Classifications

• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
  • G06V30/00—Character recognition; Recognising digital ink; Document-oriented image-based pattern recognition
  • G06V30/10—Character recognition
  • G06V30/22—Character recognition characterised by the type of writing
  • G06V30/224—Character recognition characterised by the type of writing of printed characters having additional code marks or containing code marks
• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06F—ELECTRIC DIGITAL DATA PROCESSING
  • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
  • G06F16/50—Information retrieval; Database structures therefor; File system structures therefor of still image data
  • G06F16/58—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually
• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06F—ELECTRIC DIGITAL DATA PROCESSING
  • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
  • G06F16/50—Information retrieval; Database structures therefor; File system structures therefor of still image data
  • G06F16/58—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually
  • G06F16/587—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using geographical or spatial information, e.g. location
• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06F—ELECTRIC DIGITAL DATA PROCESSING
  • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
  • G06F16/70—Information retrieval; Database structures therefor; File system structures therefor of video data
  • G06F16/78—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually
• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
  • G06V20/00—Scenes; Scene-specific elements
  • G06V20/10—Terrestrial scenes

Definitions

• tags are used to aid in the sorting, saving, and searching of digital photos.
• Tagging refers to a process of assigning keywords to digital data.
• the digital data can then be organized according to the keywords or 'tags'.
• the subject matter of a digital photo can be used to create keywords that are then associated with that digital photo as one or more tags.
• tags can be manually added to a particular digital photo to help in the categorizing and searching of the photos
• tags can be manually added to a particular digital photo to help in the categorizing and searching of the photos
• most cameras assign automatic tags of date and time to the digital photos.
• more and more cameras are including geographic location as part of the automatic tags of a photo.
• software solutions have been developed to provide automatic identification of the people in photos (and matching to a particular identity).
• information regarding certain conditions including, but not limited to, weather, geographical landmarks, architectural landmarks, and prominent ambient features can be extracted from an image.
• the time and geographic location metadata of a photo is used to extract the weather for that particular location and time. The extraction can be performed by querying weather databases to determine the weather for the particular location and time in which the photo was taken.
• geographic location metadata of a photo and image recognition is used to extract geographical and architectural landmarks.
• image recognition is used to extract prominent ambient features (including background, color, hue, and intensity) and known physical objects from images, and tags are automatically assigned to the photo based on the extracted features and objects.
• a database of keywords or object identifiers can be provided to be used as tags when one or more certain conditions are recognized in a photo.
• keywords or object identifiers associated with that particular condition are automatically assigned as tags for the photo.
• Tags previously associated with a particular photo can be used to generate additional tags.
• date information can be used to generate tags with keywords associated with that date, such as the season, school semester, holiday, and newsworthy event.
• recognized objects can be ranked by prominence and the ranking reflected as an additional tag.
• the database used in identifying the recognized objects can include various levels of specificity/granularity.
• FIG. 1 illustrates an automatic tag generation process in accordance with certain embodiments of the invention.
• FIG. 2 illustrates an image recognition process in accordance with certain embodiments of the invention.
• FIG. 3 shows an automatic tag generation process flow in accordance with certain embodiments of the invention.
• FIG. 4 illustrates a process of generating a tag by extracting an architectural landmark from a photo for an automatic tag generation process in accordance with an embodiment of the invention.
• FIG. 5 illustrates a process of generating a tag by extracting a geographical landmark from a photo for an automatic tag generation process in accordance with an embodiment of the invention.
• the automatic tagging can occur as a digital photo (or video) is loaded or otherwise transferred to a photo collection that may be stored on a local, remote, or distributed database. In other embodiments, the automatic tagging can occur upon the initiation of a user in order to tag existing photos.
• An image can include, but is not limited to, the visual representation of objects, shapes, and features of what appears in a photo or a video frame.
• an image may be captured by a digital camera (in the form of a photo or as part of a video), and may be realized in the form of pixels defined by image sensors of the digital camera.
• the term "photo image” is used herein to refer to the image of a digital photo as opposed to metadata or other elements associated with the photo and may be used interchangeably with the term “image” without departing from the scope of certain embodiments of the invention.
• the meaning of the terms "photo,” “image,” and “photo image” will be readily understood from their context.
• an image may refer to the visual representation of the electrical values obtained by the image sensors of a digital camera.
• An image file (and digital photo file) may refer to a form of the image that is computer- readable and storable in a storage device.
• the image file may include, but is not limited to, a .jpg, .gif, and .bmp.
• the image file can be reconstructed to provide the visual representation ("image") on, for example, a display device or substrate (e.g., by printing onto paper).
• Metadata written into a digital photo file often includes information identifying who owns the photo (including copyright and contact information) and the camera (and settings) that created the file, as well as descriptive information such as keywords about the photo for making the file searchable on a user's computer and/or over the Internet.
• Some metadata is written by the camera, while other metadata is input by a user either manually or automatically by software after transferring the digital photo file to a computer (or server) from a camera, memory device, or another computer.
• an image and its metadata are used to generate additional metadata.
• the additional metadata is generated by being extracted or inferred from the image and the metadata for the image.
• the metadata for the image can include the geo-location and date the image was taken, and any other information associated with the image that is available.
• the metadata for the image can be part of the image itself or provided separately.
• the metadata is part of the image itself, the data is first extracted from the digital file of the image before being used to generate the additional metadata. Once generated, the additional metadata can then be associated back to the original image or used for other purposes.
• the extracted and/or created metadata and additional metadata can be associated with the original image as a tag.
• One type of tag is a keyword tag.
• the keyword tag may be used in connection with performing operations on one or more images such as, for example, sorting, searching and/or retrieval of image files based on tags having keywords matching specified criteria.
• FIG. 1 illustrates an automatic tag generation process in accordance with certain embodiments of the invention.
• a photo having an image and corresponding metadata is received 100.
• the automatic tagging process of an embodiment of the invention can automatically begin upon receipt of the photo.
• the process can begin upon the user uploading a photo image file to a photo sharing site.
• the process can begin upon the user loading the photo from a camera onto a user's computer.
• a user's mobile phone can include an application for automatic tag generation where upon capturing an image using the mobile phone's camera or selecting the application, the tagging process can begin.
• Metadata associated with the photo is extracted 110.
• the extraction of the metadata can include reading and parsing the particular type(s) of metadata associated with the photo.
• the types of metadata that can be extracted may include, but are not limited to Exchangeable Image File Format (EXIF), International Press Telecommunication Council (IPTC), and Extensible Metadata Platform (XMP).
• image recognition is performed 120 to recognize and identify shapes and objects in the photo image.
• the particular image recognition algorithm used during the performing of the image recognition can be any suitable image or pattern recognition algorithm available for the particular application or processing constraints.
• the image recognition algorithm may be limited by available databases for providing the matching of objects in the photo to known objects.
• an image recognition algorithm can involve pre-processing of the image. Preprocessing can include, but is not limited to, adjusting the contrast of the image, converting to greyscale and/or black and white, cropping, resizing, rotating, and a combination thereof.
• a distinguishing feature such as (but not limited to) color, size, or shape, can be selected for use in detecting a particular object.
• a distinguishing feature such as (but not limited to) color, size, or shape
• multiple features providing distinguishing characteristics of the object may be used.
• Edge detection or border recognition
• Morphology may be performed in the image recognition algorithm to conduct actions on sets of pixels, including the removal of unwanted components.
• noise reduction and/or filling of regions may be performed.
• the one or more objects can each be located in the image and then classified.
• the located object(s) may be classified (i.e. identified as a particular shape or object) by evaluating the located object(s) according to particular specifications related to the distinguishing feature(s).
• the particular specifications may include mathematical calculations (or relations).
• pattern matching may be performed instead of (or in addition to) locating recognizable objects in the image. Matching may be carried out by comparing elements and/or objects in the image to "known" (previously identified or classified) objects and elements.
• the results (e.g., values) of the calculations and/or comparisons may be normalized to represent a best fit for the classifications, where a higher number (e.g., 0.9) signifies a higher likelihood of being correctly classified as the particular shape or object than a normalized result of a lower number (e.g., 0.2).
• a threshold value may be used to assign a label to the identified object.
• the image recognition algorithms can utilize neural networks (NN) and other learning algorithms.
• a video signal can be received by certain systems described herein and undergo an automatic tag generation process as described in accordance with certain embodiments of the invention.
• one or more video frames of a video signal can be received, where the video frame may include an image and metadata, and image recognition and metadata extraction can be performed.
• a first pass recognition step can be performed for an image to identify that a basic shape or object exists in the image. Once the basic shape or object is identified, a second pass recognition step is performed to obtain a more specific identification of the shape or object. For example, a first pass recognition step may identify that a building exists in the photo, and a second pass recognition step may identify the specific building. In one embodiment, the step of identifying that a building exists in the photo can be accomplished by pattern matching between the photo and a set of images or patterns available to the machine/device performing the image recognition. In certain embodiments, the result of the pattern matching for the first pass recognition step can be sufficient to identify the shape or object with sufficient specificity such that no additional recognition step is performed.
• the extracted metadata can be used to facilitate the image recognition by, for example, providing hints as to what the shape or object in the photo may be.
• geographical information extracted from the metadata can be used to facilitate the identification of the specific building.
• the performing of the image recognition 120 can be carried out using the image recognition process illustrated in FIG. 2. Referring to FIG. 2, a basic image recognition algorithm can be used to identify an object in an image 221. This image recognition algorithm is referred to as "basic" to indicate that the image recognition process in step 221 is not using the extracted metadata and should not be construed as indicating only a simplistic or otherwise limited process.
• the image recognition algorithm can be any suitable image or pattern recognition algorithm available for the particular application or processing constraints, and can also involve pre-processing of the image.
• the extracted metadata 211 can be used to obtain a name or label for the identified object by querying a database (e.g., "Identification DB") 222.
• the database can be any suitable database containing names and/or labels providing identification for the object within the constraints set by the query.
• the names and/or labels resulting from the Identification DB query can then be used to query a database (e.g., "Picture DB”) containing images to find images associated with the names and/or labels 223.
• the images resulting from the Picture DB search can then be used to perform pattern matching 224 to more specifically identify the object in the image.
• a score can be provided for how similar the images of objects resulting from the Picture DB search are to the identified object in the image undergoing the image recognition process.
• the basic image recognition 221 may be used to identify the OBJECT "building” and the algorithm may return, for example, "building,” “gray building,” or “tall building.”
• the extracted metadata 211 is the longitude and latitude at which the photo was taken (may be within a range on the order of ⁇ 10 2 feet)
• a query of an Identification DB 222 may be "find all buildings close to this geographical location" (where the geographical location is identified using the longitude and latitude provided by the extracted metadata).
• the Picture DB can be queried 223 to "find all known pictures for each of those specific buildings" (where the specific buildings are the identified buildings from the query of the Identification DB). Pattern matching 224 can then be performed to compare the images obtained by the query of the Picture DB with the image undergoing the image recognition process to determine whether there is a particularly obvious or close match.
• the relative location of objects to one another may also be recognized.
• an advanced recognition step can be performed to recognize that an identified boat is on an identified river or an identified person is in an identified pool.
• the extracted metadata and recognized/identified objects in the photo can then be used to obtain additional information for the photo by being used in querying databases for related information 130.
• Word matching can be performed to obtain results from the query. This step can include using geographical information, date/time information, identified objects in the image, or various combinations thereof to query a variety of databases to obtain related information about objects in the photo and events occurring in or near the photo.
• the results of the database querying can be received 140 and used as tags for the photo 150.
• a photo having an extracted date of November 24, 2011, an extracted location in the United states, and a recognized object of a cooked turkey on a table can result in an additional information tag of "Thanksgiving," whereas an extracted location of outside of the United states would not necessarily result in the tag of the additional information of "Thanksgiving" for the same image.
• a photo having an extracted date of the 2008 United States presidential election and an image recognized President Obama can result in an additional information tag of "presidential election" or, if the time also matches, the additional information tag can include "acceptance speech.”
• FIG. 3 illustrates an automatic tagging process in accordance with certain embodiments of the invention. Similar to the process described with respect to FIG. 1, a photo having an image 301 and corresponding metadata 302 is received. Any geographic information (310) and date/time information (320) available from the metadata 202 is extracted. If no geographic information and date/time information is available, a null result may be returned (as an end process).
• the image 301 is input into an image classifier 330 that scans for known objects (i.e. objects having been defined and/or catalogued in a database used by the image classifier) and identifies and extracts any known physical objects in the image.
• the image classifier uses a database of shapes and items (objects) to extract as much data as possible from the image.
• the image classifier can search and recognize a variety of objects, shapes, and/or features (e.g., color). Objects include, but are not limited to, faces, people, products, characters, animals, plants, displayed text, and other distinguishable content in an image.
• the database can include object identifiers (metadata) in association with the recognizable shapes and items (objects).
• the sensitivity of the image classifier can enable identification of an object even where only partial shapes or a portion of the object is available in the image.
• the metadata obtained from the image classifier process can be used as tags for the photo. The metadata may be written back into the photo or otherwise associated with the photo and stored (335).
• additional tags can be automatically generated by utilizing a combination of the metadata.
• the image can undergo one or more passes for identification and extraction of a variety of recognized features.
• a confidence value representing a probability that the recognized feature was correctly identified can be provided as part of the tag associated with the photo.
• the confidence value may be generated as part of the image recognition algorithm.
• the confidence value is the matching weight (which may be normalized) generated by the image recognition algorithm when matching a feature/object in the image to a base feature (or particular specification).
• the generated confidence value will depend on the algorithm being used and the delta between the images.
• the result may indicate a 90% match if the algorithm recognizes edges and colors, and in another case, the result may indicate a 100% match if the algorithm is only directed to edges, not color.
• the confidence values can be in the form of a table with levels of confidence.
• the table can be stored as part of the tags themselves.
• the table can include an attribute and associated certainty. For example, given a photo of a plantain (in which it is not clear that the plantain is a plantain or a banana), the photo (after undergoing an automatic tag generation process in accordance with an embodiment of the invention) may be tagged with Table 1 below. It should be understood that the table is provided for illustrative purposes only and should not be construed as limiting the form, organization, or attribute selection.
• the photo of the plantain may be obtained along with the Table 1.
• the user may, in some cases, be able to remove any attributes in the table that the user knows are incorrect and change the confidence value (or certainty) of the attribute the user knows is correct to 100% (or 1).
• the corrected table and photo can be used in an image matching algorithm to enable the image recognition algorithm to be more accurate.
• the extracted geographical information is used to facilitate a landmark recognition pass (340), through which the image is input, to identify and extract any recognized landmarks (geographical or architectural). Confidence values can also be associated with the tags generated from the landmark recognition pass. The tags generated from the landmark recognition pass can by written back into the photo image file or otherwise associated with the image and stored (345).
• a weather database is accessed to extrapolate the weather/temperature information at the time/location at which the image was captured by using the extracted metadata of geographical information and date/time information (350).
• the weather/temperature information can be written back into the photo or otherwise associated with the photo and stored (355).
• the automatic tags generated from each process may be stored in a same or separate storage location.
• the databases used by the tag generating system can be local databases or databases associated with other systems.
• a database can be included having keywords or object identifiers for use as tags when one or more specific conditions such as (but not limited to) the weather, geographical landmarks, and architectural landmarks, are determined to be present in a photo.
• This database can be part of or separate from the database used and/or accessed by the image classifier.
• the databases accessed and used for certain embodiments of the subject automatic tag generation processes can include any suitable databases available to search engines, enabling matching between images and tags.
• geotags include geographical location information such as the latitudinal and longitudinal coordinates of the location where a photo is captured.
• Automatic geotagging typically refers to using a device (e.g., digital still camera, digital video camera, mobile device with image sensor) having a geographical positioning system (GPS) when capturing the image for a photo such that the GPS coordinates are associated with the captured image when stored locally on the image capturing device (and/or uploaded into a remote database).
• GPS geographical positioning system
• CelllD also referred to as CID and which is the identifying number of a cellular network cell for a particular cell phone operator station or sector
• CID also referred to as CID and which is the identifying number of a cellular network cell for a particular cell phone operator station or sector
• a specialized automatic geotagging for geographical and architectural landmarks can be accomplished.
• the date/time and location information of a digital photo can be extracted from metadata of the digital photo and a database searched using the date/time and location codes.
• the database can be a weather database, where a query for the weather at the location and date/time extracted from the digital photo returns information (or code) related to the weather for that particular location and time.
• the result of the query can provide weather code and/or descriptions that can be used as a tag such as "Mostly Sunny,” “Sunny,” “Clear,” “Fair,” “Partly Cloudy,” “Cloudy,” “Mostly Cloudy,” “Rain,” “Showers,” “Sprinkles,” and “T-storms.”
• the weather code may include other weather related descriptors such as “Cold,” “Hot,” “Dry,” and “Humid.” Seasonal information can also be included.
• the weather database being searched may not store weather information for the exact location and time used in the query.
• a best matching search can be performed and weather information (along with a confidence value) can be provided for possible best matches to the location and date/time.
• a weather database may contain weather information updated for each hour according to city. A query of that weather database could then return the weather information for the city that the location falls within or is nearest (e.g., the location may be outside of designated city boundaries) for the closest time(s) to the particular time being searched.
• image recognition is performed on the photo image to extract feature information and a tag associated with the recognized object or feature is automatically assigned to the photo.
• prominent ambient features can be extracted from photos by using image (or pattern) recognition. Predominant colors can be identified and used as a tag.
• the image recognition algorithms can search for whether sky is a prominent feature in the photo and what colors or other highlights are in the photo. For example, the image recognition can automatically identify "blue sky” or “red sky” or “green grass” and the photo can be tagged with those terms.
• image recognition can be used to find as many objects as possible and automatically tag the photo appropriately. If a baseball bat, or a football, or a golf club, or a dog, is detected by the image recognition algorithm, tags with those terms can be automatically added as tags to the photo. In addition the objects could be automatically ranked by prominence.
• the photo can be tagged "chair,” “baseball,” and “table.”
• an extra tag can be included with an indicator that the main subject is (or is likely to be) a chair.
• the granularity of the tags can evolve.
• the database can have increasing granularity of recognizable objects, such as "automobile” to "BMW automobile” to "BMW Z4 automobile.”
• known geographic landmarks can be determined and the information extracted from a photo by using a combination of image recognition and geotagging.
• Data from the photo image itself can be extracted via image recognition and the image recognized shapes or objects compared to known geographic landmarks at or near the location corresponding to the location information extracted from the metadata or geotag of the photo.
• This can be accomplished by querying a database containing geographical landmark information.
• the database can be associated with a map having names and geographic locations of known rivers, lakes, mountains, and valleys.
• the existence of a body of water in the photo image may be recognized using image recognition.
• Combining the recognition that water is in the photograph with a geotag associated with the photograph that indicates that the location the photo image was captured is on or near a particular known body of water can result in automatic generation of tags for the photo of the name of the known body of water.
• a photo with a large body of water and a geotag indicating a location in England along the river Thames can be automatically tagged with "River Thames" and "River.”
• FIG. 4 illustrates one such process. Referring to FIG. 4, image recognition of a photo image 401 showing sunrise over a river can result in a determination that a river 402 is in the image 401.
• this information can then be extracted from the image and applied as a tag and/or used in generating the additional metadata. For example, a more specific identification for the "river" 402 can be achieved using the photo's corresponding metadata 403.
• the metadata 403 may include a variety of information such as location metadata and date time metadata.
• the combination of the location metadata (from the metadata 403) and the image-recognized identified object (402) is used to generate additional metadata.
• the metadata 403 indicates a location (not shown) near the Mississippi River and the image recognized object is a river. This results in the generation of the identifier "Mississippi River," which can be used as a tag for the photo.
• a shape or object that is recognized as being a river can be tagged with "River.”
• a shape or object that is recognized as being a beach can be tagged with "Beach” or "Coast.”
• known architectural landmarks can also be determined from a photo by using a combination of image recognition and geotagging.
• Data from the photo image itself can be extracted via image recognition and the image recognized shapes or objects compared to known architectural landmarks at or near the location corresponding to the location information extracted from the metadata or geotag of the photo. This can be accomplished by querying a database containing architectural landmark information.
• the photo can be automatically tagged with name of the architectural landmark.
• Architectural landmarks including the Eiffel tower, the Great Wall of China, or the Great Pyramid of Giza can be recognized due to their distinctive shapes and/or features.
• the existence of a particular structure in the photo may be recognized using image recognition and the photo tagged with a word associated with that structure or feature.
• the name of the particular structure determined from searching a database can be an additional tag.
• FIG. 5 illustrates one such process.
• image recognition of a photo image 501 showing a person in front of the base of the Eiffel tower can result in a determination that a building structure 502 is in the image 501.
• this information can then be extracted from the image and applied as a tag and/or used in generating the additional metadata.
• the photo can be tagged with a word or words associated with the image-recognized object of "building structure.”
• a more specific identification for the "building structure” can be achieved using the photo's corresponding metadata 503.
• the metadata 503 can include a variety of information such as location metadata and date time metadata.
• the metadata 503 of the photo can also include camera specific metadata and any user generated or other automatically generated tags. This listing of metadata 503 associated with the photo should not be construed as limiting or requiring the particular information associated with the photo and is merely intended to illustrate some common metadata.
• the combination of the location metadata (from the metadata 503) and the image-recognized identified object (502) is used to generate additional metadata.
• the metadata 503 indicates a location (not shown) near the Eiffel tower and the image recognized object is a building structure. This results in the generation of the identifier "Eiffel tower," which can be used as a tag for the photo.
• Similar processes can be conducted to automatically generate a tag of recognizable objects. For example, if a highway is recognized in a photo, the photo can be tagged as "highway.” If a known piece of art is recognized, then the photo can be tagged with the name of the piece of art. For example, a photo of Rodin's sculpture, The Thinker, can be tagged with "The Thinker" and "Rodin.”
• the known object database can be one database or multiple databases that may be accessible to the image recognition program.
• the image recognition processing can be conducted after accessing a database of images tagged or associated with the location at which the photo was taken, enabling additional datasets for comparison.
• a live video stream (having audio and visual components) can be imported and automatically tagged according to image recognized and extracted data from designated frames.
• Ambient sound can also undergo recognition algorithms to have features of the sound attached as a tag to the video.
• speech and tonal recognition, music recognition, and sound recognition e.g., car horns, clock tower bells, claps
• the video can be automatically tagged with emotive based terms, such as "angry.”
• the environment in which the automatic tagging occurs includes a user device and a tag generator provider that communicates with the user device over a network.
• the network can be, but is not limited to, a cellular (e.g., wireless phone) network, the Internet, a local area network (LAN), a wide area network (WAN), a WiFi network, or a combination thereof.
• the user device can include, but is not limited to a computer, mobile phone, or other device that can store and/or display photos or videos and send and access content (including the photos or videos) via a network.
• the tag generator provider is configured to receive content from the user device and perform automatic tag generation.
• the tag generator provider communicates with or is a part of a file sharing provider such as a photo sharing provider.
• the tag generator provider can include components providing and carrying out program modules. These components (which may be local or distributed) can include, but are not limited to, a processor (e.g., a central processing unit (CPU)) and memory.
• the automatic tagging can be accomplished via program modules directly as part of a user device (which includes components, such as a processor and memory, capable of carrying out the program modules).
• a user device which includes components, such as a processor and memory, capable of carrying out the program modules.
• no tag generator provider is used.
• the user device communicates with database providers (or other user or provider devices having databases stored thereon) over the network or accesses databases stored on or connected to the user device.
• program modules include routines, programs, objects, components, and data structures that perform particular tasks or implement particular abstract data types.
• functionality of the program modules may be combined or distributed as desired over a computing system or environment.
• Examples of computing systems, environments, and/or configurations include, but are not limited to, personal computers, server computers, hand- held or laptop devices, multiprocessor systems, microprocessor-based systems, programmable consumer electronics, and distributed computing environments that include any of the above systems or devices.
• computer readable media includes removable and nonremovable structures/devices that can be used for storage of information, such as computer readable instructions, data structures, program modules, and other data used by a computing system/environment, in the form of volatile and non-volatile memory, magnetic-based structures/de vices and optical-based structures/devices, and can be any available media that can be accessed by a user device.
• Computer readable media should not be construed or interpreted to include any propagating signals.
• any reference in this specification to "one embodiment,” “an embodiment,” “example embodiment,” etc. means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the invention.
• the appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment.
• any elements or limitations of any invention or embodiment thereof disclosed herein can be combined with any and/or all other elements or limitations (individually or in any combination) or any other invention or embodiment thereof disclosed herein, and all such combinations are contemplated within the scope of the invention without limitation thereto.

Landscapes

• Engineering & Computer Science (AREA)
• Theoretical Computer Science (AREA)
• Physics & Mathematics (AREA)
• General Physics & Mathematics (AREA)
• Library & Information Science (AREA)
• Data Mining & Analysis (AREA)
• Databases & Information Systems (AREA)
• General Engineering & Computer Science (AREA)
• Multimedia (AREA)
• Computer Vision & Pattern Recognition (AREA)
• Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
• Management Or Editing Of Information On Record Carriers (AREA)

Priority Applications (10)

Applications Claiming Priority (2)

Publications (2)

Family

ID=48426987

Family Applications (1)

Country Status (12)

Cited By (3)

Families Citing this family (112)

Family Cites Families (12)

• 2011
  • 2011-11-17 US US13/298,310 patent/US20130129142A1/en not_active Abandoned
• 2012
  • 2012-11-16 CN CN201280056443.1A patent/CN103930901A/zh active Pending
  • 2012-11-16 WO PCT/US2012/065467 patent/WO2013074895A2/en not_active Ceased
  • 2012-11-16 EP EP12850387.7A patent/EP2780863A4/en not_active Withdrawn
  • 2012-11-16 AU AU2012340354A patent/AU2012340354A1/en not_active Abandoned
  • 2012-11-16 MX MX2014006000A patent/MX2014006000A/es not_active Application Discontinuation
  • 2012-11-16 BR BR112014011739A patent/BR112014011739A8/pt not_active Application Discontinuation
  • 2012-11-16 IN IN3322CHN2014 patent/IN2014CN03322A/en unknown
  • 2012-11-16 CA CA2855836A patent/CA2855836A1/en not_active Abandoned
  • 2012-11-16 RU RU2014119859A patent/RU2608261C2/ru not_active IP Right Cessation
  • 2012-11-16 KR KR1020147013107A patent/KR20140091554A/ko not_active Withdrawn
  • 2012-11-16 JP JP2014542484A patent/JP2015501982A/ja active Pending

Non-Patent Citations (1)

Also Published As

Similar Documents

Legal Events