System and method for completing a user profile

Abstract

A system and method for at least partially completing a user profile. The method includes analyzing the user profile to identify at least one missing informational element in the user profile, wherein identifying the at least one missing element further comprises determining at least one concept based on the user profile and matching the determined at least one concept to a plurality of category concepts, each concept including a collection of signatures and metadata describing the concept, wherein each category concept is associated with at least one required informational element, wherein each missing informational element is one of the at least one required informational element that is not included in the user profile; sending a query for the missing informational element; and updating at least a portion of the user profile based on a response to the query.

Description

TECHNICAL FIELD

The present disclosure relates generally to the analysis of multimedia content associated with a user, and more specifically with determining a missing informational element associated with a user profile based on multimedia content.

BACKGROUND

As the amount of content available over the Internet continues to grow exponentially in size, the task of identifying relevant content has become increasingly cumbersome. Identifying relevant content related to user preferences is of particular interest for online advertisers in order to most effectively offer goods or services that are appropriate for each particular user. A user profile may be created based on user interests; however, the user profile must be sufficiently accurate to provide desirable results.

Existing solutions provide several tools to identify user preferences. Some solutions request active input from users to specify their interests. However, profiles generated for users based on their active input may be inaccurate, as users tend to provide only their current interests, which can change over time. Further, users may only provide partial information due to privacy concerns, resulting in an incomplete user profile. Additionally, requiring active input from users on a regular basis can easily become cumbersome and irritating for users, resulting in decreased interest of users in responding to such requests.

Other existing solutions passively track users' activity through particular web sites, such as social networks. The disadvantage of these solutions is that the information regarding the users that is revealed is typically limited, as users tend to provide only partial information due to privacy concerns. For example, users creating an account on Facebook® will often provide only the mandatory information required for the creation of the account. This information may be insufficient to build an satisfactory user profile.

Additional information about such users may be collected over time, but may take significant amounts of time (i.e., gathered via multiple social media or blog posts over a time period of weeks or months) to be sufficiently useful for accurate identification of user preferences.

Additionally, some existing solutions for determining user preferences attempt to identify and recommend content that is similar to content enjoyed by the user based on information noted by tags related to the enjoyed content including, for example, the subject matter of the content, the entity that created the content, persons appearing in the content, and the like. Such solutions also face challenges due to lack of accurate information regarding what content the user has viewed and determining whether the user has indeed enjoyed such content. As a result, user profiles created using these solutions are often incomplete or inaccurate, and typically are completed through manual correction by users.

It would therefore be advantageous to provide a solution that would overcome the challenges noted above.

SUMMARY

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

Certain embodiments disclosed herein include a method for at least partially completing a user profile. The method comprises: analyzing the user profile to identify at least one missing informational element in the user profile, wherein identifying the at least one missing element further comprises determining at least one concept based on the user profile and matching the determined at least one concept to a plurality of category concepts, each concept including a collection of signatures and metadata describing the concept, wherein each category concept is associated with at least one required informational element, wherein each missing informational element is one of the at least one required informational element that is not included in the user profile; sending a query for the missing informational element; and updating at least a portion of the user profile based on a response to the query.

Certain embodiments disclosed herein also include a non-transitory computer readable medium having stored thereon instructions for causing a processing circuitry to perform a process, the process comprising: analyzing a user profile to identify at least one missing informational element in the user profile, wherein identifying the at least one missing element further comprises determining at least one concept based on the user profile and matching the determined at least one concept to a plurality of category concepts, each concept including a collection of signatures and metadata describing the concept, wherein each category concept is associated with at least one required informational element, wherein each missing informational element is one of the at least one required informational element that is not included in the user profile; sending a query for the missing informational element; and updating at least a portion of the user profile based on a response to the query.

Certain embodiments disclosed herein also include a system for at least partially completing a user profile. 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 the user profile to identify at least one missing informational element in the user profile, wherein identifying the at least one missing element further comprises determining at least one concept based on the user profile and matching the determined at least one concept to a plurality of category concepts, each concept including a collection of signatures and metadata describing the concept, wherein each category concept is associated with at least one required informational element, wherein each missing informational element is one of the at least one required informational element that is not included in the user profile; send a query for the missing informational element; and update at least a portion of the user profile based on a response to the query.

BRIEF DESCRIPTION OF THE DRAWINGS

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

FIG. 1 is a network diagram utilized to describe the various disclosed embodiments.

FIG. 2 is an example schematic diagram of a Deep Content Classification system for creating concepts according to an embodiment.

FIG. 3 is a flowchart illustrating a method for creating a user profile according to an embodiment.

FIG. 4 is a flowchart illustrating a method for at least partially completing a user profile according to an embodiment.

FIG. 5 is a block diagram depicting the basic flow of information in the signature generator system.

FIG. 6 is a diagram showing the flow of patches generation, response vector generation, and signature generation in a large-scale speech-to-text system.

DETAILED DESCRIPTION

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

FIG. 1 shows a network diagram 100 utilized to describe the various disclosed embodiments. A user device 120, a server 130, a signature generator system (SGS) 140, a database 150, a deep content classification (DCC) system 160, and a plurality of web sources 170-1 through 170-n are communicatively connected via a network 110. The network 110 may include the Internet, the world-wide-web (WWW), a local area network (LAN), a wide area network (WAN), a metro area network (MAN), and other networks capable of enabling communication between elements of a system 100.

The user device 120 may be, but is not limited to, a mobile phone, a smartphone, a personal computer (PC), a tablet computer, a wearable computing device, and other kinds of wired and mobile devices capable of capturing, uploading, browsing, viewing, listening, filtering, and managing MMCEs as further discussed herein below. The user device 120 may have installed thereon an application 125. The application 125 may be downloaded from an application repository, such as the Apple® AppStore®, Google Play®, or any repository hosting software applications for download.

The user device 120 includes a storage (not shown) containing one or more MMCEs, such as, but not limited to, an image, a photograph, a graphic, a screenshot, a video stream, a video clip, a video frame, an audio stream, an audio clip, combinations thereof, portions thereof, and the like.

The web sources 170-1 through 170-n (hereinafter referred to collectively as web sources 170, merely for simplicity) are connected to the network 110, where ‘n’ is an integer equal to or greater than 1. The web sources 170 include data sources or files available over, for example, the Internet. To this end, the web sources 170 may include, but are not limited to, websites, web-pages, social network platforms, search engines, public and private databases, and the like. The web sources 170 include one or more multimedia content elements (MMCEs), such as, but not limited to, an image, a photograph, a graphic, a screenshot, a video stream, a video clip, a video frame, an audio stream, an audio clip, combinations thereof, portions thereof, and the like.

A server 130 is connected to the network 110 and is configured to communicate with the user device 120 and the web sources 170. The server 130 may include a processing circuitry (PC) 135 and a memory 137. The processing circuitry 135 may be realized as one or more hardware logic components and circuits. For example, and without limitation, illustrative types of hardware logic components that can be used include field programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), system-on-a-chip systems (SOCs), general-purpose microprocessors, microcontrollers, digital signal processors (DSPs), and the like, or any other hardware logic components that can perform calculations or other manipulations of information.

In an embodiment, the memory 137 is configured to store software. Software shall be construed broadly to mean any type of instructions, whether referred to as software, firmware, middleware, microcode, hardware description language, or otherwise. Instructions may include code (e.g., in source code format, binary code format, executable code format, or any other suitable format of code). The instructions, when executed by the one or more processors, cause the processing circuitry 135 to perform the various processes described herein. Specifically, the instructions, when executed, cause the processing circuitry 135 to identify a missing informational element of a user profile and send a query regarding the missing informational element, as discussed further herein below.

The SGS 140 and the DCC system 160 are utilized by the server 130 to perform the various disclosed embodiments. The SGS 140 and the DCC system 160 may be connected to the server 130 directly (not shown) or through the network 110 (as shown in FIG. 1). In certain configurations, the DCC system 160 and the SGS 140 may be embedded in the server 130. In an embodiment, the server 130 is connected to or includes an array of computational cores configured as discussed in more detail below.

In an embodiment, the server 130 is configured to access an MMCE from the user device 120 or the web sources 170, and to send the MMCE to the SGS 140, the DCC system 160, or both. The access may include receiving or retrieving the MMCE. The decision of which to be used (the SGS 140, the DCC system 160, or both) may be a default configuration, or may depend on the circumstances of the particular MMCE being analyzed, e.g., the file type, the file size of the MMCE, the clarity of the content within the MMCE, and the like. In an embodiment, the SGS 140 receives the MMCE and returns signatures generated thereto. The generated signature(s) may be robust to noise and distortion as discussed regarding FIGS. 5 and 6 below.

According to another embodiment, the analysis of the MMCE may further be based on a concept structure (hereinafter referred to as a “concept”) determined for the MMCE. A concept is a collection of signatures representing elements of the unstructured data and metadata describing the concept. As a non-limiting example, a ‘Superman concept’ is a signature-reduced cluster of signatures describing elements (such as MMCEs) related to, e.g., a Superman cartoon; and a set of metadata providing a textual representation of the Superman concept. Techniques for generating concept structures are also described in the above-referenced U.S. Pat. No. 8,266,185 to Raichelgauz et al., the contents of which are hereby incorporated by reference.

According to this embodiment, a query is sent to the DCC system 160 to match the MMCE to at least one concept. The identification of a concept matching the MMCE includes matching signatures generated for the MMCE (such signature(s) may be generated by the SGS 140 or the DCC system 160) and comparing the generated signatures to reference signatures representing predetermined concepts. The signatures to which the MMCE is compared may be stored in and accessed from the database 150. The matching can be performed across all concepts maintained by the system DCC 160.

It should be appreciated that generating signatures allows for more accurate analysis of MMCEs in comparison to, for example, relying on metadata alone. The signatures generated for the MMCEs allow for recognition and classification of MMCEs 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 SGS 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.

Based on the MMCEs, generated signatures, concepts, or a combination thereof, the server 130 is configured to identify a user preference. A user preference may be used in the generation of a user profile, where the user profile includes indications of a user interest. As an example, if a user is identified in several images riding a bicycle, the user profile may indicate that the user is interested in the subjects of “bicycle”, “sport”, “outdoor activity”, and the like. The profile may further include informational elements, such as what type of bicycle the user is interested in (e.g., road bicycle or mountain bicycle), what kind of outdoor activity (e.g., extreme or leisure), a user's favorite location(s) connected to the activity (e.g., a particular park), and the like

In an embodiment, the server 130 may be configured to identify missing informational elements in a user profile. Continuing with the above example, the server 130 may be configured to identify if the type of bicycle is unknown, e.g., it is not present within the user profile. If so, the server 130 may send a query to retrieve the missing informational element, e.g., by sending a message. The query may be sent to one of the web sources 170 (e.g., such that query results are returned by a search engine implemented in one of the web sources 170), or may be sent directly to the user device 120 (e.g., as a notification including a prompt for the missing element).

In an embodiment, the server 130 is configured to update the user profile based on a response to the query. The updated user profile may be saved, e.g., in the database 150.

It should be noted that only one user device 120 and one application 125 are discussed with reference to FIG. 1 merely for the sake of simplicity. However, the embodiments disclosed herein are applicable to a plurality of user devices that can communicate with the server 130 via the network 110, where each user device includes at least one application.

FIG. 2 shows an example diagram of a DCC system 160 for creating concepts. The DCC system 160 is configured to receive an MMCE, for example from the server 130, the user device 120, or one of the web sources 170, via a network interface 260.

The MMCE is processed by a patch attention processor (PAP) 210, resulting in a plurality of patches that are of specific interest, or otherwise of higher interest than other patches. A more general pattern extraction, such as an attention processor (AP) (not shown) may also be used in lieu of patches. The AP receives the MMCE that is partitioned into items; an item may be an extracted pattern or a patch, or any other applicable partition depending on the type of the MMCE. The functions of the PAP 210 are described herein below in more detail.

The patches that are of higher interest are then used by a signature generator, e.g., the SGS 140 of FIG. 1, to generate signatures based on the patch. It should be noted that, in some implementations, the DCC system 160 may include the signature generator. A clustering processor (CP) 230 inter-matches the generated signatures once it determines that there are a number of patches that are above a predefined threshold. The threshold may be defined to be large enough to enable proper and meaningful clustering. With a plurality of clusters, a process of clustering reduction takes place so as to extract the most useful data about the cluster and keep it at an optimal size to produce meaningful results. The process of cluster reduction is continuous. When new signatures are provided after the initial phase of the operation of the CP 230, the new signatures may be immediately checked against the reduced clusters to save on the operation of the CP 230. A more detailed description of the operation of the CP 230 is provided herein below.

A concept generator (CG) 240 is configured to create concept structures (hereinafter referred to as concepts) from the reduced clusters provided by the CP 230. Each concept comprises a plurality of metadata associated with the reduced clusters. The result is a compact representation of a concept that can now be easily compared against an MMCE to determine if the received MMCE matches a concept stored, for example, in the database 150 of FIG. 1. This can be done, for example and without limitation, by providing a query to the DCC system 160 for finding a match between a concept and a MMCE.

It should be appreciated that the DCC system 160 can generate a number of concepts significantly smaller than the number of MMCEs. For example, if one billion (10⁹) MMCEs need to be checked for a match against another one billion MMCEs, typically the result is that no less than 10⁹×10⁹=10¹⁸ matches have to take place. The DCC system 160 would typically have around 10 million concepts or less, and therefore at most only 2×10⁶×10⁹=2×10¹⁵ comparisons need to take place, a mere 0.2% of the number of matches that have had to be made by other solutions. As the number of concepts grows significantly slower than the number of MMCEs, the advantages of the DCC system 160 would be apparent to one with ordinary skill in the art.

The user profile is generated based on the generated signatures, concepts, or both, which enables determination of the user's preferences and interests.

FIG. 3 is a flowchart 300 illustrating a method for creating a user profile according to an embodiment. At S310, MMCEs are received. In an embodiment, the MMCEs are received from a user device or from a web source, which may include, but is not limited to, social networks, web blogs, news feeds, and the like. The social networks may include, for example, Google+®, Facebook®, Twitter®, Instagram®, and so on.

MMCEs may include an image, a graphic, a video stream, a video clip, an audio stream, an audio clip, a video frame, a photograph, combinations thereof and portions thereof. In an embodiment, the MMCEs are captured by a user device.

At S320, at least one signature for each received MMCE is generated. The signatures may be generated by the SGS 140 of FIG. 1, as described hereinabove. In an embodiment, the signatures are generated by a signature generation system or a deep-content classification system, as discussed herein, which may generate a signature for an MMCE via a large number of at least partially statistically independent computational cores. The signatures may be generated for one or more elements depicted within an MMCE. For example, if an MMCE is a photograph of a street corner, where the photograph includes an image of various elements, such as a person, a dog, a street sign, and a tree, a signature may be generated for each of the various elements.

At S330, at least one concept based on the at least one signature is generated for each MMCE. The concepts are generated by a process of inter-matching of the signatures once it is determined that there is a number of elements therein above a predefined threshold. That threshold needs to be large enough to enable proper and meaningful clustering.

Each concept is a collection of signatures representing MMCEs and metadata describing the concept, and acts as an abstract description of the content to which the signature was generated. As a non-limiting example, a ‘Superman concept’ is a signature-reduced cluster of signatures representing elements (such as MMCEs) related to, e.g., a Superman cartoon, and a set of metadata including a textual representation of the Superman concept. As another example, metadata of a concept represented by the signature generated for a picture showing a bouquet of red roses is “flowers.” As yet another example, metadata of a concept represented by the signature generated for a picture showing a bouquet of wilted roses is “wilted flowers”.

In one embodiment, S330 includes querying a concept-based database using the generated signatures, wherein a previously generated concept may be matched to the MMCE without requiring the generation of a new concept.

At S340, at least one user interest is determined based on the generated signatures, the concepts, or both. According to one embodiment, a user interest may be determined based on the frequency of appearance of a person or item, user interactions related to the concept or MMCE, preferences of other users connected to the user, and the like. As a non-limiting example, if a user has indicated that they like multiple pictures of a particular dog on a social network, it may be determined that the dog is included as a user interest. Further, if the user has frequently uploaded pictures of the dog to multiple social networks, it may likewise be determined that the dog is included as a user interest.

In S350, a user profile is created based on the determined user interest. The user profile may be saved, e.g., in a database, for future reference. It should be noted that if a user profile already exists in the database, the user profile may be updated to include the user interest determined in S340. For example, if a user profile related to the user exists but does not contain information related to any dogs, the profile may be updated to include the dog as a user interest.

At S360, it is checked whether there are additional MMCEs to analyze, and if so, execution continues with S320; otherwise, execution terminates.

As a non-limiting example for the process described in FIG. 3, a picture of a user riding a bicycle is uploaded to the user's profile page in Facebook®. The image is then analyzed, and a signature and at least one concept is generated respective thereto. A comment made by the user stating: “I love those field trips” is identified. Based on analysis of the concept of the uploaded picture and the user's comment, the user profile is determined as positive for field trips. The user profile is then stored or updated (if, e.g., the user profile already existed prior to this example) in a database for further use.

FIG. 4 is a flowchart 400 illustrating a method for querying for completing a user profile according to an embodiment. Specifically, the method may be utilized to partially complete a user profile missing one or more informational elements related to user interests.

At S410, a user profile is received. The user profile may be, for example, a user profile created as described herein above with respect to FIG. 3, or may be a user profile previously generated and stored, e.g., on a database. The user profile may include one or more user interests as discussed above with respect to FIG. 3.

At S420, missing informational elements are identified within the user profile. Missing informational elements are elements related to user interests that are indicated in the user profile. In an embodiment, the identification may be made by comparing concepts representing the user interests in the user profile with a predetermined set of concepts representing required informational elements related to that concept.

To this end, in an embodiment, S420 may include matching concepts representing the user interests indicated in the user profile to category concepts representing general categories of user profile information. Example category concepts may include, but are not limited to, pets, hobbies, family members, friends, place of work, home, hangout spots, specific instances thereof (e.g., dogs as pets, biking as a hobby, etc.), and the like. In a further embodiment, S420 may also include generating signatures for textual representations of the user interests and determining, based on the generated signatures, a concept representing each user interest. Determining concepts for the user interests may include matching the generated signatures to signatures of concepts in a concept database as described further herein above. Alternatively, the user profile may include predetermined concepts (e.g., concepts that were previously generated as described herein above) representing each user interest.

Each category concept may be associated with a respective predetermined set of required informational elements. As a non-limiting example, if a user interest within the user profile includes a particular dog as indicated by a concept representing the dog, the user interest concept may be matched to a category concept of representing “pet dog” that is associated with required informational elements including name, breed, age, length of ownership by the user, food preferences, recreational preferences, and the like. If, for example, the dog's age and breed are known, but its name is unknown, the name may be identified as a missing informational element.

Each informational element may be indicated in, for example, a field of a user profile. To this end, in some implementations, the missing informational elements may include informational elements corresponding to incomplete fields of the user profile. For example, when the user profile includes a field “job title” that has an empty value, job title may be determined as a missing informational element. Thus, the missing informational elements may be unknown data items in a user profile representing the user's life, routines, hobbies, and the like.

In another embodiment, the missing informational elements may be determined based on analysis of one or more clusters of MMCEs associated with the user profile. The analysis of the clusters may include, but is not limited to, generating signatures for the MMCEs, determining concepts appearing in the MMCEs, generating a validity score for each identified concept, and determining missing informational elements based on the validity scores. In an example implementation, each missing informational element corresponds to a concept having a validity score below a predetermined threshold.

In a further embodiment, the missing elements are identified by comparing concepts representing the user interests to known concepts associated with other user profiles, e.g., from a database. For example, if a user interest includes a dog, a concept related to the dog may be compared to concepts of user interests related to other user profiles in a database. If the other user profiles include a dog's name, breed, and age, and the current user profile only include the dog's breed and age, the dog's name will be identified as a missing informational element.

At S430, one or more queries is sent regarding the missing informational element. The query may be fed to a search engine, a search bar in a social network website, or sent directly to a user, e.g., to a user device. The query may include text, images, portions thereof, combinations thereof, and the like. For example, a query may include an image of a dog taken from an MMCE together with the phrase “What is this dog's name?” Additionally, the query may be gamified in order to more easily engage the user. As a non-limiting example, a plurality of faces that are determined to potentially be part of the user's family are presented together with the query “Family Album” and a request to mark which person is and is not a family member.

Each sent query may be, for example, associated with the corresponding missing informational element. As a non-limiting example, an informational element of “brands” for the category “mountain biking” may be associated with a query of “What brand of mountain bikes do you prefer?” such that, when the “brand” informational element is missing, that query may be sent.

At S440, the user profile is updated based on a response received in reply to the sent query. For example, the user interest of “dog” within the user profile may be updated to include the received response to “What is this dog's name?” The updated user profile may be saved, e.g., in a database.

At S450, it is checked whether additional missing items may exist within the user profile, and if so, execution continues with S420; otherwise, execution terminates.

As a non-limiting example, when images analyzed to identify user interests include images showing the user standing in the entrance of office buildings, user interests of a user include “office.” Signatures of a concept representing the user interest are matched to signatures representing category concepts, and a category concept representing “job” is determined as matching. The “job” concept is associated with required informational elements of “job title” and “place of employment.” When the user profile indicates a job title of “CEO” but not a place of employment, the place of employment is identified as a missing informational element. A query of “Where do you work?” associated with the “place of employment” concept is sent to the user. Based on a user response of “ABC Contractors, Manhattan branch,” the user profile may be updated with respect to place of employment.

FIGS. 5 and 6 illustrate the generation of signatures for the multimedia content elements by the SGS 120 according to one embodiment. An exemplary high-level description of the process for large scale matching is depicted in FIG. 5. In this example, the matching is for a video content.

Video content segments 2 from a Master database (DB) 6 and a Target DB 1 are processed in parallel by a large number of independent computational Cores 3 that constitute an architecture for generating the Signatures (hereinafter the “Architecture”). Further details on the computational Cores generation are provided below.

The independent Cores 3 generate a database of Robust Signatures and Signatures 4 for Target content-segments 5 and a database of Robust Signatures and Signatures 7 for Master content-segments 8. An exemplary and non-limiting process of signature generation for an audio component is shown in detail in FIG. 6. Finally, Target Robust Signatures and/or Signatures are effectively matched, by a matching algorithm 9, to Master Robust Signatures and/or Signatures database to find all matches between the two databases.

To demonstrate an example of 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. In an embodiment, the signature generator 140 is configured with a plurality of computational cores to perform matching between signatures.

The Signatures' generation process is now described with reference to FIG. 6. The first step in the process of signatures generation from a given speech-segment is to breakdown the speech-segment to K patches 14 of random length P and random position within the speech segment 12. The breakdown is performed by the patch generator component 21. The value of the number of patches K, random length P and random position parameters is determined based on optimization, considering the tradeoff between accuracy rate and the number of fast matches required in the flow process of the server 130 and SGS 140. Thereafter, all the K patches are injected in parallel into all computational Cores 3 to generate K response vectors 22, which are fed into a signature generator system 23 to produce a database of Robust Signatures and Signatures 4.

In order to generate Robust Signatures, i.e., Signatures that are robust to additive noise L (where L is an integer equal to or greater than 1) by the Computational Cores 3 a frame ‘i’ is injected into all the Cores 3. Then, Cores 3 generate two binary response vectors: one which is a Signature vector, and one 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={n_i} (1≤i≤L) may consist of a single leaky integrate-to-threshold unit (LTU) node or more nodes. The node n_i equations are:

$V_i=\sum _j{w}_{\mathrm{ij}}{k}_j$ $n_i=\theta \left(\mathrm{Vi}-\mathrm{Thx}\right)$

where, θ is a Heaviside step function; w_ij is a coupling node unit (CNU) between node i and image component j (for example, grayscale value of a certain pixel j); k_j is an image component ‘j’ (for example, grayscale value of a certain pixel j); Th_X 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 Th_X are set differently for Signature generation and for Robust Signature generation. For example, for a certain distribution of V_i values (for the set of nodes), the thresholds for Signature (Th_S) and Robust Signature (Th_RS) are set apart, after optimization, according to at least one or more of the following criteria:

1: For:V_i>Th_RS 1−p(V>Th_S)−1−(1−ε)^l<<1i.e., given that I 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, is sufficiently low (according to a system's specified accuracy).

2:p(V_i>Th_RS)≈l/L

i.e., approximately I 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 a 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 the above referenced U.S. Pat. No. 8,655,801, the contents of which are hereby incorporated by reference.

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.

As used herein, the phrase “at least one of” followed by a listing of items means that any of the listed items can be utilized individually, or any combination of two or more of the listed items can be utilized. For example, if a system is described as including “at least one of A, B, and C,” the system can include A alone; B alone; C alone; A and B in combination; B and C in combination; A and C in combination; or A, B, and C in combination.

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

Claims

1. A method for at least partially completing a user profile, comprising: analyzing, by a computerized system, the user profile to identify at least one missing informational element in the user profile, wherein the user profile is indicative of one or more user interests; wherein the one or more user interests are determined by the computerized system; wherein a missing informational element is related to at least one user interest of the one or more user interests; wherein identifying the at least one missing element comprises determining at least one concept based on the user profile and matching the determined at least one concept to a plurality of concepts, each concept of the plurality of concepts including a collection of signatures and metadata describing the concept, wherein each missing informational element is one of the at least one required informational element that is not included in the user profile;analyzing a plurality of multimedia content elements (MMCEs), wherein the analysis further comprises: generating at least one signature for each MMCE;determining at least one concept for each MMCE based on the at least one signature generated for the MMCE;wherein the one or more user interests are identified based on the determined concepts;wherein the identifying, based on the determined concepts, of the at least one user interest is responsive to a frequency of appearance of a person or item in the MMCEs;sending a query for the missing informational element; andupdating at least a portion of the user profile based on a response to the query.

2. The method of claim 1, wherein determining the at least one concept for each MMCE further comprises: querying a concept-based database using the at least one signature generated for the MMCE.

3. The method of claim 1 wherein the plurality of concepts are general categories of user profile information.

4. The method of claim 1 wherein each concept of the plurality of concepts is a category concept that is associated with at least one required informational element.

5. The method of claim 1, wherein identifying the at least one missing informational element includes comparing the user profile to the at least one required informational element for each matching category concept, wherein each missing informational element corresponds to an incomplete field of the user profile.

6. The method according to claim 1 wherein the analyzing of the plurality of MMCEs comprises processing each MMCE by a patch attention processor to find MMCE patches of higher interest that other MMCE patches, and generating a signature of the MMCE based on the MMCE patches of higher interest.

7. The method according to claim 1 wherein the identifying, based on the determined concepts, of the at least one user interest is responsive to interests of other users connected to the user.

8. The method according to claim 1 wherein the analyzing of the user profile to identify at least one missing informational element in the user profile further comprises comparing concepts representing the user interests to known concepts associated with other user profiles in a database.

9. A non-transitory computer readable medium having stored thereon instructions for causing a processing circuitry of a computerized system to perform a process, the process comprising: analyzing a user profile to identify at least one missing informational element in the user profile, wherein the user profile is indicative of one or more user interests; wherein the one or more user interests are determined by the computerized system; wherein a missing informational element is related to at least one user interest of the one or more user interests; wherein identifying the at least one missing element further comprises determining at least one concept based on the user profile and matching the determined at least one concept to a plurality of concepts, each concept of the plurality of concepts including a collection of signatures and metadata describing the concept, wherein each missing informational element is one of the at least one required informational element that is not included in the user profile;analyzing a plurality of multimedia content elements (MMCEs), wherein the analysis further comprises: generating at least one signature for each MMCE;determining at least one concept for each MMCE based on the at least one signature generated for the MMCE;identifying, based on the determined concepts, at least one user interest; andcreating the user profile based on the identified at least one user interest;wherein the identifying, based on the determined concepts, of the at least one user interest is responsive to a frequency of appearance of a person or item in the MMCEs comparing the user profile to user profiles of users;sending a query for the missing informational element; andupdating at least a portion of the user profile based on a response to the query.

10. The non-transitory computer readable medium according to claim 9, wherein the analyzing of the user profile to identify at least one missing informational element in the user profile further comprises comparing concepts representing the user interests to known concepts associated with other user profiles in a database.

11. The non-transitory computer readable medium according to claim 9, wherein the determining of the at least one concept for each MMCE further comprises: querying a concept-based database using the at least one signature generated for the MMCE.

12. The non-transitory computer readable medium according to claim 9 wherein the plurality of concepts are general categories of user profile information.

13. The non-transitory computer readable medium according to claim 9 wherein each concept of the plurality of concepts is a category concept that is associated with at least one required informational element.

14. The non-transitory computer readable medium according to claim 9, wherein the identifying of the at least one missing informational element includes comparing the user profile to the at least one required informational element for each matching category concept, wherein each missing informational element corresponds to an incomplete field of the user profile.

15. A computerized system for at least partially completing a user profile, comprising: a processing circuitry; and a memory, the memory containing instructions that, when executed by the processing circuitry, configure the system to: analyze the user profile to identify at least one missing informational element in the user profile, wherein the user profile is indicative of one or more user interests; wherein the one or more user interests are determined by the computerized system; wherein a missing informational element is related to at least one user interest of the one or more user interests; wherein identifying the at least one missing element further comprises determining at least one concept based on the user profile and matching the determined at least one concept to a plurality of concepts, each concept of the plurality of concepts including a collection of signatures and metadata describing the concept, wherein each missing informational element is one of the at least one required informational element that is not included in the user profile;analyze a plurality of multimedia content elements (MMCEs), wherein the analysis further comprises generating at least one signature for each MMCE; determine at least one concept for each MMCE based on the at least one signature generated for the MMCE; and wherein the one or more user interests are identified based on the determined concepts;wherein the identifying, based on the determined concepts, of the at least one user interest is responsive to a frequency of appearance of a person or item in the MMCEs comparing the user profile to user profiles of users;send a query for the missing informational element; andupdate at least a portion of the user profile based on a response to the query.

16. The system of claim 15, wherein the system is further configured to: query a concept-based database using the at least one signature generated for the MMCE.

17. The system of claim 15, wherein each signature is robust to noise and distortion.

18. The system of claim 15, wherein the plurality of concepts are general categories of user profile information.

19. The system of claim 15, wherein each concept of the plurality of concepts is a category concept that is associated with at least one required informational element.