shown) that is coupled to a memory (not shown), and optionally a network interface (not shown). The processing system is connected to the memory, which typically contains instructions that can be executed by the processing system. The server 130 may also include a network interface (not shown) to the network 110. In one embodiment, the processing system is realized or includes an array of computational cores configured as discussed in more detail below. In another embodiment, the processing system of each of the server 130 and SGS 140 may comprise or be a component of a larger processing system implemented with one or more processors. The one or more processors may be implemented with any combination of general-purpose microprocessors, microcontrollers, digital signal processors (DSPs), field programmable gate array (FPGAs), programmable logic devices (PLDs), controllers, state machines, gated logic, discrete hardware components, dedicated hardware finite state machines, or any other suitable entities that can perform calculations or other manipulations of information.
The context server 130 is configured to receive at least a URL of a web page hosted in an information source 150 and accessed by a web browser 125. The context server 130 is further configured to analyze the multimedia content elements contained in the web page to determine their context, thereby ascertaining the context of the web page. This is performed based on at least one signature generated for each multimedia content element. It should be noted that the context of an individual multimedia content element or a group of elements is extracted from the web page, received from a user of a web browser 125 (e.g., uploaded video clip), or retrieved from the data warehouse 160.
On a computing device 120, a user visits a web page using a web browser 125. In an embodiment, the multimedia content elements are provided from wearable computing devices 120 worn on the user. When the web page is uploaded on the user's web browser 125, a request is sent to the context server 130 to analyze the multimedia content elements contained in the web page. The request to analyze the multimedia content elements 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 information sources 150 (e.g., a web server or a publisher server) when requested to upload one 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 context server 130 is configured to analyze the multimedia content elements in the web page to determine their context. For example, if the web page contains images of palm trees, a beach, and the coast line of San Diego, the context of the web page may be determined to be “California sea shore.” The determined context can be utilized to detect one or more matching advertisements for the multimedia content elements. To this end, the SGS 140 is configured to generate at least one signature for each multimedia content element provided by the context server 130. The generated signature(s) may be robust to noise and distortions as discussed below. Then, using the generated signature(s), the context server 130 is configured to determine the context of the elements and searches the data warehouse 160 for a matching advertisement based on the context. For example, if the signature of an image indicates a “California sea shore”, then an advertisement for a swimsuit can be a potential matching advertisement.
A target area is considered a partition of a multimedia content element containing an object of interest to the user. According to the disclosed embodiments, the received multimedia content elements are partitioned by the context server 130 to a plurality of partitions. At least one of these partitions is identified as the target area of user interest based on the context of the multimedia content element. In one embodiment, metadata related to the user of the computing device 120 may be further be analyzed in order to identify the target area of user interest.
It should be noted that using signatures for determining the context and thereby for the 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 sports 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. This is especially true of images captured from wearable computing devices 120. 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 content 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.
It should be noted that the signatures generated for more than one multimedia content element may be clustered. The clustered signatures are used to determine the context of the web page and 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 125.
In S220, at least one signature for the multimedia content element extracted from the web page is generated. The signature for the multimedia content element generated by a signature generator is described below with respect to
In S230, an advertisement item is matched to the multimedia content element respective of its generated signatures and/or the determined context. 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. The signatures generated for the multimedia content elements are clustered and the cluster of signatures is matched to one or more advertisement items. 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's 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, an image that contains a plurality of multimedia content elements is identified by the context server 130 in an uploaded web page. The SGS 140 generates at least one signature for each multimedia content element executed from the image that exists in the web page. According to this embodiment, a printer and a scanner are shown in the image and the SGS 140 generates signatures respective thereto. The server 130 is configured to determine that the context of the image is office equipment. Therefore, the context server 130 is configured to match at least one advertisement suitable for office equipment.
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
To demonstrate an example of the 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 is now described with reference to
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:
where π 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 ‘j’ (for example, grayscale value of a certain pixel j); Thx is a constant Threshold value, where ‘x’ is ‘S’ 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 can be found in U.S. Pat. Nos. 8,326,775 and 8,312,031, assigned to common assignee, which are hereby incorporated by reference for all the useful information they contain.
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.
A detailed description of the computational core generation and the process for configuring such cores is discussed in more detail in U.S. Pat. No. 8,655,801 referenced above.
In S510, the uniform resource locator (URL) of the uploaded web page is received. In another embodiment, the uploaded web page includes an embedded script. The script extracts the URL of the web page, and sends the URL to, for example, the context server 130. In another embodiment, an add-on installed in the web browser 125 extracts the URL of the uploaded web page, and sends the URL to the context server 130. In yet another embodiment, an agent is installed on a user device executing the web browser 125. The agent is configured to monitor web pages uploaded to the website, extract the URLs, and send them to the context server 130. In another embodiment, a web server (e.g., server 150), hosting the requested web page, provides the context server 130 with the URL of the requested web page. It should be noted that only URLs of selected web sites can be sent to the context server 130, for example, URLs related to web sites that paid for the additional information.
In S520, the web page respective of each received URL is downloaded, for example, to the context server 130. In S525, the web page is analyzed in order to identify the existence of at least one or more multimedia content elements in the uploaded web page. It should be understood that a multimedia content element, such as an image or a video, may include a plurality of multimedia content elements. In S530, for each identified multimedia content element at least one signature is generated. In one implementation, the signatures for the multimedia content elements are generated, by the SGS 140, as described in greater detail above.
In S535, respective of the generated signatures, the context of the multimedia content element is determined. The determination of context based on the signatures is discussed in more detail below. In S540, respective of the context or the signature of the elements, one or more links to content that exist on a web server, for example, an information source 150, that can be associated with the multimedia content element is determined. A link may be a hyperlink, a URL, and the like to external resource information.
That is, the content accessed through the link may be, for example, informative web-pages such as the Wikipedia® website. The determination of the link may be made by identification of the context and/or the generated signatures. As an example, if the context of the multimedia content elements was identified as a football player, then a link to a sports website that contains information about the football player is determined.
In S550, the determined link to the content is added as an overlay to the web page, respective of the corresponding multimedia content element. According to one embodiment, a link that contains the overlay may be provided to a web browser 125 (e.g., browser 125-1) respective of a user's gesture. A user's gesture may be: a scroll on the multimedia content element, a click on the at least one multimedia content element, and/or a response to the at least one multimedia content or portion thereof.
The modified web page that includes at least one multimedia content element with the added link can be sent directly to the web browser 125-1 requesting the web page. This requires establishing a data session between the context server 130 and the web browsers 125. In another embodiment, the multimedia element including the added link is returned to a web server (e.g., source 150) hosting the requested web page. The web server returns the requested web page with the multimedia element containing the added link to the web browser 125-1 requesting the web page. Once the “modified” web page is displayed over the web browser 125-1, a detected user's gesture over the multimedia element would cause the web browser 125-1 to upload the content (e.g., a Wikipedia web-page) accessed by the link added to the multimedia element.
In S560, it is checked whether the one or more multimedia content elements contained in the web page has changed, and if so, execution continues with S525; otherwise, execution terminates.
As a non-limiting example, a web page containing an image of the movie “Pretty Woman” is uploaded to the context server 130. A signature is generated by the SGS 140 respective of the actor Richard Gere and the actress Julia Roberts, both shown in the image. The context of the signatures according to this example may be “American Movie Actors.” An overlay containing the links to Richard Gere's biography and Julia Roberts' biography on the Wikipedia® website is added over the image such that upon detection of a user's gesture, for example, a mouse clicking over the part of the image where Richard Gere is shown, the link to Richard Gere's biography on Wikipedia® is provided to the user.
According to another embodiment, a web page that contains an embedded video clip is requested by a web browser 125-1 from an information source 150-1 and a banner advertising New York City. The context server 130 receives the requested URL. The context server 130 analyzes the video content and the banner within the requested web page and a signature is generated by the SGS 140 respective of the entertainer Madonna that is shown in the video content and the banner. The context of multimedia content embedded in the web page is determined to be “live pop shows in NYC.” In response to the determined context, a link to a hosted website for purchasing show tickets is added as an overlay to the video clip. The web page together with the added link is sent to a web server (e.g., an information source 150-1), which then uploads the requested web page with the modified video element to the web browser 125-1.
The web page may contain a number of multimedia content elements; however, in some instances only a few links 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.
In S620, the uniform resource locator (URL) of the web page to be processed is received. In another embodiment, the uploaded web page includes an embedded script. The script extracts the URL of the web page, and sends the URL to, for example, the context server 130. In another embodiment, an add-on installed in the web browser 125 extracts the URL of the uploaded web page, and sends the URL to, for example, the context server 130. In yet another embodiment, an agent is installed on a user device executing the web browser 125. The agent is configured to monitor web pages uploaded to the website, extract the URLs, and send them to the, for example, context server 130. In another embodiment, the web server (e.g., an information source 150-1) hosting the requested web page, provides the context server 130 with the URL of the requested web page. It should be noted that only URLs of selected websites can be sent to the context server 130, for example, URLs related to web sites that paid for the additional information.
In S630, the web page respective of each received URL is downloaded, for example, to the server 130. In S640, the web page is analyzed in order to identify the existence of one or more multimedia content elements in the uploaded web page. Each identified multimedia content element is extracted from the web page and sent to the SGS 140.
In S650, for each identified multimedia content element at least one signature is generated. The at least one signature is robust for noise and distortion. The signatures for the multimedia content elements are generated as described in greater detail above. It should also be noted that signatures can be generated for portions of a multimedia content element. It should be noted the steps S620 through S650 may be performed as part of the method discussed with reference to
In S660, the correlation between the signatures of all extracted multimedia content elements, or portions thereof is analyzed. Specifically, each signature represents a different concept. The signatures are analyzed to determine the correlation concepts. A concept is an abstract description of the content to which the signature was generated. For example, a concept of the signature generated for an image showing a bouquet of red roses is “flowers”. The correlation between concepts can be achieved by identifying a ratio between signatures' sizes, a spatial location of each signature, and so on using probabilistic models. As noted above a signature represents a concept and is generated for a multimedia content element. Thus, identifying, for example, the ratio of signatures' sizes may also indicate the ratio between the size of their respective multimedia elements.
A context is determined as the correlation between a plurality of concepts. A strong context is determined when there are more concepts, or the plurality of concepts, satisfy the same predefined condition. As an example, signatures generated for multimedia content elements of a smiling child with a Ferris wheel in the background are analyzed. The concept of the signature of the smiling child is “amusement” and the concept of a signature of the Ferris wheel is “amusement park”. The relation between the signatures of the child and recognized wheel are further analyzed, to determine that the Ferris wheel is bigger than the child. The relation analysis determines that the Ferris wheel is used to entertain the child. Therefore, the determined context may be “amusement.”
According to one embodiment, the one or more typically probabilistic models can be utilized to determine the correlation between signatures representing concepts. The probabilistic models determine, for example, the probability that a signature may appear in the same orientation and in the same ratio as another signature. When performing the analysis, information maintained in the data warehouse 160 is utilized, for example, signatures previously analyzed. In S670, based on the analysis performed in S660, the context of a plurality of multimedia content elements that exist in the web page and in the context of the web page is determined.
The methods discussed with reference to
As an example, an image that contains a plurality of multimedia content elements is identified by the context server 130 in an uploaded web page. The at least one signature for each of the plurality of multimedia content elements that exist in the image is generated. According to this example, the multimedia contents of the singer “Adele”, “red carpet” and a “Grammy award” are shown in the image. The SGS 140 generates signatures respective thereto. The analysis of the correlation between “Adele”, “red carpet” and a “Grammy award” results in an identified context of “Adele Wining the Grammy Award”.
Following is another non-limiting example demonstrating the operation of the server 130. In this example, a web page containing a plurality of multimedia content elements is identified by the context server 130 in an uploaded web page. According to this example, the SGS 140 generates signatures for the objects such as, a “glass”, a “cutlery” and a “plate” which appear in the multimedia elements. The context server 130 then analyzes the correlation between the concepts generated by signatures respective of the data maintained in the data warehouse 160, for example, analysis of previously generated signatures. According to this example, as the all concepts of the “glass”, the “cutlery”, and the “plate” satisfy the same predefined condition, a strong context is determined. The context of such concepts may be a “table set”. The context can be also determined respective of a ratio of the sizes of the objects (glass, cutlery, and plate) in the image and the distinction of their spatial orientation.
In S680, the context of the multimedia content together with the respective signatures is stored in the data warehouse 160 for future use. In S690, it is checked whether there are additional web pages and if so execution continues with S620; otherwise, execution terminates.
In S720, the received multimedia content element is partitioned by the context server 130 to a plurality of partitions. Each partition includes at least one object. Such an object can be displayed or played on the user computing device 120. For example, an object may be a portion of a video clip which can be captured or displayed on a smart watch, bracelet, or other wearable computer device 120. In S730, at least one signature is generated for each partition of the multimedia content element wherein. As noted above each generated signature represents a concept. The signature generation is further described hereinabove with respect of
In S740, the context of the multimedia content element is determined. As noted above, this can be performed by correlating the concepts. After determining the context, in S750 at least one partition of the multimedia content is identified as the target area of user interest. In one embodiment, the signature generated for each partition is compared against the determined context. The partition of the signature that best matches the context is determined as the best match. Alternatively or collectively, metadata related to the user of the computing device 120 may further be analyzed in order to identify the target area of user interest. Such metadata may include, for example, personal variables related to the user, such as: demographic information, the user's profile, experience, a combination thereof, and so on. In one embodiment, at least one personal variable related to a user is received and a correlation above a predetermined threshold between the at least one personal variable and the at least one signature is found. It should be noted that a target area of user interest may also refer to a target object, a plurality of target objects as well as a plurality of target areas.
In S760, it is checked whether a new multimedia content element has been received; and if so execution continues with S720; otherwise, execution terminates. It should be noted that a new multimedia content element may refer to a multimedia element previously viewed, but now a different portion of such element is being viewed by the user device.
As a non-limiting example, an image of several basketball players is captured by a camera of a wearable computing device 120. The captured image is then sent to the context server 130 and is partitioned to a number of partitions, where in each partition one player is shown and a signature to each partition is generated respective thereto. Each signature represents a concept and by correlating the concepts; the context of the image is determined as the Los Angeles Lakers® basketball team. The user's experience indicates that the user searched several times for the Los Angeles Lakers® basketball player Kobe Bryant. Respective thereto, the area in which Kobe Bryant is shown is identified as the target area of user interest.
It should be noted that the identification of the target area of user interest enables publishers to show advertisements on the display of the user computing device 120 respective of the target object and/or area. For example, if a Toyota® Corola model vehicle is identified in the target area of user interest, advertisements for car agencies may be determined and shown on the web browser 125.
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 invention 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 invention, 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.
Number | Date | Country | Kind |
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171577 | Oct 2005 | IL | national |
173409 | Jan 2006 | IL | national |
185414 | Aug 2007 | IL | national |
This application claims the benefit of U.S. Provisional Application No. 61/899,225 filed on Nov. 3, 2013. This application is a continuation-in-part (CIP) of U.S. patent application Ser. No. 13/770,603 filed on Feb. 19, 2013, now pending, which is a CIP of U.S. patent application Ser. No. 13/624,397 filed on Sep. 21, 2012, now pending. The Ser. No. 13/624,397 application is a CIP of: (a) U.S. patent application Ser. No. 13/344,400 filed on Jan. 5, 2012, now pending, 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 having a filing date of Apr. 7, 2009, 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 29 Jan. 2006. All of the applications referenced above are herein incorporated by reference.
Number | Name | Date | Kind |
---|---|---|---|
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 |
5887193 | Takahashi et al. | Mar 1999 | A |
5940821 | Wical | Aug 1999 | A |
5978754 | Kumano | Nov 1999 | A |
5987454 | Hobbs | Nov 1999 | A |
6038560 | Wical | Mar 2000 | 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 |
6144767 | Bottou et al. | Nov 2000 | A |
6147636 | Gershenson | Nov 2000 | A |
6240423 | Hirata | May 2001 | B1 |
6243375 | Speicher | Jun 2001 | B1 |
6363373 | Steinkraus | Mar 2002 | B1 |
6381656 | Shankman | Apr 2002 | B1 |
6411229 | Kobayashi | Jun 2002 | B2 |
6422617 | Fukumoto et al. | Jul 2002 | B1 |
6493692 | Kobayashi et al. | Dec 2002 | B1 |
6493705 | Kobayashi et al. | Dec 2002 | B1 |
6523022 | Hobbs | Feb 2003 | B1 |
6523046 | Liu et al. | Feb 2003 | B2 |
6524861 | Anderson | Feb 2003 | B1 |
6526400 | Takata et al. | Feb 2003 | B1 |
6560597 | Dhillon et al. | May 2003 | B1 |
6594699 | Sahai et al. | Jul 2003 | B1 |
6601060 | Tomaru | Jul 2003 | B1 |
6611628 | Sekiguchi et al. | Aug 2003 | B1 |
6611837 | Schreiber | Aug 2003 | B2 |
6618711 | Ananth | Sep 2003 | B1 |
6643620 | Contolini et al. | Nov 2003 | B1 |
6643643 | Lee et al. | Nov 2003 | B1 |
6665657 | Dibachi | Dec 2003 | B1 |
6675159 | Lin et al. | Jan 2004 | B1 |
6728706 | Aggarwal et al. | Apr 2004 | B2 |
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 |
6804356 | Krishnamachari | Oct 2004 | B1 |
6819797 | Smith et al. | Nov 2004 | B1 |
6836776 | Schreiber | Dec 2004 | B2 |
6845374 | Oliver et al. | Jan 2005 | B1 |
6901207 | Watkins | May 2005 | B1 |
6970881 | Mohan et al. | Nov 2005 | B1 |
6978264 | Chandrasekar et al. | Dec 2005 | B2 |
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 |
7124149 | Smith et al. | Oct 2006 | B2 |
7199798 | Echigo et al. | Apr 2007 | B1 |
7260564 | Lynn et al. | Aug 2007 | B1 |
7277928 | Lennon | Oct 2007 | B2 |
7296012 | Ohashi | Nov 2007 | B2 |
7302117 | Sekiguchi et al. | Nov 2007 | B2 |
7313805 | Rosin et al. | Dec 2007 | B1 |
7340458 | Vaithilingam et al. | Mar 2008 | B2 |
7346629 | Kapur et al. | Mar 2008 | B2 |
7353224 | Chen et al. | Apr 2008 | B2 |
7376672 | Weare | May 2008 | B2 |
7376722 | Sim et al. | May 2008 | B1 |
7392238 | Zhou et al. | Jun 2008 | B1 |
7406459 | Chen et al. | Jul 2008 | B2 |
7433895 | Li et al. | Oct 2008 | B2 |
7450740 | Shah et al. | Nov 2008 | B2 |
7464086 | Black et al. | Dec 2008 | B2 |
7519238 | Robertson et al. | Apr 2009 | B2 |
7523102 | Bjarnestam et al. | Apr 2009 | B2 |
7526607 | Singh et al. | Apr 2009 | B1 |
7536384 | Venkataraman et al. | May 2009 | B2 |
7536417 | Walsh et al. | May 2009 | B2 |
7542969 | Rappaport et al. | Jun 2009 | B1 |
7548910 | Chu et al. | Jun 2009 | B1 |
7555477 | Bayley et al. | Jun 2009 | B2 |
7555478 | Bayley et al. | Jun 2009 | B2 |
7562076 | Kapur | Jul 2009 | B2 |
7574436 | Kapur et al. | Aug 2009 | B2 |
7574668 | Nunez et al. | Aug 2009 | B2 |
7577656 | Kawai et al. | Aug 2009 | B2 |
7660737 | Lim et al. | Feb 2010 | B1 |
7689544 | Koenig | Mar 2010 | B2 |
7694318 | Eldering | Apr 2010 | B2 |
7697791 | Chan et al. | Apr 2010 | B1 |
7769221 | Shakes et al. | Aug 2010 | B1 |
7788132 | Desikan et al. | Aug 2010 | B2 |
7788247 | Wang et al. | Aug 2010 | B2 |
7836054 | Kawai et al. | Nov 2010 | B2 |
7860895 | Scofield et al. | Dec 2010 | B1 |
7904503 | Van De Sluis | 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 |
8266185 | Raichelgauz et al. | Sep 2012 | B2 |
8312031 | Raichelgauz et al. | Nov 2012 | B2 |
8316005 | Moore | Nov 2012 | B2 |
8326775 | Raichelgauz et al. | Dec 2012 | B2 |
8332478 | Levy et al. | Dec 2012 | B2 |
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 |
8868619 | Raichelgauz et al. | Oct 2014 | B2 |
8880539 | Raichelgauz et al. | Nov 2014 | B2 |
8880566 | Raichelgauz et al. | Nov 2014 | B2 |
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 |
20010019633 | Tenze et al. | Sep 2001 | A1 |
20010056427 | Yoon et al. | Dec 2001 | A1 |
20020019881 | Bokhari et al. | Feb 2002 | A1 |
20020059580 | Kalker et al. | May 2002 | A1 |
20020087530 | Smith et al. | Jul 2002 | A1 |
20020107827 | Benitez-Jimenez et al. | Aug 2002 | A1 |
20020123928 | Eldering et al. | Sep 2002 | A1 |
20020126872 | Brunk et al. | Sep 2002 | A1 |
20020143976 | Barker et al. | Oct 2002 | A1 |
20020163532 | Thomas 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 |
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 |
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 |
20050144455 | Haitsma | Jun 2005 | A1 |
20050177372 | Wang et al. | Aug 2005 | A1 |
20060013451 | Haitsma | Jan 2006 | A1 |
20060020860 | Tardif 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 |
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 |
20060236343 | Chang | Oct 2006 | A1 |
20060242139 | Butterfield et al. | Oct 2006 | A1 |
20060242554 | Gerace et al. | Oct 2006 | A1 |
20060248558 | Barton et al. | Nov 2006 | A1 |
20060253423 | McLane et al. | Nov 2006 | A1 |
20070009159 | Fan | Jan 2007 | A1 |
20070011151 | Hagar et al. | Jan 2007 | A1 |
20070038608 | Chen | Feb 2007 | A1 |
20070042757 | Jung et al. | Feb 2007 | A1 |
20070067682 | Fang | Mar 2007 | A1 |
20070071330 | Oostveen et al. | Mar 2007 | A1 |
20070074147 | Wold | Mar 2007 | A1 |
20070130112 | Lin | Jun 2007 | A1 |
20070130159 | Gulli et al. | Jun 2007 | A1 |
20070168413 | Barletta et al. | Jul 2007 | A1 |
20070174320 | Chou | 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 |
20070268309 | Tanigawa et al. | Nov 2007 | A1 |
20070282826 | Hoeber et al. | Dec 2007 | A1 |
20070294295 | Finkelstein et al. | Dec 2007 | A1 |
20080019614 | Robertson et al. | Jan 2008 | 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 |
20080163288 | Ghosal et al. | Jul 2008 | A1 |
20080165861 | Wen | Jul 2008 | A1 |
20080172615 | Igelman 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 |
20090037408 | Rodgers | Feb 2009 | A1 |
20090089587 | Brunk et al. | Apr 2009 | A1 |
20090119157 | Dulepet | May 2009 | A1 |
20090125529 | Vydiswaran et al. | May 2009 | A1 |
20090148045 | Lee 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 |
20090254824 | Singh | 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 | 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 |
20110125727 | Zou 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 |
20120131454 | Shah | May 2012 | A1 |
20120150890 | Jeong et al. | Jun 2012 | A1 |
20120167133 | Carroll et al. | Jun 2012 | A1 |
20120191686 | Hjelm et al. | Jul 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 |
20130104251 | Moore et al. | Apr 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 |
20150289022 | Gross | Oct 2015 | A1 |
Number | Date | Country |
---|---|---|
0231764 | Apr 2002 | WO |
2007049282 | May 2007 | WO |
Entry |
---|
Boari et al, “Adaptive Routing for Dynamic Applications in Massively Parallel Architectures”, 1995 IEEE, Spring 1995. |
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. |
Garcia, “Solving the Weighted Region Least Cost Path Problem Using Transputers”, Naval Postgraduate School, Monterey, California, Dec. 1989. |
Mahdhaoui, 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. |
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. |
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. |
Theodoropoulos et al, “Simulating Asynchronous Architectures on Transputer Networks”, Proceedings of the Fourth Euromicro Workshop on Parallel and Distributed Processing, 1996. PDP '96. |
Guo et al, “AdOn: An Intelligent Overlay Video Advertising System”, SIFIG, Boston, Massachusetts, Jul. 19-23, 2009. |
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. |
Semizarov et al. “Specificity of Short Interfering RNA Determined through Gene Expression Signatures”, PNAS, 2003, pp. 6347-6352. |
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. |
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. |
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. |
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. |
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. |
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) 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. |
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. |
Verstraeten et al.: “Isolated word recognition with the Liquid State Machine; a case study”, Information Processing Letters, Amsterdam, NL, col. 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. |
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. 167-470, DOI: 10.1109/ISIMP2004.1434102 IEEE Conference Publications, Hong Kong. |
Lin, et al., “Robust Digital Signature for Multimedia Authentication: A Summary”, IEEE Circuits and Systems Magazine, 4th Quarter 2003, pp. 23-26. |
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. |
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. |
May et al., “The Transputer”, Springer-Verlag, Berlin Heidelberg, 1989, teaches multiprocessing system. |
Nam, et al., “Audio Visual Content-Based Violent Scene Characterization”, Department of Electrical and Computer Engineering, Minneapolis, MN, 1998, pp. 353-357. |
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. |
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.20142359332 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. |
Wang et al. “A Signature for Content-based Image Retrieval Using a Geometrical Transform”, ACM 1998, pp. 229-234. |
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. |
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. |
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. |
Number | Date | Country | |
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20150052086 A1 | Feb 2015 | US |
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61899225 | Nov 2013 | US |
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