DETAILED ACTION
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 .
Continued Examination Under 37 CFR 1.114
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 6/15/2026 has been entered.
Status of Claims
Claims 1-20 are currently pending in the present application. Claims 1-20 are rejected under 35 USC 103 as unpatentable in view of the references presented in this Office Action.
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, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1, 4, 7-11, 15-17 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Reiner et al. (US Patent No.: 5,742,806; Date of Patent: Apr. 21, 1998) in view of Tremblay et al. (US PGPUB No. 2022/0343250; Pub. Date: Oct. 27, 2022), Andoni et al. (US PGPUB No. 2018/0314938; Pub. Date: Nov. 1, 2018) and Lepeska et al. (US PGPUB No.: 2018/0337972; Pub. Date: Nov. 22, 2018).
Regarding independent claim 1,
Reiner discloses a system for data attribute retrieval in a multi-stage process, the system comprising: one or more memories; See Col. 98, lines 59-64, (Disclosing a system for database query processing via query decomposition. The method of query decomposition exploits the shared-memory parallel architecture of the KSR1 to speed up the execution of large ORACLE queries.) See Col. 107, lines 53-65, (SQL queries may be decomposed into separate queries that may be executed in parallel, i.e. a system for data attribute retrieval in a multi-stage process, the system comprising: one or more memories;)
and one or more processors, communicatively coupled to the one or more memories, that cause the system to:obtain configuration information for the multi-stage process, the configuration information indicating a set of data attributes to be used during the multi-stage process, See Col. 104, lines 24-27 & 43-47, (Probing queries may be generated during the gathering/analysis phase of query composition and may gather semantic information about objects referenced in the query from a data dictionary. Semantic information is used to generate a SQLDA structure describing the output columns of the original input query or of transformations of that query, i.e. obtain configuration information for the multi-stage process, the configuration information indicating a set of data attributes to be used during the multi-stage process (e.g. the system may obtain semantic information about data columns to which a query is directed).) Note Col. 8, lines 40-59 wherein the system architecture comprises a multiprocessor for executing parallel queries, i.e. one or more processors communicatively coupled to the one or more memories.
identify, based on one or more parameters of respective data attributes included in the set of data attributes, multiple data attributes to be obtained prior to a start of the multi-stage process, wherein the one or more parameters include at least one of: a data dependency parameter, or a stage parameter; See Col. 104, lines 17-20, (Probing queries are generated and executed during the gathering/analyzing phase of query decomposition and used to gather information from the query optimizer and data dictionary, which includes semantic information about objects referenced in the query. Semantic information may include data describing the output columns of the original input query or transformations of said query, i.e. identify, based on one or more parameters of respective data attributes included in the set of data attributes, multiple data attributes to be obtained prior to a start of the multi-stage process (e.g. probing queries obtain semantic data prior to query execution).) See Col. 103, lines 30-38, (Semantic information about tables, columns, clauses and expressions in the query is gathered from the data dictionary to determine the details of combining functions and queries, i.e. wherein the one or more parameters include a stage parameter (e.g. the gathered semantic information includes data describing clauses and expressions which represent different data processing steps of a query.)
execute a single query to obtain the multiple data attributes from one or more data sources prior to the start of the multi-stage process, See Col. 105, lines 4-6, (Probing query execution precedes generation of the remaining classes of queries, i.e. execute a single query to obtain the multiple data attributes from one or more data sources prior to the start of the multi-stage process (e.g. the probing query may retrieve semantic information regarding columns, tables, etc. for a query to be further processed by query decomposition).)
and wherein the one or more processors, to cause the system to obtain the multiple data attributes, are configured to cause the system to: group the multiple data attributes into one or more groups associated with the one or more data sources; See Col. 105, lines 48-55, (Parallel subqueries for a given decomposed query are directed to restricted search spaces corresponding to a specified table partition. The subqueries are generated by a series of transformations including "Select list transformations" which add columns to the select list or replace columns with other columns, i.e. wherein the one or more processors, to cause the system to obtain the multiple data attributes, are configured to cause the system to: group the multiple data attributes into one or more groups associated with the one or more data sources (e.g. the subqueries are directed to separate partitions of a table wherein the table comprises columns)
generate two or more queries for the one or more groups; See Col. 3, lines 55-65, (Generating plural subqueries which causes the DBMS to place the data request from each of a plurality of partitions in the designated database table, i.e. generate two or more queries for the one or more groups (e.g. the parallel subqueries represent partitions or groups of rows for each subquery).)
and execute, in parallel, the two or more queries; See Col. 4, lines 1-6, (The digital data processing system may execute multiple threads for applying a respective subquery to the DBMS' interface element. The threads may be executed in parallel and can be serviced by multiple server processes within the DBMS that also execute in parallel, i.e. execute, in parallel, the two or more queries;)
store, in a data cache associated with the multi-stage process, the multiple data attributes; See Col. 6, lines 45-52, (The system comprises memory elements 40A-40C which comprise cache control units 52A-52C that interface with a data storage area 54A-54C which provide physical storage space for data and instruction signals.) (388) See Col. 108, lines 40-43, (Query execution is performed using pnode trees comprising parent and child nodes, wherein pnodes are specialized row sources.) (454) See Col. 113, lines 35-45, (The process of executing parallelizable queries includes the use of cache pnodes configured to cache rows pulled from child nodes to the parent node, i.e. store, in a data cache associated with the multi-stage process, the multiple data attributes (e.g. the stored rows comprise data values of columns).)
and perform, using at least one of the multiple data attributes, one or more stages of the multi-stage process. See Col. 106, lines 13-20, (Output rows from the parallel subqueries provide the input rows to the combining functions and queries in order to dynamically merge the output streams. Note Col. 1, lines 52-53 wherein rows are identified by matching data values in one or more columns, i.e. perform, using at least one of the multiple data attributes, one or more stages of the multi-stage process (e.g. the retrieved rows are associated with one or more columns that are further processed via the dynamic combination).)
Reiner does not disclose the step wherein a data attribute of the set of data attributes is a data field that represents a characteristic of a data object and is defined using a format associated with a programming language, the programming language associated with a data repository;
Tremblay discloses the step wherein a data attribute of the set of data attributes is a data field that represents a characteristic of a data object and is defined using a format associated with a programming language, the programming language associated with a data repository; See Paragraph [0442], (A user may interact with a graphical user interface of reporting system 526 in order to specify a funnel report generated based on a set of sequential database queries. Reporting system 526 may generate a report plan embodied as an SQL query that includes pre-query database operations, query database operations and/or post-query database operations in order to query a data warehouse and generate a visualized report.) See Paragraph [0465], (A user may define report configuration parameters including field assignments. Note [0234] wherein the user may designate that the value of a particular attribute may be found in a specific field or fields of a specific database record, wherein a data attribute of the set of data attributes is a data field that represents a characteristic of a data object (e.g. the database record).) See Paragraph [0242], (Client configuration system 1602 may define one or more database objects from which MySQL database records are instantiated, i.e. and is defined using a format associated with a programming language, the programming language associated with a data repository (e.g. the MySQL database is associated with SQL used to access, modify, etc. database records).)
The examiner notes that Paragraph [0010] of Applicant's Specification discloses that "a data attribute may be defined using a syntax or format associated with a programming language used to obtain, manipulate, create, and/or delete, among other examples, data in a data repository." One of ordinary skill in the art would recognize that a MySQL database is associated with SQL queries which may be used to perform any of the operations listed in Applicant's Specification via SQL commands which are associated with a particular format and syntax.
Reiner and Tremblay are analogous art because they are in the same field of endeavor, database access optimization. It would have been obvious to anyone having ordinary skill in the art before the effective filing date to modify the system of Reiner to include the method of configuring a storage system as disclosed by Tremblay . Paragraph [0354] of Tremblay discloses that the system allows users to customize the data processing workflow which allows developers to define customized object times that results in improved speed of development for the customized objects which are then provided to users.
Reiner-Tremblay do not disclose that the system is configured to execute, during a prefetch operation, respective queries at respective data sources of multiple data sources to obtain the multiple data attributes from the multiple data sources prior to the start of the multi-stage process,
Andoni discloses that the system is configured to execute, during a prefetch operation, respective queries at respective data sources of multiple data sources to obtain the multiple data attributes from the multiple data sources prior to the start of the multi-stage process, See Paragraph [0021], (Disclosing a method for receiving input that identifies one or more data sources and determining, based on the input, a machine learning problem type of a plurality of ML problem typers supported by an automated model building (AMB) engine. Pre-processor 104 may perform various rule-based operations on raw data sources to determine an input data set 102 that is operated on by the automated model building engine.) See Paragraph [0037], (Pre-processor 104 may receive input indicating data sources from which said pre-processor is to determine the input data set 102 during a configuration stage. Note [0041] wherein the data sources include SQL servers, database servers, data archives, etc., i.e. execute, during a prefetch operation (e.g. pre-processor 104 processes data during a configuration stage), respective queries at respective data sources of multiple data sources (e.g. data sources may include SQL databases, which are accessed via SQL queries) to obtain the multiple data attributes from the multiple data sources prior to the start of the multi-stage process (e.g. Note [0043] wherein pre-processor 104 includes data profiler 320 that examines data fields and determines information regarding data fields).)
Reiner, Tremblay and Andoni are analogous art because they are in the same field of endeavor, data retrieval. It would have been obvious to anyone having ordinary skill in the art before the effective filing date to modify the system of Reiner-Tremblay to include the method of pre-processing an input dataset according to rule-based operations as disclosed by Andoni. Paragraph [0021] of Andoni discloses that pre-processor 104 may perform rule-based operations that scale, clean and modify raw data so that the resulting input dataset 102 is compatible with and/or provides computational benefits such as increased model generations peed, reduced model generation memory footprint, etc. when compared to raw data sources.
Reiner-Tremblay-DONES does not disclose the step wherein the multiple data attributes are eligible to be obtained from the multiple data sources prior to the start of the multi-stage process based on the multiple data attributes being associated with a constant data dependency value and a processing metric satisfying a prefetch threshold, the processing metric indicating an amount of processing resources used to obtain the multiple data attributes,
Lepeska discloses the step wherein the multiple data attributes are eligible to be obtained from the multiple data sources prior to the start of the multi-stage process based on the multiple data attributes being associated with a constant data dependency value, See Paragraph [0173], (Disclosing a system for determining dependency structures of a web application to improve prefetching operations to lower page load times. The system may receive and classify a plurality of URLs that are assigned to bins. When a new child URL is encountered, it is assigned to a bin that includes all fields int eh model where the bin values are those of the root URL instance responsible for the download which allows the child URLs to be prefetched.) See Paragraph [0257], (Tracking system may provide multiple prefetch lists for prefetching objects while respecting inter-object dependencies.) See Paragraph [0271], (The system may identify dependencies of the plurality of data objects received and pass the dependency information to a prefetcher, i.e. wherein the multiple data attributes are eligible to be obtained from the multiple data sources (e.g. data field information from a plurality of URLs including child URLs is received) prior to the start of the multi-stage process (e.g. the receiving ) based on the multiple data attributes being associated with a constant data dependency value (e.g. prefetching system receives dependency information for objects such as 1606, 1610).)
and a processing metric satisfying a prefetch threshold, the processing metric indicating an amount of processing resources used to obtain the multiple data attributes, See Paragraph [0260], (Prefetches are queued until an open slot below the maximum number concurrent threshold available for an additional prefetch condition, i.e. and a processing metric satisfying a prefetch threshold, the processing metric indicating an amount of processing resources used to obtain the multiple data attributes (e.g. a prefetch connection represents a processing resource used to prefetch objects. A concurrency threshold is used to control prefetching of objects).)
Reiner, Tremblay, Andoni and Lepeska are analogous art because they are in the same field of endeavor, data retrieval. It would have been obvious to anyone having ordinary skill in the art before the effective filing date to modify the system of Reiner-Tremblay-Andoni to include the method of prefetching records based on dependencies and prefetch concurrency thresholds as disclosed by Lepeska. Paragraph [0153] of Lepeska discloses that the system may calculate or synthesize fields for incoming child URL objects as appropriate information becomes available to the system. This improves the prefetch system's ability to anticipate URLs that will be requested by the browser.
Regarding dependent claim 4,
As discussed above with claim 1, Reiner-Tremblay-DONES-Lepeska discloses all of the limitations.
Andoni further discloses the step wherein the one or more processors, to identify the multiple data attributes, cause the system to: identify, based on the configuration information, that a prefetch indicator for the data attribute of the set of data attributes indicates that the data attribute is to be obtained prior to the start of the multi-stage process. See Paragraph [0041], (Pre-processor 104 may add, remove, and/or rename columns of a combined data source received from a multi-source processor 312 to generate the input data set 102 to be provided to the automated model building engine, i.e. identify, based on the configuration information (e.g. Note [0037] wherein pre-processor may determine the input data set during a configuration stage based on criteria provided by a user that specifies data sources), that a prefetch indicator for the data attribute of the set of data attributes indicates that the data attribute is to be obtained prior to the start of the multi-stage process (e.g. pre-processor 104 may determine which columns are incorporated into the input data set 102).)
Regarding dependent claim 7,
As discussed above with claim 1, Reiner-Tremblay-Andoni-Lepeska discloses all of the limitations.
Reiner further discloses the step wherein the one or more processors, to obtain the multiple data attributes, cause the system to: obtain, via the multiple data sources, the multiple data attributes based on executing the two or more queries. See FIG. 4 & Col. 9, lines 33-43, (FIG. 4 illustrates the system receiving a plurality of subquery responses which include row data comprising values of columns retrieved by each individual subquery, i.e. obtain via the multiple data sources, the multiple data attributes based on executing the two or more queries (e.g. multiple subqueries are executed (FIG. 4 illustrates 3 subqueries directed to partitions of a relational database 72, i.e. multiple data sources) which retrieve attribute values.)
Regarding dependent claim 8,
As discussed above with claim 1, Reiner-Tremblay-Andoni-Lepeska discloses all of the limitations.
Reiner further discloses the step wherein the one or more processors, to perform the one or more stages of the multi-stage process, are configured to: identify that a stage, of the one or more stages, uses the data attribute of the set of data attributes; See Col. 5, lines 48-67, (The plurality of parallel subqueries are generated by a series of transformations including select list transformations, which add columns to the select list, i.e. identify that a stage, of the one or more stages, uses the data attribute of the set of data attributes (e.g. the transformation indicates which attributes are utilized by the parallel subqueries).)
obtain, via the data cache, the data attribute; See Col. 113, lines 35-42, (Cache pnodes are configured to cache each row pulled from a child node of a query execution tree and returns the row to the parent node, i.e. obtain, via the data cache, the data attribute (e.g. the cached rows represent records of a data table having associated columns, i.e. data attributes).)
and perform, during an execution of the stage, one or more actions using the data attribute. See Col. 106, lines 13-20, (Output rows from the parallel subqueries provide the input rows to the combining functions and queries in order to dynamically merge the output streams. Note Col. 1, lines 52-53 wherein rows are identified by matching data values in one or more columns, i.e. perform, during an execution of the stage, one or more actions using the data attribute (e.g. the execution of parallel subqueries represents a stage of execution).)
Regarding dependent claim 9,
As discussed above with claim 1, Reiner-Tremblay-Andoni-Lepeska discloses all of the limitations.
Reiner further discloses the step wherein the system wherein the one or more processors, to perform the one or more stages of the multi-stage process, cause the system to: identify that a stage, of the one or more stages, uses the data attribute of the set of data attributes, wherein the data attribute of the set of data attributes is not included in the multiple data attributes; See Col. 5, lines 48-67, (The plurality of parallel subqueries are generated by a series of transformations including select list transformations, which add columns to the select list or replace columns with other columns, i.e. identify that a stage, of the one or more stages, uses the data attribute of the set of data attributes, wherein the data attribute of the set of data attributes is not included in the multiple data attributes (e.g. the transformation indicates which attributes are utilized by the parallel subqueries and may include replacing attributes, thereby not including an attribute in the parallel subqueries)
obtain, during an execution of the stage and via a data source associated with the data attribute, the data attribute; See Col. 113, lines 35-42, (Cache pnodes are configured to cache each row pulled from a child node of a query execution tree (e.g. the cache is a data source from which row data is retrieved for query execution) and returns the row to the parent node, i.e. obtain, via the data cache, the data attribute (e.g. the cached rows represent records of a data table having associated columns, i.e. data attributes).)
and perform, during the execution of the stage, one or more actions using the data attribute. See Col. 106, lines 13-20, (Output rows from the parallel subqueries provide the input rows to the combining functions and queries in order to dynamically merge the output streams. Note Col. 1, lines 52-53 wherein rows are identified by matching data values in one or more columns, i.e. perform, during an execution of the stage, one or more actions using the data attribute (e.g. the execution of parallel subqueries represents a stage of execution).)
Regarding independent claim 10,
Reiner discloses a method for data attribute retrieval, comprising: obtaining, by a device, configuration information for a multi-stage process, the configuration information indicating a set of data attributes to be used during the multi-stage process, See Col. 107, lines 53-65, (Disclosing a system for database query processing via query decomposition. SQL queries may be decomposed into separate queries that may be executed in parallel.) See Col. 104, lines 24-27 & 43-47, (Probing queries may be generated during the gathering/analysis phase of query composition and may gather semantic information about objects referenced in the query from a data dictionary. Semantic information is used to generate a SQLDA structure describing the output columns of the original input query or of transformations of that query, i.e. obtaining, by a device, configuration information for a multi-stage process, the configuration information indicating a set of data attributes to be used during the multi-stage process (e.g. the system may obtain semantic information about data columns to which a query is directed).) Note Col. 8, lines 40-59 wherein the system architecture comprises a multiprocessor for executing parallel queries, i.e. one or more processors communicatively coupled to the one or more memories.
identifying, by the device and based on one or more parameters of respective data attributes included in the set of data attributes, multiple data attributes to be obtained prior to a start of the multi-stage process, wherein the one or more parameters are indicated by the configuration information; See Col. 104, lines 17-20, (Probing queries are generated and executed during the gathering/analyzing phase of query decomposition and used to gather information from the query optimizer and data dictionary, which includes semantic information about objects referenced in the query. Semantic information may include data describing the output columns of the original input query or transformations of said query, i.e. identify, based on one or more parameters of respective data attributes included in the set of data attributes, multiple data attributes to be obtained prior to a start of the multi-stage process (e.g. probing queries obtain semantic data prior to query execution).) See Col. 103, lines 30-38, (Semantic information about tables, columns, clauses and expressions in the query is gathered from the data dictionary to determine the details of combining functions and queries, i.e. wherein the one or more parameters are indicated by the configuration information (e.g. the gathered semantic information includes indications of columns to which the query will be directed.)
executing, by the device and prior to the start of the multi-stage process, a single query to obtain the multiple data attributes from one or more data sources, See Col. 105, lines 4-6, (Probing query execution precedes generation of the remaining classes of queries, i.e. execute a single query to obtain the multiple data attributes from one or more data sources prior to the start of the multi-stage process (e.g. the probing query may retrieve semantic information regarding columns, tables, etc. for a query to be further processed by query decomposition).)
and wherein obtaining the multiple data attributes comprises: grouping the multiple data attributes into one or more groups associated with the one or more data sources; See Col. 105, lines 48-55, (Parallel subqueries for a given decomposed query are directed to restricted search spaces corresponding to a specified table partition. The subqueries are generated by a series of transformations including "Select list transformations" which add columns to the select list or replace columns with other columns, i.e. wherein the one or more processors, to cause the system to obtain the multiple data attributes, are configured to cause the system to: group the multiple data attributes into one or more groups associated with the one or more data sources (e.g. the subqueries are directed to separate partitions of a table wherein the table comprises columns)
generating two or more queries for the one or more groups; See Col. 3, lines 55-65, (Generating plural subqueries which causes the DBMS to place the data request from each of a plurality of partitions in the designated database table, i.e. generate two or more queries for the one or more groups (e.g. the parallel subqueries represent partitions or groups of rows for each subquery).)
and executing, in parallel, the two or more queries; See Col. 4, lines 1-6, (The digital data processing system may execute multiple threads for applying a respective subquery to the DBMS' interface element. The threads may be executed in parallel and can be serviced by multiple server processes within the DBMS that also execute in parallel, i.e. execute, in parallel, the two or more queries;)
and performing, by the device and using at least one of the multiple data attributes, one or more stages of the multi-stage process. See Col. 106, lines 13-20, (Output rows from the parallel subqueries provide the input rows to the combining functions and queries in order to dynamically merge the output streams. Note Col. 1, lines 52-53 wherein rows are identified by matching data values in one or more columns, i.e. perform, using at least one of the multiple data attributes, one or more stages of the multi-stage process (e.g. the retrieved rows are associated with one or more columns that are further processed via the dynamic combination).)
Reiner does not disclose the step wherein a data attribute of the set of data attributes is a data field that represents a characteristic of a data object and is defined using a format associated with a programming language, the programming language associated with a data repository;
Tremblay discloses the step wherein a data attribute of the set of data attributes is a data field that represents a characteristic of a data object and is defined using a format associated with a programming language, the programming language associated with a data repository; See Paragraph [0442], (A user may interact with a graphical user interface of reporting system 526 in order to specify a funnel report generated based on a set of sequential database queries. Reporting system 526 may generate a report plan embodied as an SQL query that includes pre-query database operations, query database operations and/or post-query database operations in order to query a data warehouse and generate a visualized report.) See Paragraph [0465], (A user may define report configuration parameters including field assignments. Note [0234] wherein the user may designate that the value of a particular attribute may be found in a specific field or fields of a specific database record, wherein a data attribute of the set of data attributes is a data field that represents a characteristic of a data object (e.g. the database record).) See Paragraph [0242], (Client configuration system 1602 may define one or more database objects from which MySQL database records are instantiated, i.e. and is defined using a format associated with a programming language, the programming language associated with a data repository (e.g. the MySQL database is associated with SQL used to access, modify, etc. database records).)
The examiner notes that Paragraph [0010] of Applicant's Specification discloses that "a data attribute may be defined using a syntax or format associated with a programming language used to obtain, manipulate, create, and/or delete, among other examples, data in a data repository." One of ordinary skill in the art would recognize that a MySQL database is associated with SQL queries which may be used to perform any of the operations listed in Applicant's Specification via SQL commands which are associated with a particular format and syntax.
Reiner and Tremblay are analogous art because they are in the same field of endeavor, database access optimization. It would have been obvious to anyone having ordinary skill in the art before the effective filing date to modify the system of Reiner to include the method of configuring a storage system as disclosed by Tremblay. Paragraph [0354] of Tremblay discloses that the system allows users to customize the data processing workflow which allows developers to define customized object times that results in improved speed of development for the customized objects which are then provided to users.
Reiner-Tremblay does not disclose the step of executing, by the device, during a prefetch operation, and prior to the start of the multi- stage process, respective queries at respective data sources of multiple data sources to obtain the multiple data attributes from the multiple data sources,
Andoni discloses the step of executing, by the device, during a prefetch operation, and prior to the start of the multi-stage process, respective queries at respective data sources of multiple data sources to obtain the multiple data attributes from the multiple data sources, See Paragraph [0021], (Disclosing a method for receiving input that identifies one or more data sources and determining, based on the input, a machine learning problem type of a plurality of ML problem typers supported by an automated model building (AMB) engine. Pre-processor 104 may perform various rule-based operations on raw data sources to determine an input data set 102 that is operated on by the automated model building engine.) See Paragraph [0037], (Pre-processor 104 may receive input indicating data sources from which said pre-processor is to determine the input data set 102 during a configuration stage. Note [0041] wherein the data sources include SQL servers, database servers, data archives, etc., i.e. execute, during a prefetch operation (e.g. pre-processor 104 processes data during a configuration stage), respective queries at respective data sources of multiple data sources (e.g. data sources may include SQL databases, which are accessed via SQL queries) to obtain the multiple data attributes from the multiple data sources prior to the start of the multi-stage process (e.g. Note [0043] wherein pre-processor 104 includes data profiler 320 that examines data fields and determines information regarding data fields).)
Reiner, Tremblay and Andoni are analogous art because they are in the same field of endeavor, data retrieval. It would have been obvious to anyone having ordinary skill in the art before the effective filing date to modify the system of Reiner-Tremblay to include the method of pre-processing an input dataset according to rule-based operations as disclosed by Andoni. Paragraph [0021] of Andoni discloses that pre-processor 104 may perform rule-based operations that scale, clean and modify raw data so that the resulting input dataset 102 is compatible with and/or provides computational benefits such as increased model generations peed, reduced model generation memory footprint, etc. when compared to raw data sources.
Reiner-Tremblay-Andoni does not disclose the step wherein the multiple data attributes are eligible to be obtained from the multiple data sources prior to the start of the multi-stage process based on the multiple data attributes being associated with a constant data dependency value,
and a processing metric satisfying a prefetch threshold, the processing metric indicating an amount of processing resources used to obtain the multiple data attributes
Lepeska discloses the step wherein the multiple data attributes are eligible to be obtained from the multiple data sources prior to the start of the multi-stage process based on the multiple data attributes being associated with a constant data dependency value, See Paragraph [0173], (Disclosing a system for determining dependency structures of a web application to improve prefetching operations to lower page load times. The system may receive and classify a plurality of URLs that are assigned to bins. When a new child URL is encountered, it is assigned to a bin that includes all fields int eh model where the bin values are those of the root URL instance responsible for the download which allows the child URLs to be prefetched.) See Paragraph [0257], (Tracking system may provide multiple prefetch lists for prefetching objects while respecting inter-object dependencies.) See Paragraph [0271], (The system may identify dependencies of the plurality of data objects received and pass the dependency information to a prefetcher, i.e. wherein the multiple data attributes are eligible to be obtained from the multiple data sources (e.g. data field information from a plurality of URLs including child URLs is received) prior to the start of the multi-stage process (e.g. the receiving ) based on the multiple data attributes being associated with a constant data dependency value (e.g. prefetching system receives dependency information for objects such as 1606, 1610).)
and a processing metric satisfying a prefetch threshold, the processing metric indicating an amount of processing resources used to obtain the multiple data attributes, See Paragraph [0260], (Prefetches are queued until an open slot below the maximum number concurrent threshold available for an additional prefetch condition, i.e. and a processing metric satisfying a prefetch threshold, the processing metric indicating an amount of processing resources used to obtain the multiple data attributes (e.g. a prefetch connection represents a processing resource used to prefetch objects. A concurrency threshold is used to control prefetching of objects).)
Reiner, Tremblay, Andoni and Lepeska are analogous art because they are in the same field of endeavor, data retrieval. It would have been obvious to anyone having ordinary skill in the art before the effective filing date to modify the system of Reiner-Tremblay-Andoni to include the method of prefetching records based on dependencies and prefetch concurrency thresholds as disclosed by Lepeska. Paragraph [0153] of Lepeska discloses that the system may calculate or synthesize fields for incoming child URL objects as appropriate information becomes available to the system. This improves the prefetch system's ability to anticipate URLs that will be requested by the browser.
Regarding dependent claim 11,
As discussed above with claim 10, Richard-Tremblay-Andoni-Lepeska discloses all of the limitations.
Reiner further discloses the step wherein the one or more parameters includes at least one of: a data dependency parameter, or a stage parameter. See Col. 103, lines 30-38, (Semantic information about tables, columns, clauses and expressions in the query is gathered from the data dictionary to determine the details of combining functions and queries, i.e. wherein the one or more parameters include a stage parameter (e.g. the gathered semantic information includes data describing clauses and expressions which represent different data processing steps of a query.)
Regarding dependent claim 15,
The claim is analogous to the subject matter of dependent claim 7 directed to a method or process and is rejected under similar rationale.
Regarding dependent claim 16,
The claim is analogous to the subject matter of dependent claim 8 directed to a method or process and is rejected under similar rationale.
Regarding independent claim 17,
The claim is analogous to the subject matter of independent claim 10 directed to a non-transitory, computer readable medium and is rejected under similar rationale.
Regarding dependent claim 20,
As discussed above with claim 1, Reiner-Tremblay-Andoni-Lepeska discloses all of the limitations.
Andoni further discloses the step wherein the one or more processors, to perform the prefetch operation, are configured to: obtain and store the multiple data attributes prior to a start of a stage of the multi- stage process where the multiple data attributes are to be used. See Paragraph [0065], (Pre-processor 104 may determine an input dataset 102 during a configuration stage. The input dataset 102 is then used by the automated model building engine. Note [0112] wherein the input data set may be stored in a memory, i.e. obtain and store the multiple data attributes prior to a start of a stage of the multi- stage process where the multiple data attributes are to be used.)
Claim(s) 2 and 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Reiner in view of Tremblay, Andoni and Lepeska as applied to claim 1 above, and further in view of V et al. (US PGPUB No. 2024/0027998; Pub. Date: Jan. 25, 2024).
Regarding dependent claim 2,
As discussed above with claim 1, Reiner-Tremblay-Andoni-Lepeska discloses all of the limitations.
Reiner-Tremblay-Andoni-Lepeska does not disclose the step wherein the data dependency parameter indicates whether the data attribute of the set of data attributes is dependent on an output of at least one stage of the multi-stage process.
V discloses the step wherein the data dependency parameter indicates whether the data attribute of the set of data attributes is dependent on an output of at least one stage of the multi-stage process. See FIG. 4 & Paragraph [0053], (Disclosing a method for executing a sequence of process steps for executing a query. The system comprises a resume module 304 configured to identify dependent transactional data indicating that an instant step is dependent on a result from a sequentially earlier/previous step before the instant process step can complete execution, i.e. wherein the data dependency parameter indicates whether the data attribute of the set of data attributes is dependent on an output of at least one stage of the multi-stage process.)
Reiner, Tremblay, Andoni, Lepeska and V are analogous art because they are in the same field of endeavor, query execution. It would have been obvious to anyone having ordinary skill in the art before the effective filing date to modify the system of Reiner-Tremblay-Andoni-Lepeska to include the method of managing data dependencies across steps of a multi-step search process as disclosed by V. Paragraph [0053] of V discloses that the execution process is configured to automatically perform transactional tasks without user intervention unless a failure occurs.
Regarding dependent claim 12,
The claim is analogous to the subject matter of dependent claim 2 directed to a method or process and is rejected under similar rationale.
Claim(s) 3 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Reiner in view of Tremblay, Andoni and Lepeska as applied to claim 1 above, and further in view of Balle et al. (US PGPUB No. 2018/0150299; Pub. Date: May 31, 2018).
Regarding dependent claim 3,
As discussed above with claim 1, Reiner-Tremblay-Andoni-Lepeska discloses all of the limitations.
Richard-Tremblay-Andoni-Lepeska does not disclose the step wherein the one or more processors, to identify the multiple data attributes, cause the system to: identify that the data dependency parameter indicates that the data attribute of the set of data attributes does not have unknown values for one or more data dependencies prior to the start of the multi-stage process.
Balle discloses the step wherein the one or more processors, to identify the multiple data attributes, cause the system to: identify that the data dependency parameter indicates that the data attribute of the set of data attributes does not have unknown values for one or more data dependencies prior to the start of the multi-stage process. See Paragraphs [0073] & [0076], (Disclosing a system for dividing work across one or more accelerator devices. Accelerator sled 1202 analyzes a plurality of jobs to determine if said jobs may be executed in parallel if certain tasks share the same data or if the tasks do or do not depend on any outputs of previous tasks as inputs, i.e. identify that the data dependency parameter indicates that the data attribute of the set of data attributes does not have unknown values for one or more data dependencies prior to the start of the multi-stage process (e.g. the method verifies that the dependencies are valid as part of scheduling jobs) prior to the start of the multi-stage process (e.g. the dependency analysis occurs before executing the job).)
Reiner, Tremblay, Andoni, Lepeska and Balle are analogous art because they are in the same field of endeavor, query execution. It would have been obvious to anyone having ordinary skill in the art before the effective filing date to modify the system of Reiner-Tremblay-Andoni-Lepeska to include the method of scheduling jobs according to dependency analysis as disclosed by Balle. Paragraph [0053] of Balle discloses that the use of accelerator devices allows the system to provide different types of acceleration such as cryptographic acceleration, compression acceleration, parallel execution, etc. that suit particular jobs best, which represents an improvement in job execution.
Regarding dependent claim 18,
The claim is analogous to the subject matter of dependent claim 3 directed to a non-transitory, computer readable medium and is rejected under similar rationale.
Claim(s) 5 and 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Reiner in view of Tremblay, Andoni and Lepeska as applied to claim 1 above, and further in view of HAJEWSKI et al. (US PGPUB No. 2022/0300417; Pub. Date: Sep. 22, 2022).
Regarding dependent claim 5,
As discussed above with claim 1, Reiner-Tremblay-Andoni-Lepeska discloses all of the limitations.
Reiner-Tremblay-Andoni-Lepeska does not disclose the step wherein the stage parameter indicates one or more stage locations of one or more stages, in an order of stages in the multi-stage process, that use a data attribute of the set of data attributes.
wherein the stage parameter indicates one or more stage locations of the one or more stages, in an order of stages in the multi-stage process, that use the data attribute of the set of data attributes.
HAJEWSKI discloses the step wherein the stage parameter indicates one or more stage locations of the one or more stages, in an order of stages in the multi-stage process, that use the data attribute of the set of data attributes.
See Paragraph [0026], (Disclosing a system for generating and implementing computational graphs that facilitate concurrent computation on data streams. A computational graph represents a sequence of nodes connected by directed edges that represent computations that are directed to a stream of data.) See Paragraph [0051], (The computational graph comprises a plurality of locations associated with a node and a corresponding path that may be identified by a stage identifier.) See Paragraph [0094], (The computational graph may be used to apply operations following a series of stages. Operations may include mathematical computations, logical operations and search operations that may be performed on one or more database records. Note [0115] wherein a record refers a data object having fields relating to a database service, i.e. wherein the stage parameter indicates one or more stage locations of the one or more stages, in an order of stages in the multi-stage process (e.g. the stage identifier of the sequence of stages of a computational graph), that use a data attribute of the set of data attributes (e.g. operations of the computational graph are directed to database records having fields).)
Reiner, Tremblay, Andoni, Lepeska and HAJEWSKI are analogous art because they are in the same field of endeavor, query execution. It would have been obvious to anyone having ordinary skill in the art before the effective filing date to modify the system of Reiner-Tremblay-Andoni-Lepeska to include the method of managing a computational graph having a plurality of stages as disclosed by HAJEWSKI. Paragraph [0147] of HAJEWSKI discloses that the system may implement a load balancer configured to distribute workload between pods which assists in improving the use of resources, increasing throughput, reducing response times and/or reducing overhead.
Regarding dependent claim 13,
The claim is analogous to the subject matter of dependent claim 5 directed to a method or process and is rejected under similar rationale.
Claim(s) 6 and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Reiner in view of Tremblay, Andoni and Lepeska as applied to claim 1 above, and further in view of HAJEWSKI et al. (US PGPUB No. 2022/0300417; Pub. Date: Sep. 22, 2022) and George et al. (US PGPUB No. 2018/0343131; Pub. Date: Nov. 29, 2018).
Regarding dependent claim 6,
As discussed above with claim 1, Reiner-Tremblay-Andoni-Lepeska discloses all of the limitations.
Reiner-Tremblay-Andoni-Lepeska does not disclose the step wherein the one or more processors, to identify the multiple data attributes, are configured to: identify, for the data attribute of the set of data attributes and based on the stage parameter, one or more stage locations of the one or more stages, in an order of stages in the multi-stage process, that use the data attribute;
and determine whether to obtain the data attribute prior to the start of the multi-stage process based on the one or more stage locations
HAJEWSKI discloses the step wherein the one or more processors, to identify the one or more data attributes, are configured to: identify, for a data attribute of the set of data attributes and based on the stage parameter, one or more stage locations of one or more stages, in an order of stages in the multi-stage process, that use the data attribute; See Paragraph [0026], (Disclosing a system for generating and implementing computational graphs that facilitate concurrent computation on data streams. A computational graph represents a sequence of nodes connected by directed edges that represent computations that are directed to a stream of data. Note [0051] wherein the computational graph comprises a plurality of locations associated with a node and a corresponding path that may be identified by a stage identifier.) See Paragraph [0036], (A computational graph is generated by applying a topological sorting to an initial computational graph. Each stage may be associated with a corresponding operation to be performed on data associated with said stage, i.e. identify, for a data attribute of the set of data attributes and based on the stage parameter, one or more stage locations of one or more stages (e.g. each stage of the computational graph is associated with a stage identifier and operations to be applied to data records having fields), in an order of stages in the multi-stage process (e.g. stages are organized in a topological ordering), that use the data attribute (e.g. stage operations are associated with particular data).)
and determine whether to obtain the data attribute prior to the start of the multi-stage process based on the one or more stage locations. See Paragraph [0039], (A computing device may obtain a single piece of data and a corresponding stage identifier that identifies a current stage in a computational graph, wherein the at least one stage is associated with a corresponding operation to be performed on data associated with said stage, i.e. determine whether to obtain the data attribute prior to the start of the multi-stage process based on the one or more stage locations (e.g. the computing device obtains data and stage identifier prior to executing the computation).)
Reiner, Tremblay, Andoni, Lepeska and HAJEWSKI are analogous art because they are in the same field of endeavor, query execution. It would have been obvious to anyone having ordinary skill in the art before the effective filing date to modify the system of Reiner-Tremblay-Andoni-Lepeska to include the method of managing a computational graph having a plurality of stages as disclosed by HAJEWSKI. Paragraph [0147] of HAJEWSKI discloses that the system may implement a load balancer configured to distribute workload between pods which assists in improving the use of resources, increasing throughput, reducing response times and/or reducing overhead.
Reiner-Tremblay-Andoni-Lepeska-HAJEWSKI does not disclose the step determine one or more metrics associated with obtaining the data attribute from a data source via which the data attribute is stored;
determine whether to obtain the data attribute prior to the start of the multi-stage process based on the one or more metrics.
George discloses the step determine one or more metrics associated with obtaining the data attribute from a data source via which the data attribute is stored; See Paragraphs [0014]-[0015], (Disclosing a method for determining optimal storage of data structures across different memory devices according to a dynamic memory allocation scheme based on a cost metric used to evaluate read/write costs for each object or object field, i.e. determine one or more metrics associated with obtaining the data attribute from a data source via which the data attribute is stored (e.g. the cost metric evaluates read/write costs, i.e. a metric associated with obtaining).)
determine whether to obtain the data attribute prior to the start of the multi-stage process based on the one or more metrics. See Paragraphs [0016] & [0018], (The total cost of retrieving an object and cost thresholds may be used to determine what objects and/or object fields are stored to a particular memory type. The system may determine that a particular memory structure is preferred for retrieving data, i.e. determine whether to obtain the data attribute prior to the start of the multi-stage process based on the one or more metrics (e.g. optimal data retrieval methods may be determined according got the cost metrics).)
Reiner, Tremblay, Andoni, Lepeska, HAJEWSKI and George are analogous art because they are in the same field of endeavor, query execution. It would have been obvious to anyone having ordinary skill in the art before the effective filing date to modify the system of Reiner-Tremblay-Andoni-Lepeska-HAJEWSKI to include the method of determining storage and retrieval optimizations for objects and object fields as disclosed by George. Paragraph [0020] of George discloses that the system may determine memory retrieval costs for each field of an object in order to determine the most suitable location to facilitate retrieval and usage of a particular field which may rely on additional metrics such as read frequency.
Regarding dependent claim 14,
The claim is analogous to the subject matter of dependent claim 6 directed to a method or process and is rejected under similar rationale.
Claim(s) 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Reiner in view of Tremblay, Andoni and Lepeska as applied to claim 1 above, and further in view of Prendergast (US PGPUB No. 2004/0199407; Pub. Date: Oct. 7, 2004).
Regarding dependent claim 19,
As discussed above with claim 1, Reiner-Tremblay-Andoni-Lepeska discloses all of the limitations.
Reiner-Tremblay-Andoni-Lepeska does not disclose the step wherein the one or more processors are configured to: determine that an other data attribute is not eligible to be prefetched based on the stage parameter indicating that the other data attribute is used in a conditional stage that is only performed based on an output associated with a prior stage.
Prendergast discloses the step wherein the one or more processors are configured to: determine that an other data attribute is not eligible to be prefetched based on the stage parameter indicating that the other data attribute is used in a conditional stage that is only performed based on an output associated with a prior stage. See Paragraph [0030], (Disclosing a system for automatically verifying partial claim data. FIG. 3 illustrates a data processing system comprising data evaluation map 4 for identifying data fields eligible for further processing.) See Paragraph [0022], (Claims engine 7 may identify and inspect all available data fields and check for predetermined data conditions in partial claim data 2, wherein data conditions may include conditions on a single data field or conditions involving multiple data fields, i.e. determine that an other data attribute is not eligible to be prefetched (e.g. data evaluation map determines eligibility) based on the stage parameter indicating that the other data attribute is used in a conditional stage that is only performed based on an output associated with a prior stage (e.g. Note [0032] wherein evaluator 22 processes pre-existing error data to create an error-to-data-dependency map 23 that indicates relationships between errors and data fields. FIG. 3 illustrates the outputs of the data evaluation map 4 and evaluator 22 are provided to result processor 40 to determine a final list of problems).)
Reiner, Tremblay, Andoni, Lepeska and Prendergast are analogous art because they are in the same field of endeavor, data retrieval. It would have been obvious to anyone having ordinary skill in the art before the effective filing date to modify the system of Reiner-Tremblay-Andoni-Lepeska to include the method of assessing the eligibility of a data field based on dependency data as disclosed by Prednergast. Paragraph [0003] of Prendergast discloses that the method of validating data may allow users to validate information as it is collected and react to invalid or incomplete information at the point of data capture.
Response to Arguments
Applicant’s arguments with respect to claim(s) 1, 10 and 17 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Applicant’s amendments necessitated the new grounds of rejection presented in this Office Action.
Applicant’s amendments have modified the scope of claims 19-20 and required the rejections currently presented above.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Fernando M Mari whose telephone number is (571)272-2498. The examiner can normally be reached Monday-Friday 7am-4pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ann J. Lo can be reached at (571) 272-9767. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/FMMV/Examiner, Art Unit 2159
/ANN J LO/Supervisory Patent Examiner, Art Unit 2159
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