Abstract: A system and method are presented for predictive live interaction offering and hosting. A mechanism may be provided for controlling outstanding offerings, or invitations, of communications with users through points of service. Offerings may include a live chat or a telephone callback request. In one embodiment, offerings may be optimized by analyzing factors such as agent history to predict when and how many agents will be available. Calculations may be continuously performed as invitations are accepted, declined, timed out, etc., to make certain more offers than can be handled are not extended.

Abstract: A system and method are presented for body language analysis of a video interaction. In a contact center system, the video interaction between an agent and a customer may be monitored and used to determine automatic actions when threshold are met and/or matches are made. Training videos comprising determined metrics may be used for comparison to real-time interactions. Scoring and/or matches may be utilized to determine a threshold to trigger pre-determined actions based on comparison to the training videos.

Abstract: A system and method are presented for determining window geometry. In an embodiment, a party to an interaction using a streaming video connection may want to share content on their display with another party to the interaction. A party may wish to annotate an on-screen target for the other party. In an embodiment, markers or magic bars may also be used to map the screens to each other in addition to coordinates. When one party annotates an on-screen target on their display, the coordinates on the party's display are translated for the other party and the annotation is placed in the intended location on that party's screen. Masking of content may also be performed such that contact on one user's screen may not be visible to the other party during the screen share.

Abstract: A system and method are presented for body language analysis of a video interaction. In a contact center system, the video interaction between an agent and a customer may be monitored and used to determine automatic actions when threshold are met and/or matches are made. Training videos comprising determined metrics may be used for comparison to real-time interactions. Scoring and/or matches may be utilized to determine a threshold to trigger pre-determined actions based on comparison to the training videos.

Abstract: A system and method are presented for predictive live interaction offering and hosting. A mechanism may be provided for controlling outstanding offerings, or invitations, of communications with users through points of service. Offerings may include a live chat or a telephone callback request. In one embodiment, offerings may be optimized by analyzing factors such as agent history to predict when and how many agents will be available. Calculations may be continuously performed as invitations are accepted, declined, timed out, etc., to make certain more offers than can be handled are not extended.

Abstract: A system and method are presented for determining window geometry. In an embodiment, a party to an interaction using a streaming video connection may want to share content on their display with another party to the interaction. A party may wish to annotate an on-screen target for the other party. In an embodiment, markers or magic bars may also be used to map the screens to each other in addition to coordinates. When one party annotates an on-screen target on their display, the coordinates on the party's display are translated for the other party and the annotation is placed in the intended location on that party's screen. Masking of content may also be performed such that contact on one user's screen may not be visible to the other party during the screen share.

Abstract: A system and method are presented for predictive live interaction offering and hosting. A mechanism may be provided for controlling outstanding offerings, or invitations, of communications with users through points of service. Offerings may include a live chat or a telephone callback request. In one embodiment, offerings may be optimized by analyzing factors such as agent history to predict when and how many agents will be available. Calculations may be continuously performed as invitations are accepted, declined, timed out, etc., to make certain more offers than can be handled are not extended.

Abstract: Techniques are described herein that are capable of selectively scanning objects for infection by malware (i.e., to determine whether one or more of the objects are infected by malware). For instance, metadata that is associated with the objects may be reviewed to determine whether update(s) have been made with regard to the objects since a determination was made that the objects were not infected by malware. An update may involve increasing a number of the objects, modifying one of the objects, etc. Objects that have been updated (e.g., added and/or modified) since the determination may be scanned. Objects that have not been updated since the determination need not necessarily be scanned. For instance, an allowance may be made to perform operations with respect to the objects that have not been updated since the determination without first scanning the objects for infection by malware.

Abstract: A system and method are presented for predictive live interaction offering and hosting. A mechanism may be provided for controlling outstanding offerings, or invitations, of communications with users through points of service. Offerings may include a live chat or a telephone callback request. In one embodiment, offerings may be optimized by analyzing factors such as agent history to predict when and how many agents will be available. Calculations may be continuously performed as invitations are accepted, declined, timed out, etc., to make certain more offers than can be handled are not extended.

Abstract: Techniques are described herein that are capable of selectively scanning objects for infection by malware (i.e., to determine whether one or more of the objects are infected by malware). For instance, metadata that is associated with the objects may be reviewed to determine whether update(s) have been made with regard to the objects since a determination was made that the objects were not infected by malware. An update may involve increasing a number of the objects, modifying one of the objects, etc. Objects that have been updated (e.g., added and/or modified) since the determination may be scanned. Objects that have not been updated since the determination need not necessarily be scanned. For instance, an allowance may be made to perform operations with respect to the objects that have not been updated since the determination without first scanning the objects for infection by malware.

Abstract: A method of identifying a malware file using multiple classifiers is disclosed. The method includes receiving a file at a client computer. The file includes static metadata. A set of metadata classifier weights are applied to the static metadata to generate a first classifier output. A dynamic classifier is initiated to evaluate the file and to generate a second classifier output. The method includes automatically identifying the file as potential malware based on at least the first classifier output and the second classifier output.

Abstract: A system for identifying specific information regarding the hardware configuration of a computer system improves resolution of problems via online crash and is usable for other purposes as well. Embodiments of the employ applications directed by remote computers functioning as servers, which use the specific computer system hardware-configuration information provided by the identifying system.

Abstract: A system for identifying specific information regarding the hardware configuration of a computer system improves resolution of problems via online crash and is usable for other purposes as well. Embodiments of the employ applications directed by remote computers functioning as servers, which use the specific computer system hardware-configuration information provided by the identifying system.

Abstract: A system for identifying specific information regarding the hardware configuration of a computer system improves resolution of problems via online crash and is usable for other purposes as well. Embodiments of the employ applications directed by remote computers functioning as servers, which use the specific computer system hardware-configuration information provided by the identifying system.