Microsoft Technology Licensing, LLC

Microsft Corporation

John S Breese

David Earl Heckerman

Eric I Horvitz

Gregory L Shaw

Carl Myers Kadie

David O Hovel

Samuel D Hobson

Adrian C Klein

Jacobus A Rommelse

John Eugene Ball

David Maxwell Chickering

James Randall Flynn

Keiji Kanazawa

Karen Jensen

Robert I Davidson

Russell P Blake

Erich-So Slashedren Finkelstein

Matching (e.g., via correlation or similarity process) entities having attributes, some of which have associated values. The values of the attributes may be adjusted based on number of entities that have values for a particular attribute so that the values decrease as the number increases. The attributes of the entities may be harmonized and provided with default values so that entities being matched have common attributes defined by the union of the attributes of the entities being matched. The attributes of the entities may be expanded and provided with default values so that the entities being matched have attributes that neither had originally. Match values may be normalized to provide a weight value which may be used to predict an attribute value of a new entity based on known attribute values of known entities. The weight values may be tuned such that relatively high weights are amplified and relatively low weights are suppressed.

Method and apparatus, using attribute set harmonization and default attribute values, for matching entities and predicting an attribute of an entity

Methods and apparatus, using expansion attributes having default, values, for matching entities and predicting an attribute of an entity

An intelligent user assistance system is disclosed. The system is constructed using a reasoning model to determine the probability of a user's intentions, goals, or informational needs in the context of a software program. This is accomplished through analysis of information about the user's actions, the program state, and spoken words. The system monitors user interaction with the software program and applies probabilistic reasoning to sense that the user may need assistance in using a particular feature or to accomplish a specific task. The inference system accesses a thorough and updateable user profile system to continually check for competencies and changes assistance that is given based on the competencies.

Intelligent user assistance facility

A general event composing and monitoring system that allows high-level events to be created from combinations of low-level events. An event specification tool allows for rapid development of a general event processor that creates high-level events from combinations of user actions. The event system, in combination with a reasoning system, is able to monitor and perform inference about several classes of events for a variety of purposes. The various classes of events include the current context, the state of key data structures in a program, general sequences of user inputs, including actions with a mouse-controlled cursor while interacting with a graphical user interface, words typed in free-text queries for assistance, visual information about users, such as gaze and gesture information, and speech information. Additionally, a method is provided for building an intelligent user interface system by constructing a reasoning model to compute the probability of alternative user's intentions, goals, or informational needs through analysis of information about a user's actions, program state, and words. The intelligent user interface system monitors user interaction with a software application and applies probabilistic reasoning to sense that the user may need assistance in using a particular feature or to accomplish a specific task. The intelligent user interface also accepts a free-text query from the user asking for help and combines the inference analysis of user actions and program state with an inference analysis of the free-text query. The inference system accesses a rich, updatable user profile system to continually check for competencies and changes assistance that is given based on user competence.

Intelligent user assistance facility for a software program

The invention is embodied in a computer user interface including an observer capable of observing user behavior, an agent capable of conveying emotion and personality by exhibiting corresponding behavior to a user, and a network linking user behavior observed by said observer and emotion and personality conveyed by said agent. The network can include an observing network facilitating inferencing user emotional and personality states from the behavior observed by the observer as well as an agent network facilitating inferencing of agent behavior from emotion and personality states to be conveyed by the agent. In addition, a policy module can dictate to the agent network desired emotion and personality states to be conveyed by the agent based upon user emotion and personality states inferred by the observing network. Typically, each network is a stochastic model. Each stochastic model is preferably a Bayesian network, so that the observing network is a first Bayesian network while the agent network is a second Bayesian network. Generally, the first and second Bayesian networks are similar copies of one another. Each of the two Bayesian networks include a first layer of multi-state nodes representing respective emotional and personality variables, and a second layer of multi-state nodes representing respective behavioral variables. Each one of the nodes includes probabilities linking each state in the one node with states of others of the nodes. More specifically, each one of the nodes in the first layer includes probabilities linking the states of the one first layer node to the states of nodes in the second layer. Similarly, each one of the nodes in the second layer include probabilities linking the states of the one second layer node to states of nodes in the first layer.

Modeling and projecting emotion and personality from a computer user interface

Modeling emotion and personality in a computer user interface

Methods and apparatus for tuning a match between entities having

A system and method for determining a workload placed on a target computer system during execution of a specified computer program. The system receives a set of performance measurements representing the performance of the target computer system during execution of the specified computer program. The system then identifies a plurality of workloads and for each identified workload, uses a model of the target computer system to predict a set of performance measurements that would results when a computer program that places the identified workload on the target computer system is executed. The system selects the identified workload whose set of predicted performance measurements most closely matches the received set of performance measurements as the determined workload that was place on the target computer system during execution of the specified computer program. The system uses the selected workload to predict the performance of the specified computer program on the target computer system with various different configurations. The system also determines the resource that is a bottleneck for each of the different configurations.

Automatic bottleneck detection by means of workload reconstruction from

An event composing and monitoring system that allows high-level events to be created from combinations of low-level events. An event specification tool, contained in the system, allows for rapidly developing a general event processor that creates high-level events from combinations of user actions. An event system, in combination with an inference system, monitors and infers, for various purposes, about several classes of events including: current program context; state of key data structures; user input sequences, including actions with a mouse-controlled cursor while interacting with a graphical user interface; words typed in free-text help queries; visual user information, such as gaze and gesture information; and user speech information. Additionally, an intelligent user interface is provided by constructing a reasoning model that computes probability of alternative user intentions, goals or information needs through analyzing information regarding program state, and that user's actions and free-text query words. Specifically, the interface monitors user interaction with a program and probabilistically reasons to sense that a user may need assistance in using a particular feature or to accomplish a specific task. This interface accepts a free-text help query from the user and combines the inference analysis of user actions and the program state with an inference analysis of the query. The inference system, using an updateable user profile, continually checks for user competencies and, based on such competencies, changes assistance that is offered.

Matching (e.g., via correlation or similarity process) entities having attributes, some of which have associated values. The values of the attributes may be adjusted based on number of entities that have values for a particular attribute so that the values decrease as the number increases. The attributes of the entities may be harmonized and provided with default values so that entities being matched have common attributes defined by the union of the attributes of the entities being matched. The attributes of the entities may be expanded and provided with default values so that the entities being matched have attributes that neither had originally. The match values may be normalized to provide a weight value which may be used to predict an attribute value of a new entity based on known attribute values of known entities. The weight values may be tuned such that relatively high weights are amplified and relatively low weights are suppressed.

Methods and apparatus for matching entities and for predicting an

Information retrieval methods and apparatus which involve: 1) the generation of estimates regarding the probability that items included in search results are already known to the user and 2) the use of such knowledge probability estimates to influence the ranking of search results, are described. By discounting the ranking, or adjusting ranking values generated by a known search engine as a function of the knowledge probability estimates, the present invention reduces or eliminates the risk of locating known information near the top of a list of search results. This is advantageous since known information is generally of little interest to a user. In various embodiments the popularity of an item is used to estimate the probability that the item is already known to a user. In addition, in various embodiments one or more user controllable parameters are used in the generation of the knowledge probability estimates and/or the ranking of the search results to give the user an opportunity to have the ranking of the search results accurately reflect the user's knowledge. The present invention is particularly well suited to collaborative filtering based search systems. This is because collaborative filters make recommendations to a user based on historical information relating to, e.g., the popularity of items being considered for recommendation. This same popularity information can be used to estimate a users knowledge of a database item. Such items may include television shows, music, Internet sites, etc.

Methods and apparatus for retrieving and/or processing retrieved

Modeling a user's emotion and personality in a computer user interface

An improved method and system for performing case-based reasoning is provided. A belief network is utilized by the preferred case-based reasoning system for assisting a user in problem resolution. After resolving a problem of a user, the preferred embodiment of the present invention updates the probabilities in the belief network so as to provide for a more accurate problem resolution upon the next invocation of the preferred embodiment. The belief network of the preferred embodiment contains six data types relating to a problem resolution scenario. The data types utilized by the belief network of the preferred embodiment include: issues, causes, resolutions, symptoms, terms, and alternates.

Method and system for case-based reasoning utilizing a belief network

The disclosed system provides an improved collaborative filtering system by utilizing a belief network, which is sometimes known as a Bayesian network. The disclosed system learns a belief network using both prior knowledge obtained from an expert in a given field of decision making and a database containing empirical data obtained from many people. The empirical data contains attributes of users as well as their preferences in the field of decision making. After initially learning the belief network, the belief network is relearned at various intervals when additional attributes are identified as having a causal effect on the preferences and data for these additional attributes can be gathered. This relearning allows the belief network to improve its accuracy at predicting preferences of a user. Upon each iteration of relearning, a cluster model is automatically generated that best predicts the data in the database. After relearning the belief network a number of times, the belief network is used to predict the preferences of a user using probabilistic inference. In performing probabilistic inference, the known attributes of a user are received and the belief network is accessed to determine the probability of the unknown preferences of the user given the known attributes. Based on these probabilities, the preference most likely to be desired by the user can be predicted.

Growing community of inventors

Mercer Island, WA, United States of America

John S Breese