Patent application number | Description | Published |
20110010335 | COLLABORATION SWARMING - A swarm can develop around a piece of content. The swarm can include the original content, changes to the original content, the persons contributing the changes, and metadata, such as comments contributed by members of the swarm. A swarm can also include statistics generated about the content, such as the size of the swarm, the growth and/or death rates of the swarm, the longevity of the swarm, the intensity of the swarm, the persistence of the swarm, and the direction of the swarm. Swarms and their behaviors can be used to validate or invalidate content. | 01-13-2011 |
20110010447 | AUTO GENERATED AND INFERRED GROUP CHAT PRESENCE - A system can include multiple individual status identifiers that correspond to multiple users and a group status identifier that corresponds to a group with which the users are associated. A status identifier update module can update the group status identifier based on a change to at least one of the individual status identifiers. | 01-13-2011 |
20110010640 | INTELLIGENT CO-BROWSING AND CO-EDITING - A leader of a group can specify how content is to be displayed to a group of users. One of these users can specify different instructions regarding the display of the content. The system can then display the content to the user, using either (or both) of the leader's instructions and the user's specific instructions. Using these various independent instructions, the system can automatically change the way the content is displayed to a group of users. | 01-13-2011 |
20110016101 | Stopping Functions For Grouping And Differentiating Files Based On Content - Methods and apparatus teach a digital spectrum of a data file. The digital spectrum is used to map a file's position in multi-dimensional space. This position relative to another file's position reveals closest neighbors. Certain of the closest neighbors are grouped together, while others are differentiated. Grouping ceases upon application of a stopping function so that rightly sized, optimum numbers of file groups are obtained. Embodiments of stopping functions relate to curve types in a mapping of numbers of groups per sequential rounds of grouping, recognizing whether groups have overlapping file members or not, and/or determining whether groups meet predetermined numbers of members, to name a few. Properly grouped files can then be further acted upon. | 01-20-2011 |
20110016124 | Optimized Partitions For Grouping And Differentiating Files Of Data - Methods and apparatus teach a digital spectrum of a data file. The digital spectrum is used to map a file's position in multi-dimensional space. This position relative to another file's position reveals closest neighbors. Certain of the closest neighbors are grouped together to define a set. Overlapping members in the groups may be further differentiated from one another by partitioning. An optimized partition of set S of N overlapping groups yields a maximum strength for groups and members in that partition. Among other things, the optimized partition includes relative strengths of every individual member in every possible partition and weighting functions applied to the relative strengths and to subgroups of files within the partitions. | 01-20-2011 |
20110016136 | Grouping and Differentiating Files Based on Underlying Grouped and Differentiated Files - Methods and apparatus teach a digital spectrum of a file. The digital spectrum is used to map a file's position. This position relative to another file's position reveals closest neighbors. When multiple such neighbors are arranged, first “patterns” of data are created that further define digital spectrums of new files. It is within this sorted new data that emergent relationships or second “patterns” are examined, according to the techniques for its underlying files, or “patterns of patterns.” Representatively, original files are stored on computing devices. If encoded, they have pluralities of symbols representing an underlying data stream of original bits of data. The original files are examined for relationships between each of the files. The original relationships are converted to new files. The new files are representatively encoded and examined for other relationships. The new files are then grouped or differentiated from one another based these new relationships yielding insight into how the original files can be grouped or differentiated. | 01-20-2011 |
20110225154 | HARVESTING RELEVANCY DATA, INCLUDING DYNAMIC RELEVANCY AGENT BASED ON UNDERLYING GROUPED AND DIFFERENTIATED FILES - Methods and apparatus teach a digital spectrum of a file representing underlying original data. The digital spectrum is used to map a file's position. This position relative to another file's position reveals closest neighbors. When multiple such neighbors are grouped together they can be used to indicate relevance in current data under consideration on a same or different computing device. Also, relevance can be found without traditional notions of needing structured data or users initiating searching for relevance or by examining metadata/administrative information associated with the files. A dynamic relevancy agent is configured for installation on the same or different computing device to monitor events regarding the current data and to initiate the examination of relevancy. It also presents to a user suggestions of data closest to the current data. Various triggering events to undertake a relevancy examination are also described as are predetermined criteria to define relative closeness. | 09-15-2011 |
20110225659 | SEMANTIC CONTROLS ON DATA STORAGE AND ACCESS - Methods and apparatus teach defining an access policy to digital data available on one or more computing devices, including identifying one or more semantic attributes of at least one first digital data set and using the identified attributes to define policy dictating user access privileges. On receipt of a user request to access at least one second digital data set, semantic attributes are compared to the at least one first digital data set and access is allowed or not allowed based on the policy. Semantic attributes are selected from at least one of a closeness attribute, a relatedness attribute, and a semantic vector attribute. Also is taught configuring a policy enforcement agent on the one or more computing devices to undertake the comparing and to allow or not allow access. In turn, computer program products and computing systems for accomplishing the foregoing are provided. | 09-15-2011 |
20110252063 | RELEVANCY FILTER FOR NEW DATA BASED ON UNDERLYING FILES - Methods and apparatus teach a digital spectrum of a file representing underlying original data. The digital spectrum is used to map a file's position. This position relative to another file's position reveals closest neighbors. When multiple such neighbors are grouped together they can be used to indicate relevance in current data under consideration on a same or different computing device. Also, relevance can be found without traditional notions of needing structured data or users initiating searching for relevance or by examining metadata/administrative information associated with the files. A plurality of original files represent underlying original bits of data from which a key is created in a mapping space for a relevancy topic. If new data is sufficiently close to this, it is related to the topic and presented to users. Various closeness measures are defined as are methods for key creation. Still other features contemplate computing arrangements and program products. | 10-13-2011 |
20120179680 | SEMANTIC ASSOCIATIONS IN DATA - Methods and apparatus teach providing semantic associations between data available on one or more computing devices, including grouping together related files and creating an association between the related grouped files and at least one anchor file to provide a semantic association for the grouped files. Also is taught configuring an agent on the one or more computing devices to undertake the grouping and to create the association without a user request. Also is taught triggering an evaluation of current files against related grouped files, and creating an association between the current files and at least one of the related grouped files and the at least one anchor file. Information may be added to the created association to create additional semantic associations for one or more of the grouped files and the current files. In turn, computer program products and computing systems for accomplishing the foregoing are provided. | 07-12-2012 |
20120197895 | ANIMATING INANIMATE DATA - Methods and computer program product relate to the animation of data. The methods and product are executable on a processing device in a computing system environment so as to effectively empower new data files to find and make associations with other applications or principles that already have built up associations or binding with data files that are similar to the new data file. | 08-02-2012 |
20120198419 | USER INPUT AUTO-COMPLETION - Methods and computer program product relate to user input auto-completion. The methods and product are executable on a processing device in a computing system environment so as to provide an auto-completion scheme with enhanced capabilities that improve user efficiency when performing a task. | 08-02-2012 |
20120215769 | STRUCTURED RELEVANCE - A MECHANISM TO REVEAL HOW DATA IS RELATED - A machine receives a description of the relationships among members of a data set. The machine constructs a graph that represents the relationships among the members of the data set, organizing the members of the data set into groups. The groups are analyzed to determine their relative strengths. Unbalanced groups can be balanced by splitting off heavy sub-trees that include too large a percentage of the nodes in the group. The machine can then use the graph to answer queries about members of the data set. | 08-23-2012 |
20120215770 | STRUCTURED RELEVANCE - A MECHANISM TO REVEAL WHY DATA IS RELATED - A machine receives a group of members of a data set. The machine identifies key symbols from the members of the group or the data set. The machine then calculates, for each key symbol, a weighted magnitude for the key symbol in the group. The machine can then sort the key symbols according to their weighted magnitudes, and filter out common key symbols. The uncommon key symbols, as sorted according to their weighted magnitudes, can form a name for the group. | 08-23-2012 |
20120221520 | SOCIAL NETWORKING CONTENT MANAGEMENT - A machine-controlled method may include receiving a file in a designated shared folder on a local device, automatically providing access to the file to a social networking website or service, and directing the social networking website or service to make the file available to users that are allowed to access a particular user account at the social networking website. | 08-30-2012 |
20120290574 | FINDING OPTIMIZED RELEVANCY GROUP KEY - Methods and apparatus filter out unused information in irrelevant patterns to find an optimized relevancy group key. Such an optimized key occupies a smaller mapping space and functions to identify relevancy groups while requiring fewer computations to perform thereby improving the overall speed and performance of the processing device. | 11-15-2012 |
20120304046 | INTEGRATED MEDIA BROWSE AND INSERTION - An application can receive a request to insert one file into another file opened for editing within the application. The request can be either in-line or through a menu system of the application. The request can include a context for the file to be inserted. The system can then identify a set of files that can satisfy the context, from which a user can choose what file is to be inserted. The chosen file can then be inserted into the file opened for editing, all without leaving the application. | 11-29-2012 |
20130024419 | COLLABORATION SWARMING - A swarm can develop around a piece of content. The swarm can include the original content, changes to the original content, the persons contributing the changes, and metadata, such as comments contributed by members of the swarm. A swarm can also include statistics generated about the content, such as the size of the swarm, the growth and/or death rates of the swarm, the longevity of the swarm, the intensity of the swarm, the persistence of the swarm, and the direction of the swarm. Swarms and their behaviors can be used to validate or invalidate content. | 01-24-2013 |
20150058304 | STOPPING FUNCTIONS FOR GROUPING AND DIFFERENTIATING FILES BASED ON CONTENT - Methods and apparatus teach a digital spectrum of a data file. The digital spectrum is used to map a file's position in multi-dimensional space. This position relative to another file's position reveals closest neighbors. Certain of the closest neighbors are grouped together, while others are differentiated. Grouping ceases upon application of a stopping function so that rightly sized, optimum numbers of file groups are obtained. Embodiments of stopping functions relate to curve types in a mapping of numbers of groups per sequential rounds of grouping, recognizing whether groups have overlapping file members or not, and/or determining whether groups meet predetermined numbers of members, to name a few. Properly grouped files can then be further acted upon. | 02-26-2015 |