Patent application number | Description | Published |
20080242420 | Adaptive Matchmaking for Games - Matchmaking processes at online game services often result in players having to wait unacceptably long times to receive a match or immediately receiving a poorly matched session. By using a matchmaking process which dynamically adapts a good balance is achieved between the quality of proposed matches (for example, in terms of how balanced, interesting and fun those matches are likely to be) and the waiting time for potential matches. A matchmaking threshold is specified. When a player seeks a match a waiting time is observed, for example, as to how long that player waits until starting a game or dropping out. Information about such waiting times is used to dynamically update the matchmaking threshold. The update is made on the basis of a relationship between information about the observed waiting time and a target waiting time. Further control may be achieved by using separate matchmaking thresholds and target waiting times for different game categories. | 10-02-2008 |
20090043593 | Event Prediction - There are many situations in which it is desired to predict outcomes of events. In an example, an event prediction system is described which receives variables for a proposed event. The system accesses learnt statistics describing belief about weights associated with the variables and uses the weights to determine probability information that the proposed event will have a specified outcome. The process involves combining the accessed statistics and mapping them into a number representing the probability. In another example, a machine learning process using assumed density filtering is used to learn the statistics from data about observed events. The event prediction system may be used as part of any suitable type of system such as an internet advertising system, an email filtering system, or a fraud detection system. | 02-12-2009 |
20090093287 | Determining Relative Player Skills and Draw Margins - A process for determining relative player skills and draw margins is described. Information about an outcome of a game between at least a first player opposing a second player is received. Also, for each player, skill statistics are received associated with a distribution representing belief about skill of that player. Draw margin statistics are received associated with a distribution representing belief about ability of that player to force a draw. An update process is performed to update the statistics on the basis of the received information about the game outcome. In an embodiment a Bayesian inference process is used during the update process which may take past and future player achievement into account. | 04-09-2009 |
20100100416 | Recommender System - A recommender system may be used to predict a user behavior that a user will give in relation to an item. In an embodiment such predictions are used to enable items to be recommended to users. For example, products may be recommended to customers, potential friends may be recommended to users of a social networking tool, organizations may be recommended to automated users or other items may be recommended to users. In an embodiment a memory stores a data structure specifying a bi-linear collaborative filtering model of user behaviors. In the embodiment an automated inference process may be applied to the data structure in order to predict a user behavior given information about a user and information about an item. For example, the user information comprises user features as well as a unique user identifier. | 04-22-2010 |
20100262568 | Scalable Clustering - A scalable clustering system is described. In an embodiment the clustering system is operable for extremely large scale applications where millions of items having tens of millions of features are clustered. In an embodiment the clustering system uses a probabilistic cluster model which models uncertainty in the data set where the data set may be for example, advertisements which are subscribed to keywords, text documents containing text keywords, images having associated features or other items. In an embodiment the clustering system is used to generate additional features for associating with a given item. For example, additional keywords are suggested which an advertiser may like to subscribe to. The additional features that are generated have associated probability values which may be used to rank those features in some embodiments. User feedback about the generated features is received and used to revise the feature generation process in some examples. | 10-14-2010 |
20110066577 | Machine Learning Using Relational Databases - Machine learning using relational databases is described. In an embodiment a model of a probabilistic relational database is formed by augmenting relation schemas of a relational database with probabilistic attributes. In an example, the model comprises constraints introduced by linking the probabilistic attributes using factor statements. For example, a compiler translates the model into a factor graph data structure which may be passed to an inference engine to carry out machine learning. For example, this enables machine learning to be integrated with the data and it is not necessary to pre-process or reformat large scale data sets for a particular problem domain. In an embodiment a machine learning system for estimating skills of players in an online gaming environment is provided. In another example, a machine learning system for data mining of medical data is provided. In some examples, missing attribute values are filled using machine learning results. | 03-17-2011 |
20110131163 | Managing a Portfolio of Experts - Managing a portfolio of experts is described where the experts may be for example, automated experts or human experts. In an embodiment a selection engine selects an expert from a portfolio of experts and assigns the expert to a specified task. For example, the selection engine has a Bayesian machine learning system which is iteratively updated each time an experts performance on a task is observed. For example, sparsely active binary task and expert feature vectors are input to the selection engine which maps those feature vectors to a multi-dimensional trait space using a mapping learnt by the machine learning system. In examples, an inner product of the mapped vectors gives an estimate of a probability distribution over expert performance. In an embodiment the experts are automated problem solvers and the task is a hard combinatorial problem such as a constraint satisfaction problem or combinatorial auction. | 06-02-2011 |
20110137629 | RACING LINE OPTIMIZATION - An automatic algorithm for finding racing lines via computerized minimization of a measure of the curvature of a racing line is derived. Maximum sustainable speed of a car on a track is shown to be inversely proportional to the curvature of the line it is attempting to follow. Low curvature allows for higher speed given that a car has some maximum lateral traction when cornering. The racing line can also be constrained, or “pinned,” at arbitrary points on the track. Pinning may be performed randomly, deterministically, or manually and allows, for example, a line designer to pin the line at any chosen points on the track, such that when the automatic algorithm is run, it will produce the smoothest line that still passes through all the specified pins. | 06-09-2011 |
20110184778 | Event Prediction in Dynamic Environments - Event prediction in dynamic environments is described. In an embodiment a prediction engine may use the learnt information to predict events in order to control a system such as for internet advertising, email filtering, fraud detection or other applications. In an example one or more variables exists for pre-specified features describing or associated with events and each variable is considered to have an associated weight and time stamp. For example, belief about each weight is represented using a probability distribution and a dynamics process is used to modify the probability distribution in a manner dependent on the time stamp for that weight. For example, the uncertainty about the associated variable's influence on prediction of future events is increased. Examples of different schedules for applying the dynamics process are given. | 07-28-2011 |
20110218946 | PRESENTING CONTENT ITEMS USING TOPICAL RELEVANCE AND TRENDING POPULARITY - A user may request a presentation of a content item set, such as a social network comprising a set of status messages or an image database comprising a set of images. However, the volume and diversity of content items of the content item set may reduce the interest of the user in the presented content items. The potential interest of the user in the presented content items may be improved by selecting content items that are associated with one or more topics of potential interest to the user, and having a positive trending popularity among users of the content item set. Moreover, the interaction of the user with a presented content item may be monitored and used to determine the interest of the user in the topics associated with the presented content item and the popularity of the content item. | 09-08-2011 |
20110231405 | Data Structures for Collaborative Filtering Systems - Data structures for collaborative filtering systems are described. In an embodiment sketches which extremely concisely represent a list of items that a user has rated are created and stored for use by a collaborative filtering system to recommend items. For example, the sketches are created by using several versions of a cryptographic hash function to permute the item list and store a minimal value from each permutation in the sketch together with a user rating. In examples the sketches are used to compute estimates of similarity measures between pairs of users such as rank correlations including Spearman's Rho and Kendall's Tau. For example, the similarity measures are used by a collaborative filtering system to accurately and efficiently recommend items to users. For example the sketches are so concise that massive amounts of data can be taken into account in order to give high quality recommendations in a practical manner. | 09-22-2011 |
20110313832 | PRICING IN SOCIAL ADVERTISING - Online recommendations are tracked through a forwarding service. The forwarding service can provide such statistics to an ad service, which can provide incentives to the recommending user and a consuming user. Example incentives may include an accumulation of points by the recommending user, a discount to the consuming user if a purchase is made in response to the recommendation, etc. To determine how much of an incentive each participant in the recommendation flow receives, a graph is created to model the recommendation flow and incentives are allocated using a cooperative game description based on this graph that associates each participant with a power index that represents that participants share of the incentive. | 12-22-2011 |
20110313833 | RECONSTRUCTING THE ONLINE FLOW OF RECOMMENDATIONS - Online recommendations are tracked through a forwarding service. For example, a user may send an email to a friend recommending a product specified at a web site identified by a URI embedded in the email. Before sending the email, the user submits the URI to a forwarding service, which returns a new URI mapped to the original URI and to the recommending user. The recommending user can then recommend the web site by forwarding the new URI to the friend. If the friend selects the recommended URI to review the web site, the forwarding service records the decision to review the web site and directs the friend to the recommended web site. The forwarding service maintains a database of recommendations made by the recommending user, recommendation consumed by the friend, etc. Incentives can be provided to the recommending user and the friend to encourage recommendations. | 12-22-2011 |
20110320767 | Parallelization of Online Learning Algorithms - Methods, systems, and media are provided for a dynamic batch strategy utilized in parallelization of online learning algorithms. The dynamic batch strategy provides a merge function on the basis of a threshold level difference between the original model state and an updated model state, rather than according to a constant or pre-determined batch size. The merging includes reading a batch of incoming streaming data, retrieving any missing model beliefs from partner processors, and training on the batch of incoming streaming data. The steps of reading, retrieving, and training are repeated until the measured difference in states exceeds a set threshold level. The measured differences which exceed the threshold level are merged for each of the plurality of processors according to attributes. The merged differences which exceed the threshold level are combined with the original partial model states to obtain an updated global model state. | 12-29-2011 |
20120071239 | STEREO VIDEO FOR GAMING - A real-time stereo video signal of a captured scene with a physical foreground object and a physical background is received. In real-time, a foreground/background separation algorithm is used on the real-time stereo video signal to identify pixels from the stereo video signal that represent the physical foreground object. A video sequence may be produced by rendering a 3D virtual reality based on the identified pixels of the physical foreground object. | 03-22-2012 |
20120089446 | Publishing Commercial Information in a Social Network - A publishing engine captures commercial information associated with a first user and automatically notifies other users in the first user's social network of this commercial information. The first user authorizes an e-commerce system to access his or her social network and to publish commercial information about the first user's commercial activity (e.g., a purchase or other commercial transaction) to users in the social network. By this automated notification, the notified users in the first user's social network can learn that the first user has completed a commercial transaction pertaining to a particular product or service. If a notified user is interested in a similar product or service, he or she can contact the first user to inquire about the first user's experience and information with the product or service. | 04-12-2012 |
20120089581 | Informing Search Results Based on Commercial Transaction Publications - A publishing engine captures capturing commercial events and other information (collectively, “commercial information”) associated with a first user and automatically notifies other users in the social network of the first user of this commercial information. The publishing engine also notifies one or more search engines of these events and information. Based on this commercial information, the search engine can augment search results of the members of the social network to include historical notifications relating to commercial transactions for similar products and/or services by others in their social network. In this manner, for example, the search engine can provide results directing the searcher to other users in their social network who have purchased such products and/or services. | 04-12-2012 |
20120101965 | TOPIC MODELS - Machine learning techniques may be used to train computing devices to understand a variety of documents (e.g., text files, web pages, articles, spreadsheets, etc.). Machine learning techniques may be used to address the issue that computing devices may lack the human intellect used to understand such documents, such as their semantic meaning. Accordingly, a topic model may be trained by sequentially processing documents and/or their features (e.g., document author, geographical location of author, creation date, social network information of author, and/or document metadata). Additionally, as provided herein, the topic model may be used to predict probabilities that words, features, documents, and/or document corpora, for example, are indicative of particular topics. | 04-26-2012 |
20120150771 | Knowledge Corroboration - Knowledge corroboration is described. In an embodiment many judges provide answers to many questions so that at least one answer is provided to each question and at least some of the questions have answers from more than one judge. In an example a probabilistic learning system takes features describing the judges or the questions or both and uses those features to learn an expertise of each judge. For example, the probabilistic learning system has a graphical assessment component which aggregates the answers in a manner which takes into account the learnt expertise in order to determine enhanced answers. In an example the enhanced answers are used for knowledge base clean-up or web-page classification and the learnt expertise is used to select judges for future questions. In an example the probabilistic learning system has a logical component that propagates answers according to logical relations between the questions. | 06-14-2012 |
20120158620 | HUMAN-ASSISTED TRAINING OF AUTOMATED CLASSIFIERS - Many computing scenarios involve the classification of content items within one or more categories. The content item set may be too large for humans to classify, but an automated classifier (e.g., an artificial neural network) may not be able to classify all content items with acceptable accuracy. Instead, the automated classifier may calculate a classification confidence while classifying respective content items. Content items having a low classification confidence may be sent to a human classifier, and may be added, along with the categories identified by the human classifier, to a training set. The automated classifier may then be retrained using the training set, thereby incrementally improving the classification confidence of the automated classifier while conserving the involvement of human classifiers. Additionally, human classifiers may be rewarded for classifying the content items, and the costs of such rewards may be considered while selecting content items for the training set. | 06-21-2012 |
20120158630 | INFORMATION PROPAGATION PROBABILITY FOR A SOCIAL NETWORK - One or more techniques and/or systems are disclosed for predicting propagation of a message on a social network. A predictive model is trained to determine a probability of propagation of information on the social network using both positive and negative information propagation feedback, which may be collected while monitoring the social network over a desired period of time for information propagation. A particular message can be input to the predictive model, which can determine a probability of propagation of the message on the social network, such as how many connections may receive at least a portion of the message and/or a likelihood of at least a portion of the message reaching respective connections in the social network. | 06-21-2012 |
20120158791 | FEATURE VECTOR CONSTRUCTION - Feature vector construction techniques are described. In one or more implementations, an input is received at a computing device that describes a graph query that specifies one of a plurality of entities to be used to query a knowledge base graph that represents the plurality of entities. A feature vector is constructed, by the computing device, having a number of indicator variables, each of which indicates observance of a sub-graph feature represented by a respective indicator variable in the knowledge base graph. | 06-21-2012 |
20120221129 | SEEDING IN A SKILL SCORING FRAMEWORK - Skill scores represent a ranking or other indication of the skill of the player based on the outcome of the game in a gaming environment. Skills scores can be used in matching compatible players on the same team and matching opposing players or teams to obtain an evenly-matched competition. An initial skill score of a player in a new gaming environment may be based in whole or in part on the skill score of that player in another game environment. The influence that the skill scores for these other game environments may have in the skill score seeding for the new game environment may be weighted based on a defined compatibility factor with the new game environment. The compatibility factor can be determined based on a game-to-game basis, compatible categories or features, game developer defined parameters, or any combination of considerations. | 08-30-2012 |
20130024448 | RANKING SEARCH RESULTS USING FEATURE SCORE DISTRIBUTIONS - Document features or document ranking values can be associated with a distribution of values. Feature values, feature value coefficients, and/or document ranking values can be generated based on sampled values from the distribution of values. This can allow the relative ranking of a document to vary. As additional information is obtained regarding the document, leading to greater certainty about the appropriate ranking of the document, the width or variation generated by the distribution can be reduced to provide more stable ranking values | 01-24-2013 |
20130282631 | INFORMATION PROPAGATION PROBABILITY FOR A SOCIAL NETWORK - One or more techniques and/or systems are disclosed for predicting propagation of a message on a social network. A predictive model is trained to determine a probability of propagation of information on the social network using both positive and negative information propagation feedback, which may be collected while monitoring the social network over a desired period of time for information propagation. A particular message can be input to the predictive model, which can determine a probability of propagation of the message on the social network, such as how many connections may receive at least a portion of the message and/or a likelihood of at least a portion of the message reaching respective connections in the social network. | 10-24-2013 |
20140156571 | TOPIC MODELS - Machine learning techniques may be used to train computing devices to understand a variety of documents (e.g., text files, web pages, articles, spreadsheets, etc.). Machine learning techniques may be used to address the issue that computing devices may lack the human intellect used to understand such documents, such as their semantic meaning. Accordingly, a topic model may be trained by sequentially processing documents and/or their features (e.g., document author, geographical location of author, creation date, social network information of author, and/or document metadata). Additionally, as provided herein, the topic model may be used to predict probabilities that words, features, documents, and/or document corpora, for example, are indicative of particular topics. | 06-05-2014 |