The Palantir Corporation

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Palantir Corporation (calera Recognition Systems)

Mindy R Bokser

A method for recognizing an object by processing reference feature vectors a collection of reference feature vectors is generated; each representing a reference pattern that belongs to one of a plurality of predefined classes. Each class has associated all reference feature vectors representing reference patterns belonging to that class. For a selected class, an associated hierarchy of one or more sets of possibility regions is generated, wherein for a selected set in the hierarchy the number of possibility regions in said selected set is significantly less than the number of reference feature vectors belonging to said selected class, and such that each reference feature vector belonging to said selected class is contained in at least one possibility region of said selected set, and such that each possibility region of said selected set contains relatively few reference feature vectors not belonging to said selected class.

Preprocessing means for use in a pattern classification system

A method for preprocessing reference feature vectors representing patterns in order to form, for each selected pattern class, collections of regions. A hierarchy of possibility regions is formed, wherein all reference feature vectors of a pattern class are contained in each level of the hierarchy of possibility regions associated with the pattern class. This hierarchy is later used to exclude a pattern class from consideration if a feature vector representing an unknown pattern is not contained in some level of its associated hierarchy of possibility regions. A collection of certainty regions is used, wherein no reference feature vector not of a pattern class is contained within any certainty region associated with the pattern class. The certainty regions are later used to classify a feature vector representing an unknown pattern as belonging to a pattern class. A collection of confidence regions is used to identify, although not with certainty, an unknown input pattern, and assign a confidence value indicating the relative confidence associated with the possibility that this unknown input pattern belongs to the pattern class.

A method of identifying an object within a set of object candidates includes the steps of:

Means for resolving ambiguities in text passed upon character context

Means for resolving ambiguities in text based upon character context

A method for preprocessing reference feature vectors representing patterns in order to form, for each selected pattern class, collections of regions. A hierarchy of possibility regions is formed, wherein all reference feature vectors of a pattern class are contained in each level of the hierarchy of possibility regions associated with the pattern class. This hierarchy is later used to exclude a pattern class from consideration if a feature vector representing an unknown pattern is not contained in some level of its associated hierarchy of possibility regions. A collection of certainty regions is used, wherein no reference feature vector not of a pattern classis contained within any certainty region associated with the pattern class. The certainty regions are later used to classify a feature vector representing an unknown pattern as belonging to a pattern class. A collection of confidence regions is used to identify, although not with certainty, an unknown input pattern, and assign a confidence value indicating the relative confidence associated with the possibility that this unknown input pattern belongs to the pattern class.

A method for classifying an input feature vector representing an unknown pattern as belonging to one of a set of pre-defined pattern classes, wherein the input feature vector is compared with pre-constructed regions. A hierarchy of possibility regions is used to exclude a pattern class from consideration. Certainty regions are used to classify with certainty the input feature vector as belonging to some pattern class. Confidence regions are used to identify, although not with certainty, the unknown input pattern and assign a confidence value indicating the relative confidence associated with the possiblity that this unknown pattern belongs to given pattern class.

Pattern classification means using feature vector regions preconstructed

A method for identifying unknown input data, such as patterns or characters. In order to classify unknown input characters, first, during a preprocessing phase, large number of reference data is collected and analyzed in order to form 'ringed clusters' for each class of input data. For example, if the input data are characters, a set of ringed clusters is associated with each character class, such as all 'e'. These ringed clusters may be coarse, medium or fine depending upon the desired accuracy in classifying the input characters. The ringed clusters include 'certainty spheres' which are used to identify with certainty an unknown input character if it lies within such a sphere. The ringed clusters also include 'confidence spheres' which are used to identify, although not with certainty, the unknown input character, and assign a confidence value indicating the relative confidence associated with the possibility that this unknown character corresponds to the reference data class of the ringed cluster.

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San Francisco, CA, United States of America

Mindy R Bokser