DETAILED ACTION
1. This is a Final Office Action Correspondence in response to amendments/arguments for U.S. Application No. 18/166035 filed on August 15, 2025.
Notice of Pre-AIA or AIA Status
2. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
3. Applicants’ arguments have been considered but are not persuasive.
On Pg. 9-11 of remarks in regards to 35 U.S.C. 103, relating to claim 1, Applicant argues the amended limitations.
Examiner replies that a new reference is presented below to address the amended limitations.
Applicants argument have been considered but are not persuasive.
On Pg. 9 of remarks in regards to 35 U.S.C. 103, relating to claim 1. Applicant states “There does not appear to be any discussion in Walters, Beecham or Lott of a "sparse index," "building the sparse index," "building the sparse index includes: indicating one pointer for a selected data record of the range of data records; and indicating another pointer for another selected data record of the range of data records," or "building the sparse index includes: indicating one pointer for a selected data record of the range of data records; and indicating another pointer for another selected data record of the range of data records, wherein there is one sparse index for a plurality of data records included in the range of data records specified by the one pointer and the another pointer." For one or more of these reasons, applicant respectfully requests an indication of allowance for independent claim 1.”
Examiner replies that Rivers does teach this concept. Col. 10 Lines 38-48 Rivers discloses creating indices to store the data set. Col. 8 Lines 37-47 Rivers discloses the indices includes sparse and dense indices that point to data. Col. 15 Lines 55-59 Rivers discloses the data is skewed. The most similar dataset is seen as the skewed data.
On Pg. 11 of remarks in regards to 35 U.S.C. 103, relating to claim 1. Applicant states “applicant respectfully submits that independent claim 11 is patentable over the combination of Walters, Beecham and Lott, since it appears that the combination of references fails to describe, teach or suggest one or more of the following features: "determining that a dataset includes skewed data and indicating in storage the skewed data, wherein the skewed data is a non-uniform distribution of data in the dataset that is concentrated in one direction; building one or more sparse indexes for the skewed data of the dataset, based on determining that the dataset includes skewed data, wherein a sparse index of the one or more sparse indexes is for a range of data records of the skewed data, and wherein the building the sparse index includes: indicating one pointer for a selected data record of the range of data records; and indicating another pointer for another selected data record of the range of data records, wherein there is one sparse index for a plurality of data records included in the range of data records specified by the one pointer and the another pointer; and storing in selected storage the sparse index, the sparse index to be used in a query of the dataset.”
Examiner replies that Rivers does teach this concept. (Col. 10 Lines 38-48 Rivers discloses creating indices to store the data set. Col. 8 Lines 37-47 Rivers discloses the indices includes sparse and dense indices that point to data. Col. 15 Lines 55-59 Rivers discloses the data is skewed. The most similar dataset is seen as the skewed data);
On Pg. 14 of remarks in regards to 35 U.S.C. 103, relating to claim 7. Applicant states “As another example, applicant respectfully submits that it appears that the combination of Walters, Beecham and Lott fails to describe, teach or suggest, at least, applicant's claimed aspect of "wherein the analyzing the data determines that the one or more types of data include the skewed data and non-skewed data, and the selecting indicates that the one or more sparse indexes are to be built for the skewed data and one or more dense indexes are to be built for the non-skewed data," as claimed in one or more aspects in dependent claim 5. There does not appear to be a description, teaching or suggestion in Walters, Beecham or Lott, alone or in combination, of, for instance, "selecting indicates that the one or more sparse indexes are to be built for the skewed data and one or more dense indexes are to be built for the non-skewed data," as claimed in one or more aspects.”
Examiner replies that Rivers does teach this concept. Col. 21 Lines 31-35 Rivers discloses adapting the indices based upon the changes in the data within the database.
On Pg. 14 of remarks in regards to 35 U.S.C. 103, relating to claim 7. Applicant states, “In the Office Action it is stated that "Par. 0091 Lott modifying the parameters that are used to generate the schema with the data distribution. The modified values that change the distribution is seen as adjusting the index distribution." Applicant respectfully disagrees that "modifying the parameters that are used to generate the schema with the data distribution. The modified values that change the distribution is seen as adjusting the index distribution" is a description, teaching or suggestion of, for instance, "wherein the adjusting the index distribution includes changing a type of an index of the one or more indexes from sparse to dense," as claimed in one or more aspects in dependent claim 8. Again, there does not appear to be a description, teaching or suggestion in any of the references, alone or in combination, of for instance, "sparse indexes" and/or of "wherein the adjusting the index distribution includes changing a type of an index of the one or more indexes from sparse to dense." For at least one or more of these reasons, applicant respectfully requests an indication of allowance for dependent claim 8.”
Examiner replies that Rivers does teach this concept. Col. 21 Lines 31-35 Rivers discloses adapting the indices based upon the changes in the data within the database.
Claim Rejections - 35 USC § 112
4. Claims 1, 11 and 16 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. The claims contains the language “determining that a dataset includes skewed data that is concentrated in one direction” which is not described in the specification.
Claim Rejections - 35 USC § 103
5. 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.
6. 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.
7. Claim(s) 1-13, and 15-21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Rivers et al. U.S. Patent No. 11,321,296 (herein as ‘Rivers’) and further in view of Beecham et al. U.S. Patent Application Publication No. 2018/0307857 (herein as ‘Beecham’) and Lott U.S. Patent Application Publication No. 2021/0191908 (herein as ‘Lott’).
As to claim 1 Rivers teaches a computer-implemented method of facilitating processing within a computing environment, the computer-implemented method comprising: building, using a computing device of the computing environment, one or more sparse indexes for the skewed data of the dataset, based on determining that the dataset includes skewed data, wherein a sparse index of the one or more sparse indexes is for a range of data records of the skewed data, and wherein the building the sparse index includes: indicating one pointer for a selected data record of the range of data records;
(Col. 10 Lines 38-48 Rivers discloses creating indices to store the data set. Col. 8 Lines 37-47 Rivers discloses the indices includes sparse and dense indices that point to data. Col. 15 Lines 55-59 Rivers discloses the data is skewed. The most similar dataset is seen as the skewed data);
Rivers does not teach but Beecham teaches and indicating another pointer for another selected data record of the range of data records, wherein there is one sparse index for a plurality of data records included in the range of data records specified by the one pointer and the another pointer (Par. 0223 Beecham discloses the data having multiple instances of the same unique identifiers to identify the same data).
Rivers and Beecham are analogous art because they are in the same field of endeavor, skewed data processing. It would have been obvious to one of ordinary skill in the art, before the effective filing date, to modify indices of data of Rivers to include the skewed data of Beecham, to allow for accessing content in order to improve the search efficiency (Par. 0038 Beecham);
Walters does not teach but Lott teaches determining that a dataset includes skewed data that is concentrated in one direction (Par. 0041 and 0042 Lott discloses having skewed data that is expected to have more house numbers than actually occurs and building a datasets based upon the skewed data);
and storing in selected storage the sparse index the sparse index to be used in a query of the dataset and indicating in storage the skewed data, wherein the skewed data is a non-uniform distribution of data in the dataset (Par. 0070 Lott discloses a non-unform set of distribution data that is skewed);
Rivers and Lott are analogous art because they are in the same field of endeavor, skewed data processing. It would have been obvious to one of ordinary skill in the art, before the effective filing date, to modify indices of data of Rivers to include the skewed data of Evans, to allow for accessing content in order to improve the quality, efficient and quality of data access used for data modeling (Par. 0002-0004 Lott);
As to claim 2 Rivers in combination with Lott teaches each and every limitation of claim 1.
In addition Lott teaches wherein the determining that the dataset includes the skewed data includes computing one or more statistical measures to be used to determine that the dataset includes the skewed data (Par. 0041 and 0042 Lott discloses having skewed data that is expected to have more house numbers than actually occurs and building a datasets based upon the skewed data);
As to claim 3 Rivers in combination with Lott teaches each and every limitation of claim 1.
In addition Walters teaches wherein the dataset further includes non-skewed data, and wherein the computer-implemented method further includes building one or more dense indexes for the non-skewed data (Par. 0042 Rivers discloses identifying synthetic data having a data set that ideal for given numerical field).
As to claim 4 Rivers in combination with Lott teaches each and every limitation of claim 1.
In addition Rivers teaches further comprising: analyzing data of the dataset to determine one or more types of data included in the dataset, wherein the skewed data is one type of data of the one or more types of data of the dataset; and selecting one or more types of indexes to be built based on the one or more types of data of the dataset (Col. 5 Lines 35-45 Rivers discloses selecting indices based upon the data criteria).
As to claim 5 Rivers in combination with Lott teaches each and every limitation of claim 1.
In addition Rivers teaches wherein the analyzing the data determines that the one or more types of data include the skewed data and non-skewed data, and the selecting indicates that the one or more sparse indexes are to be built for the skewed data and one or more dense indexes are to be built for the non-skewed data (Col. 7 Lines 6-20 Rivers discloses the index selector, selecting the indexes based upon the data of the database).
As to claim 7 Rivers in combination with Lott teaches each and every limitation of claim 1.
In addition Rivers teaches wherein the adjusting the index distribution includes changing a type of an index of the one or more indexes from dense to sparse (Col. 21 Lines 31-35 Rivers discloses adapting the indices based upon the changes in the data within the database).
As to claim 8 Rivers in combination with Lott teaches each and every limitation of claim 1.
In addition Rivers teaches wherein the adjusting the index distribution includes changing a type of an index of the one or more indexes from sparse to dense (Col. 21 Lines 31-35 Rivers discloses adapting the indices based upon the changes in the data within the database).
As to claim 9 Rivers in combination with Lott teaches each and every limitation of claim 1.
In addition Rivers teaches further comprising analyzing one or more query patterns of one or more queries of the data of the dataset to facilitate selection of the one or more types of indexes to be built (Col. 15 Lines 14-17 Rivers discloses selecting an index based upon a learned index).
As to claim 10 Rivers in combination with Lott and Beecham teaches each and every limitation of claim 1.
In addition Rivers teaches further comprising analyzing one or more search terms used to query the data of the dataset to facilitate selection of the one or more types of indexes to be built (Col. 15 Lines 14-17 Rivers discloses selecting an index).
As to claim 11 Rivers teaches a computer system for facilitating processing within a computing environment, the computer system comprising: a memory; and one or more processors in communication with the memory, wherein the computer system is configured to perform a method, said method comprising: building one or more sparse indexes for the skewed data of the dataset, based on determining that the dataset includes skewed data, wherein a sparse index of the one or more sparse indexes is for a range of data of the skewed data, and wherein the building the sparse index includes: indicating one pointer for a selected data record of the range of data (Col. 10 Lines 38-48 Rivers discloses creating indices to store the data set. Col. 8 Lines 37-47 Rivers discloses the indices includes sparse and dense indices that point to data. Col. 15 Lines 55-59 Rivers discloses the data is skewed. The most similar dataset is seen as the skewed data);
Rivers does not teach but Beecham teaches and indicating another pointer for another selected record of the range of data; and indicating another pointer for another selected record of the range of data records, wherein there is one sparse index for a plurality of data records included in the range of data records specified by the one pointer and the another pointer (Par. 0223 Beecham discloses the data having multiple instances of the same unique identifiers to identify the same data).
Rivers and Beecham are analogous art because they are in the same field of endeavor, skewed data processing. It would have been obvious to one of ordinary skill in the art, before the effective filing date, to modify indices of data of Rivers to include the skewed data of Beecham, to allow for accessing content in order to improve the search efficiency (Par. 0038 Beecham);
and providing the sparse index to be used in a query of the dataset (Col. 8 Lines 37-47 Rivers discloses the indices includes sparse and dense indices that point to data);
Rivers does not teach but Lott teaches determining that a dataset includes skewed data (Par. 0041 and 0042 Lott discloses having skewed data that is expected to have more house numbers than actually occurs and building a datasets based upon the skewed data);
data and indicating in storage the skewed data, wherein the skewed data is a non-uniform distribution of data in the dataset that is concentrated in one direction (Par. 0041, 0042 and Par. 0070 Lott discloses a non-unform set of distribution data that is skewed);
Rivers and Lott are analogous art because they are in the same field of endeavor, skewed data processing. It would have been obvious to one of ordinary skill in the art, before the effective filing date, to modify indices of data of Rivers to include the skewed data of Evans, to allow for accessing content in order to improve the quality, efficient and quality of data access used for data modeling (Par. 0002-0004 Lott).
As to claim 12 Rivers in combination with Lott teaches each and every limitation of claim 1.
In addition Walters teaches wherein the dataset further includes non- skewed data, and wherein the method further includes building one or more dense indexes for the non-skewed data (Fig. 5 Par. 0075-0080 Walters discloses identifying the most similar data set. The most similar dataset is seen as the skewed data).
As to claim 13 Rivers in combination with Lott teaches each and every limitation of claim 11.
In addition Rivers teaches further comprising: analyzing data of the dataset to determine one or more types of data included in the dataset, wherein the skewed data is one type of data of the one or more types of data of the dataset; and selecting one or more types of indexes to be built based on the one or more types of data of the dataset (Col. 5 Lines 35-45 Rivers discloses selecting indices based upon the data criteria).
As to claim 16 Rivers teaches a computer program product for facilitating processing within a computing environment, said computer program product comprising: one or more computer readable storage media and program instructions collectively stored on the one or more computer readable storage media readable by at least one processing circuit to perform a method comprising: building one or more sparse indexes for the skewed data of the dataset based on determining that the dataset includes skewed data, wherein a sparse index of the one or more sparse indexes is for a range of data of the skewed data, and wherein the building the sparse index includes: indicating one pointer for a selected data record of the range of data; (Col. 10 Lines 38-48 Rivers discloses creating indices to store the data set. Col. 8 Lines 37-47 Rivers discloses the indices includes sparse and dense indices that point to data. The most similar dataset is seen as the skewed data);
Rivers does not teach but Beecham teaches and indicating another pointer for another selected data record of the range of data records, wherein there is one sparse index for a plurality of data records included in the range of data records specified by the one pointer and the another pointer and providing the sparse index to be used in a query of the dataset (Fig. 5 Par. 0075-0080 Walters discloses identifying the most similar data set. The most similar dataset is seen as the skewed data).
Rivers and Beecham are analogous art because they are in the same field of endeavor, skewed data processing. It would have been obvious to one of ordinary skill in the art, before the effective filing date, to modify indices of data of Rivers to include the skewed data of Beecham, to allow for accessing content in order to improve the search efficiency (Par. 0038 Beecham);
Rivers does not teach but Lott teaches determining that a dataset includes skewed data (Par. 0041 and 0042 Lott discloses having skewed data that is expected to have more house numbers than actually occurs and building a datasets based upon the skewed data);
data and indicating in storage the skewed data, wherein the skewed data is a non-uniform distribution of data in the dataset that is concentrated in one direction (Par. 0041, 0042 and Par. 0070 Lott discloses a non-unform set of distribution data that is skewed);
Rivers and Lott are analogous art because they are in the same field of endeavor, skewed data processing. It would have been obvious to one of ordinary skill in the art, before the effective filing date, to modify indices of data of Rivers to include the skewed data of Evans, to allow for accessing content in order to improve the quality, efficient and quality of data access used for data modeling (Par. 0002-0004 Lott).
As to claim 17 Rivers in combination with Lott and Beecham teaches each and every limitation of claim 1.
In addition Rivers teaches wherein the dataset further includes non-skewed data, and wherein the method further includes building one or more dense indexes for the non-skewed data (Par. 0042 Rivers discloses identifying synthetic data having a data set that ideal for given numerical field).
As to claim 18 Walters in combination with Lott and Beecham teaches each and every limitation of claim 1.
In addition Walters teaches wherein the method further comprises: analyzing the data of the dataset to determine one or more types of data included in the dataset, wherein the skewed data is one type of data of the one or more types of data of the dataset; and selecting one or more types of indexes to be built based on the one or more types of data of the dataset (Fig. 5 Par. 0075-0080 Walters discloses identifying the most similar data set. The most similar dataset is seen as the skewed data. Par. 0042 Walters discloses data in different data types).
As to claim 19 Rivers in combination with Lott and Beecham teaches each and every limitation of claim 1.
In addition Walters teaches wherein the analyzing the data determines that the one or more types of data include the skewed data and non-skewed data, and the selecting indicates that the one or more sparse indexes are to be built for the skewed data and one or more dense indexes are to be built for the non-skewed data (Col. 21 Lines 31-35 Rivers discloses adapting the indices based upon the changes in the data within the database).
As to claim 21 Walters in combination with Lott and Beecham teaches each and every limitation of claim 1.
In addition Beecham teaches wherein the one sparse index is used for the plurality of data records included within the range of data records specified by the one pointer and the another pointer, rather than building an index for each data record of the plurality of data records (Col. 10 Lines 38-48 Rivers discloses creating indices to store the data set. Col. 8 Lines 37-47 Rivers discloses the indices includes sparse and dense indices that point to data. The most similar dataset is seen as the skewed data);
Allowable Subject Matter
8. Claims 6, 15 and 20 do not contain any prior art.
Claims 6, 15 and 20 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
9. THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/J.A.M/ April 16, 2026
Examiner, Art Unit 2159
/ANN J LO/Supervisory Patent Examiner, Art Unit 2159
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