DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This final office action is in response to the amendment filed 22 September 2025.
Claims 1-8 and 11-20 are pending. Claims 1, 11, and 18 are independent claims.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-8 and 11-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
When considering subject matter eligibility under 35 USC 101, it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter (Step 1; MPEP 2106.03). If the claim falls within one of the statutory categories, the second step in the analysis is to determine whether the claim is directed toward a judicial exception (Step 2A; MPEP 2106.04). This step is broken into two prongs.
The first prong (Step 2A, Prong 1) determines whether or not the claims recite a judicial exception (e.g., mathematical concepts, mental processes, certain methods of organizing human activity). If it is determined at Step 2A, Prong 1 that the claims recite a judicial exception, the analysis proceeds to the second prong (Step 2A, Prong 2; MPEP 2106.04). The second prong (Step 2A, Prong 2) determines whether the claims integrate the judicial exception into a practical application. If the claims do not integrate the judicial exception into a practical application, the analysis proceeds to determine whether the claim is a patent-eligible exception (Step 2B; MPEP 2106.05).
If an abstract idea is present int the claim, in order to recite statutory subject matter, any element or combination of elements in the claim must be sufficient to ensure that the claim integrates the judicial exception into a practical application or amounts to significantly more than the abstract idea itself (see: 2019 PEG).
Step 1:
According to Step 1 of the two Step analysis, claims 1-8 are directed toward a system (machine). Claims 11-17 are directed toward a method (process). Claims 18-20 are directed toward a computer program product (manufacture). Therefore, each of these claims falls within one of the four statutory categories.
Claim 1:
Step 2A, Prong 1:
Following the determination that the claims fall within one of the statutory categories (Step 1), it must be determined inf the claims recite a judicial exception (Step 2A, Prong 1). In this instance, the claims are determined to recite a judicial exception (mental process)
With respect to claim 1, the claim recites:
identifying a plurality of tables comprising user data for one or more users stored across a plurality of data sources (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses a user observing tables to identify a plurality of tables having user data across a plurality of data sources)
filtering at least some of the user data from the plurality of tables based on a filter criterion and determining a cluster of tables from a remainder data of the plurality of tables (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses a user performing a judgment on a plurality of tables to filter the data into a data set and a remainder set)
generating… a plurality of links, wherein each link of the plurality of links is representative of a correlation between a pair of columns from respective tables of the cluster of tables, wherein each link is generated in response to the respective pair of columns comprising respective data satisfying a respective relatedness criterion (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses a user making a judgement to identify a link between columns of tables based upon a relatedness criterion (evaluation))
classifying… the each link into one or more classes according to a respective link classification criterion, wherein in response to the each link satisfying the respective link classification criterion, associating a respective label indicative of a respective class of the one or more classes with each link, the respective link classification criterion being a respective data privacy requirement for the user data (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses a user performing a judgement to link the tables when they satisfy a criterion (judgement) of the user and associated the tables with a label based upon an evaluation)
identifying, in response to a target request, one or more links of the plurality of links subject to the data privacy requirement from the cluster of tables… based on the label associated with the each link (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses a user performing an observation to identify links, subject to an evaluation of data privacy requirements, based on labels associated with each link)
extracting, in response to the target request, one or more pairs of columns of data from the cluster of tables based on the identified one or more tasks (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses a user performing a evaluation to determine one or more pairs of columns associated with the task for extraction)
Step 2A, Prong 2:
Accordingly, after determining that a claim recites a judicial exception in Step 2A Prong One, examiners should evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception in Step 2A Prong Two. A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception (MPEP 2106.04(d)).
The claims disclose the following additional limitations:
a processor
a non-transitory computer-readable medium having stored thereon computer-executable instructions that are executable by the system to cause the system to perform operations
The judicial exception is not integrated into a practical application. Specifically, these elements which are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Additionally, the claim recites:
using a neural network
The judicial exception is not integrated into a practical application. In particular, the claimed element amounts to generally linking the use of a judicial exception to a particular technological environment or field of use (using a neural network). As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application (MPEP § 2106.05(h))
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B:
Based on the determination in Step 2A of the analysis that the claims are directed toward a judicial exception, in must be determined if any claims contain any element or combination of elements sufficient to ensure that the claims amount to significantly more than the judicial exception (Step 2B).
The claims disclose the following additional limitations:
a processor
a non-transitory computer-readable medium having stored thereon computer-executable instructions that are executable by the system to cause the system to perform operations
The judicial exception is not integrated into a practical application. Specifically, these elements which are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Additionally, the claim recites:
using a neural network
The judicial exception is not integrated into a practical application. In particular, the claimed element amounts to generally linking the use of a judicial exception to a particular technological environment or field of use (using a neural network). As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application (MPEP § 2106.05(h))
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Claim 2:
Step 2A, Prong 2:
The claim recites the additional element:
storing data represented in the pair or columns in a temporary data store
The judicial exception is not integrated into a practical application. Specifically, these elements which are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. The additional elements of storing data represented in a pair of columns in a temporary data store includes elements which are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Claim 3:
Step 2A, Prong 1:
With respect to claim 3, the claim recites:
verifying the classification of the link using a sample join query (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses a user performing a judgement to verify the classification using a sample join query (evaluation))
in response to a determination that the link comprises a positive link (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses a user performing a determination (judgement))
Step 2A, Prong 2:
The claim recites the additional element:
storing data represented in the pair of columns in a final linkage inventory
These elements are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. The additional elements of storing data represented in a pair of columns in a final linkage inventory includes elements which are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Claim 4:
Step 2A, Prong 1:
With respect to claim 4, the claim recites:
verifying the classification of the link using a sample join query (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses a user performing a judgement to verify the classification using a sample join query (evaluation))
in response to a determination that the link comprises a false-positive link (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses a user performing a determination (judgement))
Step 2A, Prong 2:
The claim recites the additional element:
generating feedback data associated with the false-positive link
These limitations also amount to extra solution activity because it is a mere nominal or tangential addition to the claim, amounting to mere data output (see MPEP 2106.05(g)).
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. The additional elements of generating feedback data associated with the false-positive link includes elements which are merely a nominal or tangential addition to the claim, amounting to mere data output (MPEP 2106.05(g)).
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Claim 5:
Step 2A, Prong 2:
The claim recites the additional elements of wherein the neural network comprises a Siamese neural network.
The judicial exception is not integrated into a practical application. In particular, the claimed element amounts to generally linking the use of a judicial exception to a particular technological environment or field of use (using a neural network). As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application (MPEP § 2106.05(h))
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B:
Based on the determination in Step 2A of the analysis that the claims are directed toward a judicial exception, in must be determined if any claims contain any element or combination of elements sufficient to ensure that the claims amount to significantly more than the judicial exception (Step 2B).
The claims disclose the following additional limitation:
wherein the neural network comprises a Siamese neural network
The judicial exception is not integrated into a practical application. In particular, the claimed element amounts to generally linking the use of a judicial exception to a particular technological environment or field of use (using a neural network). As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application (MPEP § 2106.05(h))
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Claim 6:
Step 2A, Prong 2:
The claim recites the additional elements of adjusting the neural network based upon a result of verifying the classification of the link using a join query.
The judicial exception is not integrated into a practical application. In particular, the claimed element amounts to generally linking the use of a judicial exception to a particular technological environment or field of use (adjusting/retraining a neural network based upon data). As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application (MPEP § 2106.05(h))
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B:
Based on the determination in Step 2A of the analysis that the claims are directed toward a judicial exception, in must be determined if any claims contain any element or combination of elements sufficient to ensure that the claims amount to significantly more than the judicial exception (Step 2B).
The claims disclose the following additional limitation:
adjusting the neural network based upon a result of verifying the classification of the link using a join query
The judicial exception is not integrated into a practical application. In particular, the claimed element amounts to generally linking the use of a judicial exception to a particular technological environment or field of use (adjusting/retraining a neural network based upon data). As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application (MPEP § 2106.05(h))
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Claim 7:
Step 2A, Prong 2:
The claim recites the additional elements of introducing augmented data into the neural network, wherein the neural network is adjusted based on a result of classifying the augmented data.
The judicial exception is not integrated into a practical application. In particular, the claimed element amounts to generally linking the use of a judicial exception to a particular technological environment or field of use (adjusting/retraining a neural network based upon data). As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application (MPEP § 2106.05(h))
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B:
Based on the determination in Step 2A of the analysis that the claims are directed toward a judicial exception, in must be determined if any claims contain any element or combination of elements sufficient to ensure that the claims amount to significantly more than the judicial exception (Step 2B).
The claims disclose the following additional limitation:
introducing augmented data into the neural network, wherein the neural network is adjusted based on a result of classifying the augmented data
The judicial exception is not integrated into a practical application. In particular, the claimed element amounts to generally linking the use of a judicial exception to a particular technological environment or field of use (adjusting/retraining a neural network based upon data). As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application (MPEP § 2106.05(h))
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Claim 8:
Step 2A, Prong 2:
The claim recites the additional elements wherein the neural network has been applied to past links between other pairs of columns other than the pair of column.
The judicial exception is not integrated into a practical application. In particular, the claimed element amounts to generally linking the use of a judicial exception to a particular technological environment or field of use (adjusting/retraining a neural network based upon data). As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application (MPEP § 2106.05(h))
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B:
Based on the determination in Step 2A of the analysis that the claims are directed toward a judicial exception, in must be determined if any claims contain any element or combination of elements sufficient to ensure that the claims amount to significantly more than the judicial exception (Step 2B).
The claims disclose the following additional limitation:
wherein the neural network has been applied to past links between other pairs of columns other than the pair of column
The judicial exception is not integrated into a practical application. In particular, the claimed element amounts to generally linking the use of a judicial exception to a particular technological environment or field of use (adjusting/retraining a neural network based upon data). As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application (MPEP § 2106.05(h))
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Claim 11:
Step 2A, Prong 1:
Following the determination that the claims fall within one of the statutory categories (Step 1), it must be determined inf the claims recite a judicial exception (Step 2A, Prong 1). In this instance, the claims are determined to recite a judicial exception (mental process)
With respect to claim 11, the claim recites:
determining… a data subgroup comprising a subgroup of data tables of a group of data tables by filtering at least some of the data from the group of data tables based on a first criteria (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses a user observing tables to identify a cluster of tables from a plurality of tables)
determining… correlated data comprising a correlation between data from respective data tables of the subgroup of data tables, wherein the correlated data satisfy a cluster criterion (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses a user making a judgement to identify a link between columns of tables)
classifying… the correlated data into one or more classes according to classification criterion, wherein, in response to the correlated data satisfying the classification criterion, a label indicative of each of the one or more classes is associated with the correlated data, the classification criterion being a regulatory compliance criterion associated with user data (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses a user performing a judgement to link the tables when they satisfy a criterion (judgement) of the user and associated the tables with a label based upon an evaluation)
identifying, in response to a data removal request, a subset of the correlated data subject to the regulatory compliance criterion from the data subgroup based on the labels associated with the correlated data (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses a user performing an observation to identify links, subject to an evaluation of data privacy requirements, based on labels associated with each link)
extracting the identified subset of the correlated data from the data subgroup, the extracted subset of the correlated data comprising one or more pair of columns data (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses a user performing a evaluation to determine one or more pairs of columns associated with the task for extraction)
Step 2A, Prong 2:
Accordingly, after determining that a claim recites a judicial exception in Step 2A Prong One, examiners should evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception in Step 2A Prong Two. A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception (MPEP 2106.04(d)).
The claims disclose the following additional limitations:
a computer system comprising a processor
The judicial exception is not integrated into a practical application. Specifically, these elements which are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Additionally, the claim recites:
using machine learning
The judicial exception is not integrated into a practical application. In particular, the claimed element amounts to generally linking the use of a judicial exception to a particular technological environment or field of use (using machine learning). As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application (MPEP § 2106.05(h))
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B:
Based on the determination in Step 2A of the analysis that the claims are directed toward a judicial exception, in must be determined if any claims contain any element or combination of elements sufficient to ensure that the claims amount to significantly more than the judicial exception (Step 2B).
The claims disclose the following additional limitations:
a computer system comprising a processor
The judicial exception is not integrated into a practical application. Specifically, these elements which are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Additionally, the claim recites:
using machine learning
The judicial exception is not integrated into a practical application. In particular, the claimed element amounts to generally linking the use of a judicial exception to a particular technological environment or field of use (using machine learning). As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application (MPEP § 2106.05(h))
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Claim 12:
Step 2A, Prong 2:
The claim recites the additional element:
generating, by the computer system, a graphical user interface representative of the correlated data
This limitation amounts to extra-solution activity of gathering data for use in the claimed process. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity to a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
Step 2B:
Based on the determination in Step 2A of the analysis that the claims are directed toward a judicial exception, in must be determined if any claims contain any element or combination of elements sufficient to ensure that the claims amount to significantly more than the judicial exception (Step 2B).
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements of generating, by the computer system, a graphical user interface representative of the correlated data amounts to extra-solution activity of gathering data for use in the claimed process. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity to a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Claim 13:
Step 2A, Prong 2:
The claim recites the additional element:
wherein the group of data tables are received via the graphical user interface
This limitation amounts to extra-solution activity of gathering data for use in the claimed process. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity to a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
Step 2B:
Based on the determination in Step 2A of the analysis that the claims are directed toward a judicial exception, in must be determined if any claims contain any element or combination of elements sufficient to ensure that the claims amount to significantly more than the judicial exception (Step 2B).
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements of “wherein the group of data tables are received via the graphical user interface,” which amounts to extra-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Claim 14:
Step 2A, Prong 2:
The claim recites the additional elements:
wherein the correlated data comprise respective metadata associated with the group of data tables
This limitation amounts to extra-solution activity of gathering data for use in the claimed process. As described in MPEP 2106.05(g), limitations that amount to extra-solution activity because it is merely a nominal or tangential addition to the claim.
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B:
Based on the determination in Step 2A of the analysis that the claims are directed toward a judicial exception, in must be determined if any claims contain any element or combination of elements sufficient to ensure that the claims amount to significantly more than the judicial exception (Step 2B).
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements of “wherein the correlated data comprise respective metadata associated with the group of data tables,” amounts to extra solution activity because it is a mere nominal or tangential addition to the claim, amounting to mere data output (see MPEP 2106.05(g)).
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Claim 15:
Step 2A, Prong 1:
With respect to claim 15, the claim recites:
wherein the classification criterion is based in part on a group of classification factors, and wherein the group of classification factors are weighted… according to respective relative importance (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses a user making an observation to classify tables based upon the evaluation of classification factors, including a judgement regarding the relative importance of factors)
Step 2A, Prong 2:
Additionally, the claim recites:
using machine learning
The judicial exception is not integrated into a practical application. In particular, the claimed element amounts to generally linking the use of a judicial exception to a particular technological environment or field of use (using machine learning). As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application (MPEP § 2106.05(h))
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B:
Based on the determination in Step 2A of the analysis that the claims are directed toward a judicial exception, in must be determined if any claims contain any element or combination of elements sufficient to ensure that the claims amount to significantly more than the judicial exception (Step 2B).
Additionally, the claim recites:
using machine learning
The judicial exception is not integrated into a practical application. In particular, the claimed element amounts to generally linking the use of a judicial exception to a particular technological environment or field of use (using machine learning). As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application (MPEP § 2106.05(h))
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Claim 16:
Step 2A, Prong 2:
Additionally, the claim recites:
wherein the group of classification factors comprises at least one of table name, column name, and data type
This limitation amounts to extra-solution activity of gathering data for use in the claimed process. As described in MPEP 2106.05(g), limitations that amount to extra-solution activity because it is merely a nominal or tangential addition to the claim.
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B:
Based on the determination in Step 2A of the analysis that the claims are directed toward a judicial exception, in must be determined if any claims contain any element or combination of elements sufficient to ensure that the claims amount to significantly more than the judicial exception (Step 2B).
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements of “wherein the group of classification factors comprises at least one of table name, column name, and data type,” amounts to extra solution activity because it is a mere nominal or tangential addition to the claim, amounting to mere data output (see MPEP 2106.05(g)).
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Claim 17:
Step 2A, Prong 2:
The claim recites the additional element:
wherein the group classification factors comprise at least one of column length, last access time, and timestamp
This limitation amounts to extra-solution activity of gathering data for use in the claimed process. As described in MPEP 2106.05(g), limitations that amount to extra-solution activity because it is merely a nominal or tangential addition to the claim.
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B:
Based on the determination in Step 2A of the analysis that the claims are directed toward a judicial exception, in must be determined if any claims contain any element or combination of elements sufficient to ensure that the claims amount to significantly more than the judicial exception (Step 2B).
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements of “wherein the group classification factors comprise at least one of column length, last access time, and timestamp,” amounts to extra solution activity because it is a mere nominal or tangential addition to the claim, amounting to mere data output (see MPEP 2106.05(g)).
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Claim 18:
Step 2A, Prong 1:
Following the determination that the claims fall within one of the statutory categories (Step 1), it must be determined inf the claims recite a judicial exception (Step 2A, Prong 1). In this instance, the claims are determined to recite a judicial exception (mental process)
With respect to claim 1, the claim recites:
determining a data cluster of tables by filtering data from a plurality of tables based on a first criterion (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses a user observing tables to identify a cluster of tables from a plurality of tables)
determining… a link between a pair of columns from respective tables of the cluster of tables, wherein the link between the pair of columns satisfies a relatedness criterion (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses a user making a judgement to identify a link between columns of tables)
classifying… the link according to a link classification criterion, wherein the link satisfies the link classification criterion, wherein a label indicative of the link satisfying the link classification criterion is associated with the link (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses a user performing an observation to identify links, subject to an evaluation of data privacy requirements, based on labels associated with each link)
extracting, in response to the target request, the pair of columns from the data cluster based on the label associated with the link (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses a user performing a evaluation to determine one or more pairs of columns associated with the task for extraction)
Step 2A, Prong 2:
Accordingly, after determining that a claim recites a judicial exception in Step 2A Prong One, examiners should evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception in Step 2A Prong Two. A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception (MPEP 2106.04(d)).
The claims disclose the following additional limitations:
a computer-program product comprising a computer-readable medium having program instructions embedded therewith, the program instructions executable by a computer system to cause the computer system to perform operations
The judicial exception is not integrated into a practical application. Specifically, these elements which are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Additionally, the claim recites:
using a neural network
The judicial exception is not integrated into a practical application. In particular, the claimed element amounts to generally linking the use of a judicial exception to a particular technological environment or field of use (using a neural network). As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application (MPEP § 2106.05(h))
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B:
Based on the determination in Step 2A of the analysis that the claims are directed toward a judicial exception, in must be determined if any claims contain any element or combination of elements sufficient to ensure that the claims amount to significantly more than the judicial exception (Step 2B).
The claims disclose the following additional limitations:
a computer-program product comprising a computer-readable medium having program instructions embedded therewith, the program instructions executable by a computer system to cause the computer system to perform operations
The judicial exception is not integrated into a practical application. Specifically, these elements which are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Additionally, the claim recites:
using a neural network
The judicial exception is not integrated into a practical application. In particular, the claimed element amounts to generally linking the use of a judicial exception to a particular technological environment or field of use (using a neural network). As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application (MPEP § 2106.05(h))
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Claim 19:
Step 2A, Prong 1:
With respect to claim 19, the claim recites:
receiving a target for the link based upon a data privacy compliance requirement (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses a user purging columns based upon an observation)
in response to the link being determined to satisfy the link classification criterion, purging data associated with the link from the plurality of tables (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses a user purging columns based upon an evaluation that items are subject to a privacy requirement)
Claim 20:
Step 2A, Prong 1:
With respect to claim 20, the claim recites:
in response to the link being determined to satisfy the link classification criterion (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses a user performing a judgement to verify the classification (evaluation))
Step 2B, Prong 2:
The claim recites the additional elements:
adjusting the link classification criterion using a tuning model, wherein the tuning model has been generated using machine learning applied to past link classification information representative of past links of other pairs of columns in other tables other than the plurality of tables
The judicial exception is not integrated into a practical application. In particular, the claimed element amounts to generally linking the use of a judicial exception to a particular technological environment or field of use (using a neural network). As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application (MPEP § 2106.05(h))
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B:
Based on the determination in Step 2A of the analysis that the claims are directed toward a judicial exception, in must be determined if any claims contain any element or combination of elements sufficient to ensure that the claims amount to significantly more than the judicial exception (Step 2B).
Additionally, the claim recites:
adjusting the link classification criterion using a tuning model, wherein the tuning model has been generated using machine learning applied to past link classification information representative of past links of other pairs of columns in other tables other than the plurality of tables
The judicial exception is not integrated into a practical application. In particular, the claimed element amounts to generally linking the use of a judicial exception to a particular technological environment or field of use (using a neural network). As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application (MPEP § 2106.05(h))
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Claim Rejections - 35 USC § 103
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 (i.e., changing from AIA to pre-AIA ) 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.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-2 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Helft et al. (US 2021/0248311, filed 16 April 2021, hereafter Helft) and further in view of Khosravy (US 11301523, filed 18 June 2015) and further in view of Kakumanu et al. (US 2023/0015123, filed 15 July 2021, hereafter Kakumanu).
As per independent claim 1, Helft discloses a system, comprising:
a processor (paragraph 0049: Here, a processor is disclosed)
a non-transitory computer-readable medium having stored thereon computer-executable instructions that are executable by the system to cause the system to perform operations (paragraph 0050: Here, a non-transitory computer-readable medium is disclosed), comprising:
generating, using a neural network (paragraph 0069), a link between a pair of columns from respective tables of the cluster of tables (paragraph 0062: Here, at least two tables are selected (cluster) for consolidation from among a plurality of tables), wherein the pair of columns satisfies a relatedness criterion (paragraph 0063: Here, a link is determined between columns of a set of tables based upon the columns having a common characteristic. This common characteristic includes common data type, common headings, common data, or similar formats)
classifying, using the neural network, the each link according to a respective link classification criterion, wherein the link satisfies the link classification criterion (paragraphs 0069-0071: Here, the data type of a column is determined using a neural network. These columns may then be linked and consolidated by common metadata associated with each column)
Helft fails to specifically disclose:
identifying a plurality of tables comprising user data for one or more users stored across a plurality of data sources
filtering at least some of the user data from the plurality of tables based on a first criterion and determining a cluster of tables from a remaining data of the plurality of tables
generating a plurality of links, wherein each link of the plurality of links is representative of a correlation between a pair of columns from respective tables of the cluster of tables, wherein each link is generated in response to the respective pair of columns comprising respective data satisfying a respective relatedness criterion
classifying, using the neural network, the each link into one or more classes according to a respective link classification criterion, wherein in response to the each link satisfying the respective link classification criterion, associating a respective label indicative of a respective class of the one or more classes with each link, the respective link classification criterion being a respective data privacy requirement for the user data
identifying, in response to a target request, one or more links of the plurality of links subject to the data privacy requirement from the cluster of tables based on the label associated with the each link
extracting, in response to the target request, one or more pair of columns of data from the cluster of tables based on the identified one or more links
However, Khosravy, which is analogous to the claimed invention because it is directed toward linking data columns, discloses:
identifying a plurality of tables comprising user data for one or more users stored across a plurality of data sources (column 3, line 46- column 4, line 12: here, a plurality of data sets, arranged as tables, are provided by different content providers. Each of these data sets associated with content providers constitutes a data source).
filtering at least some of the user data from the plurality of tables based on a first criterion and determining a cluster of tables from a remaining data of the plurality of tables (column 4, lines 43-60: Here, data is extracted based upon semantic analysis (filtered). The semantically similar data is clustered for comparison. Data that is not clustered is remaining data)
generating a plurality of links, wherein each link of the plurality of links is representative of a correlation between a pair of columns from respective tables of the cluster of tables, wherein each link is generated in response to the respective pair of columns comprising respective data satisfying a respective relatedness criterion (column 11, lines 33-40: Here, a linking component identifies columns of disparate data sets having identical semantic types. This includes identifying that the data is semantically similar within a validation threshold (relatedness criterion) (column 9, line 52- column 10, line 12))
classifying the each link into one or more classes according to a respective link classification criterion, wherein in response to the each link satisfying the respective link classification criterion, associating a respective label indicative of a respective class of the one or more classes with each link (Figure 11; column 12, lines 12-29: Here, a relationship between datasets is visualized via a user interface. This includes applying a label, such as “credit risk” or “real estate.” These classes of data may then be selected and data from the associated data set displayed)
identifying, in response to a target request, one or more links of the plurality of links from the cluster of tables based on the label associated with the each link (Figure 11; column 12, lines 12-29: Here, a relationship between datasets is visualized via a user interface. This includes applying a label, such as “credit risk” or “real estate.” These classes of data may then be selected and data from the associated data set displayed)
extracting, in response to the target request, one or more pair of columns of data from the cluster of tables based on the identified one or more links (Figure 11; column 12, lines 12-29: Here, a relationship between datasets is visualized via a user interface. This includes applying a label, such as “credit risk” or “real estate.” These classes of data may then be selected and data from the associated data set displayed)
It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Khosravy’s teaching of associating, labeling, and classifying data using semantic analysis with Helft’s teaching of classifying data via a neural network, with a reasonable expectation of success, as it would have provided a robust analysis of data for linkage based upon a learned relationships to expose previously unknown correlations between data sets (Khosravy: column 2, lines 20-29).
Further, Kakumanu, which is analogous to the claimed invention because it is directed toward classifying information, discloses a classification criterion being a respective data privacy requirement for the user data (Figure 2; paragraph 0028 and 0030: Here, the data element is classified based upon its metadata to identify whether it contains personally identifiable information (privacy data)). It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Kakumanu with Helft-Khosravy, with a reasonable expectation of success, as it would have allowed for classifying data while maintaining privacy of personally identifiable information (Kakumanu: paragraph 0030).
As per dependent claim 2, Helft discloses wherein the operations further comprises storing data represented in the pair of columns in a temporary data store (Figure 7, item 702; paragraph 0092: Here, a data structure stores a plurality of tables. Each of these stored tables includes one or more columns).
As per dependent claim 19, Helft and Khosravy disclose the limitations similar to those in claim 18 (see below), and the same rejection is incorporated herein. Khosravy further discloses:
receiving the target request for the link (Figure 11; column 12, lines 12-29)
identifying, in response to a target request, the link from the cluster base on the label associated with the link (Figure 11; column 12, lines 12-29: Here, a relationship between datasets is visualized via a user interface. This includes applying a label, such as “credit risk” or “real estate.” These classes of data may then be selected and data from the associated data set displayed)
It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Khosravy’s teaching of associating, labeling, and classifying data using semantic analysis with Helft’s teaching of classifying data via a neural network, with a reasonable expectation of success, as it would have provided a robust analysis of data for linkage based upon a learned relationships to expose previously unknown correlations between data sets (Khosravy: column 2, lines 20-29).
Helft fails to specifically disclose a classification criterion being a respective data privacy requirement for the user data (Figure 2; paragraph 0028 and 0030: Here, the data element is classified based upon its metadata to identify whether it contains personally identifiable information (privacy data)). It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Kakumanu with Helft-Khosravy, with a reasonable expectation of success, as it would have allowed for classifying data while maintaining privacy of personally identifiable information (Kakumanu: paragraph 0030).
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Helft, Khosravy, and Kakumanu and further in view of Goyal et al. (US 11537785, filed 14 July 2021, hereafter Goyal).
As per dependent claim 3, Helft, Khosravy, and Kakumanu disclose the limitations similar to those in claim 1, and the same rejection is incorporated herein. Helft discloses verifying the classification of the link using a sample join query (paragraphs 0093-0095: Here, a join (merge) request is processed to determine if columns are similar based upon shared characteristics) and responsive to determining that the link comprises a positive link, performing an action (paragraph 0096: Here, based upon an affirmative determination of common characteristics, the columns are associated).
Helft fails to specifically disclose storing data represented in the pair of columns in a final linkage inventory. However, Goyal, which is analogous to the claimed invention because it is directed toward storing data, discloses storing data represented in the pair of columns in a final linkage inventory (claim 10: Here, merged data is stored in a merged table in a database). It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Goyal with Helft, with a reasonable expectation of success, as it would have allowed for storing merged data together (Goyal: claim 10). This would have facilitated grouping and storing similar data together for efficient data retrieval.
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Helft, Khosravy, and Kakumanu and further in view of Lekakis et al. (US 11397750, filed 27 November 2019, hereafter Lekakis).
As per dependent claim 4, Helft, Khosravy, and Kakumanu disclose the limitations similar to those in claim 1, and the same rejection is incorporated herein. Helft discloses verifying the classification of the link using a sample join query (paragraphs 0093-0095: Here, a join (merge)).
Helft fails to specifically disclose in response to a determination that the link comprises a false-positive link, generating feedback data associated with the false-positive link. However, Lekakis, which is analogous to the claimed invention because it is directed toward determining a false-positive regarding a merge, discloses a determination that the link comprises a false-positive link, generating feedback data associated with the false-positive link (column 11, line 49- column 12, line 3: Here, a merge operation is attempted. If it cannot be performed (false-positive), a failure notification (feedback) is provided to users). It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Lekakis with Helft, with a reasonable expectation of success, as it would have allowed for determining failure and notifying users (Lekakis: column 11, line 49- column 12, line 3). This would have provided a user with the advantage of knowing that a merge/join operation failed.
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Helft, Khosravy, and Kakumanu and further in view of Sheth et al. (US 2020/0090334, published 19 March 2020, hereafter Sheth).
As per dependent claim 5, Helft, Khosravy, and Kakumanu disclose the limitations similar to those in claim 1, and the same rejection is incorporated herein. Helft fails to specifically disclose wherein the neural network comprises a Siamese neural network.
However, Sheth, which is analogous to the claimed invention because it is directed toward using a neural network, discloses a Siamese neural network (paragraph 0042: Here, a Siamese neural network comprises identical neural networks with identical weights to learn similarities and differences). It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Sheth with Helft, with a reasonable expectation of success, as it would have allowed for using multiple neural networks to perform processing (Sheth: paragraph 0042). This would have allowed for improved processing speed when using a neural network, as the data would be able to be analyzed in parallel.
Claims 6-8 are rejected under 35 U.S.C. 103 as being unpatentable over Helft, Khosravy, and Kakumanu and further in view of Grauman et al. (US 2021/0174817, filed 6 December 2019, hereafter Grauman).
As per dependent claim 6, Helft, Khosravy, and Kakumanu disclose the limitations similar to those in claim 1, and the same rejection is incorporated herein. Helft discloses using a neural network to verify the classification of a link using a sample join query (paragraphs 0068-0069: Here, a neural network is used to combine (merge/join) table columns).
Helft fails to specifically disclose adjusting the neural network. However, Grauman, which is analogous to the claimed invention because it is directed toward training a neural network, discloses adjusting the neural network (paragraph 0082: Here, the neural network is adjusted based upon adjusting the weight values used to train the neural network). It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Grauman with Helft, with a reasonable expectation of success, as it would have allowed for modifying the neural network to improve results (Grauman: paragraph 0082). This would have allowed a user greater control over the neural network.
As per dependent claim 7, Helft, Khosravy, Kakumanu, and Grauman disclose the limitations similar to those in claim 6, and the same rejection is incorporated herein. Grauman discloses introducing augmented data into the neural network, wherein the neural network is adjusted based on a result of classifying the augmented data (paragraph 0037). It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Grauman with Helft, with a reasonable expectation of success, as it would have allowed for modifying the neural network to improve results (Grauman: paragraph 0082). This would have allowed a user greater control over the neural network.
As per dependent claim 8, Helft, Khosravy, and Kakumanu disclose the limitations similar to those in claim 1, and the same rejection is incorporated herein. Helft fails to specifically disclose wherein the neural network has been applied to past links between other pairs of columns other than the pair of columns.
However, Grauman, which is analogous to the claimed invention because it is directed toward training a neural network, discloses wherein the neural network has been applied to past data sets (paragraph 0026: Here, the neural network is trained on historical data). It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Grauman with Helft, with a reasonable expectation of success, as it would have allowed for training based upon past data sets (Grauman: paragraph 0026). This would have provided improved accuracy in classifying by the network (Grauman: paragraph 0026).
Claims 11-14 are rejected under 35 U.S.C. 103 as being unpatentable over Helft and further in view of Khosravy, and further in view of Kakumanu and further in view of Herman et al. (US 2022/0366082; filed 11 May 2021, hereafter Herman).
As per independent claim 11, Helft discloses a computer-implemented method, comprising:
determining, by a computer system comprising a processor (paragraph 0049), a data subgroup comprising a subgroup of data tables of a group of data tables by filtering the group of data tables (paragraph 0062: Here, at least two tables (a cluster) are selected for consolidation from among a plurality of tables. This selection of two data tables is interpreted to be filtering based upon a selection)
determining, by a computer system and using machine learning (paragraph 0069), correlated data comprising a correlation between data from respective data tables of the subgroup of data tables, wherein the correlation data satisfy a cluster criterion (paragraph 0063: Here, a link is determined between columns of a set of tables based upon the columns having a common characteristic. This common characteristic includes common data type, common headings, common data, or similar formats)
classifying, by the computer system and using the machine learning, the correlated data according to a classification criterion, wherein the correlated data satisfy the classification criterion (paragraphs 0069-0071: Here, the data type of a column is determined using a neural network. These columns may then be linked and consolidated by common metadata associated with each column)
determining, by the computer system and using machine learning(paragraph 0069), correlated data between data from respective data tables of a subgroup of data tables, wherein the correlated data satisfy a cluster criterion (paragraph 0062: Here, at least two tables are selected (cluster) for consolidation from among a plurality of tables), wherein the pair of columns satisfies a relatedness criterion (paragraph 0063: Here, a link is determined between columns of a set of tables based upon the columns having a common characteristic. This common characteristic includes common data type, common headings, common data, or similar formats)
classifying, by the computer system and using machine learning, the each link according to a respective link classification criterion, wherein the link satisfies the link classification criterion (paragraphs 0069-0071: Here, the data type of a column is determined using a neural network. These columns may then be linked and consolidated by common metadata associated with each column)
Helft fails to specifically disclose:
classifying the correlated data into one or more classes according to a classification criterion, wherein, in response to the correlated data satisfying the classification criterion, a label indicative of each of the one or more classes is associated with the correlated data, the classifying criterion being a regulatory compliance criterion associated with user data
identifying, in response to a data removal request, a subset of the correlated data subject to the regulatory compliance criterion from the data subgroup based on the labels associated with the correlated data
extracting the identified subset of the correlated data from the data subgroup, the extracted subset of the correlated data comprising one or more pair of columns of data
However, Khosravy, which is analogous to the claimed invention because it is directed toward linking data columns, discloses:
classifying the correlated data into one or more classes according to a classification criterion, wherein, in response to the correlated data satisfying the classification criterion, a label indicative of each of the one or more classes is associated with the correlated data (column 3, line 46- column 4, line 12: here, a plurality of data sets, arranged as tables, are provided by different content providers. Each of these data sets associated with content providers constitutes a data source. Further, data is extracted based upon semantic analysis (filtered). The semantically similar data is clustered for comparison (column 4, lines 43-60). Additionally, a linking component identifies columns of disparate data sets having identical semantic types (column 11, lines 33-40). This includes identifying that the data is semantically similar within a validation threshold (relatedness criterion) (column 9, line 52- column 10, line 12). Finally, a relationship between datasets is visualized via a user interface. This includes applying a label, such as “credit risk” or “real estate.” These classes of data may then be selected and data from the associated data set displayed (Figure 11; column 12, lines 12-29)
identifying, in response to a data removal request, a subset of the correlated data subject to the regulatory compliance criterion from the data subgroup based on the labels associated with the correlated data (Figure 11; column 12, lines 12-29: Here, a relationship between datasets is visualized via a user interface. This includes applying a label, such as “credit risk” or “real estate.” These classes of data may then be selected and data from the associated data set displayed)
extracting the identified subset of the correlated data from the data subgroup, the extracted subset of the correlated data comprising one or more pair of columns of data (Figure 11; column 12, lines 12-29: Here, a relationship between datasets is visualized via a user interface. This includes applying a label, such as “credit risk” or “real estate.” These classes of data may then be selected and data from the associated data set displayed)
It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Khosravy’s teaching of associating, labeling, and classifying data using semantic analysis with Helft’s teaching of classifying data via a neural network, with a reasonable expectation of success, as it would have provided a robust analysis of data for linkage based upon a learned relationships to expose previously unknown correlations between data sets (Khosravy: column 2, lines 20-29).
Further, Kakumanu, which is analogous to the claimed invention because it is directed toward classifying information, discloses a classification criterion being a regulatory compliance criterion associated with the user data (Figure 2; paragraphs 0003, 0028, and 0030: Here, the data element is classified based upon its metadata to identify whether it contains personally identifiable information (privacy data)). It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Kakumanu with Helft-Khosravy, with a reasonable expectation of success, as it would have allowed for classifying data while maintaining privacy of personally identifiable information (Kakumanu: paragraph 0030).
Finally, Herman, which is analogous to the claimed invention because it is directed toward removing personally identifiable data, discloses a data removal request paragraph 0005: Here, based upon a removal request of personally identifiable information, data is removed). It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Herman with Helft-Khosravy-Kakumanu, with a reasonable expectation of success, as it would have allowed for user control over personally identifiable information to maintain compliance General Data Protection Regulation (Herman: paragraph 0013).
As per dependent claim 12, Helft discloses the limitations similar to those in claim 11, and the same rejection is incorporated herein. Helft discloses generating, by the computer system, a graphical user interface representative of the correlation data (Figure 6; paragraph 0088: Here, a user interface is displayed including options for merging the column data).
As per dependent claim 13, Helft discloses wherein the group of data tables are received via the graphical user interface (Figure 6; paragraphs 0088-0090: Here, a user specifies multiple tables to merge (items 400A, 400B, and 400C) via a user interface).
As per dependent claim 14, Helft discloses wherein the correlated data comprise respective metadata associated with the group of tables (paragraph 0063: Here, metadata is associated with the columns to specify characteristics of the data, such as data type).
Claims 15-17 are rejected under 35 U.S.C. 103 as being unpatentable over Helft, Khosravy, Kakumanu, and Herman and further in view of Xu et al. (US 2021/0150407, filed 14 November 2019, hereafter Xu).
As per dependent claim 15, Helft, Khosravy, Kakumanu, and Herman disclose wherein the classification criterion is based in part on a group of classification factors (paragraph 0063). Helft fails to specifically disclose wherein the group of classification factors are weighted using the machine learning according to respective relative importance.
However, Xu, which is analogous to the claimed invention because it is directed toward using weight factors for classifying data, discloses wherein the group of classification factors are weighted using the machine learning according to respective relative importance (paragraph 0045: Here, a set of classification factors include weights which are increased based upon the contents being used to correctly classify data (importance in correctly classifying data)). It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Xu with Helft, with a reasonable expectation of success, as it would have allowed for weighting factors differently based upon their effect on classification (Xu: paragraph 0045). This weighting would have allowed for improved classification of data.
As per dependent claim 16, Helft, Khosravy, Kakumanu, Herman, and Xu disclose the limitations similar to those in claim 15, and the same rejection is incorporated herein. Helft discloses wherein the group of classification factors comprise at least one of table name, column name, and data type (paragraph 0063: Here, data type is disclosed as a classification factor for merging columns).
As per dependent claim 17, Helft, Khosravy, Kakumanu, Herman, and Xu disclose the limitations similar to those in claim 15, and the same rejection is incorporated herein. Helft fails to specifically disclose wherein the classification factors comprise at least one of column length, last access time, and timestamp.
However, the examiner takes official notice that it was notoriously well-known in the art at the time of the applicant’s effective filing date to have stored data such as last access time and timestamp in columns of a database associated with data items. It would have been obvious to one of ordinary skill in the art at the time of the applicant’s invention to have combined the well-known with Helft-Xu, with a reasonable expectation of success, as it would have allowed for considering these factors when categorizing data. This would have allowed for categorizing data based upon factors such as last access time and timestamps associated with the data.
Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over Helft, and further in view of Khosravy.
As per independent claim 18, Helft discloses a system, comprising:
a computer-program product comprising a computer-readable medium having program instructions embedded thereon, the programming instructions executed by a computer system to cause the computer system to perform (paragraphs 0049-0050)
determining a data cluster of tables from a plurality of tables (paragraph 0062: Here, at least two tables (a cluster) are selected for consolidation from among a plurality of tables)
determining, using a neural network (paragraph 0069), a link between a pair of columns from respective tables of the cluster of tables, wherein the pair of columns satisfies a relatedness criterion (paragraph 0063: Here, a link is determined between columns of a set of tables based upon the columns having a common characteristic. This common characteristic includes common data type, common headings, common data, or similar formats)
classifying, using the neural network, the link according to a link classification criterion, wherein the link satisfies the link classification criterion (paragraphs 0069-0071: Here, the data type of a column is determined using a neural network. These columns may then be linked and consolidated by common metadata associated with each column)
Helft fails to specifically disclose:
determining a data cluster by filtering data from a plurality of tables based on a first criterion
classifying the link according to a link classification criterion, wherein a label indicative of the link satisfying the link classification criterion is associated with the link
extracting, in response to a target request, the pair of columns from the data cluster based on the label associated with the link
However, Khosravy, which is analogous to the claimed invention because it is directed toward linking data columns, discloses:
determining a data cluster by filtering data from a plurality of tables based on a first criterion (column 3, line 46- column 4, line 12: here, a plurality of data sets, arranged as tables, are provided by different content providers. Each of these data sets associated with content providers constitutes a data source. Additionally, data is extracted based upon semantic analysis (filtered). The semantically similar data is clustered for comparison. Data that is not clustered is remaining data (column 4, lines 43-60)
classifying the link according to a link classification criterion, wherein a label indicative of the link satisfying the link classification criterion is associated with the link (column 11, lines 33-40: Here, a linking component identifies columns of disparate data sets having identical semantic types. This includes identifying that the data is semantically similar within a validation threshold (relatedness criterion) (column 9, line 52- column 10, line 12). Further, a relationship between datasets is visualized via a user interface. This includes applying a label, such as “credit risk” or “real estate.” These classes of data may then be selected and data from the associated data set displayed (Figure 11; column 12, lines 12-29)
extracting, in response to a target request, the pair of columns from the data cluster based on the label associated with the link (Figure 11; column 12, lines 12-29: Here, a relationship between datasets is visualized via a user interface. This includes applying a label, such as “credit risk” or “real estate.” These classes of data may then be selected and data from the associated data set displayed)
It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Khosravy’s teaching of associating, labeling, and classifying data using semantic analysis with Helft’s teaching of classifying data via a neural network, with a reasonable expectation of success, as it would have provided a robust analysis of data for linkage based upon a learned relationships to expose previously unknown correlations between data sets (Khosravy: column 2, lines 20-29).
Claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over Helft and Khosravy, and further in view of Grauman.
As per dependent claim 20, Helft and Khosravy disclose the limitations similar to those in claim 18, and the same rejection is incorporated herein. Helft discloses in response to the link being determined to satisfy the link classification criterion, performing linking (paragraphs 0092-0095).
Helft fails to specifically disclose adjusting the link classification criterion using a tuning model, wherein the tuning model has been generated using machine learning applied to past link classification information respective of past links to other pairs of columns in other tables other than the plurality of tables.
However, Grauman, which is analogous to the claimed invention because it is directed toward adjusting a classification using a tuning model, wherein the tuning model has been generated using machine learning applied to past classification information (paragraph 0026: Here, the neural network is trained on historical data). It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Grauman with Helft, with a reasonable expectation of success, as it would have allowed for training based upon past data sets (Grauman: paragraph 0026). This would have provided improved accuracy in classifying by the network (Grauman: paragraph 0026).
Response to Arguments
Applicant’s arguments with respect to the rejection of claims under 35 USC 102 and 35 USC 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Helft and further in view of Khosravy.
Applicant's arguments with respect to the rejection of claims under 35 USC 101 have been fully considered but they are not persuasive.
The examiner notes that the applicant’s arguments appear to focus on the amended limitations. Specifically, the applicant argues that the amended limitations provide an improvement to computer functionality or technical improvement by transforming datasets to conform to the criterion, such as, for example, the data privacy requirements (page 9). Further, the applicant argues that the claim recites additional limitations that are significantly more than the judicial exception (page 9).
It is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements. See the discussion of Diamond v. Diehr, 450 U.S. 175, 187 and 191-92, 209 USPQ 1, 10 (1981)) in subsection II, below. In addition, the improvement can be provided by the additional element(s) in combination with the recited judicial exception. See MPEP § 2106.04(d) (discussing Finjan, Inc. v. Blue Coat Sys., Inc., 879 F.3d 1299, 1303-04, 125 USPQ2d 1282, 1285-87 (Fed. Cir. 2018))
It is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology. For example, in Trading Technologies Int’l v. IBG, 921 F.3d 1084, 1093-94, 2019 USPQ2d 138290 (Fed. Cir. 2019), the court determined that the claimed user interface simply provided a trader with more information to facilitate market trades, which improved the business process of market trading but did not improve computers or technology (MPEP 2106.05(a)(II).
In this instance, the claimed improvement appears to be achieved via the mental process limitations. Specifically, Howeas noted by the examiner, the following limitations are mental processes:
identifying a plurality of tables comprising user data for one or more users stored across a plurality of data sources (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses a user observing tables to identify a plurality of tables having user data across a plurality of data sources)
filtering at least some of the user data from the plurality of tables based on a filter criterion and determining a cluster of tables from a remainder data of the plurality of tables (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses a user performing a judgment on a plurality of tables to filter the data into a data set and a remainder set)
generating… a plurality of links, wherein each link of the plurality of links is representative of a correlation between a pair of columns from respective tables of the cluster of tables, wherein each link is generated in response to the respective pair of columns comprising respective data satisfying a respective relatedness criterion (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses a user making a judgement to identify a link between columns of tables based upon a relatedness criterion (evaluation))
classifying… the each link into one or more classes according to a respective link classification criterion, wherein in response to the each link satisfying the respective link classification criterion, associating a respective label indicative of a respective class of the one or more classes with each link, the respective link classification criterion being a respective data privacy requirement for the user data (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses a user performing a judgement to link the tables when they satisfy a criterion (judgement) of the user and associated the tables with a label based upon an evaluation)
identifying, in response to a target request, one or more links of the plurality of links subject to the data privacy requirement from the cluster of tables… based on the label associated with the each link (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses a user performing an observation to identify links, subject to an evaluation of data privacy requirements, based on labels associated with each link)
extracting, in response to the target request, one or more pairs of columns of data from the cluster of tables based on the identified one or more tasks (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses a user performing a evaluation to determine one or more pairs of columns associated with the task for extraction)
For this reason, these arguments are not persuasive.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Huang et al. (US 2023/0058870): Discloses fulfilling a customer request to access and/or remove personal identifiable information from a data set consistent with data privacy regulations (paragraph 0013)
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KYLE R STORK whose telephone number is (571)272-4130. The examiner can normally be reached 8am - 2pm; 4pm - 6pm.
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/KYLE R STORK/Primary Examiner, Art Unit 2128