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 19/051,565 filed 2/12/2025 (with a provisional app. 63/376,522 with a filing date of 09/21/2022) has been examined.
In this Office Action, Claims 1-20 are currently pending.
Examiner’s Note:
Claim 1 recites (and similar claim 13 and claim 20):
“accessing a corresponding plurality of relational database rows in at least one relational database table to determine a set of input rows based on;”
However, it appears it was Applicant’s intent to rather claim:
“accessing a corresponding plurality of relational database rows in at least one relational database table to determine a set of input rows ;”
Additionally, claim 6 ends in limitation “applied to the window function,.”(emphasis added)
It appears it was Applicants intent to remove the unnecessary comma before the period at the end of claim 6.
Appropriate clarification/correction 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. 12,265,550. Although the claims at issue are not identical, they are not patentably distinct from each other because claim 1 is generic to all that is recited in claim 1 of U.S. Patent No. 12,265,550. That is, claim 1 of U.S. Patent No. 12,265,550 falls entirely within the scope of claim 1 or, in other words, claim 1 is anticipated by claim 1 of U.S. Patent No. 12,265,550.
Specifically, because instant claim 1 recites: "utilizing a thread pool of the database system to generate a plurality of sets of output values via a plurality of parallelized threads of the thread pool executing a plurality of parallelized processes" this limitation is/are a species of the generic category defined by “utilizing a thread pool of the database system to generate the plurality of required binomial coefficient values as a function of the configured non-integer numeric value via a plurality of parallelized threads of the thread pool” (see claim 1, U.S. Patent No. 12,265,550) the process of claim 1 reciting “utilizing a thread pool of the database system to generate a plurality of sets of output values via a plurality of parallelized threads of the thread pool executing a plurality of parallelized processes” is anticipated by claim 1 of U.S. Patent No. 12,265,550 reciting “utilizing a thread pool of the database system to generate the plurality of required binomial coefficient values as a function of the configured non-integer numeric value via a plurality of parallelized threads of the thread pool”.
Current Application
US Pat. 12265550 B2 (App. 18/330,455)
1. A database system includes:
at least one processor; and
a memory that stores operational instructions that, when executed by the at least one processor, cause the database system to:
receive a query expression indicating a query for execution against at least one relational database table; and
execute the query based on:
accessing a corresponding plurality of relational database rows in at least one relational database table to determine a set of input rows based on;
determining a plurality of subsets of the set of input rows for separate performance of a differentiation operation indicated in the query expression; and
utilizing a thread pool of the database system to generate a plurality of sets of output values via a plurality of parallelized threads of the thread pool executing a plurality of parallelized processes,
wherein each of the plurality of parallelized processes is executed by a corresponding thread of the plurality of parallelized threads to generate a corresponding set of output values of the plurality of sets of output values as output of executing the differentiation operation upon a corresponding subset of the plurality of subsets of the set of input rows in parallel with other ones of the plurality of parallelized processes being executed by other ones corresponding thread of the plurality of parallelized threads to generate other corresponding sets of output values of the plurality of sets of output values.
2. The database system of claim 1, wherein executing the query is further based on:
identifying a plurality of required binomial coefficient values required for generating the plurality of sets of output values for the query based on a configured non-integer numeric value included in the query expression;
generating the plurality of required binomial coefficient values as a function of the configured non-integer numeric value;
and
storing the plurality of required binomial coefficient values in cache memory resources;
wherein generating each output value of plurality of sets of output values is based on:
identifying a subset of the plurality of required binomial coefficient values for generating the each output value; and
accessing the subset of the plurality of required binomial coefficient values in the cache memory resources, wherein the each output value is generated as a function of the subset of the plurality of required binomial coefficient values.
3. The database system of claim 2, wherein the plurality of required binomial coefficient values are generated via a corresponding plurality of parallelized threads of the thread pool, wherein each of the corresponding plurality of parallelized threads generates a corresponding subset of the plurality of required binomial coefficient values in parallel with other ones of the corresponding plurality of parallelized threads generating other corresponding subsets of the plurality of required binomial coefficient values.
4. The database system of claim 1, wherein the plurality of sets of output values are generated based on performing a window function upon each row in the corresponding subset of the plurality of subsets of the set of input rows.
5. The database system of claim 4, wherein the plurality of subsets of the set of input rows are identified based on window partitioning applied to the window function.
6. The database system of claim 5, wherein the window partitioning is performed in accordance with implementing functionality of a PARTITION BY clause applied to the window function.
7. The database system of claim 1, wherein the set of input rows includes a plurality of columns, wherein the plurality of subsets of the set of input rows are identified based on grouping ones of the set of input rows having a same value for a first column of the plurality of columns into a same one of the plurality of subsets, and wherein the differentiation operation is performed based on processing values of a second column of the plurality of columns.
8. The database system of claim 7, wherein the first column of the plurality of columns corresponds to a category identifier type, and wherein the second column of the plurality of columns corresponds to a numeric value type.
9. The database system of claim 8, wherein the differentiation operation is performed based on processing values of the second column of the plurality of columns for the corresponding subset of the plurality of subsets of the set of input rows in accordance with applying a corresponding window function to an ordered subset of rows ordered by a third column of the plurality of columns different from the first column and the second column.
10. The database system of claim 9, wherein the third column stores temporal values, and wherein the corresponding window function is applied to the ordered subset of rows based on the corresponding subset being ordered by the temporal values.
11. The database system of claim 1, further comprising:
determining a query expression for execution indicating performance of a degree of the differentiation operation based on including a call to a differentiation function included in the query expression having a configured numeric value as a configurable degree parameter indicating the degree of the differentiation operation, wherein each set of output values of the plurality of sets of output values are generated in accordance with the degree of the differentiation operation.
12. The database system of claim 11, wherein at least one of:
the degree of the differentiation operation is implemented as derivation based on the configured numeric value being a positive numeric value;
the degree of the differentiation operation is implemented as derivation based on the configured numeric value being a negative numeric value; or
the degree of the differentiation operation is implemented as fractional order differentiation based on the configured numeric value being a non-integer numeric value.
13. A method for execution by a database system, comprising:
receiving a query expression indicating a query for execution against at least one relational database table; and
executing the query based on:
accessing a corresponding plurality of relational database rows in at least one relational database table to determine a set of input rows based on;
determining a plurality of subsets of the set of input rows for separate performance of a differentiation operation indicated in the query expression; and
utilizing a thread pool of the database system to generate a plurality of sets of output values via a plurality of parallelized threads of the thread pool executing a plurality of parallelized processes, wherein each of the plurality of parallelized processes is executed by a corresponding thread of the plurality of parallelized threads to generate a corresponding set of output values of the plurality of sets of output values as output of executing the differentiation operation upon a corresponding subset of the plurality of subsets of the set of input rows.
14. The method of claim 13, wherein the plurality of sets of output values are generated based on performing a window function upon each row in the corresponding subset of the plurality of subsets of the set of input rows.
15. The method of claim 14, wherein the plurality of subsets of the set of input rows are identified based on applying window partitioning to the window function based on executing the plurality of parallelized processes.
16. The method of claim 13, wherein the set of input rows includes a plurality of columns, wherein the plurality of subsets of the set of input rows are identified based on grouping ones of the set of input rows having a same value for a first column of the plurality of columns into a same one of the plurality of subsets, and wherein the differentiation operation is performed based on processing values of a second column of the plurality of columns.
17. The method of claim 16, wherein the first column of the plurality of columns corresponds to a category identifier type, and wherein the second column of the plurality of columns corresponds to a numeric value type.
18. The method of claim 17, wherein the differentiation operation is performed based on processing values of the second column of the plurality of columns for the corresponding subset of the plurality of subsets of the set of input rows in accordance with applying a corresponding window function to an ordered subset of rows ordered by a third column of the plurality of columns different from the first column and the second column.
19. The method of claim 13, further comprising:
determining a query expression for execution indicating performance of a degree of the differentiation operation based on including a call to a differentiation function included in the query expression having a configured numeric value as a configurable degree parameter indicating the degree of the differentiation operation, wherein each set of output values of the plurality of sets of output values are generated in accordance with the degree of the differentiation operation.
20. A non-transitory computer readable storage medium comprises:
at least one memory section that stores operational instructions that, when executed by a processing module that includes a processor and a memory, causes the processing module to:
receive a query expression indicating a query for execution against at least one relational database table; and
execute the query based on:
accessing a corresponding plurality of relational database rows in at least one relational database table to determine a set of input rows based on;
determining a plurality of subsets of the set of input rows for separate performance of a differentiation operation indicated in the query expression; and
utilizing a thread pool of a database system to generate a plurality of sets of output values via a plurality of parallelized threads of the thread pool executing a plurality of parallelized processes, wherein each of the plurality of parallelized processes is executed by a corresponding thread of the plurality of parallelized threads to generate a corresponding set of output values of the plurality of sets of output values as output of executing the differentiation operation upon a corresponding subset of the plurality of subsets of the set of input rows in parallel with other ones of the plurality of parallelized processes being executed by other ones corresponding thread of the plurality of parallelized threads to generate other corresponding sets of output values of the plurality of sets of output values.
1. A method for execution by a database system, comprising:
receiving a query expression indicating a query for execution against at least one relational database table; and
executing the query based on:
identifying a plurality of required binomial coefficient values required for generating a set of output values for the query based on a configured non-integer numeric value included in the query expression;
utilizing a thread pool of the database system to generate the plurality of required binomial coefficient values as a function of the configured non-integer numeric value via a plurality of parallelized threads of the thread pool,
wherein each of the plurality of parallelized threads generates a corresponding subset of the plurality of required binomial coefficient values in parallel with other ones of the plurality of parallelized threads generating other corresponding subsets of the plurality of required binomial coefficient values;
storing the plurality of required binomial coefficient values in cache memory resources; and
generating the set of output values, wherein generating each output value of the set of output values is based on:
identifying a subset of the plurality of required binomial coefficient values for generating the each output value;
accessing the subset of the plurality of required binomial coefficient values in the cache memory resources, wherein the each output value is generated as a function of the subset of the plurality of required binomial coefficient values;
generating a query resultant for the query based on the set of output values; and
removing the plurality of required binomial coefficient values from the cache memory resources based on completing execution of the query.
2. The method of claim 1, wherein generating a first one of the set of output values is based on accessing a first proper subset of the plurality of required binomial coefficient values in the cache memory resources, wherein generating a second one of the set of output values is based on accessing a second proper subset of the plurality of required binomial coefficient values in the cache memory resources, wherein the first proper subset and the second proper subset have a non-null intersection that includes at least one of the plurality of required binomial coefficient values.
3. The method of claim 1, wherein the at least one of the plurality of required binomial coefficient values is computed exactly once for the query based on generating the plurality of required binomial coefficient values.
4. The method of claim 1, wherein executing the query is further based on:
determining an ordered set of input rows based on accessing a corresponding plurality of relational database rows in the at least one relational database table; and
generating the set of output values as an ordered set of output values based on performing a window function upon each row in the ordered set of input rows to return an output value for the each row as a function of the each row and further as a function of at least one binomial coefficient value of the plurality of required binomial coefficient values, wherein the output value for the each row is generated based on accessing the least one binomial coefficient value in the cache memory resources.
5. The method of claim 4, wherein the output value for the each row is generated further as a function of all prior consecutive rows in the ordered set of input rows from the each row.
6. The method of claim 1, wherein the query expression indicates performance of a fractional degree of differentiatio, and wherein the plurality of required binomial coefficient values are identified based on a fractional value of the fractional degree of differentiation.
7. The method of claim 6, wherein a request to perform the fractional degree of differentiation is indicated in a call to a differentiation function included in the query expression based on the call to the differentiation function having the configured non-integer numeric value as a configurable degree parameter indicating the fractional degree of differentiation.
8. The method of claim 7, wherein one of:
the fractional degree of differentiation is implemented as fractional degree derivation based on the configured non-integer numeric value being a positive numeric value; or
the fractional degree of differentiation is implemented as fractional degree integration based on the configured non-integer numeric value being a negative numeric value.
9. The method of claim 7, further comprising:
receiving a second query expression that includes a second call to the differentiation function having a second fractional value for the configurable degree parameter indicating a different non-integer numeric value corresponding to a different degree of fractional differentiation;
executing the second query expression based on:
identifying a second plurality of required binomial coefficient values required for generating a second set of output values, wherein the second plurality of required binomial coefficient values are different from the plurality of required binomial coefficient values based on the second fractional value being different from the fractional value;
generating the second plurality of required binomial coefficient values;
storing the second plurality of required binomial coefficient values in the cache memory resources; and
generating a second set of output values based on accessing binomial coefficient values of the second plurality of required binomial coefficient values in the cache memory resources.
10. The method of claim 1, wherein the least one relational database table includes a corresponding plurality of relational database rows corresponding to time series data having temporal values indicated in at least one column, and wherein the set of output values is generated as an ordered set of output values based processing an ordered set of input values generated based on ordering by the temporal values in the at least one column.
11. The method of claim 10, wherein the ordered set of output values meet a stationary data condition based on performing a fractional degree of differentiation, further comprising:
executing at least one time series forecasting algorithm upon the ordered set of output values based on the ordered set of output values meeting the stationary data condition.
12. The method of claim 1, further comprising:
receiving a second query expression indicating a second query for execution against the at least one relational database table; and
executing the second query based on:
identifying, based on the second query expression, a second plurality of required binomial coefficient values required for generating a second set of output values for the second query based on a second configured non-integer numeric value indicated in the second query expression, wherein the second plurality of required binomial coefficient values are different from the plurality of required binomial coefficient values based on the second configured non-integer numeric value of the second query expression being different from the configured non-integer numeric value of the query expression;
generating the second plurality of required binomial coefficient values as a function of the second configured non-integer numeric value;
storing the second plurality of required binomial coefficient values in the cache memory resources; and
generating the second set of output values, wherein generating each output value of the second set of output values is based on:
identifying a subset of the second plurality of required binomial coefficient values for generating the each output value of the second set of output values; and
accessing the subset of the second plurality of required binomial coefficient values in the cache memory resources, wherein the each output value of the second set of output values is generated as a function of the subset of the second plurality of required binomial coefficient values;
generating a second query resultant for the second query based on the second set of output values; and
removing the second plurality of required binomial coefficient values from the cache memory resources based on completing execution of the second query.
13. The method of claim 1, wherein execution of the query is completed at a first time, wherein the plurality of required binomial coefficient values are removed from the cache memory resources at a second time after the first time, and wherein, during a temporal period between the first time and the second time, the method further comprises:
determining a second query for execution;
executing the second query based on:
identifying a second plurality of required binomial coefficient values required for generating a second set of output values for the second query, wherein the second plurality of required binomial coefficient values has a non-null set intersection with the plurality of required binomial coefficient values;
generating an additional set of binomial coefficient values set corresponding to only ones of the second plurality of required binomial coefficient values not included in the plurality of required binomial coefficient values;
storing the additional set of binomial coefficient values in the cache memory resources;
generating the second set of output values, wherein generating each output value of the second set of output values is based on:
identifying a second subset of the second plurality of required binomial coefficient values for generating the each output value; and
accessing the second subset of the second plurality of required binomial coefficient values in the cache memory resources, wherein the each output value is generated as a function of the subset of the plurality of required binomial coefficient values, and wherein at least one output value of the second set of output values is generated based on accessing at least one binomial coefficient value generated for the query based on being included in the non-null set intersection with the plurality of required binomial coefficient values.
14. A database system includes:
at least one processor; and
a memory that stores operational instructions that, when executed by the at least one processor, cause the database system to:
receive a query expression indicating a query for execution against at least one relational database table; and
execute the query based on:
identifying a plurality of required binomial coefficient values required for generating a set of output values for the query based on a configured non-integer numeric value included in the query expression;
utilizing a thread pool of the database system to generate the plurality of required binomial coefficient values as a function of the configured non-integer numeric value via a plurality of parallelized threads of the thread pool, wherein each of the plurality of parallelized threads generates a corresponding subset of the plurality of required binomial coefficient values in parallel with other ones of the plurality of parallelized threads generating other corresponding subsets of the plurality of required binomial coefficient values;
storing the plurality of required binomial coefficient values in cache memory resources; and
generating the set of output values, wherein generating each output value of the set of output values is based on:
identifying a subset of the plurality of required binomial coefficient values for generating the each output value; and
accessing the subset of the plurality of required binomial coefficient values in the cache memory resources, wherein the each output value is generated as a function of the subset of the plurality of required binomial coefficient values;
generating a query resultant for the query based on the set of output values; and
removing the plurality of required binomial coefficient values from the cache memory resources based on completing execution of the query.
15. The database system of claim 14, wherein generating a first one of the set of output values is based on accessing a first proper subset of the plurality of required binomial coefficient values in the cache memory resources, wherein generating a second one of the set of output values is based on accessing a second proper subset of the plurality of required binomial coefficient values in the cache memory resources, wherein the first proper subset and the second proper subset have a non-null intersection that includes at least one of the plurality of required binomial coefficient values.
16. The database system of claim 14, wherein executing the query is further based on:
determining an ordered set of input rows based on accessing a corresponding plurality of relational database rows in the at least one relational database table; and
generating the set of output values as an ordered set of output values based on performing a window function upon each row in the ordered set of input rows to return an output value for the each row as a function of the each row and further as a function of at least one binomial coefficient value of the plurality of required binomial coefficient values, wherein the output value for the each row is generated based on accessing the least one binomial coefficient value in the cache memory resources.
17. The database system of claim 14, wherein the query expression indicates performance of a fractional degree of differentiation, and wherein the plurality of required binomial coefficient values are identified based on a fractional value of the fractional degree of differentiation.
18. The database system of claim 14, wherein the least one relational database table includes a corresponding plurality of relational database rows corresponding to time series data having temporal values indicated in at least one column, and wherein the set of output values is generated as an ordered set of output values based processing an ordered set of input values generated based on ordering by the temporal values in the at least one column.
19. The database system of claim 14, wherein the operational instructions, when executed by the at least one processor, further cause the database system to:
receive a second query expression indicating a second query for execution against the at least one relational database table; and
execute the second query based on:
identifying, based on the second query expression, a second plurality of required binomial coefficient values required for generating a second set of output values for the second query based on a second configured non-integer numeric value indicated in the second query expression, wherein the second plurality of required binomial coefficient values are different from the plurality of required binomial coefficient values based on the second configured non-integer numeric value of the second query expression being different from the configured non-integer numeric value of the query expression;
generating the second plurality of required binomial coefficient values as a function of the second configured non-integer numeric value;
storing the second plurality of required binomial coefficient values in the cache memory resources; and
generating the second set of output values, wherein generating each output value of the second set of output values is based on:
identifying a subset of the second plurality of required binomial coefficient values for generating the each output value of the second set of output values; and
accessing the subset of the second plurality of required binomial coefficient values in the cache memory resources, wherein the each output value of the second set of output values is generated as a function of the subset of the second plurality of required binomial coefficient values;
generating a second query resultant for the second query based on the second set of output values; and
removing the second plurality of required binomial coefficient values from the cache memory resources based on completing execution of the second query.
20. A non-transitory computer readable storage medium comprises:
at least one memory section that stores operational instructions that, when executed by a processing module that includes a processor and a memory, causes the processing module to:
receive a query expression indicating a query for execution against at least one relational database table; and
execute the query based on:
identifying a plurality of required binomial coefficient values required for generating a set of output values for the query based on a configured non-integer numeric value included in the query expression;
utilizing a thread pool to generate the plurality of required binomial coefficient values as a function of the configured non-integer numeric value via a plurality of parallelized threads of the thread pool, wherein each of the plurality of parallelized threads generates a corresponding subset of the plurality of required binomial coefficient values in parallel with other ones of the plurality of parallelized threads generating other corresponding subsets of the plurality of required binomial coefficient values;
storing the plurality of required binomial coefficient values in cache memory resources; and
generating the set of output values, wherein generating each output value of the set of output values is based on:
identifying a subset of the plurality of required binomial coefficient values for generating the each output value;
accessing the subset of the plurality of required binomial coefficient values in the cache memory resources, wherein the each output value is generated as a function of the subset of the plurality of required binomial coefficient values;
generating a query resultant for the query based on the set of output values; and
removing the plurality of required binomial coefficient values from the cache memory resources based on completing execution of the query.
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.
Note: The Examiner notes additionally the claimed "database system" limitations (see claim 1) are composed of processors/computing devices. See instant specification para. [0132, 0163].
Claim 1 recites: (Step 2a, Prong One)
determining subsets of input rows for a differentiation operation indicated in a query expression.
The limitation of determining subsets of input rows for a differentiation operation indicated in a query expression, 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 language, “determining” in the context of this claim encompasses the user manually determining generic “rows” from generic “input rows” for queries/operations using generic “determining” subsets steps. Similarly, the limitation(s) of receiving and rendering, 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 processor language, determining rows in the context of this claim encompasses the user manually receiving generic “input rows” and a “query expression” and performing generic “determining” of subsets 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 determining subsets of rows steps of generic input rows using generic query expressions is a method of human activity in commercial or legal interactions.
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 with a memory to perform both the receiving;
executing; accessing; utilizing; and determining steps. The processor with a memory in both steps is recited at a high level of generality (i.e., as a generic processor performing a generic computer function of “determining”) 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 with a memory to perform
both the receiving; executing; accessing; utilizing; and determining 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 “identifying a plurality of required binomial coefficient values required for generating the plurality of sets of output values for the query based on a configured non-integer numeric value included in the query expression; generating the plurality of required binomial coefficient values as a function of the configured non-integer numeric value; and
storing the plurality of required binomial coefficient values in cache memory resources;
wherein generating each output value of plurality of sets of output values is based on:
identifying a subset of the plurality of required binomial coefficient values for generating the each output value; and accessing the subset of the plurality of required binomial coefficient values in the cache memory resources, wherein the each output value is generated as a function of the subset of the plurality of required binomial coefficient values”.
(Step 2a, Prong Two)
This judicial exception is not integrated into a practical application. In particular, the claim only
recites the additional elements of “identifying a plurality of required binomial coefficient values required for generating the plurality of sets of output values for the query based on a configured non-integer numeric value included in the query expression; generating the plurality of required binomial coefficient values as a function of the configured non-integer numeric value; and
storing the plurality of required binomial coefficient values in cache memory resources;
wherein generating each output value of plurality of sets of output values is based on:
identifying a subset of the plurality of required binomial coefficient values for generating the each output value; and accessing the subset of the plurality of required binomial coefficient values in the cache memory resources, wherein the each output value is generated as a function of the subset of the plurality of required binomial coefficient values” steps to perform both the aforementioned receiving; executing; accessing; utilizing; and determining 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 “identifying a plurality of required binomial coefficient values required for generating the plurality of sets of output values for the query based on a configured non-integer numeric value included in the query expression; generating the plurality of required binomial coefficient values as a function of the configured non-integer numeric value; and storing the plurality of required binomial coefficient values in cache memory resources; wherein generating each output value of plurality of sets of output values is based on: identifying a subset of the plurality of required binomial coefficient values for generating the each output value; and accessing the subset of the plurality of required binomial coefficient values in the cache memory resources, wherein the each output value is generated as a function of the subset of the plurality of required binomial coefficient values” steps to perform both the aforementioned receiving; executing; accessing; utilizing; and determining 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 “wherein the plurality of required binomial coefficient values are generated
via a corresponding plurality of parallelized threads of the thread pool, wherein each of the corresponding plurality of parallelized threads generates a corresponding subset of the plurality of required binomial coefficient values in parallel with other ones of the corresponding plurality of parallelized threads generating other corresponding subsets of the plurality of required binomial coefficient values”.
(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 plurality of required binomial coefficient values are generated via a corresponding plurality of parallelized threads of the thread pool, wherein each of the corresponding plurality of parallelized threads generates a corresponding subset of the plurality of required binomial coefficient values in parallel with other ones of the corresponding plurality of parallelized threads generating other corresponding subsets of the plurality of required binomial coefficient values” steps to perform both the aforementioned receiving; executing; accessing; utilizing; and determining 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 plurality of required binomial coefficient values are generated via a corresponding plurality of parallelized threads of the thread pool, wherein each of the corresponding plurality of parallelized threads generates a corresponding subset of the plurality of required binomial coefficient values in parallel with other ones of the corresponding plurality of parallelized threads generating other corresponding subsets of the plurality of required binomial coefficient values” steps to perform both the aforementioned receiving; executing; accessing; utilizing; and determining 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 “wherein the plurality of sets of output values are generated based on performing a window function upon each row in the corresponding subset of the plurality of subsets of the set of input rows”.
(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 plurality of sets of output values are generated based on performing a window function upon each row in the corresponding subset of the plurality of subsets of the set of input rows” steps to perform both the aforementioned receiving; executing; accessing; utilizing; and determining 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 plurality of sets of output values are generated based on performing a window function upon each row in the corresponding subset of the plurality of subsets of the set of input rows” steps to perform both the aforementioned receiving; executing; accessing; utilizing; and determining 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 “wherein the plurality of subsets of the set of input rows are identified based
on window partitioning applied to the window function”.
(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 plurality of subsets of the set of input rows are identified based on window partitioning applied to the window function” steps to perform both the aforementioned receiving; executing; accessing; utilizing; and determining 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 plurality of subsets of the set of input rows are identified based on window partitioning applied to the window function” steps to perform both the aforementioned receiving; executing; accessing; utilizing; and determining 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 window partitioning is performed in accordance with
implementing functionality of a PARTITION BY clause applied to the window function”.
(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 window partitioning is performed in accordance with implementing functionality of a PARTITION BY clause applied to the window function” steps to perform both the aforementioned receiving; executing; accessing; utilizing; and determining 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 window partitioning is performed in accordance with implementing functionality of a PARTITION BY clause applied to the window function” steps to perform both the aforementioned receiving; executing; accessing; utilizing; and determining 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 the set of input rows includes a plurality of columns, wherein the
plurality of subsets of the set of input rows are identified based on grouping ones of the set of input rows having a same value for a first column of the plurality of columns into a same one of the plurality of subsets, and wherein the differentiation operation is performed based on processing values of a second column of the plurality of columns”.
(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 set of input rows includes a plurality of columns, wherein the plurality of subsets of the set of input rows are identified based on grouping ones of the set of input rows having a same value for a first column of the plurality of columns into a same one of the plurality of subsets, and wherein the differentiation operation is performed based on processing values of a second column of the plurality of columns” steps to perform both the aforementioned receiving; executing; accessing; utilizing; and determining 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 set of input rows includes a plurality of columns, wherein the plurality of subsets of the set of input rows are identified based on grouping ones of the set of input rows having a same value for a first column of the plurality of columns into a same one of the plurality of subsets, and wherein the differentiation operation is performed based on processing values of a second column of the plurality of columns” steps to perform both the aforementioned receiving; executing; accessing; utilizing; and determining 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 first column of the plurality of columns corresponds to a category
identifier type, and wherein the second column of the plurality of columns corresponds to a numeric value type”.
(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 first column of the plurality of columns corresponds to a category identifier type, and wherein the second column of the plurality of columns corresponds to a numeric value type” steps to perform both the aforementioned receiving; executing; accessing; utilizing; and determining 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 first column of the plurality of columns corresponds to a category identifier type, and wherein the second column of the plurality of columns corresponds to a numeric value type” steps to perform both the aforementioned receiving; executing; accessing; utilizing; and determining 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 differentiation operation is performed based on processing values
of the second column of the plurality of columns for the corresponding subset of the plurality of subsets of the set of input rows in accordance with applying a corresponding window function to an ordered subset of rows ordered by a third column of the plurality of columns different from the first column and the second column”.
(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 differentiation operation is performed based on processing values of the second column of the plurality of columns for the corresponding subset of the plurality of subsets of the set of input rows in accordance with applying a corresponding window function to an ordered subset of rows ordered by a third column of the plurality of columns different from the first column and the second column” steps to perform both the aforementioned receiving; executing; accessing; utilizing; and determining 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 differentiation operation is performed based on processing values of the second column of the plurality of columns for the corresponding subset of the plurality of subsets of the set of input rows in accordance with applying a corresponding window function to an ordered subset of rows ordered by a third column of the plurality of columns different from the first column and the second column” steps to perform both the aforementioned receiving; executing; accessing; utilizing; and determining 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 third column stores temporal values, and wherein the corresponding window function is applied to the ordered subset of rows based on the corresponding subset being ordered by the temporal values”.
(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 third column stores temporal values, and wherein the corresponding window function is applied to the ordered subset of rows based on the corresponding subset being ordered by the temporal values” steps to perform both the aforementioned receiving; executing; accessing; utilizing; and determining 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 third column stores temporal values, and wherein the corresponding window function is applied to the ordered subset of rows based on the corresponding subset being ordered by the temporal values” steps to perform both the aforementioned receiving; executing; accessing; utilizing; and determining 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 “determining a query expression for execution indicating performance of a degree of the differentiation operation based on including a call to a differentiation function included in the query expression having a configured numeric value as a configurable degree parameter indicating the degree of the differentiation operation, wherein each set of output values of the plurality of sets of output values are generated in accordance with the degree of the differentiation operation”.
(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 a query expression for execution indicating performance of a degree of the differentiation operation based on including a call to a differentiation function included in the query expression having a configured numeric value as a configurable degree parameter indicating the degree of the differentiation operation, wherein each set of output values of the plurality of sets of output values are generated in accordance with the degree of the differentiation operation” steps to perform both the aforementioned receiving; executing; accessing; utilizing; and determining 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 a query expression for execution indicating performance of a degree of the differentiation operation based on including a call to a differentiation function included in the query expression having a configured numeric value as a configurable degree parameter indicating the degree of the differentiation operation, wherein each set of output values of the plurality of sets of output values are generated in accordance with the degree of the differentiation operation” steps to perform both the aforementioned receiving; executing; accessing; utilizing; and determining 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 12, (Step 2a, Prong One) this further merely performs an additional abstract
mental step of “wherein at least one of: the degree of the differentiation operation is implemented as derivation based on the configured numeric value being a positive numeric value; the degree of the differentiation operation is implemented as derivation based on the configured numeric value being a negative numeric value; or the degree of the differentiation operation is implemented as fractional order differentiation based on the configured numeric value being a non-integer numeric value”.
(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 at least one of: the degree of the differentiation operation is implemented as derivation based on the configured numeric value being a positive numeric value; the degree of the differentiation operation is implemented as derivation based on the configured numeric value being a negative numeric value; or the degree of the differentiation operation is implemented as fractional order differentiation based on the configured numeric value being a non-integer numeric value” steps to perform both the aforementioned receiving; executing; accessing; utilizing; and determining 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 at least one of: the degree of the differentiation operation is implemented as derivation based on the configured numeric value being a positive numeric value; the degree of the differentiation operation is implemented as derivation based on the configured numeric value being a negative numeric value; or the degree of the differentiation operation is implemented as fractional order differentiation based on the configured numeric value being a non-integer numeric value” steps to perform both the aforementioned receiving; executing; accessing; utilizing; and determining 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.
Note: The Examiner notes additionally the claimed "database system" limitations (see claim 1) are composed of processors/computing devices. See instant specification para. [0132, 0163].
Claim 13 recites: (Step 2a, Prong One)
determining subsets of input rows for a differentiation operation indicated in a query expression.
The limitation of determining subsets of input rows for a differentiation operation indicated in a query expression, 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 database system, nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the processor language, “determining” in the context of this claim encompasses the user manually determining generic “rows” from generic “input rows” for queries/operations using generic “determining” subsets steps. Similarly, the limitation(s) of receiving and rendering, 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 database system language, determining rows in the context of this claim encompasses the user manually receiving generic “input rows” and a “query expression” and performing generic “determining” of subsets 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 determining subsets of rows steps of generic input rows using generic query expressions is a method of human activity in commercial or legal interactions.
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 database system to perform both the receiving;
executing; accessing; utilizing; and determining steps. The processor with a memory in both steps is recited at a high level of generality (i.e., as a generic database system/processor performing a generic computer function of “determining”) 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 database system to perform
both the receiving; executing; accessing; utilizing; and determining 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 “wherein the plurality of sets of output values are generated based on performing a window function upon each row in the corresponding subset of the plurality of subsets of the set of input rows”.
(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 plurality of sets of output values are generated based on performing a window function upon each row in the corresponding subset of the plurality of subsets of the set of input rows” steps to perform both the aforementioned receiving; executing; accessing; utilizing; and determining 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 plurality of sets of output values are generated based on performing a window function upon each row in the corresponding subset of the plurality of subsets of the set of input rows” steps to perform both the aforementioned receiving; executing; accessing; utilizing; and determining 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.
Referring to claim 15, (Step 2a, Prong One) this further merely performs an additional abstract
mental step of “wherein the plurality of subsets of the set of input rows are identified based on
applying window partitioning to the window function based on executing the plurality of parallelized processes”.
(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 plurality of subsets of the set of input rows are identified based on applying window partitioning to the window function based on executing the plurality of parallelized processes” steps to perform both the aforementioned receiving; executing; accessing; utilizing; and determining 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 plurality of subsets of the set of input rows are identified based on applying window partitioning to the window function based on executing the plurality of parallelized processes” steps to perform both the aforementioned receiving; executing; accessing; utilizing; and determining 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.
Referring to claim 16, (Step 2a, Prong One) this further merely performs an additional abstract
mental step of “wherein the set of input rows includes a plurality of columns, wherein the plurality of subsets of the set of input rows are identified based on grouping ones of the set of input rows having a same value for a first column of the plurality of columns into a same one of the plurality of subsets, and wherein the differentiation operation is performed based on processing values of a second column of the plurality of columns”.
(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 set of input rows includes a plurality of columns, wherein the plurality of subsets of the set of input rows are identified based on grouping ones of the set of input rows having a same value for a first column of the plurality of columns into a same one of the plurality of subsets, and wherein the differentiation operation is performed based on processing values of a second column of the plurality of columns” steps to perform both the aforementioned receiving; executing; accessing; utilizing; and determining 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 set of input rows includes a plurality of columns, wherein the plurality of subsets of the set of input rows are identified based on grouping ones of the set of input rows having a same value for a first column of the plurality of columns into a same one of the plurality of subsets, and wherein the differentiation operation is performed based on processing values of a second column of the plurality of columns” steps to perform both the aforementioned receiving; executing; accessing; utilizing; and determining 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.
Referring to claim 17, (Step 2a, Prong One) this further merely performs an additional abstract
mental step of “wherein the first column of the plurality of columns corresponds to a category
identifier type, and wherein the second column of the plurality of columns corresponds to a numeric value type”.
(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 first column of the plurality of columns corresponds to a category identifier type, and wherein the second column of the plurality of columns corresponds to a numeric value type” steps to perform both the aforementioned receiving; executing; accessing; utilizing; and determining 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 first column of the plurality of columns corresponds to a category identifier type, and wherein the second column of the plurality of columns corresponds to a numeric value type” steps to perform both the aforementioned receiving; executing; accessing; utilizing; and determining 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.
Referring to claim 18, (Step 2a, Prong One) this further merely performs an additional abstract
mental step of “wherein the differentiation operation is performed based on processing values of the second column of the plurality of columns for the corresponding subset of the plurality of subsets of the set of input rows in accordance with applying a corresponding window function to an ordered subset of rows ordered by a third column of the plurality of columns different from the first column and the second column”.
(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 differentiation operation is performed based on processing values of the second column of the plurality of columns for the corresponding subset of the plurality of subsets of the set of input rows in accordance with applying a corresponding window function to an ordered subset of rows ordered by a third column of the plurality of columns different from the first column and the second column” steps to perform both the aforementioned receiving; executing; accessing; utilizing; and determining 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 differentiation operation is performed based on processing values of the second column of the plurality of columns for the corresponding subset of the plurality of subsets of the set of input rows in accordance with applying a corresponding window function to an ordered subset of rows ordered by a third column of the plurality of columns different from the first column and the second column” steps to perform both the aforementioned receiving; executing; accessing; utilizing; and determining 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.
Referring to claim 19, (Step 2a, Prong One) this further merely performs an additional abstract
mental step of “determining a query expression for execution indicating performance of a degree of the differentiation operation based on including a call to a differentiation function included in the query ex-pression having a configured numeric value as a configurable degree parameter indicating the degree of the differentiation operation, wherein each set of output values of the plurality of sets of output values are generated in accordance with the degree of the differentiation operation”.
(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 a query expression for execution indicating performance of a degree of the differentiation operation based on including a call to a differentiation function included in the query ex-pression having a configured numeric value as a configurable degree parameter indicating the degree of the differentiation operation, wherein each set of output values of the plurality of sets of output values are generated in accordance with the degree of the differentiation operation” steps to perform both the aforementioned receiving; executing; accessing; utilizing; and determining 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 a query expression for execution indicating performance of a degree of the differentiation operation based on including a call to a differentiation function included in the query ex-pression having a configured numeric value as a configurable degree parameter indicating the degree of the differentiation operation, wherein each set of output values of the plurality of sets of output values are generated in accordance with the degree of the differentiation operation” steps to perform both the aforementioned receiving; executing; accessing; utilizing; and determining 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.
Note: The Examiner notes additionally the claimed "database system" limitations (see claim 1) are composed of processors/computing devices. See instant specification para. [0132, 0163].
Claim 13 recites: (Step 2a, Prong One)
determining subsets of input rows for a differentiation operation indicated in a query expression.
The limitation of determining subsets of input rows for a differentiation operation indicated in a query expression, 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/medium, nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the processor language, “determining” in the context of this claim encompasses the user manually determining generic “rows” from generic “input rows” for queries/operations using generic “determining” subsets steps. Similarly, the limitation(s) of receiving and rendering, 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 processor language, determining rows in the context of this claim encompasses the user manually receiving generic “input rows” and a “query expression” and performing generic “determining” of subsets 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 determining subsets of rows steps of generic input rows using generic query expressions is a method of human activity in commercial or legal interactions.
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 with a memory/medium to perform both the receiving;
executing; accessing; utilizing; and determining steps. The processor with a memory in both steps is recited at a high level of generality (i.e., as a generic processor performing a generic computer function of “determining”) 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 with a memory/medium to perform both the receiving; executing; accessing; utilizing; and determining 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, 4-5, 7-11, 13-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kondiles et al., US Pub. No. 2021/0240717 A1, in view of Amel et al., US Pub. No. 2017/0228460 A1, in view of Lee et al., US Pub. No. 2020/0012734 A1.
As to claim 1 (and substantially similar claim 13 and claim 20), Kondiles discloses:
a database system includes:
at least one processor; and
a memory that stores operational instructions that, when executed by the at least one processor,
(Kondiles Fig. 8, para. 0261-0263, 0298 )
cause the database system to:
receive a query expression indicating a query for execution against at least one relational database table;
(Kondiles [0086] The Q&R sub-system 13 allows for multiple queries regarding one or more tables to be processed concurrently. For example, a set of processing core resources of a
computing device ( e.g., one or more processing core resources) processes a first query and a second set of processing core resources of the computing device ( or a different computing device) processes a second query.; see also [0073] In addition, the assigned node parses the query to create an abstract syntax tree. As a specific example, the assigned node converts an SQL (Standard Query Language) statement into a database instruction set.)
and execute the query based on:
accessing a corresponding plurality of relational database rows in at least one relational database table to determine a set of input rows based on;
(Kondiles [0128] As discussed herein, each data segment can indicate a particular
subset of rows of a particular table, where a subset of fields and/or columns or an entirety of fields and/or columns of each row in the particular subset of rows is included in the
segment.;
See also [0275] In the method illustrated in FIG. 25K, step 2560 includes determining a plurality of queries for concurrent execution that includes a first query and a second query. Step 2562 includes determining a plurality of sets of segments required to execute the plurality of queries. The plurality of sets of segments includes a first set of segments that each include a first set of rows required to execute the first query and a second set of segments that each include a second set of rows required to execute the second query.)
wherein each of the plurality of parallelized processes is executed by a corresponding thread of the plurality of parallelized threads to generate a corresponding set of output values of the plurality of sets of output values as output of executing the differentiation operation upon a corresponding subset of the plurality of subsets of the set of input rows in parallel with other ones of the plurality of parallelized processes being executed by other ones corresponding thread of the plurality of parallelized threads to generate other corresponding sets of output values of the plurality of sets of output values
(Kondiles teaches different parallel processing threads process different segments with the assigned operators of the query to generate a corresponding plurality of query resultants
See [0142] Some or all of the plurality of partial executions of a query required to fulfill the node's execution of the query can be facilitated by the node concurrently, for example, where different parallel processing threads of the same or different processing core resource 48 of the node process different segments in accordance with the assigned operators of the query.
see also [0147] can be achieved via the segment processing module 2430, where different parallel processing threads of the segment processing module 2430 can perform partial executions of different queries concurrently, for example, as discussed in conjunction with FIGS. 26A-26C. Each query in the query set can be executed in its own set of sequential time slices,
where different queries in the query set can have overlapping or non-overlapping sets of sequential time slices. Within a plurality of sequential time slices, execution of some or all
of the set of queries 2405-1-2405-N can be facilitated by the segment processing module 2430 to ultimately generate a corresponding plurality of query resultants 2432-1-2432-N, where each one of the plurality of query resultants 2432 is based on a set of partial results generated for the corresponding one of the plurality of queries by processing the corresponding set of segments in the query's segment set 2418.;
see also [0154] For example, a particular processing core resource 48, processing thread, and/or other processing resource allocated for execution of a particular one of the set of queries in the query set can indicate that it has completed processing of at least one previously selected segment, and is thus ready to process a new segment, for example, via a notification to the
segment scheduler, via an update to query set 2415 indicating completion of processing of the previous segment, and/or via an indication that no segments of the query are currently being processed;; see also [0136] the resultant generated by a particular node's full execution of a query via retrieval and processing of the node's entire segment set 2418 may correspond to a plurality of rows that need to be further filtered, aggregated, and/or processed via one or
more other node's execution of the query)
Kondiles does not disclose:
determining a plurality of subsets of the set of input rows for separate performance of a differentiation operation indicated in the query expression; and
However, Amel discloses:
determining a plurality of subsets of the set of input rows for separate performance of a differentiation operation indicated in the query expression; and
(Amel teaches applying delta analysis (i.e. differentiation operation) on rows/lines based on query expressions
See [0160] Delta analysis may then be performed on the generated sets of results. In some embodiments, the generated results are correlated or combined using combining
engine 2218. The combined or correlated results may then be analyzed by delta analysis engine 2220 to determine deltas between the results.;
see also [0121] FIG. 18 illustrates interface 1700 after Alice has entered a query term into box 1702 (1802) and selected start button 1706. As indicated in region 1804, a total of 2,885
individual messages (e.g., log lines) pertaining to the disk controller collector were generated in the time frame selected by Alice (1806). A graph depicting when, over the time frame, the messages were generated is shown in region 1812.;
see also [0154] In this example, each row (e.g., row 2402) corresponds to a cluster (represented by a corresponding signature). In this example, for each cluster, a count of the
number of messages that are included in the cluster is also displayed (2404));
It would have been obvious to one having ordinary skill in the art at the time of the effective filing date to apply delta analysis as taught by Amel, to the system of Kondiles, since it was known in the art that database systems provide delta analysis which may enable query rewriting, correlation/combining, and delta analysis, thus providing efficient comparison of collected information where the use and leveraging of computing resources is also made more efficient, as more queries and processing may be run per unit time ( e.g., allowing for parallel processing of queries and processing of query results), rather than waiting on the user, for example, to write a query, save the output of the query, and then run the query again over another time range and thus, querying and comparison of data is made more efficient. (Amel [0160]).
Kondiles/Amel do not disclose:
utilizing a thread pool of the database system to generate a plurality of sets of output values via a plurality of parallelized threads of the thread pool executing a plurality of parallelized processes;
However, Lee discloses:
utilizing a thread pool of the database system to generate a plurality of sets of output values via a plurality of parallelized threads of the thread pool executing a plurality of parallelized processes
(Lee teaches an execution plan for the query where each job execution thread of the job execution is part of a thread pool and is executed in parallel, i.e. “utilizing a thread pool of the database system to generate a plurality of sets of output values via a plurality of parallelized threads of the thread pool”
See [0059] In certain situations, such as if the query involves complex or internally-parallelized operations or sub-operations, the query executor 220 can send operations or suboperations
of the query to a job executor component 254, which can include a thread pool 256. An execution plan for the query can include a plurality of plan operators. Each job execution thread of the job execution thread pool 256, in a particular implementation, can be assigned to an individual plan operator. The job executor component 254 can be used to execute at least a portion of the operators of the query in parallel.;
see also [0054] For example, the query can be sent to an execution thread of the thread pool 224 determined by the session manager 208 or the application manager 210.;
See also [0052] The query interface 212 can communicate with a query language processor 216, such as a structured query language processor. For example, the query interface 212
may forward to the query language processor 216 query language statements or other database operation requests from the client 204. The query language processor 216 can include a query language executor 220, such as a SQL executor, which can include a thread pool 224.).
It would have been obvious to one having ordinary skill in the art at the time of the effective filing date to apply delta analysis as taught by Lee, to the system of Kondiles/Amel, since it was known in the art that database systems provide a query executor which can send operations or suboperations of the query to a job executor component which can include a thread pool where an execution plan for the query can include a plurality of plan operators where each job execution thread of the job execution thread pool, in a particular implementation, can be assigned to an individual plan operator and the job executor component can be used to execute at least a portion of the operators of the query in parallel where using the job executor component can increase the load on one or more processing units of the database server, and can improve execution time of the query. (Lee [0059]).
As to claim 4, Amel as modified discloses the database system of claim 1, wherein the plurality of sets of output values are generated based on performing a window function upon each row in the corresponding subset of the plurality of subsets of the set of input rows
(Amel teaches custom time window analysis corresponding to lines, i.e. “sets of output values are generated based on performing a window function upon each row in the corresponding subset of the plurality of subsets of the set of input rows”
See [0133] In some cases, such as where the time window specified by Alice via dropdown 1704 is small, text that is variable will erroneously be treated as if it is static. As one
example, if Alice selected a shorter time period than what is shown, the messages corresponding to line 1908 might be generated with respect to a single IP address ( e.g., 10.0.0.1 ), rather than multiple IP addresses.
See [0151] Suppose that in this example, a time range of the last 15 minutes (e.g., 15 minute time range/window) was selected. For example, the most recent 15 minute time
window may be selected by Alice or by default. Alice may also select any custom time range as desired ( e.g., by specifying expressions for the start and end times). FIG. 23 illustrates an example embodiment of a user interface including query results. In some embodiments, the interface of FIG. 23 is provided by platform 2202;
see also [0197] At 3006, the target and baseline queries are performed against a data store including collected metrics. At 3008, for each of the target and baseline queries, corresponding target and baseline sets of results are obtained. For example, a set of metrics values corresponding to the target query with a target characteristic (e.g., target time window) are obtained.).
As to claim 5, Amel as modified discloses the database system of claim 4, wherein the plurality of subsets of the set of input rows are identified based on window partitioning applied to the window function
(Amel [0065] Such tags can be used to partition data and significantly improve the amount of time it takes to process queries against that data; see also [0080] message is indexed, the result will be more useful when performing searches (e.g., by allowing the data to be partitioned in more ways).).
As to claim 7, Amel as modified discloses the database system of claim 1, wherein the set of input rows includes a plurality of columns, wherein the plurality of subsets of the set of input rows are identified based on grouping ones of the set of input rows having a same value for a first column of the plurality of columns into a same one of the plurality of subsets, and wherein the differentiation operation is performed based on processing values of a second column of the plurality of columns
(Amel teaches sets of results/columns are combined or correlated/clustered based on values see [0186-0187] [0186] At 3006, the generated target and baseline queries are performed against log data in a data store including log messages collected from monitored devices (e.g., collected using blades/collectors, as described above). At 3008, corresponding sets of target results (for logs in the target time range that match the query parameters) and baseline results
(for logs in the baseline time range that match the query parameters) are obtained. In some embodiments, clustering is executed for each set of results. For example, as described
above, for each set of results, the returned logs are clustered into inferred classes, where each class is associated with a corresponding log signature. In some embodiments, the
count of the number of logs included in a cluster is determined for each cluster/class. As one example, a set of results includes two columns, where one column is a column of
signatures, and the second column includes the count of the number of logs matching to the signatures.
[0187] At 3010, the target and baseline sets of results are combined or correlated. As one example, the column of signatures/class names is treated as a primary key column, and the target and baseline sets of log message results are joined on the signatures/classes.
See also [0104] The output of the process shown in FIG. 12 can be used to automatically select a schema for which portions of the raw data should be extracted (and how they should be
labeled). For example, while a particular raw message may include a total of ten columns' worth of data, the selected schema may state that the first column ("time") and third
column ("temperature") should be extracted separately from the other columns, that column two should be discarded, and that columns four through ten should be merged into a single
column in the structured store and assigned a collective label.).
As to claim 8, Amel as modified discloses the database system of claim 7, wherein the first column of the plurality of columns corresponds to a category identifier type, and wherein the second column of the plurality of columns corresponds to a numeric value type
(Amel teaches key and value columns, i.e. category columns and value columns see [0165] Delta analysis engine 2220 is configured to determine a delta or variance between the two generated sets of results shown in Tables 1 and 2. In some embodiments,
performing the delta analysis includes combining or correlating the target and baseline results using combining engine 2218. For example, if the query results have joinable key
columns, then the deltas between the value columns may be computed, visualized, or otherwise determined to provide insights. For example, key columns may be correlated in
such a way that differences or variances in the values in value columns may be determined. In this example, the combining engine combines the two tables by performing a join on the classes between the tables (e.g., where the table have the same number of columns and include key and value columns, and where the tables are joined or combined or correlated on the primary key columns of class/cluster/ signature). With the tables correlated on the key column of
class name, the values in the value columns ("count" column in this example) may be analyzed to determine a variance between the values in the target set of results and the results
returned from the baseline query.).
As to claim 9, Amel as modified discloses the database system of claim 8, wherein the differentiation operation is performed based on processing values of the second column of the plurality of columns for the corresponding subset of the plurality of subsets of the set of input rows in accordance with applying a corresponding window function to an ordered subset of rows ordered by a third column of the plurality of columns different from the first column and the second column
(Amel teaches using window functions and using a time/temperature/merged column i.e. “applying a corresponding window function to an ordered subset of rows ordered by a third column” see [0104] The output of the process shown in FIG. 12 can be used to automatically select a schema for which portions of the raw data should be extracted (and how they should be labeled). For example, while a particular raw message may include a total of ten columns' worth of data, the selected schema may state that the first column ("time") and third column ("temperature") should be extracted separately from the other columns, that column two should be discarded, and that columns four through ten should be merged into a single column in the structured store and assigned a collective label.;
see also [0159] For example, in response to the clicking of the button, two queries are generated, one for the original time range, and one for the same time range 24 hours prior. In some embodiments, the target query checks for variances against the baseline query, for a window time. The set of results from each of the generated queries for the shifted time ranges is then clustered (using clustering engine 2212), resulting in target clustered results set 2214 ( corresponding, for example, to the results of the target query for the last 15 minute range of time), and baseline clustered results set 2216 (corresponding, for example, to the request
of the query for the same 15 minute period, 24 hours ago). In some embodiments, the results comprise table data structures).
As to claim 10, Amel as modified discloses the database system of claim 9, wherein the third column stores temporal values, and wherein the corresponding window function is applied to the ordered subset of rows based on the corresponding subset being ordered by the temporal values
(Amel teaches using window functions and using a time/temperature/merged column i.e. “the corresponding window function is applied to the ordered subset of rows based on the corresponding subset being ordered by the temporal values” see [0104] The output of the process shown in FIG. 12 can be used to automatically select a schema for which portions of the raw data should be extracted (and how they should be labeled). For example, while a particular raw message may include a total of ten columns' worth of data, the selected schema may state that the first column ("time") and third column ("temperature") should be extracted separately from the other columns, that column two should be discarded, and that columns four through ten should be merged into a single column in the structured store and assigned a collective label.;
see also [0159] For example, in response to the clicking of the button, two queries are generated, one for the original time range, and one for the same time range 24 hours prior. In some embodiments, the target query checks for variances against the baseline query, for a window time. The set of results from each of the generated queries for the shifted time ranges is then clustered (using clustering engine 2212), resulting in target clustered results set 2214 ( corresponding, for example, to the results of the target query for the last 15 minute range of time), and baseline clustered results set 2216 (corresponding, for example, to the request
of the query for the same 15 minute period, 24 hours ago). In some embodiments, the results comprise table data structures).
As to claim 11, Amel as modified discloses the database system of claim 1, further comprising:
determining a query expression for execution indicating performance of a degree of the differentiation operation based on including a call to a differentiation function included in the query expression having a configured numeric value as a configurable degree parameter indicating the degree of the differentiation operation, wherein each set of output values of the plurality of sets of output values are generated in accordance with the degree of the differentiation operation
(Amel teaches various customizable numeric options for delta analysis, i.e. “a degree of the differentiation operation” based on a “query expression having a configured numeric value as a configurable degree parameter indicating the degree of the differentiation operation” [0156-0157] [0156] In addition to classifying and counting the occurrence of the various classes/clusters of log messages in a search result, as described above, suppose that Alice would like to compare the results of her search against the results for the same search, but a different time range. Using the techniques described herein, such an analysis of the delta between what has happened previously with what is happening now may be performed (e.g., comparison of data in a target period against a baseline period). [0157] The time range may be selected by default, from a list of presets, or customized (where a user can specify ( e.g., in an expression) any baseline and compare it to the target time period). For purposes of illustration, the older time range is referred to as the baseline, and the more current time range is referred to as the target. A user may specify which results of which query should be designated as a baseline or target.;
See also [0174] FIG. 29A is an example embodiment of a user interface for customizing log comparison. In some embodiments, the interface of FIG. 29A is provided by platform
2202. In this example, a user interface for customizing/ configuring time comparison is shown. In some embodiments, the comparison operator shown allows a user to compare current search results with data from a past time period for aggregate searches. Shown in this example are
fields for entering customized values for the time comparison. In this example, a user has customized a comparison of a query to a 1 day historical time shift (2902) with 5 time
periods (2904) using an average of historical results (2906).).
Referring to claim 14, this dependent claim recites similar limitations as claim 4; therefore, the arguments above regarding claim 4 are also applicable to claim 14.
As to claim 15, Amel as modified discloses the method of claim 14, wherein the plurality of subsets of the set of input rows are identified based on applying window partitioning to the window function based on executing the plurality of parallelized processes
(Amel [0141] In various embodiments, multiple iterations of portions 2104 and 2106 of process 2100 are performed, and/or portions 2104 and 2106 are performed in parallel.;
see also [0160] Alice need not write several, different, complex queries for different time ranges in order to perform a comparison. Instead, by surfacing, for example, a button, which when selected by Alice, performs the query rewriting, correlation/combining, and delta analysis
described above, efficient comparison of collected information is provided. Use and leveraging of computing resources is also made more efficient, as more queries and processing may be run per unit time ( e.g., allowing for parallel processing of queries and processing of query results), rather than waiting on the user, for example, to write a query, save the output of the query, and then run the query again over another time range. Thus, querying and comparison of data is made more efficient.).
Referring to claim 16, this dependent claim recites similar limitations as claim 7;
therefore, the arguments above regarding claim 7 are also applicable to claim 16.
Referring to claim 17, this dependent claim recites similar limitations as claim 8;
therefore, the arguments above regarding claim 8 are also applicable to claim 17.
Referring to claim 18, this dependent claim recites similar limitations as claim 9;
therefore, the arguments above regarding claim 9 are also applicable to claim 18.
Referring to claim 19, this dependent claim recites similar limitations as claim 11;
therefore, the arguments above regarding claim 11 are also applicable to claim 19.
Claim(s) 2-3 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kondiles et al., US Pub. No. 2021/0240717 A1, in view of Amel et al., US Pub. No. 2017/0228460 A1, in view of Lee et al., US Pub. No. 2020/0012734 A1, in view of Dotzler et al., US Pub. No. 2019/0089785 A1, in view of Drake et al., AU2011254041 A1.
As to claim 2, Kondiles/Amel/Lee do not disclose:
wherein executing the query is further based on:
identifying a plurality of required binomial coefficient values required for generating the plurality of sets of output values for the query
generating the plurality of required binomial coefficient values as a function of the configured non-integer numeric value; and
storing the plurality of required binomial coefficient values in cache memory resources;
wherein generating each output value of plurality of sets of output values is based on:
identifying a subset of the plurality of required binomial coefficient values for generating the each output value; and
accessing the subset of the plurality of required binomial coefficient values in the cache memory resources, wherein the each output value is generated as a function of the subset of the plurality of required binomial coefficient values;
However, Dotzler discloses:
the database system of claim 1, wherein executing the query is further based on:
identifying a plurality of required binomial coefficient values required for generating the plurality of sets of output values for the query
(Dotzler teaches a pre-calculated table of binomial coefficients, i.e. "identifying a plurality of
required binomial coefficient values required for generating a set of output values" see [0015]
Advantageously, instead of an individual calculation of the binominal coefficient a pre-calculated
table of these coefficients is used. Thereby, the required computational power is further
reduced, by accepting a small increase in the need for memory.;
See also claim 9: "in calculating the binomial coefficient, determining the binomial coefficient
based on the association table instead of calculation of the binomial coefficient.")
generating the plurality of required binomial coefficient values as a function of the configured non-integer numeric value; and
(Dotzler teaches a pre-calculated table of binomial coefficients, i.e. " generating the plurality of required binomial coefficient values as a function of the configured non-integer numeric value" see [0015] Advantageously, instead of an individual calculation of the binominal coefficient a pre-calculated table of these coefficients is used. Thereby, the required computational power is further reduced, by accepting a small increase in the need for memory.)
storing the plurality of required binomial coefficient values in cache memory resources;
(Dotzler teaches storing via cache memories for storing the encoding tables/buffers [0015] Advantageously, instead of an individual calculation of the binominal coefficient
a pre-calculated table of these coefficients is used. Thereby, the required computational
power is further reduced, by accepting a small increase in the need for memory.;
see also Dotzler teaches various cache memories for storing the encoding tables/buffers see [0009] Furthermore, the disclosure provides a cache assigning device, comprising an identification decoding device, by means of which a list of elements can be generated from a number of group identifications, an encoding parameter, a group identification, a data block identification and a file identification.;
see also [0074] A cache assigning device 300, as depicted in FIG. 6, may be provided for the
purpose for selecting the data blocks 22 to be stored in the cache memory 60-65. The cache
assigning device 300 obtains the system parameter N and k, as well as a group identification i
and a data block identification c as input parameters.;
see also [0075] The cache assigning device 300 has an identification decoding device 302
configured for performing above described method 200-220 for assigning a list of elements to
an identification. The identification encoding device 302 processes input parameters N, k and c
to a list S of group identifications of groups 70-73, in which data block 22 having data block
identification c is to be held. [0049] In the closing step 122, the content of buffer c is passed on.
The content represents the determined identification and is output by the identification assigning
device 30 as the identification c of the list of elements S. This identification may be a global/or a
local data block identification c, which describes a data block 22 which is cached on the
end user devices 16a-16/ contained in the list of elements S.
see also [0034] This is considered by applying so called index coding. This may be
implemented on the end user devices 16a-16f, as depicted in FIG. 4. Each of the end user
devices 16a-16/ comprises a memory device 20a-20f, also called a cache memory, which is
able to store data blocks 22;
see also [0053] In a first step 204, a binomial coefficient.. is calculated and stored in a buffer z.)
wherein generating each output value of plurality of sets of output values is based on:
identifying a subset of the plurality of required binomial coefficient values for generating the each output value; and
(Dotzler teaches determining a buffer of binomial coefficients, i.e.
"subset of the plurality of required binomial coefficient values" see [0053] In a first step 204, a
binomial coefficient is calculated and stored in a buffer z.
See also [0050] The list of elements S to be determined should have a pre-determined
cardinality, which is stored in a cardinality counter k. Moreover, a set of elements, for which the
elements of the list of elements S are to be selected, has a pre-determined total number N of
elements.
[0058] In closing step 220, the list of elements S is output
by the identification decoding device 40 as a result.
See also [0063] The outputting of the result in steps 122, 220 may
be achieved by passing on the result to a further processing
device or by storing it in a memory portion .... "index - subset_to_index(subset, N, K)")
accessing the subset of the plurality of required binomial coefficient values in the cache memory resources, wherein the each output value is generated as a function of the subset of the plurality of required binomial coefficient values.
(Dotzler teaches using the buffer Z filled with the binomial coefficients, i.e. "accessing the subset
of the plurality of required binomial coefficient value in the cache" see [0054] In a second step
206, the value of the identification buffer c and the buffer z are compared with each other. If the
identification buffer c has a value, which is greater than or equal to the value of buffer z, the
method continues with a sixth step 214 described below.)
It would have been obvious to one having ordinary skill in the art at the time the time of the
effective filing date to apply pre-calculated binomial coefficients as taught by Dotzler, to the system of Kondiles/Amel/Lee since it was known in the art that caching/decoding systems provide that instead of an individual calculation of the binominal coefficient a pre-calculated table of these coefficients is used, thereby, the required computational power is further reduced, by accepting a small increase in the need for
memory (Dotzler [0015]).
Kondiles/Amel/Lee/Dotzler do not disclose:
based on a configured non-integer numeric value included in the query expression;
However, Drake discloses
based on a configured non-integer numeric value included in the query expression;
(Drake teaches a floating point simplex number used to determine binomial coefficients for encoding/matching requests, i.e. “based on a configured non-integer numeric value included in the query expression” see abstract: “the method comprising the steps of (a)
determining ( 1310) the height of a simplex having a volume equal to the numerical identifier (1410); (b) determining (1320) a bound for a parameter defining a corresponding binomial coefficient, said bound being dependent upon the height of said simplex; ( c) determining (1330), constrained by the determined bound, the largest value of the parameter such that the value of the corresponding binomial coefficient is the largest value less than or equal to the numerical identifier; and ( d) determining the coordinate of the quantised feature vector based on the inverse of said largest binomial coefficient, said quantised feature vector representing the portion of the image.”
see also p. 3 “(b) computer software code for determining, dependent upon the height of said
simplex, a bound for a parameter defining a corresponding binomial coefficient;”;
see also p. 24: “By using a table to represent log(i!) and standard floating point mathematics functions, the
height of the simplex can be determined efficiently and accurately for large values of i and L
using only double precision arithmetic. The method then proceeds to a step 1320”
see also p. 43: “11. The method of claim 2 wherein the height of the simplex is determined from a logarithm of the numerical identifier using double precision floating point arithmetic.” )
It would have been obvious to one having ordinary skill in the art at the time the time of the
effective filing date to apply floating point generated binomial coefficients as taught by Drake, to the system of Kondiles/Amel/Lee/Dotzler, since it was known in the art that database encoding/compression systems provide encoding arrangements which use other measures of closeness between feature vectors to find matching feature vectors where the efficient means to convert labels back to vectors described above allows matching to be performed using matching techniques that rely on the Euclidean distance (or other distance)as a measure of closeness between feature vectors where the criteria used for determining a match between two labels can involve a predefined distance threshold, where this provides the advantage that comparison of features is fast, since image features are represented by labels and labels are compact in size where this approach also has a further advantage, in that the computer memory is able to store more labels than feature vectors due to the compressed size of the labels, and so labels can be accessed faster than feature vectors because of the faster access to memory. (Drake p. 26-27).
As to claim 3, Lee as modified discloses the database system of claim 2, wherein the plurality of required binomial coefficient values are generated via a corresponding plurality of parallelized threads of the thread pool, wherein each of the corresponding plurality of parallelized threads generates a corresponding subset of the plurality of required binomial coefficient values in parallel with other ones of the corresponding plurality of parallelized threads generating other corresponding subsets of the plurality of required binomial coefficient values
(Lee teaches an execution plan for the query where each job execution thread of the job execution is part of a thread pool and is executed in parallel, i.e. “corresponding plurality of parallelized threads generates a corresponding subset of the plurality of required binomial coefficient values in parallel with other ones of the corresponding plurality of parallelized threads generating other corresponding subsets of the plurality of required binomial coefficient values”
See [0059] In certain situations, such as if the query involves complex or internally-parallelized operations or sub-operations, the query executor 220 can send operations or suboperations
of the query to a job executor component 254, which can include a thread pool 256. An execution plan for the query can include a plurality of plan operators. Each job execution thread of the job execution thread pool 256, in a particular implementation, can be assigned to an individual plan operator. The job executor component 254 can be used to execute at least a portion of the operators of the query in parallel.;
see also [0054] For example, the query can be sent to an execution thread of the thread pool 224 determined by the session manager 208 or the application manager 210.;
See also [0052] The query interface 212 can communicate with a query language processor 216, such as a structured query language processor. For example, the query interface 212
may forward to the query language processor 216 query language statements or other database operation requests from the client 204. The query language processor 216 can include a query language executor 220, such as a SQL executor, which can include a thread pool 224.).
Claim(s) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kondiles et al., US Pub. No. 2021/0240717 A1, in view of Amel et al., US Pub. No. 2017/0228460 A1, in view of Lee et al., US Pub. No. 2020/0012734 A1, in view of Li et al., US Pub. No. 2014/0214799 A1.
As to claim 6, Kondiles/Amel/Lee do not disclose:
wherein the window partitioning is performed in accordance with implementing functionality of a PARTITION BY clause applied to the window function;
However, Li discloses:
the database system of claim 5, wherein the window partitioning is performed in accordance with implementing functionality of a PARTITION BY clause applied to the window function
(Li teaches a window function as specified in a "PARTITION BY" clause, i.e.
See General Overview [ 0021] A ranking or cumulative window function in a database
statement refers to an analytical function ( e.g., as specified in a "PARTITION BY" clause with an "ORDER BY" clause) that is evaluated against a sliding window of data from unbounded preceding up to a current row and returns a row number, rank, dense rank, sum, average, minimum, maximum, count, variance, standard deviation, first value, last value, etc., for each row in the sliding window up to that row (including its duplicates) in a result set. The database statement specifies one or more partition-by columns ( or partition by keys) in the "PARTITION-BY" clause and one or more order-by colunms (or order-by keys) in the "ORDER BY" clause.;
see also [0034-0035] Ranking and Cumulative Window Functions
[0034] A ranking or cumulative window function may be defined in the form of:
[0035] FUNCTION_NAME([exprl] ... ) OVER (PARTITION
BY expr2 [, expr3, ... ]ORDER BY expr4 [, expr5, ] [RANGEIROWS BETWEEN
UNBOUNDED PRECEDING AND CURRENT ROW])
[0036] As can be seen above, a ranking or cumulative reporting function is specified with one or more partition-by keys such as "expr2", "expr3", etc., in a partition-by clause and one or more order-by keys such as "expr4", "expr5", etc., in an order-by clause as shown above. The one or more partition-by keys as represented by the one or more expressions such as "expr2", "expr3", etc.).
It would have been obvious to one having ordinary skill in the art at the time the time of the
effective filing date to apply a window function as specified in a "PARTITION BY" clause as taught by Li, to the system of Kondiles/Amel/Lee, since it was known in the art that database systems provide a window function as specified in a "PARTITION BY" clause to provide a scalable computation algorithm as described herein selects a data distribution key (referred to as "extended data-distribution key") not just based on partition-by keys but also based on order-by keys where additionally or optionally, other columns or temporary distribution variables may be used as part of the data distribution key or used in a distribution function based on the data distribution key where because of the presence of the order-by keys, other columns, and temporary distribution variables, the number of distinct values of the data distribution key in the scalable computation algorithm can be made sufficiently large to distribute input data to a large number of parallel executing processes where this data distribution strategy is effective as it can scale up to many parallel executing processes ( e.g., according to what is indicated by a parallel processing related parameter such as a degree of parallelism) without being limited to only the number of distinct combinations of values of the partition-by keys, resulting in a more efficient use of a large number of resources available in database systems than otherwise. (Li [0025]).
Claim(s) 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kondiles et al., US Pub. No. 2021/0240717 A1, in view of Amel et al., US Pub. No. 2017/0228460 A1, in view of Lee et al., US Pub. No. 2020/0012734 A1, in view of Hickman et al., US Pub. No., 2019/0384575 A1.
As to claim 12, Kondiles/Amel/Lee do not disclose:
the degree of the differentiation operation is implemented as derivation based on the configured numeric value being a positive numeric value;
the degree of the differentiation operation is implemented as derivation based on the configured numeric value being a negative numeric value; or
the degree of the differentiation operation is implemented as fractional order differentiation based on the configured numeric value being a non-integer numeric value.
However, Hickman discloses:
the database system of claim 11, wherein at least one of:
the degree of the differentiation operation is implemented as derivation based on the configured numeric value being a positive numeric value;
(Hickman teaches range specific coefficient sets for corresponding negative and positive ranges [0044] Symmetry Mode Register-This register specifies the symmetry of a function. For example, the symmetry mode may be none, y-axis, or origin. Some unary functions
(e.g., some deep learning functions) have symmetry, so instead of storing coefficient sets for corresponding negative and positive ranges, a single series of sets may be stored for both ranges. When the symmetry mode is none, no symmetry optimization is applied. When the symmetry mode is y-axis, the function may be evaluated using the absolute value of the input value. When the symmetry mode is origin, the function may be evaluated using the absolute value of the input value and the sign of the output is then flipped if the original input was negative to produce the final output. Each function may have its own Symmetry Mode Register.;
See also [0043] Shift Register-This register stores a value representing a shift amount applied to an input value's offset within a range ( e.g., the value obtained after subtracting the
offset value from the input value). In some embodiments, this value may be provided by a user. In a particular embodiment, this value is based on the number of coefficient sets within the range.)
the degree of the differentiation operation is implemented as derivation based on the configured numeric value being a negative numeric value; or
(Hickman teaches range specific coefficient sets for corresponding negative and positive ranges [0044] Symmetry Mode Register-This register specifies the symmetry of a function. For example, the symmetry mode may be none, y-axis, or origin. Some unary functions
(e.g., some deep learning functions) have symmetry, so instead of storing coefficient sets for corresponding negative and positive ranges, a single series of sets may be stored for both ranges. When the symmetry mode is none, no symmetry optimization is applied. When the symmetry mode is y-axis, the function may be evaluated using the absolute value of the input value. When the symmetry mode is origin, the function may be evaluated using the absolute value of the input value and the sign of the output is then flipped if the original input was negative to produce the final output. Each function may have its own Symmetry Mode Register.;
See also [0043] Shift Register-This register stores a value representing a shift amount applied to an input value's offset within a range ( e.g., the value obtained after subtracting the
offset value from the input value). In some embodiments, this value may be provided by a user. In a particular embodiment, this value is based on the number of coefficient sets within the range.)
the degree of the differentiation operation is implemented as fractional order differentiation
based on the configured numeric value being a non-integer numeric value
(Hickman abstract: “each entry of the plurality of entries comprising a set of coefficients defining a power series approximation; selecting first entry of the plurality of entries based on a determination that a floating point input value is within a portion of the range of input values that is associated with the first entry;
See also [0043] Shift Register-This register stores a value representing a shift amount applied to an input value's offset within a range ( e.g., the value obtained after subtracting the
offset value from the input value). In some embodiments, this value may be provided by a user. In a particular embodiment, this value is based on the number of coefficient sets within the range.).
It would have been obvious to one having ordinary skill in the art at the time the time of the
effective filing date to apply range specific coefficient sets, as taught by Hickman, to the system of Kondiles/Amel/Lee, since it was known in the art that database systems provide range specific coefficient sets and provide robust solutions for performing unary functions having an FP number as an input where unary functions are implemented by a set of power series approximations arranged successively across the possible values for the input where for example, a tabulated function result may be determined by an evaluation of a power series and in particular embodiments, the coefficients are determined by a two-stage process where first,
the input FP value is compared against successive ranges where the start value of each range may be an arbitrary FP number while the end value of a particular range is the FP value that
is one ULP less than the next range's start value and second, once the input value's range is determined, a coefficient set (e.g., a0 , a1 , and a2 ) is selected based on the input value's
offset within the range (thus different ranges may be associated with different series of coefficient sets and different sections of a range may be associated with different coefficient sets where the coefficient set is then used in conjunction with the input value x to calculate the result of the unary function. (Hickman [0018]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Pal et al., US Pub. No.: US 2019/0138639 A1, teaches systems and methods are disclosed for receiving, at a first data intake and query system, a query that includes an indication to process data managed by another data intake and query system. The first data intake and query system
identifies a second data intake and query system that manages the data to be processed and generates a subquery for execution by the second data intake and query system, generates instructions for one or more worker nodes to receive and process results of the subquery from the second data intake and query system, and instructs the worker nodes to provide results of the processing to the first data intake and query system;
George et al., US Pub. No.: US 2012/0191699 A1, teaches systems and apparatuses are provided for a distributed aggregate user defined function processing system. A non-transitory computer readable medium stores instructions for a query compiler to identify a call to a distributed aggregate user defined function within a query of the distributed analytical data processing system, retrieve metadata associated with the distributed aggregate user defined function, and validate and resolve respective inputs and outputs of a plurality of function partitions associated with the distributed aggregate user defined function according to the retrieved metadata. A distributed processing engine includes a plurality of processing nodes to execute the plurality of function partitions and organize data flow from the plurality of function partitions to produce an output for the distributed aggregate user defined function;
Bowman et al., US Pub. No. US 2021/0026806 A1, teaches an apparatus includes a processor to : within each reading thread , retrieve a data set part and corresponding part metadata from storage device ( s ) , analyze row group meta data for each row group within the data set part to identify candidate row group ( s ) meeting specified criteria , and store the candidate row group ( s ) and corresponding row group metadata within a data buffer of a queue ; operate the queue
as a FIFO buffer ; within each provision thread , retrieve one of multiple row groups and corresponding metadata from within the data buffer , use information in the metadata to
identify rows meeting the criteria , and provide those rows to the requesting device or an application ; and in response to each instance of storage of a data set part within a data buffer
of the queue , analyze the availability of storage space and / or of processing resources to determine whether to dynamically adjust the quantity of reading thread.
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/Evan Aspinwall/Primary Examiner, Art Unit 2152 3/30/2026
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