Abstract: The disclosed embodiments illustrate a method and a system for predicting future activities of a user on a social media platform. The method includes extracting a first time series of one or more historical activities performed by the user from a social media platform server. The method further includes receiving a second time series of one or more future events from a requestor-computing device. The method further includes determining a first set of forecast values and a second set of forecast values based on the first time series and/or the second time series, wherein the first set of forecast values is determined using an ARIMA technique, and the second set of forecast values is determined using a regression modelling technique. The method further includes predicting the future activities of the user based on the first set of forecast values and the second set of forecast values.

Abstract: The disclosed embodiments illustrate methods data processing for real-time text analysis. The method includes receiving text content from a plurality of user-computing devices, wherein the text content comprises at least a current text segment and a previous text segment. The method further includes extracting one or more first features from the current text segment, wherein at least a first feature of the one or more first features corresponds to a difference between timestamps associated with each of the current text segment and the previous text segment. The method further includes categorizing the current text segment into a predetermined category of one or more predetermined categories, based on at least the one or more first features, automatically by utilizing a classifier. Further, the method includes predicting a likelihood of evolution of an attribute in the text content, based on the predetermined category associated with the current text segment.

Abstract: The disclosed embodiments illustrate methods and systems for summary generation of a real-time conversation. The method includes receiving a real-time conversation from a plurality of computing devices over a communication network. The method further includes determining one or more first features of the real-time conversation between at least a first user and a second user. The method further includes extracting one or more second features from the one or more first features, based on one or more pre-defined criteria. The method further includes generating a summary content of the real-time conversation, based on at least the extracted one or more second features and one or more annotations associated with the determined one or more first features by use of one or more trained classifier. Further, the method includes rendering the generated summary content on a user interface displayed on at least one of the plurality of computing devices.

Abstract: The disclosed embodiments illustrate methods and systems for summary generation of a real-time conversation. The method includes receiving a real-time conversation from a plurality of computing devices over a communication network. The method further includes determining one or more first features of the real-time conversation between at least a first user and a second user. The method further includes extracting one or more second features from the one or more first features, based on one or more pre-defined criteria. The method further includes generating a summary content of the real-time conversation, based on at least the extracted one or more second features and one or more annotations associated with the determined one or more first features by use of one or more trained classifier. Further, the method includes rendering the generated summary content on a user interface displayed on at least one of the plurality of computing devices.

Abstract: The disclosed embodiments illustrate methods data processing for real-time text analysis. The method includes receiving text content from a plurality of user-computing devices, wherein the text content comprises at least a current text segment and a previous text segment. The method further includes extracting one or more first features from the current text segment, wherein at least a first feature of the one or more first features corresponds to a difference between timestamps associated with each of the current text segment and the previous text segment. The method further includes categorizing the current text segment into a predetermined category of one or more predetermined categories, based on at least the one or more first features, automatically by utilizing a classifier. Further, the method includes predicting a likelihood of evolution of an attribute in the text content, based on the predetermined category associated with the current text segment.

Abstract: The disclosed embodiments illustrate a method and a system for predicting future activities of a user on a social media platform. The method includes extracting a first time series of one or more historical activities performed by the user from a social media platform server. The method further includes receiving a second time series of one or more future events from a requestor-computing device. The method further includes determining a first set of forecast values and a second set of forecast values based on the first time series and/or the second time series, wherein the first set of forecast values is determined using an ARIMA technique, and the second set of forecast values is determined using a regression modelling technique. The method further includes predicting the future activities of the user based on the first set of forecast values and the second set of forecast values.

Abstract: The disclosed embodiments illustrate methods and systems for managing a conversation between a first user and a customer care agent. The method includes extracting one or more features from a first conversation between said first user and said customer care agent. The first conversation corresponds to an ongoing conversation over an electronic communication medium. The method includes determining a score for a feature. The score indicates a deviation of a value of said feature from an average of values of said feature determined from historical conversations involving said first user. The method includes aggregating said score for each of said one or more features of said first conversation. Thereafter, the method includes redirecting said first conversation of said first user to a third user during said first conversation based on said aggregation. The third user manages said first conversation between said first user and said customer care agent.

Abstract: An embodiment of the disclosure can receive a composite resource document containing at least one resource. An updated manifest resource can be obtained. The updated manifest resource can list all resources in the composite resource document. A set of zero or more (0 . . . N) resources can be indicated. Each indicated resource is one that is to be subtracted from the list of resources in the updated manifest resource in order to create a generated signature reference list of identified resources to be signed. A hash token can be generated using the resources identified in the generated signature reference list to form a signature hash token. The signature hash token can be encrypted with a secret key.

Abstract: An embodiment of the disclosure can receive a composite resource document containing at least one resource. An updated manifest resource can be obtained. The updated manifest resource can list all resources in the composite resource document. A set of zero or more (0 . . . N) resources can be indicated. Each indicated resource is one that is to be subtracted from the list of resources in the updated manifest resource in order to create a generated signature reference list of identified resources to be signed. A hash token can be generated using the resources identified in the generated signature reference list to form a signature hash token. The signature hash token can be encrypted with a secret key.