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
A request for continued examination under 37 CFR 1.114, including the fee set
forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this
application is eligible for continued examination under 37 CFR 1.114, and the fee set
forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action
has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 06/03/26
has been entered.
Response to Amendment
This action is in response to applicant's arguments and amendments filed on
06/03/26. which are in response to USPTO Office Action mailed on 03/06/26.
Applicant's arguments and amendments have been considered with the results that
follow: THIS ACTION IS MADE NON-FINAL.
Claim Rejections - 35 U.S.C. §103
2. 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.
3. 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.
4. 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 Mamou et al. (US 2005/0262188 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), and 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, Mamou teaches as a function of the determined structure of the operational database, (See Mamou paragraph [0332], customer marketing databases, and inventory synchronization functions. In manufacturing and logistics operations) deploying one or more transformation jobs, in accordance with the at least one determined metadata-based modeling technique, (See Mamou, paragraph [0296], a high-level architecture is represented for a data integration platform 2700, which may be deployed…The data integration…transformation, cleansing, discovery, metadata, parallel execution, and similar facilities that are required to perform data integration jobs.)), for executing the at least one determined metadata-based modeling technique to transform the data for storage in the lakehouse, (See Mamou, paragraph [0405], intermediate representation of connectivity in a transformation process enables deployment of any automation strategies, and selection of different combinations of execution engines, as well as optimization based on, for example, metadata or profiling).
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 Mamou in order to identifying the code module in the registry of services; and facilitating access to the code module in real time from the registry of services via a services oriented architecture.
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, Mamou, teaches using a central transition table maintaining data state information, (See Mamou paragraph [0206], the data integration system may also include a data transformation stage 308 to transform, enrich and deliver transformed data. The data transformation stage 308 may perform transitional services such as reorganization and reformatting of data), and metadata pertaining to transitions of the data between states, states representing operational phases of the device, (See Mamou paragraph [0203], The data may have been stored in the database 112 in a transformed condition or in its original state. For example, the data may be stored in a transformed condition such that the data from a number of data sources 102 can be combined in another transformation process).
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 Mamou in order to identifying the code module in the registry of services; and facilitating access to the code module in real time from the registry of services via a services oriented architecture.
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, Mamou teaches deploy one or more transformation jobs,
in accordance with the at least one determined metadata-based modeling technique, (See Mamou, paragraph [0296], a high-level architecture is represented for a data integration platform 2700, which may be deployed…The data integration…transformation, cleansing, discovery, metadata, parallel execution, and similar facilities that are required to perform data integration jobs.)), to be executed on the data during movement of the data from the operational database to the analytical database, (See Mamou, paragraph [0405], intermediate representation of connectivity in a transformation process enables deployment of any automation strategies, and selection of different combinations of execution engines, as well as optimization based on, for example, metadata or profiling).
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 Mamou in order to identifying the code module in the registry of services; and facilitating access to the code module in real time from the registry of services via a services oriented architecture.
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 access 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, Mamou, teaches ORUN does not explicitly disclose access object storage in which the data is stored after transformation of the data, (See Mamou, paragraph [0276], a data integration system 104 may be used to collect, cleanse, transform or otherwise manipulate the data from the several data sources 1902A, 1902B and 1902C and to store the data in a common data warehouse or database 1908, which may be any of the databases…such that it can be accessed from various tools, targets, or other computing systems), the transformation of the data having been performed in accordance with transformation jobs, (See Mamou, paragraph [0034], The data integration job may include an extraction job. The data integration job may include a data transformation job).
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 ORUN does not explicitly disclose access 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 Mamou in order to identifying the code module in the registry of services; and facilitating access to the code module in real time from the registry of services via a services oriented architecture.
5. Claims 5-6, 9-13, 16-17 and 19-20 are rejected under 35 U.S.C. 103 as being
unpatentable over ORUN (US 2022/0237202 A1) in view of Mamou, et
al. (US 2005/0262188 A1) and further in view of SASSIN (US 2019/0318272 A1).
Regarding claim 5, ORUN together with Mamou, taught the method of Claim 2, as described above.
ORUN together with Mamou 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 6, ORUN taught the method of Claim 5, as described above.
ORUN 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 9, ORUN together with Mamou taught the method of Claim 2, as described above.
ORUN together with Mamou 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 Mamou taught the method of Claim 2, as described above.
ORUN together with Mamou 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 tables of SASSIN for extracting data from the source schema and loading the extracted data into the target schema.
Regarding claim 11, ORUN together with Mamou taught the method of Claim 2, as described above.
ORUN together with Mamou 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 together with Mamou taught the method of Claim 2, as described above.
ORUN together with Mamou 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 together with Mamou taught the method of Claim 12, as described above.
ORUN together with Mamou 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 together with Mamou 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 Mamou 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 17, ORUN taught the system of Claim 16, as described above.
ORUN 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 19, ORUN together with Mamou 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 Mamou 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 20, ORUN taught the analytical database of Claim 16, as described above.
ORUN 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.
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
The prior art made of record and not relied upon is considered pertinent
to applicant’s disclosure. See form PTO-892.
Edwards et al. (US 2022/0269978 A1), a data catalog configured to store logic metadata, a derived data library, and memory storing instructions that, when executed by the one or more processors, are configured to cause the system to receive, by the data gathering module, input data from a data source, transform, according to the transformation logic and by the compute engine, the input data to produce a derived data output.
Greene et al. (US 2016/0062767 A1) The analytical rule includes one or more operations and invoking the analytical rule performs the operations to analyze one or more job components associated with the corresponding feature as represented in the job model and to extract information pertaining to that feature.
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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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Tony Mahmoudi can be reached at 5712724078. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MULUEMEBET GURMU/Primary Examiner, Art Unit 2163
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