Abstract: Apparatus and method to combine unstructured free text with structured data to make predictive modeling easier and better. Structured data is received from applying an extractor to the unstructured free text or from a database query of a related database. This permits unstructured model-building to be used when data also comes from structured data, also facilitating explanations of inferences based upon both unstructured and structured key passages.

Abstract: This disclosure describes, among other things, an apparatus for generating an inference engine about a document. The apparatus includes at least one processor and a memory with instructions. The memory including instructions that, when executed by the at least one processor, cause the at least one processor to perform a number of processes. The processor accesses a set of documents. Each document has a corresponding inference. The processor also generates a vector representation for each document in the set of documents. First, the processor parses text of the document into groups of words, and generates a vector representation for each group.

Abstract: This disclosure describes, among other things, an apparatus for generating an inference engine about a document. The apparatus includes at least one processor and a memory with instructions. The memory including instructions that, when executed by the at least one processor, cause the at least one processor to perform a number of processes. The processor accesses a set of documents. Each document has a corresponding inference. The processor also generates a vector representation for each document in the set of documents. First, the processor parses text of the document into groups of words, and generates a vector representation for each group.

Abstract: A disclosed method of developing a drug comprises, in one embodiment: selecting a sub-group of constituent drugs from a potential group, wherein the selection is made on the basis that an expected effectiveness of a combination drug comprising all of the compounds of the selected sub-group exceeds a pre-determined minimum threshold measure of expected effectiveness against a target of the drug, and such that a side-effect measure for each expected side-effect of the combination drug is less than a corresponding pre-determined maximum threshold measure of side-effect tolerability for each side-effect; and selecting a dosage for each drug of the sub-group such that each dosage is less than a dosage that would be used as a fully effective dosage of the drug when used individually against the target of the drug. Related methods of treating patients, combination drug formulations, and computer-implementations of the methods are also disclosed.

Abstract: A safety system reduces personal risk by establishing a Web-based method of verifying that an individual returns from a date, a hike, or vacation when expected and, if not, of automatically alerting friends and relatives and summoning help from the police. The safety system also enables elderly or infirm individuals to routinely indicate their well-being on a regular basis, e.g. daily, an alert being sent in the absence of a signal. Further, the safety system provides a Web-based repository for personal information and a mechanism of controlled access to this information by other parties.

Abstract: A method for generating context vectors for use in a document storage and retrieval system. A context vector is a fixed length list of component values generated to approximate conceptual relationships. A context vector is generated for each word stem. The component values may be manually determined on the basis of conceptual relationships to word-based features for a core group of word stems The core group of context vectors are used to generate the remaining context vectors based on the proximity of a word stem to words and the context vectors assigned to those words. The core group may also be generated by initially assigning each core word stem a row vector from an identity matrix and then performing the proximity based algorithm. Context vectors may be revised as new records are added to the system, based on the proximity relationships between word stems in the new records.

Abstract: A method for storing and searching documents also useful in disambiguating word senses and a method for generating a dictionary of context vectors. The dictionary of context vectors provides a context vector for each word stem in the dictionary. A context vector is a fixed length list of component values corresponding to a list of word-based features, the component values being an approximate measure of the conceptual relationship between the word stem and the word-based feature. Documents are stored by combining the context vectors of the words remaining in the document after uninteresting words are removed. The summary vector obtained by adding all of the context vectors of the remaining words is normalized. The normalized summary vector is stored for each document. The data base of normalized summary vectors is searched using a query vector and identifying the document whose vector is closest to that query vector.

Abstract: An expert system in which an inference engine is controlled by a matrix of learning coefficients and a method for generating a matrix controlled expert system from examples. The inference engine determines likely values and final determinations for resultant variables in the matrix, the variables being representative of responses or actions. The inference engine also determines an input variable which will contribute to making a final determination of a resultant variable. The matrix may be generated by training examples and/or rules and it may be modified dynamically by additional examples as the system operates.