The present disclosure relates generally to the analysis of multimedia content, and more specifically to identifying a plurality of multimedia content elements with respect to context.
With the abundance of data made available through various means in general and through the Internet and world-wide web (WWW) in particular, a need to understand likes and dislikes of users has become essential for on-line businesses.
Existing solutions provide various tools to identify user preferences. In particular, some of these existing solutions determine user preferences based on user inputs. These existing solutions actively require an input from the user that indicates the user's interests. However, profiles generated for users based on their inputs may be inaccurate, as the users tend to provide only their current interests, or only partial information due to their privacy concerns.
Other existing solutions passively track user activity through web sites such as social networks. The disadvantage with such solutions is that typically limited information regarding the users is revealed because users provide minimal information due to, e.g., privacy concerns. For example, users creating an account on Facebook® typically provide only the mandatory information required for the creation of the account.
Further, user inputs that may be utilized to determine user preferences may be duplicative. For example, a user may provide multiple images of his or her pet to illustrate that he or she has a user preference related to dogs. Such duplicative user inputs require additional memory usage, and may obfuscate the user's true interests. For example, if the user provides 10 images of his or her pet taken around the same time, the system receiving the images typically stores all 10 images, and any user preferences determined therefrom may appear to disproportionately revolve around pets.
It would therefore be advantageous to provide a solution that overcomes the deficiencies of the prior art.
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.
Certain embodiments disclosed herein include a method for removing contextually identical multimedia content elements. The method comprises analyzing a plurality of multimedia content elements to identify at least two multimedia content elements of the plurality of multimedia content elements that are contextually identical; selecting, from among the at least two contextually identical multimedia content elements, at least one optimal multimedia content element; and removing, from a storage, all multimedia content elements of the group of contextually identical multimedia content elements other than the at least one optimal multimedia content element.
Certain embodiments disclosed herein also include a non-transitory computer readable medium having stored thereon instructions for causing one or more processing units to execute a method, the method comprising: analyzing a plurality of multimedia content elements to identify at least two multimedia content elements of the plurality of multimedia content elements that are contextually identical; selecting, from among the at least two contextually identical multimedia content elements, at least one optimal multimedia content element; and removing, from a storage, all multimedia content elements of the group of contextually identical multimedia content elements other than the at least one optimal multimedia content element.
Certain embodiments disclosed herein also include system for removing contextually identical multimedia content elements. The system comprises: a processing circuitry; and a memory, the memory containing instructions that, when executed by the processing circuitry, configure the system to: analyze a plurality of multimedia content elements to identify at least two multimedia content elements of the plurality of multimedia content elements that are contextually identical; select, from among the at least two contextually identical multimedia content elements, at least one optimal multimedia content element; and remove, from a storage, all multimedia content elements of the group of contextually identical multimedia content elements other than the at least one optimal multimedia content element.
The subject matter that is regarded 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.
Certain embodiments disclosed herein include a system and method for determining whether multimedia content elements are contextually identical. A plurality of multimedia content elements to identify contextually identical multimedia content elements. In an embodiment, the analysis includes generating at least one signature for each multimedia content element. In a further embodiment, the analysis includes matching among the generated signatures to identify signatures representing multimedia content elements that are contextually identical. In another embodiment, the analysis may include determining contextual identifiers for the plurality of multimedia content elements.
Contextually identical multimedia content elements are multimedia content elements associated with the same or nearly the same content. Contextually identical multimedia content elements may be determined to be contextually identical based on, e.g., features of the multimedia content elements (e.g., people and things captured in an image or video, sounds in audio or video, etc.), contextual insights related to the multimedia content elements (e.g., time of capture or receipt, location of capture, device which captured the multimedia content elements, etc.), and the like. For example, two images taken at a concert of a singer that were captured by two users standing next to each other may be contextually identical. As another example, two audio recordings of a song performed by the singer captured at different locations in the concert venue may be contextually identical.
Removing contextually identical multimedia content elements may be useful for, e.g., eliminating duplicative multimedia content elements or multimedia content elements that otherwise include essentially the same content. This elimination may reduce the amount of storage space needed and allows for removal of unnecessary duplicate multimedia content elements. For example, if a user accidentally presses the “capture” button on a camera multiple times when trying to take a picture of a group of friends, multiple images showing essentially the same scene will be captured. As another example, multiple people in a social media group may store multiple instances of the same video. In either example, a essentially duplicate identical multimedia content elements.
In an embodiment, upon identification of contextually identical multimedia content elements, a notification may be generated and sent. In another embodiment, at least one optimal multimedia content element may be determined from among the contextually identical multimedia content elements. The notification may also include a recommendation of the determined at least one optimal multimedia content element. The optimal multimedia content element may be determined based on, but not limited to, features of the multimedia content elements (e.g., resolution, focus, clarity, frame, texture, etc.); matching with other multimedia content elements (e.g., multimedia content elements ranked highly in a social network or liked by a particular user); a combination thereof; and the like. In some embodiments, multimedia content elements that are contextually identical to the optimal multimedia content element may be removed from, e.g., a storage.
As a non-limiting example, a user of a user device captures a series of 10 images determined as self-portrait photographs, which are typically referred to as “selfies”, within a time span of a few minutes. The selfie images are analyzed. In this example, the images are analyzed by at least generating and matching signatures. Based on the analysis, it is determined that the 10 images are contextually identical. Upon determining that the 10 images are contextually identical, an optimal image from among the 10 images is determined and a recommendation of the optimal image is provided. Upon receiving a gesture from a user responsive to the recommendation, images of the contextually identical selfie images other than the optimal image are removed from the storage.
The user device 120 may be, but is not limited to, a personal computer (PC), a personal digital assistant (PDA), a mobile phone, a tablet computer, a smart phone, a wearable computing device, and the like. Each user device 120 may have installed therein an agent 125-1 through 125-n (hereinafter referred to individually as an agent 125 and collectively as agents 125, merely for simplicity purposes), respectively. The agent 125 may be a dedicated application, script, or any program code stored in a memory (not shown) of the user device 120 and is executable, for example, by the operating system (not shown) of the user device 120. The agent 120 may be configured to perform some or all of the processes disclosed herein.
The user device 120 is configured to capture multimedia content elements, to receive multimedia content elements, to display multimedia content elements, or a combination thereof. The multimedia content elements displayed on the user device 120 may be, e.g., downloaded from one of the data sources 150, or may be embedded in a web-page displayed on the user device 120. Each of the data sources 150 may be, but is not limited to, a server (e.g., a web server), an application server, a data repository, a database, a website, an e-commerce website, a content website, and the like. The multimedia content elements can be locally saved in the user device 120 or can be captured by the user device 120.
For example, the multimedia content elements may include an image captured by a camera (not shown) installed in the user device 120, a video clip saved in the device, an image received by the user device 120, and so on. A multimedia content element may be, but is not limited to, an image, a graphic, a video stream, a video clip, an audio stream, an audio clip, a video frame, a photograph, an image of signals (e.g., spectrograms, phasograms, scalograms, etc.), a combination thereof, a portion thereof, and the like.
The various embodiments disclosed herein may be realized using the server 130, a signature generator system (SGS) 140, or both.
In an embodiment, a tracking agent such as, for example, the agent 125, may be configured to collect and send a plurality of multimedia content elements captured or displayed by the user device 120 to the server 130. In an embodiment, the server 130 may be configured to receive the collected multimedia content elements and to analyze the received multimedia content elements to determine whether and which of the multimedia content elements are contextually identical. The analysis may be based on, but is not limited to, signatures generated for each multimedia content element, concepts determined based on the multimedia content elements, contextual insights for each multimedia content element, a combination thereof, and the like.
In an embodiment, the server 130 is configured to preprocess the multimedia content elements to determine similarities between multimedia content elements of the plurality of multimedia content elements, and only multimedia content elements having similarities above a predetermined threshold are analyzed to determine contextually identical multimedia content elements. In an embodiment, the preprocessing may include analyzing factors including any of the signatures generated for each multimedia content element, the concepts determined based on the multimedia content elements, and the contextual insights for each multimedia content element before analyzing the other factors. For example, it may first be checked if the multimedia content elements were captured within a time period below a predetermined threshold and, if not, the multimedia content elements may be determined not to be contextually identical without generating signatures or determining concepts.
In an embodiment, the server 130 may be configured to send the received multimedia content elements to the signature generator system 140. In an embodiment, the signature generator system 140 is configured to generate at least one signature for each of the multimedia content elements. The process for generating the signatures is explained in more detail herein below with respect to
In a further embodiment, the server 130 is further configured to receive the generated signatures from the signature generator system 140. In another embodiment, the server 130 may be configured to generate the at least one signature for each multimedia content element or portion thereof as discussed further herein below.
In an embodiment, whether multimedia content elements are contextually identical may be based on matching between signatures of the multimedia content elements. In a further embodiment, if the matching between the signatures is above a predetermined threshold, the signatures may be determined to be contextually identical.
It should be appreciated that signatures may be used for profiling the user's interests, because signatures allow more accurate recognition of multimedia content elements in comparison to, for example, utilization of metadata. The signatures generated by the signature generator system 140 for the multimedia content elements 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. For example, a signature generated by the signature generator system 140 for a picture showing a car enables accurate recognition of the model of the car from any angle at which the picture was taken.
In yet a further embodiment, the server 130 may be configured to match the generated signatures against a database of concepts (not shown) to identify a concept that can be associated with each signature, and hence the corresponding multimedia element.
A concept is a collection of signatures representing at least one multimedia content element and metadata describing the concept. The collection of signatures is a signature reduced cluster generated by inter-matching signatures generated for the at least one multimedia content element represented by the concept. The concept is represented using at least one signature. Generating concepts by inter-matching signatures is described further in U.S. patent application Ser. No. 14/096,901, filed on Dec. 4, 2013, assigned to the common assignee, which is hereby incorporated by reference.
In a further embodiment, matching the generated signatures against the database of concepts further includes matching the generated signatures to signatures representing the concepts. The signatures representing the concepts may be, but are not limited to, signatures included in the concepts or signature clusters representing the concepts.
In an embodiment, whether multimedia content elements are contextually identical may be based at least in part on whether the multimedia content elements are associated with the same or similar concepts. In a further embodiment, determining whether multimedia content elements are associated with the same or similar concepts may be utilized to preprocess and determine multimedia content elements that are not likely contextually identical. That is, in an embodiment, if two or more multimedia content elements are not associated with a similar concept, other factors for determining whether they are contextually identical (e.g., matching between signatures of the multimedia content elements or determination of contextual identifiers) may not be performed. As an example, if a first image is associated with concepts of “books” and “library” while a second image is associated with concepts of “flowers” and “sidewalk”, the first image and the second image may be determined to not be contextually identical without requiring matching between signatures of the first and second images or consideration of time and location of capture of the images.
In another embodiment, the server 130 is further configured to generate at least one contextual insight of the received multimedia content elements. Contextual insights are conclusions related to the context of each multimedia content element, in particular relative to other contexts. In a further embodiment, the contextual insights may be based on metadata associated with each multimedia content element. To this end, in an embodiment, the server 130 is configured to parse the multimedia content elements to determine metadata associated with each multimedia content element.
The metadata may include, but is not limited to, a time pointer associated with a capture or display of a multimedia content element, a location pointer associated with a capture of a multimedia content element, details related to a device (e.g., the user device 120) that captured the multimedia content element, combinations thereof, and the like. In an embodiment, multimedia content elements may be contextually identical if the multimedia content elements were captured or displayed by the same user device 120, at the same (or roughly the same time), at the same (or roughly the same) location, or a combination thereof. Multimedia content elements may be captured or displayed at roughly the same time or location if a difference in the time or location between captures or displays is below a predetermined threshold. For example, if 15 images were captured within a time period of 30 seconds, the 15 images may be determined to be contextually identical. As another example, if two images were captured within 15 feet of each other, the two images may be determined to be contextually identical.
Based on the analysis, the server 130 is configured to determine whether at least two of the received multimedia content elements are contextually identical. As noted above, multimedia content elements may be contextually identical if, for example, signatures of the multimedia content elements match above a predetermined threshold; the multimedia content elements are associated with the same or similar concepts; contextual insights of the multimedia content elements indicate that the multimedia content elements were captured, displayed, or received at the same or similar time; the contextual insights indicate that the multimedia content elements were captured at the same or similar location; the contextual insights indicate that the multimedia content elements were captured by the same device; or a combination thereof.
In an embodiment, when it is determined that at least two multimedia contents are contextually identical, the server 130 is configured to send a notification indicating the at least two contextually identical multimedia content elements. In a further embodiment, the server 130 may be configured to receive a selection of one of the at least two contextually identical multimedia content elements. In yet a further embodiment, the server 130 is configured to remove, from a storage (e.g., one of the data sources 160), multimedia content elements of the at least two multimedia content elements other than the selected multimedia content element. Removing unselected contextually identical multimedia content elements reduces
In a further embodiment, the server 130 may be configured to determine at least one optimal multimedia content element from among the at least two contextually identical multimedia content elements. The at least one optimal multimedia content element is a multimedia content element selected to represent the at least two contextually identical multimedia content elements. The at least one optimal multimedia content element may be determined based on, but not limited to, features of the multimedia content elements (e.g., resolution, focus, clarity, frame, texture, etc.); matching with other multimedia content elements (e.g., multimedia content elements ranked highly in a social network or liked by a particular user); a combination thereof; and the like.
In a further embodiment, the server 130 is configured to determine the optimal multimedia content based on, but not limited to, matching between signatures representing the at least two contextually identical multimedia content elements and signatures representing concepts a particular user is interested in. In yet a further embodiment, the contextually identical multimedia content element having the signature with the highest matching to the user interest concept signatures may be determined as the optimal multimedia content element.
To this end, each concept may be associated with at least one user interest. For example, a concept of flowers may be associated with a user interest in ‘flowers’ or ‘gardening.’ In an embodiment, the user interest may simply be the identified concept. In another embodiment, the user interest may be determined using an association table which associates one or more identified concepts with a user interest. For example, the concepts of ‘flowers’ and ‘spring’ may be associated, in an association table with a user interest of ‘gardening’. Such an association table may be maintained in, e.g., the server 130 or the database 160.
In an embodiment, the notification may further indicate the at least one optimal multimedia content element. In a further embodiment, the notification including the at least one optimal multimedia content element is then provided to the user device 120 and the user device 120 is prompted to confirm selection of the at least one optimal multimedia content element. When the selection is confirmed, the server 130 is configured to remove the multimedia content element(s) of the at least two contextually identical multimedia content elements which were not determined as optimal from, e.g., a storage. In an embodiment, the server 130 is configured to remove the non-optimal multimedia content elements in real-time. In another embodiment, the server 130 may be configured to automatically remove the non-optimal multimedia content elements when at least one optimal multimedia content element is determined.
Each of the server 130 and the signature generator system 140 typically includes a processing circuitry (not shown) that is coupled to a memory (not shown). The memory typically contains instructions that can be executed by the processing circuitry. The server 130 also includes an interface (not shown) to the network 110. In an embodiment, the signature generator system 140 can be integrated in the server 130. In an embodiment, the server 130, the signature generator system 140, or both may include a plurality of computational cores having properties that are at least partly statistically independent from other of the plurality of computational cores. The computational cores are discussed further herein below.
The server 130 includes an interface 210 at least for receiving multimedia content elements captured or displayed by the user device 120 and for sending notifications indicating contextually identical multimedia content elements, optimal multimedia content elements, or both, to the user device 120. The server 130 further includes a processing circuitry 220 such as a processor coupled to a memory (mem) 230. The memory 230 contains instructions that, when executed by the processing circuitry 220, configures the server 130 to identify contextually identical multimedia content elements as further described herein.
In an embodiment, the server 130 also includes a signature generator (SG) 240. The signature generator 240 includes a plurality of computational cores having properties that are at least partly statistically independent from other of the plurality of computational cores. The signature generator 240 is configured to generate signatures for multimedia content elements. In an embodiment, the signatures are robust to noise, distortion, or both. In another embodiment, the server 130 may be configured to send, to an external signature generator (e.g., the signature generator system 140), one or more multimedia content elements and to receive, from the external signature generator, signatures generated to the sent one or more multimedia content elements.
In another embodiment, the server 130 includes a data storage 250. The data storage may store, for example, signatures of multimedia content elements, signatures of concepts, contextually identical multimedia content elements, optimal multimedia content elements, combinations thereof, and the like.
At optional S310, the plurality of multimedia content elements may be preprocessed. The preprocessing allows for, e.g., reduced usage of computing resources. To this end, in an embodiment, S310 includes, but is not limited to, determining at least one contextual insight (e.g., time, location, or device of capture or display) for each of the plurality of multimedia content elements, determining a concept associated with each of the plurality multimedia content elements, or both. Determining contextual insights and concepts for multimedia content elements are described further herein above with respect to
At S320, the multimedia content elements are analyzed to identify at least one group of contextually identical multimedia content elements. Each group of contextually identical multimedia content elements includes at least two multimedia content elements that are contextually identical to each other. In an embodiment, the analysis may be based on, but not limited to, at least one contextual insight of each multimedia content element, at least one concept associated with each multimedia content element, at least one signature of each multimedia content element, or a combination thereof. Analyzing multimedia content elements to identify contextually identical multimedia content elements is described further herein below with respect to
In another embodiment, S320 may include sending, to a signature generator system (e.g., the signature generator system 140) the multimedia content elements and receiving, from the signature generator system, at least one signature for each sent multimedia content element.
At S330, it is determined, based on the analysis, whether any multimedia content elements were identified as being contextually identical to each other. If so, execution continues with S340; otherwise, execution terminates.
At S340, at least one optimal multimedia content element may be determined from among the identified contextually identical multimedia content elements. In an embodiment, the at least one optimal multimedia content element may be determined based on, but not limited to, features of the multimedia content elements (e.g., resolution, focus, clarity, frame, texture, etc.); matching with other multimedia content elements (e.g., multimedia content elements ranked highly in a social network or liked by a particular user); a combination thereof; and the like.
In a further embodiment, one optimal multimedia content element may be selected for each group of contextually identical multimedia content elements that are contextually identical to each other. As an example, if the plurality of multimedia content elements includes 3 images showing a dog that are contextually identical and 5 videos showing a cat that are contextually identical, an optimal image may be selected from among the 3 contextually identical dog images and an optimal video may be selected from among the 5 contextually identical cat videos.
At S350, for each group of contextually identical multimedia content elements, all multimedia contents of the set other than the at least one optimal multimedia content are removed from, e.g., a storage. The removal may be automatic and in real-time. Alternatively, in another embodiment, S350 may include sending, to a user device, a notification indicating the selecting optimal multimedia content elements and prompting a user to confirm selection of the optimal multimedia content elements. In a further embodiment, upon receiving confirmation of the selection of the optimal multimedia content elements, S350 includes automatically removing all non-optimal multimedia content elements. In yet a further embodiment, S350 may further include receiving a selection of at least one alternative optimal multimedia content element. In such an embodiment, all multimedia content elements other than the at least one alternative optimal multimedia content may be removed from the storage.
As a non-limiting example, a plurality of images is received. The plurality of images is stored in a web server of a social network. The plurality of images includes 10 images showing a group of friends and one image showing an ocean. The plurality of images are preprocessed by determining contextual insights for each image. Each image is parsed to identify metadata, and the metadata is analyzed to determine the contextual insights. Based on the contextual insights, it is determined that the image showing the ocean was captured one hour after the images showing the group of friends, and that the images showing the group of friends were captured within 1 minute of each other. Accordingly, the images showing the group of friends are determined to be potentially contextually identical, and the image of the ocean is filtered out.
The remaining images showing the group of friends is analyzed by generating and matching signatures for each of the images. Based on the signature matching, it is determined that all of the images showing the group of friends match above a predetermined threshold. Thus, it is determined that the 10 images of the group of friends are contextually identical. Features of the contextually identical images are analyzed. Based on the feature analysis, it is determined that one of the contextually identical images has a higher resolution than other of the contextually identical images. The higher resolution image is selected as the optimal image, and the other images of the group of friends are removed from the web server.
At S410, at least one signature for each multimedia element identified is caused to be generated. In an embodiment, S410 may further include sending, to a signature generator system, the plurality of multimedia content elements and receiving, from the signature generator system, signatures generated for the plurality of multimedia content elements. Generation of signatures is described further herein below with respect to
At S420, the generated signatures are matched. Matching between signatures is described further herein below with respect to
At S430, it is determined, based on the signature matching, whether any of the plurality of multimedia content elements are contextually identical and, if so, execution continues with S440; otherwise, execution terminates. In an embodiment, S430 includes determining, based on the matching, whether signatures representing any of the plurality of multimedia content elements match above a predefined threshold, where two or more multimedia content elements are contextually identical to each other when signatures representing the two or more multimedia contents match above a predetermined threshold.
At S440, when it is determined that at least two of the multimedia content elements are contextually identical, at least one group of contextually identical multimedia content elements is identified. Each set includes at least two multimedia content elements that are contextually identical to each other.
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 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, θ 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 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:
i.e., given that l nodes (cores) constitute a Robust Signature of a certain image I, the probability that not all of these l nodes will belong to the Signature of a same, but noisy image, Ĩ is sufficiently low (according to a system's specified accuracy).
i.e., approximately l out of the total L nodes can be found to generate a Robust Signature according to the above definition.
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 U.S. Pat. Nos. 8,326,775 and 8,312,031, assigned to common assignee, and 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:
Detailed description of the Computational Core generation and the process for configuring such cores is discussed in more detail in the U.S. Pat. No. 8,655,801 referenced above.
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.
This application claims the benefit of U.S. Provisional Patent Application No. 62/310,742 filed on Mar. 20, 2016. This application is a continuation-in-part of U.S. patent application Ser. No. 14/643,694 filed on Mar. 10, 2015, now pending, which is a continuation of U.S. patent application Ser. No. 13/766,463 filed on Feb. 13, 2013, now U.S. Pat. No. 9,031,999. The Ser. No. 13/766,463 application is a continuation-in-part of U.S. patent application Ser. No. 13/602,858 filed on Sep. 4, 2012, now U.S. Pat. No. 8,868,619. The Ser. No. 13/602,858 application is a continuation of U.S. patent application Ser. No. 12/603,123 filed on Oct. 21, 2009, now U.S. Pat. No. 8,266,185. The Ser. No. 12/603,123 application is a continuation-in-part of: (1) 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 Jan. 29, 2006; (2) U.S. patent application Ser. No. 12/195,863 filed on 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 above-referenced U.S. patent application Ser. No. 12/084,150; (3) U.S. patent application Ser. No. 12/348,888 filed on Jan. 5, 2009, now pending, which is a continuation-in-part of the above-referenced U.S. patent application Ser. No. 12/084,150 and the above-referenced U.S. patent application Ser. No. 12/195,863; and (4) U.S. patent application Ser. No. 12/538,495 filed on Aug. 10, 2009, now U.S. Pat. No. 8,312,031, which is a continuation-in-part of the above-referenced U.S. patent application Ser. No. 12/084,150, the above-referenced U.S. patent application Ser. No. 12/195,863, and the above-referenced U.S. patent application Ser. No. 12/348,888. All of the applications referenced above are hereby incorporated by reference.
Number | Name | Date | Kind |
---|---|---|---|
4733353 | Jaswa | Mar 1988 | A |
4932645 | Schorey et al. | Jun 1990 | A |
4972363 | Nguyen et al. | Nov 1990 | A |
5214746 | Fogel et al. | May 1993 | A |
5307451 | Clark | Apr 1994 | A |
5412564 | Ecer | May 1995 | A |
5436653 | Ellis et al. | Jul 1995 | A |
5568181 | Greenwood et al. | Oct 1996 | A |
5638425 | Meador et al. | Jun 1997 | A |
5745678 | Herzberg et al. | Apr 1998 | A |
5763069 | Jordan | Jun 1998 | A |
5806061 | Chaudhuri et al. | Sep 1998 | A |
5835901 | Duvoisin et al. | Nov 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 |
5940821 | Wical | Aug 1999 | A |
5978754 | Kumano | Nov 1999 | A |
5987454 | Hobbs | Nov 1999 | A |
5991306 | Burns et al. | Nov 1999 | A |
6038560 | Wical | Mar 2000 | A |
6052481 | Grajski et al. | Apr 2000 | A |
6070167 | Qian et al. | May 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 |
6163510 | Lee et al. | Dec 2000 | A |
6240423 | Hirata | May 2001 | B1 |
6243375 | Speicher | Jun 2001 | B1 |
6243713 | Nelson et al. | Jun 2001 | B1 |
6275599 | Adler et al. | Aug 2001 | B1 |
6329986 | Cheng | Dec 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 |
6507672 | Watkins et al. | Jan 2003 | 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 |
6550018 | Abonamah et al. | Apr 2003 | B1 |
6557042 | He et al. | Apr 2003 | B1 |
6560597 | Dhillon et al. | May 2003 | B1 |
6594699 | Sahai et al. | Jul 2003 | B1 |
6601026 | Appelt et al. | Jul 2003 | B2 |
6601060 | Tomaru | Jul 2003 | B1 |
6611628 | Sekiguchi et al. | Aug 2003 | B1 |
6611837 | Schreiber | Aug 2003 | B2 |
6618711 | Ananth | Sep 2003 | B1 |
6640015 | Lafruit | Oct 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 |
6681032 | Bortolussi et al. | Jan 2004 | B2 |
6704725 | Lee | Mar 2004 | B1 |
6728706 | Aggarwal et al. | Apr 2004 | B2 |
6732149 | Kephart | May 2004 | B1 |
6742094 | Igari | May 2004 | B2 |
6751363 | Natsev et al. | Jun 2004 | B1 |
6751613 | Lee et al. | Jun 2004 | B1 |
6754435 | Kim | Jun 2004 | B2 |
6763069 | Divakaran et al. | Jul 2004 | B1 |
6763519 | McColl et al. | Jul 2004 | B1 |
6774917 | Foote et al. | Aug 2004 | B1 |
6795818 | Lee | Sep 2004 | B1 |
6804356 | Krishnamachari | Oct 2004 | B1 |
6813395 | Kinjo | Nov 2004 | B1 |
6819797 | Smith et al. | Nov 2004 | B1 |
6836776 | Schreiber | Dec 2004 | B2 |
6845374 | Oliver et al. | Jan 2005 | B1 |
6877134 | Fuller et al. | Apr 2005 | B1 |
6901207 | Watkins | May 2005 | B1 |
6938025 | Lulich et al. | Aug 2005 | B1 |
6961463 | Loui | Nov 2005 | B1 |
6963975 | Weare | Nov 2005 | B1 |
6970881 | Mohan et al. | Nov 2005 | B1 |
6978264 | Chandrasekar et al. | Dec 2005 | B2 |
6985172 | Rigney et al. | Jan 2006 | B1 |
7006689 | Kasutani | Feb 2006 | B2 |
7013051 | Sekiguchi et al. | Mar 2006 | B2 |
7020654 | Najmi | Mar 2006 | B1 |
7023979 | Wu et al. | Apr 2006 | B1 |
7043473 | Rassool et al. | May 2006 | B1 |
7124149 | Smith et al. | Oct 2006 | B2 |
7158681 | Persiantsev | Jan 2007 | B2 |
7199798 | Echigo et al. | Apr 2007 | B1 |
7215828 | Luo | May 2007 | B2 |
7260564 | Lynn et al. | Aug 2007 | B1 |
7277928 | Lennon | Oct 2007 | B2 |
7296012 | Ohashi | Nov 2007 | B2 |
7299261 | Oliver et al. | Nov 2007 | B1 |
7302117 | Sekiguchi et al. | Nov 2007 | B2 |
7313805 | Rosin et al. | Dec 2007 | B1 |
7340358 | Yoneyama | Mar 2008 | B2 |
7346629 | Kapur et al. | Mar 2008 | B2 |
7353224 | Chen et al. | Apr 2008 | B2 |
7376672 | Weare | May 2008 | B2 |
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 |
7523102 | Bjarnestam et al. | Apr 2009 | B2 |
7526607 | Singh et al. | Apr 2009 | B1 |
7529659 | Wold | May 2009 | B2 |
7536384 | Venkataraman 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 |
7657100 | Gokturk et al. | Feb 2010 | B2 |
7660468 | Gokturk et al. | Feb 2010 | B2 |
7694318 | Eldering et al. | Apr 2010 | B2 |
7801893 | Gulli | Sep 2010 | B2 |
7836054 | Kawai et al. | Nov 2010 | B2 |
7920894 | Wyler | Apr 2011 | B2 |
7921107 | Chang et al. | Apr 2011 | B2 |
7933407 | Keidar 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 |
8023739 | Hohimer et al. | Sep 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 |
8275764 | Jeon | 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 |
RE44225 | Aviv | May 2013 | E |
8457827 | Ferguson et al. | Jun 2013 | B1 |
8495489 | Everingham | Jul 2013 | B1 |
8527978 | Sallam | Sep 2013 | B1 |
8548828 | Longmire | Oct 2013 | B1 |
8634980 | Urmson | Jan 2014 | B1 |
8635531 | Graham et al. | Jan 2014 | B2 |
8655801 | Raichelgauz et al. | Feb 2014 | B2 |
8655878 | Kulkarni et al. | Feb 2014 | B1 |
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 |
8781152 | Momeyer | Jul 2014 | B2 |
8782077 | Rowley | Jul 2014 | B1 |
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 |
8868861 | Shimizu 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 |
8923551 | Grosz | Dec 2014 | B1 |
8959037 | Raichelgauz et al. | Feb 2015 | B2 |
8990125 | Raichelgauz et al. | Mar 2015 | B2 |
8990199 | Ramesh et al. | Mar 2015 | B1 |
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 |
9165406 | Gray et al. | Oct 2015 | B1 |
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 |
9298763 | Zack | Mar 2016 | B1 |
9323754 | Ramanathan et al. | Apr 2016 | B2 |
9330189 | Raichelgauz et al. | May 2016 | B2 |
9392324 | Maltar | Jul 2016 | B1 |
9438270 | Raichelgauz et al. | Sep 2016 | B2 |
9440647 | Sucan | Sep 2016 | B1 |
9466068 | Raichelgauz et al. | Oct 2016 | B2 |
9646006 | Raichelgauz et al. | May 2017 | B2 |
9679062 | Schillings et al. | Jun 2017 | B2 |
9734533 | Givot | Aug 2017 | B1 |
9807442 | Bhatia et al. | Oct 2017 | B2 |
9875445 | Amer et al. | Jan 2018 | B2 |
9984369 | Li et al. | May 2018 | B2 |
10133947 | Yang | Nov 2018 | B2 |
10157291 | Kenthapadi et al. | Dec 2018 | B1 |
10347122 | Takenaka | Jul 2019 | B2 |
10491885 | Hicks | Nov 2019 | B1 |
20010019633 | Tenze et al. | Sep 2001 | A1 |
20010038876 | Anderson | Nov 2001 | A1 |
20010056427 | Yoon et al. | Dec 2001 | A1 |
20020010682 | Johnson | Jan 2002 | A1 |
20020010715 | Chinn et al. | Jan 2002 | A1 |
20020019881 | Bokhari et al. | Feb 2002 | A1 |
20020032677 | Morgenthaler et al. | Mar 2002 | A1 |
20020037010 | Yamauchi | Mar 2002 | A1 |
20020038299 | Zernik et al. | Mar 2002 | A1 |
20020042914 | Walker et al. | Apr 2002 | A1 |
20020059580 | Kalker et al. | May 2002 | A1 |
20020072935 | Rowse et al. | Jun 2002 | A1 |
20020087530 | Smith et al. | Jul 2002 | A1 |
20020087828 | Arimilli et al. | Jul 2002 | A1 |
20020099870 | Miller et al. | Jul 2002 | A1 |
20020103813 | Frigon | Aug 2002 | A1 |
20020107827 | Benitez-Jimenez et al. | Aug 2002 | A1 |
20020113812 | Walker et al. | Aug 2002 | A1 |
20020123928 | Eldering et al. | Sep 2002 | A1 |
20020126872 | Brunk et al. | Sep 2002 | A1 |
20020129140 | Peled et al. | Sep 2002 | A1 |
20020129296 | Kwiat et al. | Sep 2002 | A1 |
20020143976 | Barker et al. | Oct 2002 | A1 |
20020147637 | Kraft et al. | Oct 2002 | A1 |
20020152087 | Gonzalez | 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 |
20020184505 | Mihcak et al. | Dec 2002 | A1 |
20030005432 | Ellis et al. | Jan 2003 | A1 |
20030028660 | Igawa et al. | Feb 2003 | A1 |
20030037010 | Schmelzer | 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 |
20030089216 | Birmingham et al. | May 2003 | A1 |
20030093790 | Logan et al. | May 2003 | A1 |
20030101150 | Agnihotri | May 2003 | A1 |
20030105739 | Essafi et al. | Jun 2003 | A1 |
20030115191 | Copperman et al. | Jun 2003 | A1 |
20030126147 | Essafi et al. | Jul 2003 | A1 |
20030182567 | Barton et al. | Sep 2003 | A1 |
20030184598 | Graham | Oct 2003 | A1 |
20030191764 | Richards | Oct 2003 | A1 |
20030200217 | Ackerman | Oct 2003 | A1 |
20030217335 | Chung et al. | Nov 2003 | A1 |
20030229531 | Heckerman et al. | Dec 2003 | A1 |
20040003394 | Ramaswamy | Jan 2004 | A1 |
20040025180 | Begeja et al. | Feb 2004 | A1 |
20040047461 | Weisman | Mar 2004 | A1 |
20040059736 | Willse | Mar 2004 | A1 |
20040068510 | Hayes et al. | Apr 2004 | A1 |
20040091111 | Levy | May 2004 | A1 |
20040095376 | Graham et al. | May 2004 | A1 |
20040098671 | Graham et al. | May 2004 | A1 |
20040107181 | Rodden | Jun 2004 | A1 |
20040111432 | Adams et al. | Jun 2004 | A1 |
20040111465 | Chuang et al. | Jun 2004 | A1 |
20040117367 | Smith et al. | Jun 2004 | A1 |
20040117638 | Monroe | Jun 2004 | A1 |
20040119848 | Buehler | 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 |
20040230572 | Omoigui | Nov 2004 | A1 |
20040249779 | Nauck et al. | Dec 2004 | A1 |
20040260688 | Gross | Dec 2004 | A1 |
20040267774 | Lin et al. | Dec 2004 | A1 |
20050021394 | Miedema et al. | Jan 2005 | A1 |
20050114198 | Koningstein et al. | May 2005 | A1 |
20050131884 | Gross et al. | Jun 2005 | A1 |
20050144455 | Haitsma | Jun 2005 | A1 |
20050163375 | Grady | Jul 2005 | A1 |
20050172130 | Roberts | Aug 2005 | A1 |
20050177372 | Wang et al. | Aug 2005 | A1 |
20050193015 | Logston | Sep 2005 | A1 |
20050193093 | Mathew et al. | Sep 2005 | A1 |
20050238198 | Brown et al. | Oct 2005 | A1 |
20050238238 | Xu et al. | Oct 2005 | A1 |
20050245241 | Durand et al. | Nov 2005 | A1 |
20050249398 | Khamene et al. | Nov 2005 | A1 |
20050256820 | Dugan et al. | Nov 2005 | A1 |
20050262428 | Little et al. | Nov 2005 | A1 |
20050281439 | Lange | Dec 2005 | A1 |
20050289163 | Gordon et al. | Dec 2005 | A1 |
20050289590 | Cheok et al. | 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 |
20060033163 | Chen | Feb 2006 | A1 |
20060041596 | Stirbu et al. | Feb 2006 | A1 |
20060048191 | Xiong | Mar 2006 | A1 |
20060064037 | Shalon et al. | Mar 2006 | A1 |
20060082672 | Peleg | Apr 2006 | A1 |
20060100987 | Leurs | May 2006 | A1 |
20060112035 | Cecchi et al. | May 2006 | A1 |
20060120626 | Perlmutter | Jun 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 |
20060217828 | Hicken | Sep 2006 | A1 |
20060218191 | Gopalakrishnan | Sep 2006 | A1 |
20060224529 | Kermani | Oct 2006 | A1 |
20060236343 | Chang | Oct 2006 | A1 |
20060242130 | Sadri | Oct 2006 | A1 |
20060242139 | Butterfield et al. | Oct 2006 | A1 |
20060242554 | Gerace et al. | Oct 2006 | A1 |
20060247983 | Dalli | Nov 2006 | A1 |
20060248558 | Barton et al. | Nov 2006 | A1 |
20060251292 | Gokturk | Nov 2006 | A1 |
20060251338 | Gokturk | Nov 2006 | A1 |
20060251339 | Gokturk | Nov 2006 | A1 |
20060253423 | McLane et al. | Nov 2006 | A1 |
20060288002 | Epstein et al. | Dec 2006 | A1 |
20070009159 | Fan | Jan 2007 | A1 |
20070011151 | Hagar et al. | Jan 2007 | A1 |
20070019864 | Koyama et al. | Jan 2007 | A1 |
20070022374 | Huang et al. | Jan 2007 | A1 |
20070033163 | Epstein et al. | Feb 2007 | A1 |
20070038608 | Chen | Feb 2007 | A1 |
20070038614 | Guha | Feb 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 |
20070083611 | Farago et al. | Apr 2007 | A1 |
20070091106 | Moroney | Apr 2007 | A1 |
20070130112 | Lin | Jun 2007 | A1 |
20070130159 | Gulli et al. | Jun 2007 | A1 |
20070156720 | Maren | Jul 2007 | A1 |
20070168413 | Barletta et al. | Jul 2007 | A1 |
20070174320 | Chou | Jul 2007 | A1 |
20070195987 | Rhoads | Aug 2007 | A1 |
20070196013 | Li | 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 |
20070298152 | Baets | Dec 2007 | A1 |
20080046406 | Seide et al. | Feb 2008 | A1 |
20080049629 | Morrill | Feb 2008 | A1 |
20080049789 | Vedantham et al. | Feb 2008 | A1 |
20080072256 | Boicey et al. | Mar 2008 | A1 |
20080079729 | Brailovsky | Apr 2008 | A1 |
20080091527 | Silverbrook et al. | Apr 2008 | A1 |
20080109433 | Rose | May 2008 | A1 |
20080152231 | Gokturk | Jun 2008 | A1 |
20080159622 | Agnihotri et al. | Jul 2008 | A1 |
20080163288 | Ghosal et al. | Jul 2008 | A1 |
20080165861 | Wen et al. | Jul 2008 | A1 |
20080166020 | Kosaka | Jul 2008 | A1 |
20080172615 | Igelman et al. | Jul 2008 | A1 |
20080189609 | Larson | Aug 2008 | A1 |
20080201299 | Lehikoinen et al. | Aug 2008 | A1 |
20080201314 | Smith et al. | Aug 2008 | A1 |
20080201361 | Castro et al. | Aug 2008 | A1 |
20080204706 | Magne et al. | Aug 2008 | A1 |
20080228995 | Tan et al. | Sep 2008 | A1 |
20080237359 | Silverbrook et al. | Oct 2008 | A1 |
20080253737 | Kimura et al. | Oct 2008 | A1 |
20080263579 | Mears et al. | Oct 2008 | A1 |
20080270373 | Oostveen et al. | Oct 2008 | A1 |
20080270569 | McBride | Oct 2008 | A1 |
20080294278 | Borgeson et al. | Nov 2008 | A1 |
20080307454 | Ahanger et al. | Dec 2008 | A1 |
20080313140 | Pereira et al. | Dec 2008 | A1 |
20090013414 | Washington et al. | Jan 2009 | A1 |
20090022472 | Bronstein | Jan 2009 | A1 |
20090024641 | Quigley et al. | Jan 2009 | A1 |
20090034791 | Doretto | Feb 2009 | A1 |
20090037408 | Rodgers | Feb 2009 | A1 |
20090043637 | Eder | Feb 2009 | A1 |
20090043818 | Raichelgauz | Feb 2009 | A1 |
20090080759 | Bhaskar | Mar 2009 | A1 |
20090089587 | Brunk et al. | Apr 2009 | A1 |
20090119157 | Dulepet | 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 |
20090208106 | Dunlop et al. | Aug 2009 | A1 |
20090208118 | Csurka | Aug 2009 | A1 |
20090216761 | Raichelgauz | Aug 2009 | A1 |
20090220138 | Zhang et al. | Sep 2009 | A1 |
20090245573 | Saptharishi et al. | Oct 2009 | A1 |
20090245603 | Koruga et al. | Oct 2009 | A1 |
20090253583 | Yoganathan | Oct 2009 | A1 |
20090254572 | Redlich et al. | Oct 2009 | A1 |
20090277322 | Cai et al. | Nov 2009 | A1 |
20090278934 | Ecker | Nov 2009 | A1 |
20090282218 | Raichelgauz et al. | Nov 2009 | A1 |
20090297048 | Slotine et al. | Dec 2009 | A1 |
20100042646 | Raichelgauz | Feb 2010 | A1 |
20100082684 | Churchill | Apr 2010 | A1 |
20100104184 | Bronstein et al. | Apr 2010 | A1 |
20100111408 | Matsuhira | May 2010 | A1 |
20100125569 | Nair et al. | May 2010 | A1 |
20100162405 | Cook et al. | Jun 2010 | A1 |
20100173269 | Puri et al. | Jul 2010 | A1 |
20100198626 | Cho et al. | Aug 2010 | A1 |
20100212015 | Jin et al. | Aug 2010 | A1 |
20100268524 | Nath et al. | Oct 2010 | A1 |
20100284604 | Chrysanthakopoulos | Nov 2010 | A1 |
20100306193 | Pereira | Dec 2010 | A1 |
20100312736 | Kello | Dec 2010 | A1 |
20100318493 | Wessling | Dec 2010 | A1 |
20100322522 | Wang et al. | Dec 2010 | A1 |
20100325138 | Lee et al. | Dec 2010 | A1 |
20100325581 | Finkelstein et al. | Dec 2010 | A1 |
20110029620 | Bonforte | Feb 2011 | A1 |
20110038545 | Bober | Feb 2011 | A1 |
20110052063 | McAuley et al. | Mar 2011 | A1 |
20110055585 | Lee | Mar 2011 | A1 |
20110145068 | King et al. | Jun 2011 | A1 |
20110164180 | Lee | Jul 2011 | A1 |
20110164810 | Zang et al. | Jul 2011 | A1 |
20110202848 | Ismalon | Aug 2011 | A1 |
20110208744 | Chandiramani | Aug 2011 | A1 |
20110218946 | Stern et al. | Sep 2011 | A1 |
20110246566 | Kashef | Oct 2011 | A1 |
20110251896 | Impollonia et al. | Oct 2011 | A1 |
20110276680 | Rimon | Nov 2011 | A1 |
20110296315 | Lin et al. | Dec 2011 | A1 |
20110313856 | Cohen et al. | Dec 2011 | A1 |
20120041969 | Priyadarshan et al. | Feb 2012 | A1 |
20120082362 | Diem et al. | Apr 2012 | A1 |
20120131454 | Shah | May 2012 | A1 |
20120133497 | Sasaki | May 2012 | A1 |
20120150890 | Jeong et al. | Jun 2012 | A1 |
20120167133 | Carroll et al. | Jun 2012 | A1 |
20120179642 | Sweeney et al. | Jul 2012 | A1 |
20120179751 | Ahn | Jul 2012 | A1 |
20120185445 | Borden et al. | Jul 2012 | A1 |
20120197857 | Huang et al. | Aug 2012 | A1 |
20120221470 | Lyon | Aug 2012 | A1 |
20120227074 | Hill et al. | Sep 2012 | A1 |
20120239690 | Asikainen et al. | Sep 2012 | A1 |
20120239694 | Avner et al. | Sep 2012 | A1 |
20120294514 | Saunders | Nov 2012 | A1 |
20120299961 | Ramkumar et al. | Nov 2012 | A1 |
20120301105 | Rehg et al. | Nov 2012 | A1 |
20120330869 | Durham | Dec 2012 | A1 |
20120331011 | Raichelgauz et al. | Dec 2012 | A1 |
20130031489 | Gubin et al. | Jan 2013 | A1 |
20130066856 | Ong et al. | Mar 2013 | A1 |
20130067035 | Amanat et al. | Mar 2013 | A1 |
20130067364 | Berntson et al. | Mar 2013 | A1 |
20130086499 | Dyor et al. | Apr 2013 | A1 |
20130089248 | Remiszewski et al. | Apr 2013 | A1 |
20130103814 | Carrasco | Apr 2013 | A1 |
20130104251 | Moore et al. | Apr 2013 | A1 |
20130137464 | Kramer | May 2013 | A1 |
20130159298 | Mason et al. | Jun 2013 | A1 |
20130173635 | Sanjeev | Jul 2013 | A1 |
20130212493 | Krishnamurthy | Aug 2013 | A1 |
20130226820 | Sedota, Jr. | Aug 2013 | A1 |
20130226930 | Arngren et al. | Aug 2013 | A1 |
20130273968 | Rhoads | Oct 2013 | A1 |
20130283401 | Pabla et al. | Oct 2013 | A1 |
20130325550 | Varghese et al. | Dec 2013 | A1 |
20130332951 | Gharaat et al. | Dec 2013 | A1 |
20140019264 | Wachman et al. | Jan 2014 | A1 |
20140025692 | Pappas | Jan 2014 | A1 |
20140059443 | Tabe | Feb 2014 | A1 |
20140095425 | Sipple | Apr 2014 | A1 |
20140111647 | Atsmon | Apr 2014 | A1 |
20140147829 | Jerauld | May 2014 | A1 |
20140152698 | Kim et al. | Jun 2014 | A1 |
20140169681 | Drake | Jun 2014 | A1 |
20140176604 | Venkitaraman et al. | Jun 2014 | A1 |
20140188786 | Raichelgauz et al. | Jul 2014 | A1 |
20140193077 | Shiiyama et al. | Jul 2014 | A1 |
20140198986 | Marchesotti | Jul 2014 | A1 |
20140201330 | Lozano Lopez | Jul 2014 | A1 |
20140250032 | Huang et al. | Sep 2014 | A1 |
20140282655 | Roberts | Sep 2014 | A1 |
20140300722 | Garcia | Oct 2014 | A1 |
20140310825 | Raichelgauz et al. | Oct 2014 | A1 |
20140317480 | Chau | Oct 2014 | A1 |
20140330830 | Raichelgauz et al. | Nov 2014 | A1 |
20140341476 | Kulick et al. | Nov 2014 | A1 |
20140379477 | Sheinfeld | Dec 2014 | A1 |
20150033150 | Lee | Jan 2015 | A1 |
20150100562 | Kohlmeier et al. | Apr 2015 | A1 |
20150117784 | Lin | Apr 2015 | A1 |
20150120627 | Hunzinger et al. | Apr 2015 | A1 |
20150134688 | Jing | May 2015 | A1 |
20150254344 | Kulkarni et al. | Sep 2015 | A1 |
20150286742 | Zhang et al. | Oct 2015 | A1 |
20150289022 | Gross | Oct 2015 | A1 |
20150324356 | Gutierrez et al. | Nov 2015 | A1 |
20150363644 | Wnuk | Dec 2015 | A1 |
20160007083 | Gurha | Jan 2016 | A1 |
20160026707 | Ong et al. | Jan 2016 | A1 |
20160210525 | Yang | Jul 2016 | A1 |
20160221592 | Puttagunta | Aug 2016 | A1 |
20160283483 | Jiang | Sep 2016 | A1 |
20160306798 | Guo et al. | Oct 2016 | A1 |
20160342683 | Lim et al. | Nov 2016 | A1 |
20160357188 | Ansari | Dec 2016 | A1 |
20170017638 | Satyavarta et al. | Jan 2017 | A1 |
20170032257 | Sharifi | Feb 2017 | A1 |
20170041254 | Agara Venkatesha Rao | Feb 2017 | A1 |
20170109602 | Kim | Apr 2017 | A1 |
20170154241 | Shambik et al. | Jun 2017 | A1 |
20170255620 | Raichelgauz | Sep 2017 | A1 |
20170262437 | Raichelgauz | Sep 2017 | A1 |
20170323568 | Inoue | Nov 2017 | A1 |
20180081368 | Watanabe | Mar 2018 | A1 |
20180101177 | Cohen | Apr 2018 | A1 |
20180157916 | Doumbouya | Jun 2018 | A1 |
20180158323 | Takenaka | Jun 2018 | A1 |
20180204111 | Zadeh | Jul 2018 | A1 |
20190005726 | Nakano | Jan 2019 | A1 |
20190039627 | Yamamoto | Feb 2019 | A1 |
20190043274 | Hayakawa | Feb 2019 | A1 |
20190045244 | Balakrishnan | Feb 2019 | A1 |
20190056718 | Satou | Feb 2019 | A1 |
20190065951 | Luo | Feb 2019 | A1 |
20190188501 | Ryu | Jun 2019 | A1 |
20190220011 | Della Penna | Jul 2019 | A1 |
20190317513 | Zhang | Oct 2019 | A1 |
20190364492 | Azizi | Nov 2019 | A1 |
20190384303 | Muller | Dec 2019 | A1 |
20190384312 | Herbach | Dec 2019 | A1 |
20190385460 | Magzimof | Dec 2019 | A1 |
20190389459 | Berntorp | Dec 2019 | A1 |
20200004248 | Healey | Jan 2020 | A1 |
20200004251 | Zhu | Jan 2020 | A1 |
20200004265 | Zhu | Jan 2020 | A1 |
20200005631 | Visintainer | Jan 2020 | A1 |
20200018606 | Wolcott | Jan 2020 | A1 |
20200018618 | Ozog | Jan 2020 | A1 |
20200020212 | Song | Jan 2020 | A1 |
20200050973 | Stenneth | Feb 2020 | A1 |
20200073977 | Montemerlo | Mar 2020 | A1 |
20200090484 | Chen | Mar 2020 | A1 |
20200097756 | Hashimoto | Mar 2020 | A1 |
20200133307 | Kelkar | Apr 2020 | A1 |
20200043326 | Tao | Jun 2020 | A1 |
Number | Date | Country |
---|---|---|
1085464 | Jan 2007 | EP |
0231764 | Apr 2002 | WO |
0231764 | Apr 2002 | WO |
2003005242 | Jan 2003 | WO |
2003067467 | Aug 2003 | WO |
2004019527 | Mar 2004 | WO |
2005027457 | Mar 2005 | WO |
2007049282 | May 2007 | WO |
WO2007049282 | May 2007 | WO |
2014076002 | May 2014 | WO |
2014137337 | Sep 2014 | WO |
2016040376 | Mar 2016 | WO |
2016070193 | May 2016 | WO |
WO-2016127478 | Aug 2016 | WO |
Entry |
---|
Bilyana Taneva et al. “Gathering and Ranking Photos of Named Entities with High Precision, High Recall, and Diversity”, WSDM '10 , Feb. 4-6, 2010, New York City, New York USA ACM 2010, 10 pages. |
Brecheisen, et al., “Hierarchical Genre Classification for Large Music Collections”, ICME 2006, pp. 1385-1388. |
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/WoWMoM.2011.5986211 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. |
Santos, et al., “SCORM-MPEG: an Ontology of Interoperable Metadata for Multimedia and e-Leaming”, 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. |
Zeevi, Y. et al.: “Natural Signal Classification by Neural Cliques and Phase-Locked Attractors”, IEEE World Congress an Computational Intelligence, IJCNN2006, Vancouver, Canada, Jul. 2006 (Jul. 2006), XP002466252. |
Boari et al, “Adaptive Routing for Dynamic Applications in Massively Parallel Architectures”, 1995 IEEE, Spring 1995. |
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. |
Chuan-Yu Cho, et al., “Efficient Motion-Vector-Based Video Search Using Query by Clip”, 2004, IEEE, Taiwan, pp. 1-4. |
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. |
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). |
Gomes et al., “Audio Watermaking and Fingerprinting: For Which Applications?” University of Rene Descartes, Paris, France, 2003. |
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. |
Guo et al, “AdOn: An Intelligent Overlay Video Advertising System”, SIGIR, Boston, Massachusetts, Jul. 19-23, 2009. |
Ihab Al Kabary, et al., “SportSense: Using Motion Queries to Find Scenes in Sports Videos”, Oct. 2013, ACM, Switzerland, pp. 1-3. |
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; dated 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; dated Jul. 28, 2009. |
International Search Report for the corresponding International Patent Application PCT/IL2006/001235; dated Nov. 2, 2008. |
IPO Examination Report under Section 18(3) for corresponding UK application No. GB1001219.3, dated Sep. 12, 2011. |
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. |
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. |
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, 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. |
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. |
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. |
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. |
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. |
May et al., “The Transputer”, Springer-Verlag, Berlin Heidelberg, 1989, teaches multiprocessing system. |
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. |
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. |
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. |
Nam, et al., “Audio Visual Content-Based Violent Scene Characterization”, Department of Electrical and Computer Engineering, Minneapolis, MN, 1998, pp. 353-357. |
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. |
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. |
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. |
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. |
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. |
Shih-Fu Chang, et al., “VideoQ: A Fully Automated Video Retrieval System Using Motion Sketches”, 1998, IEEE, , New York, pp. 1-2. |
Theodoropoulos et al, “Simulating Asynchronous Architectures on Transputer Networks”, Proceedings of the Fourth Euromicro Workshop On Parallel and Distributed Processing, 1996. PDP '96. |
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. |
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. |
Wang et al. “A Signature for Content-based Image Retrieval Using a Geometrical Transform”, ACM 1998, pp. 229-234. |
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. |
Whitby-Strevens, “The Transputer”, 1985 IEEE, Bristol, UK. |
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. |
Yanai, “Generic Image Classification Using Visual Knowledge on the Web,” MM'03, Nov. 2-8, 2003, Tokyo, Japan, pp. 167-176. |
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. |
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; 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. |
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. |
Johnson, John L., “Pulse-Coupled Neural Nets: Translation, Rotation, Scale, Distortion, and Intensity Signal Invariance for Images.” Applied Optics, vol. 33, No. 26, 1994, pp. 6239-6253. |
The International Search Report and the Written Opinion for PCT/US2016/050471, ISA/RU, Moscow, RU, dated May 4, 2017. |
The International Search Report and the Written Opinion for PCT/US2016/054634 dated Mar. 16, 2017, ISA/RU, Moscow, RU. |
The International Search Report and the Written Opinion for PCT/US2017/015831, ISA/RU, Moscow, Russia, dated Apr. 20, 2017. |
Hogue, “Tree Pattern Inference and Matching for Wrapper Induction on the World Wide Web”, Master's Thesis, Massachusetts institute of Technology, 2004, pp. 1-106. |
Johnson et al, “Pulse-Coupled Neural Nets: Translation, Rotation, Scale, Distortion, and Intensity Signal Invariance for Images”, Applied Optics, vol. 33, No. 26, 1994, pp. 6239-6253. |
McNamara et al., “Diversity Decay in opportunistic Content Sharing Systems”, 2011 IEEE International Symposium an a World of Wireless, Mobile and Multimedia Networks, pp. 1-3. |
Rui, Yong et al. “Relevance feedback: a power tool for interactive content-based image retrieval.” IEEE Transactions an circuits and systems for video technology 8.5 (1998): 644-655. |
“Computer Vision Demonstration Website”, Electronics and Computer Science, University of Southampton, 2005, USA. |
Big Bang Theory Series 04 Episode 12, aired Jan. 6, 2011; [retrieved from Internet: ]. |
Boari et al, “Adaptive Routing for Dynamic Applications in Massively Parallel Architectures”, 1995 IEEE, Spring 1995, pp. 1-14. |
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, pp. 1-4. |
Chinchor, Nancy A. et al.; Multimedia Analysis + Visual Analytics = Multimedia Analytics; IEEE Computer Society 2010; pp. 52-60. (Year: 2010). |
Fathy et al, “A Parallel Design and Implementation For Backpropagation Neural Network Using MIMD Architecture”, 8th Mediterranean Electrotechnical Conference, 19'96. MELECON '96, Date of Conference: May 13-16, 1996, vol. 3 pp. 1472-1475, vol. 3. |
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 (Year: 2009). |
Hogue, “Tree Pattern Inference and Matching for Wrapper Induction on the World Wide Web”, Master's Thesis, Massachusetts Institute of Technology, Jun. 2004, pp. 1-106. |
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. |
Hua et al., “Robust Video Signature Based on Ordinal Measure”, Image Processing, 2004, 2004 International Conference on Image Processing (ICIP), vol. 1, IEEE, pp. 685-688, 2004. |
International Search Report and Written Opinion for PCT/US2016/050471, ISA/RU, Moscow, RU, dated May 4, 2017. |
International Search Report and Written Opinion for PCT/US2016/054634, ISA/RU, Moscow, RU, dated Mar. 16, 2017. |
International Search Report and Written Opinion for PCT/US2017/015831, ISA/RU, Moscow, RU, dated Apr. 20, 2017. |
Johnson et al, “Pulse-Coupled Neural Nets: Translation, Rotation, Scale, Distortion, and Intensity Signal Invariance tor Images”, Applied Optics, vol. 33, No. 26, 1994, pp. 6239-6253. |
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, 2008, pp. 98-103. |
Li et al (“Matching Commercial Clips from TV Streams Using a Unique, Robust and Compact Signature” 2005) (Year: 2005). |
Lin et al., “Generating robust digital signature for image/video authentication”, Multimedia and Security Workshop at ACM Multimedia '98, Bristol, U.K., Sep. 1998, pp. 245-251. |
Lu et al, “Structural Digital Signature for Image Authentication: An Incidental Distortion Resistant Scheme”, IEEE Transactions on Multimedia, vol. 5, No. 2, Jun. 2003, pp. 161-173. |
Lyon, “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. |
Marian Stewart B et al., “Independent component representations for face recognition”, Proceedings of the SPIE Symposium on Electronic Imaging: Science and Technology; Conference on Human Vision and Electronic Imaging III, San Jose, California, Jan. 1998, pp. 1-12. |
May et al, “The Transputer”, Springer-Verlag Berlin Heidelberg 1989, vol. 41. |
McNamara et al., “Diversity Decay in opportunistic Content Sharing Systems”, 2011 IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks, pp. 1-3. |
Morad et al., “Performance, Power Efficiency and Scalability of Asymmetric Cluster Chip Multiprocessors”, Computer Architecture Letters, vol. 4, 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 Publication No. 427, IEE 1996. |
Natschlager 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. |
Odinaev et al, “Cliques in Neural Ensembles as Perception Carriers”, Technion—Institute of Technology, 2006 International Joint Conference on neural Networks, Canada, 2006, pp. 285-292. |
Ortiz-Boyer et al, “CIXL2: A Crossover Operator for Evolutionary Algorithms Based on Population Features”, Journal of Artificial Intelligence Research 24 (2005) Submitted Nov. 2004; published Jul. 2005, pp. 1-48. |
Pandya etal. A Survey on QR Codes: in context of Research and Application. International Journal of Emerging Technology and U Advanced Engineering. ISSN 2250-2459, ISO 9001:2008 Certified Journal, vol. 4, Issue 3, Mar. 2014 (Year: 2014). |
Queluz, “Content-Based Integrity Protection of Digital Images”, SPIE Conf. on Security and Watermarking of Multimedia Contents, San Jose, Jan. 1999, pp. 85-93. |
Rui, Yong et al. “Relevance feedback: a power tool for interactive content-based image retrieval.” IEEE Transactions on circuits and systems for video technology 8.5 (1998): 644-655. |
Santos et al., “SCORM-MPEG: an Ontology of Interoperable Metadata for multimediaand E-Learning”, 23rd International Conference on Software, Telecommunications and Computer Networks (SoftCom), 2015, pp. 224-228. |
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 publication, ISBN 2-930307-06-4, pp. 1-12. |
Schneider et al, “A Robust Content based Digital Signature for Image Authentication”, Proc. ICIP 1996, Lausane, Switzerland, Oct. 1996, pp. 227-230. |
Srihari et al., “Intelligent Indexing and Semantic Retrieval of Multimodal Documents”, Kluwer Academic Publishers, May 2000, vol. 2, Issue 2-3, pp. 245-275. |
Srihari, Rohini K. “Automatic indexing and content-based retrieval of captioned images” Computer 0 (1995): 49-56. |
Stolberg et al (“Hibrid-Soc: A Multi-Core Soc Architecture for Multimedia Signal Processing” 2003). |
Stolberg et al, “Hibrid-Soc: A Mul Ti-Core Soc Architecture for Mul Timedia Signal Processing”, 2003 IEEE, pp. 189-194. |
Theodoropoulos et al, “Simulating Asynchronous Architectures on Transputer Networks”, Proceedings of the Fourth Euromicro Workshop on Parallel and Distributed Processing, 1996. PDP '96, pp. 274-281. |
Vallet et al (“Personalized Content Retrieval in Context Using Ontological Knowledge” Mar. 2007) (Year: 2007). |
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 onlline Jul. 14, 2005, pp. 521-528. |
Wang et al., “Classifying Objectionable Websites Based onImage Content”, Stanford University, pp. 1-12. |
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. |
Whitby-Strevens, “The transputer”, 1985 IEEE, pp. 292-300. |
Wilk et al., “The Potential of Social-Aware Multimedia Prefetching on Mobile Devices”, International Conference and Workshops on networked Systems (NetSys), 2015, pp. 1-5. |
Yanagawa et al, “Columbia University's Baseline Detectors for 374 LSCOM Semantic Visual Concepts”, Columbia University ADVENT Technical Report # 222-2006-8, Mar. 20, 2007, pp. 1-17. |
Yanagawa et al., “Columbia University's Baseline Detectors for 374 LSCOM Semantic Visual Concepts”, Columbia University ADVENT Technical Report #222, 2007, pp. 2006-2008. |
Zhou et al, “Ensembling neural networks: Many could be better than all”, National Laboratory for Novel Software Technology, Nanjing University, Hankou Road 22, Nanjing 210093, PR China, Available online Mar. 12, 2002, pp. 239-263. |
Ma Et El. (“Semantics modeling based image retrieval system using neural networks” 2005 (Year: 2005). |
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, Mar. 2003, pp. 37-42. |
Zhu et al., “Technology-Assisted Dietary Assesment”, Proc SPIE. Mar. 20, 2008, pp. 1-15. |
Zou et al., “A Content-Based Image Authentication System with Lossless Data Hiding”, ICME 2003, pp. 213-216. |
Jasinschi et al., A Probabilistic Layered Framework for Integrating Multimedia Content and Context Information, 2002, IEEE, p. 2057-2060. (Year: 2002). |
Jones et al., “Contextual Dynamics of Group-Based Sharing Decisions”, 2011, University of Bath, p. 1777-1786. (Year: 2011). |
Iwamoto, “Image Signature Robust to Caption Superimpostion for Video Sequence Identification”, IEEE, pp. 3185-3188 (Year: 2006). |
Cooperative Multi-Scale Convolutional Neural, Networks for Person Detection, Markus Eisenbach, Daniel Seichter, Tim Wengefeld, and Horst-Michael Gross Ilmenau University of Technology, Neuroinformatics and Cognitive Robotics Lab (Year; 2016) |
Chen, Yixin, James Ze Wang, and Robert Krovetz. “Clue: cluster-based retrieval of images by unsupervised learning.” IEEE transactions on Image Processing 14.8 (2005); 1187-1201. (Year: 2005). |
Wusk et al (Non-Invasive detection of Respiration and Heart Rate with a Vehicle Seat Sensor; www.mdpi.com/journal/sensors; Published: May 8, 2018). (Year: 2018). |
Chen, Tiffany Yu-Han, et al. “Glimpse: Continuous, real-time object recognition on mobile devices.” Proceedings of the 13th ACM Confrecene on Embedded Networked Sensor Systems. 2015. (Year: 2015). |
Number | Date | Country | |
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20170046343 A1 | Feb 2017 | US |
Number | Date | Country | |
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62310742 | Mar 2016 | US |
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Parent | 13766463 | Feb 2013 | US |
Child | 14643694 | US |
Number | Date | Country | |
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Parent | 14643694 | Mar 2015 | US |
Child | 15296551 | US |