Patent Document ID: 8583419
Application ID: 12450575

Base Claim:
1. A computer-implemented method for advance and/or unsupervised machine learning by Latent Metonymical Analysis and Indexing (LMai), said method comprising steps of: a. inputting natural documents; b. eliminating special characters to count a number of words within the given document, filtering the contents of the document based on a list of predefined stop-words and calculating a fraction of the stop-words present in the document; c. determining a Significant Single Value Term data set and a Significant Multi Value Term data set from the document; d. decomposing words in the Significant Single Value Term data set and the Significant Multi Value Term data set to extract Keywords of the document being processed; e. optionally, determining KeyTerms and their respective hand-in-hand (HiH) words automatically for further decomposition, wherein the hand-in-hand (HiH) words are words that go together as one word; f. identifying a Topic in an unsupervised manner based not just on a File Name but also by manipulating/comparing with various combinations of document attributes that are extracted to select Best Topic candidates and thereafter defining an appropriate Topic based on predefined rules using a computer; and g. analyzing relationship between the identified Topics and the Keywords and thereafter indexing the Topics and their related Keywords, KeyTerms and their respective hand-in-hand terms into a Metonymy cluster and a KeyTerms HiH cluster respectively.

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Claim 9:
9. The computer-implemented method as claimed in claim 1 , said method further comprising steps of: a. checking the document being processed to identify a prerequisite minimal size of data and/or word articles/words; b. storing the data or words in the document in a sequential order as per their occurrence in the document; c. creating two identical instances of the data to facilitate the identification of the Significant Single Value Term data set and the Significant Multi Value Term data set; d. determining a Significant Single Value Term from one of the instance of the data set and a Significant Multi Value Term from the other instance of the data set starting from the highest hand-in-hand words range predefined, followed by consecutive hand-in-hand range terms of lesser dimension; e. storing the identified Significant Single Value Term and the identified Significant Multi Value Term of different hand-in-hand range in their respective data sets; f. comparing words in the Significant Multi Value Term data set with the words in the Significant Single Value Term data set to extract those words in the respective hand-in-hand range of each Significant Multi Value Term data set as Best-Terms, which have at least one instance of Single Value Terms within their range and the rest of the hand-in-hand terms are decomposed; and g. comparing the data sets in such a way that every individual hand-in-hand range term that has at least one instance of any term in the Significant Single Value Term data set that is extracted as a Keyword and the rest are decomposed to determine the KeyTerms.