Patent Document ID: 9256805
Application ID: 14591175

Base Claim:
1. A server-side method of identifying a wine entity from text in a digital image of a wine menu comprising: obtaining a digital image from a mobile device, wherein the digital image comprises a digital photograph of a physical text, wherein at least a portion of the physical text is related to a pre-defined topic, wherein the digital image comprises a digital photograph of a wine menu, wherein the pre-defined topic comprises a wine-related topic, and wherein the digital photograph is obtained with a digital camera system in the mobile device of a user; converting the digital photograph of the physical text to a text in a computer-readable format; providing a word dictionary, wherein the word dictionary comprises a set of words related to the pre-defined topic; matching a set of words of the text to similar words in the set of words in the word dictionary; identifying a word cluster in the text, wherein each word in the word cluster is associated with a category of a single entity, wherein the single entity is a member of a class of entities demarcated by the pre-defined topic, wherein the class of entities demarcated by the pre-defined topic comprises a set of wine items, and wherein a set of categories of the wine item comprises a wine varietal, a wine producer and a wine vintage; searching a database comprising a list of members of the class of entities demarcated by the pre-defined topic for one or more entities matching one or more of word-category associations of the word cluster; receiving a user instruction that identifies the word cluster; and implementing a linear n-gram scanning processes to convert a set of character strings of each word in the set of words of the text to words related to the pre-defined topic according to a statistical algorithm.

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Claim 5:
5. The method of claim 1 further comprising: returning a sorted list of the one Or more entities matching the one or more of word-category associations of the word cluster, Wherein in the list is ranked based on the number of matches between the word-category associations of the word cluster for each entity in the list.