Numerous organizations, including industry and government entities, recognize that important conclusions can be drawn if massive data sets can be analyzed to identify patterns of behavior that suggest dangers to public safety or evidence illegality. These analyses often involve matching data associated with a person or thing of interest with other data associated with the same person or thing to determine that the same person or thing has been involved in multiple acts that raise safety or criminal concerns.
Yet, the quality of the analytical result arising from use of sophisticated analytical tools can be limited by the quality of data the tool utilizes. For certain types of analyses, an acceptable error rate must be literally or nearly zero for an analytical conclusion drawn from the data to be sound. Achieving this zero or near-zero error rate for datasets comprising tens or hundreds of millions of records can be problematic. Present data comparison tools are not well suited to solve these issues.
The issues discussed above are particularly acute for analyses involving data related to identifying persons or things for inquiries relating to public safety. For example, analytical tools for identifying potential safety threats generally do not have an acceptable error rate greater than zero because the cost of mistakenly identifying the presence of a safety threat (i.e., a “false positive”) or allowing a safety threat to go undetected (i.e., a “false negative”) is unacceptably high. Therefore, tools supporting public safety must correctly relate data associated with persons or things of interest with other data related to the same person or thing.
Some tools exist for accurately comparing data, but they are computationally impractical to use with datasets containing millions of records. For example, one solution to determining whether two particular objects are associated with the same person or thing of interest is to compare each element of one object to a corresponding element in the second object. For example, for objects containing M elements, a first element in the first object may be compared to a corresponding first element in the second object, and corresponding comparisons may be made for each of the remaining M−1 elements common to the first and second objects. If the elements within each object are collectively adequate to uniquely identify the represented person or thing with certainty, and corresponding elements within the first and second objects match, a conclusion may reasonably be drawn that the objects reflect the same person or thing. As an alternative, each object could be converted (serialized) into a single string reflecting the contents of each element to be compared. Thereafter, a string generated from one object could be compared to a string generated from another object as a form of object comparison.
For certain datasets, the above approaches may consume little memory or system resources, because the objects or their serialized strings can be stored on disk rather than in main memory. However, the above approaches may quickly become impractical with large or non-trivial datasets. As the number of objects to compare increases, the number of comparisons and thus the processing time of the comparisons increases exponentially; i.e., proportional to n2/2, where n represents the number of objects to be compared. Thus, a comparison of 500 objects using a serialized approach, whose processing time may be approximated as the time to perform 125,000 string comparisons, may be computationally tractable. However, a comparison of 100 million (100M) records using that approach, whose processing time may be approximated as the time to perform 5 quadrillion (5e15) string comparisons, may be computationally intractable. Additionally, reading strings from disk rather than reading them from memory may add additional processing time.
Another solution for identifying matching objects within a corpus of objects is to store each object in a multimap. This multimap is an associative array that stores multiple values for each key. Importing the objects into the multimap leads to objects with the same element data being stored in a single entry of the multimap. Thus, use of a multimap associates identical objects.
One drawback to using a multimap for object comparisons is that the multimap is typically stored in main memory, due to algorithmic considerations related to key organization within the multimap, so an object comparator must have sufficient main memory to hold a multimap comprising the entire corpus in memory. Therefore, a multimap solution can be impractical for datasets at or above 100M objects. Similar drawbacks exist to each approach as applied to other object comparison problems, such as efficiently identifying unique objects within a corpus of objects and efficiently comparing a single object to all objects within a corpus of object.
Neither solution is viable for datasets approaching or exceeding 100M objects. Yet, object datasets comprising 100M or more objects are not uncommon today. Therefore, the problems described above are quite real and a need exists for improved object comparators.
Reference will now be made to the accompanying drawings showing example embodiments of the present application, and in which:
Reference will now be made in detail to the embodiments, examples of which are illustrated in the accompanying drawings. Whenever possible, consistent reference numbers will be used throughout the drawings to refer to the same or like parts.
Embodiments of the present disclosure can avoid the shortcomings of traditional object comparators by providing computer-implemented systems and methods for comparing objects in a way that allows for greater computational throughput and acceptable memory consumption without a reduction in comparison accuracy and for dataset sizes that were previously impractical or impossible at acceptable levels of computational throughput.
Embodiments of the present disclosure address a class of computational problems related to object comparison. One member of this class involves efficient object comparison of a particular object to a corpus of objects. Another member of this class involves efficient comparison of each object in a corpus to all other objects in the corpus. An additional member of this class involves efficient identification of unique objects within a corpus of objects.
The following detailed description begins with a general overview of object comparison. Some examples of objects to be compared or analyzed are provided. The description then explains an exemplary embodiment that addresses the first class of problem discussed above (i.e., efficiently comparing one object to all objects in a corpus). The description then expands the solution to the first class of problem to address the second class of problem discussed above (i.e., efficient comparison of each object in a corpus to all other objects in the corpus). The detailed description then discloses a solution to the third class of problem (i.e., efficient identification of unique objects within a corpus of objects). An introduction to objects and an overview of object comparison follows.
Several types of objects exist within the field of computer science. One type of object that is well known within the field of computer science is an object in the object-oriented sense. Wikipedia describes an object of this type as a set of elements (i.e., data structures) and methods, which are similar to functions. Without necessarily endorsing that rather simplistic description, embodiments implementing the object comparison solutions discussed herein are compatible with comparing objects of this type.
Another type of object within the field of computer science field is a data structure that reflects the properties of a person or thing relevant to a particular task or data processing environment. In some embodiments, these properties are reflected by strings. In other embodiments, properties may be reflected by strings, integers, real numbers, times or dates, binary values, structures in the C programming sense, enumerated variables, and/or other forms of data. In some embodiments, properties within either type of object may be converted to strings prior to comparison. In other embodiments, some properties may be strings or may be converted to strings while other properties may not be strings and may not be converted to strings. The embodiments of the present disclosure may operate on string or non-string properties.
Moreover, the notion of a “data structure” is very flexible in this context. The term “data structure” can reflect any type of structured data, from information stored in a database (with table columns reflecting elements within an object or data structure and table rows reflecting instances of the object or data structure) to formatted text in a text file (such as data within an XML structure) to data stored within an executing computer program. Accordingly, because a data structure broadly encompasses the types of structured data described above, objects also broadly encompass these types of structured data. Moreover, the object comparison solutions discussed herein are also compatible with comparing objects of these types.
In some embodiments, effective object comparison involves considering which properties of the objects to be compared are relevant to performing the comparison because the entities (e.g., persons or things) reflected by those objects may have different relevant properties in different environments. For example, an object can store properties of an automobile that may be relevant to a state's motor vehicle department by storing the following information: vehicle identification number (VIN), year of manufacture, make, model, expiration date of the vehicle's registration, and a direct or indirect indication of the person that owns the vehicle.
For automobiles being sold on an auction website such as eBay, however, the relevant properties of an automobile may differ from those relevant to the state's motor vehicle department. For example, a data structure for storing properties of an automobile listed for sale on eBay may include: VIN, year, make, model, odometer reading, condition of the automobile, minimum auction bid, and a direct or indirect indication of the person listing the vehicle for sale. Thus, properties of an entity (e.g., a person or thing) relevant to one environment may differ from properties of the entity relevant to another environment. Accordingly, an object's properties considered during object comparison in one environment may differ from those considered during object comparison in a second environment.
In some embodiments, effective data comparison may also involve considering which properties tend to distinguish an entity (e.g., a person or thing) from other instances of the entity. For example, a VIN for an automobile should by design be unique to that automobile. However, occasional situations may arise where a VIN is not unique to a particular automobile. Such situations may arise from intentional errors or accidental errors. An example of an intentional error is attempting fraudulent registration of a stolen vehicle under an assumed VIN. An example of an accidental error occurs when a smog check worker incorrectly enters a VIN into a computer at a smog check station, which leads to a smog check record with an incorrect VIN subsequently being communicated to a state database. Data errors exist in real world data processing environments, so some embodiments of the present disclosure minimize or eliminate errors by identifying objects through a combination of several object properties rather than identifying objects through use of a single object property.
In some embodiments, one or more identifying properties of an object are extracted from the object and stored in a data structure. This data structure is referred to as a “slug”; it contains information that may be sufficient to uniquely identify an entity (e.g., a person or thing) with some degree of information redundancy to allow for detecting errors in the properties within the slug. In some embodiments, the slug comprises a concatenation of strings separated by a delimiter character. In some embodiments, the delimiter character is a NULL character while in other embodiments the delimiter character may be a character not otherwise present in the concatenated string. In some embodiments, the concatenated strings may be delimited by a delimiter string (e.g., “--”) rather than a delimiter character. In embodiments employing a delimiter string, the delimiter string may be any string that is not otherwise present in the strings that were concatenated. In other embodiments, the slug comprises a data structure such as an object, array, structure, or associative array.
For example, in one embodiment, slug for an automobile may contain properties reflecting a VIN, make, model, and year for the automobile. Inclusion of make, model, and year properties for the automobile within the slug provides a capability for detecting errors in the VIN property because the VIN property is not the only object property being compared. For slugs associated with two automobiles to match in the presence of an error in the VIN property of one automobile object, an automobile object with the same VIN property as the erroneous VIN must also have the same make, model, and year properties.
The odds of this coincidental match of multiple properties between two or more objects may be fleetingly low. Therefore, inclusion of some degree of information redundancy should avoid or at least substantially reduce erroneous object comparison matches relative to object comparisons only comparing a single property between objects notwithstanding that the single property was intended to uniquely identify its corresponding entity (e.g., person or thing).
Exemplary embodiments will now be described that solve the first problem discussed above, i.e., efficiently comparing a particular object (hereinafter a “target object”) to all objects in a corpus. The disclosed embodiments utilize a Bloom filter to identify slugs associated with objects in the corpus that do not match the slug for the target object. This quick recognition is performed by discarding slugs that are associated with a different bin in the Bloom filter than the bin associated with the slug for the target object.
Bloom filters have the property that two slugs falling into different bins within the Bloom filter are certain to have different properties and thus reflect different objects. Therefore, if the slug for the target object does not fall into the same bin as the slug for a particular object in the corpus, the target object does not match the particular object in the corpus and may thus be removed from future consideration in such embodiments.
As illustrated, in step 102, a Bloom filter is sized and created with consideration for the error rate that will result for the corpus size that is being processed. For example, increasing the number of bins in a Bloom filter may tend to decrease the error rate for a specific corpus size while reducing the number of bins in a Bloom filter may tend to increase the error rate for a specific corpus size. Techniques for sizing a Bloom filter to achieve a target error rate for a specific corpus size are well known in the art, so these techniques are not discussed herein.
In step 104, a slug for the target object (i.e., the object against which all objects in the corpus will be compared) is generated. Considerations for selecting which properties of an object to include in a slug were discussed above. In step 106, a Bloom filter bin corresponding to the slug for the target object is determined. In some embodiments, a Bloom filter bin for a slug may be determined by inputting the slug to a Bloom filter and directing the Bloom filter to disclose the bin into which the slug was added.
In other embodiments, a Bloom filter bin for a slug may be determined by presenting the slug as a input to a software function associated with the Bloom filter without storing the slug in the Bloom filter. In additional embodiments, a bin for a slug may be determined by inputting the slug into a software function reflecting a bin selection algorithm for a Bloom filter in the absence of using an actual and/or complete Bloom filter and receiving the Bloom filter bin as an output of that software function. In other embodiments, other approaches to yielding a Bloom filter bin from a slug may be utilized. These approaches for identifying a Bloom filter bin for a slug, consistent with the embodiments discussed above, are collectively referred to in steps 106, 108. The determined Bloom filter bin will be utilized to identify slug comparison matches, some of which may be “false positives”, using the Bloom filter as discussed below.
In step 108, a slug for each object in the corpus is generated. In step 110, a Bloom filter bin for each object in the corpus is determined. In some embodiments, a Bloom filter bin for an object may be determined by inputting the object's slug into the Bloom filter and directing the Bloom filter to disclose the bin into which the slug was added.
After completion of step 110, slugs corresponding to the bin identified in step 108 reflect matches with the slug for the target object. Some of these matches, however, may be false positive matches rather than true matches. Therefore, steps 112 and 114 filter out the false positive matches through use of a multimap.
In step 112, for each slug corresponding to an object in the corpus whose bin in the Bloom filter is the same bin as the slug for the target object, the slug corresponding to an object in the corpus and its corresponding object in the corpus is added to a multimap. When adding the slug and its corresponding object to the multimap, the slug represents the key to the multimap and the object in the corpus represents the value to the multimap. This multimap will be utilized to remove false positives from processing. In step 114, the process concludes by selecting the true positive matches identified in the multimap. These non-false positive matches can be retrieved from the multimap by reading data from the multimap with the slug for the target object as a key.
In some embodiments, process 100 may be distributed across multiple processors. For example, a Bloom filter may exist on each of several processors and steps 102 through 114 can be executed on each of the several processors. The corpus of objects may be distributed among the various processors so that all objects are processed by one processor, but no object is processed by more than one processor. In such embodiments, each of the multiple processors outputs a portion of the objects in the corpus that match the target object.
Exemplary embodiments will now be described that solve the second problem discussed above, i.e., efficiently comparing all objects to all objects in a corpus. These embodiments utilize a counting Bloom filter to quickly identify slugs associated with objects in the corpus that do not match the slug for the target object. Counting Bloom filters are well known in the art, so their structure and construction are not discussed herein.
In particular, if a bin in the counting Bloom filter has a value of zero or one after slugs for all of the objects in the corpus have been input to the Bloom filter, no object whose slug is associated with that bin could match another slug, so these slugs are removed from further consideration. These slugs can be removed because those skilled in the art will recognize that Bloom filters can have false positives but they cannot have false negatives. Therefore, a counting Bloom filter bin whose count is less than two reflects an accurate determination that no match exists between slugs associated with that bin because any match would create a count of at least two. However, false positive may exist among objects whose slugs are associated with the same Bloom filter bin, so false positives may be removed through additional processing, as discussed below.
In some embodiments, the counting Bloom filter may comprise an N-bit counter and these counters may be implemented as two-bit counters (i.e., N=2). In other embodiments, these counters may be one-bit counters or counters of more than two bits. In additional embodiments, these counters are saturation counters; i.e., these counters will count up to a maximum value and then not exceed that value.
In step 204, a slug for each object in the corpus is generated. In step 206, each slug is input to the counting Bloom filter, which causes a counter in a bin corresponding to a slug to be incremented. After completion of step 206, bins whose counters have a value greater than one reflect one or more matching slugs. Some of these matches, however, may be false positive matches rather than true matches. Therefore, steps 208 and 210 filter out the false positive matches through use of a multimap.
In step 208, for slugs associated with bins in the counting Bloom filter whose counters have a value greater than 1, the slug and its associated object are added to a multimap. When adding the slug and its corresponding object to the multimap, the slug represents the key to the multimap and the object in the corpus represents the value to the multimap. This multimap will be utilized to remove false positives from processing. In step 210, the process 200 concludes by outputting a value for any key in the multimap that has two or more values. The outputted values reflect objects whose slugs matched slugs of at least one other object in the corpus. Thus, the objects outputted identify objects whose selected properties, as reflected in an object's slug, unambiguously match at least one other object in the corpus.
In some embodiments, process 200 may be distributed across multiple processors. For example, a counting Bloom filter may exist on each of several processors and steps 202, 204, and 206 can be executed on each of the several processors. The corpus of objects may be distributed among the various processors so that all objects are processed by one processor, but no object is processed by more than one processor. In such embodiments, prior to executing step 208, counters for each bin in the counting Bloom filter are summed together with counters for the same bin in counting Bloom filters on other processors. Thereafter, process 200 continues by executing steps 208 and 210 on a single processor.
Exemplary embodiments will now be described that solve the third problem discussed above, i.e., efficiently identifying unique objects in a corpus. These embodiments utilize a counting Bloom filter and a multimap to quickly identify unique objects. Upon inputting slugs for all objects in the corpus into the counting Bloom filter, any bin with a count value of one reflects a unique object because Bloom filters do not generate false negatives. Additionally, to the extent that bins have count values of two or more, those count values could reflect false positives. Therefore, a multimap allows a determination of whether the matches reflected in the count values were false or true positives.
In some embodiments, the counting Bloom filter may comprise an N-bit counter and these counters may be implemented as two-bit counters (i.e., N=2). In other embodiments, these counters may be one-bit counters or counters of more than two bits. In additional embodiments, these counters are saturation counters; i.e., these counters will count up to a maximum value and then not exceed that value.
In step 304, a slug for each object in the corpus is generated. In step 306, each slug is input to the counting Bloom filter, which causes a counter in a bin corresponding to the slug to be incremented. As previously discussed, after slugs for all objects in the corpus have been input to the counting Bloom filter, any bin with a count value of one reflects a unique object within the corpus because the counting Bloom filter does not generate false negatives. Therefore, in step 308, for each slug whose counter in the counting Bloom filter is one, the slug's corresponding object is output as a unique object within the corpus.
After completion of step 308, bins whose counters have a value greater than one reflect one or more matching slugs; i.e., slugs that are not unique. Some of these matches, however, may be false positive matches rather than true matches due to the nature of Bloom filters, as discussed above. Therefore, steps 310 and 312 filter out the false positive matches through use of a multimap.
Steps 310 and 312 determine whether the counting Bloom filter is masking the existence of other unique objects because the Bloom filter allows for false positives. In step 310, for each slug whose associated bin has a counter value greater than one, the slug is input as a key to a multimap and the object corresponding to the slug is input as a value for that key. In step 312, the process terminates after outputting each value in the multimap for keys that have only one value. Unique objects within the corpus are reflected by the collection of objects output from step 308 and the collection of objects output by step 312 because the former reflects objects whose slugs were the only slug in a counting Bloom filter's bin and were therefore unique among slugs associated with objects in the corpus while the latter reflects slugs that were false positives within the counting Bloom filter but were disambiguated by the multimap.
In some embodiments, process 300 may be distributed across multiple processors. For example, a counting Bloom filter may exist on each of several processors and steps 302, 304, and 306 can be executed on each of several processors. The corpus of objects may be distributed among the various processors so that all objects are processed by one processor, but no object is processed by more than one processor. In such embodiments, prior to executing step 308, counters for each bin in the counting Bloom filter are summed together with counters for the same bin in counting Bloom filters on other processors. Thereafter, process 300 continues by executing steps 308, 310, and 312 on a single processor.
Computer system 400 includes a bus 402 or other communication mechanism for communicating information, and a hardware processor 404 coupled with bus 402 for processing information. In some embodiments, hardware processor 404 can be, for example, a general-purpose microprocessor or it can be a reduced instruction set microprocessor.
Computer system 400 also includes a main memory 406, such as a random access memory (RAM) or other dynamic storage device, coupled to bus 402 for storing information and instructions to be executed by processor 404. Main memory 406 also can be used for storing temporary variables or other intermediate information during execution of instructions by processor 404. Such instructions, when stored in non-transitory storage media accessible to processor 404, render computer system 400 into a special-purpose machine that is customized to perform the operations specified in the instructions.
In some embodiments, computer system 400 further includes a read only memory (ROM) 408 or other static storage device coupled to bus 402 for storing static information and instructions for processor 404. A storage device 410, such as a magnetic disk or optical disk, is provided and coupled to bus 402 for storing information and instructions.
Computer system 400 can be coupled via bus 402 to a display 412, such as a cathode ray tube (CRT) or LCD panel, for displaying information to a computer user. An input device 414, including alphanumeric and other keys, is coupled to bus 402 for communicating information and command selections to processor 404. Another type of user input device is cursor control 416, such as a mouse, a trackball, or cursor direction keys for communicating direction information and command selections to processor 404 and for controlling cursor movement on display 412. The input device typically has degrees of freedom in two axes, a first axis (for example, x) and a second axis (for example, y), that allows the device to specify positions in a plane.
Computer system 400 can implement the processes and techniques described herein using customized hard-wired logic, one or more ASICs or FPGAs, firmware and/or program logic which in combination with the computer system causes or programs computer system 400 to be a special-purpose machine. In some embodiments, the processes and techniques herein are performed by computer system 400 in response to processor 404 executing one or more sequences of one or more instructions contained in main memory 406. Such instructions can be read into main memory 406 from another storage medium, such as storage device 410. Execution of the sequences of instructions contained in main memory 406 causes processor 404 to perform the process steps described herein. In other embodiments, hard-wired circuitry can be used in place of or in combination with software instructions.
The term “storage media” as used herein refers to any non-transitory media that store data and/or instructions that cause a machine to operate in a specific manner. Such storage media can comprise non-volatile media and/or volatile media. Non-volatile media includes, for example, optical or magnetic disks, such as storage device 410. Volatile media includes dynamic memory, such as main memory 406. Common forms of storage media include, for example, a floppy disk, a flexible disk, hard disk, solid state drive, magnetic tape, or any other magnetic data storage medium, a CD-ROM, any other optical data storage medium, any physical medium with patterns of holes, a RAM, a PROM, and EPROM, a FLASH-EPROM, NVRAM, any other memory chip or cartridge.
Storage media is distinct from but can be used in conjunction with transmission media. Transmission media participates in transferring information between storage media. For example, transmission media includes coaxial cables, copper wire and fiber optics, including the wires that comprise bus 402. Transmission media can also take the form of acoustic or light waves, such as those generated during radio-wave and infra-red data communications.
Various forms of media can be involved in carrying one or more sequences of one or more instructions to processor 404 for execution. For example, the instructions can initially be carried on a magnetic disk or solid state drive of a remote computer. The remote computer can load the instructions into its dynamic memory and send the instructions over a telephone line using a modem. A modem local to computer system 400 can receive the data on the telephone line and use an infra-red transmitter to convert the data to an infra-red signal. An infra-red detector can receive the data carried in the infra-red signal and appropriate circuitry can place the data on bus 402. Bus 402 carries the data to main memory 406, from which processor 404 retrieves and executes the instructions. The instructions received by main memory 406 can optionally be stored on storage device 410 either before or after execution by processor 404.
Computer system 400 also includes a communication interface 418 coupled to bus 402. Communication interface 418 provides a two-way data communication coupling to a network link 420 that is connected to a local network 422. For example, communication interface 418 can be an integrated services digital network (ISDN) card, cable modem, satellite modem, or a modem to provide a data communication connection to a corresponding type of telephone line. As another example, communication interface 418 can be a local area network (LAN) card to provide a data communication connection to a compatible LAN. Wireless links can also be implemented. In any such implementation, communication interface 418 sends and receives electrical, electromagnetic or optical signals that carry digital data streams representing various types of information.
Network link 420 typically provides data communication through one or more networks to other data devices. For example, network link 420 can provide a connection through local network 422 to a host computer 424 or to data equipment operated by an Internet Service Provider (ISP) 426. ISP 426 in turn provides data communication services through the world wide packet data communication network now commonly referred to as the “Internet” 428. Local network 422 and Internet 428 both use electrical, electromagnetic or optical signals that carry digital data streams. The signals through the various networks and the signals on network link 420 and through communication interface 418, which carry the digital data to and from computer system 400, are example forms of transmission media.
Computer system 400 can send messages and receive data, including program code, through the network(s), network link 420 and communication interface 418. In the Internet example, a server 430 might transmit a requested code for an application program through Internet 428, ISP 426, local network 422 and communication interface 418. The received code can be executed by processor 404 as it is received, and/or stored in storage device 410, or other non-volatile storage for later execution.
This application is a continuation of U.S. Non-Provisional application Ser. No. 14/552,336, filed on Nov. 24, 2014, which is a continuation of U.S. Non-Provisional application Ser. No. 14/099,661, filed on Dec. 6, 2013, which claims the benefit of priority to U.S. Provisional Patent Application No. 61/801,297, filed on Mar. 15, 2013, the disclosures of which are expressly incorporated herein by reference in their entireties.
Number | Name | Date | Kind |
---|---|---|---|
5241625 | Epard et al. | Aug 1993 | A |
5826021 | Mastors et al. | Oct 1998 | A |
5832218 | Gibbs et al. | Nov 1998 | A |
5845300 | Corner | Dec 1998 | A |
5878434 | Draper et al. | Mar 1999 | A |
5897636 | Kaeser | Apr 1999 | A |
5966706 | Biliris et al. | Oct 1999 | A |
5999911 | Berg et al. | Dec 1999 | A |
6006242 | Poole et al. | Dec 1999 | A |
6057757 | Arrowsmith et al. | May 2000 | A |
6065026 | Cornelia et al. | May 2000 | A |
6134582 | Kennedy | Oct 2000 | A |
6232971 | Haynes | May 2001 | B1 |
6237138 | Hameluck et al. | May 2001 | B1 |
6243706 | Moreau et al. | Jun 2001 | B1 |
6243717 | Gordon et al. | Jun 2001 | B1 |
6370538 | Lamping et al. | Apr 2002 | B1 |
6430305 | Decker | Aug 2002 | B1 |
6463404 | Appleby | Oct 2002 | B1 |
6519627 | Dan et al. | Feb 2003 | B1 |
6523019 | Borthwick | Feb 2003 | B1 |
6665683 | Meltzer | Dec 2003 | B1 |
6820135 | Dingman | Nov 2004 | B1 |
6850317 | Mullins et al. | Feb 2005 | B2 |
6944821 | Bates et al. | Sep 2005 | B1 |
6967589 | Peters | Nov 2005 | B1 |
6978419 | Kantrowitz | Dec 2005 | B1 |
6980984 | Huffman et al. | Dec 2005 | B1 |
7058648 | Lightfoot et al. | Jun 2006 | B1 |
7086028 | Davis et al. | Aug 2006 | B1 |
7089541 | Ungar | Aug 2006 | B2 |
7168039 | Bertram | Jan 2007 | B2 |
7174377 | Bernard et al. | Feb 2007 | B2 |
7194680 | Roy et al. | Mar 2007 | B1 |
7213030 | Jenkins | May 2007 | B1 |
7392254 | Jenkins | Jun 2008 | B1 |
7403942 | Bayliss | Jul 2008 | B1 |
7441182 | Beilinson et al. | Oct 2008 | B2 |
7441219 | Perry et al. | Oct 2008 | B2 |
7461077 | Greenwood | Dec 2008 | B1 |
7461158 | Rider et al. | Dec 2008 | B2 |
7617232 | Gabbert et al. | Nov 2009 | B2 |
7627489 | Schaeffer et al. | Dec 2009 | B2 |
7739246 | Mooney et al. | Jun 2010 | B2 |
7756843 | Palmer | Jul 2010 | B1 |
7757220 | Griffith et al. | Jul 2010 | B2 |
7765489 | Shah et al. | Jul 2010 | B1 |
7877421 | Berger et al. | Jan 2011 | B2 |
7880921 | Dattilo et al. | Feb 2011 | B2 |
7899796 | Borthwick et al. | Mar 2011 | B1 |
7912842 | Bayliss | Mar 2011 | B1 |
7917376 | Bellin et al. | Mar 2011 | B2 |
7941321 | Greenstein et al. | May 2011 | B2 |
7941336 | Robin-Jan | May 2011 | B1 |
7966199 | Frasher | May 2011 | B1 |
7958147 | Turner et al. | Jun 2011 | B1 |
7962495 | Jain et al. | Jun 2011 | B2 |
8010507 | King et al. | Aug 2011 | B2 |
8036971 | Aymeloglu et al. | Oct 2011 | B2 |
8037046 | Udezue et al. | Oct 2011 | B2 |
8046283 | Burns | Oct 2011 | B2 |
8054756 | Chand et al. | Nov 2011 | B2 |
8073857 | Sreekanth | Dec 2011 | B2 |
8117022 | Linker | Feb 2012 | B2 |
8126848 | Wagner | Feb 2012 | B2 |
8147715 | Bruckhaus et al. | Apr 2012 | B2 |
8214490 | Vos et al. | Jul 2012 | B1 |
8229902 | Vishniac et al. | Jul 2012 | B2 |
8290838 | Thakur et al. | Oct 2012 | B1 |
8302855 | Ma et al. | Nov 2012 | B2 |
8364642 | Garrod | Jan 2013 | B1 |
8386377 | Xiong et al. | Feb 2013 | B1 |
8429527 | Arbogast | Apr 2013 | B1 |
8473454 | Evanitsky et al. | Jun 2013 | B2 |
8484115 | Aymeloglu et al. | Jul 2013 | B2 |
8489641 | Seefeld et al. | Jul 2013 | B1 |
8554719 | McGrew | Oct 2013 | B2 |
8577911 | Stepinski et al. | Nov 2013 | B1 |
8589273 | Creeden et al. | Nov 2013 | B2 |
8601326 | Kim | Dec 2013 | B1 |
8639552 | Chen et al. | Jan 2014 | B1 |
8682696 | Shanmugam | Mar 2014 | B1 |
8688573 | Rukonic et al. | Apr 2014 | B1 |
8732574 | Burr et al. | May 2014 | B2 |
8744890 | Bernier | Jun 2014 | B1 |
8799313 | Satlow | Aug 2014 | B2 |
8799799 | Cervelli et al. | Aug 2014 | B1 |
8806355 | Twiss et al. | Aug 2014 | B2 |
8807948 | Luo et al. | Aug 2014 | B2 |
8812444 | Garrod et al. | Aug 2014 | B2 |
8812960 | Sun et al. | Aug 2014 | B1 |
8838538 | Landau et al. | Sep 2014 | B1 |
8855999 | Elliot | Oct 2014 | B1 |
8903717 | Elliot | Dec 2014 | B2 |
8924388 | Elliot et al. | Dec 2014 | B2 |
8924389 | Elliot et al. | Dec 2014 | B2 |
8930874 | Duff et al. | Jan 2015 | B2 |
8938434 | Jain et al. | Jan 2015 | B2 |
8938686 | Erenrich et al. | Jan 2015 | B1 |
8949164 | Mohler | Feb 2015 | B1 |
8984390 | Aymeloglu et al. | Mar 2015 | B2 |
9058315 | Burr et al. | Jun 2015 | B2 |
9069842 | Melby | Jun 2015 | B2 |
9100428 | Visbal | Aug 2015 | B1 |
9105000 | White et al. | Aug 2015 | B1 |
9111281 | Stibel et al. | Aug 2015 | B2 |
9129219 | Robertson et al. | Sep 2015 | B1 |
9165100 | Begur et al. | Oct 2015 | B2 |
9230060 | Friedlander et al. | Jan 2016 | B2 |
9256664 | Chakerian et al. | Feb 2016 | B2 |
20020032677 | Moregenthaler et al. | Mar 2002 | A1 |
20020035590 | Eibach et al. | Mar 2002 | A1 |
20020065708 | Senay et al. | May 2002 | A1 |
20020095360 | Joao | Jul 2002 | A1 |
20020095658 | Shulman | Jul 2002 | A1 |
20020103705 | Brady | Aug 2002 | A1 |
20020147805 | Leshem et al. | Oct 2002 | A1 |
20020194058 | Eldering | Dec 2002 | A1 |
20020196229 | Chen et al. | Dec 2002 | A1 |
20030036927 | Bowen | Feb 2003 | A1 |
20030088438 | Maughan et al. | May 2003 | A1 |
20030093401 | Czahowski et al. | May 2003 | A1 |
20030093755 | O'Carroll | May 2003 | A1 |
20030105759 | Bess et al. | Jun 2003 | A1 |
20030115481 | Baird et al. | Jun 2003 | A1 |
20030126102 | Borthwick | Jul 2003 | A1 |
20030171942 | Gaito | Sep 2003 | A1 |
20030177112 | Gardner | Sep 2003 | A1 |
20030182313 | Federwisch et al. | Sep 2003 | A1 |
20030212718 | Tester | Nov 2003 | A1 |
20040003009 | Wilmot | Jan 2004 | A1 |
20040006523 | Coker | Jan 2004 | A1 |
20040034570 | Davis | Feb 2004 | A1 |
20040044648 | Anfindsen et al. | Mar 2004 | A1 |
20040083466 | Dapp et al. | Apr 2004 | A1 |
20040088177 | Travis et al. | May 2004 | A1 |
20040111480 | Yue | Jun 2004 | A1 |
20040117387 | Civetta et al. | Jun 2004 | A1 |
20040153418 | Hanweck | Aug 2004 | A1 |
20040205492 | Newsome | Oct 2004 | A1 |
20040210763 | Jonas | Oct 2004 | A1 |
20040236688 | Bozeman | Nov 2004 | A1 |
20040236711 | Nixon et al. | Nov 2004 | A1 |
20050010472 | Quatse et al. | Jan 2005 | A1 |
20050028094 | Allyn | Feb 2005 | A1 |
20050039116 | Slack-Smith | Feb 2005 | A1 |
20050086207 | Heuer et al. | Apr 2005 | A1 |
20050091186 | Alon | Apr 2005 | A1 |
20050097441 | Herbach et al. | May 2005 | A1 |
20050102328 | Ring et al. | May 2005 | A1 |
20050125715 | Di Franco et al. | Jun 2005 | A1 |
20050131935 | O'Leary et al. | Jun 2005 | A1 |
20050154628 | Eckart et al. | Jul 2005 | A1 |
20050154769 | Eckart et al. | Jul 2005 | A1 |
20050262512 | Schmidt et al. | Nov 2005 | A1 |
20060010130 | Leff et al. | Jan 2006 | A1 |
20060026120 | Carolan et al. | Feb 2006 | A1 |
20060026170 | Kreitler et al. | Feb 2006 | A1 |
20060026561 | Bauman et al. | Feb 2006 | A1 |
20060031779 | Theurer et al. | Feb 2006 | A1 |
20060053097 | King et al. | Mar 2006 | A1 |
20060053170 | Hill et al. | Mar 2006 | A1 |
20060059423 | Lehmann et al. | Mar 2006 | A1 |
20060080139 | Mainzer | Apr 2006 | A1 |
20060080283 | Shipman | Apr 2006 | A1 |
20060080316 | Gilmore et al. | Apr 2006 | A1 |
20060129746 | Porter | Jun 2006 | A1 |
20060136513 | Ngo et al. | Jun 2006 | A1 |
20060143034 | Rothermel | Jun 2006 | A1 |
20060143075 | Carr et al. | Jun 2006 | A1 |
20060143079 | Basak et al. | Jun 2006 | A1 |
20060155654 | Plessis et al. | Jul 2006 | A1 |
20060178915 | Chao | Aug 2006 | A1 |
20060178954 | Thukral et al. | Aug 2006 | A1 |
20060218206 | Bourbonnais et al. | Sep 2006 | A1 |
20060218941 | Grossman et al. | Sep 2006 | A1 |
20060253502 | Raman et al. | Nov 2006 | A1 |
20060265417 | Amato et al. | Nov 2006 | A1 |
20060277460 | Forstall et al. | Dec 2006 | A1 |
20070000999 | Kubo et al. | Jan 2007 | A1 |
20070011304 | Error | Jan 2007 | A1 |
20070038646 | Thota | Feb 2007 | A1 |
20070043686 | Teng et al. | Feb 2007 | A1 |
20070061752 | Cory | Mar 2007 | A1 |
20070067285 | Blume | Mar 2007 | A1 |
20070113164 | Hansen et al. | May 2007 | A1 |
20070136095 | Weinstein | Jun 2007 | A1 |
20070150801 | Chidlovskii et al. | Jun 2007 | A1 |
20070156673 | Maga | Jul 2007 | A1 |
20070162454 | D'Albora et al. | Jul 2007 | A1 |
20070168871 | Jenkins | Jul 2007 | A1 |
20070178501 | Rabinowitz et al. | Aug 2007 | A1 |
20070185850 | Walters et al. | Aug 2007 | A1 |
20070185867 | Maga | Aug 2007 | A1 |
20070192122 | Routson et al. | Aug 2007 | A1 |
20070233756 | D'Souza et al. | Oct 2007 | A1 |
20070245339 | Bauman et al. | Oct 2007 | A1 |
20070271317 | Carmel | Nov 2007 | A1 |
20070284433 | Domenica et al. | Dec 2007 | A1 |
20070295797 | Herman et al. | Dec 2007 | A1 |
20070299697 | Friedlander et al. | Dec 2007 | A1 |
20080005063 | Seeds | Jan 2008 | A1 |
20080016155 | Khalatian | Jan 2008 | A1 |
20080065655 | Chakravarthy et al. | Mar 2008 | A1 |
20080077642 | Carbone et al. | Mar 2008 | A1 |
20080091693 | Murthy | Apr 2008 | A1 |
20080109714 | Kumar et al. | May 2008 | A1 |
20080126344 | Hoffman et al. | May 2008 | A1 |
20080126951 | Sood et al. | May 2008 | A1 |
20080140387 | Linker | Jun 2008 | A1 |
20080172607 | Baer | Jul 2008 | A1 |
20080177782 | Poston et al. | Jul 2008 | A1 |
20080195672 | Hamel et al. | Aug 2008 | A1 |
20080208735 | Balet et al. | Aug 2008 | A1 |
20080222295 | Robinson et al. | Sep 2008 | A1 |
20080228467 | Womack et al. | Sep 2008 | A1 |
20080249820 | Pathria et al. | Oct 2008 | A1 |
20080255973 | El Wade et al. | Oct 2008 | A1 |
20080267386 | Cooper | Oct 2008 | A1 |
20080270316 | Guidotti et al. | Oct 2008 | A1 |
20080281580 | Zabokritski | Nov 2008 | A1 |
20080294663 | Heinley et al. | Nov 2008 | A1 |
20080313132 | Hao et al. | Dec 2008 | A1 |
20080313243 | Poston et al. | Dec 2008 | A1 |
20090031401 | Cudich et al. | Jan 2009 | A1 |
20090043801 | LeClair et al. | Feb 2009 | A1 |
20090055487 | Moraes et al. | Feb 2009 | A1 |
20090089651 | Herberger et al. | Apr 2009 | A1 |
20090094270 | Alirez et al. | Apr 2009 | A1 |
20090106178 | Chu | Apr 2009 | A1 |
20090106242 | McGrew et al. | Apr 2009 | A1 |
20090112678 | Luzardo | Apr 2009 | A1 |
20090112745 | Stefanescu | Apr 2009 | A1 |
20090125359 | Knapic | May 2009 | A1 |
20090125459 | Norton et al. | May 2009 | A1 |
20090132953 | Reed, Jr. et al. | May 2009 | A1 |
20090150868 | Chakra et al. | Jun 2009 | A1 |
20090157732 | Hao et al. | Jun 2009 | A1 |
20090164387 | Armstrong et al. | Jun 2009 | A1 |
20090177962 | Gusmorino et al. | Jul 2009 | A1 |
20090187546 | Whyte et al. | Jul 2009 | A1 |
20090187548 | Ji et al. | Jul 2009 | A1 |
20090199106 | Jonsson et al. | Aug 2009 | A1 |
20090248757 | Havewala et al. | Oct 2009 | A1 |
20090249178 | Ambrosino et al. | Oct 2009 | A1 |
20090249244 | Robinson et al. | Oct 2009 | A1 |
20090254842 | Leacock et al. | Oct 2009 | A1 |
20090271343 | Vaiciulis et al. | Oct 2009 | A1 |
20090281839 | Lynn et al. | Nov 2009 | A1 |
20090282068 | Shockro et al. | Nov 2009 | A1 |
20090299830 | West et al. | Dec 2009 | A1 |
20090307049 | Elliott et al. | Dec 2009 | A1 |
20090313311 | Hoffmann et al. | Dec 2009 | A1 |
20090313463 | Pang et al. | Dec 2009 | A1 |
20090319418 | Herz | Dec 2009 | A1 |
20090319891 | MacKinlay | Dec 2009 | A1 |
20100030722 | Goodson et al. | Feb 2010 | A1 |
20100031141 | Summers et al. | Feb 2010 | A1 |
20100042922 | Bradeteanu et al. | Feb 2010 | A1 |
20100057622 | Faith et al. | Mar 2010 | A1 |
20100070531 | Aymeloglu et al. | Mar 2010 | A1 |
20100070842 | Aymeloglu et al. | Mar 2010 | A1 |
20100082541 | Kottomtharayil | Apr 2010 | A1 |
20100082671 | Li et al. | Apr 2010 | A1 |
20100098318 | Anderson | Apr 2010 | A1 |
20100106752 | Eckardt et al. | Apr 2010 | A1 |
20100114817 | Gilbert et al. | May 2010 | A1 |
20100114887 | Conway et al. | May 2010 | A1 |
20100131502 | Fordham | May 2010 | A1 |
20100145909 | Ngo | Jun 2010 | A1 |
20100161735 | Sharma | Jun 2010 | A1 |
20100191563 | Schlaifer et al. | Jul 2010 | A1 |
20100204983 | Chung et al. | Aug 2010 | A1 |
20100211535 | Rosenberger | Aug 2010 | A1 |
20100223260 | Wu | Sep 2010 | A1 |
20100235915 | Memon et al. | Sep 2010 | A1 |
20100238174 | Haub et al. | Sep 2010 | A1 |
20100262688 | Hussain et al. | Oct 2010 | A1 |
20100262901 | DiSalvo | Oct 2010 | A1 |
20100280851 | Merkin | Nov 2010 | A1 |
20100293174 | Bennett et al. | Nov 2010 | A1 |
20100306285 | Shah et al. | Dec 2010 | A1 |
20100312837 | Bodapati et al. | Dec 2010 | A1 |
20100313239 | Chakra et al. | Dec 2010 | A1 |
20110047540 | Williams et al. | Feb 2011 | A1 |
20110061013 | Billicki et al. | Mar 2011 | A1 |
20110066497 | Gopinath et al. | Mar 2011 | A1 |
20110074788 | Regan et al. | Mar 2011 | A1 |
20110078173 | Seligmann et al. | Mar 2011 | A1 |
20110093327 | Fordyce et al. | Apr 2011 | A1 |
20110099133 | Chang et al. | Apr 2011 | A1 |
20110153384 | Horne et al. | Jun 2011 | A1 |
20110161409 | Nair et al. | Jun 2011 | A1 |
20110173093 | Psota et al. | Jul 2011 | A1 |
20110179048 | Satlow | Jul 2011 | A1 |
20110208565 | Ross et al. | Aug 2011 | A1 |
20110208724 | Jones et al. | Aug 2011 | A1 |
20110208822 | Rathod | Aug 2011 | A1 |
20110213655 | Henkin | Sep 2011 | A1 |
20110218955 | Tang | Sep 2011 | A1 |
20110225482 | Chan et al. | Sep 2011 | A1 |
20110252282 | Meek et al. | Oct 2011 | A1 |
20110258216 | Supakkul et al. | Oct 2011 | A1 |
20110270604 | Qi et al. | Nov 2011 | A1 |
20110270834 | Sokolan et al. | Nov 2011 | A1 |
20110289397 | Eastmond et al. | Nov 2011 | A1 |
20110295649 | Fine | Dec 2011 | A1 |
20110314007 | Dassa et al. | Dec 2011 | A1 |
20110314024 | Chang et al. | Dec 2011 | A1 |
20120004894 | Butler et al. | Jan 2012 | A1 |
20120004904 | Shin et al. | Jan 2012 | A1 |
20120011238 | Rathod | Jan 2012 | A1 |
20120011245 | Gillette et al. | Jan 2012 | A1 |
20120013684 | Lucia | Jan 2012 | A1 |
20120022945 | Falkenborg et al. | Jan 2012 | A1 |
20120054284 | Rakshit | Mar 2012 | A1 |
20120059853 | Jagota | Mar 2012 | A1 |
20120066166 | Curbera et al. | Mar 2012 | A1 |
20120078595 | Balandin et al. | Mar 2012 | A1 |
20120079363 | Folting et al. | Mar 2012 | A1 |
20120084117 | Tavares et al. | Apr 2012 | A1 |
20120084184 | Raleigh et al. | Apr 2012 | A1 |
20120084287 | Lakshminarayan et al. | Apr 2012 | A1 |
20120123989 | Yu et al. | May 2012 | A1 |
20120131512 | Takeuchi et al. | May 2012 | A1 |
20120144335 | Abeln et al. | Jun 2012 | A1 |
20120158527 | Cannelongo et al. | Jun 2012 | A1 |
20120159362 | Brown et al. | Jun 2012 | A1 |
20120173381 | Smith | Jul 2012 | A1 |
20120188252 | Law | Jul 2012 | A1 |
20120191446 | Binsztok et al. | Jul 2012 | A1 |
20120197657 | Prodanovic | Aug 2012 | A1 |
20120197660 | Prodanovich | Aug 2012 | A1 |
20120215784 | King et al. | Aug 2012 | A1 |
20120221553 | Wittmer et al. | Aug 2012 | A1 |
20120226523 | Weiss | Sep 2012 | A1 |
20120226590 | Love et al. | Sep 2012 | A1 |
20120245976 | Kumar et al. | Sep 2012 | A1 |
20120284670 | Kashik et al. | Nov 2012 | A1 |
20120323888 | Osann, Jr. | Dec 2012 | A1 |
20130006947 | Akinyemi et al. | Jan 2013 | A1 |
20130016106 | Yip et al. | Jan 2013 | A1 |
20130054306 | Bhalla | Feb 2013 | A1 |
20130057551 | Ebert et al. | Mar 2013 | A1 |
20130096988 | Grossman et al. | Apr 2013 | A1 |
20130097130 | Bingol et al. | Apr 2013 | A1 |
20130110746 | Ahn | May 2013 | A1 |
20130124193 | Holmberg | May 2013 | A1 |
20130132348 | Garrod | May 2013 | A1 |
20130151305 | Akinola et al. | Jun 2013 | A1 |
20130151453 | Bhanot et al. | Jun 2013 | A1 |
20130166348 | Scotto | Jun 2013 | A1 |
20130166480 | Popescu et al. | Jun 2013 | A1 |
20130185245 | Anderson | Jul 2013 | A1 |
20130185307 | El-Yaniv et al. | Jul 2013 | A1 |
20130226318 | Procyk | Aug 2013 | A1 |
20130226879 | Talukder et al. | Aug 2013 | A1 |
20130226944 | Baid et al. | Aug 2013 | A1 |
20130238616 | Rose et al. | Sep 2013 | A1 |
20130246170 | Gross et al. | Sep 2013 | A1 |
20130246316 | Zhao et al. | Sep 2013 | A1 |
20130246537 | Gaddala | Sep 2013 | A1 |
20130246597 | Iizawa et al. | Sep 2013 | A1 |
20130208565 | Castellanos et al. | Oct 2013 | A1 |
20130263019 | Castellanos et al. | Oct 2013 | A1 |
20130268520 | Fisher et al. | Oct 2013 | A1 |
20130282696 | John et al. | Oct 2013 | A1 |
20130290825 | Arndt et al. | Oct 2013 | A1 |
20130297619 | Chandrasekaran et al. | Nov 2013 | A1 |
20130304770 | Boero et al. | Nov 2013 | A1 |
20140006404 | McGrew et al. | Jan 2014 | A1 |
20140012796 | Petersen et al. | Jan 2014 | A1 |
20140040371 | Gurevich et al. | Feb 2014 | A1 |
20140058914 | Song et al. | Feb 2014 | A1 |
20140068487 | Steiger et al. | Mar 2014 | A1 |
20140095509 | Patton | Apr 2014 | A1 |
20140108074 | Miller et al. | Apr 2014 | A1 |
20140108380 | Gotz et al. | Apr 2014 | A1 |
20140108985 | Scott et al. | Apr 2014 | A1 |
20140123279 | Bishop et al. | May 2014 | A1 |
20140129936 | Richards | May 2014 | A1 |
20140136285 | Carvalho | May 2014 | A1 |
20140143009 | Brice et al. | May 2014 | A1 |
20140156527 | Grigg et al. | Jun 2014 | A1 |
20140157172 | Peery et al. | Jun 2014 | A1 |
20140164502 | Khodorenko et al. | Jun 2014 | A1 |
20140189536 | Lange et al. | Jul 2014 | A1 |
20140195515 | Baker et al. | Jul 2014 | A1 |
20140208281 | Ming | Jul 2014 | A1 |
20140222521 | Chait | Aug 2014 | A1 |
20140222793 | Sadkin et al. | Aug 2014 | A1 |
20140229554 | Grunin et al. | Aug 2014 | A1 |
20140244284 | Smith | Aug 2014 | A1 |
20140344230 | Krause et al. | Nov 2014 | A1 |
20140358829 | Hurwitz | Dec 2014 | A1 |
20140366132 | Stiansen et al. | Dec 2014 | A1 |
20150012509 | Kirn | Jan 2015 | A1 |
20150026622 | Roaldson et al. | Jan 2015 | A1 |
20150046481 | Elliot | Feb 2015 | A1 |
20150073929 | Psota et al. | Mar 2015 | A1 |
20150073954 | Braff | Mar 2015 | A1 |
20150089353 | Folkening | Mar 2015 | A1 |
20150095773 | Gonsalves et al. | Apr 2015 | A1 |
20150100897 | Sun et al. | Apr 2015 | A1 |
20150106170 | Bonica | Apr 2015 | A1 |
20150106379 | Elliot et al. | Apr 2015 | A1 |
20150134599 | Banerjee et al. | May 2015 | A1 |
20150135256 | Hoy et al. | May 2015 | A1 |
20150188872 | White | Jul 2015 | A1 |
20150212663 | Papale et al. | Jul 2015 | A1 |
20150242401 | Liu | Aug 2015 | A1 |
20150254220 | Burr et al. | Sep 2015 | A1 |
20150338233 | Cervelli et al. | Nov 2015 | A1 |
20150379413 | Robertson et al. | Dec 2015 | A1 |
20160004764 | Chakerian et al. | Jan 2016 | A1 |
20160062555 | Ward et al. | Mar 2016 | A1 |
Number | Date | Country |
---|---|---|
2013251186 | Nov 2015 | AU |
102546446 | Jul 2012 | CN |
103167093 | Jun 2013 | CN |
102054015 | May 2014 | CN |
102014204827 | Sep 2014 | DE |
102014204830 | Sep 2014 | DE |
102014204834 | Sep 2014 | DE |
102014213036 | Jan 2015 | DE |
1672527 | Jun 2006 | EP |
2487610 | Aug 2012 | EP |
2778913 | Sep 2014 | EP |
2778914 | Sep 2014 | EP |
2858018 | Apr 2015 | EP |
2869211 | May 2015 | EP |
2889814 | Jul 2015 | EP |
2892197 | Jul 2015 | EP |
2963595 | Jan 2016 | EP |
2993595 | Mar 2016 | EP |
2366498 | Mar 2002 | GB |
2513472 | Oct 2014 | GB |
2513721 | Nov 2014 | GB |
2517582 | Feb 2015 | GB |
2013134 | Jan 2015 | NL |
WO 2001025906 | Apr 2001 | WO |
WO 2001088750 | Nov 2001 | WO |
WO 20050116851 | Dec 2005 | WO |
WO 2007133206 | Nov 2007 | WO |
WO 2009051987 | Apr 2009 | WO |
WO 2010030913 | Mar 2010 | WO |
WO 2010030914 | Mar 2010 | WO |
WO 2010030919 | Mar 2010 | WO |
WO 2012119008 | Sep 2012 | WO |
Entry |
---|
“A Real-World Problem of Matching Records,” Nov. 2006, <http://grupoweb.upf.es/bd-web/slides/ullman.pdf> pp. 1-16. |
“A Tour of Pinboard,” <http://pinboard.in/tour> as printed May 15, 2014 in 6 pages. |
Abbey, Kristen, “Review of Google Docs,” May 1, 2007, pp. 2. |
Adams et al., “Worklets: A Service-Oriented Implementation of Dynamic Flexibility in Workflows,” R. Meersman, Z. Tari et al. (Eds.): OTM 2006, LNCS, 4275, pp. 291-308, 2006. |
Amnet, “5 Great Tools for Visualizing Your Twitter Followers,” posted Aug. 4, 2010, http://www.amnetblog.com/component/content/article/115-5-grate-tools-for-visualizing-your-twitter-followers.html. |
Appacts, “Smart Thinking for Super Apps,” <http://www.appacts.com> Printed Jul. 18, 2013 in 4 pages. |
Apsalar, “Data Powered Mobile Advertising,” “Free Mobile App Analytics” and various analytics related screen shots <http://apsalar.com> Printed Jul. 18, 2013 in 8 pages. |
Bluttman et al., “Excel Formulas and Functions for Dummies,” 2005, Wiley Publishing, Inc., pp. 280, 284-286. |
Brandel, Mary, “Data Loss Prevention Dos and Don'ts,” <http://web.archive.org/web/20080724024847/http://www.csoonline.com/article/221272/Dos_and__Don_ts_for_Data_Loss_Prevention>, Oct. 10, 2007, pp. 5. |
Capptain—Pilot Your Apps, <http://www.capptain.com> Printed Jul. 18, 2013 in 6 pages. |
Celik, Tantek, “CSS Basic User Interface Module Level 3 (CSS3 UI),” Section 8 Resizing and Overflow, Jan. 17, 2012, retrieved from internet http://www.w3.org/TR/2012/WD-css3-ui-20120117/#resizing-amp-overflow retrieved on May 18, 2015. |
Chaudhuri et al., “An Overview of Business Intelligence Technology,” Communications of the ACM, Aug. 2011, vol. 54, No. 8. |
Cohn et al., “Semi-supervised Clustering with User Feedback,” Constrained Clustering: Advances in Algorithms, Theory, and Applications 4.1, 2003, pp. 17-32. |
Conner, Nancy, “Google Apps: The Missing Manual,” May 1, 2008, pp. 15. |
Countly Mobile Analytics, <http://count.ly/> Printed Jul. 18, 2013 in 9 pages. |
Delicious, <http://delicious.com/> as printed May 15, 2014 in 1 page. |
Distimo—App Analytics, <http://www.distimo.com/app-analytics> Printed Jul. 18, 2013 in 5 pages. |
“E-MailRelay,” <http://web.archive.org/web/20080821175021/http://emailrelay.sourceforge.net/> Aug. 21, 2008, pp. 2. |
Flurry Analytics, <http://www.flurry.com/> Printed Jul. 18, 2013 in 14 pages. |
Galliford, Miles, “SnagIt Versus Free Screen Capture Software: Critical Tools for Website Owners,” <http://www.subhub.com/articles/free-screen-capture-software>, Mar. 27, 2008, pp. 11. |
Google Analytics Official Website—Web Analytics & Reporting, <http://www.google.com/analytics.index.html> Printed Jul. 18, 2013 in 22 pages. |
Gorr et al., “Crime Hot Spot Forecasting: Modeling and Comparative Evaluation,” Grant 98-IJ-CX-K005, May 6, 2002, 37 pages. |
“GrabUp—What a Timesaver!” <http://atlchris.com/191/grabup/>, Aug. 11, 2008, pp. 3. |
Gu et al., “Record Linkage: Current Practice and Future Directions,” Jan. 15, 2004, pp. 32. |
Hansen et al. “Analyzing Social Media Networks with NodeXL: Insights from a Connected World”, Chapter 4, pp. 53-67 and Chapter 10, pp. 143-164, published Sep. 2010. |
Hua et al., “A Multi-attribute Data Structure with Parallel Bloom Filters for Network Services” HiPC 2006, LNCS 4297, pp. 277-288, 2006. |
“HunchLab: Heat Map and Kernel Density Calculation for Crime Analysis,” Azavea Journal, printed from www.azavea.com/blogs/newsletter/v4i4/kernel-density-capabilities-added-to-hunchlab/ on Sep. 9, 2014, 2 pages. |
JetScreenshot.com, “Share Screenshots via Internet in Seconds,” <http://web.archive.org/web/20130807164204/http://www.jetscreenshot.com/>, Aug. 7, 2013, pp. 1. |
Johnson, Maggie “Introduction to YACC and Bison”. |
Johnson, Steve, “Access 2013 on demand,” Access 2013 on Demand, May 9, 2013, Que Publishing. |
Keylines.com, “An Introduction to KeyLines and Network Visualization,” Mar. 2014, <http://keylines.com/wp-content/uploads/2014/03/KeyLines-White-Paper.pdf> downloaded May 12, 2014 in 8 pages. |
Keylines.com, “KeyLines Datasheet,” Mar. 2014, <http://keylines.com/wp-content/uploads/2014/03/KeyLines-datasheet.pdf> downloaded May 12, 2014 in 2 pages. |
Keylines.com, “Visualizing Threats: Improved Cyber Security Through Network Visualization,” Apr. 2014, <http://keylines.com/wp-content/uploads/2014/04/Visualizing-Threats1.pdf> downloaded May 12, 2014 in 10 pages. |
Kontagent Mobile Analytics, <http://www.kontagent.com/> Printed Jul. 18, 2013 in 9 pages. |
Kwout, <http://web.archive.org/web/20080905132448/http://www.kwout.com/> Sep. 5, 2008, pp. 2. |
Lim et al., “Resolving Attribute Incompatibility in Database Integration: An Evidential Reasoning Approach,” Department of Computer Science, University of Minnesota, 1994, <http://reference.kfupm.edu.sa/content/r/e/resolving_attribute_incompatibility_in_d_531691.pdf> pp. 1-10. |
Litwin et al., “Multidatabase Interoperability,” IEEE Computer, Dec. 1986, vol. 19, No. 12, http://www.lamsade.dauphine.fr/˜litwin/mdb-interoperability.pdf, pp. 10-18. |
Localytics—Mobile App Marketing & Analytics, <http://www.localytics.com/> Printed Jul. 18, 2013 in 12 pages. |
Manno et al., “Introducing Collaboration in Single-user Applications through the Centralized Control Architecture,” 2010, pp. 10. |
Microsoft, “Registering an Application to a URI Scheme,” <http://msdn.microsoft.com/en-us/library/aa767914.aspx>, printed Apr. 4, 2009 in 4 pages. |
Microsoft, “Using the Clipboard,” <http://msdn.microsoft.com/en-us/library/ms649016.aspx>, printed Jun. 8, 2009 in 20 pages. |
Microsoft Windows, “Microsoft Windows Version 2002 Print Out 2,” 2002, pp. 1-6. |
Mixpanel—Mobile Analytics, <https://mixpanel.com/> Printed Jul. 18, 2013 in 13 pages. |
Nadeau et al., “A Survey of Named Entity Recognition and Classification,” Jan. 15, 2004, pp. 20. |
Nin et al., “On the Use of Semantic Blocking Techniques for Data Cleansing and Integration,” 11th International Database Engineering and Applications Symposium, 2007, pp. 9. |
Nitro, “Trick: How to Capture a Screenshot as PDF, Annotate, Then Share it,” <http://blog.nitropdf.com/2008/03/04/trick-how-to-capture-a-screenshot-as-pdf-annotate-it-then-share/>, Mar. 4, 2008, pp. 2. |
Online Tech Tips, “Clip2Net—Share files, folders and screenshots easily,” <http://www.online-tech-tips.com/free-software-downloads/share-files-folders-screenshots/>, Apr. 2, 2008, pp. 5. |
Open Web Analytics (OWA), <http://www.openwebanalytics.com/> Printed Jul. 19, 2013 in 5 pages. |
O'Reilly.com, <http://oreilly.com/digitalmedia/2006/01/01/mac-os-x-screenshot-secrets.html> published Jan. 1, 2006 in 10 pages. |
Piwik—Free Web Analytics Software. <http://piwik.org/> Printed Jul. 19, 2013 in18 pages. |
Pythagoras Communications Ltd., “Microsoft CRM Duplicate Detection,” Sep. 13, 2011, https://www.youtube.com/watch?v=j-7Qis0D0Kc. |
Qiang et al., “A Mutual-Information-Based Approach to Entity Reconciliation in Heterogeneous Databases,” Proceedings of 2008 International Conference on Computer Science & Software Engineering, IEEE Computer Society, New York, NY, Dec. 12-14, 2008, pp. 666-669. |
“Refresh CSS Ellipsis When Resizing Container—Stack Overflow,” Jul. 31, 2013, retrieved from internet http://stackoverflow.com/questions/17964681/refresh-css-ellipsis-when-resizing-container, retrieved on May 18, 2015. |
Sekine et al., “Definition, Dictionaries and Tagger for Extended Named Entity Hierarchy,” May 2004, pp. 1977-1980. |
Schroder, Stan, “15 Ways to Create Website Screenshots,” <http://mashable.com/2007/08/24/web-screenshots/>, Aug. 24, 2007, pp. 2. |
Sigrist et al., “PROSITE, a Protein Domain Database for Functional Characterization and Annotation,” Nucleic Acids Research 38.Suppl 1, 2010, pp. D161-D166. |
SnagIt, “SnagIt Online Help Guide,” <http://download.techsmith.com/snagit/docs/onlinehelp/enu/snagit_help.pdf>, TechSmith Corp., Version 8.1, printed Feb. 7, 2007, pp. 284. |
SnagIt, “SnagIt 8.1.0 Print Out,” Software release date Jun. 15, 2006, pp. 6. |
SnagIt, “SnagIt 8.1.0 Print Out 2,” Software release date Jun. 15, 2006, pp. 1-3. |
StatCounter—Free Invisible Web Tracker, Hit Counter and Web Stats, <http://statcounter.com/> Printed Jul. 19, 2013 in 17 pages. |
TestFlight—Beta Testing on the Fly, <http://testflightapp.com/> Printed Jul. 18, 2013 in 3 pages. |
trak.io, <http://trak.io/> printed Jul. 18, 2013 in 3 pages. |
UserMetrix, <http://usermetrix.com/android-analytics> printed Jul. 18, 2013 in 3 pages. |
Valentini et al., “Ensembles of Learning Machines,” M. Marinaro and R. Tagliaferri (Eds.): WIRN VIETRI 2002, LNCS 2486, pp. 3-20. |
Vose et al., “Help File for ModelRisk Version 5,” 2007, Vose Software, pp. 349-353. [Uploaded in 2 Parts]. |
Wang et al., “Research on a Clustering Data De-Duplication Mechanism Based on Bloom Filter,” IEEE 2010, 5 pages. |
Warren, Christina, “TUAW Faceoff: Screenshot apps on the firing line,” <http://www.tuaw.com/2008/05/05/tuaw-faceoff-screenshot-apps-on-the-firing-line/>, May 5, 2008, pp. 11. |
Wikipedia, “Multimap,” Jan. 1, 2013, https://en.wikipedia.org/w/index.php?title=Multimap&oldid=530800748. |
Zhao et al., “Entity Matching Across Heterogeneous Data Sources: An Approach Based on Constrained Cascade Generalization,” Data & Knowledge Engineering, vol. 66, No. 3, Sep. 2008, pp. 368-381. |
Notice of Allowance for U.S. Appl. No. 14/265,637 dated Feb. 13, 2015. |
Notice of Allowance for U.S. Appl. No. 14/479,863 dated Mar. 31, 2015. |
Notice of Allowance for U.S. Appl. No. 14/304,741 dated Apr. 7, 2015. |
Notice of Allowance for U.S. Appl. No. 14/225,084 dated May 4, 2015. |
Notice of Allowance for U.S. Appl. No. 14/319,161 dated May 4, 2015. |
Notice of Allowance for U.S. Appl. No. 14/323,935 dated Oct. 1, 2015. |
Notice of Allowance for U.S. Appl. No. 14/552,336 dated Nov. 3, 2015. |
Notice of Allowance for U.S. Appl. No. 12/556,307 dated Jan. 4, 2016. |
Notice of Allowance for U.S. Appl. No. 14/746,671 dated Jan. 21, 2016. |
Notice of Allowance for U.S. Appl. No. 14/094,418 dated Jan. 25, 2016. |
Notice of Allowance for U.S. Appl. No. 14/858,647 dated Mar. 4, 2016. |
Official Communication for U.S. Appl. No. 14/225,160 dated Jul. 29, 2014. |
Official Communication for U.S. Appl. No. 14/304,741 dated Aug. 6, 2014. |
Official Communication for U.S. Appl. No. 14/225,084 dated Sep. 2, 2014. |
Official Communication for U.S. Appl. No. 14/225,006 dated Sep. 10, 2014. |
Official Communication for U.S. Appl. No. 14/451,221 dated Oct. 21, 2014. |
Official Communication for U.S. Appl. No. 14/225,160 dated Oct. 22, 2014. |
Official Communication for U.S. Appl. No. 14/463,615 dated Nov. 13, 2014. |
Official Communication for U.S. Appl. No. 13/827,491 dated Dec. 1, 2014. |
Official Communication for U.S. Appl. No. 14/479,863 dated Dec. 26, 2014. |
Official Communication for U.S. Appl. No. 14/319,161 dated Jan. 23, 2015. |
Official Communication for U.S. Appl. No. 14/483,527 dated Jan. 28, 2015. |
Official Communication for U.S. Appl. No. 14/463,615 dated Jan. 28, 2015. |
Official Communication for U.S. Appl. No. 14/225,160 dated Feb. 11, 2015. |
Official Communication for U.S. Appl. No. 14/225,084 dated Feb. 20, 2015. |
Official Communication for U.S. Appl. No. 14/225,006 dated Feb. 27, 2015. |
Official Communication for U.S. Appl. No. 14/304,741 dated Mar. 3, 2015. |
Official Communication for U.S. Appl. No. 14/571,098 dated Mar. 11, 2015. |
Official Communication for U.S. Appl. No. 13/669,274 dated May 6, 2015. |
Official Communication for U.S. Appl. No. 14/225,160 dated May 20, 2015. |
Official Communication for U.S. Appl. No. 14/463,615 dated May 21, 2015. |
Official Communication for U.S. Appl. No. 12/556,307 dated Jun. 9, 2015. |
Official Communication for U.S. Appl. No. 14/014,313 dated Jun. 18, 2015. |
Official Communication for U.S. Appl. No. 13/827,491 dated Jun. 22, 2015. |
Official Communication for U.S. Appl. No. 14/483,527 dated Jun. 22, 2015. |
Official Communication for U.S. Appl. No. 12/556,321 dated Jul. 7, 2015. |
Official Communication for U.S. Appl. No. 14/552,336 dated Jul. 20, 2015. |
Official Communication for U.S. Appl. No. 14/676,621 dated Jul. 30, 2015. |
Official Communication for U.S. Appl. No. 14/571,098 dated Aug. 5, 2015. |
Official Communication for U.S. Appl. No. 14/225,160 dated Aug. 12, 2015. |
Official Communication for U.S. Appl. No. 14/571,098 dated Aug. 24, 2015. |
Official Communication for U.S. Appl. No. 13/669,274 dated Aug. 26, 2015. |
Official Communication for U.S. Appl. No. 14/225,006 dated Sep. 2, 2015. |
Official Communication for U.S. Appl. No. 14/631,633 dated Sep. 10, 2015. |
Official Communication for U.S. Appl. No. 14/463,615 dated Sep. 10, 2015. |
Official Communication for U.S. Appl. No. 14/225,084 dated Sep. 11, 2015. |
Official Communication for U.S. Appl. No. 14/562,524 dated Sep. 14, 2015. |
Official Communication for U.S. Appl. No. 14/813,749 dated Sep. 28, 2015. |
Official Communication for U.S. Appl. No. 14/746,671 dated Sep. 28, 2015. |
Official Communication for U.S. Appl. No. 14/141,252 dated Oct. 8, 2015. |
Official Communication for U.S. Appl. No. 13/827,491 dated Oct. 9, 2015. |
Official Communication for U.S. Appl. No. 14/483,527 dated Oct. 28, 2015. |
Official Communication for U.S. Appl. No. 14/676,621 dated Oct. 29, 2015. |
Official Communication for U.S. Appl. No. 14/571,098 dated Nov. 10, 2015. |
Official Communication for U.S. Appl. No. 14/562,524 dated Nov. 10, 2015. |
Official Communication for U.S. Appl. No. 14/746,671 dated Nov. 12, 2015. |
Official Communication for U.S. Appl. No. 14/842,734 dated Nov. 19, 2015. |
Official Communication for U.S. Appl. No. 14/306,138 dated Dec. 3, 2015. |
Official Communication for U.S. Appl. No. 14/222,364 dated Dec. 9, 2015. |
Official Communication for U.S. Appl. No. 14/463,615 dated Dec. 9, 2015. |
Official Communication for U.S. Appl. No. 14/800,447 dated Dec. 10, 2015. |
Official Communication for U.S. Appl. No. 14/225,006 dated Dec. 21, 2015. |
Official Communication for U.S. Appl. No. 14/306,147 dated Dec. 24, 2015. |
Official Communication for U.S. Appl. No. 14/306,138 dated Dec. 24, 2015. |
Official Communication for U.S. Appl. No. 14/883,498 dated Dec. 24, 2015. |
Official Communication for U.S. Appl. No. 14/225,084 dated Jan. 4, 2016. |
Official Communication for U.S. Appl. No. 14/526,066 dated Jan. 21, 2016. |
Official Communication for U.S. Appl. No. 14/225,160 dated Jan. 25, 2016. |
Official Communication for U.S. Appl. No. 14/319,765 dated Feb. 1, 2016. |
Official Communication for U.S. Appl. No. 14/929,584 dated Feb. 4, 2016. |
Official Communication for U.S. Appl. No. 14/871,465 dated Feb. 9, 2016. |
Official Communication for U.S. Appl. No. 14/741,256 dated Feb. 9, 2016. |
Official Communication for U.S. Appl. No. 14/841,338 dated Feb. 18, 2016. |
Official Communication for U.S. Appl. No. 14/715,834 dated Feb. 19, 2016. |
Official Communication for U.S. Appl. No. 14/571,098 dated Feb. 23, 2016. |
Official Communication for U.S. Appl. No. 12/556,321 dated Feb. 25, 2016. |
Official Communication for U.S. Appl. No. 14/014,313 dated Feb. 26, 2016. |
Official Communication for U.S. Appl. No. 12/556,307 dated Sep. 2, 2011. |
Official Communication for U.S. Appl. No. 12/556,307 dated Feb. 13, 2012. |
Official Communication for U.S. Appl. No. 12/556,307 dated Oct. 1, 2013. |
Official Communication for U.S. Appl. No. 12/556,307 dated Mar. 14, 2014. |
Notice of Acceptance for Australian Patent Application No. 2013251186 dated Nov. 6, 2015. |
Notice of Acceptance for Australian Patent Application No. 2014203669 dated Jan. 21, 2016. |
Official Communication for Australian Patent Application No. 2014201506 dated Feb. 27, 2015. |
Official Communication for Australian Patent Application No. 2014201507 dated Feb. 27, 2015. |
Official Communication for Australian Patent Application No. 2013251186 dated Mar. 12, 2015. |
Official Communication for Australian Patent Application No. 2014203669 dated May 29, 2015. |
Official Communication for Canadian Patent Application No. 2831660 dated Jun. 9, 2015. |
European Search Report for European Patent Application No. 09813700.3 dated Apr. 3, 2014. |
Extended European Search Report for European Patent Application No. 14158958.0 dated Jun. 3, 2014. |
Extended European Search Report for European Patent Application No. 14158977.0 dated Jun. 10, 2014. |
Official Communication for European Patent Application No. 14187996.5 dated Feb. 12, 2015. |
Official Communication for European Patent Application No. 14158977.0 dated Apr. 16, 2015. |
Official Communication for European Patent Application No. 14158958.0 dated Apr. 16, 2015. |
Official Communication for European Patent Application No. 14200298.9 dated May 13, 2015. |
Official Communication for European Patent Application No. 14191540.5 dated May 27, 2015. |
Official Communication for European Patent Application No. 14200246.8 dated May 29, 2015. |
Official Communication for European Patent Application No. 12181585.6 dated Sep. 4, 2015. |
Official Communication for European Patent Application No. 15181419.1 dated Sep. 29, 2015. |
Official Communication for European Patent Application No. 15184764.7 dated Dec. 14, 2015. |
Official Communication for European Patent Application No. 15188106.7 dated Feb. 3, 2016. |
Official Communication for European Patent Application No. 15190307.7 dated Feb. 19, 2016. |
Official Communication for Great Britain Patent Application No. 1404499.4 dated Aug. 20, 2014. |
Official Communication for Great Britain Patent Application No. 1404486.1 dated Aug. 27, 2014. |
Official Communication for Great Britain Patent Application No. 1404489.5 dated Aug. 27, 2014. |
Official Communication for Great Britain Patent Application No. 1404499.4 dated Sep. 29, 2014. |
Official Communication for Great Britain Patent Application No. 1404489.5 dated Oct. 6, 2014. |
Official Communication for Great Britain Patent Application No. 1411984.6 dated Dec. 22, 2014. |
Official Communication for Great Britain Patent Application No. 1404486.1 dated May 21, 2015. |
Official Communication for Great Britain Patent Application No. 1404489.5 dated May 21, 2015. |
Official Communication for Great Britain Patent Application No. 1404499.4 dated Jun. 11, 2015. |
Official Communication for Great Britain Patent Application No. 1411984.6 dated Jan. 8, 2016. |
Official Communication for Netherlands Patent Application No. 2013134 dated Apr. 20, 2015. |
Official Communication for Netherlands Patent Application No. 2011729 dated Aug. 13, 2015. |
Official Communication for Netherlands Patents Application No. 2012421 dated Sep. 18, 2015. |
Official Communication for Netherlands Patents Application No. 2012417 dated Sep. 18, 2015. |
Official Communication for Netherlands Patent Application 2012438 dated Sep. 21, 2015. |
Official Communication for New Zealand Patent Application No. 622389 dated Mar. 20, 2014. |
Official Communication for New Zealand Patent Application No. 622404 dated Mar. 20, 2014. |
Official Communication for New Zealand Patent Application No. 622439 dated Mar. 24, 2014. |
Official Communication for New Zealand Patent Application No. 622473 dated Mar. 27, 2014. |
Official Communication for New Zealand Patent Application No. 622513 dated Apr. 3, 2014. |
Official Communication for New Zealand Patent Application No. 622439 dated Jun. 6, 2014. |
Official Communication for New Zealand Patent Application No. 622473 dated Jun. 19, 2014. |
Official Communication for New Zealand Patent Application No. 628161 dated Aug. 25, 2014. |
Number | Date | Country | |
---|---|---|---|
20160179927 A1 | Jun 2016 | US |
Number | Date | Country | |
---|---|---|---|
61801297 | Mar 2013 | US |
Number | Date | Country | |
---|---|---|---|
Parent | 14552336 | Nov 2014 | US |
Child | 15053305 | US | |
Parent | 14099661 | Dec 2013 | US |
Child | 14552336 | US |