2516 results about "Pattern matching" patented technology

A method for determining the presence of a face from image data includes a face detection algorithm having two separate algorithmic steps: a first step of prescreening image data with a first component of the algorithm to find one or more face candidate regions of the image based on a comparison between facial shape models and facial probabilities assigned to image pixels within the region; and a second step of operating on the face candidate regions with a second component of the algorithm using a pattern matching technique to examine each face candidate region of the image and thereby confirm a facial presence in the region, whereby the combination of these components provides higher performance in terms of detection levels than either component individually. In a camera implementation, a digital camera includes an algorithm memory for storing an algorithm comprised of the aforementioned first and second components and an electronic processing section for processing the image data together with the algorithm for determining the presence of one or more faces in the scene. Facial data indicating the presence of faces may be used to control, e.g., exposure parameters of the capture of an image, or to produce processed image data that relates, e.g., color balance, to the presence of faces in the image, or the facial data may be stored together with the image data on a storage medium.

A fact extraction tool set (“FEX”) finds and extracts targeted pieces of information from text using linguistic and pattern matching technologies, and in particular, text annotation and fact extraction. Text annotation tools break a text, such as a document, into its base tokens and annotate those tokens or patterns of tokens with orthographic, syntactic, semantic, pragmatic and other attributes. A user-defined “Annotation Configuration” controls which annotation tools are used in a given application. XML is used as the basis for representing the annotated text. A tag uncrossing tool resolves conflicting (crossed) annotation boundaries in an annotated text to produce well-formed XML from the results of the individual annotators. The fact extraction tool is a pattern matching language which is used to write scripts that find and match patterns of attributes that correspond to targeted pieces of information in the text, and extract that information.

A prosthetic heart valve is provided with a cuff (85, 285, 400) having features which promote sealing with the native tissues even where the native tissues are irregular. The cuff may include a portion (90) adapted to bear native aortic valve. The valve may include elements (210, 211, 230, 252, 253) for biasing the cuff outwardly with respect to the stent body when the stent body is in an expanded condition. The cuff may have portions of different thickness (280) distributed around the circumference of the valve in a pattern matching the shape of the opening defined by the native tissue. All or part (402) of the cuff may be movable relative to the stent during implantation.

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.

A system, method and computer program product for conducting questions and answers with deferred type evaluation based on any corpus of data. The method includes processing a query including waiting until a “Type” (i.e. a descriptor) is determined AND a candidate answer is provided; the Type is not required as part of a predetermined ontology but is only a lexical/grammatical item. Then, a search is conducted to look (search) for evidence that the candidate answer has the required LAT (e.g., as determined by a matching function that can leverage a parser, a semantic interpreter and/or a simple pattern matcher). In another embodiment, it may be attempted to match the LAT to a known Ontological Type and then look for a candidate answer up in an appropriate knowledge-base, database, and the like determined by that type. Then, all the evidence from all the different ways to determine that the candidate answer has the expected lexical answer type (LAT) is combined and one or more answers are delivered to a user.

Enabling dynamic, computer-driven, context-based detection of social network patterns within an input graph representing a social network. A Social Network Aware Pattern Detection (SNAP) system and method utilizes a highly-scalable, computationally efficient integration of social network analysis (SNA) and graph pattern matching. Social network interaction data is provided as an input graph having nodes and edges. The graph illustrates the connections and/or interactions between people, objects, events, and activities, and matches the interactions to a context. A sample graph pattern of interest is identified and/or defined by the user of the application. With this sample graph pattern and the input graph, a computational analysis is completed to (1) determine when a match of the sample graph pattern is found, and more importantly, (2) assign a weight (or score) to the particular match, according to a pre-defined criteria or context.

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.

A programmable pattern matching engine efficiently parses the contents of network messages for regular expressions and executes pre-defined actions or treatments on those messages that match the regular expressions. The pattern matching engine is preferably a logic circuit designed to perform its pattern matching and execution functions at high speed, e.g., at multi-gigabit per second rates. It includes, among other things, a message buffer for storing the message being evaluated, a decoder circuit for decoding and executing corresponding actions or treatments, and one or more content-addressable memories (CAMs) that are programmed to store the regular expressions used to search the message. The CAM may be associated with a second memory device, such as a random access memory (RAM), as necessary, that is programmed to contain the respective actions or treatments to be applied to messages matching the corresponding CAM entries. The RAM provides its output to the decoder circuit, which, in response, decodes and executes the specified action or treatment.

Systems and methods for analyzing electronic messages are disclosed. In some embodiments, the method comprises receiving a new received message from an indicated sender, the new received message having a first message characteristic of the indicated sender and a second message characteristic, identifying an actual sender message characteristic pattern of an actual sender using the first message characteristic, probabilistically comparing the second message characteristic to the actual sender message characteristic pattern, determining a degree of similarity of the second message characteristic to the actual sender message characteristic pattern, and influencing a probability that the indicated sender is the actual sender based upon the degree of similarity. There may be multiple message characteristics and patterns. In some embodiments, the methods may utilize pattern matching techniques, recipient background information, quality measures, threat intelligence data or URL information to help determine whether the new received message is from the actual sender.

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.

The present invention provides an innovative technique for rapidly and accurately determining whether two audio samples match, as well as being immune to various kinds of transformations, such as playback speed variation. The relationship between the two audio samples is characterized by first matching certain fingerprint objects derived from the respective samples. A set (230) of fingerprint objects (231,232), each occurring at a particular location (242), is generated for each audio sample (210). Each location (242) is determined in dependence upon the content of the respective audio sample (210) and each fingerprint object (232) characterizes one or more local features (222) at or near the respective particular location (242). A relative value is next determined for each pair of matched fingerprint objects. A histogram of the relative values is then generated. If a statistically significant peak is found, the two audio samples can be characterized as substantially matching.

The present invention is a system, method, and program product that comprises a computer with a collection of documents to be searched. The documents contain free form (natural language) text. We define a set of labels called QA-Tokens, which function as abstractions of phrases or question-types. We define a pattern file, which consists of a number of pattern records, each of which has a question template, an associated question word pattern, and an associated set of QA-Tokens. We describe a query-analysis process which receives a query as input and matches it to one or more of the question templates, where a priority algorithm determines which match is used if there is more than one. The query-analysis process then replaces the associated question word pattern in the matching query with the associated set of QA-Tokens, and possibly some other words. This results in a processed query having some combination of original query tokens, new tokens from the pattern file, and QA-Tokens, possibly with weights. We describe a pattern-matching process that identifies patterns of text in the document collection and augments the location with corresponding QA-Tokens. We define a text index data structure which is an inverted list of the locations of all of the words in the document collection, together with the locations of all of the augmented QA-Tokens. A search process then matches the processed query against a window of a user-selected number of sentences that is slid across the document texts. A hit-list of top-scoring windows is returned to the user.

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 systems and methods of the client agent describe herein provides a solution to obtaining, recognizing and taking an action on text displayed by an application that is performed in a non-intrusive and application agnostic manner. In response to detecting idle activity of a cursor on the screen, the client agent captures a portion of the screen relative to the position of the cursor. The portion of the screen may include a textual element having text, such as a telephone number or other contact information. The client agent calculates a desired or predetermined scanning area based on the default fonts and screen resolution as well as the cursor position. The client agent performs optical character recognition on the captured image to determine any recognized text. By performing pattern matching on the recognized text, the client agent determines if the text has a format or content matching a desired pattern, such as phone number. In response to determining the recognized text corresponds to a desired pattern, the client agent displays a user interface element on the screen near the recognized text. The user interface element may be displayed as an overlay or superimposed to the textual element such that it seamlessly appears integrated with the application. The user interface element is selectable to take an action associated with the recognized text.