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 .
DETAILED ACTION
Application 19/244,158 filed 6/20/2025 (with a Parent application Filing Date of 06/17/2024)
has been examined.
In this Office Action, Claims 1-20 are currently pending.
Examiner’s Note:
Claim 8 line 2 recites:
“goupings comprises:”
It appears it was Applicant’s intent to rather claim:
“groupings comprises:”
Appropriate correction of claim 8 is requested.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1‐20 are rejected on the ground of nonstatutory double patenting as being
unpatentable over claims 1‐20 of U.S. Patent No. 12,367,195. Although the claims at
issue are not identical, they are not patentably distinct from each other because
because claim 1 is generic to all that is recited in claim 1 of U.S. Patent No.
12,367,195. That is, claim 1 of U.S. Patent No. 12,367,195 falls entirely within
the scope of claim 1 or, in other words, claim 1 is anticipated by claim 1 of U.S.
Patent No. 12,367,195.
Specifically, because instant claim 1 recites: "determining, by the server, an estimated record count for the search query by comparing the search query with the historical data and applying an estimation equation that uses the total distinct-value counts and the correlations" this limitation is/are a species of the generic category defined by “computing, by the server, an estimated record count for the search query on the multi-dimensional database based on a comparison between the search query and the previously executed search queries of the first computed data source and an estimation equation comprising data values from the second computed data source and the third computed data source” (see claim 1, U.S. Patent No. 12,367,195) the process of claim 1 reciting “determining, by the server, an estimated record count for the search query by comparing the search query with the historical data and applying an estimation equation that uses the total distinct-value counts and the correlations” is
anticipated by claim 1 of U.S. Patent No. 12,367,195 reciting “computing, by the server, an estimated record count for the search query on the multi-dimensional database based on a comparison between the search query and the previously executed search queries of the first computed data source and an estimation equation comprising data values from the second computed data source and the third computed data source”.
Current Application
US Pat. No. 12367195 B1 (App.18/745,820)
1. A method for processing a plurality of search queries, the method comprising:
receiving, by a server, a search query comprising search parameters to retrieve data from a multi-dimensional database;
receiving, by the server, a data packet comprising:
historical data describing previously executed search queries on the multidimensional
database and actual record counts returned by the previously executed search queries,
a total distinct-value count for each of a plurality of attribute groupings segmented by predetermined categories in the multi-dimensional database, and
for at least one pair of the attribute groupings, a correlation between their respective total distinct-value count;
determining, by the server, an estimated record count for the search query by comparing the search query with the historical data and applying an estimation equation that uses the total distinct-value counts and the correlations; and
executing, by the server, the search query based on the estimated record count not
exceeding a predetermined record-count threshold.
2. The method of claim 1, comprising:
comparing, by the server, the search query to the historical data to determine whether
the search query matches a previously executed search query.
3. The method of claim 1, comprising:
determining, by the server, that the estimated record count exceeds the predetermined
record-count threshold.
4. The method of claim 3, comprising:
cancelling, by the server, execution of the search query based on the estimated record
count exceeding the predetermined record-count threshold.
5. The method of claim 1, comprising:
selecting, by the server, a database-processing system to execute the search query
based on the search parameters and the estimated record count.
6. The method of claim 1, wherein the data packet is updated after each completed search
query.
7. The method of claim 1, wherein each correlation in the data packet is derived from the
respective total distinct-value counts of the paired attribute groupings.
8. The method of claim 7, wherein the correlation for every ordered pair of attribute
goupings comprises:
executing a first database query that, for a first grouping G1 and a second grouping G2,
returns a distinct-value count for G1, a distinct-value count for G2, and a joint distinct-value
count of G1 and G2 over a defined set of time-period categories; and
executing a second database query that repeats the foregoing for an ordered pair (G2,
G1), to produce ratio values usable as the correlation between G1 and G2.
9. The method of claim 7, wherein the total distinct-value counts are recomputed at a first
predetermined interval, and upon completion of the recomputation, each correlation is
recomputed at a second predetermined interval based on updated total distinct-value counts.
10. The method of claim 1, wherein the predetermined categories include a month category.
11. The method of claim 1, wherein the multi-dimensional database contains at least 1
trillion records.
12. A method for processing a plurality of search queries, the method comprising:
receiving, by a server, a search query comprising search parameters to retrieve data
from a multi-dimensional database;
generating, by the server, a data packet comprising:
historical data describing previously executed search queries and
corresponding actual record counts;
total distinct-value counts for each of a plurality of attribute groupings
segmented by predetermined categories; and
correlations between the total distinct-value counts of at least one pair of
the attribute groupings;
determining, by the server, an estimated record count for the search query via an
estimation equation that combines the total distinct-value counts, the correlations, and
a comparison between the search query and the historical data; and
executing, by the server, the search query based on the estimated record count not
exceeding a predetermined record-count threshold.
13. The method of claim 12, comprising:
comparing, by the server, the search query to historical data to identify whether the
search query matches a previously executed search query.
14. The method of claim 12, comprising:
determining, by the server, that the estimated record count exceeds the predetermined
record count threshold; and
cancelling, by the server, the search query based on the estimated record count
exceeding the predetermined record count.
15. The method of claim 12, comprising:
selecting, by the server, a database-processing system to execute the search query
based on the search parameters and the estimated record count.
16. The method of claim 12, comprising:
updating, by the server, the historical data after each completed search query to include
the search query and its actual record count.
17. The method of claim 12, wherein the correlation is derived from the total distinct-value
counts of the paired attribute groupings. of the total count of distinct values between at least two
of the plurality of groupings is generated.
18. The method of claim 17, wherein deriving each correlation for every ordered pair of
attribute groupings comprises:
executing a first database query for an ordered pair (G1, G2) to obtain the distinct-value
count of G1, the distinct-value count of G2, and a joint distinct-value count of G1 and G2; and
executing a second database query for the ordered pair (G2, G1) to obtain
corresponding counts, the first and second query results together defining ratio values usable as
the correlation.
19. The method of claim 17, wherein the total distinct-value counts are recomputed at a first
predetermined interval; and
upon completion of that recomputation, each correlation is recomputed at a second
predetermined interval based on updated total distinct-value counts.
20. A system for processing a plurality of search queries, the system comprising:
one or more databases, including a multi-dimensional database; and
a server having one or more processors and non-transitory memory storing
instructions that, when executed, cause the processors to:
receive a search query including search parameters for retrieving data from the
multi-dimensional database;
receive a metadata packet comprising historical data describing previously
executed search queries and corresponding actual record counts, total distinct-value
counts for each of a plurality of attribute groupings segmented by predetermined
categories, and correlations between the total distinct-value counts of at least one pair of
the attribute groupings;
determine an estimated record count for the search query by applying an
estimation equation that uses the historical data, the total distinct-value counts, and the
correlations; and
execute the search query only when the estimated record count does not exceed
a predetermined record-count threshold.
1. A method for processing a plurality of search queries, the method comprising:
receiving, by a server, a search query comprising search parameters to retrieve data from a multi-dimensional database;
receiving, by the server, a first computed data source, a second computed data source, and a third computed data source, wherein:
the first computed data source comprises
historical data associated with previously executed search queries on the multi-dimensional database and actual record counts returned by the previously executed search queries,
the second computed data source comprises
a total count of distinct values for each of a plurality of groupings segmented by predetermined categories within the multi-dimensional database, and
the third computed data source comprises a correlation of the total count of distinct values between at least two of the plurality of groupings;
computing, by the server, an estimated record count for the search query on the multi-dimensional database based on a comparison between the search query and the previously executed search queries of the first computed data source and an estimation equation comprising data values from the second computed data source and the third computed data source; and
executing, by the server, the search query based on the estimated record count not exceeding a predetermined record count.
2. The method of claim 1, further comprising:
comparing, by the server, the search query and the previously executed search queries of the first computed data source to determine whether the search query matches one of the previously executed search queries.
3. The method of claim 1, further comprising:
determining, by the server, the estimated record count exceeds the predetermined record count.
4. The method of claim 3, further comprising:
cancelling, by the server, the search query based on the estimated record count exceeding the predetermined record count.
5. The method of claim 1, further comprising:
determining, by the server, a database processing system to execute the search query based on the search parameters of the search query and the estimated record count.
6. The method of claim 1, wherein the first computed data source is updated after each completed search query.
7. The method of claim 1, wherein the third computed data source is generated based on the second computed data source.
8. The method of claim 7, wherein the third computed data source is generated based on a first query for a first ratio value and a second query for a second ratio value for all combinations of the plurality of groupings in the multi-dimensional database, wherein the first ratio value for a first grouping, grouping1, and a second grouping, grouping2, of the plurality of groupings is calculated by:
select ‘grouping1, ‘grouping2’ as grouping_name, count(distinct lower(grouping1)), count(distinct lower(grouping2)), count(distinct lower(grouping1),lower(grouping2)), month from table where month in (‘month1’, ‘month2’, . . . , ‘monthN’) group by month,
and wherein the second ratio value for the first grouping, grouping1, and the second grouping, grouping2, is calculated by:
select ‘grouping2, ‘grouping1’ as grouping_name, count(distinct lower(grouping2)), count(distinct lower(grouping1)), count(distinct lower(grouping2),lower(grouping1)), month from the multi-dimensional database where month in (‘month1’, ‘month2’, . . . , ‘monthN’) group by month.
9. The method of claim 7, wherein the second computed data source and the third computed data source are recomputed on a predetermined interval, wherein the second computed data source is recomputed before the third computed data source, and the third computed data source is subsequently recomputed based on the second computed data source.
10. The method of claim 1, wherein the predetermined categories of the second computed data source comprises a month category.
11. The method of claim 1, wherein the multi-dimensional database of records comprises at least 1 trillion records.
12. A method for processing a plurality of search queries, the method comprising:
receiving, by a server, a search query comprising search parameters to retrieve data from a multi-dimensional database;
generating, by the server, a first computed data source, a second computed data source, and a third computed data source, wherein:
the first computed data source comprises historical data associated with previously executed search queries on the multi-dimensional database and actual record counts returned by the previously executed search queries,
the second computed data source comprises a total count of distinct values for each of a plurality of groupings segmented by predetermined categories within the multi-dimensional database, and
the third computed data source comprises a correlation of the total count of distinct values between at least two of the plurality of groupings;
computing, by the server, an estimated record count for the search query on the multi-dimensional database based on a comparison between the search query and the previously executed search queries of the first computed data source and an estimation equation comprising data values from the second computed data source and the third computed data source; and
executing, by the server, the search query based on the estimated record count not exceeding a predetermined record count.
13. The method of claim 12, further comprising:
comparing, by the server, the search query and the previously executed search queries of the first computed data source to determine whether the search query matches one of the previously executed search queries.
14. The method of claim 12, further comprising:
determining, by the server, the estimated record count exceeds the predetermined record count; and
cancelling, by the server, the search query based on the estimated record count exceeding the predetermined record count.
15. The method of claim 12, further comprising:
determining, by the server, a database processing system to execute the search query based on the search parameters of the search query and the estimated record count.
16. The method of claim 12, further comprising:
updating, by the server, the first computed data source after each completed search query, wherein an update to the first computed data source comprises the search query and an actual record count for the search query.
17. The method of claim 12, wherein the third computed data source is generated based on the second computed data source.
18. The method of claim 17, wherein the third computed data source is generated based on a first query for a first ratio value and a second query for a second ratio value for all combinations of the plurality of groupings in the multi-dimensional database, wherein the first ratio value for a first grouping, grouping1, and a second grouping, grouping2, of the plurality of groupings is calculated by:
select ‘grouping1, ‘grouping2’ as grouping_name, count(distinct lower(grouping1)), count(distinct lower(grouping2)), count(distinct lower(grouping1), lower(grouping2)), and wherein the second ratio value for the first grouping, grouping1, and the second grouping, grouping2, is calculated by:
select ‘grouping2, ‘grouping1’ as grouping_name, count(distinct lower(grouping2)),
count(distinct lower(grouping1)),count(distinct lower(grouping2),lower(grouping1)).
19. The method of claim 17, wherein the second computed data source and the third computed data source are recomputed on a predetermined interval, wherein the second computed data source is recomputed before the third computed data source, and the third computed data source is subsequently recomputed based on the second computed data source.
20. A method for processing a plurality of search queries, the method comprising:
receiving, by a server, a search query comprising search parameters to retrieve data from a multi-dimensional database;
receiving, by the server, a first computed data source, a second computed data source, and a third computed data source, wherein:
the first computed data source comprises historical data associated with previously executed search queries on the multi-dimensional database and actual record counts returned by the previously executed search queries,
the second computed data source comprises a total count of distinct values for each of a plurality of groupings segmented by predetermined categories within the multi-dimensional database, and
the third computed data source comprises a correlation of the total count of distinct values between at least two of the plurality of groupings;
comparing, by the server, the search query and the previously executed search queries of the first computed data source to determine whether the search query matches one of the previously executed search queries;
computing, by the server, an estimated record count for the search query on the multi-dimensional database based on a comparison between the search query and the previously executed search queries of the first computed data source and an estimation equation comprising data values from the second computed data source and the third computed data source; and
one of:
executing, by the server, the search query based on the estimated record count not exceeding a predetermined record count; or
cancelling, by the server, the search query based on the estimated record count exceeding the predetermined record count.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is
directed to an abstract idea without significantly more.
Claim 1 recites: (Step 2a, Prong One)
executing a search query based on the estimated record count not exceeding a record-count threshold.
The limitation of executing a search query based on the estimated record count not exceeding a record-count threshold, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting a generic method/server, nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the method/server language, “executing” in the context of this claim encompasses the user manually determining/executing a generic “query” using a generic “record count threshold” steps. Similarly, the limitation(s) of receiving; receiving and determining, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. For example, but for the method/server language, receiving; receiving and determining in the context of this claim encompasses the user manually receiving generic “historical data” and “record counts” and performing generic determining of “record counts” using “estimating” steps. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas (concepts performed in the human mind (including an observation, evaluation, judgment, opinion)).
Further, these concepts also recite “Certain Methods of Organizing Human Activity”; (such as
commercial or legal interactions (including agreements in the form of contracts; legal
obligations; advertising, marketing or sales activities or behaviors; business relations) where
performing generic executing of generic search queries using steps of generic “estimating” and
“thresholds” is a method of human activity in commercial or legal interactions (for example, “record keeping”).
Accordingly, the claim recites an abstract idea.
(Step 2a, Prong Two)
This judicial exception is not integrated into a practical application. In particular, the claim only
recites one additional element – using a method with a server/database to perform both the receiving; receiving and determining; and executing steps. The method with a server/database in both steps is recited at a high level of generality (i.e., as a generic processor performing a generic computer function of “executing”) such that it amounts no more than mere instructions to apply the exception using a generic computer component.
Accordingly, this additional element does not integrate the abstract idea into a practical
application because it does not impose any meaningful limits on practicing the abstract idea.
The claim is directed to an abstract idea. The claim does not include additional elements that
are sufficient to amount to significantly more than the judicial exception.
(Step 2b)
The claim does not include additional elements that are sufficient to amount to significantly
more than the judicial exception. As discussed above with respect to integration of the abstract
idea into a practical application, the additional element of a method with a server/database to perform both the receiving; receiving and determining; and executing steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim(s) is/are not patent eligible.
Referring to claim 2, (Step 2a, Prong One) this further merely performs an additional abstract
mental step of “comparing, by the server, the search query to the historical data to determine whether the search query matches a previously executed search query”.
(Step 2a, Prong Two)
This judicial exception is not integrated into a practical application. In particular, the claim only
recites the additional elements of “comparing, by the server, the search query to the historical data to determine whether the search query matches a previously executed search query” steps to perform both the aforementioned receiving; receiving and determining; and executing steps. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
(Step 2b)
The claim does not include additional elements that are sufficient to amount to significantly
more than the judicial exception. As discussed above with respect to integration of the abstract
idea into a practical application, the additional element of using “comparing, by the server, the search query to the historical data to determine whether the search query matches a previously executed search query” steps to perform both the aforementioned receiving; receiving and determining; and executing steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
The claim(s) is/are not patent eligible.
Referring to claim 3, (Step 2a, Prong One) this further merely performs an additional abstract
mental step of “determining, by the server, that the estimated record count exceeds the predetermined record-count threshold”.
(Step 2a, Prong Two)
This judicial exception is not integrated into a practical application. In particular, the claim only
recites the additional elements of “determining, by the server, that the estimated record count exceeds the predetermined record-count threshold” steps to perform both the aforementioned receiving; receiving and determining; and executing steps. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
(Step 2b)
The claim does not include additional elements that are sufficient to amount to significantly
more than the judicial exception. As discussed above with respect to integration of the abstract
idea into a practical application, the additional element of using “determining, by the server, that the estimated record count exceeds the predetermined record-count threshold” steps to perform both the aforementioned receiving; receiving and determining; and executing steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
The claim(s) is/are not patent eligible.
Referring to claim 4, (Step 2a, Prong One) this further merely performs an additional abstract
mental step of “cancelling, by the server, execution of the search query based on the estimated record count exceeding the predetermined record-count threshold”.
(Step 2a, Prong Two)
This judicial exception is not integrated into a practical application. In particular, the claim only
recites the additional elements of “cancelling, by the server, execution of the search query based on the estimated record count exceeding the predetermined record-count threshold” steps to perform both the aforementioned receiving; receiving and determining; and executing steps. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
(Step 2b)
The claim does not include additional elements that are sufficient to amount to significantly
more than the judicial exception. As discussed above with respect to integration of the abstract
idea into a practical application, the additional element of using “cancelling, by the server, execution of the search query based on the estimated record count exceeding the predetermined record-count threshold” steps to perform both the aforementioned receiving; receiving and determining; and executing steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
The claim(s) is/are not patent eligible.
Referring to claim 5, (Step 2a, Prong One) this further merely performs an additional abstract
mental step of “selecting, by the server, a database-processing system to execute the search query based on the search parameters and the estimated record count”.
(Step 2a, Prong Two)
This judicial exception is not integrated into a practical application. In particular, the claim only
recites the additional elements of “selecting, by the server, a database-processing system to execute the search query based on the search parameters and the estimated record count” steps to perform both the aforementioned receiving; receiving and determining; and executing steps. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
(Step 2b)
The claim does not include additional elements that are sufficient to amount to significantly
more than the judicial exception. As discussed above with respect to integration of the abstract
idea into a practical application, the additional element of using “selecting, by the server, a database-processing system to execute the search query based on the search parameters and the estimated record count” steps to perform both the aforementioned receiving; receiving and determining; and executing steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
The claim(s) is/are not patent eligible.
Referring to claim 6, (Step 2a, Prong One) this further merely performs an additional abstract
mental step of “wherein the data packet is updated after each completed search query”.
(Step 2a, Prong Two)
This judicial exception is not integrated into a practical application. In particular, the claim only
recites the additional elements of “wherein the data packet is updated after each completed search query” steps to perform both the aforementioned receiving; receiving and determining; and executing steps. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
(Step 2b)
The claim does not include additional elements that are sufficient to amount to significantly
more than the judicial exception. As discussed above with respect to integration of the abstract
idea into a practical application, the additional element of using “wherein the data packet is updated after each completed search query” steps to perform both the aforementioned receiving; receiving and determining; and executing steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
The claim(s) is/are not patent eligible.
Referring to claim 7, (Step 2a, Prong One) this further merely performs an additional abstract
mental step of “wherein each correlation in the data packet is derived from the respective total distinct-value counts of the paired attribute groupings”.
(Step 2a, Prong Two)
This judicial exception is not integrated into a practical application. In particular, the claim only
recites the additional elements of “wherein each correlation in the data packet is derived from the respective total distinct-value counts of the paired attribute groupings” steps to perform both the aforementioned receiving; receiving and determining; and executing steps. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
(Step 2b)
The claim does not include additional elements that are sufficient to amount to significantly
more than the judicial exception. As discussed above with respect to integration of the abstract
idea into a practical application, the additional element of using “wherein each correlation in the data packet is derived from the respective total distinct-value counts of the paired attribute groupings” steps to perform both the aforementioned receiving; receiving and determining; and executing steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
The claim(s) is/are not patent eligible.
Referring to claim 8, (Step 2a, Prong One) this further merely performs an additional abstract
mental step of “wherein the correlation for every ordered pair of attribute groupings comprises:
executing a first database query that, for a first grouping G1 and a second grouping G2,
returns a distinct-value count for G1, a distinct-value count for G2, and a joint distinct-value
count of G1 and G2 over a defined set of time-period categories; and
executing a second database query that repeats the foregoing for an ordered pair (G2,
G1), to produce ratio values usable as the correlation between G1 and G2”.
(Step 2a, Prong Two)
This judicial exception is not integrated into a practical application. In particular, the claim only
recites the additional elements of “wherein the correlation for every ordered pair of attribute groupings comprises: executing a first database query that, for a first grouping G1 and a second grouping G2, returns a distinct-value count for G1, a distinct-value count for G2, and a joint distinct-value count of G1 and G2 over a defined set of time-period categories; and
executing a second database query that repeats the foregoing for an ordered pair (G2,
G1), to produce ratio values usable as the correlation between G1 and G2” steps to perform both the aforementioned receiving; receiving and determining; and executing steps. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
(Step 2b)
The claim does not include additional elements that are sufficient to amount to significantly
more than the judicial exception. As discussed above with respect to integration of the abstract
idea into a practical application, the additional element of using “wherein the correlation for every ordered pair of attribute groupings comprises:
executing a first database query that, for a first grouping G1 and a second grouping G2,
returns a distinct-value count for G1, a distinct-value count for G2, and a joint distinct-value
count of G1 and G2 over a defined set of time-period categories; and
executing a second database query that repeats the foregoing for an ordered pair (G2,
G1), to produce ratio values usable as the correlation between G1 and G2” steps to perform both the aforementioned receiving; receiving and determining; and executing steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
The claim(s) is/are not patent eligible.
Referring to claim 9, (Step 2a, Prong One) this further merely performs an additional abstract
mental step of “wherein the total distinct-value counts are recomputed at a first predetermined interval, and upon completion of the recomputation, each correlation is recomputed at a second predetermined interval based on updated total distinct-value counts”.
(Step 2a, Prong Two)
This judicial exception is not integrated into a practical application. In particular, the claim only
recites the additional elements of “wherein the total distinct-value counts are recomputed at a first predetermined interval, and upon completion of the recomputation, each correlation is
recomputed at a second predetermined interval based on updated total distinct-value counts” steps to perform both the aforementioned receiving; receiving and determining; and executing steps. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
(Step 2b)
The claim does not include additional elements that are sufficient to amount to significantly
more than the judicial exception. As discussed above with respect to integration of the abstract
idea into a practical application, the additional element of using “wherein the total distinct-value counts are recomputed at a first predetermined interval, and upon completion of the recomputation, each correlation is recomputed at a second predetermined interval based on updated total distinct-value counts” steps to perform both the aforementioned receiving; receiving and determining; and executing steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
The claim(s) is/are not patent eligible.
Referring to claim 10, (Step 2a, Prong One) this further merely performs an additional abstract
mental step of “wherein the predetermined categories include a month category”.
(Step 2a, Prong Two)
This judicial exception is not integrated into a practical application. In particular, the claim only
recites the additional elements of “wherein the predetermined categories include a month category” steps to perform both the aforementioned receiving; receiving and determining; and executing steps. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
(Step 2b)
The claim does not include additional elements that are sufficient to amount to significantly
more than the judicial exception. As discussed above with respect to integration of the abstract
idea into a practical application, the additional element of using “wherein the predetermined categories include a month category” steps to perform both the aforementioned receiving; receiving and determining; and executing steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
The claim(s) is/are not patent eligible.
Referring to claim 11, (Step 2a, Prong One) this further merely performs an additional abstract
mental step of “wherein the multi-dimensional database contains at least 1 trillion records”.
(Step 2a, Prong Two)
This judicial exception is not integrated into a practical application. In particular, the claim only
recites the additional elements of “wherein the multi-dimensional database contains at least 1 trillion records” steps to perform both the aforementioned receiving; receiving and determining; and executing steps. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
(Step 2b)
The claim does not include additional elements that are sufficient to amount to significantly
more than the judicial exception. As discussed above with respect to integration of the abstract
idea into a practical application, the additional element of using “wherein the multi-dimensional database contains at least 1 trillion records” steps to perform both the aforementioned receiving; receiving and determining; and executing steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
The claim(s) is/are not patent eligible.
Claim 12 recites: (Step 2a, Prong One)
executing a search query based on the estimated record count not exceeding a record-count threshold.
The limitation of executing a search query based on the estimated record count not exceeding a record-count threshold, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting a generic method/server, nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the method/server language, “executing” in the context of this claim encompasses the user manually determining/executing a generic “query” using a generic “record count threshold” steps. Similarly, the limitation(s) of receiving; receiving and determining, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. For example, but for the method/server language, receiving; receiving and determining in the context of this claim encompasses the user manually receiving generic “historical data” and “record counts” and performing generic determining of “record counts” using “estimating” steps. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas (concepts performed in the human mind (including an observation, evaluation, judgment, opinion)).
Further, these concepts also recite “Certain Methods of Organizing Human Activity”; (such as
commercial or legal interactions (including agreements in the form of contracts; legal
obligations; advertising, marketing or sales activities or behaviors; business relations) where
performing generic executing of generic search queries using steps of generic “estimating” and
“thresholds” is a method of human activity in commercial or legal interactions (for example, “record keeping”).
Accordingly, the claim recites an abstract idea.
(Step 2a, Prong Two)
This judicial exception is not integrated into a practical application. In particular, the claim only
recites one additional element – using a method with a server/database to perform both the receiving; receiving and determining; and executing steps. The method with a server/database in both steps is recited at a high level of generality (i.e., as a generic processor performing a generic computer function of “executing”) such that it amounts no more than mere instructions to apply the exception using a generic computer component.
Accordingly, this additional element does not integrate the abstract idea into a practical
application because it does not impose any meaningful limits on practicing the abstract idea.
The claim is directed to an abstract idea. The claim does not include additional elements that
are sufficient to amount to significantly more than the judicial exception.
(Step 2b)
The claim does not include additional elements that are sufficient to amount to significantly
more than the judicial exception. As discussed above with respect to integration of the abstract
idea into a practical application, the additional element of a method with a server/database to perform both the receiving; receiving and determining; and executing steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim(s) is/are not patent eligible.
Referring to claim 13, (Step 2a, Prong One) this further merely performs an additional abstract
mental step of “comparing, by the server, the search query to historical data to identify whether the search query matches a previously executed search query”.
(Step 2a, Prong Two)
This judicial exception is not integrated into a practical application. In particular, the claim only
recites the additional elements of “comparing, by the server, the search query to historical data to identify whether the search query matches a previously executed search query” steps to perform both the aforementioned receiving; receiving and determining; and executing steps. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
(Step 2b)
The claim does not include additional elements that are sufficient to amount to significantly
more than the judicial exception. As discussed above with respect to integration of the abstract
idea into a practical application, the additional element of using “comparing, by the server, the search query to historical data to identify whether the search query matches a previously executed search query” steps to perform both the aforementioned receiving; receiving and determining; and executing steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
The claim(s) is/are not patent eligible.
Referring to claim 14, (Step 2a, Prong One) this further merely performs an additional abstract
mental step of “determining, by the server, that the estimated record count exceeds the predetermined record count threshold; and cancelling, by the server, the search query based on the estimated record count exceeding the predetermined record count”.
(Step 2a, Prong Two)
This judicial exception is not integrated into a practical application. In particular, the claim only
recites the additional elements of “determining, by the server, that the estimated record count exceeds the predetermined record count threshold; and cancelling, by the server, the search query based on the estimated record count exceeding the predetermined record count” steps to perform both the aforementioned receiving; receiving and determining; and executing steps. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
(Step 2b)
The claim does not include additional elements that are sufficient to amount to significantly
more than the judicial exception. As discussed above with respect to integration of the abstract
idea into a practical application, the additional element of using “determining, by the server, that the estimated record count exceeds the predetermined record count threshold; and cancelling, by the server, the search query based on the estimated record count exceeding the predetermined record count” steps to perform both the aforementioned receiving; receiving and determining; and executing steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
The claim(s) is/are not patent eligible.
Referring to claim 15, (Step 2a, Prong One) this further merely performs an additional abstract
mental step of “selecting, by the server, a database-processing system to execute the search query based on the search parameters and the estimated record count”.
(Step 2a, Prong Two)
This judicial exception is not integrated into a practical application. In particular, the claim only
recites the additional elements of “selecting, by the server, a database-processing system to execute the search query based on the search parameters and the estimated record count” steps to perform both the aforementioned receiving; receiving and determining; and executing steps. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
(Step 2b)
The claim does not include additional elements that are sufficient to amount to significantly
more than the judicial exception. As discussed above with respect to integration of the abstract
idea into a practical application, the additional element of using “selecting, by the server, a database-processing system to execute the search query based on the search parameters and the estimated record count” steps to perform both the aforementioned receiving; receiving and determining; and executing steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
The claim(s) is/are not patent eligible.
Referring to claim 16, (Step 2a, Prong One) this further merely performs an additional abstract
mental step of “updating, by the server, the historical data after each completed search query to include the search query and its actual record count”.
(Step 2a, Prong Two)
This judicial exception is not integrated into a practical application. In particular, the claim only
recites the additional elements of “updating, by the server, the historical data after each completed search query to include the search query and its actual record count” steps to perform both the aforementioned receiving; receiving and determining; and executing steps. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
(Step 2b)
The claim does not include additional elements that are sufficient to amount to significantly
more than the judicial exception. As discussed above with respect to integration of the abstract
idea into a practical application, the additional element of using “updating, by the server, the historical data after each completed search query to include the search query and its actual record count” steps to perform both the aforementioned receiving; receiving and determining; and executing steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
The claim(s) is/are not patent eligible.
Referring to claim 17, (Step 2a, Prong One) this further merely performs an additional abstract
mental step of “wherein the correlation is derived from the total distinct-value counts of the paired attribute groupings. of the total count of distinct values between at least two of the plurality of groupings is generated”.
(Step 2a, Prong Two)
This judicial exception is not integrated into a practical application. In particular, the claim only
recites the additional elements of “wherein the correlation is derived from the total distinct-value counts of the paired attribute groupings. of the total count of distinct values between at least two of the plurality of groupings is generated” steps to perform both the aforementioned receiving; receiving and determining; and executing steps. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
(Step 2b)
The claim does not include additional elements that are sufficient to amount to significantly
more than the judicial exception. As discussed above with respect to integration of the abstract
idea into a practical application, the additional element of using “wherein the correlation is derived from the total distinct-value counts of the paired attribute groupings. of the total count of distinct values between at least two of the plurality of groupings is generated” steps to perform both the aforementioned receiving; receiving and determining; and executing steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
The claim(s) is/are not patent eligible.
Referring to claim 18, (Step 2a, Prong One) this further merely performs an additional abstract
mental step of “executing a first database query for an ordered pair (G1, G2) to obtain the distinct-value count of G1, the distinct-value count of G2, and a joint distinct-value count of G1 and G2; and executing a second database query for the ordered pair (G2, G1) to obtain
corresponding counts, the first and second query results together defining ratio values usable as
the correlation”.
(Step 2a, Prong Two)
This judicial exception is not integrated into a practical application. In particular, the claim only
recites the additional elements of “executing a first database query for an ordered pair (G1, G2) to obtain the distinct-value count of G1, the distinct-value count of G2, and a joint distinct-value count of G1 and G2; and executing a second database query for the ordered pair (G2, G1) to obtain corresponding counts, the first and second query results together defining ratio values usable as the correlation” steps to perform both the aforementioned receiving; receiving and determining; and executing steps. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
(Step 2b)
The claim does not include additional elements that are sufficient to amount to significantly
more than the judicial exception. As discussed above with respect to integration of the abstract
idea into a practical application, the additional element of using “executing a first database query for an ordered pair (G1, G2) to obtain the distinct-value count of G1, the distinct-value count of G2, and a joint distinct-value count of G1 and G2; and executing a second database query for the ordered pair (G2, G1) to obtain
corresponding counts, the first and second query results together defining ratio values usable as
the correlation” steps to perform both the aforementioned receiving; receiving and determining; and executing steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
The claim(s) is/are not patent eligible.
Referring to claim 19, (Step 2a, Prong One) this further merely performs an additional abstract
mental step of “wherein the total distinct-value counts are recomputed at a first predetermined interval; and upon completion of that recomputation, each correlation is recomputed at a second
predetermined interval based on updated total distinct-value counts”.
(Step 2a, Prong Two)
This judicial exception is not integrated into a practical application. In particular, the claim only
recites the additional elements of “wherein the total distinct-value counts are recomputed at a first predetermined interval; and upon completion of that recomputation, each correlation is recomputed at a second predetermined interval based on updated total distinct-value counts” steps to perform both the aforementioned receiving; receiving and determining; and executing steps. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
(Step 2b)
The claim does not include additional elements that are sufficient to amount to significantly
more than the judicial exception. As discussed above with respect to integration of the abstract
idea into a practical application, the additional element of using “wherein the total distinct-value counts are recomputed at a first predetermined interval; and upon completion of that recomputation, each correlation is recomputed at a second
predetermined interval based on updated total distinct-value counts” steps to perform both the aforementioned receiving; receiving and determining; and executing steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
The claim(s) is/are not patent eligible.
Claim 20 recites: (Step 2a, Prong One)
executing a search query based on the estimated record count not exceeding a record-count threshold.
The limitation of executing a search query based on the estimated record count not exceeding a record-count threshold, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting a generic processor/memory, nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the processor/memory language, “executing” in the context of this claim encompasses the user manually determining/executing a generic “query” using a generic “record count threshold” steps. Similarly, the limitation(s) of receiving; receiving and determining, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. For example, but for the method/server language, receiving; receiving and determining in the context of this claim encompasses the user manually receiving generic “historical data” and “record counts” and performing generic determining of “record counts” using “estimating” steps. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas (concepts performed in the human mind (including an observation, evaluation, judgment, opinion)).
Further, these concepts also recite “Certain Methods of Organizing Human Activity”; (such as
commercial or legal interactions (including agreements in the form of contracts; legal
obligations; advertising, marketing or sales activities or behaviors; business relations) where
performing generic executing of generic search queries using steps of generic “estimating” and
“thresholds” is a method of human activity in commercial or legal interactions (for example, “record keeping”).
Accordingly, the claim recites an abstract idea.
(Step 2a, Prong Two)
This judicial exception is not integrated into a practical application. In particular, the claim only
recites one additional element – using a processor/memory/database to perform both the receiving; receiving and determining; and executing steps. The processor/memory/database in both steps is recited at a high level of generality (i.e., as a generic processor performing a generic computer function of “executing”) such that it amounts no more than mere instructions to apply the exception using a generic computer component.
Accordingly, this additional element does not integrate the abstract idea into a practical
application because it does not impose any meaningful limits on practicing the abstract idea.
The claim is directed to an abstract idea. The claim does not include additional elements that
are sufficient to amount to significantly more than the judicial exception.
(Step 2b)
The claim does not include additional elements that are sufficient to amount to significantly
more than the judicial exception. As discussed above with respect to integration of the abstract
idea into a practical application, the additional element of a processor/memory/database to perform both the receiving; receiving and determining; and executing steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim(s) is/are not patent eligible.
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, 3, 5-7, 12, 15, 17 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ramanathan et al., US Pub. No. 2020/0334267 A1.
As to claim 1 (and substantially similar claim 12 and claim 20),
Ramanathan discloses
a method for processing a plurality of search queries,
(Ramanathan abstract; [0046-0052])
the method comprising:
receiving, by a server, a search query comprising search parameters to retrieve data
from a multi-dimensional database;
(Ramanathan teaches querying data warehouse/multi-dimensional databases for facts/fields etc., i.e. parameters see [0123] Once data has been loaded into their data warehouse instance, customers can create business database views that combine tables from both their customer schema and the software analytic application schema; and can query their data warehouse instance using an interface provided, for example, by a business productivity and analytics product suite, or by a SQL tool of the customer's choice.;
See also [0038] For example, the data warehouse environment or component can be provided as a multi-dimensional database that employs online analytical processing (OLAP);
See also [0080] For example, dimensions can include categories of data such as, for example, "name," "address," or "age". Fact generation includes the generation of values that data can
take, or "measures." Facts are associated with appropriate dimensions in the data warehouse instance.)
receiving, by the server, a data packet comprising:
historical data describing previously executed search queries on the multidimensional
database and actual record counts returned by the previously executed search queries,
(Ramanathan teaches historical data with record counts [0112] For example, the historical data can include size of extraction, count of extraction, extraction time, size of warehouse, transform time, publish (load) time, view object extract size, view object extract record count, view object
extract time, warehouse table count, count of records processed for a table, warehouse table transform time, publish table count, and publish time. Such historical data can be used to estimate and plan current and future activation plans in order to organize various tasks;
see also [0024] FIG. 15 illustrates an example of various column data with value sets for a warehouse file, in accordance with an embodiment. [0025] FIG. 16 illustrates another example of various column data with value sets for a warehouse file, in accordance with an embodiment.;
see also [0056] The provisioning component can then provision the requested data warehouse
instance, including a customer schema of the data warehouse; and populate the data warehouse instance with the appropriate information supplied by the customer.)
a total distinct-value count for each of a plurality of attribute groupings segmented by predetermined categories in the multi-dimensional database,
(Ramanathan [0168-0169] [0169] In the illustrated example, the value of "ntotal" is the total number of rows in the column, and the value of "nunique" is the number of unique values in the column. The value set displays the unique values in each column.
See also [0151] In accordance with an embodiment, various examples of dynamic data-driven asserts for integer values can include: [0152] Unique value asserts, for example wherein a column holds only a single value, such as a legal entity value, or the update ID for the process that updated the warehouse; see also [0159] For each column in the value set, the
system can determine if the values in the column are unique, strictly sequential, cyclic or non-negative (as described above). If the values for any column are indeed determined to be unique, strictly sequential, cyclic or non-negative, then the system can generate the appropriate assert, as described above.)
and for at least one pair of the attribute groupings, a correlation between their respective total distinct-value count;
(Ramanathan teaches determining/updating dynamic data-driven asserts for integer values can include unique value asserts with forecasting assert values, i.e. “correlations” between distinct values see [0151] In accordance with an embodiment, various examples of dynamic data-driven asserts for integer values can include: [0152] Unique value asserts, for example wherein a column holds only a single value, such as a legal entity value, or the update ID for the process that updated the warehouse; see also [0159] For each column in the value set, the
system can determine if the values in the column are unique, strictly sequential, cyclic or non-negative (as described above). If the values for any column are indeed determined to be unique, strictly sequential, cyclic or non-negative, then the system can generate the appropriate assert, as described above;
see also [0208] asserts are maintained or updated on an ongoing basis by, for example, forecasting assert values using a current trend; performing a forecast quality evaluation; and accepting the forecast if it meets a threshold for quality; and/or re-computing the assert probabilities based on the forecast data.)
determining, by the server, an estimated record count for the search query by comparing
the search query with the historical data and applying an estimation equation that uses the total
distinct-value counts and the correlations; and
(Ramanathan teaches the historical data/counts can be used to estimate and plan current and future activation plans in order to organize various tasks see [0112] For example, the historical data can include size of extraction, count of extraction, extraction time, size of
warehouse, transform time, publish (load) time, view object extract size, view object extract record count, view object extract time, warehouse table count, count of records processed
for a table, warehouse table transform time, publish table count, and publish time. Such historical data can be used to estimate and plan current and future activation plans in order to organize various tasks to, such as, for example, run in sequence or in parallel to arrive at a minimum time to run an activation plan. In addition, the gathered historical data can be used to optimize across multiple activation plans for a tenant. In some embodiments, the optimization of
activation plans (i.e., a particular sequence of jobs, such as ETLs) based upon historical data can be automatic.)
executing, by the server, the search query based on the estimated record count not
exceeding a predetermined record-count threshold.
(Ramanathan teaches putting assertions into production if they are stable based on not exceeding a threshold ( Count (Assert_violations)<some threshold), i.e. “executing, by the server, the search query based on the estimated record count not exceeding a predetermined record-count threshold”
See [0199] In accordance with an embodiment, the asserts can be put into production when it is stable. Stability can be defined, e.g., as Count (Assert_violations)<some threshold
in the evaluation period, or as some percentage of customers affected by the assert violation.
See also [0206] At step 274, the asserts are validated against the data, for example in accordance with an embodiment by computing a number of instances ( e.g., customers) for which a particular assert was violated, and if the number is above a particular threshold ( e.g., 25% of instances or customers), then the assert is updated or modified to include any otherwise
offending values.
See also [0207] At step 276, the modified asserts are executed against the data; and if, for example, the number of instances (e.g., customers) for which a particular assert was violated
is seen to decrease, then the modified assert is accepted.)
While Ramanathan does explicitly teach the term “a data packet”, Ramanathan does teach instances/data warehouse files (see Ramanathan [0024-0025, 0056])
It would have been obvious to one having ordinary skill in the art at the time of the effective filing date to apply thresholds and assertions from historical data to for executing multidimensional data warehouse queries from instances/data warehouse files as taught by Ramanathan, since it was known in the art that database systems provide in order to effectively use asserts for data verification during an ETL process, the system evaluates dynamic data-driven asserts where once an organization has gathered sufficient data from its applications, databases, or processes, the data itself can provide clues as to what should be considered valid data, and what should not be considered valid data where dynamic data driven asserts can similarly enable the system to learn over time, which data is considered valid data, and which data is
considered to be not valid (invalid) data and when used with data warehouses offered in SaaS and other shared computing environments, data values can also be compared across multiple customer data warehouses, to create or improve upon the use of dynamic data-driven asserts where assertions on numeric values are of particular interest, since such enterprises often make financial decisions based on numeric values; and accordingly, errors in such numeric values are of particular consequence. (Ramanathan [0140-0141]).
As to claim 3, Ramanathan as modified discloses the method of claim 1, comprising:
determining, by the server, that the estimated record count exceeds the predetermined
record-count threshold
(Ramanathan [0158] In accordance with an embodiment, for columns of data holding real value data, a dynamic data-driven assert verifying the probability of a data value exceeding a threshold can be generated.).
As to claim 5, Ramanathan as modified discloses the method of claim 1, comprising:
selecting, by the server, a database-processing system to execute the search query
based on the search parameters and the estimated record count
(Ramanathan [0111-0112] [0111] In accordance with an embodiment, based on a
determination of historical performance data recorded over a period of time, the system can optimize the execution of activation plans, e.g., for one or more functional areas
associated with a particular tenant, or across a sequence of activation plans associated with multiple tenants, to address utilization of the VMs and service level agreements (SLAs) for those tenants. Such historical data can include statistics of load volumes and load times.
[0112] For example, the historical data can include size of extraction, count of extraction, extraction time, size of warehouse, transform time, publish (load) time, view object extract size, view object extract record count, view object extract time, warehouse table count, count of records processed for a table, warehouse table transform time, publish table count, and publish time. Such historical data can be used to estimate and plan current and future activation plans
in order to organize various tasks See also [0072] In accordance with an embodiment, a data pipeline or process can be scheduled to execute at intervals (e.g., hourly/daily/weekly) to extract transactional data from an enterprise software application or data environment, such as,
for example, business productivity software applications and corresponding transactional databases 106 that are provisioned in the SaaS environment.;
see also [0151-0152] [0151] In accordance with an embodiment, various examples of dynamic data-driven asserts for integer values can include: [0152] Unique value asserts, for example wherein a column holds only a single value, such as a legal entity value, or the update ID for the process that updated the warehouse;).
As to claim 6, Ramanathan as modified discloses the method of claim 1, wherein the data packet is updated after each completed search query
(Ramanathan see Fig. 18 item 278 “Asserts are maintained or updated on an ongoing basis by, for example, forecasting assert values using a current trend; performing a forecast quality evaluation; and accepting the forecast if it meets a threshold for quality; and/or re-computing the assert probabilities based on the forecast data”).
As to claim 7, Ramanathan as modified discloses the method of claim 1, wherein each correlation in the data packet is derived from the respective total distinct-value counts of the paired attribute groupings
(Ramanathan teaches the historical data/counts can be used to estimate and plan current and future activation plans in order to organize various tasks, i.e. “correlation in the data packet is derived from the respective total distinct-value counts” see [0112] For example, the historical data can include size of extraction, count of extraction, extraction time, size of
warehouse, transform time, publish (load) time, view object extract size, view object extract record count, view object extract time, warehouse table count, count of records processed
for a table, warehouse table transform time, publish table count, and publish time. Such historical data can be used to estimate and plan current and future activation plans in order to organize various tasks to, such as, for example, run in sequence or in parallel to arrive at a minimum time to run an activation plan. In addition, the gathered historical data can be used to optimize across multiple activation plans for a tenant. In some embodiments, the optimization of
activation plans (i.e., a particular sequence of jobs, such as ETLs) based upon historical data can be automatic.).
Referring to claim 15, this dependent claim recites similar limitations as claim 5;
therefore, the arguments above regarding claim 5 are also applicable to claim 15.
Referring to claim 17, this dependent claim recites similar limitations as claim 7;
therefore, the arguments above regarding claim 7 are also applicable to claim 17.
Claim(s) 2, 13, 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over
Ramanathan et al., US Pub. No. 2020/0334267 A1, in view of Bell et al., US Pub. No.: US 2023/0138193 A1.
As to claim 2, Ramanathan does not disclose:
comparing, by the server, the search query to the historical data to determine whether
the search query matches a previously executed search query;
However, Bell discloses:
the method of claim 1, comprising:
comparing, by the server, the search query to the historical data to determine whether
the search query matches a previously executed search query
(Bell abstract: “In one aspect, a search query is received and determined to match a pattern having an uncategorized variable placeholder portion. In response to determining the
search query matches the pattern, the pattern is used to identify a first portion of the search query based on the first portion of the search query corresponding to the uncategorized
variable placeholder portion of the pattern”;
see also [0010] In some aspects of the method, the first queries are identified based on a database mapping the first queries to their particular category. Some aspects of the method
include searching a historical query database to identify the unique second queries, the historical query database mapping historical queries to respective categories.).
It would have been obvious to one having ordinary skill in the art at the time of the effective filing date to apply historical query matching as taught by Bell, to the system of Ramanathan, since it was known in the art that database systems provide for an improved ability to automatically categorize results for a query received by a query response system where for example, when a query is received, the disclosed methods and systems may enhance an ability to provide results for the query that are within a single category or subcategory where this filtering of results for specific categories is designed to provide query results to a user more related to items they seek via the query; where categories and subcategories use of the term category or subcategory may be considered equivalent where for example, references a category in the description of the methods and systems below may be considered to include references to a sub-category in some aspects (Bell [0036]).
Referring to claim 13, this dependent claim recites similar limitations as claim 2;
therefore, the arguments above regarding claim 2 are also applicable to claim 13.
As to claim 16, Bell as modified discloses the method of claim 12, comprising:
updating, by the server, the historical data after each completed search query to include
the search query and its actual record count.
(Bell [0054-0055] [0054] The rank column 372 may be generated after all count entries 368 are generated for at least a unique query 362 value. The rank column 372 indicates an ordinal rank of a value in the count field 368 relative to other values in other count fields 368 for rows of the query statistics database 360 having equivalent query 362 values. [0055] FIG. 3 also illustrates a top category match database 375. Top category match database 375 stores a query
380. The query 380 may be equivalent to a query 362 stored in the query statistics database 360.).
Claim(s) 4, 9, 14, 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over
Ramanathan et al., US Pub. No. 2020/0334267 A1, in view of Bell et al., US Pub. No.: US 2023/0138193 A1, in view of Day et al., US Pub. No.: 2006/0074874 A1.
As to claim 4, Ramanathan/Bell do not disclose:
cancelling, by the server, execution of the search query based on the estimated record
count exceeding the predetermined record-count threshold;
However, Day discloses:
the method of claim 3, comprising:
cancelling, by the server, execution of the search query based on the estimated record
count exceeding the predetermined record-count threshold
(Day teaches re-evaluating a query/killing a query execution based on a threshold exceeding/violation by monitoring the number of records evaluated see [0065] If any further records remain to be evaluated, the 'Y' branch is taken from step 512, the evaluation loop counter is incremented (step 513), and the query engine tests whether the loop threshold has been exceeded (step 514). If the threshold is exceeded (the 'Y' branch from step 514), the
query engine re-evaluates the primary execution strategy, beginning with step 521. If the threshold is not exceeded, the query engine continues to step 509 to select the next record for evaluation. When all records have been thus evaluated, the 'N' branch is taken from step 512;
see also [0074] In the case of the alternative thread, after all records have been evaluated, the 'N' branch is taken from step 534, and the primary strategy thread is killed (step 535). In either case, the query engine then continues to step 517.;
see also [0019] In the preferred embodiment, the query engine monitors the number of records evaluated and selected by the index value condition during execution of the query, and
dynamically initiates an alternative query strategy when this number appears to be out of proportion to expectations.).
It would have been obvious to one having ordinary skill in the art at the time of the effective filing date to apply killing query execution based on thresholds as taught by Day, to the system of Ramanathan/Bell, since it was known in the art that database systems provide for
a query engine that monitors the number of records evaluated and selected by the index value condition during execution of the query, and dynamically initiates an alternative query strategy when this number appears to be out of proportion to expectations where once or more during execution, the number selected is extrapolated based on the total number in the database, and if the minimum number is not met, the alternative (index search) strategy is attempted where the query engine initiates an alternative search strategy and concurrently continues the original search strategy, the two strategies executing as separate and independent threads where search results are obtained from whichever thread completes first, and the other thread is terminated, where it would alternatively be possible to select only one strategy in this instance, and to execute the selected strategy; where by dynamically and automatically altering the execution strategy of queries where it appears that the assumptions upon which a strategy was optimized are erroneous, the selection of an optimal search strategy is improved, thus improving the utilization of computer resources and/or response time to the requestor. (Day [0019-0021]).
As to claim 9, Day as modified discloses the method of claim 7, wherein the total distinct-value counts are recomputed at a first predetermined interval, and upon completion of the recomputation, each correlation is recomputed at a second predetermined interval based on updated total distinct-value counts
(Day teaches periodic re-evaluation/ loop threshold T may be adjusted, i.e. recomputation see [0068] The query engine then adjusts loop threshold (T) to an appropriate value (step 524). The loop threshold must be adjusted to avoid re-evaluation with every loop iteration. The loop threshold T may be adjusted to some value greater than TotalRecs, so that re-evaluation is performed only once. Alternatively, the loop threshold may be adjusted by some fixed incremental amount substantially greater than one, so that re-evaluation is performed periodically during the table scan. The query engine then returns to the table scan at step 509.).
As to claim 14, Ramanathan as modified discloses the method of claim 12, comprising:
determining, by the server, that the estimated record count exceeds the predetermined
record count threshold; and
(Ramanathan [0158] In accordance with an embodiment, for columns of data holding real value data, a dynamic data-driven assert verifying the probability of a data value exceeding a threshold can be generated.).
And Day as modified discloses
cancelling, by the server, the search query based on the estimated record count
exceeding the predetermined record count
(Day teaches re-evaluating a query/killing a query execution based on a threshold exceeding/violation by monitoring the number of records evaluated see [0065] If any further records remain to be evaluated, the 'Y' branch is taken from step 512, the evaluation loop counter is incremented (step 513), and the query engine tests whether the loop threshold has been exceeded (step 514). If the threshold is exceeded (the 'Y' branch from step 514), the
query engine re-evaluates the primary execution strategy, beginning with step 521. If the threshold is not exceeded, the query engine continues to step 509 to select the next record for evaluation. When all records have been thus evaluated, the 'N' branch is taken from step 512;
see also [0074] In the case of the alternative thread, after all records have been evaluated, the 'N' branch is taken from step 534, and the primary strategy thread is killed (step 535). In either case, the query engine then continues to step 517.;
see also [0019] In the preferred embodiment, the query engine monitors the number of records evaluated and selected by the index value condition during execution of the query, and
dynamically initiates an alternative query strategy when this number appears to be out of proportion to expectations.).
Referring to claim 19, this dependent claim recites similar limitations as claim 9;
therefore, the arguments above regarding claim 9 are also applicable to claim 19.
Claim(s) 8, 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over
Ramanathan et al., US Pub. No. 2020/0334267 A1, in view of Jagota et al., US Pub. No. 2019/0236475.
As to claim 8, Ramanathan as modified discloses the method of claim 7, wherein the correlation for every ordered pair of attribute groupings comprises:
executing a first database query that, for a first grouping G1 and a second grouping G2,
returns a distinct-value count for G1, a distinct-value count for G2, and a joint distinct-value
count of G1 and G2 over a defined set of time-period categories;
(Ramanathan [0165] At step 258, if the generated assertion (e.g., ValueSet assert) is determined to be the same across all of the columns, then the system can set or otherwise determine a higher confidence value associated with the generated assertion.)
And
Ramanathan does not disclose:
executing a second database query that repeats the foregoing for an ordered pair (G2,
G1), to produce ratio values usable as the correlation between G1 and G2
However, Jagota discloses:
executing a second database query that repeats the foregoing for an ordered pair (G2,
G1), to produce ratio values usable as the correlation between G1 and G2
(Jagota [0063] A cross-hierarchy probability can be can be the extent to which something is likely to occur, measured by the ratio of the favorable cases to the whole number of
cases possible, within arrangements or classifications of things according to relative importance or inclusiveness.;
see also [0064] After identifying cross-hierarchy probabilities, an estimated count of a query result set is output, the estimated count generated from cross-hierarchy probabilities, probabilities that values of a first attribute are associated with values corresponding to a first node in a first hierarchy, and probabilities that values of a second attribute are associated
with values corresponding to a second node in a second hierarchy, block 222. The system uses the probabilities of the recorded correlation between hierarchies of attributes to estimate a query result set count.;
see also [0053-0053] [0052] After an influence between two attributes' values is identified, a hierarchy is optionally created, the hierarchy including a node representing an attribute, and another node representing another attribute, and a directed arc connecting the node representing the attribute to the other node representing the other attribute, block 204. The system creates a hierarchy of attributes. By way of example and without limitation, this can include the database system creating the hierarchy 118 that includes a zip code node 128, a state node130, and a directed arc 132 connecting the zip code node 128 to the state node 130, as depicted in FIG. lD. In another example, the database system creates the hierarchy 118 that
includes a city node 134, the state node 130, and a directed arc 136 connecting the city node 134 to the state node 130, as depicted in FIG. lD. In yet another example, the database system creates the hierarchy 120 that includes a sub-industry node 138, the industry node 140, and the directed arc 126 connecting the sub-industry node 138 to the industry node 140, as depicted in FIG. lD. A hierarchy can be an arrangement or classification of things according to relative importance or inclusiveness. A node can be a point at which lines or pathways intersect or branch; a central or connecting point. A directed arc can be a connection representing an
effect on things that are arranged or classified according to relative importance or inclusiveness.
[0053] Following the identification of an influence between two attributes' values, an additional influence by values of one of the attributes on probabilities of values of an additional attribute is optionally identified, block 206. The system identifies additional hierarchical attributes)
It would have been obvious to one having ordinary skill in the art at the time of the effective filing date to apply killing query execution based on thresholds as taught by Day, to the system of Ramanathan, since it was known in the art that database systems provide for identification of an influence between two attributes' values, an additional influence by values of one of the attributes on probabilities of values of an additional attribute is optionally identified, where the system identifies additional hierarchical attributes and where this can include the database system identifying an influence by the state attribute's values on probabilities of the country attribute's values. (Jagota [0053]).
Referring to claim 18, this dependent claim recites similar limitations as claim 8;
therefore, the arguments above regarding claim 8 are also applicable to claim 18.
Claim(s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ramanathan et al., US Pub. No. 2020/0334267 A1, in view of Shen et al., US Pub. No. 2016/0350305 A1.
As to claim 10, Ramanathan does not disclose:
wherein the predetermined categories include a month category;
However, Shen discloses:
the method of claim 1, wherein the predetermined categories include a month category
(Shen [0041] By associating the pre-fetch range with a particular dimension, pre-fetching of
analytic results may be restricted to a particular drill through path. As an example, there may be multiple possible drill through paths for a particular level of data that is being currently viewed. If the user is viewing the sales of a particular product categorized by month and state, a user
may drill down and/or drill up along different dimensions such as the product dimension, the time dimension, and the state dimension;
see also [0049] As an example, an analytic result set may include total sales across different combinations of years, countries, and products at one level of data. If drilling down on the time dimension, the analytic result set may include total sales across different combinations of months, countries, and products for a second level of data. As another example, a slice may choose a single value for the country dimension attribute ( e.g., U.S.) and aggregate different combinations of dimension attribute values for the month and product dimension attributes.)
It would have been obvious to one having ordinary skill in the art at the time of the effective filing date to apply killing query execution based on thresholds as taught by Shen, to the system of Ramanathan, since it was known in the art that database systems provide a month dimension in order to provide that an analytic result set may include total sales across different combinations of years, countries, and products at one level of data, where if drilling down on the time dimension, the analytic result set may include total sales across different combinations of months, countries, and products for a second level of data and where as another example, a slice may choose a single value for the country dimension attribute ( e.g., U.S.) and aggregate different combinations of dimension attribute values for the month and product dimension attributes. (Shen [0049]).
Claim(s) 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over
Ramanathan et al., US Pub. No. 2020/0334267 A1, in view of Zhao US Pub. No.: 2022/0100726 A1.
As to claim 11, Ramanathan does not disclose:
wherein the multi-dimensional database contains at least 1 trillion records;
however, Zhao discloses:
the method of claim 1, wherein the multi-dimensional database contains at least 1
trillion records
(Zhao teaches trillions of multi-dimensional data records see [0055] The data aggregator 210 may apply a scalable distributed system . The data aggregator 210 may be deployed in clusters of tens to hundreds of servers , and may
offer ingest rates of millions of records / sec , retention of trillions of records , and query latencies of sub - second to a few seconds . These factors may further enable the second based response time for real - time data aggregation and analytics;
See also [ 0050 ] The data aggregator 210 may use compressed bitmap indexes to create indexes that power fast filtering and real - time queries . The data aggregator 210 may first partition
data by time and may also additionally partition data based on one or more other fields . Such multi - layered partitioning may lead to significant performance improvements for time based queries . The data aggregator 210 may be deployed in clusters of tens to hundreds of servers and may process a query in parallel across the entire cluster , which offers ingest rates of millions of records / sec , retention of trillions of records , and query latencies of sub - second to a few seconds . Such factors may enable the data aggregator 210 to support real - time analytics;
see also [0024] Implementations of the disclosed subject matter are directed to methods and systems for real - time data aggregation and analysis on a multi-dimensional dataset by using
a data aggregator that receives indexed data from a data broker ,)
It would have been obvious to one having ordinary skill in the art at the time of the effective filing date to apply trillions of multi-dimensional records, as taught by Zhao, to the system of Ramanathan, since it was known in the art that database systems provide a data aggregator that may first partition data by time and may also additionally partition data based on one or more other fields where multi - layered partitioning may lead to significant performance improvements for time based queries where the data aggregator may be deployed in
clusters of tens to hundreds of servers and may process a query in parallel across the entire cluster , which offers ingest rates of millions of records / sec , retention of trillions of records, and query latencies of sub - second to a few seconds where such factors may enable the data aggregator to support real - time analytics and where the data aggregator may be used to perform any applicable aggregation tasks including , but not limited to , clickstream analytics ( e.g. , web and mobile analytics ) , network telemetry analytics ( e.g. , network performance
monitoring ) , server metrics storage , supply chain analytics ( e.g. , manufacturing metrics ) , application performance metrics , digital marketing / advertising analytics , business intelligence OLAP, or the like. (Zhao [0050-0051]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Gerweck et al., US Pub. No. 2016/0335318 A1, teaches a methods, and in computer
program products for dynamic aggregate generation and updating for high performance querying of large datasets. Certain embodiments are directed to technological solutions for determining at least one aggregate of selected virtual cube attributes (e.g., measures, dimensions, etc.) describing a virtual multidimensional data model of a subject database, and generating an aggregate table and a set of aggregate metadata for the aggregate. In some embodiments, an aggregate database statement configured to operate on the subject database can be issued to generate the aggregate table and/or aggregate metadata. Further, the aggregate can be dynamically determined responsive to receiving a database statement configured to operate on the virtual multidimensional data model representing the subject database. Also, the aggregate table can comprise one or more partitions in an aggregate view to facilitate aggregate management and/or quality;
Bar-Yossef et al., US Patent No.: 8,065,309 B1, teaches a method for counting one or more unique search results within a plurality of search results includes creating hash values for information in each of the search results using a first hash function. The first hash function has a predetermined hash value range size. The method further includes identifying a predetermined
number of smallest hash values within the created hash values. The method further includes estimating a first number of unique search results based on the predetermined hash value range size, the predetermined number, and a largest hash value in the smallest hash values.
CONTACT INFORMATION
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/Evan Aspinwall/Primary Examiner, Art Unit 2156
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