Abstract: Embodiments of the present systems and methods may provide techniques by which bots may be analyzed using improved representations of bot structure and a means of assessing conversational quality that may provide improved efficiency. For example a method may comprise training, at a computer system comprising a processor, memory accessible by the processor, and computer program instructions stored in the memory and executable by the processor, a neural network model to learn representations that capture characteristics of the graphs of chatbots, wherein the captured characteristics include at least a content-based representation based on user utterances that are relevant to the nodes and based on the chatbot response for the nodes.

Abstract: Embodiments of the present systems and methods may provide techniques by which bots may be analyzed using improved representations of bot structure and a means of assessing conversational quality that may provide improved efficiency. For example a method may comprise training, at a computer system comprising a processor, memory accessible by the processor, and computer program instructions stored in the memory and executable by the processor, a neural network model to learn representations that capture characteristics of the graphs of chatbots, wherein the captured characteristics include at least a content-based representation based on user utterances that are relevant to the nodes and based on the chatbot response for the nodes.

Abstract: For each intent associated with a feature in a discordant conversation, one or more preceding discordant user utterances and one or more following discordant user utterances are collected. A discordant distribution over terms of the one or more preceding discordant user utterances and the one or more following discordant user utterances is created. For each intent associated with a feature in a non-discordant conversation, one or more preceding non-discordant user utterances and one or more following non-discordant user utterances are collected. A non-discordant distribution over terms of the one or more preceding non-discordant user utterances and the one or more following non-discordant user utterances is created. The discordant and non-discordant distributions are compared and the top-k terms that are most specific to user utterances associated with the corresponding discordance feature using Kullback-Leibler divergence are determined.

Abstract: Utilizing a computing device to detect and respond to emotion in dialog systems. The computing device receives a dialog structure comprising a plurality of dialog nodes. The computing device determines a node emotion level for each of the dialog nodes in the dialog structure based on analysis of one or more intents of each of the dialog nodes in the dialog structure. The computing device determines emotional hotspot nodes in the dialog structure, the node emotion level for each of the emotional hotspot nodes exceeding an emotional threshold. The computing device generates one or more responses modifying the node emotion level of each of the emotional hotspot nodes.

Abstract: For each intent associated with a feature in a discordant conversation, one or more preceding discordant user utterances and one or more following discordant user utterances are collected. A discordant distribution over terms of the one or more preceding discordant user utterances and the one or more following discordant user utterances is created. For each intent associated with a feature in a non-discordant conversation, one or more preceding non-discordant user utterances and one or more following non-discordant user utterances are collected. A non-discordant distribution over terms of the one or more preceding non-discordant user utterances and the one or more following non-discordant user utterances is created. The discordant and non-discordant distributions are compared and the top-k terms that are most specific to user utterances associated with the corresponding discordance feature using Kullback-Leibler divergence are determined.

Abstract: Utilizing a computing device to detect and respond to emotion in dialog systems. The computing device receives a dialog structure comprising a plurality of dialog nodes. The computing device determines a node emotion level for each of the dialog nodes in the dialog structure based on analysis of one or more intents of each of the dialog nodes in the dialog structure. The computing device determines emotional hotspot nodes in the dialog structure, the node emotion level for each of the emotional hotspot nodes exceeding an emotional threshold. The computing device generates one or more responses modifying the node emotion level of each of the emotional hotspot nodes.

Abstract: Utilizing a computing device to detect and respond to emotion in dialog systems. The computing device receives a dialog structure comprising a plurality of dialog nodes. The computing device determines a node emotion level for each of the dialog nodes in the dialog structure based on analysis of one or more intents of each of the dialog nodes in the dialog structure. The computing device determines emotional hotspot nodes in the dialog structure, the node emotion level for each of the emotional hotspot nodes exceeding an emotional threshold. The computing device generates one or more responses modifying the node emotion level of each of the emotional hotspot nodes.

Abstract: Utilizing a computing device to detect and respond to emotion in dialog systems. The computing device receives a dialog structure comprising a plurality of dialog nodes. The computing device determines a node emotion level for each of the dialog nodes in the dialog structure based on analysis of one or more intents of each of the dialog nodes in the dialog structure. The computing device determines emotional hotspot nodes in the dialog structure, the node emotion level for each of the emotional hotspot nodes exceeding an emotional threshold. The computing device generates one or more responses modifying the node emotion level of each of the emotional hotspot nodes.

Abstract: Machines, systems and methods for identification of a consumer are provided. The method comprises capturing a unique identifier (ID) associated with a computing device, wherein the computing device is configured to access content stored on one or more content servers; and associating the unique ID with tracking data associated with the computing device, wherein when the computing device submits a request to a content server to access content, wherein in response to retrieving at least one of the unique ID or the tracking data of the computing device, the computing device is identified and content pages accessed by the computing device are tracked by a machine that is aware of the association between the unique ID and the tracking data for the computing device.

Abstract: Machines, systems and methods for identification of a consumer are provided. The method comprises capturing a unique identifier (ID) associated with a computing device, wherein the computing device is configured to access content stored on one or more content servers; and associating the unique ID with tracking data associated with the computing device, wherein when the computing device submits a request to a content server to access content, wherein in response to retrieving at least one of the unique ID or the tracking data of the computing device, the computing device is identified and content pages accessed by the computing device are tracked by a machine that is aware of the association between the unique ID and the tracking data for the computing device.

Abstract: Machines, systems and methods for determining position of a target device in a target area are provided. The method comprises positioning sensors 1 through N in a target area, wherein a sensor counts the number of data frames transmitted by a target device and captured at the sensor during a time period; calculating relative captured frame count (RCFC) values for sensors 1 through N for the target device; and comparing the calculated RCFC values for the target device with pre-existing RCFC values calculated for a plurality of sample points in the target area to find at least X points from among the plurality of sample points that are most similar to the calculated RCFC values for the target device.