Motorola Corporation

Guidewire Software, Inc.

John Lome Seybold

Giovanni Seni

Clark Allan Heydon

A computing platform () of choice can be configured and arranged to access (and) a first memory () that stores attributes which specifically characterize a particular candidate insurance entity and a second memory () that stores mappings which relate insurance policy element availability for a plurality of insurance policy elements to various corresponding insurance entity characterizing attributes. The aforementioned attributes, by one approach, correspond to respective end user-configurable dimensions. This platform can then serve () as a matching component by automatically using the aforementioned mappings to determine available insurance policy elements for the particular candidate insurance entity as a function of these attributes and then automatically using () those available insurance policy elements to configure an insurance policy consistent with these mappings.

Method and apparatus to facilitate determining insurance policy element availability

Handwritten ink is scanned to identify potential diacriticals. A list of diacriticals ( ) is generated by traversing the ink. Potential diacritical-containing characters are processed by scoring them with and without a diacritical to generate a first and second score. The first score is compared to the second score to in order to make a decision as to which variant of the potential diacritical-containing character produced a highest score. The highest score is used as a score for a theory and the decision is recorded. A data structure ( ) is added to the theory. Each data unit in the data structure ( ) corresponds to an entry in the list of diacriticals ( ). As a new theory is created by propagation, contents of the data structure ( ) are copied into the new theory. Thus, the data structure ( ) is used to ensure that all handwritten ink is used and is used only once.

Diacritical processing for unconstrained, on-line handwriting recognition using a forward search

In a method of recognition of handwritten input, a preprocessing module (,) of a recognizer (,) having a recognition module (,), segments handwritten input into strokes, to provide stroke vectors. A search module (,) generates hypothetical character strings for scoring by the recognition module (,). A character hypothesis is generated which is a data set of a character, a start vector and an end vector. The recognizer (,) provides a non-cumulative score for the character hypothesis, where the score represents a likelihood of the character being present in the handwritten input at a position starting at the start vector and ending at the end vector. The recognizer (,) delivers to the search module a result that is either the score for the selected first character hypothesis or a result that allows the search module to temporarily ignore the hypothesis or to kill it altogether. The search module maintains or disregards the first hypothetical character string dependent on the result. The process is repeated for other character hypotheses and other hypothetical character strings.

Text recognizer and method using non-cumulative character scoring in a forward search

A cache is used to provide additional information to a recognition system about what words are likely to be used in a current context. Words are added to the cache only if they are explicitly corrected by the user. If the user forgets to correct (or postpones correcting) a recognition error, nothing is added to the cache, and therefore, there is no negative impact on the recognition system.

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Palo Alto, CA, United States of America

John Lome Seybold