Patent Document ID: 9710547
Application ID: 14550082

Base Claim:
1. A method for performing a semantic matching process, the method, with at least one computing device, comprising: detecting one or more meanings of a query, comparing the one or more meanings with one or more detected meanings of one or more pieces of content, and outputting at least one response of the comparing; wherein detecting one or more meanings of the query further comprises: detecting and formalizing all meanings of the query into a global semantic representation, wherein the global semantic representation gives a full meaning of the query by transforming individual or groups of words of the query into semantic representations comprising pairs of lemma and a semantic category retrieved from a lexicon and lexical functions assignments and rules database, and weighting the semantic representations in a basis of their category index and their frequency to generate a global weighted semantic representation of the query; wherein detecting one or more meanings of the one or more pieces of content further comprises: detecting and formalizing one or more meanings of the one or more pieces of content into a global semantic representation, wherein the global semantic representation gives a full meaning of the one or more pieces of content by transforming individual or groups of words of the one or more pieces of content into semantic representations comprising of pairs of lemma and a semantic category retrieved from the lexicon and lexical functions assignments and rules database; and weighting the semantic representations in a basis of their category index and their frequency to generate a global weighted semantic representation of the one or more pieces of content; wherein the comparing further comprises: calculating a semantic matching degree and assigning a score between the global weighted semantic representation of the query and the global weighted semantic representation of the one or more pieces of content, and retrieving at least one piece of content of the one or more pieces of content based on the at least one piece of content having the best assigned score and output the retrieved at least one piece of content as the response; wherein the one or more pieces of content are formed of phrases or expressions obtained from a contents database.

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Claim 8:
8. The method of claim 1 , wherein the matching process between the query and the one or more pieces of content comprises: for each semantic representation of the global semantic representation of the query: assigning a category index that is proportional to importance of the semantic category; normalizing the category index assignment to determine a semantic weight based on category wherein all category indexes for the global semantic representation of the query equal 1, and dividing the category index by the sum of category indexes of all semantic representation of the global semantic representation of the query; assigning a frequency index to each semantic representation through a precalculated meaning-frequency table; and calculating and normalizing a frequency balanced semantic weight by dividing the category by a log function of the meaning-frequency value for each meaning of the global semantic representation in the query and normalizing them in order that all frequencies of the global semantic representation of the query add up to 1; for each of the semantic representations of the global semantic representation of the one or more pieces of content: assigning a category index that is proportional to importance of the semantic category; normalizing the category index to determine a semantic weight based on category wherein all category indexes for a global semantic representation of the one or more pieces of content add up to 1, and dividing the category index by the sum of category indexes of all semantic representations of the global semantic representation of the one or more pieces of content; assigning a frequency index to each semantic representation through a precalculated meaning-frequency table, wherein each meaning has a computed frequency that represents the number of times it appears in different pieces of content and a semantic approximation factor that defines the quality of each appearance using a frequency index application, wherein the frequency index application is based on that as information decreases as probability of a meaning increases, based on a logarithmic proportion; and calculating and normalizing a frequency balanced semantic weight by dividing the category by a log function of the meaning-frequency value for each meaning of the global semantic representation of the one or more pieces of content and normalizing them in order that all the frequency balanced semantic weights of the global semantic representation of the one or more pieces of content add up to 1.