Patent application number | Description | Published |
20080246986 | METHODS AND APPARATUS FOR IMPROVED OPERATION OF NETWORKED PRINTING SYSTEM - Methods and systems are presented for performing one or more printer device management functions in a network, in which affinities between printers are determined from job tracking data to indicate associations between printer devices and user devices, and the affinity data is used to perform one or more printer management functions such as determining printer connections for new or roaming user devices, print job redirection, and identification of underutilized printer device assets. | 10-09-2008 |
20080246987 | METHODS AND SYSTEMS FOR SOFT FAILURE DETECTION FOR NETWORKED PRINTERS - Methods and systems are presented for identifying potential printer failures in a networked printing enterprise, in which job tracking data is gathered for print jobs in the network, affinity data is derived from the job tracking data indicating associations between printer devices and user devices, and potential printer failures are identified based on changes in the affinity data. | 10-09-2008 |
20080300879 | FACTORIAL HIDDEN MARKOV MODEL WITH DISCRETE OBSERVATIONS - A method for analyzing hidden dynamics, includes acquiring discrete observations, each discrete observation having an observed value selected from two or more allowed discrete values. A factorial hidden Markov model (FHMM) relating the discrete observations with a plurality of hidden dynamics is constructed. A contribution of the state of each hidden dynamic to the discrete observation may be represented in the FHMM as a parameter of a nominal distribution which is scaled by a function of the state of the hidden dynamic. States of the hidden dynamics are inferred from the discrete observations based on the FHMM. Information corresponding to at least one inferred state of at least one of the hidden dynamics is output. The parameters of the contribution of each dynamic to the hidden states may be learnt from a large number of observations. An example of a networked printing system is used to demonstrate the applicability of the method. | 12-04-2008 |
20090216700 | TEMPORAL EVENTS ANALYSIS EMPLOYING TREE INDUCTION - An events analysis method comprises: optimizing respective to a set of training data a set of branching transition likelihood parameters associating parent events of type k with child events of type k′ in branching processes; inferring a most probable branching process for a set of input data comprising events based on the optimized set of branching transition likelihood parameters; and identifying rare or unusual events of the set of input data based on the inferred most probable branching process. An events analysis apparatus includes a probabilistic branching process learning engine configured to optimize the set of branching transition likelihood parameters, and a probabilistic branching process inference engine configured to infer the most probable branching process. | 08-27-2009 |
20090271433 | CLUSTERING USING NON-NEGATIVE MATRIX FACTORIZATION ON SPARSE GRAPHS - Object clustering techniques are disclosed. A nonnegative sparse similarity matrix is constructed for a set of objects. Nonnegative factorization of the nonnegative sparse similarity matrix is performed. Objects of the set of objects are allocated to clusters based on factor matrices generated by the nonnegative factorization of the nonnegative sparse similarity matrix. | 10-29-2009 |
20100058121 | VISUALIZATION OF USER INTERACTIONS IN A SYSTEM OF NETWORKED DEVICES - As set forth herein, a system identifies soft failures of devices. An interface captures transactional data between one or more users and one or more devices within the system. A data log receives the transactional data from the interface and stores the data as historical data for subsequent retrieval. A warning system evaluates the historical data in the data log to identify one or more devices that have a soft failure condition, wherein an alarm is output for each soft failure identified. A display module combines the historical data from the data log and one or more alarms from the warning system into a single display for review. | 03-04-2010 |
20100088073 | FAST ALGORITHM FOR CONVEX OPTIMIZATION WITH APPLICATION TO DENSITY ESTIMATION AND CLUSTERING - A method of maximizing a concave log-likelihood function comprises: selecting a pair of parameters from a plurality of adjustable parameters of a concave log-likelihood function; maximizing a value of the concave log-likelihood function respective to an adjustment value to generate an optimal adjustment value, wherein the value of one member of the selected pair of parameters is increased by the adjustment value and the value of the other member of the selected pair of parameters is decreased by the adjustment value; updating values of the plurality of adjustable parameters by increasing the value of the one member of the selected pair of parameters by the optimized adjustment value and decreasing the value of the other member of the selected pair of parameters by the optimized adjustment value; and repeating the selecting, maximizing, and updating for different pairs of parameters to identify optimized values of the plurality of adjustable parameters. | 04-08-2010 |
20100094787 | CLUSTERING AND CLASSIFICATION EMPLOYING SOFTMAX FUNCTION INCLUDING EFFICIENT BOUNDS - A function optimization method includes the operations of: constructing an upper bound using a double majorization bounding process to a sum-of-exponentials function including a summation of exponentials of the form | 04-15-2010 |
20100145647 | SYSTEM AND METHOD FOR IMPROVING FAILURE DETECTION USING COLLECTIVE INTELLIGENCE WITH END-USER FEEDBACK - Systems and methods are described that facilitate using end-user feedback to automatically distinguish between a normal behavior and a device failure which can be a hard failure (e.g., a device malfunction) or a soft failure. For instance, upon detection of a usage switch from a first device to a second device by a user, a survey message is sent to the user to solicit information regarding the reasons for the switch. If the switch was triggered by a device malfunction, the detected device failure is verified and an alert is sent to an administrator and/or potentially impacted users. If the switch was triggered by the user's need for functionality (e.g., color printing, collation, etc.) not provided by the first device, which is otherwise functioning properly, then the detected failure is determined to be a failure and the failure detection algorithm is updated accordingly. | 06-10-2010 |
20100324950 | OPTIMAL MAPPING OF A SPATIAL PRINT INFRASTRUCTURE - Disclose are embodiments for selecting an advantageous, feasible and suitable location for placing a selected printing device within a space. A mathematical formula identifies a most advantageous location for placing the selected printing device. Next, successive contour regions surrounding this most advantageous location are defined such that any inner contour region is considered more advantageous than any outer contour region. A mark representing the most advantageous location and contour lines indicating the successive contour regions are plotted onto a floor plan of the space. The edited floor plan is then evaluated (e.g., either visually by a user or automatically) to determine whether the mark overlaps any fixed shapes and/or restricted-use areas. If the mark overlaps a fixed shape or restricted-use area, a different location can be selected that is within a closest possible contour region without overlapping any other fixed shapes or restricted-use areas. | 12-23-2010 |
20110265086 | USER AND DEVICE LOCALIZATION USING PROBABILISTIC DEVICE LOG TRILATERATION - A system and method of localizing elements (shared devices and/or their users) in a device infrastructure, such as a printing network, are provided. The method includes mapping a structure in which the elements of a device infrastructure are located, the elements comprising shared devices and users of the shared devices. Probable locations of fewer than all of the elements in the structure are mapped, with at least some of the elements being initially assigned to an unknown location. Usage logs for a plurality of the shared devices are acquired. The acquired usage log for each device includes a user identifier for each of a set of uses of the device, each of the uses being initiated from a respective location within the mapped structure by one of the users. Based on the acquired usage logs and the input probable locations of some of the elements, locations of at least some of the elements initially assigned to an unknown location are predicted. The prediction is based a model which infers that for each of a plurality of the users, a usage of at least some of the shared devices by the user is a function of respective distances between the user and each of those devices. | 10-27-2011 |
20130226839 | ROBUST BAYESIAN MATRIX FACTORIZATION AND RECOMMENDER SYSTEMS USING SAME - In a recommender method, Bayesian Matrix Factorization (BMF) is performed on a matrix having user and item dimensions and matrix elements containing user ratings for items made by users in order to train a probabilistic collaborative filtering model. A recommendation is generated for a user using the probabilistic collaborative filtering model. The recommendation may comprise a predicted item rating, or an identification of one or more recommended items. The recommender method is suitably performed by an electronic data processing device. The BMF may employ non-Gaussian priors, such as Student-t priors. The BMF may additionally or alternatively employ a heteroscedastic noise model comprising priors that include (1) a row dependent variance component that depends upon the matrix row and (2) a column dependent variance component that depends upon the matrix column. | 08-29-2013 |
20140156231 | PROBABILISTIC RELATIONAL DATA ANALYSIS - A multi-relational data set is represented by a probabilistic multi-relational data model in which each entity of the multi-relational data set is represented by a D-dimensional latent feature vector. The probabilistic multi-relational data model is trained using a collection of observations of relations between entities of the multi-relational data set. The collection of observations includes observations of at least two different relation types. A prediction is generated for an observation of a relation between two or more entities of the multi-relational data set based on a dot product of the optimized D-dimensional latent feature vectors representing the two or more entities. The training may comprise optimizing the D-dimensional latent feature vectors to maximize likelihood of the collection of observations, for example by Bayesian inference performed using Gibbs sampling. | 06-05-2014 |
20140156579 | CONVEX COLLECTIVE MATRIX FACTORIZATION - A method operates on observed relationship data between pairs of entities of a set of entities including entities of at least two (and optionally at least three) different entity types. An observed collective symmetric matrix is constructed in which element (n,m)=element (m,n) stores the observed relationship between entities indexed n and m when the observed relationship data includes this observed relationship. A prediction collective symmetric matrix is optimized in order to minimize a loss function comparing the observed collective symmetric matrix and the prediction collective symmetric matrix. A relationship between two entities of the set of entities is predicted using the optimized prediction collective symmetric matrix. Entities of the same entity type may be indexed using a contiguous set of indices such that the entity type maps to a contiguous set of rows and corresponding contiguous set of columns in the observed collective symmetric matrix. | 06-05-2014 |