The disclosed systems and methods include pre-calculation, per object, of object feature bin values, for identifying close matches between objects, such as text documents, that have numerous weighted features, such as specific-length word sequences. Predetermined feature weights get scaled with two or more selected adjacent scaling factors, and randomly rounded. The expanded set of weighted features of an object gets min-hashed into a predetermined number of feature bins. For each feature that qualifies to be inserted by min-hashing into a particular feature bin, and across successive feature bins, the expanded set of weighted features get min-hashed and circularly smeared into the predetermined number of feature bins. Completed pre-calculated sets of feature bin values for each scaling of the object, together with the scaling factor, are stored for use in comparing sampled features of the object with sampled features of other objects by calculating an estimated Jaccard similarity index.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method of pre-calculation, per object, of object feature bin values for identifying close matches between objects that have numerous weighted features, the method including during min-hashing of a set of weighted features into a predetermined number of feature bins, circularly smearing a feature that qualifies to be inserted by min-hashing into a particular feature bin across successive feature bins, using a processor and memory coupled to the processor, the memory holding objects, sets of weighted features of the objects, and values placed in the feature bins by min-hashing, the circularly smearing including: treating the feature bins as a circular buffer so that a first bin follows a last bin for smearing; when a min-hashed value of a particular feature qualifies to be inserted into bin (i), a qualifying min-hashed value is considered for insertion into bin (i+1) and subsequent bins, including: increasing a min-hashed value by a predetermined increment at each successive step of insertion consideration; when an increased min-hashed value is considered for insertion into the bin (i+1) and subsequent bins, inserting the increased min-hashed value when a bin under consideration is empty or holds a bin value greater than the increased min-hashed value under consideration; and continuing consideration of the bin (i+2) and subsequent bins, through the circular buffer, until the increased min-hashed value fails to qualify to be inserted into a bin under consideration; and saving a completed set of the feature bins for one or more scalings of weights of the object together with a scaling factor or sum of feature weights for use in comparing sampled features of the object with sampled features of other objects when calculating an estimated Jaccard similarity index. 2. The method of claim 1, further including min-hashing by inserting min-hashed values into bins using a fractional part of a min-hashed value produced by hashing a feature value or, in an expanded set of feature values, the feature value combined with a sequence number, into a range spanning the predetermined number of feature bins. 3. The method of claim 1, further including min-hashing by inserting min-hashed values into bins using a fractional part of a min-hashed value produced by hashing a feature value, and, in an expanded set of feature values, the feature value combined with a sequence number, into a range spanning the predetermined number of feature bins, inserting a new minimum fractional part value into a feature bin under consideration when the fractional part of the min-hashed value is less than a current contents of the feature bin under consideration. 4. The method of claim 1, further including min-hashing by inserting min-hashed values into bins using a fractional part of a min-hashed value produced by hashing a feature value and, in an expanded set of feature values, the feature value combined with a sequence number, into a range spanning the predetermined number of feature bins, inserting a new maximum fractional part value into a feature bin under consideration when the fractional part of the min-hashed value is greater than a current contents of the feature bin under consideration. 5. The method of claim 1, further including, for a first and second object, each having the scaling factor or the sum of feature weights: determining a maximum possible similarity between the first and second object by comparing sums of the feature weights or scaled-weights of the features of the first and second object; and when the maximum possible similarity between the first and second object is less than a predetermined threshold, rejecting similarity between the first and second object without counting matches of the values in the feature bins saved for the first object with the values in the feature bins saved for the second object. 6. The method of claim 5, further including, for a first and second object, each having a completed set of feature bins: calculating the estimated Jaccard similarity index based on a count of matches of the values in the feature bins saved for the first object with the values in the feature bins saved for the second object. 7. The method of claim 6, further including: selecting two or more scaling factors to apply to the set of weighted features and mapping the scaled-weights into two or more respective sets of feature bins. 8. The method of claim 7, further including, for a first and second object, each having a completed set of feature bins: calculating the estimated Jaccard similarity index based on a comparison of the values in selected feature bins saved for the first object with the values in the feature bins saved for a second object, wherein the selected feature bins for the first and second object have matching scaling factors. 9. The method of claim 1, further including scaling weights of a weighted set such that the sum of weights is less than or equal to an oversampling factor of samples per bin times the number of bins to fill. 10. A device that provides pre-calculation, per object, of object feature bin values for identifying close matches between objects that have numerous weighted features, the device including: a processor, memory coupled to the processor, the memory holding objects, sets of weighted features of the objects, and values placed in the feature bins by min-hashing, and computer instructions loaded into the memory that, when executed, cause the processor to implement a process that includes: during min-hashing of a set of weighted features into a predetermined number of feature bins, circularly smearing a feature that qualifies to be inserted by min-hashing into a particular feature bin across successive feature bins, the circularly smearing including: treating the feature bins as a circular buffer so that a first bin follows a last bin for smearing; when a min-hashed value of a particular feature qualifies to be inserted into bin (i), a qualifying min-hashed value is considered for insertion into bin (i+1) and subsequent bins, including: increasing a min-hashed value by a predetermined increment at each successive step of insertion consideration; when an increased min-hashed value is considered for insertion into the bin (i+1) and subsequent bins, inserting the increased min-hashed value when a bin under consideration is empty or holds a bin value greater than the increased min-hashed value under consideration; and continuing consideration of the bin (i+2) and subsequent bins, through the circular buffer, until the increased min-hashed value fails to qualify to be inserted into a bin under consideration; and saving a completed set of the feature bins for one or more scalings of weights of the object together with a scaling factor or sum of feature weights for use in comparing sampled features of the object with sampled features of other objects when calculating an estimated Jaccard similarity index. 11. The device of claim 10, further including min-hashing by inserting min-hashed values into bins using a fractional part of a min-hashed value produced by hashing a feature value or, in an expanded set of feature values, the feature value combined with a sequence number, into a range spanning the predetermined number of feature bins. 12. The device of claim 10, further including min-hashing by inserting min-hashed values into bins using a fractional part of a min-hashed value produced by hashing a feature value, and, in an expanded set of feature values, the feature value combined with a sequence number, into a range spanning the predetermined number of feature bins, inserting a new minimum fractional part value into a feature bin under consideration when the fractional part of the min-hashed value is less than a current contents of the feature bin under consideration. 13. The device of claim 10, further including, for a first and second object, each having the scaling factor or the sum of feature weights: determining a maximum possible similarity between the first and second object by comparing sums of the feature weights or scaled-weights of the features of the first and second object; and when the maximum possible similarity between the first and second object is less than a predetermined threshold, rejecting similarity between the first and second object without counting matches of the values in the feature bins saved for the first object with the values in the feature bins saved for the second object. 14. The device of claim 10, further including, for a first and second object, each having a completed set of feature bins: calculating the estimated Jaccard similarity index based on a count of matches of the values in the feature bins saved for the first object with the values in the feature bins saved for the second object. 15. The device of claim 10, further including, for a first and second object, each having a completed set of feature bins: calculating the estimated Jaccard similarity index based on a comparison of the values in selected feature bins saved for the first object with the values in the feature bins saved for a second object, wherein the selected feature bins for the first and second object have matching scaling factors. 16. The device of claim 10, further including scaling weights of a weighted set such that the sum of weights is less than or equal to an oversampling factor of samples per bin times the number of bins to fill. 17. A tangible non-transitory computer readable storage medium that stores program instructions that, when executed, cause a computer to implement a method for pre-calculation, per object, of object feature bin values for identifying close matches between objects that have numerous weighted features, the method including: during min-hashing of a set of weighted features into a predetermined number of feature bins, circularly smearing a feature that qualifies to be inserted by min-hashing into a particular feature bin across successive feature bins, using a processor and memory coupled to the processor, the memory holding objects, sets of weighted features of the objects, and values placed in the feature bins by min-hashing, the circularly smearing including: treating the feature bins as a circular buffer so that a first bin follows a last bin for smearing; when a min-hashed value of a particular feature qualifies to be inserted into bin (i), a qualifying min-hashed value is considered for insertion into bin (i+1) and subsequent bins, including: increasing a min-hashed value by a predetermined increment at each successive step of insertion consideration; when an increased min-hashed value is considered for insertion into the bin (i+1) and subsequent bins, inserting the increased min-hashed value when a bin under consideration is empty or holds a bin value greater than the increased min-hashed value under consideration; and continuing consideration of the bin (i+2) and subsequent bins, through the circular buffer, until the increased min-hashed value fails to qualify to be inserted into a bin under consideration; and saving a completed set of the feature bins for one or more scalings of weights of the object together with a scaling factor or sum of feature weights for use in comparing sampled features of the object with sampled features of other objects when calculating an estimated Jaccard similarity index. 18. The tangible non-transitory computer readable storage medium of claim 17, further including min-hashing by inserting min-hashed values into bins using a fractional part of a min-hashed value produced by hashing a feature value or, in an expanded set of feature values, the feature value combined with a sequence number, into a range spanning the predetermined number of feature bins. 19. The tangible non-transitory computer readable storage medium of claim 17, further including min-hashing by inserting min-hashed values into bins using a fractional part of a min-hashed value produced by hashing a feature value, and, in an expanded set of feature values, the feature value combined with a sequence number, into a range spanning the predetermined number of feature bins, inserting a new minimum fractional part value into a feature bin under consideration when the fractional part of the min-hashed value is less than a current contents of the feature bin under consideration. 20. The tangible non-transitory computer readable storage medium of claim 17, further including, for a first and second object, each having the scaling factor or the sum of feature weights: determining a maximum possible similarity between the first and second object by comparing sums of the feature weights or scaled-weights of the features of the first and second object; and when the maximum possible similarity between the first and second object is less than a predetermined threshold, rejecting similarity between the first and second object without counting matches of the values in the feature bins saved for the first object with the values in the feature bins saved for the second object. 21. The tangible non-transitory computer readable storage medium of claim 17, further including, for a first and second object, each having a completed set of feature bins: calculating the estimated Jaccard similarity index based on a count of matches of the values in the feature bins saved for the first object with the values in the feature bins saved for the second object. 22. The tangible non-transitory computer readable storage medium of claim 17, further including, for a first and second object, each having a completed set of feature bins: calculating the estimated Jaccard similarity index based on a comparison of the values in selected feature bins saved for the first object with the values in the feature bins saved for a second object, wherein the selected feature bins for the first and second object have matching scaling factors. 23. The tangible non-transitory computer readable storage medium of claim 17, further including scaling weights of a weighted set such that the sum of weights is less than or equal to an oversampling factor of samples per bin times the number of bins to fill. 24. A method of pre-calculation, per object, of object feature bin values for identifying close matches between objects that have numerous weighted features, the method including during min-hashing of an expanded set of weighted features into a predetermined number of feature bins, circularly smearing a feature that qualifies to be inserted by min-hashing into a particular feature bin across successive feature bins, using a processor and memory coupled to the processor, the memory holding objects, sets of weighted features of the objects, and values placed in the feature bins by min-hashing, for initial weighted features of an object and the predetermined number of the feature bins used to receive the values produced by the min-hashing of the weighted features, as part of calculating an estimated Jaccard similarity index among objects: scaling initial weights to produce scaled-weighted features, using scaling factors automatically selected based on a sum of the initial weights divided by a number of features in a feature set, applying two or more of the selected scaling factors to scale the initial weights for min-hashing into one set of feature bins per scaling factor, expanding the scaled-weighted features into two or more expanded sets of weighted features of the object that includes at least as many weighted features as the predetermined number of feature bins, expanding particular scaled-weighted features into multiple feature samples in proportion to respective weights of the particular scaled weighted features; the circularly smearing including: treating the feature bins as a circular buffer so that a first bin follows a last bin for smearing; when a min-hashed value of a particular feature qualifies to be inserted into bin (i), a qualifying min-hashed value is considered for insertion into bin (i+1) and subsequent bins, including: increasing a min-hashed value by a predetermined increment at each successive step of insertion consideration; when an increased min-hashed value is considered for insertion into the bin (i+1) and subsequent bins, inserting the increased min-hashed value when a bin under consideration is empty or holds a bin value greater than the increased min-hashed value under consideration; and continuing consideration of the bin (i+2) and subsequent bins, through the circular buffer, until the increased min-hashed value fails to qualify to be inserted into a bin under consideration; and saving completed sets of the feature bins for each scaling of the object together with a scaling factor or sum of the initial weights for use in comparing sampled features of the object with sampled features of other objects by calculating an estimated Jaccard similarity index. 25. A tangible non-transitory computer readable storage medium with instructions that are combinable with a processor and memory coupled to the processor to implement the method of claim 24.