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 .
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.
1.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 is/are rejected under 35 U.S.C. 103 as being unpatentable over applicant disclosed prior art (ADPA) in view of Bodner (US 8,491,311).
Examiner Note: Hereafter, above references are combination A.
installing a structured query language (SQL) server application on a host server;
installing statistical analysis modules on the host server;
Specification [0040] In one or more embodiments, the host server 102 may be a x64 or larger server loaded with at least 32 processing cores and at least 64 Gb of RAM (e.g., in a Gig-E network) to serve as the host machine for the SQL server application 104 and the statistical analysis modules 106. The statistical analysis modules 106 may include traditional statistical application systems like SPSS, SAS, NVIVO or R.
executing the statistical analysis modules within a relational database of the SQL server
application to analyze data stored in the relational database; and
ADPA discloses elements of the claimed invention as noted but does not disclose above limitation. However, Bodner discloses:
Bodner col 2 line 65 – col 3 line 8 Additionally included is the system wherein the computer network is the Internet. Additionally included is the system wherein the analysis data is stored on a relational database, and wherein statistical and data mining analysis is performed on the analysis data to determine a specific learning problem of the student and to generate a remedial recommendation. Additionally included is the system wherein the analysis data is stored on a relational database, and wherein statistical and data mining analysis is performed on the analysis data to determine one or more universals of learning encoded in the analysis data.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify ADPA to obtain above limitation based on the teachings of Bodner for the purpose of performing comprehensive data analysis and statistical computations, see specification.
generating outputs based on the execution of the statistical analysis modules within the relational database.
Specification [0019] In one or more embodiments, a large data set including capital, assets, purchasing, and invoice records may be built and integrated into a capital spend data mart and stored in a SQL server database. R scripts may be used to analyze the data mart and product text and graphical outputs of common R libraries run inside the database along with SQL scripts. For example, the data set may include billions of records for a one-year period of financial operations, and the data mart may include over a million records of aggregated and summarized data. The R scripts may produce a trending spend and forecast budget run rate, for example.
Claim(s) 2 is/are rejected under 35 U.S.C. 103 as being unpatentable over combination
A in view of Mozes (US 2017/0308809)
Combination A discloses elements of the claimed invention as noted but does not disclose wherein generating the outputs occurs without exporting the data stored in the relational database. However, Mozes discloses:
Mozes [0067] At block 230, the data partitions are analyzed within the relational database system to generate partition metrics data. The partition metrics data include a number of partitions in the training data, a size of each partition in the training data, and a respective value for the partition key associated with each partition in the training data. Other partition metrics data may be possible as well, in accordance with other embodiments. In accordance with one embodiment, metrics logic 125 of analytics logic 110 of FIG. 1 is configured to generate the partition metrics data.
Interpreted per specification [0020]
It would have been obvious to one of ordinary skill in the art before the effective filing date of
the claimed invention to modify reference combination A to obtain above limitation based on the
teachings of Mozes for the purpose of providing a relational database system, having a
computing device configured with analytics logic, for generating a composite model object of
partitioned data mining models built on partitioned training data, and scoring records of scoring
data using the composite model object, see [0006] FIG. 1.
Claim(s) 3 is/are rejected under 35 U.S.C. 103 as being unpatentable over reference combination
A in view of Ward (US 2020/0304384).
Combination A discloses elements of the claimed invention as noted but does not disclose wherein the host server comprises 32 processing cores and 64 gigabytes of random access memory. However, Ward discloses:
Ward [0040] For example, in the illustrated embodiment, the various instance types (small,
medium, large and extra large) of the standard instance family 210 may be implementable using
a single server type "S1" with 32 processing cores, 64 gigabytes of available main memory and
1600 gigabytes of available disk storage.
It would have been obvious to one of ordinary skill in the art before the effective filing date of
the claimed invention to modify reference combination A to obtain above limitation based on the
teachings of Ward for the purpose of managing flexible capacity pool reservations for network-
accessible resources, see [0016]
Claim(s) 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over combination
A in view of Sihavong (US 2022/0277383) in view of Clode (US 2013/0054440)
Combination A discloses elements of the claimed invention as noted but does not disclose wherein the outputs comprise a spending trend. However, Sihavong
discloses:
Sihavong [0023] Account data 1271 includes financial data from the user's financial accounts.
User financial accounts may include user savings accounts, checking accounts, credit card
accounts, and any other types of payment accounts. Account data 1271 may include, but is not
limited to, a plurality of data features, such as a user identifier (ID), an account number, cash
flow, account balances, payment due dates of financial cards accounts, interest charges of the
financial card accounts, savings goals, budgets, credit scores, spending trend data, etc. Account
data 1271 is associated with transaction data 1272 of corresponding user accounts and
corresponding historical user behavior data 1273.
It would have been obvious to one of ordinary skill in the art before the effective filing date of
the claimed invention to modify reference combination A to obtain above limitation based on the
teachings of Sihavong for the purpose of recommending personalized digital nudges designed to
influence the user in a manner that furthers a savings goal, see abstract.
Reference combination A discloses elements of the claimed invention as noted but does not disclose wherein the outputs comprise a forecast budget run rate. However,
Clode discloses:
Clode [0047] Established budget values may be run through the budget rates or the
transactional rates. While assembling a budget VS. transaction report, the actual values may be
run through the transactional rates to track finances or through the budget rates to determine
budget accuracy. If there is a significant discrepancy in the budget with respect to the actual
values, the actual values may be run through the budget rate to identify specific transactions or
financial activities that may have caused the discrepancy. If the rate projections are substantially
accurate, information may be provided regarding the accuracy of the budget. If the result is near
the target budget but differs greatly when using the actual rates, a determination can be made
regarding how much of the discrepancy is due to exchange rate variance.
It would have been obvious to one of ordinary skill in the art before the effective filing date of
the claimed invention to modify reference combination A to obtain above limitation based on the
teachings of Clode for the purpose of running through the transactional rates to track finances or
through the budget rates to determine budget accuracy.
Claim(s) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over combination
A in view of Gilbert (US 2011/0167115).
Combination A discloses elements of the claimed invention as noted but does not disclose wherein the statistical analysis modules use an R geo-statistical Model. However, Gilbert discloses:
Gilbert [0152] The core We Meddle tie strength engine is written in Perl, appropriating the
output of an R statistical model, although the engine is not limited by the software language used
to implement it. When a user first signs in to We Meddle, the system needs to build a database of
tie strengths for each account the user follows. The sign-in forks off hundreds and sometimes
thousands of API requests against Twitter. This was the main technical hurdle: overlapping the
relational data requests in precisely the right way to support hundreds of simultaneous users.
It would have been obvious to one of ordinary skill in the art before the effective filing date of
the claimed invention to modify reference combination A to obtain above limitation based on the
teachings of Gilbert for the purpose of building a database of tie strengths for each account the
user follows.
Claim(s) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over applicant disclosed prior art (ADPA) in view of Bodner (US 8,491,311).
Examiner Note: Hereafter, above references are combination A.
install a structured query language (SQL) server application on a host server;
install statistical analysis modules on the host server;
Specification [0040] In one or more embodiments, the host server 102 may be a x64 or larger server loaded with at least 32 processing cores and at least 64 Gb of RAM (e.g., in a Gig-E network) to serve as the host machine for the SQL server application 104 and the statistical analysis modules 106. The statistical analysis modules 106 may include traditional statistical application systems like SPSS, SAS, NVIVO or R.
execute the statistical analysis modules within a relational database of the SQL server
application to analyze data stored in the relational database; and
ADPA discloses elements of the claimed invention as noted but does not disclose above limitation. However, Bodner discloses:
Bodner col 2 line 65 – col 3 line 8 Additionally included is the system wherein the computer network is the Internet. Additionally included is the system wherein the analysis data is stored on a relational database, and wherein statistical and data mining analysis is performed on the analysis data to determine a specific learning problem of the student and to generate a remedial recommendation. Additionally included is the system wherein the analysis data is stored on a relational database, and wherein statistical and data mining analysis is performed on the analysis data to determine one or more universals of learning encoded in the analysis data.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify ADPA to obtain above limitation based on the teachings of Bodner for the purpose of performing comprehensive data analysis and statistical computations, see specification.
generate outputs based on the execution of the statistical analysis modules within the relational database.
Specification [0019] In one or more embodiments, a large data set including capital, assets, purchasing, and invoice records may be built and integrated into a capital spend data mart and stored in a SQL server database. R scripts may be used to analyze the data mart and product text and graphical outputs of common R libraries run inside the database along with SQL scripts. For example, the data set may include billions of records for a one-year period of financial operations, and the data mart may include over a million records of aggregated and summarized data. The R scripts may produce a trending spend and forecast budget run rate, for example.
Claim(s) 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over combination
A in view of Mozes (US 2017/0308809)
Combination A discloses elements of the claimed invention as noted but does not disclose wherein to generate the outputs occurs without exporting the data stored in the relational database. However, Mozes discloses:
Mozes [0067] At block 230, the data partitions are analyzed within the relational database system to generate partition metrics data. The partition metrics data include a number of partitions in the training data, a size of each partition in the training data, and a respective value for the partition key associated with each partition in the training data. Other partition metrics data may be possible as well, in accordance with other embodiments. In accordance with one embodiment, metrics logic 125 of analytics logic 110 of FIG. 1 is configured to generate the partition metrics data.
Interpreted per specification [0020]
It would have been obvious to one of ordinary skill in the art before the effective filing date of
the claimed invention to modify reference combination A to obtain above limitation based on the
teachings of Mozes for the purpose of providing a relational database system, having a
computing device configured with analytics logic, for generating a composite model object of
partitioned data mining models built on partitioned training data, and scoring records of scoring
data using the composite model object, see [0006] FIG. 1.
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over reference combination
A in view of Ward (US 2020/0304384).
Combination A discloses elements of the claimed invention as noted but does not disclose wherein the host server comprises 32 processing cores and 64 gigabytes of random access memory. However, Ward discloses:
Ward [0040] For example, in the illustrated embodiment, the various instance types (small,
medium, large and extra large) of the standard instance family 210 may be implementable using
a single server type "S1" with 32 processing cores, 64 gigabytes of available main memory and
1600 gigabytes of available disk storage.
It would have been obvious to one of ordinary skill in the art before the effective filing date of
the claimed invention to modify reference combination A to obtain above limitation based on the
teachings of Ward for the purpose of managing flexible capacity pool reservations for network-
accessible resources, see [0016]
Claim(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over combination
A in view of Sihavong (US 2022/0277383) in view of Clode (US 2013/0054440)
Combination A discloses elements of the claimed invention as noted but does not disclose wherein the outputs comprise a spending trend. However, Sihavong
discloses:
Sihavong [0023] Account data 1271 includes financial data from the user's financial accounts.
User financial accounts may include user savings accounts, checking accounts, credit card
accounts, and any other types of payment accounts. Account data 1271 may include, but is not
limited to, a plurality of data features, such as a user identifier (ID), an account number, cash
flow, account balances, payment due dates of financial cards accounts, interest charges of the
financial card accounts, savings goals, budgets, credit scores, spending trend data, etc. Account
data 1271 is associated with transaction data 1272 of corresponding user accounts and
corresponding historical user behavior data 1273.
It would have been obvious to one of ordinary skill in the art before the effective filing date of
the claimed invention to modify reference combination A to obtain above limitation based on the
teachings of Sihavong for the purpose of recommending personalized digital nudges designed to
influence the user in a manner that furthers a savings goal, see abstract.
Reference combination A discloses elements of the claimed invention as noted but does not disclose wherein the outputs comprise a forecast budget run rate. However,
Clode discloses:
Clode [0047] Established budget values may be run through the budget rates or the
transactional rates. While assembling a budget VS. transaction report, the actual values may be
run through the transactional rates to track finances or through the budget rates to determine
budget accuracy. If there is a significant discrepancy in the budget with respect to the actual
values, the actual values may be run through the budget rate to identify specific transactions or
financial activities that may have caused the discrepancy. If the rate projections are substantially
accurate, information may be provided regarding the accuracy of the budget. If the result is near
the target budget but differs greatly when using the actual rates, a determination can be made
regarding how much of the discrepancy is due to exchange rate variance.
It would have been obvious to one of ordinary skill in the art before the effective filing date of
the claimed invention to modify reference combination A to obtain above limitation based on the
teachings of Clode for the purpose of running through the transactional rates to track finances or
through the budget rates to determine budget accuracy.
Claim(s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over combination
A in view of Gilbert (US 2011/0167115).
Combination A discloses elements of the claimed invention as noted but does not disclose wherein the statistical analysis modules use an R geo-statistical Model. However, Gilbert discloses:
Gilbert [0152] The core We Meddle tie strength engine is written in Perl, appropriating the
output of an R statistical model, although the engine is not limited by the software language used
to implement it. When a user first signs in to We Meddle, the system needs to build a database of
tie strengths for each account the user follows. The sign-in forks off hundreds and sometimes
thousands of API requests against Twitter. This was the main technical hurdle: overlapping the
relational data requests in precisely the right way to support hundreds of simultaneous users.
It would have been obvious to one of ordinary skill in the art before the effective filing date of
the claimed invention to modify reference combination A to obtain above limitation based on the
teachings of Gilbert for the purpose of building a database of tie strengths for each account the
user follows.
Claim(s) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over applicant disclosed prior art (ADPA) in view of Bodner (US 8,491,311).
Examiner Note: Hereafter, above references are combination A.
install a structured query language (SQL) server application on a host server;
install statistical analysis modules on the host server;
Specification [0040] In one or more embodiments, the host server 102 may be a x64 or larger server loaded with at least 32 processing cores and at least 64 Gb of RAM (e.g., in a Gig-E network) to serve as the host machine for the SQL server application 104 and the statistical analysis modules 106. The statistical analysis modules 106 may include traditional statistical application systems like SPSS, SAS, NVIVO or R.
execute the statistical analysis modules within a relational database of the SQL server
application to analyze data stored in the relational database; and
ADPA discloses elements of the claimed invention as noted but does not disclose above limitation. However, Bodner discloses:
Bodner col 2 line 65 – col 3 line 8 Additionally included is the system wherein the computer network is the Internet. Additionally included is the system wherein the analysis data is stored on a relational database, and wherein statistical and data mining analysis is performed on the analysis data to determine a specific learning problem of the student and to generate a remedial recommendation. Additionally included is the system wherein the analysis data is stored on a relational database, and wherein statistical and data mining analysis is performed on the analysis data to determine one or more universals of learning encoded in the analysis data.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify ADPA to obtain above limitation based on the teachings of Bodner for the purpose of performing comprehensive data analysis and statistical computations, see specification.
generate outputs based on the execution of the statistical analysis modules within the relational database.
Specification [0019] In one or more embodiments, a large data set including capital, assets, purchasing, and invoice records may be built and integrated into a capital spend data mart and stored in a SQL server database. R scripts may be used to analyze the data mart and product text and graphical outputs of common R libraries run inside the database along with SQL scripts. For example, the data set may include billions of records for a one-year period of financial operations, and the data mart may include over a million records of aggregated and summarized data. The R scripts may produce a trending spend and forecast budget run rate, for example.
Claim(s) 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over combination
A in view of Mozes (US 2017/0308809)
Combination A discloses elements of the claimed invention as noted but does not disclose wherein generating the outputs occurs without exporting the data stored in the relational database. However, Mozes discloses:
Mozes [0067] At block 230, the data partitions are analyzed within the relational database system to generate partition metrics data. The partition metrics data include a number of partitions in the training data, a size of each partition in the training data, and a respective value for the partition key associated with each partition in the training data. Other partition metrics data may be possible as well, in accordance with other embodiments. In accordance with one embodiment, metrics logic 125 of analytics logic 110 of FIG. 1 is configured to generate the partition metrics data.
Interpreted per specification [0020]
It would have been obvious to one of ordinary skill in the art before the effective filing date of
the claimed invention to modify reference combination A to obtain above limitation based on the
teachings of Mozes for the purpose of providing a relational database system, having a
computing device configured with analytics logic, for generating a composite model object of
partitioned data mining models built on partitioned training data, and scoring records of scoring
data using the composite model object, see [0006] FIG. 1.
Claim(s) 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over reference combination
A in view of Ward (US 2020/0304384).
Combination A discloses elements of the claimed invention as noted but does not disclose wherein the host server comprises 32 processing cores and 64 gigabytes of random access memory. However, Ward discloses:
Ward [0040] For example, in the illustrated embodiment, the various instance types (small,
medium, large and extra large) of the standard instance family 210 may be implementable using
a single server type "S1" with 32 processing cores, 64 gigabytes of available main memory and
1600 gigabytes of available disk storage.
It would have been obvious to one of ordinary skill in the art before the effective filing date of
the claimed invention to modify reference combination A to obtain above limitation based on the
teachings of Ward for the purpose of managing flexible capacity pool reservations for network-
accessible resources, see [0016]
Claim(s) 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over combination
A in view of Sihavong (US 2022/0277383) in view of Clode (US 2013/0054440)
Combination A discloses elements of the claimed invention as noted but does not disclose wherein the outputs comprise a spending trend. However, Sihavong
discloses:
Sihavong [0023] Account data 1271 includes financial data from the user's financial accounts.
User financial accounts may include user savings accounts, checking accounts, credit card
accounts, and any other types of payment accounts. Account data 1271 may include, but is not
limited to, a plurality of data features, such as a user identifier (ID), an account number, cash
flow, account balances, payment due dates of financial cards accounts, interest charges of the
financial card accounts, savings goals, budgets, credit scores, spending trend data, etc. Account
data 1271 is associated with transaction data 1272 of corresponding user accounts and
corresponding historical user behavior data 1273.
It would have been obvious to one of ordinary skill in the art before the effective filing date of
the claimed invention to modify reference combination A to obtain above limitation based on the
teachings of Sihavong for the purpose of recommending personalized digital nudges designed to
influence the user in a manner that furthers a savings goal, see abstract.
Reference combination A discloses elements of the claimed invention as noted but does not disclose wherein the outputs comprise a forecast budget run rate. However,
Clode discloses:
Clode [0047] Established budget values may be run through the budget rates or the
transactional rates. While assembling a budget VS. transaction report, the actual values may be
run through the transactional rates to track finances or through the budget rates to determine
budget accuracy. If there is a significant discrepancy in the budget with respect to the actual
values, the actual values may be run through the budget rate to identify specific transactions or
financial activities that may have caused the discrepancy. If the rate projections are substantially
accurate, information may be provided regarding the accuracy of the budget. If the result is near
the target budget but differs greatly when using the actual rates, a determination can be made
regarding how much of the discrepancy is due to exchange rate variance.
It would have been obvious to one of ordinary skill in the art before the effective filing date of
the claimed invention to modify reference combination A to obtain above limitation based on the
teachings of Clode for the purpose of running through the transactional rates to track finances or
through the budget rates to determine budget accuracy.
Response to Arguments
Applicant Argues
Independent claim 1 of the present application, from which claims 2-5 depend, recites,
inter alia, a method for analyzing data stored in a relational database that includes "executing the statistical analysis modules within a relational database of the SQL server application to
analyze data stored in the relational database" (emphasis added). Independent claims 6 and 11, from which claims 7-10 and 12-14 depend, respectively, include similar recitations written in different formats. For the reasons discussed below, the Applicant respectfully submits that the cited references fail to disclose or suggest the above recitation.
At page 12 of the Office Action, the Office acknowledges that Brezis and Rosner fail to disclose or suggest the above recitation. Instead, the Office alleges that Cabrera-Arevalo discloses the claimed analytics, referring specifically to paragraph [0035] thereof. However, Cabrera-Arevalo is directed toward the training of a neural network inside of a database engine (para. [0029]). Cabrera-Arevalo is silent as to the execution ofstatistical analysis modules within a relational database, as recited in the present claims. In fact, Cabrera-Arevalo is entirely silent as to the use of such models or the use of statistical analysis entirely, let alone execution within a database. Paragraph [0035] of Cabrera-Arevalo simply discloses that the training of a neural network can enrich the analytics within a database management system without any suggestion or reference to the execution of anything within the database management system, let alone the execution of statistical analysis modules within the database itself, as recited in the present claims. Thus, for at least these reasons, Cabrera-Arevalo fails to disclose or suggest the above recitations of the present claims.
Rathakrishnan, Mozes, Ward, Sihavong, Clode, and Gilbert are each cited by the Office as allegedly disclosing additional features recited in various other recitations of the present application. Assuming arguendo that the Office's characterizations of these additional references are accurate, the Applicant respectfully submits that each of the additional references fails to remedy the deficiencies of Brezis, Rosner, and Cabrera-Arevalo, as each fail to disclose or suggest "executing the statistical analysis modules within a relational database of the SQL
server application to analyze data stored in the relational database," as recited in the present claims. Thus, each of the cited references, whether taken alone or in combination, fail to disclose or suggest each of the recitations of the present claims. Accordingly, for at least this reason, the Applicant respectfully submits that the claims of the present application are patentable over the cited references. Withdrawal of the rejection is respectfully requested.
Examiner Responds
Applicant’s arguments with respect to claim(s) 1-14 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ETIENNE PIERRE LEROUX whose telephone number is (571)272-4022. The examiner can normally be reached M-F 8:00 am to 4:30 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Apu Mofiz can be reached at 571 272 4080. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ETIENNE P LEROUX/Primary Examiner of Art Unit 2161
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