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 .
1. In response to the Office Action dated on 09/15/2026, applicant(s) amend the application as follow:
Claims amended:
Claims canceled: 12-18 and 20
Claims newly added: 21-30
Claims pending: 1-11, 19 and 21-30
Response to Arguments
2. Applicant’s arguments with respect to claim(s) 1 and 19 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Applicant(s) argues the obviousness double patenting rejection. The new Double Patenting rejection was introduced.
Applicant(s) argues “applicant respectfully disagrees with this rejection and the reasoning thereof. Nevertheless, the applicant believes the claims as amended, overcome the present rejection.”
Examiner respectfully disagrees with the above argument. Please see the new rejection below.
Information Disclosure Statement
3. The information disclosure statement (IDS) submitted on 03/16/2026. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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.
4. Claims 1-11, 19 and 21-30 provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of copending Application No. 19/044,883 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other because both applications direct to similar subject matter including energy utilization estimation function based on input data to generate energy utilization estimate data for the query and apply energy efficient bed on the energy utilization estimate data for the query. While the instant application include nodes which storage data and executing the query; however, the 883 application also include the estimation was based on the historic energy utilization execution data. Therefore, it would have been obvious to one ordinary skill in the art to modify 883 to include energy utilization based on historic energy utilization execution data to arrive the same invention as claimed.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
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.
5. Claims 1-11, 19 and 21-30 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea without significantly more.
Step 1 (See MPEP 2106)
Claims 1-12, 19 and 21-30 are directed to a method and system and a tangible , non-transitory computer readable medium which belongs to a statutory class.
Step 2A, Prong One:
Claims recites
“Monitoring pluralities of computing nodes of pluralities of computing devices of a plurality of computing device clusters of a query and response sub-system of the parallelized database system for incoming queries, wherein the parallelized database system includes pluralities of computing device clusters, wherein a first computing device cluster of the plurality of computing device clusters includes a first plurality of computing devices of the pluralities of computing devices, wherein a first computing device of the first plurality of computing devices includes a first plurality of computing nodes of the pluralities of computing nodes of the parallelized database system;
“Detecting a query regarding a dataset, wherein the dataset is stored by a set of storage computing nodes of pluralities of storage computing nodes of a store and compute sub-system of the parallelized database system” is a mental process.
“Detecting an indication to generate energy utilization estimate data for executing the query” is an mental process.
“Determining a set of query energy utilization estimation input data points for the query” is a mental process.
These are the processes which under its broadest reasonable interpretation, covers performance of the limitation by Mental Process, but for the recitation of generic computer components. Nothing in the claim element precludes the steps from practically being performed in the human mind. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation by mental process, but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A, Prong Two:
Claims recite computing devices including computing nodes to process the abstract idea. These are generic computer components which use to perform abstract ideas.
“Storing at least some query data regarding the query in a query data record;
“Obtaining a query energy utilization estimate data model based on the indication and the query data” is the process of retrieving data.
“Applying the set of query energy utilization estimation data points to the energy utilization estimation data model to produce energy utilization estimate data for the query” is the process of executing query estimation get the result.
“Adding the energy utilization estimate data to the query data record” is the process for storing data for later uses.
“Updating one or more energy utilization estimation data models of a plurality of energy
utilization estimation data models with at least some of: the energy utilization estimate data, the set of query energy utilization estimation points, and the query data” is the process of provide new information..
The limitation is thus insignificant extra-solution activity. Limitations that the courts have found not to be enough to qualify as "significantly more” when recited in a claim with a judicial exception include: i. Adding the words "apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp., 134 S. Ct. at 2360, 110 USPQ2d at 1984 (see MPEP § 2106.05(f)). 2106.05(g)--Insignificant Extra-Solution Activity.
At Step 2B:
The conclusions for the mere implementation using a computer are carried over and does not provide significantly more.
Looking at the claim as a whole does not change this conclusion and the claim is ineligible.
As to claims 2 and 21, the limitation “the query data comprises energy utilization estimation input data includes at least one of: a query operation type; a plurality of query operations of the query; a requester of the query; a level of parallelization for executing the query operation, wherein the energy utilization estimate data is generated as a function of the level of parallelization; size of the dataset data; execution duration of time for execution of the query operation, wherein the energy storage information regarding the dataset” is only further defined what query data including and insignificantly to amount significantly more.
As to claims 3 and 23, the limitation “the indication comprises one or more of: a request to produce the energy utilization estimate data; a default setting to produce the energy utilization estimate data, wherein the default setting is associated with a one or more features of the query or the parallelized database system; detection of one or more features of the query that do not compare favorably with a set of energy utilization metrics; and detection of one or more features of the parallelized database system that do not compare favorably with the set of energy utilization metric” is only further included what indication is an insignificantly to amount significantly more.
As to claims 4 and 24, the imitation “the set of energy utilization metrics comprises: a level of query execution complexity; an amount of query operations of the query; a query operation type of the query operations of the query; a peak power level threshold; a maximum time frame for query execution; availability of the pluralities of storage computing nodes; and a threshold amount of concurrent incoming queries to the query and response sub-system” is further defined what the set of energy utilization metrics are which insignificantly to amount significantly more.
As to claims 5 and 25, the limitation “the determining the set of query energy utilization estimation data points comprises: determining, from the energy utilization estimation data model, a list of required data points as the set of query energy utilization estimation data points, wherein the set of query energy utilization estimation data points include the query data and one or more of: current availability of the pluralities of computing nodes of the query and response sub-system; status of the set of storage computing nodes; concurrent incoming queries pertaining to at least a portion of the dataset; user preferences associated with a requester of the query; restrictions associated with the requester; a resource distribution strategy; and optimal query execution plan parameters” is a mental process for determining the set of query energy utilization estimation data points.
As to claims 6 and 26, the limitation “providing the energy utilization estimate data to one or more of: a requester of the query; an administrator of the parallelized database system; a set of computing nodes of the pluralities of computing nodes; and one or more storage computing nodes of the set of storage computing nodes” is the process for requester for further processing and insignificant to amount significantly more.
As to claims 7 and 27, the limitation “the energy utilization estimate data includes
total energy utilization estimate data that indicates at least one of:
“An estimated value for total energy utilization amount” is a mental process.
“An estimated value for total energy utilization cost” is a mental process.
“An estimated value range for the total energy utilization amount” is a mental process.
“An estimated value range for the total energy utilization amount; probability distribution data for value of total energy utilization amount” is a mental process.
“probability distribution data for value of total energy utilization cost” is a mental process.
As to claim 8 and 28, the energy utilization estimate data includes
temporal energy utilization distribution estimate data that indicates at least one of:
“An estimated execution duration of time” is a metal process.
“Estimated peak power timing data indicating when peak power is estimated to occur within the estimated execution duration of time” is a mental process. or
“Distribution of power consumption over the estimated execution duration of time” is a mental process.
As to claims 9 and 29, the limitations the energy utilization estimate data includes
temporal energy utilization distribution estimate data that indicates at least one of:
“An estimated execution duration of time” is mental process.
“Estimated peak power timing data indicating when peak power is estimated to occur within the estimated execution duration of time” is mental process. or
“Distribution of power consumption over the estimated execution duration of time” is the process provide data or power to other process.
As to claims 10 and 30, the limitation “the energy utilization estimate data includes
resource-based energy utilization distribution estimate data that includes:
“drive-based energy utilization estimate data indicating a proportion of total energy utilization induced by drive resources involved in executing the query operation” is a mental process” is a mental process.
“processor-based energy utilization estimate data indicating a proportion of total energy utilization induced by processor resources involved in executing the query operation” is an estimation process which is a mental process.
“memory-based energy utilization estimate data indicating a proportion of total energy utilization induced by memory resources involved in executing the query operation” is an estimation process which is a mental process. or
“network-based energy utilization estimate data indicating a proportion of total energy
utilization induced by network resources involved in executing the query operation” is an estimation process which is a mental process.
As to claim 11, the limitation “wherein the obtaining the query energy utilization estimation model comprises: generating the query energy utilization estimation model by performing a model training function upon training data that includes historic energy utilization data associated with the query data and historic query execution data associated with the query data” is further defined what obtaining step and only to retrieve data which is insignificantly to amount significantly more.
AS to claim 12, the limitation “the query data further comprises: a query execution plan for executing the plurality of query operations. further comprising: executing a plurality of prior operations; and generating a plurality of energy utilization measurements for the plurality of prior operations, wherein the training data includes the plurality of energy utilization measurements for the plurality of prior operations” is only further defined what query data is and insignificantly to amount significantly more.
As to claim 22, the limitation “the query data further comprises: a query execution plan for executing the plurality of query operations” is only further what query plan is and insignificant to amount significantly more.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
6. Claim(s) 1-7, 9-11, 19 and 21-30 is/are rejected under 35 U.S.C. 103 as being unpatentable over Liu et al. (Pub. No. US 2018/0314735 A1) in view of Yamaguchi (Pub. No. US 2020/0034768 A1).
As to claim 1. (Currently amended) Liu discloses a method for execution by an energy utilization processing sub-system of a parallelized at least one processor of a database system, comprising the method comprises:
monitoring pluralities of computing nodes of pluralities of computing devices of a plurality of computing device clusters of a query and response sub-system of the parallelized database
system for incoming queries, wherein the parallelized database system includes pluralities of
computing device clusters, wherein a first computing device cluster of the plurality of computing
device clusters includes a first plurality of computing devices of the pluralities of computing
devices, wherein a first computing device of the first plurality of computing devices includes a
first plurality of computing nodes of the pluralities of computing nodes of the parallelized
database system (… a method of using machine learning to estimate query resource consumption in a MPPDS. A MPPDB is a database management system that partitions data across multiple servers or nodes for enabling queries to be split into a set of coordinated processes that are executed in parallel on the one or more nodes to achieve faster results) (Paragraph 0035);
detecting a query regarding a dataset, wherein the dataset is stored by a set of storage computing nodes of pluralities of storage computing nodes of a store and compute sub-system of the parallelized database system (the resource is shared by all concurrent queries…) (paragraph 0036);
storing at least some query data regarding the query in a query data record (storing the data) (paragraph 0085);
detecting an indication to generate energy utilization estimate data for executing the query (extreme events discovery ) (paragraph 0041);
obtaining a query energy utilization estimate data model based on the indication and the query data (a machine learning model, which has the capability to jointly learn the models…) (paragraph 0041);
determining a set of query energy utilization estimation input data points for the query (query resource consumption estimation) (paragraph 0041);
applying the set of query energy utilization estimation data points to the energy utilization estimation data model to produce energy utilization estimate data for the query (a machine learning module which has the capability to jointly learn the models of both query resource consumption estimation and extreme events discovery) (paragraph 0041); and
updating one or more energy utilization estimation data models of a plurality of energy
utilization estimation data models with at least some of: the energy utilization estimate data, the set of query energy utilization estimation points, and the query data (model training phrase) (Paragraph 0043).
Liu discloses adding the energy utilization estimate data to the query data record. Yamaguchi disclose adding the energy utilization estimate data to the query data record (the database collects and records the energy consumption data and the measurement content of the meteorological station and further records calculation results of the usage energy consumption estimation program, the energy savable amount calculation program, and the energy saving simulation program) (paragraph 0036). This suggests adding the energy utilization estimate data to the query data record. Therefore, it would have been obvious to one ordinary skill in the art before the effective filling date of the instant application to modify teaching of Liu to include adding the energy utilization estimate data to the query data record as disclosed by Yamaguchi in order to keep the record for further processing.
As to claim 2. (Currently amended) Liu discloses the method of claim 1, wherein the query data comprises energy utilization estimation input data includes at least one of:
a query operation type (orders/sequences of operators) (paragraph 0046);
a plurality of query operations of the query (query operation plans) (paragraph 0045);
a requester of the query;
a level of parallelization for executing the query operation, wherein the energy utilization
estimate data is generated as a function of the level of parallelization;
size of the dataset data (the size of data) (paragraph 0046);
execution duration of time for execution of the query operation, wherein the energy
storage information regarding the dataset.
As to claim 3. (Currently amended) Liu discloses the method of claim 1, wherein the indication comprises one or more of:
a request to produce the energy utilization estimate data (query resource consumption estimation) (paragraph 005);
a default setting to produce the energy utilization estimate data, wherein the default setting is associated with a one or more features of the query or the parallelized database system;
detection of one or more features of the query that do not compare favorably with a set of energy utilization metrics; and
detection of one or more features of the parallelized database system that do not compare favorably with the set of energy utilization metric.
As to claim 4. (Currently amended) Liu discloses the method of claim 3, wherein the set of energy utilization metrics comprises:
a level of query execution complexity (data size to be processed) (paragraph 0056);
an amount of query operations of the query (arithmetic operations) (paragraph 0056);
a query operation type of the query operations of the query (query operation) (paragraph 0045);
a peak power level threshold (peak value) (paragraph 0044);
a maximum time frame for query execution (time range) (paragraph 0036);
availability of the pluralities of storage computing nodes (computing servers and node) (; and
a threshold amount of concurrent incoming queries to the query and response sub-system (one or more queries are passed to a query optimization…) (paragraph 0078)
As to claim 5. (Currently amended) Liu discloses the method of claim 1, wherein the determining the set of query energy utilization estimation data points comprises:
determining, from the energy utilization estimation data model, a list of required data points as the set of query energy utilization estimation data points (a machine learning model, which has the capability to jointly learn the models…) (paragraph 0041), wherein the set of query energy utilization estimation data points include the query data and one or more of:
current availability of the pluralities of computing nodes of the query and response sub-
system;
status of the set of storage computing nodes;
concurrent incoming queries pertaining to at least a portion of the dataset;
user preferences associated with a requester of the query;
restrictions associated with the requester;
a resource distribution strategy; and
optimal query execution plan parameters (generate a query plan for the query by parsing the query to determine operators) (paragraph 0014).
As to claim 6. (Currently amended) Liu discloses the method of claim 1 further comprises:
providing the energy utilization estimate data to one or more of:
a requester of the query;
an administrator of the parallelized database system;
a set of computing nodes of the pluralities of computing nodes; and
one or more storage computing nodes of the set of storage computing nodes (database management system that partitions data across multiples servers or nodes….) (paragraph 0035)..
As to claim 7. (Original) Liu discloses the method of claim 1, wherein the energy utilization estimate data includes total energy utilization estimate data that indicates at least one of:
an estimated value for total energy utilization amount (the query resource consumption estimation) (paragraph 0017);
an estimated value for total energy utilization cost;
an estimated value range for the total energy utilization amount;
an estimated value range for the total energy utilization amount;
probability distribution data for value of total energy utilization amount;
probability distribution data for value of total energy utilization cost.
As to claim 9, (Original) Liu discloses the method of claim 1, wherein the energy utilization estimate data includes temporal energy utilization distribution estimate data that indicates at least one of:
an estimated execution duration of time (an execution time range) (paragraph 0036);
estimated peak power timing data indicating when peak power is estimated to occur within the estimated execution duration of time; or
distribution of power consumption over the estimated execution duration of time.
As to claim 10, (Original) Liu disclose the method of claim 1, wherein the energy utilization estimate data includes resource-based energy utilization distribution estimate data that includes:
drive-based energy utilization estimate data indicating a proportion of total energy
utilization induced by drive resources involved in executing the query operation (optionally in any of the preceding aspects, another implementation of aspect provides that the machine learning technology jointly performs query resource consumption estimation for a query and resource extreme events detection together) (paragraph 0012);
processor-based energy utilization estimate data indicating a proportion of total energy
utilization induced by processor resources involved in executing the query operation;
memory-based energy utilization estimate data indicating a proportion of total energy
utilization induced by memory resources involved in executing the query operation; or
network-based energy utilization estimate data indicating a proportion of total energy
utilization induced by network resources involved in executing the query operation.
As to claim 11, (Currently amended) Liu discloses the method of claim 1, wherein the obtaining the query energy utilization estimation model comprises:
generating the query energy utilization estimation model by performing a model training function upon training data that includes historic energy utilization data associated with the query data and historic query execution data associated with the query data (training model) (paragraph 0061).
As to claim 12. (Currently amended) Liu discloses the method of claim 2, wherein the query data further comprises: a query execution plan for executing the plurality of query operations. further comprising: executing a plurality of prior operations (historical data) (paragraph 0048); and generating a plurality of energy utilization measurements for the plurality of prior operations, wherein the training data includes the plurality of energy utilization measurements for the plurality of prior operations (predictive model) (paragraph 0049).
Claims 13-18. (Canceled)
Claim 19 is rejected under the same reason as to claim 1, Liu discloses a parallelized database system comprises: pluralities of computing device clusters (database management system that partitions data across multiple servers or nodes for enabling queries to be split into a set of coordinated processes that are executed in parallel on the one or more nodes to achieve faster results) (paragraph 0035).
20. (Canceled)
Claim 21 is rejected under the same reason as to claim 2.
As to claim 22, (Newly added) Liu discloses the parallelized database system of claim 21, wherein the query data further comprises: a query execution plan for executing the plurality of query operations (input queries are forwarded to the query plan generation module 104, which generates query operation plans using a DB optimizer…) (paragraph 0045).
Claim 23 is rejected under the same reason as to claim 4.
Claim 24 is rejected under the same reason as to claim 5.
Claim 26 is rejected under the same reason as to claim 6.
Claim 27 is rejected under the same reason as to claim 7.
Claim 28 is rejected under the same reason as to claim 8.
Claim 29 is rejected under the same reason as to claim 9.
Claim 30 is rejected under the same reason as to claim 10.
7. Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Liu et al. (Pub. No. US 2018/0314735 A1) in view of Yamaguchi (Pub. No. US 2020/0034768 A1) and further in view of ITOTH et al. (Pub. No. US 2017/0133866 A1).
As to claim 8, (Original) Liu discloses the method of claim 1 excepting for wherein the energy utilization estimate data includes peak power estimate data that indicates at least one of: an estimated value for peak power amount; an estimated value for peak power cost; an estimated value range for the peak power amount; an estimated value range for the peak power cost; probability distribution data for value of peak power amount; or probability distribution data for value of peak power cost. However, ITO discloses the energy utilization estimate data includes peak power estimate data that indicates at least one of: an estimated value for peak power amount; an estimated value for peak power cost (the storage unit 704 store the power consumption estimate data 741 and the peak power upper limit value 742. The power consumption estimate data 741 is the estimate data generated by estimate variant with time in the power consumption in the facility…) (paragraph 0047); an estimated value range for the peak power amount; an estimated value range for the peak power cost; probability distribution data for value of peak power amount; or probability distribution data for value of peak power cost. Therefore, it would have been obvious to one ordinary skill in the art before the effective filing date of the instant application to modify teaching of Liu to include the energy utilization estimate data includes peak power estimate data that indicates at least one of: an estimated value for peak power amount; an estimated value for peak power cost; an estimated value range for the peak power amount; an estimated value range for the peak power cost; probability distribution data for value of peak power amount; or probability distribution data for value of peak power cost as disclosed by ITOTH in order to provide cost for execution.
Conclusion
8. Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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BAOQUOC N. TO
Examiner
Art Unit 2154
/BAOQUOC N TO/Primary Examiner, Art Unit 2154
Prosecution Insights