Abstract: Disclosed are computer systems with intelligent or autonomous decision systems which include means for determining relevancy, i.e., the threats to and opportunities of the autonomous decision system. Also disclosed are such autonomous decision systems using an efficient ontology to interact sociably with humans, including the use of natural languages and bonding. The desired “whether concrete is included in abstract” computation system is enhanced by the ontology system using categorizing of natural objects using as primitives a set of self tendencies suitable, when hierarchically assigned to objects, to do incremental simulation of “future” situations (including such objects) from a presented situation. Using such primitives and computation system, planning, learning, languaging, etc., are efficiently accomplished.

Abstract: An email object is provided in a multi-tenant database system that can be related to multiple people (e.g., contact, lead, user) or any object represented for storage in the multi-tenant database system via sharing relationships. The email object follows a sharing model such that an email inherits the sharing model or attributes of a parent record specified via sharing relationships. In various aspects, a sharing model provides users with a true value of a multi-tenant database system as users are more informed about communications concerning people or other objects represented for storage in the multi-tenant database system.

Abstract: An object cache framework client selects a rule-set object in an object cache framework. The rule-set object comprises a rule-set (set of business rules). The object cache framework client evaluates criteria of the rule-set against properties of a plurality of rules engines. The object cache framework client determines if the properties of a rules engine, from a plurality of rules engines, satisfy the criteria of the rule-set. The object cache framework client supplies the rule-set object to the rules engine for execution of the rule-set.

Abstract: An indexed hierarchical tree search structure converts each registration sample into an equivalent registration model based on the clustering of its registration item descriptors in the leaf nodes of the hierarchical tree. Query item descriptors from a query sample from someone wanting to be recognized are distributed into the hierarchical tree. A query model is defined based on the clustering of query item descriptors at the leaf nodes, and registration and verification are made based on comparison of the query model and the registration models.

Abstract: In particular embodiments, a method includes accessing a first binary decision diagram (BDD) representing a data stream from a first sensor and a second BDD representing a data stream from a second sensor, determining whether the first sensor data and the second sensor data correlate by: constructing a third BDD by performing an OR operation between the first and second BDDs, determining sizes of the first, second, and third BDDs, and comparing the third size to the sum of the first size and the second size; where the degree that the first sensor data and the second sensor data correlate is indicated by the amount that the third size is less than the sum of the first size and the second size.

Abstract: Estimating a remaining useful life (RUL) of an apparatus is disclosed. A computer device may obtain a priori RUL data of an apparatus. The a priori RUL data identifies a priori RULs values of the apparatus as a function of time. Buckets are then defined in the a priori RUL data, wherein each of the buckets corresponds to a different set of the a priori RUL values in the a priori RUL data. An operational event indicator may then be obtained for the apparatus that indicates a current operational event of the apparatus. The RUL of an apparatus is estimated by determining probability values throughout a time period. Probability values are then determined based on the operational event indicator where each probability value quantifies a probability that a current RUL value of the first apparatus is within one of the buckets.

Abstract: A system includes a centralized repository for tracking rule content and managing subscriptions to rule content by organizations and providers utilizing the system; a rule-evaluation server for receiving requests for rule-evaluations for specific patients, wherein the server determines content needing to be evaluated and retrieves the content to be used; a rule engine for performing the evaluations, wherein content, patient data, and rule evaluation parameters are provided to the engine, and the engine returns recommendations triggered by the evaluation, if any; an aggregator for aggregating recommendations from multiple sources, detecting and coordinating related recommendations, and applying configuration settings based on the patient and/or provider in context; and a client component for coordinating communication between an electronic health records system, the server, and the aggregator.

Abstract: A phrase identification system and method are provided. The method comprises: identifying one or more phrase candidates in the electronic document; selecting one of the phrase candidates; numerically representing features of the selected phrase candidates to obtain a numeric feature representation associated with that phrase candidate; and inputting the numeric feature representation into a machine learning classifier, the machine learning classifier being configured to determine, based on each numeric feature representation, whether the phrase candidate associated with that numeric feature representation is a phrase.

Abstract: The present invention provides a System and Method to Define, Validate and Extract Data for Predictive Models. A system of sensors is deployed in an environment, with additional sensors for ambient data whose output as a form of metadata can characterize performance conditions including background ambient conditions. A format or sequence of processes is the basis for a math model to establish a logical weight to data for predictive modeling and event reporting. The present invention provides a computer or other sensor interface system with a primary sensor or sensors, network connection, and supplementary sensors to measure the conditions in which the primary data is captured. A software process allows for user inputs of data in order to establish the methods and rules for normal function.

Abstract: To provide a discriminant model learning device capable of efficiently learning a discriminant model on which domain knowledge indicating user's knowledge or analysis intention for a model is reflected while keeping fitting to data. A query candidate storage means 81 stores candidates of a query as a model to be given with domain knowledge indicating a user's intention. A regularization function generation means 82 generates a regularization function indicating compatibility with domain knowledge based on the domain knowledge to be given to the query candidates. A model learning means 83 learns a discriminant model by optimizing a function defined by a loss function and the regularization function predefined per discriminant model.

Abstract: CMOS-memristor circuit is constructed to behave as a trainable artificial synapse for neuromorphic hardware systems. The invention relies on the memristance of a memristor at the input side of the device to act as a reconfigurable weight that is adjusted to realize a desired function. The invention relies on charge sharing at the output to enable the summation of signals from multiple synapses at the input node of a neuron circuit, implemented using a CMOS amplifier circuit. The combination of several memristive synapses and a neuron circuit constitute a neuromorphic circuit capable of learning and implementing a multitude of possible functionalities.

Abstract: Computer-readable media, computer systems, and computing devices facilitate providing a comparison experience to a user in response to a search query. Upon receiving a search query from the user, entities are extracted from the query. The entities are associated with entity classes. The entities, entity classes, previous user behavior, and other information are used to infer whether the user likely is engaging in a comparison task. If the inference indicates that the user likely is engaging in a comparison task, a comparison experience is generated and access to the comparison experience is provided to the user.

Abstract: The disclosure relates to identifying a solution to a problem wherein the problem and solution are in different disciplines. An exemplary embodiment concerns a system for identifying a solution comprising a bridging component for generating a bridged ontology from data elements of prime and secondary ontologies if the number the data elements common to the ontologies meets a threshold value. The system may comprise an inference engine for identifying, from the bridged ontology, data elements related to the problem and the solution. The inference engine may further determine relationships between the data elements associated with the problem and those associated with a solution to confirm the correctness of a solution. The problem and solution may be represented as RDF data objects.

Abstract: A method and system for filtering irrelevant items using machine-readable emotional codes from a plurality of database entities is disclosed. The entity can be a human user, a product, or a service. The emotional codes are category labels out of a plurality of categories and the plurality of categories are generated by categorizing user responses. After understanding the sending parties' emotional profile, the receiving party can filter irrelevant items to achieve more optimum results.

Abstract: One or more machine learning classifiers are trained to classify cases in one or more categories using one or more sets of labeled training data. A first distribution of scores for positive cases in the training set is determined for each category, and a second distribution of scores for negative cases in the training set is determined for each category. A third distribution of scores is generated by each classifier classifying cases in a set of target data is also determined. A proportion of cases in the target set that are positive cases for a category is estimated by fitting the first distribution and the second distribution for the category to the third distribution.

Abstract: A method is provided for “but reasoning” with an inconsistent knowledge base. The method not only gives an answer to a Boolean question but also additional and instructive information. The method first finds a bridge statement that explains a contradiction between the answer and the inconsistent knowledge base. The method then selects a but statement that causes the contradiction with the answer, and constructs and validates a but answer with the answer, the but statement, and optionally the bridge statement.

Abstract: A collection of labeled training cases is received, where each of the labeled training cases has at least one original feature and a label with respect to at least one class. Non-linear transformation of values of the original feature in the training cases is applied to produce transformed feature values that are more linearly related to the class than the original feature values. The non-linear transformation is based on computing probabilities of the training cases that are positive with respect to the at least one class. The transformed feature values are used to train a classifier.

Abstract: An operation support apparatus includes: an operation record storage unit (303) in which an operation record of a user is stored; a target function candidate storage unit (309) which defines and stores target function candidates; an operation strategy determination unit (305) which determines that the user's operation strategy is an operation strategy which originates from content selection, in the case where the operation included in the operation record is a predetermined operation performed when the appliance is in a status in which content can be displayed or selected; a target function inference unit (308) which infers the target function according to a method suited to the operation strategy determined by the operation strategy determination unit (305); and an operation support determination unit (310) which determines an operation support method for the user, using the inferred target function.

Abstract: An automated method and system are provided for receiving an input of flow cytometry data and analyzing the data using one or more support vector machines to generate an output in which the flow cytometry data is classified into two or more categories. The one or more support vector machines utilize a kernel that captures distributional data within the input data. Such a distributional kernel is constructed by using a distance function (divergence) between two distributions. In the preferred embodiment, a kernel based upon the Bhattacharyya affinity is used. The distributional kernel is applied to classification of flow cytometry data obtained from patients suspected having myelodysplastic syndrome.

Abstract: A method of monitoring an evolving system, the method including the steps of: obtaining a plurality of sensor data streams relating to outputs from sensors monitoring said system, wherein at least one of said sensors monitors a condition of said system, and wherein at least one of said sensors monitors a causal agent for said condition; iteratively constructing a plurality of functional nests, each functional nest being a functional formed from a combination of selected functionals from a basic set of functionals; determining an output data stream for each functional nest by inputting said sensor data streams into said functional nests; selecting a functional nest from said plurality of functional nests based on said output data streams; and using said selected functional nest to monitor said system.