DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
This action is responsive to the Applicant’s Application filed on February 26, 2026.
No claims have been amended.
Claims 1, 13, and 17 are independent. As a result claims 1-20 are pending in this office action.
Response to Arguments
Applicant's arguments filed February 26, 2026 regarding the rejection of claims 1, 13, and 17 under 35 U.S.C 103 have been fully considered but they are not persuasive.
Applicant argues, regarding claims 1, 13, and 17 Kusnoto does not teach or suggest the following limitation, inputting the one or more dynamically evaluated data features to a machine-learned index selection model and obtaining, as output of the machine-learned index selection model, weights of the one or more dynamically evaluated features as disclosed in Applicants’ invention.
Examiner respectfully disagrees with applicant’s assertions.
With regards to a), Examiner appreciates the interpretation of the description given by Applicant in the response. In Fig. 1, para [0002-0003], Kusnoto teaches " to rank business processes based on multi-factorials and multi-facets parameters of best practice and using Artificial Intelligence (AI) to interpret and verify commonly used individual parameter data-points for specific business processes, weighted with predefined order and class (ranking), in which the sum of the total weight of all the parameters will be the dynamically morphing, applying AI deep learning output to create a dynamic value index (BVI—Best Value Index). [0003] The Best Value Index (BVI) method helps a patient to identify the best clinic based on parameters such as clinical condition, treatment price, availability and feedback rating based on dynamic weighting.”, para [0012], Kusnoto teaches “automatically deriving and analyzing quantitative BVI data is its ability to be adaptive and dynamic in arranging sequence based on parameters set (such as distance, availability, price, reviews, etc.)”, para [0020], Kusnoto teaches “Dynamic BVI is BVI in which the weight of each parameters adapts to the profile and the preference of each users. Some users prefer price over time over distance, others prefer convenient over price or services. Deep learning dynamic BVI, is the integration of AI and its deep learning capability to recognize those subtle individual preferences thus making inference of those based on each user profile”. Therefore, Best Value Index, BVI (machine learned index selection model) dynamically weighting parameters (evaluated features).
In response to applicant's argument that the references fail to show certain features of applicant’s invention, it is noted that the features upon which applicant relies (i.e. outputting weights to select a computational database index (such as a learned index versus a non-learned B-tree index) from a plurality of indexes) and (outputs weights specifically used to select a database access index) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
Claim Rejections - 35 USC § 103
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 of this title, 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
Claims 1, 2, 10-11, 13, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Hoang et al. (US 2021/0240727) (hereinafter Hoang) in view of Kusnoto et al. (US 2020/0401887)(hereinafter Kusnoto).
Regarding claim 1, Hoang teaches a system, comprising: one or more memory devices comprising a plurality of indexes for accessing data stored in a database, the plurality of indexes including at least one non-learned index that is formed and maintained without using machine learning and at least one learned index that is created or maintained using machine learning (see Fig. 4, Fig. 6A, para [0058], para [0068-0069], discloses an indexer component associated with machine learning model component for selecting and building indices (machine learning index) and an indexer selecting and building indices such as a binary search tree data index (non-learned index)); and a server infrastructure comprising at least one processor configured to perform operations (see Fig. 1, para [0011], discloses processor) that comprise: responsive to a query, dynamically evaluating one or more data features of said data in view of at least one characteristic obtained from the query (see Fig. 4, para [0018], para [0057, 0059], discloses automated feature model that includes a set of queries to execute scoring operation, maximizing a scoring (obtained characteristic) query throughput based on constraints in a relational database); selecting an index from the plurality of indexes (see Fig. 4, para [0058-0059], discloses selecting and building indices using indexer component associated with machine learning component); accessing the database using the selected index (see Fig. 4, Fig. 6A, para [0058], para [0068], discloses accessing database using selected indices); and outputting results for the query, based at least on portions of the data obtained by the accessing (see Fig. 6A, Fig. 6C, para [0069], para [0071], discloses results from joining path of joined table in which filtered (portion) record table is joined with main table).
Hoang does not explicitly teach inputting the one or more dynamically evaluated data features to a machine-learned index selection model and obtaining, as output of the machine-learned index selection model, weights of the one or more dynamically evaluated features; wherein the selecting is based on the weights of the one or more dynamically evaluated features and a set of rules including one or more threshold values.
Kusnoto teaches inputting the one or more dynamically evaluated data features to a machine-learned index selection model and obtaining, as output of the machine-learned index selection model, weights of the one or more dynamically evaluated features (see Fig. 1, para [0002-0003], para [0012, 0020], discloses Best Value Index, BVI (machine learned index selection model) dynamically weighting parameters (evaluated features)); wherein the selecting is based on the weights of the one or more dynamically evaluated features and a set of rules including one or more threshold values (see Fig. 2, para [0012, 0016], para [0020], discloses predefined order and class ranking (set of rules including threshold values) based on user profile and preferences in order to dynamically arrange sequence of parameters).
Hoang/Kusnoto are analogous arts as they are each from the same field of endeavor of database systems.
Before the effective filing date of the invention it would have been obvious to a person of ordinary skill in the art to modify the system of Hoang to utilize weights of dynamically evaluated features from disclosure of Kusnoto. The motivation to combine these arts is disclosed by Kusnoto as “create better products solving cognitive problems commonly associated with human intelligence to grow its business, improve its customer experience and selection” (para [0006]) and utilizing weights of dynamically evaluated features is well known to persons of ordinary skill in the art, and therefore one of ordinary skill would have good reason to pursue the known options within his or her technical grasp that would lead to anticipated success.
Regarding claim 13, Hoang teaches a method for accessing a database having a plurality of indexes, the method, performed by one or more processors in a server infrastructure (see Fig. 1, para [0011], discloses processor), comprising: receiving a query for accessing data stored in the database (see para [0017], discloses receiving a query to compute features in database tables), the plurality of indexes including at least one learned index that is created or maintained using machine learning and at least one non-learned index that is formed and maintained without using machine learning (see Fig. 4, Fig. 6A, para [0058], para [0068-0069], discloses an indexer component associated with machine learning model component for selecting and building indices (machine learning index) and an indexer selecting and building indices such as a binary search tree data index (non-learned index)); responsive to the query, dynamically evaluating one or more data features of said data in view of at least one characteristic obtained from the query (see Fig. 4, para [0018], para [0057, 0059], discloses automated feature model that includes a set of queries to execute scoring operation, maximizing a scoring (obtained characteristic) query throughput based on constraints in a relational database); selecting an index from the plurality of indexes (see Fig. 4, para [0058-0059], discloses selecting and building indices using indexer component associated with machine learning component); accessing the database using the selected index (see Fig. 4, Fig. 6A, para [0058], para [0068], discloses accessing database using selected indices); and outputting results for the query, based on portions of the data obtained by the accessing (see Fig. 6A, Fig. 6C, para [0069], para [0071], discloses results from joining path of joined table in which filtered (portion) record table is joined with main table).
Hoang does not explicitly teach inputting the one or more dynamically evaluated data features to a machine-learned index selection model and obtaining, as output of the machine- learned index selection model, weights of the one or more dynamically evaluated features; wherein the selecting is based on the weights of the one or more dynamically evaluated features and a set of rules including one or more threshold values.
Kusnoto teaches inputting the one or more dynamically evaluated data features to a machine-learned index selection model and obtaining, as output of the machine-learned index selection model, weights of the one or more dynamically evaluated features (see Fig. 1, para [0002-0003], para [0012, 0020], discloses Best Value Index, BVI (machine learned index selection model) dynamically weighting parameters (evaluated features)); wherein the selecting is based on the weights of the one or more dynamically evaluated features and a set of rules including one or more threshold values (see Fig. 2, para [0012, 0016], para [0020], discloses predefined order and class ranking (set of rules including threshold values) based on user profile and preferences in order to dynamically arrange sequence of parameters).
Hoang/Kusnoto are analogous arts as they are each from the same field of endeavor of database systems.
Before the effective filing date of the invention it would have been obvious to a person of ordinary skill in the art to modify the system of Hoang to utilize weights of dynamically evaluated features from disclosure of Kusnoto. The motivation to combine these arts is disclosed by Kusnoto as “create better products solving cognitive problems commonly associated with human intelligence to grow its business, improve its customer experience and selection” (para [0006]) and utilizing weights of dynamically evaluated features is well known to persons of ordinary skill in the art, and therefore one of ordinary skill would have good reason to pursue the known options within his or her technical grasp that would lead to anticipated success.
Regarding claim 17, Hoang teaches a non-transitory computer-readable storage medium having stored therein a program comprising instructions, that when executed by one or more processors of a server infrastructure (see Fig. 1, para [0011], discloses processor), causes the server infrastructure to perform operations comprising: receiving a query for accessing data stored in the database (see para [0017], discloses receiving a query to compute features in database tables), the plurality of indexes including at least one learned index that is created or maintained using machine learning and at least one non-learned index that is formed and maintained without using machine learning (see Fig. 4, Fig. 6A, para [0058], para [0068-0069], discloses an indexer component associated with machine learning model component for selecting and building indices (machine learning index) and an indexer selecting and building indices such as a binary search tree data index (non-learned index)); responsive to the query, dynamically evaluating one or more data features of said data in view of at least one characteristic obtained from the query (see Fig. 4, para [0018], para [0057, 0059], discloses automated feature model that includes a set of queries to execute scoring operation, maximizing a scoring (obtained characteristic) query throughput based on constraints in a relational database); selecting an index from the plurality of indexes (see Fig. 4, para [0058-0059], discloses selecting and building indices using indexer component associated with machine learning component); accessing the database using the selected index (see Fig. 4, Fig. 6A, para [0058], para [0068], discloses accessing database using selected indices); and outputting results for the query, based on portions of the data obtained by the accessing (see Fig. 6A, Fig. 6C, para [0069], para [0071], discloses results from joining path of joined table in which filtered (portion) record table is joined with main table).
Hoang does not explicitly teach inputting the one or more dynamically evaluated data features to a machine-learned index selection model and obtaining, as output of the machine- learned index selection model, weights of the one or more dynamically evaluated features; wherein the selecting is based on the weights of the one or more dynamically evaluated features and a set of rules including one or more threshold values.
Kusnoto teaches inputting the one or more dynamically evaluated data features to a machine-learned index selection model and obtaining, as output of the machine-learned index selection model, weights of the one or more dynamically evaluated features (see Fig. 1, para [0002-0003], para [0012, 0020], discloses Best Value Index, BVI (machine learned index selection model) dynamically weighting parameters (evaluated features)); wherein the selecting is based on the weights of the one or more dynamically evaluated features and a set of rules including one or more threshold values (see Fig. 2, para [0012, 0016], para [0020], discloses predefined order and class ranking (set of rules including threshold values) based on user profile and preferences in order to dynamically arrange sequence of parameters).
Hoang/Kusnoto are analogous arts as they are each from the same field of endeavor of database systems.
Before the effective filing date of the invention it would have been obvious to a person of ordinary skill in the art to modify the system of Hoang to utilize weights of dynamically evaluated features from disclosure of Kusnoto. The motivation to combine these arts is disclosed by Kusnoto as “create better products solving cognitive problems commonly associated with human intelligence to grow its business, improve its customer experience and selection” (para [0006]) and utilizing weights of dynamically evaluated features is well known to persons of ordinary skill in the art, and therefore one of ordinary skill would have good reason to pursue the known options within his or her technical grasp that would lead to anticipated success.
Regarding claim 2, Hoang/Kusnoto teach a system of claim 1.
Hoang further teaches wherein the at least one non-learned index and the at least one learned index are configured to access a same data stored in the database (see Figs. 6A-6C, para [0068], para [0070], discloses trained automated feature engineering model and binary tree accessing same database).
Regarding claim 10, Hoang/Kusnoto teach a system of claim 1.
Hoang further teaches wherein the one or more data features include at least one preconfigured data feature and at least one derived data feature that is derived automatically by a learning system based at least on the data in the database. (see Figs. 4-5, para [0063], para [0066], discloses original features (preconfigured data feature) of tables in database and generated features using trained automated feature engineering model).
Regarding claim 11, Hoang/Kusnoto teach a system of claim 1.
Hoang further teaches wherein the learning system includes a neural network that is trained on a plurality of queries including data store queries and data load queries (see Figs. 4-5, para [0058, 0060], para [0063], discloses machine learning model components building indices using queries and trained operations).
Claims 3-5, 12, 15, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Hoang et al. (US 2021/0240727) (hereinafter Hoang) in view of Kusnoto as applied to claims 1, 13, and 17, and in further view of Hombaiah et al. (US 2022/0004918) (hereinafter Hombaiah).
Regarding claims 3, 15, and 19, Hoang/Kusnoto teach a system of claim 1, method of claim 13, and medium of claim 17.
Hoang/Kusnoto do not explicitly teach wherein the one or more threshold values include at least one threshold value that is dynamically determined based at least on the data.
Hombaiah teaches wherein the one or more threshold values include at least one threshold value that is dynamically determined based at least on the data (see para [0013], discloses thresholds being dynamically determined based on properties (data) of the query).
Hoang/Kusnoto/Hombaiah are analogous arts as they are each from the same field of endeavor of database systems.
Before the effective filing date of the invention it would have been obvious to a person of ordinary skill in the art to modify the system of Hoang/Kusnoto to include threshold values from disclosure of Hombaiah. The motivation to combine these arts is disclosed by Hombaiah as “reducing the corpus of content items that are searched” (para [0010]) and including threshold values is well known to persons of ordinary skill in the art, and therefore one of ordinary skill would have good reason to pursue the known options within his or her technical grasp that would lead to anticipated success.
Regarding claim 4, Hoang/Kusnoto teach a system of claim 1.
Hoang/Kusnoto do not explicitly teach wherein the one or more threshold values include at least one threshold value that is dynamically reconfigured based at least on the query.
Hombaiah teaches wherein the one or more threshold values include at least one threshold value that is dynamically reconfigured based at least on the received query (see para [0013], discloses threshold dynamically determined based on classification of the query).
Hoang/Kusnoto/Hombaiah are analogous arts as they are each from the same field of endeavor of database systems.
Before the effective filing date of the invention it would have been obvious to a person of ordinary skill in the art to modify the system of Hoang/Kusnoto to include threshold values from disclosure of Hombaiah. The motivation to combine these arts is disclosed by Hombaiah as “reducing the corpus of content items that are searched” (para [0010]) and including threshold values is well known to persons of ordinary skill in the art, and therefore one of ordinary skill would have good reason to pursue the known options within his or her technical grasp that would lead to anticipated success.
Regarding claim 5, Hoang/Kusnoto teach a system of claim 1.
Hoang/Kusnoto do not explicitly teach wherein the at least one processor is further configured to perform operations comprising training the index selection model based at least on the received query and/or results from said accessing the database.
Hombaiah teaches wherein the at least one processor is further configured to perform operations comprising training the index selection model based at least on the received query and/or results from said accessing the database (see Figs. 1-2, Fig. 5, para [0073], para [0099], discloses training model responsive to a query and identifying a query trending).
Hoang/Kusnoto/Hombaiah are analogous arts as they are each from the same field of endeavor of database systems.
Before the effective filing date of the invention it would have been obvious to a person of ordinary skill in the art to modify the system of Hoang/Kusnoto to include threshold values from disclosure of Hombaiah. The motivation to combine these arts is disclosed by Hombaiah as “reducing the corpus of content items that are searched” (para [0010]) and including threshold values is well known to persons of ordinary skill in the art, and therefore one of ordinary skill would have good reason to pursue the known options within his or her technical grasp that would lead to anticipated success.
Regarding claim 12, Hoang/Kusnoto teach a system of claim 1.
Hoang/Kusnoto do not explicitly teach wherein said selecting comprises comparing one or more estimated values to the one or more threshold values, wherein the one or more estimated value are determined based at least on a characteristic of the query or a characteristic of the data.
Hombaiah teaches wherein said selecting the index comprises comparing one or more estimated values to one or more threshold values (see para [0013], discloses comparing predicted user measures to threshold), wherein the one or more estimated value are determined based at least on a characteristic of the query or a characteristic of the data (see Fig. 3, para [0055], discloses generating predicted user measures using labeled quality and popularity measures (characteristics of data)).
Hoang/Kusnoto/Hombaiah are analogous arts as they are each from the same field of endeavor of database systems.
Before the effective filing date of the invention it would have been obvious to a person of ordinary skill in the art to modify the system of Hoang/Kusnoto to include threshold values from disclosure of Hombaiah. The motivation to combine these arts is disclosed by Hombaiah as “reducing the corpus of content items that are searched” (para [0010]) and including threshold values is well known to persons of ordinary skill in the art, and therefore one of ordinary skill would have good reason to pursue the known options within his or her technical grasp that would lead to anticipated success.
Claims 6-7, 16, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Hoang et al. (US 2021/0240727) (hereinafter Hoang) in view of Kusnoto as applied to claims 1, 13, and 17, and in further view of Wang et al. (US 2022/0207059) (hereinafter Wang).
Regarding claims 6, 16, and 20, Hoang/Kusnoto teach a system of claim 1, method of claim 13, and medium of claim 17.
Hoang/Kusnoto do not explicitly teach wherein the at least one processor is further configured to perform operations comprising, upon the at least one learned index being the selected index, predicting, using the selected index, a location in the database of a key for data to be accessed by the received query.
Wang teaches wherein the at least one processor is further configured to perform operations comprising, upon the at least one learned index being the selected index, predicting, using the selected index, a location in the database of a key for data to be accessed by the received query (see Fig. 1, Fig 5, para [0022], para [0044], discloses reservoir simulation model predicting location based on attributes of a simulation run, allowing access to different perspectives of the simulation runs).
Hoang/Kusnoto/Wang are analogous arts as they are each from the same field of endeavor of database systems.
Before the effective filing date of the invention it would have been obvious to a person of ordinary skill in the art to modify the system of Hoang/Kusnoto to include predicting location of a key in a database from disclosure of Wang. The motivation to combine these arts is disclosed by Wang as “This facilitates efficiently building a reservoir model with reuse of different dimensions of simulation runs by attributes” (para [0022]) and including predicting location of a key in a database is well known to persons of ordinary skill in the art, and therefore one of ordinary skill would have good reason to pursue the known options within his or her technical grasp that would lead to anticipated success.
Regarding claim 7, Hoang/Kusnoto teach a system of claim 1.
Hoang/Kusnoto do not explicitly teach wherein the at least one processor is further configured to perform operations comprising training the selected learned index based at least on the received query and/or results from said accessing the database.
Wang teaches wherein the at least one processor is further configured to perform operations comprising training the selected learned index based at least on the received query and/or results from said accessing the database (see Fig. 1, Fig. 5, para [0023], para [0044], discloses training reservoir simulation run is training data for predictive model training based on attributes indicated in reservoir simulation run request).
Hoang/Kusnoto/Wang are analogous arts as they are each from the same field of endeavor of database systems.
Before the effective filing date of the invention it would have been obvious to a person of ordinary skill in the art to modify the system of Hoang/Kusnoto to include predicting location of a key in a database from disclosure of Wang. The motivation to combine these arts is disclosed by Wang as “This facilitates efficiently building a reservoir model with reuse of different dimensions of simulation runs by attributes” (para [0022]) and including predicting location of a key in a database is well known to persons of ordinary skill in the art, and therefore one of ordinary skill would have good reason to pursue the known options within his or her technical grasp that would lead to anticipated success.
Claims 8-9, 14, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Hoang et al. (US 2021/0240727) (hereinafter Hoang) in view of Kusnoto as applied to claims 1, 13, and 17, and in further view of Chaudhuri et al. (US 2019/0378028) (hereinafter Chaudhuri).
Regarding claim 8, Hoang/Kusnoto teach a system of claim 1.
Hoang/Kusnoto do not explicitly teach wherein the one or more data features include at least one data feature which characterizes data in the database in terms of data distribution density levels of one or more selected attributes of the data, and wherein the selecting an index is based at least on an estimated value of the data distribution density levels, wherein the estimated value is determined based at least on a characteristic of the query or a characteristic of the data.
Chaudhuri teaches wherein the one or more data features include at least one data feature which characterizes data in the database in terms of data distribution density levels of one or more selected attributes of the data (see Fig. 9, para [0102-0103], discloses labels that characterize blobs density at a location in which a set of labeled blobs belong to), and wherein the selecting an index is based at least on an estimated value of the data distribution density levels, wherein the estimated value is determined based at least on a characteristic of the query or a characteristic of the data (see Fig. 5, para [0088-0089], discloses labeled input for relevant clauses based on query plan).
Hoang/Kusnoto/Chaudhuri are analogous arts as they are each from the same field of endeavor of database systems.
Before the effective filing date of the invention it would have been obvious to a person of ordinary skill in the art to modify the system of Hoang/Kusnoto to include data distribution density levels of attributes from disclosure of Chaudhuri. The motivation to combine these arts is disclosed by Chaudhuri as “to execute such machine learning inference queries efficiently” (para [0032]) and including data distribution density levels of attributes is well known to persons of ordinary skill in the art, and therefore one of ordinary skill would have good reason to pursue the known options within his or her technical grasp that would lead to anticipated success.
Regarding claims 9, 14, and 18, Hoang/Kusnoto teach a system of claim 1 and method of claim 13, and a medium of claim 17.
Hoang/Kusnoto do not explicitly teach wherein the at least one learned index automatically, based at least on machine learning, groups data into a respective plurality of chunks for each of a plurality of attributes of the data, and indexes the chunks, and wherein the one or more data features include at least one of: an average data per chunk for one of more of said attributes, standard deviation of data in chunks for one or more of said attributes, a variance of data in chunks for one or more of said attributes, a retrieval time of said results for one or more of said attributes, a retrieval rate of said results for one or more of said attributes, a total amount of data scanned for one or more of said attributes, and a sparsity and/or density factor for one or more of said attributes.
Chaudhuri teaches wherein the at least one learned index automatically, based at least on machine learning, groups data into a respective plurality of chunks for each of a plurality of attributes of the data, and indexes the chunks (see Fig. 8, para [0091-0094], discloses training dataset that includes respective labeled set of blobs (chunks for each attributes) and linear support vector machine classifier), and wherein the one or more data features include at least one of: an average data per chunk for one of more of said attributes, standard deviation of data in chunks for one or more of said attributes, a variance of data in chunks for one or more of said attributes, a retrieval time of said results for one or more of said attributes, a retrieval rate of said results for one or more of said attributes, a total amount of data scanned for one or more of said attributes, and a sparsity and/or density factor for one or more of said attributes. (see Fig. 9, para [0102-0103], discloses density location indicating likelihood of blob belonging to a set).
Hoang/Kusnoto/Chaudhuri are analogous arts as they are each from the same field of endeavor of database systems.
Before the effective filing date of the invention it would have been obvious to a person of ordinary skill in the art to modify the system of Hoang/Kusnoto to include data distribution density levels of attributes from disclosure of Chaudhuri. The motivation to combine these arts is disclosed by Chaudhuri as “to execute such machine learning inference queries efficiently” (para [0032]) and including data distribution density levels of attributes is well known to persons of ordinary skill in the art, and therefore one of ordinary skill would have good reason to pursue the known options within his or her technical grasp that would lead to anticipated success.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/Courtney Harmon/Primary Examiner, Art Unit 2159