Abstract: A system is provided for automatically predicting actions a user is likely to take in response to receiving data. The system may be configured to monitor and observe a user's interactions with incoming data and to identify patterns of actions the user may take in response to the incoming data. The system may enable a trainer component and a classifier component to determine the probability a user may take a particular action and to make predictions of likely user actions based on the observations of the user and the identified pattern of the user's actions. The system may also be configured to continuously observe the user's actions to fine-tune and adjust the identified patterns of user actions and to update the probabilities of likely user actions in order increase the accuracy of the predicted user action in response to incoming data.

Abstract: User interfaces for tools for estimating a probability that a future event will occur based on user input are described. One set of interfaces include rating, trend, cohort record and source controls each of which when selected provide corresponding data from one predictive model that produces predictions of the likelihood of an event occurring in the future based on analysis of data in a database. The system further displays a process of content produced by a model builder that populates the interfaces, and outputs thereof.

Abstract: Disclosed is a novel system, and method to evaluate a prediction of a possibly unknown outcome out of a plurality of predictions of that outcome. The method begins with accessing a particular prediction of an outcome out of a plurality of predictions of that outcome in which the outcome may be unknown. Next, a subsample of the plurality of predictions of the outcome is accessed. The subsample can possibly include the particular prediction. A consensus prediction of the outcome based on the subsample of the plurality of predictions is determined. A proximity of the particular prediction to the consensus prediction is determined. Each prediction is ranked out of the plurality of predictions in an order of a closest in proximity to the consensus prediction to a farthest in proximity to the consensus prediction.

Abstract: In one embodiment, prior to similarity measure computation, concept expansion based on disjunctive normal form (DNF) decomposition and non-conventional reasoning is applied to an original ontology to generate an expanded ontology having the original concepts plus one or more pseudo concepts. As a result, the similarity measure computation can produce results that more accurately reflect a human point of view than convention techniques.

Abstract: A method and a system for providing personal assistance in daily activities. A method and a system for automatically and actively providing personal assistance, using a vehicle or a portable electronic device, based on detected data regarding a user and the user's environment. The method and system may include a processor, at least one sensor, an output device, a communications unit, and a database. The database may further include a memory and cloud-based database and computing. The method and system may actively and automatically provide personal assistance regarding health, exercise, diet, or nutrition. The method and system may assist the user or a health professional in health diagnosis and treatment.

Abstract: Exemplary embodiments provide systems, devices, one or more non-transitory computer-readable media and computer-executable methods for managing publication of online advertising. In exemplary embodiments, computer-based publication techniques may include, but is not limited to, automatically determining whether the content of a particular web page article is suitable or unsuitable for accompaniment with one or more advertisements, automatically determining whether an advertisement is suitable or unsuitable for publication on a web page associated with a web page article, and automatically determining a category that may be used to classify the content of a web page article in order to select one or more categories of advertisements suitable for accompaniment with the web page article.

Abstract: A method of evaluating a value of reference information and an apparatus for evaluating a value of reference information are disclosed. According to an embodiment of the present invention, the method includes calculating similarity values of reference relationships between the subject document and each of neighbor documents, respectively, and summing the calculated similarity values to calculate a first sum, multiplying each of the similarity values of reference relationships between the subject document and each of the neighbor documents by a corresponding value of reference information of the first reference document for each of the neighbor documents, and summing the multiplied values to calculate a second sum, and evaluating a value of reference information using a ratio of the first and second sums.

Abstract: Disclosed herein is an information processing apparatus including an evaluation information extraction section configured to extract evaluation information including an object targeted to be evaluated and an evaluation of the object targeted to be evaluated from a linguistic expression given as information expressed linguistically by a user of interest; an identification section configured to identify whether the evaluation information is of a first type regarding content or of a second type regarding another user; and an evaluation prediction section configured to predict the evaluation by the user of interest regarding the content, based on the evaluation information of the first type given by the user of interest and on the evaluation information given by the other user in the evaluation information of the second type given by the user of interest.

Abstract: Systems and methods are provided for a well control expert system that provides well control recommendations for a drilling system. The well control expert system includes a well control Bayesian decision network (BDN) model that receives inputs and outputs recommendations based on Bayesian probability determinations. The well control BDN model includes a circulation section, a well control practices section, and a troubleshooting section.

Abstract: Example methods and systems for determining when to launch vehicles into a fleet of autonomous vehicles are described. A method comprises receiving a sequence of coverage requirements for a region over a period of time. The region may be characterized by landmarks and the period of time can be divided into time intervals. The method also includes defining a landmark as a launch site representative of a landmark at which a given vehicle can be added to a plurality of operating vehicles, and determining for a respective landmark, estimated landmarks that can be reached by a vehicle starting from the respective landmark by an end of a time interval. The method also includes based on the sequence of coverage requirements and the estimated landmarks, determining a given launch site and corresponding time interval at which to add the given vehicle to the plurality of operating vehicles.

Abstract: A system and method for processing information in unstructured or structured form, comprising a computer running in a distributed network with one or more data agents. Associations of natural language artifacts may be learned from natural language artifacts in unstructured data sources, and semantic and syntactic relationships may be learned in structured data sources, using grouping based on a criteria of shared features that are dynamically determined without the use of a priori classifications, by employing conditional probability constraints.

Abstract: A method of computerized content analysis that gives “approximately unbiased and statistically consistent estimates” of a distribution of elements of structured, unstructured, and partially structured source data among a set of categories. In one embodiment, this is done by analyzing a distribution of small set of individually-classified elements in a plurality of categories and then using the information determined from the analysis to extrapolate a distribution in a larger population set. This extrapolation is performed without constraining the distribution of the unlabeled elements to be equal to the distribution of labeled elements, nor constraining a content distribution of content of elements in the labeled set (e.g., a distribution of words used by elements in the labeled set) to be equal to a content distribution of elements in the unlabeled set.

Abstract: Methods, systems, and apparatus, including computer programs encoded on one or more computer storage devices, for training a predictive model. In one aspect, a method includes receiving over a network predictive modeling training data from a client computing system. The training data and multiple training functions obtained from a repository of training functions are used to train multiple predictive models. A score is generated for each of the trained predictive models, where each score represents an estimation of the effectiveness of the respective trained predictive model. A first trained predictive model is selected from among the trained predictive models based on the generated scores. Access to the first trained predictive model is provided over the network.

Abstract: Systems and methods for predicting an identity of a person are provided. In some aspects, a list of subject activities accessed by a subject person is received. For each of a plurality of stored persons, a stored list of activities accessed with by the stored person is accessed in one or more data repositories. An intersection is calculated between the list of subject activities and the stored list of activities for at least one stored person from the plurality of stored persons. That the subject person is likely to correspond to the at least one stored person from among the plurality of stored persons is predicted, based on the calculated intersection. An indication that the subject person is likely to correspond to the at least one stored person from among the plurality of stored persons is provided.

Abstract: Data-driven rules are eliminated from an application, and replaced with goal-driven rules that formally and completely characterize high-level business concepts, while enabling the resulting goal-driven rules to be executed eagerly using a forward chaining rule engine in a way that preserves the data-driven behavior of the original rules. A computer-implemented method for executing business rules provides a unified rule that encapsulates a goal-oriented rule with triggering information selected from dependencies of the goal-oriented rule. The unified rule may be eagerly executed in response to changes in data specified by the dependencies, or lazily executed in response to a request for the value computed by the rule.

Abstract: A method of generating training documents for training a classifying device comprises, with a processor, sampling from a distribution of words in a number of original documents, and creating a number of pseudo-documents from the distribution of words, the pseudo-documents comprising a similar distribution of words as the original documents. A device for classifying textual documents comprises a processor; and a memory communicatively coupled to the processor, the memory comprising a sampling module to, when executed by the processor, determine the distribution of words in a number of original documents, a pseudo-document creation module to, when executed by the processor, create a number of pseudo-documents from the distribution of words, the pseudo-documents comprising a similar distribution of words as the original documents, and a training module to, when executed by the processor, train the device to classify textual documents based on the pseudo-documents.

Abstract: Embodiments relate to making predictions for values or states to follow multiple time steps after receiving a certain input data in a spatial and temporal memory system. During a training stage, relationships between states of the spatial and temporal memory system at certain times and spatial patterns of the input data detected a plurality of time steps later after the certain time steps are established. Using the established relationships, the spatial and temporal memory system can make predictions multiple time steps into the future based on the input data received at a current time.

Abstract: The present disclosure provides a geographically localized recommendation to a user through a computer-based advice facility, comprising collecting a recommendation from an Internet source, wherein the recommendation is determined to have an interestingness aspect and a geographic location aspect, comparing the collected recommendation to a derived user taste and the user's current geographic location, determining at least one recommendation for the user based on processing on the comparison, and delivering at least one recommendation to a user's mobile communications device, wherein the user is enabled to at least one of view, save, and share the recommendation via an application at least in part resident on the computer-based advice facility.

Abstract: A real-time predictive intelligence platform comprises: receiving from a user through a meta API definitions for predictive intelligence (PI) artifacts that describe a domain of an online transaction system for least one business entity, each of the PI artifacts including types, component modules and behavior bundles; exposing an entity API based on the PI artifacts for receiving entity events from the online transaction system comprising records of interactions and transactions between customers and the online transaction system; responsive to receiving an entity event through the entity API, executing the component modules and behavior bundles to analyze relationships found between past entity events and metrics associated with the past entity events, and computing a probabilistic prediction and/or a score, which is then returned to the online transaction system in real-time; and processing entity event replicas using modified versions of the PI artifacts for experimentation.

Abstract: Optical flow for a moving platform may be encoded into pulse output. Optical flow contribution induced due to the platform self-motion may be cancelled. The cancellation may be effectuated by (i) encoding the platform motion into pulse output; and (ii) inhibiting pulse generation by neurons configured to encode optical flow component optical flow that occur based on self-motion. The motion encoded may be coupled to the optical flow encoder via one or more connections. Connection propagation delay may be configured during encoder calibration in the absence of obstacles so as to provide system specific delay matrix. The inhibition may be based on a coincident arrival of the motion spiking signal via the calibrated connections to the optical flow encoder neurons. The coincident motion pulse arrival may be utilized in order to implement an addition of two or more vector properties.