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
Response to Amendment
This Office Action is in response to the amendment filed on 01/23/26
The applicant’s remarks and amendments to the claims were considered and results as
follow: THIS ACTION IS MADE FINAL.
Claims 1, 14 and 18 has been amended. No claims have been cancelled. No
claims have been added. As a result, claims 1-20 are now pending in this office action.
Claim Rejections - 35 U.S.C. §103
4. 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 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.
5. 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.
6. Claims 1-3, 7-8, 14-15 and 18 are rejected under 35 U.S.C. 103 as being
unpatentable over ORUN (US 2022/0237202 A1) in view of Ramanasankaran et
al. (US 2023/0205158 A1 A1).
Regarding claim 1, ORUN teaches a method, comprising:
analyzing, by a data transformation system operatively connected to an operational database and a lakehouse, (See ORUN paragraph [0111], a data lake may be a single store of all enterprise data including source system data and transformed data used for tasks such as reporting, visualization, analytics and machine learning), a schema of the operational database to identify a structure of the operational database, (See ORUN paragraph [0101], a schema, where the columns of the relational database table are different ones of the fields from the plurality of records…the fields of a record are defined by the structure of the database);
in response to a query to access data maintained in the operational database, (See ORUN paragraph [0078], in response the system 340 (e.g., one or more servers in system 340) automatically may generate one or more Structured Query Language (SQL) statements (e.g., one or more SQL queries) that are designed to access the desired information from the multi-tenant database(s) 346 and/or system data), selecting, by the transformation system, an analytical modeling approach, (See ORUN paragraph [0014], selected options based on predictive analytics, such as automating decision processes); integrated analytics (e.g., allowing developed analytical models to be integrated within information), comprising at least one metadata-based modeling technique to be applied to the data, (See ORUN, paragraph [0025], a metadata repository 1B21-1 including logical data model 1B50-1 and the app/service 1A30-N includes a metadata repository 1B21-N including logical data model 1B50-N).
ORUN does not explicitly disclose as a function of the determined structure of the operational database, deploying one or more transformation jobs in accordance with the at least one determined metadata-based modeling technique for executing the at least one determined metadata-based modeling technique to transform the data for storage in the lakehouse.
However, Ramanasankaran, teaches as a function of the determined structure of the operational database, (See Ramanasankaran paragraph [0078], The query manager 165 can query the graph projection database 162 with the query parameters to retrieve a result…the query manager 165 can select the twin function based on the context and/or perform operates based on the context. In some embodiments, the query manager 165 is configured to perform the operations) deploying one or more transformation jobs, in accordance with the at least one determined metadata-based modeling technique, (See Ramanasankaran, paragraph [0004], the analytics model is trained based on the building data, and deploy the analytics model to operate based on data of the one or more buildings and generate one or more analytic results based on the data of the one or more buildings)), for executing the at least one determined metadata-based modeling technique to transform the data for storage in the lakehouse, (See Ramanasankaran paragraph [0122], ETL’ed (extracted, transformed, loaded) for model training, etc. The hub 610 can further receive data feeds and external references 620 for performing training and/or execution on, e.g., external weather forecast feeds, Dataminr threat incidents, etc. The hub 610 can support data streaming and/or historian data loading, in some embodiments. The data of the hub 610 can, in some embodiments, be stored in a lakehouse 622 or other data storage component).
It would have been obvious to one with ordinary skill in the art before the
effective filing date of the claimed invention was made, to modify as a function of the determined structure of the operational database, deploying one or more transformation jobs in accordance with the at least one determined metadata-based modeling technique for executing the at least one determined metadata-based modeling technique to transform the data for storage in the lakehouse of Ramanasankaran in order to control the building based on the collected data and/or generate analytics based on the collected data.
Regarding claim 2, ORUN taught the method of Claim 1, as described above. ORUN further teaches wherein the at least one metadata-based modeling technique, (See ORUN paragraph [0025], a metadata repository 1B21-1 including logical data model), comprises one of state machine modeling, aggregate modeling, adaptive modeling, path denormalization, edge denormalization, tree denormalization, and log denormalization of the data at the operational database, (See ORUN paragraph [0012], Data refinement involves organizing data into shareable data stores such as data lakes, data warehouses, and master data/reference data hubs (e.g., repositories 1A21 in FIG. 1A). Data cleansing, integration, aggregation, and other types of data transformations may also be performed).
Regarding claim 3, ORUN taught the method of Claim 2, as described above. ORUN further teaches wherein the state machine modeling comprises representing the data, when the data characterizes a device’s lifecycle, (See ORUN paragraph [0099], Customer relationship management (CRM) is a term that refers to practices, strategies, and/or technologies that companies (e.g., vendors) use to manage and analyze customer interactions and data throughout the customer lifecycle).
ORUN does not explicitly disclose using a central transition table maintaining data state information and metadata pertaining to transitions of the data between states, states representing operational phases of the device.
However, Ramanasankaran, teaches using a central transition table maintaining data state information and metadata pertaining to transitions of the data between states, states representing operational phases of the device, (See Ramanasankaran paragraph [0122], The hub 610 can perform data reconciliation, pre-validation, tagging, labeling, enrichment, ETL’ed (extracted, transformed, loaded) for model training, etc. The hub 610 can further receive data feeds and external references 620 for performing training and/or execution on).
It would have been obvious to one with ordinary skill in the art before the
effective filing date of the claimed invention was made, to modify using a central transition table maintaining data state information and metadata pertaining to transitions of the data between states, states representing operational phases of the device of Ramanasankaran in order to control the building based on the collected data and/or generate analytics based on the collected data.
Regarding claim 7, ORUN taught the method of Claim 2, as described above. ORUN further teaches wherein the adaptive modeling comprises monitoring query patterns and statistics of queries to the lakehouse that involve at least one of joins or aggregations, (See ORUN paragraph [0095], analyzing a data set, including searching for patterns or specific items in a data set…finding patterns or specific items rapid and intuitive. Data discovery may leverage statistical and data mining techniques to accomplish these goals).
Regarding claim 8, ORUN taught the method of Claim 7, as described above. ORUN further teaches wherein the adaptive modeling further comprises generating metadata for at least one of a join recipe or an aggregation recipe based on the monitored query patterns and statistics, (See ORUN paragraph [0095, organizing data into shareable data stores such as data lakes, data warehouses, and master data/reference data hubs (e.g., repositories 1A21 in FIG. 1A). Data cleansing, integration, aggregation..manage the simultaneous ingestion, processing, and analysis applied to both static and streaming data).
Regarding claim 14, ORUN teaches a system, comprising:
a processor, (See ORUN paragraph [0056], one or more processors); and
a memory comprising instructions that when executed cause the processor to, (a computer- or processor-executable instructions or commands on a physical non-transitory computer-readable medium…. read only memory (ROM)): analyze a schema of the operational database, (The metadata imports include database schemas), wherein the system is operative between an analytical database and the operational database, (metadata repositories 1A21 that provide listings of data elements/objects that are of interest to an enterprise (e.g., analytics, customer data platform (CDP), compliance operations, etc.), the apps/services 1A30 and/or databases that use the data elements/objects);
in response to a query to access data maintained in the operational database and based on the determined structure of the operational database, (See ORUN paragraph [0078], in response the system 340 (e.g., one or more servers in system 340) automatically may generate one or more Structured Query Language (SQL) statements (e.g., one or more SQL queries) that are designed to access the desired information from the multi-tenant database(s) 346 and/or system data),
determine at least one metadata-based modeling technique to be applied to the data, (See ORUN, paragraph [0025], a metadata repository 1B21-1 including logical data model 1B50-1 and the app/service 1A30-N includes a metadata repository 1B21-N including logical data model 1B50-N); and
ORUN does not explicitly disclose deploy one or more transformation jobs in accordance with the at least one determined metadata-based modeling technique to be executed on the data during movement of the data from the operational database to the analytical database.
However, Ramanasankaran, teaches deploy one or more transformation jobs,
in accordance with the at least one determined metadata-based modeling technique, (See Ramanasankaran, paragraph [0004], the analytics model is trained based on the building data, and deploy the analytics model to operate based on data of the one or more buildings and generate one or more analytic results based on the data of the one or more buildings))), to be executed on the data during movement of the data from the operational database to the analytical database, (See Ramanasankaran paragraph [0004], the analytics model is trained based on the building data, and deploy the analytics model to operate based on data of the one or more buildings and generate one or more analytic results based on the data of the one or more buildings)).
It would have been obvious to one with ordinary skill in the art before the
effective filing date of the claimed invention was made, to modify deploy one or more transformation jobs in accordance with the at least one determined metadata-based modeling technique to be executed on the data during movement of the data from the operational database to the analytical database of Ramanasankaran in order to control the building based on the collected data and/or generate analytics based on the collected data.
Regarding claim 15, ORUN taught the system of Claim 14, as described above. ORUN further teaches wherein the analytical database comprises a data lakehouse, (See ORUN paragraph [0111], a data lake may be a single store of all enterprise data including source system data and transformed data used for tasks such as reporting, visualization, analytics and machine learning).
Regarding claim 18, ORUN teaches an analytical database, comprising:
a processor, (See ORUN paragraph [0056], one or more processors);
a memory comprising instructions that when executed cause the processor to, , (a computer- or processor-executable instructions or commands on a physical non-transitory computer-readable medium…. read only memory (ROM)):
receive a query to access data maintained in an operational database communicatively connected to the analytical database, (See ORUN paragraph [0078], The user devices 380A-380S communicate with the server(s) of system 340 to request…to access the desired information from the multi-tenant database(s) 346 and/or system data storage 350); comprising application of a metadata-based modeling technique to the data, the metadata-based modeling technique, (See ORUN, paragraph [0025], a metadata repository 1B21-1 including logical data model 1B50-1 and the app/service 1A30-N includes a metadata repository 1B21-N including logical data model 1B50-N), having been selected in accordance with a schema of the operational database and based on the received query to access the data, (See ORUN, paragraph [0080], The query servers may be used to retrieve information from one or more file servers. For example, the query system may receive requests for information from the application servers and then transmit queries to the NFS located outside the pod. The ACS servers may control access to data, hardware resources, or software resources).
ORUN does not explicitly disclose object storage in which the data is stored after transformation of the data, the transformation of the data having been performed in accordance with transformation jobs
However, Ramanasankaran, teaches object storage in which the data is stored after transformation of the data, the transformation of the data having been performed in accordance with transformation jobs, (See Ramanasankaran paragraph [0122], ETL’ed (extracted, transformed, loaded) for model training, etc. The hub 610 can further receive data feeds and external references 620 for performing training and/or execution on, e.g., external weather forecast feeds, Dataminr threat incidents, etc. The hub 610 can support data streaming and/or historian data loading, in some embodiments. The data of the hub 610 can, in some embodiments, be stored in a lakehouse 622 or other data storage component).
It would have been obvious to one with ordinary skill in the art before the
effective filing date of the claimed invention was made, to modify object storage in which the data is stored after transformation of the data, the transformation of the data having been performed in accordance with transformation jobs of Ramanasankaran in order to control the building based on the collected data and/or generate analytics based on the collected data.
7.. Claims 5, 9-13, 16 and 19 are rejected under 35 U.S.C. 103 as being
unpatentable over ORUN (US 2022/0237202 A1) in view of Ramanasankaran et
al. (US 2023/0205158 A1 A1) and further in view of SASSIN (US 2019/0318272 A1).
Regarding claim 5, ORUN together with Ramanasankaran taught the method of Claim 2, as described above.
ORUN together with Ramanasankaran does not explicitly disclose wherein the aggregate modeling comprises storing the data in accordance with an aggregation schema comprising at least one or more source tables or columns, one or more rollup operations or formulae, and one or more destination tables or columns with a desired aggregation window.
However, SASSIN, teaches wherein the aggregate modeling comprises storing the data in accordance with an aggregation schema, (See SASSIN paragraph [0112]], An aggregate table is typically derived within the target schema from a table), comprising at least one or more source tables or columns, one or more rollup operations or formulae, and one or more destination tables or columns with a desired aggregation window, (See SASSIN paragraph [0116]-[0117], source tables with a type-subtype relationship are mapped to one target table, Aggregate table pattern: An aggregate table is typically derived within the target schema from a table with finer-grain data using aggregation functions).
It would have been obvious to one with ordinary skill in the art before the
effective filing date of the claimed invention was made, to modify comprises storing the data in accordance with an aggregation schema, comprising at least one or more source tables or columns, one or more rollup operations or formulae, and one or more destination tables or columns with a desired aggregation window of SASSIN for extracting data from the source schema and loading the extracted data into the target schema.
Regarding claim 9, ORUN together with Ramanasankaran taught the method of Claim 2, as described above.
ORUN together with Ramanasankaran does not explicitly disclose wherein the path denormalization comprises creating a denormalized table for every path of the data.
However, SASSIN, teaches wherein the path denormalization comprises creating a denormalized table for every path of the data, (See SASSIN paragraph [0112], multiple tables representing a dimension hierarchy are joined to produce a denormalized dimension table).
It would have been obvious to one with ordinary skill in the art before the
effective filing date of the claimed invention was made, to modify wherein the path denormalization comprises creating a denormalized table for every path of the data of SASSIN for extracting data from the source schema and loading the extracted data into the target schema.
Regarding claim 10, ORUN together with Ramanasankaran taught the method of Claim 2, as described above.
ORUN together with Ramanasankaran does not explicitly disclose wherein the edge denormalization comprises creating a denormalized table based on joins of linked tables.
. However, SASSIN, teaches wherein the edge denormalization comprises creating a denormalized table based on joins of linked tables, (See SASSIN paragraph [0112], multiple tables representing a dimension hierarchy are joined to produce a denormalized dimension table).
It would have been obvious to one with ordinary skill in the art before the
effective filing date of the claimed invention was made, to modify wherein the edge denormalization comprises creating a denormalized table based on joins of linked of SASSIN for extracting data from the source schema and loading the extracted data into the target schema.
Regarding claim 11, ORUN together with Ramanasankaran taught the method of Claim 2, as described above.
ORUN together with Ramanasankaran does not explicitly disclose wherein the tree denormalization comprises creating a denormalized table representative of all tables of the schema.
However, SASSIN, teaches wherein the tree denormalization comprises creating a denormalized table representative of all tables of the schema, (See SASSIN paragraph [0037], multiple related tables while a star schema has dimensions that are denormalized with each dimension being represented by a single table).
It would have been obvious to one with ordinary skill in the art before the
effective filing date of the claimed invention was made, to modify wherein the tree denormalization comprises creating a denormalized table representative of all tables of the schema of SASSIN for extracting data from the source schema and loading the extracted data into the target schema.
Regarding claim 12, ORUN taught the method of Claim 2, as described above.
ORUN together with of Ramanasankaran does not explicitly disclose wherein the log denormalization comprises updating multiple related tables of the schema as part of a single transaction, and wherein an extract-transform-load operation moves the data from the operational system to the lakehouse.
However, SASSIN, teaches wherein the log denormalization comprises updating multiple related tables of the schema as part of a single transaction, (See SASSIN paragraph [0037], a snowflake data schema includes dimensions that are normalized into multiple related tables while a star schema has dimensions that are denormalized with each dimension being represented by a single table), and wherein an extract-transform-load operation moves the data from the operational system to the lakehouse, (See SASSIN paragraph [0004], Using the machine learning algorithm and based on the source schema, target schema, and extracted features, one or more ETL rules can be predicted that define logic for extracting data from the source schema and loading the extracted data into the target schema).
It would have been obvious to one with ordinary skill in the art before the
effective filing date of the claimed invention was made, to modify denormalization comprises updating multiple related tables of the schema as part of a single transaction, and wherein an extract-transform-load operation moves the data from the operational system to the lakehouse of SASSIN for extracting data from the source schema and loading the extracted data into the target schema.
Regarding claim 13, ORUN taught the method of Claim 12, as described above.
ORUN together with Ramanasankaran does not explicitly disclose further comprising performing CDC on the data, wherein CDC events contain a reference to a transaction identifier for a table participating in the single transaction.
However, SASSIN, teaches further comprising performing CDC on the data, wherein CDC events, (See SASSIN paragraph [0033], data changed in a source system based on Oracle® Golden Gate with Change Data Capture (“CDC”) mechanisms), contain a reference to a transaction identifier for a table participating in the single transaction, (See SASSIN paragraph [0162], Tables can be referenced via foreign keys within the mapping structure. Filters 604 can be applied to tables 602, for example applied to the source tables).
It would have been obvious to one with ordinary skill in the art before the
effective filing date of the claimed invention was made, to modify further comprising performing CDC on the data, wherein CDC events, contain a reference to a transaction identifier for a table participating in the single transaction to the lakehouse of SASSIN for extracting data from the source schema and loading the extracted data into the target schema.
Regarding claim 16, ORUN taught the system of Claim 14, as described above. ORUN further teaches wherein the at least one metadata-based modeling technique, , (See ORUN, paragraph [0025], a metadata repository 1B21-1 including logical data model 1B50-1 and the app/service 1A30-N includes a metadata repository 1B21-N including logical data model 1B50-N).
ORUN together with Ramanasankaran does not explicitly disclose comprises one of state machine modeling, aggregate modeling, adaptive modeling, path denormalization, edge denormalization, tree denormalization, and log denormalization of the data at the operational database.
However, SASSIN, teaches comprises one of state machine modeling, aggregate modeling, adaptive modeling, path denormalization, edge denormalization, tree denormalization, and log denormalization of the data at the operational database, (See SASSIN paragraph [0135], Multiple solution patterns are established for denormalizing data, mapping type-subtype patterns…Pattern names can be supplied as metadata to machine learning component 110).
It would have been obvious to one with ordinary skill in the art before the
effective filing date of the claimed invention was made, to modify comprises one of state machine modeling, aggregate modeling, adaptive modeling, path denormalization, edge denormalization, tree denormalization, and log denormalization of the data at the operational database of SASSIN for extracting data from the source schema and loading the extracted data into the target schema.
Regarding claim 19, ORUN taught the analytical database of Claim 18, as described above. ORUN further teaches wherein the metadata-based modeling technique, (See ORUN paragraph [0025], a metadata repository 1B21-1 including logical data model).
ORUN together with Ramanasankaran does not explicitly disclose comprises one of state machine modeling, aggregate modeling, adaptive modeling, path denormalization, edge denormalization, tree denormalization, and log denormalization of the data at the operational database.
. However, SASSIN, teaches comprises one of state machine modeling, aggregate modeling, adaptive modeling, path denormalization, edge denormalization, tree denormalization, and log denormalization of the data at the operational database, (See SASSIN paragraph [0135], Multiple solution patterns are established for denormalizing data, mapping type-subtype patterns…Pattern names can be supplied as metadata to machine learning component 110).
It would have been obvious to one with ordinary skill in the art before the
effective filing date of the claimed invention was made, to modify comprises one of state machine modeling, aggregate modeling, adaptive modeling, path denormalization, edge denormalization, tree denormalization, and log denormalization of the data at the operational database of SASSIN for extracting data from the source schema and loading the extracted data into the target schema.
8. Claims 6, 17 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over ORUN (US 2022/0237202 A1) in view of Ramanasankaran et al. (US 2023/0205158 A1 A1) in view of SASSIN (US 2019/0318272 A1). and further in view of Mamou, et al. (US 2005/0240592 A1).
Regarding claim 6, ORUN together with Ramanasankaran and SASSIN taught the method of Claim 5, as described above.
ORUN together with Ramanasankaran and SASSIN does not explicitly disclose wherein a generic job template applies the one or more rollup operations or formulae to the data that is incoming from the operational database.
However, Mamou, teaches wherein a generic job template, (See Mamou paragraph [0037], a template job), applies the one or more rollup operations or formulae to the data that is incoming from the operational database, (See Mamou paragraph [0360], A translation engine may perform translation operations with respect to one or more semantic identifiers, databases 112, databases 112).
It would have been obvious to one with ordinary skill in the art before the
effective filing date of the claimed invention was made, to modify wherein a generic job template, applies the one or more rollup operations or formulae to the data that is incoming from the operational database of Mamou, in order to allow a user to check in and check out a version of a data integration job in order to use the data integration job.
Regarding claim 17, ORUN together with Ramanasankaran and SASSIN taught the system of Claim 16, as described above.
ORUN together with Ramanasankaran and SASSIN does not explicitly disclose wherein a generic job template applies the one or more rollup operations or formulae to the data that is incoming from the operational database.
However, Mamou, teaches wherein the determination of the at least one metadata-based modeling technique depends on at least one of type of data structure used in the schema, size of the data structure used in the schema, (See Mamou paragraph [0420], the database content analysis module 8000 may provide a statistical analysis of numerical data in columns of a database, or report on the frequency of empty records, or report the number and size of tables, and so on. The database content analysis module 8000 may also characterize database structure), dependencies within the data structure used in the schema, and type of analysis use-case associated with the query, (See Mamou paragraph [0225], The class structure may include a main class 1402, two subclasses 1404 for containers and handles that depend from the main class 1402, and two lower-level subclasses 1408 for sides and bases, both of which depend from the container subclass 1404, See Mamou paragraph [0206], a data integration job. In this embodiment, the discovery data stage 302 queries a database…to determine the content and structure of data in the database 402).
It would have been obvious to one with ordinary skill in the art before the
effective filing date of the claimed invention was made, to modify wherein a generic job template, applies the one or more rollup operations or formulae to the data that is incoming from the operational database of Mamou, in order to allow a user to check in and check out a version of a data integration job in order to use the data integration job.
Regarding claim 20, ORUN together with Ramanasankaran and SASSIN taught the analytical database of Claim 16, as described above.
ORUN together with Ramanasankaran and SASSIN does not explicitly disclose wherein the selection of the metadata-based modeling technique depends on at least one of type of data structure used in the schema, size of the data structure used in the schema, dependencies within the data structure used in the schema, and type of analysis use-case associated with the query.
However, Mamou, teaches wherein the selection of the metadata-based modeling technique depends on at least one of type of data structure used in the schema, size of the data structure used in the schema, See Mamou paragraph [0420], the database content analysis module 8000 may provide a statistical analysis of numerical data in columns of a database, or report on the frequency of empty records, or report the number and size of tables, and so on. The database content analysis module 8000 may also characterize database structure), dependencies within the data structure used in the schema, and type of analysis use-case associated with the query, (See Mamou paragraph [0225], The class structure may include a main class 1402, two subclasses 1404 for containers and handles that depend from the main class 1402, and two lower-level subclasses 1408 for sides and bases, both of which depend from the container subclass 1404, See Mamou paragraph [0206], a data integration job. In this embodiment, the discovery data stage 302 queries a database…to determine the content and structure of data in the database 402).
It would have been obvious to one with ordinary skill in the art before the
effective filing date of the claimed invention was made, to modify wherein the selection of the metadata-based modeling technique depends on at least one of type of data structure used in the schema, size of the data structure used in the schema, dependencies within the data structure used in the schema, and type of analysis use-case associated with the query of Mamou, in order to allow a user to check in and check out a version of a data integration job in order to use the data integration job.
Applicant's arguments with respect to claims 1-3, 5-20 have been considered but are moot in view of the new ground(s) of rejection.
Allowable Subject Matter
Claim 4 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusions/Points of Contacts
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MULUEMEBET GURMU whose telephone number is (571)270-7095. The examiner can normally be reached M-F 9am - 5pm.
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/MULUEMEBET GURMU/Primary Examiner, Art Unit 2163