Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed.
Claims 1-10, filed February 07, 2025, are examined on the merits.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-10 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1:
The claims recite an apparatus and a method which are statutory categories of invention.
Step 2A Prong One:
Claim 1 recites “constructs a hierarchical histogram…”, “creates integer value conversion data…”, and “a multidimensional sorting algorithm” at a high level of generality such that the claimed invention is accomplished by the steps of mathematical calculations. The instant specification discloses the hierarchical histogram, integer value conversion, and the sorting algorithm are merely mathematical calculations of division, aggregation, and sorting (pages 15-17). Therefore, the limitations, under their broadest reasonable interpretation, covers performance of the limitation by mathematical calculations but for generic computer components. These limitations, as drafted, are processes that, under their broadest reasonable interpretation, can be performed a mathematical process.
Claim 10 is directed to a method embodying the same abstract idea as claim 1. These claims are similarly rejected under the same rationale as claim 1, supra.
Step 2A Prong Two
The judicial exception is not integrated into a practical application. In particular, the claims recite additional element of “an integer value conversion unit and a multidimensional sorting unit” wherein the elements are generic elements of the apparatus and method. The “an integer value conversion unit and a multidimensional sorting unit” are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using a generic component (MPEP 2106.05(f)). The limitation of “places the data…in a database from which data is read” amounts to extra-solution activity (MPEP 2106.05(g): i.e. pre-solution activity of gathering data for use in the claimed process.
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Step 2B
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional elements of “an integer value conversion unit and a multidimensional sorting unit” are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component (MPEP 2106.05(f)). The limitation of “places the data…in a database from which data is read” amounts to extra-solution activity (MPEP 2106.05(g): i.e. pre-solution activity of gathering data for use in the claimed process.
Thus taken alone, the individual elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology.
Claim 2 recites wherein, for each of the columns, the converted integer value is a bin id of any bin in the hierarchical histogram of the column, the integer value conversion unit assigns bin ids of bins in the hierarchical histogram equally in a range equal to or greater than a first integer value and equal to or smaller than a second integer value, and the first integer value is a predetermined integer value, and the second integer value is based on the base and a given degree of the column. These limitations further narrow the abstract idea, additional elements, or extra-solution activity, but are nonetheless part of the abstract idea identified in claim 1. They also do not amount to significantly more than the abstract idea. The claims are similarly rejected under the same rationale as claim 1, supra.
Claim 3 recites wherein, for each of the columns, (A) when there is an area j related to the column and having a degree of 1 or more, the integer value conversion unit creates an equal-width histogram by dividing the area j into X equal parts with respect to the area j, X being the base ^, the degree of the area j, (B) the integer value conversion unit determines whether there is an empty bin in the equal-width histogram, (C) when a result of determination in (B) is true, (c1) the integer value conversion unit divides the equal-width histogram into a plurality of areas j' by removing empty bins from the equal-width histogram, each of the plurality of areas j' being composed of one or more consecutive bins in which data is present, (c2) the integer value conversion unit reduces, for each of the plurality of areas j', a degree of the area j' from a degree of the original area j of the area j', and then performs (A) on each area j' as the area j. These limitations further narrow the abstract idea, additional elements, or extra-solution activity, but are nonetheless part of the abstract idea identified in claim 1. They also do not amount to significantly more than the abstract idea. The claims are similarly rejected under the same rationale as claim 1, supra.
Claim 4 recites wherein, in (c1), the integer value conversion unit determines whether the number of areas j' is the n-th power of the base (n is an integer equal to or greater than 1 and equal to or less than the degree of the original area j of the areas j') (c11), and when the number of areas j' is the n-th power of the base, for each of the plurality of areas i', the integer value conversion unit reduces the degree of the area j' by n from the degree of the original area j of the area j' in (c2). These limitations further narrow the abstract idea, additional elements, or extra-solution activity, but are nonetheless part of the abstract idea identified in claim 1. They also do not amount to significantly more than the abstract idea. The claims are similarly rejected under the same rationale as claim 1, supra.
Claim 5 recites wherein, when the number of areas j' is not the n-th power of the base, the integer value conversion unit further divides an area j' with the largest area width into two equal areas j' and performs (c11). These limitations further narrow the abstract idea, additional elements, or extra-solution activity, but are nonetheless part of the abstract idea identified in claim 1. They also do not amount to significantly more than the abstract idea. The claims are similarly rejected under the same rationale as claim 1, supra.
Claim 6 recites wherein, when the number of areas j' is not the n-th power of the base, the integer value conversion unit further divides an area j' with the largest area width into two equal areas j' and performs (c11) 6. The database management apparatus according to claim 3, wherein, for each of the columns, (D) when the result of determination in (B) is false, the integer value conversion unit (d1) allocates equal integers in the range equal to or greater than 0 and equal to or smaller than the base^ (the degree of the area j - 1) as bin ids to a plurality of bins in the equal-width histogram, and (d2) changes the degree of the area j to 0. These limitations further narrow the abstract idea, additional elements, or extra-solution activity, but are nonetheless part of the abstract idea identified in claim 1. They also do not amount to significantly more than the abstract idea. The claims are similarly rejected under the same rationale as claim 1, supra.
Claim 7 recites wherein, for each of the columns, when degrees of all areas j related to the column are 0 or less, the integer value conversion unit updates a bin id of a bin in the hierarchical histogram of the column to a value obtained by combining bin ids in an area including the bin, in order, from a bin id of a higher tier that has the original area of the bin in a-ary notation, and converting the combined value to a decimal value. These limitations further narrow the abstract idea, additional elements, or extra-solution activity, but are nonetheless part of the abstract idea identified in claim 1. They also do not amount to significantly more than the abstract idea. The claims are similarly rejected under the same rationale as claim 1, supra.
Claim 8 recites wherein, when there is an empty area in the hierarchical histogram, the integer value conversion unit adjusts a value range width of each bin in the hierarchical histogram such that the empty area disappears. These limitations further narrow the abstract idea, additional elements, or extra-solution activity, but are nonetheless part of the abstract idea identified in claim 1. They also do not amount to significantly more than the abstract idea. The claims are similarly rejected under the same rationale as claim 1, supra.
Claim 9 recites wherein, for each of the columns, the integer value conversion unit obtains sampling data by randomly extracting data from the input data, the hierarchical histogram of data distribution with respect to the column is a histogram created from the sampling data, and the number of pieces of data randomly extracted is based on the degree of the column. These limitations further narrow the abstract idea, additional elements, or extra-solution activity, but are nonetheless part of the abstract idea identified in claim 1. They also do not amount to significantly more than the abstract idea. The claims are similarly rejected under the same rationale as claim 1, supra.
Claim 10 directed to a method which embodies the same abstract idea as claim 1. The claim is similarly rejected under the same rationale as claims 1, supra.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1, 9, and 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Shah et al. (Shah hereafter, US 10114846 B1) in view of Beavin et al. (Beavin hereafter, US 5,995,957A).
Claim 1, Shah discloses a database management apparatus comprising an integer value conversion unit, wherein, for each column in input data having a plurality of columns (Abstract, e.g. columns containing string data values may be converted to equally-sized integer data values. The data values of columns may be evaluated to determine buckets representing the ranges of data values within the columns for depth-balanced histograms of the columns. Multi-column sort order values may be generated for individual entries in the database table according to bucket values assigned to the buckets that include the columns values of the individual entries), the integer value conversion unit constructs a hierarchical histogram of data distribution with respect to the column by repeating division into a prescribed number of areas based on a degree and a base suitable for and creation of an equal-width histogram as long as an empty bin is present in a histogram (column 3, line 58, column 4, line 20, e.g. two the data values in identified columns for the multi-column sort order, column A and column B, may be evaluated to generate respective depth-balanced histograms for the data values in each column. Column A values 102 may be evaluated to generate a depth-balanced histogram 122 for column A. Likewise, column B values 104 may be evaluated to generate column B depth-balanced histograms. Depth-balanced histograms may be histograms where the buckets of the histogram may include a balanced number of entries in each bucket (e.g., the same or near the same number of entries in each bucket). For example, as illustrated in FIG. 1B, the number of column A values in each bucket 112 is the same (or approximately the same). Similarly, the number column B values in each bucket 114 is the same (or approximately the same). To achieve a depth balanced histogram, the range of data values represented by a bucket may vary in order to include a same number of entries (as illustrated by the varying bucket widths of buckets and corresponding ranges for column A values 102 and column B values 104). In order to determine the range of data values for buckets for a depth-balanced histogram, a number of quantiles corresponding to the number of buckets may be calculated. A quantile may be a partition of the data values in a set (e.g., a column) that is equal (or nearly equal). By setting bucket data range values equivalent to include quantiles for the data values of a column, each bucket may have an equal (or near equal) number of entries within the range of data values corresponding to the bucket. Examiner Interpretation: the phrase of “as long as…” is a conditional state wherein the “creation of an equal-width histogram…” is performed with the condition of “as long as…” is satisfied. However, as recited in the claims are not specifically limited to “creation of an equal-width histogram…” Therefore, under BRI Shah describes the claimed invention), and
creates integer value conversion data that maps a value range width of data in the input data to a converted integer value on the basis of the hierarchical histogram (Abstract, e.g. columns containing string data values may be converted to equally-sized integer data values, and column 3, line 58, column 4, line 20, e.g. o achieve a depth balanced histogram, the range of data values represented by a bucket may vary in order to include a same number of entries (as illustrated by the varying bucket widths of buckets and corresponding ranges for column A values 102 and column B values 104). In order to determine the range of data values for buckets for a depth-balanced histogram, a number of quantiles corresponding to the number of buckets may be calculated. A quantile may be a partition of the data values in a set (e.g., a column) that is equal (or nearly equal). By setting bucket data range values equivalent to include quantiles for the data values of a column, each bucket may have an equal (or near equal) number of entries within the range of data values corresponding to the bucket).
However, Shah does not disclose a multidimensional sorting algorithm…and wherein the multidimensional sorting unit places the data, which is in the input data, in a database from which data is read in units of segment by multidimensionally sorting the input data according to the multidimensional sorting algorithm on the basis of the integer value conversion data of each column. Beavin discloses a multidimensional sorting algorithm…and wherein the multidimensional sorting unit places the data, which is in the input data, in a database from which data is read in units of segment by multidimensionally sorting the input data according to the multidimensional sorting algorithm on the basis of the integer value conversion data of each column (column 10, lines 26-41, e.g. The multi-column quantile statistics take n-dimensional space (the domain of all possible tuples) and divides it into segments, with a low and high multi-column value defining the ends of each segment. This works well when dealing with `linear` correlation because the answer set can also be represented by a single segment, bounded by a low and high value. When dealing with `polygonal` correlation, however, the answer set is characterized by an n-dimensional polygon (within the domain of all possible tuples). Trying to divide the n-dimensional space representing the answer set into segments, while possible, is not practical or efficient, as an unlimited number of segments could be generated).
Beavin discloses an invention to improve performance for queries that have predicates on multiple columns of a table (column 2, lines 23-25). One of ordinary skill in the art at the time prior to the effective filing date of the instant invention would have been motivated by Beavin to improve the apparatus of Shah. Therefore, it would have been obvious for one of ordinary skill in the art to use the apparatus of Shah with the teachings of Beaving. The benefit would be to improve performance for queries that have predicates on multiple columns of a table.
Claim 9, Shah as modified discloses wherein, for each of the columns, the integer value conversion unit obtains sampling data by randomly extracting data from the input data, the hierarchical histogram of data distribution with respect to the column is a histogram created from the sampling data, and the number of pieces of data randomly extracted is based on the degree of the column (Shah, column 11, lines 53-67, e.g. histogram generation 610 may select a random sample of column data values and determine approximate quantiles based on the random sample of column data. Histogram generation 610 may also perform exact or non-approximate quantile calculations over the entire set of column data values).
Claim 10, Shah as modified discloses a method (column 1, lines 49-50) for implementing the above cited apparatus above.
Claim(s) 2 is/are rejected under 35 U.S.C. 103 as being unpatentable over Shah et al. (Shah hereafter, US 10114846 B1) in view of Beavin et al. (Beavin hereafter, US 5,995,957A), as applied to claims 1, 9, and 10 above, in further view of Li et al. (A sketched-based sampling algorithm on sparse data. Technical report, Department of Statistics, Stanford University (2006)).
Claim 2, Shah as modified discloses the claimed invention except for the limitation of wherein, for each of the columns, the converted integer value is a bin id of any bin in the hierarchical histogram of the column, the integer value conversion unit assigns bin ids of bins in the hierarchical histogram equally in a range equal to or greater than a first integer value and equal to or smaller than a second integer value, and the first integer value is a predetermined integer value, and the second integer value is based on the base and a given degree of the column. Li discloses wherein, for each of the columns, the converted integer value is a bin id of any bin in the hierarchical histogram of the column, the integer value conversion unit assigns bin ids of bins in the hierarchical histogram equally in a range equal to or greater than a first integer value and equal to or smaller than a second integer value, and the first integer value is a predetermined integer value, and the second integer value is based on the base and a given degree of the column (page 11, e.g. the sketches store “ID (Binned data value).”
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Li discloses an improvement that can improve the accuracy of estimators (page 5). One of ordinary skill in the art at the time prior to the effective filing date of the instant invention would have been motivated by Li to improve the apparatus of Shah as modified. Therefore, it would have been obvious for one of ordinary skill in the art to use the teachings of Li with the disclosure of Shah as modified. The benefit would be to improve the accuracy.
RELATED PRIOR ART
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Swamidass (US 9378382 B1) discloses he number of times each of the public identifiers appears in the set may be stored in each bin. This list of all integers in all bins, corresponding to a histogram of public identifiers comprises the cryptoset. The cryptoset may be stored as a vector indexed by public identifier from 0 to L−1, denoted as (Xi).sub.0.sup.L−1, where X is the integer value for the i.sup.th bin corresponding to the total number of private identifiers mapped to the i.sup.th public identifier by the degenerate mapping rule, and L is the total number of bins corresponding to the total number of public identifiers (column 14, lines 51-60).
Claims 3-8 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
CONCLUSION
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Any inquiry concerning this communication or earlier communications from the examiner should be directed to C. Dune Ly, whose telephone number is (571) 272-0716. The examiner can normally be reached on Monday-Friday from 8 A.M. to 4 PM ET.
If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, Neveen Abel-Jalil, can be reached on 571-270-0474.
/Cheyne D Ly/
Primary Examiner, Art Unit 2152
3/24/2026