Abstract: Automatic analyses of customer-agent interactions provide valuable, actionable feedback for managers, agents, and customers. To provide such analyses, methods for recording and analysis of customer-agent interactions using a customer relationship management (CRM) system are disclosed. A recorder application records the customer-agent interaction, and sensitive information may be identified. Sensitive portions of the recording may then be redacted and removed from the recording. The redacted recording is then analyzed to generate useful summary and analytics information.

Abstract: A semantic similarity based configurable system for automatic scenario detection in customer-agent conversations is disclosed. The system understands intent from the vector space semantic similarity between speaker sentences, which is agnostic to the use of synonyms and tolerates a large amount of paraphrasing. This approach scales easily to a large number of customers and can be fed more data to increase accuracy and precision. Furthermore, the system is configurable in real-time so that the client is able to control which intents are detected and how. In some embodiments, the semantic similarity based configurable system comprises a scenario detection system, a conversation tag system, a bi-encoder, and a cross-encoder, where the scenario detection system receives inputs of sample phrases and customer-agent utterances and generates results. The sample phrases may be phrases and keywords that describe a scenario expressing the behavior of a customer or call agent.

Abstract: Automatic analyses of customer-agent interactions provide valuable, actionable feedback for managers, agents, and customers. To provide such analyses, methods for recording and analysis of customer-agent interactions using a customer relationship management (CRM) system are disclosed. A recorder application records the customer-agent interaction, and sensitive information may be identified. Sensitive portions of the recording may then be redacted and removed from the recording. The redacted recording is then analyzed to generate useful summary and analytics information.

Abstract: Automatic analyses of customer-agent interactions provide valuable, actionable feedback for managers, agents, and customers. To provide such analyses, methods for recording and analysis of customer-agent interactions using a customer relationship management (CRM) system are disclosed. A recorder application records the customer-agent interaction, and sensitive information may be identified. Sensitive portions of the recording may then be redacted and removed from the recording. The redacted recording is then analyzed to generate useful summary and analytics information.

Abstract: A semantic similarity based configurable system for automatic scenario detection in customer-agent conversations is disclosed. The system understands intent from the vector space semantic similarity between speaker sentences, which is agnostic to the use of synonyms and tolerates a large amount of paraphrasing. This approach scales easily to a large number of customers and can be fed more data to increase accuracy and precision. Furthermore, the system is configurable in real-time so that the client is able to control which intents are detected and how. In some embodiments, the semantic similarity based configurable system comprises a scenario detection system, a conversation tag system, a bi-encoder, and a cross-encoder, where the scenario detection system receives inputs of sample phrases and customer-agent utterances and generates results. The sample phrases may be phrases and keywords that describe a scenario expressing the behavior of a customer or call agent.

Abstract: A semantic similarity based configurable system for automatic scenario detection in customer-agent conversations is disclosed. The system understands intent from the vector space semantic similarity between speaker sentences, which is agnostic to the use of synonyms and tolerates a large amount of paraphrasing. This approach scales easily to a large number of customers and can be fed more data to increase accuracy and precision. Furthermore, the system is configurable in real-time so that the client is able to control which intents are detected and how. In some embodiments, the semantic similarity based configurable system comprises a scenario detection system, a conversation tag system, a bi-encoder, and a cross-encoder, where the scenario detection system receives inputs of sample phrases and customer-agent utterances and generates results. The sample phrases may be phrases and keywords that describe a scenario expressing the behavior of a customer or call agent.

Abstract: A cloud learning system for smart windows is provided. The system includes at least one server configured to couple via a network to a plurality of window systems, each of the plurality of window systems having at least one control system and a plurality of windows with electrochromic windows and sensors, wherein the at least one server includes at least one physical server or at least one virtual server implemented using physical computing resources. The at least one server is configured to gather first information from the plurality of window systems, and configured to gather second information from sources on the network and external to the plurality of window systems. The at least one server is configured to form at least one rule or control algorithm usable by a window system, based on the first information and the second information, and configured to download the at least one rule or control algorithm to at least one of the plurality of window systems.

Abstract: A cloud learning system for smart windows is provided. The system includes at least one server configured to couple via a network to a plurality of window systems, each of the plurality of window systems having at least one control system and a plurality of windows with electrochromic windows and sensors, wherein the at least one server includes at least one physical server or at least one virtual server implemented using physical computing resources. The at least one server is configured to gather first information from the plurality of window systems, and configured to gather second information from sources on the network and external to the plurality of window systems. The at least one server is configured to form at least one rule or control algorithm usable by a window system, based on the first information and the second information, and configured to download the at least one rule or control algorithm to at least one of the plurality of window systems.

Abstract: A cloud learning system for smart windows is provided. The system includes at least one server configured to couple via a network to a plurality of window systems, each of the plurality of window systems having at least one control system and a plurality of windows with electrochromic windows and sensors, wherein the at least one server includes at least one physical server or at least one virtual server implemented using physical computing resources. The at least one server is configured to gather first information from the plurality of window systems, and configured to gather second information from sources on the network and external to the plurality of window systems. The at least one server is configured to form at least one rule or control algorithm usable by a window system, based on the first information and the second information, and configured to download the at least one rule or control algorithm to at least one of the plurality of window systems.