Abstract: An end-effector may be summarized substantially as described and illustrated herein.

Abstract: An end-effector may be summarized substantially as described and illustrated herein.

Abstract: Content Based Similarity Detection. A computer implemented method includes computing a hash of each word in a collection of books to produce a numerical integer token using a reduced representation and computing an Inverse Document Frequency (IDF) vector comprising the number of books the token appears in, for every token in the collection of books. The method also includes creating a token occurrence count vector for each book in the collection and normalizing the token occurrence count vector using the IDF vector to create a Term Frequency-Inverse Document Frequency (TF-IDF) vector. Further, the method includes reducing each TF-IDF vector by using random projections to obtain a final signature representing each book in the collection, reducing each TF-IDF vector by using random projections to obtain a final signature representing each book in the collection and using a trained machine learning algorithm, determining whether each of the list of candidate books is similar to the target book.

Abstract: Methods and systems for supplementing an e-Book's metadata with a unique identifier are provided. The method accesses metadata associated with the e-Book on a computing device. In the metadata a name portion of e-Book series metadata conflated with non-name related information is found. A plurality of rules is used on the name portion of the e-Book series metadata conflated with non-name related information to determine a distinct e-Book series name. The distinct e-Book series name is used to generate a unique series identifier for each e-Book in the same series. The metadata is then supplemented with the unique series identifier.

Abstract: Methods and systems for disambiguating an e-Book series number for an e-Book are provided. The method accesses metadata associated with the e-Book. In the metadata, a portion of e-Book series metadata comprising series related information is found. A plurality of rules is used on the metadata comprising series related information to generate a standardized e-Book series number.

Abstract: Methods and systems for disambiguating an e-Book series name for an e-Book are provided. The method accesses, on an e-Reading device, metadata associated with the e-Book; finds, in the metadata, a name portion of e-Book series metadata conflated with non-name related information; and utilizes a plurality of rules on the name portion of the e-Book series metadata conflated with non-name related information to determine a distinct c-Book series name.

Abstract: Content Based Similarity Detection. A computer implemented method includes computing a hash of each word in a collection of books to produce a numerical integer token using a reduced representation and computing an Inverse Document Frequency (IDF) vector comprising the number of books the token appears in, for every token in the collection of books. The method also includes creating a token occurrence count vector for each book in the collection and normalizing the token occurrence count vector using the IDF vector to create a Term Frequency-Inverse Document Frequency (TF-IDF) vector. Further, the method includes reducing each TF-IDF vector by using random projections to obtain a final signature representing each book in the collection, reducing each TF-IDF vector by using random projections to obtain a final signature representing each book in the collection and using a trained machine learning algorithm, determining whether each of the list of candidate books is similar to the target book.