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2225 results about "Recursion" patented technology

Recursion (adjective: recursive) occurs when a thing is defined in terms of itself or of its type. Recursion is used in a variety of disciplines ranging from linguistics to logic. The most common application of recursion is in mathematics and computer science, where a function being defined is applied within its own definition. While this apparently defines an infinite number of instances (function values), it is often done in such a way that no loop or infinite chain of references can occur.

System for bandwidth extension of Narrow-band speech

A system and method are disclosed for extending the bandwidth of a narrowband signal such as a speech signal. The method applies a parametric approach to bandwidth extension but does not require training. The parametric representation relates to a discrete acoustic tube model (DATM). The method comprises computing narrowband linear predictive coefficients (LPCs) from a received narrowband speech signal, computing narrowband partial correlation coefficients (parcors) using recursion, computing Mnb area coefficients from the partial correlation coefficient, and extracting Mwb area coefficients using interpolation. Wideband parcors are computed from the Mwb area coefficients and wideband LPCs are computed from the wideband parcors. The method further comprises synthesizing a wideband signal using the wideband LPCs and a wideband excitation signal, highpass filtering the synthesized wideband signal to produce a highband signal, and combining the highband signal with the original narrowband signal to generate a wideband signal. In a preferred variation of the invention, the Mnb area coefficients are converted to log-area coefficients for the purpose of extracting, through shifted-interpolation, Mwb log-area coefficients. The Mwb log-area coefficients are then converted to Mwb area coefficients before generating the wideband parcors.
Owner:CERENCE OPERATING CO

Methods and systems for generating natural language descriptions from data

The invention is directed to a natural language generation (NLG) software system that generates rich, content-sensitive human language descriptions based on unparsed raw domain-specific data. In one embodiment, the NLG software system may include a data parser / normalizer, a comparator, a language engine, and a document generator. The data parser / normalizer may be configured to retrieve specification information for items to be described by the NLG software system, to extract pertinent information from the raw specification information, and to convert and normalize the extracted information so that the items may be compared specification by specification. The comparator may be configured to use the normalized data from the data parser / normalizer to compare the specifications of the items using comparison functions and interpretation rules to determine outcomes of the comparisons. The language engine may be configured to cycle through all or a subset of the normalized specification information, to retrieve all sentence templates associated with each of the item specifications, to call the comparator to compute or retrieve the results of the comparisons between the item specifications, and to recursively generate every possible syntactically legal sentence associated with the specifications based on the retrieved sentence templates. The document generator may be configured to select one or more discourse models having instructions regarding the selection, organization and modification of the generated sentences, and to apply the instructions of the discourse model to the generated sentences to generate a natural language description of the selected items.
Owner:CLASSIFIED VENTURES

Method and apparatus for prefetching recursive data structures

Computer systems are typically designed with multiple levels of memory hierarchy. Prefetching has been employed to overcome the latency of fetching data or instructions from or to memory. Prefetching works well for data structures with regular memory access patterns, but less so for data structures such as trees, hash tables, and other structures in which the datum that will be used is not known a priori. The present invention significantly increases the cache hit rates of many important data structure traversals, and thereby the potential throughput of the computer system and application in which it is employed. The invention is applicable to those data structure accesses in which the traversal path is dynamically determined. The invention does this by aggregating traversal requests and then pipelining the traversal of aggregated requests on the data structure. Once enough traversal requests have been accumulated so that most of the memory latency can be hidden by prefetching the accumulated requests, the data structure is traversed by performing software pipelining on some or all of the accumulated requests. As requests are completed and retired from the set of requests that are being traversed, additional accumulated requests are added to that set. This process is repeated until either an upper threshold of processed requests or a lower threshold of residual accumulated requests has been reached. At that point, the traversal results may be processed.
Owner:DIGITAL CACHE LLC +1

Method and apparatus of a self-configured, model-based adaptive, predictive controller for multi-zone regulation systems

A control system simultaneously controls a multi-zone process with a self-adaptive model predictive controller (MPC), such as temperature control within a plastic injection molding system. The controller is initialized with basic system information. A pre-identification procedure determines a suggested system sampling rate, delays or “dead times” for each zone and initial system model matrix coefficients necessary for operation of the control predictions. The recursive least squares based system model update, control variable predictions and calculations of the control horizon values are preferably executed in real time by using matrix calculation basic functions implemented and optimized for being used in a S7 environment by a Siemens PLC. The number of predictions and the horizon of the control steps required to achieve the setpoint are significantly high to achieve smooth and robust control. Several matrix calculations, including an inverse matrix procedure performed at each sample pulse and for each individual zone determine the MPC gain matrices needed to bring the system with minimum control effort and variations to the final setpoint. Corrective signals, based on the predictive model and the minimization criteria explained above, are issued to adjust system heating/cooling outputs at the next sample time occurrence, so as to bring the system to the desired set point. The process is repeated continuously at each sample pulse.
Owner:SIEMENS IND INC

Binary prediction tree modeling with many predictors and its uses in clinical and genomic applications

The statistical analysis described and claimed is a predictive statistical tree model that overcomes several problems observed in prior statistical models and regression analyses, while ensuring greater accuracy and predictive capabilities. Although the claimed use of the predictive statistical tree model described herein is directed to the prediction of a disease in individuals, the claimed model can be used for a variety of applications including the prediction of disease states, susceptibility of disease states or any other biological state of interest, as well as other applicable non-biological states of interest. This model first screens genes to reduce noise, applies k-means correlation-based clustering targeting a large number of clusters, and then uses singular value decompositions (SVD) to extract the single dominant factor (principal component) from each cluster. This generates a statistically significant number of cluster-derived singular factors, that we refer to as metagenes, that characterize multiple patterns of expression of the genes across samples. The strategy aims to extract multiple such patterns while reducing dimension and smoothing out gene-specific noise through the aggregation within clusters. Formal predictive analysis then uses these metagenes in a Bayesian classification tree analysis. This generates multiple recursive partitions of the sample into subgroups (the “leaves” of the classification tree), and associates Bayesian predictive probabilities of outcomes with each subgroup. Overall predictions for an individual sample are then generated by averaging predictions, with appropriate weights, across many such tree models. The model includes the use of iterative out-of-sample, cross-validation predictions leaving each sample out of the data set one at a time, refitting the model from the remaining samples and using it to predict the hold-out case. This rigorously tests the predictive value of a model and mirrors the real-world prognostic context where prediction of new cases as they arise is the major goal.
Owner:DUKE UNIV

View navigation for creation, update and querying of data objects and textual annotations of relations between data objects

A method (and corresponding database system) for displaying in a view window information characterizing semantics of relations between objects. For each given relation between at least one subject object and at least one direct object, bi-directional modifier data is stored that represents first text characterizing semantics of a relationship of the at least one first object to the at least one second object, and represents second text characterizing semantics of a relationship of the at least one second object to the at least one first object. In response to predetermined user input associated with an object node displayed in the view window, a set of relations whose at least one subject object or at least one direct object is associated with the object node is identified. For at least one relation in the set of relations, the view window is updated to include a second node comprising a graphical representation of: the first text of the given relation in the event that the given object is a subject object in the given relation, or the second text of the given relation in the event that the given object is a direct object in the given relation. The second node may be a relation node associated with a given relation, or a mixed node associated with a relation-type pair. In response to predetermined input with a second node, the second node may be expanded to identify and display one or more object nodes (identifying direct object(s) of relations derived from expansion of a subject object associated therewith or identifying subject object(s) of relations derived from expansion of a direct object associated therewith). Preferably, this expansion routine is recursive in nature.The method (and database system) of the present invention may be used in a wide assortment of software applications, including enterprise applications (such as e-business applications, supply chain management applications, customer relationship management applications, decision support applications), the file system in operating systems, web browsers, e-mail applications and personal information management applications. Importantly, the method (and database system) provides an easy, user friendly and efficient mechanism to define, view and query the organization of the data elements (and the relationships therebetween) stored and accessed in such software applications, in a manner that conveys the real-world meaning of such relationships.
Owner:REVELINK

Voice identification method using long-short term memory model recurrent neural network

The invention discloses a voice identification method using a long-short term memory model recurrent neural network. The voice identification method comprises training and identification. The training process comprises steps of introducing voice data and text data to generate a commonly-trained acoustic and language mode, and using an RNN sensor to perform decoding to form a model parameter. The identification process comprises steps of converting voice input to a frequency spectrum graph through Fourier conversion, using the recursion neural network of the long-short term memory model to perform orientational searching decoding and finally generating an identification result. The voice identification method adopts the recursion neural network (RNNs) and adopts connection time classification (CTC) to train RNNs through an end-to-end training method. These LSTM units combining with the long-short term memory have good effects and combines with multi-level expression to prove effective in a deep network; only one neural network model (end-to-end model) exits from a voice characteristic (an input end) to a character string (an output end) and the neural network can be directly trained by a target function which is a some kind of a proxy of WER, which avoids to cost useless work to optimize an individual target function.
Owner:SHENZHEN WEITESHI TECH
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