Abstract: Computer implemented techniques for classifying mental states of individuals and providing tailored support are described. The techniques determine sets of features that are associated with multiple groups having different mental status, and a classification model is used to classify one group against another group. The techniques also include receiving user set goal, querying a system database to determine whether there is a machine learning model to predict risk associated with the received goal assessing changes in a real-time risk value associated with the goal, generating an automated dialog associated with assessed changes in a real-time risk value associated with the goal, posting to a buddy system the real time risk value with the generated dialog, tracking edits made on the buddy system, and finally a comparison of users and their assisted goal accomplishment.

Abstract: Computer implemented techniques for classifying mental states of individuals are described. The techniques determine sets of words that are associated with multiple groups having different mental status, and a classification model is used to classify one group against another group. Furthermore, by determining points of intersection of words between a first group and second group, words that are statistically predictive terms and that are unique to each group, to provide further predictive features for differentiating the multiple cohorts.

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: Techniques to estimate the probability of a future event occurring are described. The techniques include decomposing a data input stream to build a database of precursor data and building predictive models using the precursor data. Also disclosed are techniques in which by using a search engine to search a database of models to find a model and a user can query a found model to develop an inference of the likelihood of the future event.

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: Computer implemented techniques for classifying mental states of individuals are described. The techniques determine sets of words that are associated with multiple groups having different mental status, and a classification model is used to classify one group against another group. Furthermore, by determining points of intersection of words between a first group and second group, words that are statistically predictive terms and that are unique to each group, to provide further predictive features for differentiating the multiple cohorts.

Abstract: Techniques to estimate the probability of a future event occurring are described. The techniques include decomposing a data input stream to build a database of precursor data and building predictive models using the precursor data. Also disclosed are techniques in which by using a search engine to search a database of models to find a model and a user can query a found model to develop an inference of the likelihood of the future event.

Abstract: Techniques for estimating a probability that a future event will occur based on user input includes decomposing a data input stream to build a database of precursor data to build at least one predictive model, building a database of at least one model generated by a model building process using the precursor data in the database, with the at least one model being a model that produces predictions of the likelihood of an event occurring in the future based on analysis of data in the database and storing the at least one model in a second database that stores models, with the database being searchable, to permit the models in the database to be accessed by users. Also disclosed are techniques in which by using a search engine to search a database of models to find a model and a user can query a found model to develop an inference of the likelihood of a future event.

Abstract: Techniques for estimating the structure and meaning of data using probability are described. The techniques include retrieving data as data strings from a data source, producing a dataset from the retrieved data strings and building a statistical model of parent-child relationships from data strings in the dataset. Building the statistical model includes determining incidence values for the data strings in the dataset and concatenating the incident values with the data strings to provide child variables. The techniques include analyzing the child variables and the parent variables to produce statistical relationships between the child variables and a parent variable, determining probabilities values based on the determined parent child relationships and building an ontological representation of the data based on subsequent conditional probabilities values.

Abstract: Techniques for estimating a probability that a future event will occur based on user input includes decomposing a data input stream to build a database of precursor data to build at least one predictive model, building a database of at least one model generated by a model building process using the precursor data in the database, with the at least one model being a model that produces predictions of the likelihood of an event occurring in the future based on analysis of data in the database and storing the at least one model in a second database that stores models, with the database being searchable, to permit the models in the database to be accessed by users. Also disclosed are techniques in which by using a search engine to search a database of models to find a model and a user can query a found model to develop an inference of the likelihood of a future event.

Abstract: A method of performing transactions over an electronic network includes defining data entries for objects represented in the network. The data entries including metadata represented as a web-readable document for an object and the entries including a keyword that represents network information or user process information related to the object. The method associates an object file with an entry that corresponds to the object being represented.

Abstract: A method of performing transactions over an electronic network, the method includes defining data entries for objects represented in the network the data entries including metadata represented as a web-readable document for an object and the entries including a keyword that represents network information or user process information related to the object and associating an object file with an entry that corresponds to the object being represented.