1611 results about "Match algorithm" patented technology

A system and method is described for instantly connecting and matching people and business entities with reciprocal interests in the location of their presence in real-time. Portable communication devices using wireless communication are used for transmitting data between users with reciprocal interests connected through a peer-to-peer network or in a client-server environment. Telephone users utilize an Interactive Voice Response system to communicate with other users of reciprocal interest. A matching algorithm running in a remote computer connected to the devices through network makes an initial assessment about the likelihood of a match, and then with the permission of the requester and the respondent, sets up communication sessions.

Methods and systems are provided for routing callers to agents in a call-center routing environment. An exemplary method includes pooling incoming callers, and causing a caller from the pool of callers to be routed. The caller may be routed from the pool of callers to an agent, placed in another pool of callers, or placed in a queue of callers. The caller data may include demographic or psychographic data. The caller may be routed from the pool of callers based on comparing the caller data with agent data associated with an agent via a pattern matching algorithm and/or computer model for predicting a caller-agent pair outcome. Additionally, if a caller is held beyond a hold threshold (e.g., a time, “cost” function, or the like) the caller may be routed to the next available agent.

Methods and systems are provided for routing callers to agents in a call-center routing environment. An exemplary method includes identifying caller data for a caller in a queue of callers, and jumping or moving the caller to a different position within the queue based on the caller data. The caller data may include one or both of demographic data and psychographic data. The caller can be jumped forward or backward in the queue relative to at least one other caller. Jumping the caller may further be based on comparing the caller data with agent data via a pattern matching algorithm and/or computer model for predicting a caller-agent pair outcome. Additionally, if a caller is held beyond a hold threshold (e.g., a time, “cost” function, or the like) the caller may be routed to the next available agent.

Methods and systems are provided for routing callers to agents in a call-center routing environment. An exemplary method includes routing a caller from a pool of callers based on at least one caller data associated with the caller, where a pool of callers includes, e.g., a set of callers that are not chronologically ordered and routed based on a chronological order or hold time of the callers. The caller may be routed from the pool of callers to an agent, placed in another pool of callers, or placed in a queue of callers. The caller data may include demographic or psychographic data. The caller may be routed from the pool of callers based on comparing the caller data with agent data associated with an agent via a pattern matching algorithm and/or computer model for predicting a caller-agent pair outcome. Additionally, if a caller is held beyond a hold threshold (e.g., a time, “cost” function, or the like) the caller may be routed to the next available agent.

Systems and methods are disclosed for routing callers to agents in a contact center, along with an intelligent routing system. Exemplary methods include routing a caller from a set of callers to an agent from a set of agents based on a performance based routing and/or pattern matching algorithm(s) utilizing caller data associated with the caller and the agent data associated with the agent. For performance based routing, the performance or grading of agents may be associated with time data, e.g., a grading or ranking of agents based on time. Further, for pattern matching algorithms, one or both of the caller data and agent data may include or be associated with time effect data. Examples of time effect data include probable performance or output variables as a function of time of day, day of week, time of month, or time of year. Time effect data may also include the duration of the agent's employment.

Systems and methods are disclosed for routing callers to agents in a contact center, along with an intelligent routing system. An exemplary method includes combining multiple output variables of a pattern matching algorithm (for matching callers and agents) into a single metric for use in the routing system. The pattern matching algorithm may include a neural network architecture, where the exemplary method combines output variables from multiple neural networks. The method may include determining a Z-score of the variable outputs and determining a linear combination of the determined Z-scores for a desired output. Callers may be routed to agents via the pattern matching algorithm to maximize the output value or score of the linear combination. The output variables may include revenue generation, cost, customer satisfaction performance, first call resolution, cancellation, or other variable outputs from the pattern matching algorithm of the system.

Methods and systems are provided for routing callers to agents in a call-center routing environment. An exemplary method includes identifying caller data for at least one of a set of callers on hold and causing a caller of the set of callers to be routed to an agent based on a comparison of the caller data and the agent data. The caller data and agent data may be compared via a pattern matching algorithm and/or computer model for predicting a caller-agent pair having the highest probability of a desired outcome. As such, callers may be pooled and routed to agents based on comparisons of available caller and agent data, rather than a conventional queue order fashion. If a caller is held beyond a hold threshold the caller may be routed to the next available agent. The hold threshold may include a predetermined time, “cost” function, number of times the caller may be skipped by other callers, and so on.

Methods and systems are provided for routing callers to agents in a call-center routing environment. An exemplary method includes identifying caller data for at least one caller in a queue of callers, and skipping a caller at the front of the queue of callers for another caller based on the identified caller data. The caller data may include one or both of demographic data and psychographic data. Skipping the caller may be further based on comparing caller data with agent data associated with an agent via a pattern matching algorithm such as a correlation algorithm. In one example, if the caller at the front of the queue has been skipped a predetermined number of times the caller at the front is the next routed (and cannot be skipped again).

The invention relates to a method and an apparatus for recognising speech, more particularly to a speech recognition system and method utilising a speech recognition dictionary supplemented by a lexicon containing frequently occurring word sequences (orthographies). In typical speech recognition systems, the process of speech recognition consists of scanning the vocabulary database or dictionary by using a fast match algorithm to find the top N candidates that potentially match the input speech. In a second pass the N candidates are re-scored using more precise likelihood computations. The novel method comprises the introduction of a step in the search stage that consists of forcing the insertion in the list of N candidates entries selected from a lexicon containing frequently used orthographies to increase the probability of occurrence of certain text combinations.

Systems and methods are disclosed for routing callers to agents in a contact center, along with an intelligent routing system. An exemplary interface for use with a contact center includes a graphical user element for adjusting a time limit for a caller to be held before routed to an agent according to a pattern matching algorithm where the caller is compared to a set of agents via the pattern matching algorithm and routed to the best matching agent of the set of agents if available prior to the time limit and routed to the best matching agent of available agents if the time limit is exceeded. The interface may further display an estimated effect of the time limit on at least one outcome variable, which may include revenue generation, cost, customer satisfaction, first call resolution, cancellation, or other variable outputs from the pattern matching algorithm(s) of the system.

An automated system for matching orders to buy and sell securities at the midpoint of a best bid and offer (“BBO”), wherein the automated system is adapted to (i) operate on a fully-anonymous and continuous basis, (ii) process multiple, computer-generated orders, of any size, transmitted by algorithmic, program, and other automated trading systems, (iii) automatically match multiple, executable orders according to a priority scheme, and (iv) execute a matching algorithm that maximizes tradable volume when an executable order can not be matched according to the priority scheme. The system may be executed at a server or participant system, or combination of the two.

Systems and methods are disclosed for preprocessing caller-agent pairs in a contact routing center, along with an intelligent routing system. A method includes determining at least one agent data for each agent of a set of agents, determining at least one caller data for each caller of a set of callers, and determining a score for each possible caller-agent pair of the set of agents and the set of callers using the agent data and the caller data in a pattern matching algorithm. The scores for each possible caller-agent pair may then be stored, e.g., in a look-up table, for retrieval, where matching a caller of the set of callers to an agent of the set of agents is based on the previously determined or calculated scores. For example, the caller-agent pair having the best score of all potential caller-agent pairs may be connected.

A method, system, and computer program product for utilizing a mapping of activity occurring at and between devices on a computer network to detect and prevent network intrusions. An enhanced graph matching intrusion detection system (eGMIDS) is provided that provides data collection functions, data fusion techniques, graph matching algorithms, and secondary and other search mechanisms. Threats are modeled as a set of entities and interrelations between the entities and sample threat patterns are stored within a database. The eGMIDS utility initiates a graph matching algorithm by which the threat patterns are compared within the generated activity graph via subgraph isomorphism. A multi-layered approach including a targeted secondary layer search following a match during a primary layer search is provided. Searches are tempered by attributes and constraints and the eGMIDS reduces the number of threat patterns searched by utilizing ontological generalization.

An intrusion detection system (IDS) is enhanced to operate in a cluster of such systems, and IDSs organized into a cluster cooperate to exchange IP reputation influencing events information between or among the cooperating systems in real-time to enhance overall system response time and to prevent otherwise hidden attacks from damaging network resources. An IDS includes an IP reputation analytics engine to analyze new and existing events, correlate information, and to raise potential alerts. The IP reputation analytics engines may implement an algorithm, such as a pattern matching algorithm, a continuous data mining algorithm, or the like, to facilitate this operation. Clustering IDS endpoints to share IP reputation influencing events, using the cluster-wide view to determine IP reputation, and feeding the cluster-wide view back to the IDS endpoints, provides for enhanced and early detection of threats that is much more reliable and scalable as compared to prior art techniques.

A method is described for augmenting sponsored search results in a search engine, which includes extracting attribute data from a plurality of natural searches for a search term linked to a plurality of uniform resource locators (URLs), analyzing the attribute data of one or more attributes for clickers and non-clickers to determine at least one greatest distinguishing factor between the clickers and non-clickers, and integrating the at least one greatest distinguishing factor into a matching algorithm used by the search engine to rank order and display a plurality of the most relevant ads corresponding to the plurality of URLs in response to a search for the term. The method may also integrate the at least one greatest distinguishing factor into a marketer algorithm to enable a marketer of a URL to strategically choose a search term, along with the at least one greatest distinguishing factor, on which to bid.