INSIDESALES.COM, INC. Patent applications |
Patent application number | Title | Published |
20160105562 | Call Center Input/Output Agent Utilization Arbitration Method and System - Disclosed herein are systems and methods that provide for maintenance of the status of availability of call-center agents through the use of local arbitration between processes and applications that may interact with more than one resource of telephone contacts between differing activities or work for these call-center agents. Detailed information on various example embodiments of the inventions are provided in the Detailed Description below, and the inventions are defined by the appended claims. | 04-14-2016 |
20140372344 | Adaptive User Interfaces - According to various embodiments, user performance and/or motivation for a computing system may be maximized by optimizing one or more target components of a user interface of the computing system. The target components may be aspects of the user interface that is perceived by the user. One or more input features and one or more output features may be identified, and data regarding these input and output features may be gathered. This data may be compared with the results generated by a set of candidate artificial intelligence algorithms to determine which of them provides the best fit with the data collected. Then, the selected artificial intelligence algorithm may be applied to the user interface to iteratively change the target components over time until the optimal settings for each user are discovered. | 12-18-2014 |
20140279739 | RESOLVING AND MERGING DUPLICATE RECORDS USING MACHINE LEARNING - According to various embodiments of the present invention, an automated technique is implemented for resolving and merging fields accurately and reliably, given a set of duplicated records that represents a same entity. In at least one embodiment, a system is implemented that uses a machine learning (ML) method, to train a model from training data, and to learn from users how to efficiently resolve and merge fields. In at least one embodiment, the method of the present invention builds feature vectors as input for its ML method. In at least one embodiment, the system and method of the present invention apply Hierarchical Based Sequencing (HBS) and/or Multiple Output Relaxation (MOR) models in resolving and merging fields. Training data for the ML method can come from any suitable source or combination of sources. | 09-18-2014 |
20140180978 | INSTANCE WEIGHTED LEARNING MACHINE LEARNING MODEL - An instance weighted learning (IWL) machine learning model. In one example embodiment, a method of employing an IWL machine learning model may include identifying a temporal sequence of reinforcement learning machine learning training instances with each of the training instances including a state-action pair, determining a first quality value for a first training instance in the temporal sequence of reinforcement learning machine learning training instances determining a second quality value for a second training instance in the temporal sequence of reinforcement learning machine learning training instances, associating the first quality value with the first training instance, and associating the second quality value with the second training instance. In this example embodiment, the first quality value is higher than the second quality value. | 06-26-2014 |
20140180975 | INSTANCE WEIGHTED LEARNING MACHINE LEARNING MODEL - An instance weighted learning (IWL) machine learning model. In one example embodiment, a method of employing an IWL machine learning model to train a classifier may include determining a quality value that should be associated with each machine learning training instance in a temporal sequence of reinforcement learning machine learning training instances, associating the corresponding determined quality value with each of the machine learning training instances, and training a classifier using each of the machine learning training instances. Each of the machine learning training instances includes a state-action pair and is weighted during the training based on its associated quality value using a weighting factor that weights different quality values differently such that the classifier learns more from a machine learning training instance with a higher quality value than from a machine learning training instance with a lower quality value. | 06-26-2014 |
20140143344 | Systems and Methods for Transferring Personal Session Information for Telephonic Use - Disclosed herein are systems and associated methods for operating web interactive services in conjunction with communication services, linking the communication with the interaction by means of a session-specific identifier such as a telephone number. During the course of a web session, user interaction information may be collected, that information potentially indicating subjects of interest to a user associated with the session-specific identifier. In the event the user uses the identifier make a contact regarding the information, the identifier can be used to associate the interaction information and subjects of interest, such that the contact may have that information and those subjects available to assist a user making contact. Interaction and subject information may also be used to customize the interaction with a contacting user with regard to the routing of a telephone call, a greeting, a product or service offering, or other communication. | 05-22-2014 |
20140114997 | System and Method for Responding to Web Form Inquiries - An apparatus, system, and method are disclosed for generating contact plans and responding to web form inquires using the contact plans. | 04-24-2014 |
20140052678 | HIERARCHICAL BASED SEQUENCING MACHINE LEARNING MODEL - A hierarchical based sequencing (HBS) machine learning model. In one example embodiment, a method of employing an HBS machine learning model to predict multiple interdependent output components of an MOD output decision may include determining an order for multiple interdependent output components of an MOD output decision. The method may also include sequentially training a classifier for each component in the selected order to predict the component based on an input and based on any previous predicted component(s). | 02-20-2014 |