Abstract: The present invention is related to a system and method of considering time segments or intervals in a collaborative filtering model. The present invention extends collaborative filtering approaches by integrating considerations of temporality into the training and/or vote input associated with the usage of collaborative filtering models. The present invention also applies filtering to the output with temporal models, so as to view a most appropriate subset of recommended content, centering on content that may be available at a target time. The present invention applies time to a collaborative filtering model by allowing weight to be associated with selections within a current time segment, selections historically watched within the current time segment by the user and selections historically watched within the current time segment by a large group of users.

Abstract: A system for optimizing the value of communications between communicating parties is provided. The system includes a communication group manager that facilitates specifying policies, preferences and/or automated analysis of ideal communication channels, routing and/or scheduling in terms of communicating party groups that can be pre-populated clusters of communicating parties, assembled based on relationships (e.g., organizational), and/or assembled based on satisfying inclusion criteria (e.g., age, location, competence, communication history, meeting history). The communication group manager maps communicating parties into predefined and/or dynamically created groups that facilitate specifying and/or automatically computing ideal communication actions like selecting a channel, displaying lists of potential channels sorted by communicating party preferences, and (re)scheduling communications to different channels and/or times.

Abstract: Systems and methods are provided for the identification of genotype-phenotype associations in genome-wide association (GWA) studies. In an illustrative implementation, a data correlation environment comprises a population structure engine and at least one instruction set to instruct the population structure engine to process pedigree or population genetic data to generate a population structure sub-model according to a selected graphical model-based data correlation paradigm. Illustratively, the parameter of the resulting generalized linear mixed model can be learned using a variational approximation.

Abstract: The invention relates to a system for filtering messages—the system includes a seed filter having associated therewith a false positive rate and a false negative rate. A new filter is also provided for filtering the messages, the new filter is evaluated according to the false positive rate and the false negative rate of the seed filter, the data used to determine the false positive rate and the false negative rate of the seed filter are utilized to determine a new false positive rate and a new false negative rate of the new filter as a function of threshold. The new filter is employed in lieu of the seed filter if a threshold exists for the new filter such that the new false positive rate and new false negative rate are together considered better than the false positive and the false negative rate of the seed filter.

Abstract: The present invention provides collaborative filtering systems and methods employing statistical smoothing to provide quickly creatable models that can efficiently predict probability that a user likes an item and/or similarities between items. Smoothing is accomplished by utilizing statistical methods such as support cutoff, single and multiple prior on counts, and prior on measure of association and the like. By improving model-based collaborative filtering with such techniques, performance is increased with regard to product-to-product recommendations. The present invention also provides improvements over systems based on dependency nets (DN) in both areas of quality of recommendations and speed of model creation. It can also be complementary to DN to improve the value of an existing collaborative filtering system's overall efficiency. It is also employable with low frequency user preference data.

Abstract: A system and method is provided for personalizing and refining policies within a general notification platform. The system includes a profile definition and selection system that receives contextual information relating to a user state. The profile definition and selection system generates and/or relays a set of control parameters based at least partially upon the contextual information, and a notification manager selectively sends a user notification and/or communication based upon the set of control parameters. Methods are provided for tuning the notification platform. This may include defining one or more context profiles, assigning values to parameters employed in one or more context profiles, determining a current user context, determining which of the one or more user profiles is consistent with the current user context, and utilizing the parameter value associated with the one or more context profiles consistent with the current user context to adjust the notification system.

Abstract: The claimed subject matter provides systems and/or methods that determines a number of non-spurious arcs associated with a learned graphical model. The system can include devices and mechanisms that utilize learning algorithms and datasets to generate learned graphical models and graphical models associated with null permutations of the datasets, ascertaining the average number of arcs associated with the graphical models associated with null permutations of the datasets, enumerating the total number of arcs affiliated with the learned graphical model, and presenting a ratio of the average number of arcs to the total number of arcs, the ratio indicative of the number of non-spurious arcs associated the learned graphical model.

Abstract: A system and method are provided to learn and infer the time until a user will be available for communications, collaboration, or information access, given evidence about such observations as time of day, calendar, location, presence, and activity. The methods can be harnessed to coordinate communications between parties via particular modalities of interaction. The system includes a user state identifier that determines a user's state from background knowledge, the flow of time, or one or more context information sources. A data log can be employed to store information about user state changes and observational evidence to accumulate statistics and build inferential models of the availability and unavailability of users for different kinds of communication, collaboration, and information access.

Abstract: A system and method are provided to learn and infer the time until a user will be available for communications, collaboration, or information access, given evidence about such observations as time of day, calendar, location, presence, and activity. The methods can be harnessed to coordinate communications between parties via particular modalities of interaction. The system includes a user state identifier that determines a user's state from background knowledge, the flow of time, or one or more context information sources. A data log can be employed to store information about user state changes and observational evidence to accumulate statistics and build inferential models of the availability and unavailability of users for different kinds of communication, collaboration, and information access.

Abstract: The present invention relates to a system and methodology to facilitate multiattribute adjustments and control associated with messages and other communications and informational items that are directed to a user via automated systems. An interface, specification language, and controls are provided for defining a plurality of variously configured groups that may attempt to communicate respective items. Controls include the specification of priorities and preferences as well as the modification of priorities and preferences that have been learned from training sets via machine learning methods. The system provides both a means for assessing parameters used in the control of messaging and communications and for the inspection and modification of parameters that have been learned autonomously.

Abstract: The present invention is related to a system and method of caching data employing probabilistic predictive techniques. The system and method has particular application to multimedia systems for providing local storage of a subset of available viewing selections by assigning a value to a selection and retaining selections in the cache depending on the value and size of the selection. The value assigned to an item can represent the time-dependent likelihood that a user will review an item at some time in the future. An initial value of an item can be based on the user's viewing habits, the user's viewing habit over particular time segment (e.g., early morning, late morning, early afternoon, late afternoon, primetime, late night) and/or viewing habits of a group of user's during a particular time segment.

Abstract: The present invention relates to a system and methodology to enable a variety of information associated with one or more notification sources to be directed to one or more notification sinks via a notification platform architecture. The architecture includes a context analyzer for determining a user's state such as location and attentional focus, wherein the user's state is employed by a notification manager to make decisions regarding what, when and how information generated by the notification sources should be forwarded to the notification sinks, for example. These decisions can include a cost benefit analysis wherein considerations are given as to whether the benefits of notifying the user are outweighed by the costs of disrupting the user. Decision-theoretic policies and/or somewhat less formal heuristic policies can be employed to enable the decision-making process within the notification manager.

Abstract: The present invention is related to a system and method of caching data employing probabilistic predictive techniques. The system and method provides local storage of a subset of available viewing selections by assigning a value to a selection and retaining selections in the cache depending on the value and size of the selection. The value assigned to an item can represent the time-dependent likelihood that a user will review an item at some time in the future. An initial value of an item can be based on the user's viewing habits, the user's viewing habit over particular time segment and/or viewing habits of a group of user's during a particular time segment. A value assigned to a selection dynamically changes according to a set of cache retention policies, where the value can be time-dependent functions that decay based on the class of the item, as determined by inference about the class or via a label associated with the item.

Abstract: A system and method of caching multimedia data employing probabilistic predictive techniques is provided for facilitating local storage of a subset of available viewing selections by assigning a value to a selection and retaining selections in the cache depending on the value and size of the selection. The value assigned to an item can represent the time-dependent likelihood that a user (or group of users) will review an item at some time in the future, current viewing habits as well as habits over a particular time segment. A value assigned to a selection dynamically changes according to a set of cache retention policies, where the value can be time-dependent functions that decay based on the class of the item, for example. A selections value may also change based on changes in viewing habits, time segments or a user's modification of the cache retention policies.

Abstract: The subject invention leverages data sampling techniques to provide an efficient means to determine co-occurrence count estimations for objects and features from relational data, simplifying measure-of-association determinations. By providing an efficient mechanism to estimate co-occurrence counts, instances of the subject invention can be incorporated directly into a database, increasing its versatility and performance for such uses as collaborative filtering recommendations. Other instances of the subject invention can be utilized for enhancing database query selectivity, optimizing query performance through employment of á priori co-occurrence counts.

Abstract: A system and methods are provided to learn and infer the time until a user will be available for communications, collaboration, or information access, given evidence about such observations as time of day, calendar, location, presence, and activity. The methods can be harnessed to coordinate communications between parties via particular modalities of interaction. The system includes a user state identifier that determines a user's state from background knowledge, the flow of time, or one or more context information sources. A data log can be employed to store information about user state changes and observational evidence to accumulate statistics and build inferential models of the availability and unavailability of users for different kinds of communication, collaboration, and information access.

Abstract: The present invention relates to a system and methodology to facilitate multiattribute adjustments and control associated with messages and other communications and informational items that are directed to a user via automated systems. An interface, specification language, and controls are provided for defining a plurality of variously configured groups that may attempt to communicate respective items. Controls include the specification of priorities and preferences as well as the modification of priorities and preferences that have been learned from training sets via machine learning methods. The system provides both a means for assessing parameters used in the control of messaging and communications and for the inspection and modification of parameters that have been learned autonomously.

Abstract: The present invention is related to caching data by employing probabilistic predictive techniques. The invention has particular application to multimedia systems for providing local storage of a subset of available viewing selections by assigning a value to a selection and retaining selections in the cache depending on the value and size of the selection. The value assigned to an item can represent the time-dependent likelihood that a user will review an item at some time in the future. An initial value of an item can be based on the user's viewing habits, the user's or a group of user's viewing habit over particular time segment. A value assigned to a selection dynamically changes according to a set of cache retention policies. A selection's value may be reduced based upon age, changes on a user's viewing habits, changes in time segments or a user's modification of the cache retention policies.

Abstract: Epitope prediction models are described herein. By way of example, a system for predicting epitope information relating to a epitope can include a classification model (e.g., logistic regression model). The trained classification model can illustratively operatively execute one ore logistic functions on received protein data, and incorporate one or more of hidden binary variables and shift variables that when processed represent the identification (e.g., prediction) of one or more desired epitopes. The classification model can be configured to predict the epitope information by processing data including various features of an epitope, MHC, MHC supertype, and Boolean combinations thereof.

Abstract: Statistical models for identifying associations are described herein. By way of example, a system for identifying associations between variables can include a model builder and an association identifier. The model builder can receive observations about the variables and generate a null model and a non-null model. The association identifier can assess the strength of the association between the variables by determining how much the non-null model better explains the observed data than the null model. Additionally or alternatively, the structure of the observed data can be inferred simultaneously with the statistical model.