DETAILED ACTION
This action is in response to the Applicant Response filed 05 July 2024 for application 18/765,042 filed 05 July 2024.
Claim(s) 1-20 is/are pending.
Claim(s) 1-20 is/are rejected.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Objections
Claim(s) 2-3, 9-10, 12-13, 18-19 is/are objected to because of the following informalities:
Claim 2, line 2, among for respective model features should read “
Claim 9, line 5, the second of features should read “the second set of features”
Claim 10, line 2, using large language model should read “using a large language model”
Claim 12, lines 3-4, among for respective model features should read “
Claim 18, line 5, the second of features should read “the second set of features”
Claim 19, line 2, using large language model should read “using a large language model”
Claims 3, 10, 13, 19 are objected to due to their dependence, either directly or indirectly, on claims 2, 9-10, 12, 18-19
Appropriate correction is required.
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.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 101, because the claim(s) is/are directed to an abstract idea, and because the claim elements, whether considered individually or in combination, do not amount to significantly more than the abstract idea, see Alice Corporation Pty. Ltd. V. CLS Bank International et al., 573 US 208 (2014).
Regarding claim 1, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 1 is directed to a system with a processor, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) system for dynamic data operations modeling.
The limitation of generate, ... at each respective time stage from the sequence of time stages, a categorical prediction associated with the data operations using the meta attributes ..., as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application.
The claim recites additional element(s) – system, processor, memory, processor-executable instructions. The additional element(s) is/are recited at a high-level of generality (i.e., as generic computer components performing generic computer functions of executing instructions on the computers) such that it amounts to no more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(b)).
The claim recites additional element(s) – detection model, multinomial logistic regression. The additional element(s) is/are recited at a high-level of generality such that it amounts to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h)).
The claim recites receive a set of data records associated with meta attributes representing operations on the data records, the operations performed over a sequence of time stages; transmit, following the sequence of time stages, one or more signals representing one or more from the categorical predictions for dynamically updating the user interface for communicating an interim categorical prediction during data operations execution, which is simply acquiring and transmitting data recited at a high level of generality. This is nothing more than insignificant extra-solution activity (MPEP 2106.05(g)).
The claim recites ... the detection model based on a multinomial logistic regression providing the categorical prediction for adapting multi-label predictions which is simply additional information regarding the model, and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of:
system, processor, memory, processor-executable instructions amount(s) to no more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(b))
acquiring and transmitting data amount(s) to no more than insignificant extra-solution activity (MPEP 2106.05(g)), wherein the insignificant extra-solution activity is the well-understood routine and conventional activit(y/ies) of receiving or transmitting data over a network and/or storing and retrieving information in memory (MPEP 2016.05(d))
detection model, multinomial logistic regression amount(s) to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h))
additional information regarding the model do(es) not apply the exception in a meaningful way (MPEP 2106.05(e))
The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 2, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 2 is directed to a system with a processor, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) system for dynamic data operations modeling.
The limitation of train the detection model by determining one or more regression coefficients based on odds ratio among for respective model features, as drafted, is a process that, under its broadest reasonable interpretation, covers a mathematical concept. The limitation encompasses calculating coefficients.
If a claim limitation, under its broadest reasonable interpretation, covers performance of mathematical concepts, then it falls within the "Mathematical Concepts" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application.
The claim recites wherein the detection model is based on a set of model features considered for the categorical prediction which is simply additional information regarding the model, and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of:
additional information regarding the model do(es) not apply the exception in a meaningful way (MPEP 2106.05(e))
The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 3, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 3 is directed to a system with a processor, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) system for dynamic data operations modeling.
The limitation of determine statistical association among respective multi-label predictions for excluding at least one model feature from training the detection model, as drafted, is a process that, under its broadest reasonable interpretation, covers a mathematical concept. The limitation encompasses calculating a statistical association.
If a claim limitation, under its broadest reasonable interpretation, covers performance of mathematical concepts, then it falls within the "Mathematical Concepts" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated
into a practical application. The claim does not recite any additional elements which integrate the
abstract idea into a practical application and, therefore, does not impose any meaningful limits on
practicing the abstract idea. Therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception. As discussed above with respect to the integration of the
abstract idea into a practical application, the claim does not recite any additional elements which
provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 4, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 4 is directed to a system with a processor, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) system for dynamic data operations modeling. The Step 2A Prong One Analysis for claim 1 is applicable here since claim 4 carries out the system of claim 1 but for the recitation of additional element(s) of wherein the sequence of time stages are at non-periodic time intervals.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application. In particular, the claim recites additional information regarding the time stages and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of additional information regarding the time stages do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Not applying the exception in a meaningful way does not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 5, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 5 is directed to a system with a processor, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) system for dynamic data operations modeling.
The limitation of determine variance inflation factors for generating class weights to associate with respective multi-label predictions, as drafted, is a process that, under its broadest reasonable interpretation, covers a mathematical concept. The limitation encompasses calculating variance inflation factors.
If a claim limitation, under its broadest reasonable interpretation, covers performance of mathematical concepts, then it falls within the "Mathematical Concepts" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated
into a practical application. The claim does not recite any additional elements which integrate the
abstract idea into a practical application and, therefore, does not impose any meaningful limits on
practicing the abstract idea. Therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception. As discussed above with respect to the integration of the
abstract idea into a practical application, the claim does not recite any additional elements which
provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 6, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 6 is directed to a system with a processor, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) system for dynamic data operations modeling. The Step 2A Prong One Analysis for claim 1 is applicable here since claim 6 carries out the system of claim 1 but for the recitation of additional element(s) of wherein the detection model is based on an ordinal regression for determining variance inflation factors.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application.
The claim recites wherein the detection model is based on an ordinal regression for determining variance inflation factors which is simply additional information regarding the model, and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)).
The claim recites additional element(s) – ordinal regression. The additional element(s) is/are recited at a high-level of generality such that it amounts to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of:
ordinal regression amount(s) to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h))
additional information regarding the model do(es) not apply the exception in a meaningful way (MPEP 2106.05(e))
The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 7, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 7 is directed to a system with a processor, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) system for dynamic data operations modeling. The Step 2A Prong One Analysis for claim 1 is applicable here since claim 7 carries out the system of claim 1 but for the recitation of additional element(s) of wherein the multi-label predictions includes a satisfactory, a require improvement, or an unsatisfactory prediction.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application. In particular, the claim recites additional information regarding the predictions and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of additional information regarding the predictions do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Not applying the exception in a meaningful way does not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 8, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 8 is directed to a system with a processor, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) system for dynamic data operations modeling.
The limitation of generate a first set of features based on the set of data records using feature engineering, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
The limitation of generate, ... at each respective time stage from the sequence of time stages based on the first set of features, the categorical prediction associated with the data operations using the meta attributes, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application.
The claim recites additional element(s) – feature engineering. The additional element(s) is/are recited at a high-level of generality such that it amounts to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h)).
The claim recites receive user input representing an audit type; transmit the first set of features to the detection model, which is simply acquiring and transmitting data recited at a high level of generality. This is nothing more than insignificant extra-solution activity (MPEP 2106.05(g)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of:
acquiring and transmitting data amount(s) to no more than insignificant extra-solution activity (MPEP 2106.05(g)), wherein the insignificant extra-solution activity is the well-understood routine and conventional activit(y/ies) of receiving or transmitting data over a network and/or storing and retrieving information in memory (MPEP 2016.05(d))
feature engineering amount(s) to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h))
The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 9, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 9 is directed to a system with a processor, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) system for dynamic data operations modeling.
The limitation of generate a second set of features based on the set of data records using feature engineering, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
The limitation of generate, ... at each respective time stage from the sequence of time stages based on the second set of features, the categorical prediction associated with the data operations using the meta attributes, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application.
The claim recites receive a second user input representing a second audit type; transmit the second of features to the detection model, which is simply acquiring and transmitting data recited at a high level of generality. This is nothing more than insignificant extra-solution activity (MPEP 2106.05(g)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of:
acquiring and transmitting data amount(s) to no more than insignificant extra-solution activity (MPEP 2106.05(g)), wherein the insignificant extra-solution activity is the well-understood routine and conventional activit(y/ies) of receiving or transmitting data over a network and/or storing and retrieving information in memory (MPEP 2016.05(d))
The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 10, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 10 is directed to a system with a processor, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) system for dynamic data operations modeling. The Step 2A Prong One Analysis for claim 9 is applicable here since claim 10 carries out the system of claim 9 but for the recitation of additional element(s) of wherein at least one of the first and second set of features are generated using large language model.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application.
The claim recites additional element(s) – large language model. The additional element(s) is/are recited at a high-level of generality such that it amounts to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h)).
The claim recites wherein at least one of the first and second set of features are generated using large language model which is simply applying a model recited at a high level of generality and amounts to the recitation of the words “apply it” (or an equivalent) or amounts to no more than mere instructions to implement an abstract idea or other exception on a computer (MPEP 2106.05(f)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of:
applying a model amount(s) to no more than mere instructions to apply the exception (MPEP 2106.05(f))
large language model amount(s) to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h))
The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 11, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 11 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) computer-implemented method for dynamic data operations modelling.
The limitation of generating, ... at each respective time stage from the sequence of time stages, a categorical prediction associated with the data operations using the meta attributes ..., as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application.
The claim recites additional element(s) – computer-implemented. The additional element(s) is/are recited at a high-level of generality (i.e., as generic computer components performing generic computer functions of executing instructions on the computers) such that it amounts to no more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(b)).
The claim recites additional element(s) – detection model, multinomial logistic regression. The additional element(s) is/are recited at a high-level of generality such that it amounts to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h)).
The claim recites receiving a set of data records associated with meta attributes representing operations on the data records, the operations performed over a sequence of time stages; transmitting, following the sequence of time stages, one or more signals representing one or more from the categorical predictions for dynamically updating the user interface for communicating an interim categorical prediction during data operations execution, which is simply acquiring and transmitting data recited at a high level of generality. This is nothing more than insignificant extra-solution activity (MPEP 2106.05(g)).
The claim recites ... the detection model based on a multinomial logistic regression providing the categorical prediction for adapting multi-label predictions which is simply additional information regarding the model, and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of:
computer-implemented amount(s) to no more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(b))
acquiring and transmitting data amount(s) to no more than insignificant extra-solution activity (MPEP 2106.05(g)), wherein the insignificant extra-solution activity is the well-understood routine and conventional activit(y/ies) of receiving or transmitting data over a network and/or storing and retrieving information in memory (MPEP 2016.05(d))
detection model, multinomial logistic regression amount(s) to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h))
additional information regarding the model do(es) not apply the exception in a meaningful way (MPEP 2106.05(e))
The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 12, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 12 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) computer-implemented method for dynamic data operations modelling.
The limitation of training the detection model by determining one or more regression coefficients based on odds ratio among for respective model features, as drafted, is a process that, under its broadest reasonable interpretation, covers a mathematical concept. The limitation encompasses calculating coefficients.
If a claim limitation, under its broadest reasonable interpretation, covers performance of mathematical concepts, then it falls within the "Mathematical Concepts" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application.
The claim recites wherein the detection model is based on a set of model features considered for the categorical prediction which is simply additional information regarding the model, and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of:
additional information regarding the model do(es) not apply the exception in a meaningful way (MPEP 2106.05(e))
The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 13, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 13 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) computer-implemented method for dynamic data operations modelling.
The limitation of determining statistical association among respective multi-label predictions for excluding at least one model feature from training the detection model, as drafted, is a process that, under its broadest reasonable interpretation, covers a mathematical concept. The limitation encompasses calculating a statistical association.
If a claim limitation, under its broadest reasonable interpretation, covers performance of mathematical concepts, then it falls within the "Mathematical Concepts" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated
into a practical application. The claim does not recite any additional elements which integrate the
abstract idea into a practical application and, therefore, does not impose any meaningful limits on
practicing the abstract idea. Therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception. As discussed above with respect to the integration of the
abstract idea into a practical application, the claim does not recite any additional elements which
provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 14, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 14 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) computer-implemented method for dynamic data operations modelling.
The limitation of determining variance inflation factors for generating class weights to associate with respective multi-label predictions, as drafted, is a process that, under its broadest reasonable interpretation, covers a mathematical concept. The limitation encompasses calculating variance inflation factors.
If a claim limitation, under its broadest reasonable interpretation, covers performance of mathematical concepts, then it falls within the "Mathematical Concepts" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated
into a practical application. The claim does not recite any additional elements which integrate the
abstract idea into a practical application and, therefore, does not impose any meaningful limits on
practicing the abstract idea. Therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception. As discussed above with respect to the integration of the
abstract idea into a practical application, the claim does not recite any additional elements which
provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 15, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 15 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) computer-implemented method for dynamic data operations modelling. The Step 2A Prong One Analysis for claim 11 is applicable here since claim 15 carries out the method of claim 11 but for the recitation of additional element(s) of wherein the detection model is based on an ordinal regression for determining variance inflation factors.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application.
The claim recites wherein the detection model is based on an ordinal regression for determining variance inflation factors which is simply additional information regarding the model, and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)).
The claim recites additional element(s) – ordinal regression. The additional element(s) is/are recited at a high-level of generality such that it amounts to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of:
ordinal regression amount(s) to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h))
additional information regarding the model do(es) not apply the exception in a meaningful way (MPEP 2106.05(e))
The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 16, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 16 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) computer-implemented method for dynamic data operations modelling. The Step 2A Prong One Analysis for claim 11 is applicable here since claim 16 carries out the method of claim 11 but for the recitation of additional element(s) of wherein the multi-label predictions includes a satisfactory, a require improvement, or an unsatisfactory prediction.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application. In particular, the claim recites additional information regarding the predictions and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of additional information regarding the predictions do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Not applying the exception in a meaningful way does not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 17, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 17 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) computer-implemented method for dynamic data operations modelling.
The limitation of generating a first set of features based on the set of data records using feature engineering, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
The limitation of generating, ... at each respective time stage from the sequence of time stages based on the first set of features, the categorical prediction associated with the data operations using the meta attributes, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application.
The claim recites additional element(s) – feature engineering. The additional element(s) is/are recited at a high-level of generality such that it amounts to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h)).
The claim recites receiving user input representing an audit type; transmitting the first set of features to the detection model, which is simply acquiring and transmitting data recited at a high level of generality. This is nothing more than insignificant extra-solution activity (MPEP 2106.05(g)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of:
acquiring and transmitting data amount(s) to no more than insignificant extra-solution activity (MPEP 2106.05(g)), wherein the insignificant extra-solution activity is the well-understood routine and conventional activit(y/ies) of receiving or transmitting data over a network and/or storing and retrieving information in memory (MPEP 2016.05(d))
feature engineering amount(s) to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h))
The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 18, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 18 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) computer-implemented method for dynamic data operations modelling.
The limitation of generating a second set of features based on the set of data records using feature engineering, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
The limitation of generating, ... at each respective time stage from the sequence of time stages based on the second set of features, the categorical prediction associated with the data operations using the meta attributes, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application.
The claim recites receiving a second user input representing a second audit type; transmitting the second of features to the detection model, which is simply acquiring and transmitting data recited at a high level of generality. This is nothing more than insignificant extra-solution activity (MPEP 2106.05(g)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of:
acquiring and transmitting data amount(s) to no more than insignificant extra-solution activity (MPEP 2106.05(g)), wherein the insignificant extra-solution activity is the well-understood routine and conventional activit(y/ies) of receiving or transmitting data over a network and/or storing and retrieving information in memory (MPEP 2016.05(d))
The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 19, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 19 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) computer-implemented method for dynamic data operations modelling.
The limitation of generating at least one of the first and second set of features ..., as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application.
The claim recites additional element(s) – large language model. The additional element(s) is/are recited at a high-level of generality such that it amounts to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h)).
The claim recites … using large language model which is simply applying a model recited at a high level of generality and amounts to the recitation of the words “apply it” (or an equivalent) or amounts to no more than mere instructions to implement an abstract idea or other exception on a computer (MPEP 2106.05(f)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of:
applying a model amount(s) to no more than mere instructions to apply the exception (MPEP 2106.05(f))
large language model amount(s) to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h))
The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 20, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 20 is directed to a computer-readable medium or media, which is directed to an article of manufacture, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) computer-readable medium or media … to perform a computer-implemented method of dynamic data operations modelling.
The limitation of generating, ... at each respective time stage from the sequence of time stages, a categorical prediction associated with the data operations using the meta attributes ..., as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application.
The claim recites additional element(s) – computer-readable medium or media, machine interpretable instructions, processor, computer-implemented. The additional element(s) is/are recited at a high-level of generality (i.e., as generic computer components performing generic computer functions of executing instructions on the computers) such that it amounts to no more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(b)).
The claim recites additional element(s) – detection model, multinomial logistic regression. The additional element(s) is/are recited at a high-level of generality such that it amounts to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h)).
The claim recites receiving a set of data records associated with meta attributes representing operations on the data records, the operations performed over a sequence of time stages; transmitting, following the sequence of time stages, one or more signals representing one or more from the categorical predictions for dynamically updating the user interface for communicating an interim categorical prediction during data operations execution, which is simply acquiring and transmitting data recited at a high level of generality. This is nothing more than insignificant extra-solution activity (MPEP 2106.05(g)).
The claim recites ... the detection model based on a multinomial logistic regression providing the categorical prediction for adapting multi-label predictions which is simply additional information regarding the model, and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of:
computer-readable medium or media, machine interpretable instructions, processor, computer-implemented amount(s) to no more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(b))
acquiring and transmitting data amount(s) to no more than insignificant extra-solution activity (MPEP 2106.05(g)), wherein the insignificant extra-solution activity is the well-understood routine and conventional activit(y/ies) of receiving or transmitting data over a network and/or storing and retrieving information in memory (MPEP 2016.05(d))
detection model, multinomial logistic regression amount(s) to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h))
additional information regarding the model do(es) not apply the exception in a meaningful way (MPEP 2106.05(e))
The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1, 4, 7-9, 11, 16-18, 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Misler et al. (US 2024/0338520 A1 – Machine Learning System for Generating Recommended Electronic Actions, hereinafter referred to as “Misler”) in view of Barker et al. (US 2021/0192334 A1 – Selecting Computational Kernel Variants Using Neural Networks, hereinafter referred to as “Barker”).
Regarding claim 1, Misler teaches a system for dynamic data operations modeling (Misler, [0016] – teaches dynamic data operations modeling) comprising:
a processor (Misler, [0014] - teaches computerized environment with processor and memory); and
a memory coupled to the processor and storing processor-executable instructions that, when executed, configure the processor to (Misler, [0014] - teaches computerized environment with processor and memory):
receive a set of data records associated with meta attributes representing operations on the data records (Misler, [0016] – teaches receiving input data; Misler, [0034]-[0035] – teaches analyzing input data to determine proper digital actions from insights derived from the input data), the operations performed over a sequence of time stages (Misler, [0021] – teaches iteratively adjusting the visualizations based on input data);
generate, by a detection model (Misler, [0034] - teaches machine learning analysis model) at each respective time stage from the sequence of time stages (Misler, [0021] - teaches real-time dynamic adjustments of digital actions based on sequences of inputs; Misler, [0025]-[0026] - teaches iteratively processing input to update a user profile state until a desired state in reached; see also, Misler, [0035], [0069]-[0070]), a categorical prediction associated with the data operations using the meta attributes (Misler, [0034]-[0035] – teaches analyzing input data to determine proper digital actions, including categorical visualizations, from insights derived from the input data) …; and
transmit, following the sequence of time stages (Misler, [0021] - teaches real-time dynamic adjustments of digital actions based on sequences of inputs; Misler, [0025]-[0026] - teaches iteratively processing input to update a user profile state until a desired state in reached; see also, Misler, [0035], [0069]-[0070]), one or more signals representing one or more from the categorical predictions for dynamically updating the user interface for communicating an interim categorical prediction during data operations execution (Misler, [0034]-[0035] – teaches analyzing input data to determine proper digital actions, including categorical visualizations, from insights derived from the input data to dynamically update the user interface).
While Misler teaches using machine learning to predict updates applied to a user interface, Misler does not explicitly teach using a multinomial logistic regression model.
Barker teaches
generate, by a detection model …, a categorical prediction associated with the data operations using the meta attributes (Barker, [0013] - teaches using a model to make predictions for an optimal kernel based on characteristics of the input data), the detection model based on a multinomial logistic regression providing the categorical prediction for adapting multi-label predictions (Barker, [0049] – teaches using multinomial logistic regression models for multiclass models).
It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to modify Misler with the teachings of Barker in order to optimize parameters to maximize computational efficiency in the field of predicting data operations based on input data (Barker, [0002] – “When a computation on a matrix or set of matrices is performed, a number of factors can affect the time it will take to complete the computation. Because multiple kernels may exist for performing the operation, it is often difficult to assess which of the kernels to utilize to perform the operation. By selecting an inefficient kernel to perform a computation, performance may be less than optimal. Therefore, selection of an optimal kernel is important in maximizing computation performance. However, the time and resource cost of selecting a kernel must be minimized in order to prevent the selection process from taking up more time than would be saved in using a possibly sub-optimal kernel.”).
Regarding claim 4, Misler in view of Barker teaches all of the limitations of the system of claim 1 as noted above. Misler further teaches wherein the sequence of time stages are at non-periodic time intervals (Misler, [0021] - teaches real-time dynamic adjustments of digital actions based on sequences of inputs from user feedback; Misler, [0025]-[0026] - teaches iteratively processing input to update a user profile state until a desired state in reached; see also, Misler, [0035], [0069]-[0070] [Because state changes are based on user feedback and not time steps, the intervals are non-periodic]).
It would have been obvious to one of ordinary skill in the art before the filing data of the claimed invention to combine the teachings of Misler and Barker for the same reasons as disclosed in claim 1 above.
Regarding claim 7, Misler in view of Barker teaches all of the limitations of the system of claim 1 as noted above. Barker further teaches wherein the multi-label predictions includes a satisfactory, a require improvement, or an unsatisfactory prediction (Barker, [0030] – teaches that kernels are selected based on a score such as perfect [satisfactory] and impossible [unsatisfactory]).
It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to combine the teachings of Misler and Barker in order to identify the labels to optimize parameters to maximize computational efficiency (Barker, [0002]).
Regarding claim 8, Misler in view of Barker teaches all of the limitations of the system of claim 1 as noted above. Misler further teaches wherein the processor is configured to:
receive user input representing an audit type (Misler, [0041] - teaches a multi-rater assessment on how users rate capabilities; see also Misler, [0034] – teaches multimodal machine learning analysis);
generate a first set of features based on the set of data records using feature engineering (Misler, [0034]-[0035] – teaches generating features from the input data to determine recommended digital actions);
transmit the first set of features to the detection model (Misler, [0034]-[0035] – teaches using machine learning to determine recommended digital actions based on input data features); and
generate, by the detection model (Misler, [0034] - teaches machine learning analysis model0 at each respective time stage from the sequence of time stages (Misler, [0021] - teaches real-time dynamic adjustments of digital actions based on sequences of inputs; Misler, [0025]-[0026] - teaches iteratively processing input to update a user profile state until a desired state in reached; see also, Misler, [0035], [0069]-[0070]) based on the first set of features (Misler, [0034]-[0035] – teaches generating features from the input data to determine recommended digital actions), the categorical prediction associated with the data operations using the meta attributes (Misler, [0034]-[0035] – teaches analyzing input data to determine proper digital actions, including categorical visualizations, from insights derived from the input data).
It would have been obvious to one of ordinary skill in the art before the filing data of the claimed invention to combine the teachings of Misler and Barker for the same reasons as disclosed in claim 1 above.
Regarding claim 9, Misler in view of Barker teaches all of the limitations of the system of claim 8 as noted above. Misler further teaches wherein the processor is configured to:
receive a second user input representing a second audit type (Misler, [0042] - teaches ratings relating to users from different categories of entities; see also Misler, [0034] – teaches multimodal machine learning analysis);
generate a second set of features based on the set of data records using feature engineering (Misler, [0034]-[0035] – teaches generating features from the input data to determine recommended digital actions);
transmit the second of features to the detection model (Misler, [0034]-[0035] – teaches using machine learning to determine recommended digital actions based on input data features); and
generate, by the detection model (Misler, [0034] - teaches machine learning analysis model) at each respective time stage from the sequence of time stages (Misler, [0021] - teaches real-time dynamic adjustments of digital actions based on sequences of inputs; Misler, [0025]-[0026] - teaches iteratively processing input to update a user profile state until a desired state in reached; see also, Misler, [0035], [0069]-[0070]) based on the second set of features (Misler, [0034]-[0035] – teaches generating features from the input data to determine recommended digital actions), the categorical prediction associated with the data operations using the meta attributes (Misler, [0034]-[0035] – teaches analyzing input data to determine proper digital actions, including categorical visualizations, from insights derived from the input data).
It would have been obvious to one of ordinary skill in the art before the filing data of the claimed invention to combine the teachings of Misler and Barker for the same reasons as disclosed in claim 8 above.
Regarding claim 11, it is the method embodiment of claim 1 with similar limitations to claim 1 and is rejected using the same reasoning found in claim 1.
Regarding claim 16, the rejection of claim 11 is incorporated herein. Further, the limitations in this claim are taught by Misler in view of Barker for the reasons set forth in the rejection of claim 7.
Regarding claim 17, the rejection of claim 11 is incorporated herein. Further, the limitations in this claim are taught by Misler in view of Barker for the reasons set forth in the rejection of claim 8.
Regarding claim 18, the rejection of claim 17 is incorporated herein. Further, the limitations in this claim are taught by Misler in view of Barker for the reasons set forth in the rejection of claim 9.
Regarding claim 20, it is the computer-readable medium or media embodiment of claim 1 with similar limitations to claim 1 and is rejected using the same reasoning found in claim 1. Misler further teaches a non-transitory computer-readable medium or media having stored thereon machine interpretable instructions which, when executed by a processor, cause the processor to (Misler, [0014] - teaches computerized environment with processor and memory) perform a computer-implemented method of dynamic data operations modelling (Misler, [0016] – teaches dynamic data operations modeling) …
It would have been obvious to one of ordinary skill in the art before the filing data of the claimed invention to combine the teachings of Misler and Barker for the same reasons as disclosed in claim 1 above.
Claim(s) 2-3, 12-13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Misler in view of Barker and further in view of Dugger et al. (US 2024/0005150 A1 – Explainable Machine Learning Modeling Using Wavelet Predictor Variable Data, hereinafter referred to as “Dugger”).
Regarding claim 2, Misler in view of Barker teaches all of the limitations of the system of claim 1 as noted above. Misler further teaches wherein the detection model is based on a set of model features considered for the categorical prediction (Misler, [0034]-[0035] – teaches generating features from the input data to determine recommended digital actions).
However, Misler in view of Barker does not explicitly teach train the detection model by determining one or more regression coefficients based on odds ratio among for respective model features.
Dugger teaches wherein the detection model is based on a set of model features considered for the categorical prediction (Dugger, [0182] – teaches using predictor variables to determine predictions), wherein the processor is configured to:
train the detection model by determining one or more regression coefficients based on odds ratio among for respective model features (Dugger, [0182]-[0184] – teaches generating training data [for training] using regression coefficients for the predictor variable based on odds ratio).
It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to modify Misler in view of Barker with the teachings of Dugger in order to improve machine-implemented operating environments in the field of logistic regression modeling (Dugger, [0053] – “Certain aspects can include operations and data structures with respect to neural networks or other models that improve how computing systems service analytical queries or otherwise update machine-implemented operating environments. For instance, a particular set of rules are employed in the training of timing-prediction models that are implemented via program code. This particular set of rules allow, for example, different models to be trained over different timing windows, for monotonicity to be introduced as a constraint in the optimization problem involved in the training of the models, or both. Employment of these rules in the training of these computer-implemented models can allow for more effective prediction of the timing of certain events, which can in turn facilitate the adaptation of an operating environment based on that timing prediction (e.g., modifying an industrial environment based on predictions of hardware failures, modifying an interactive computing environment based on risk assessments derived from the predicted timing of adverse events, etc.). Thus, certain aspects can effect improvements to machine-implemented operating environments that are adaptable based on the timing of target events with respect to those operating environments.”).
Regarding claim 3, Misler in view of Barker and further in view of Dugger teaches all of the limitations of the system of claim 2 as noted above. Dugger further teaches wherein the processor is configured to determine statistical association among respective multi-label predictions for excluding at least one model feature from training the detection model (Dugger, [0182] – teaches determining statistical relationships among variables and outputs, and excluding predictor variables based on the relationships).
It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to combine the teachings of Misler, Barker and Dugger in order to exclude features to improve machine-implemented operating environments (Dugger, [0053]).
Regarding claim 12, the rejection of claim 11 is incorporated herein. Further, the limitations in this claim are taught by Misler in view of Barker and further in view of Dugger for the reasons set forth in the rejection of claim 2.
Regarding claim 13, the rejection of claim 12 is incorporated herein. Further, the limitations in this claim are taught by Misler in view of Barker and further in view of Dugger for the reasons set forth in the rejection of claim 3.
Claim(s) 5-6, 14-15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Misler in view of Barker and further in view of Wang et al. (US 2023/0196133 A1 – Systems and Methods for Weight of Evidence Based Feature Engineering and Machine Learning, hereinafter referred to as “Wang”).
Regarding claim 5, Misler in view of Barker teaches all of the limitations of the system of claim 1 as noted above. However, Misler in view of Barker does not explicitly teach determine variance inflation factors for generating class weights to associate with respective multi-label predictions.
Wang teaches determine variance inflation factors for generating class weights to associate with respective multi-label predictions (Wang, [0070]-[0072] – teaches using variance inflation factors to determine strong predictors of the class).
It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to modify Misler in view of Barker with the teachings of Wang in order to quickly and accurately deploy logit models in the field of multinomial logistic regression (Wang, [0003] – “Current development and deployment of logit models requires detailed understanding of machine learning processes, including underlying mathematic and programming concepts. The specialized knowledge required for logit model deployment limits the scenarios for which logit models can be deployed. Current computer systems are thus limited in their ability to quickly and accurately deploy logit models as part of automated or partially automated processes.”).
Regarding claim 6, Misler in view of Barker teaches all of the limitations of the system of claim 1 as noted above. However, Misler in view of Barker does not explicitly teach wherein the detection model is based on an ordinal regression for determining variance inflation factors.
Wang teaches wherein the detection model is based on an ordinal regression for determining variance inflation factors (Wang, [0002] – teaches ordinal logistic regression logit models; Wang, [0070]-[0073] – teaches determining variance inflation factors for the logit model).
It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to modify Misler in view of Barker with the teachings of Wang in order to quickly and accurately deploy logit models in the field of multinomial logistic regression (Wang, [0003] – “Current development and deployment of logit models requires detailed understanding of machine learning processes, including underlying mathematic and programming concepts. The specialized knowledge required for logit model deployment limits the scenarios for which logit models can be deployed. Current computer systems are thus limited in their ability to quickly and accurately deploy logit models as part of automated or partially automated processes.”).
Regarding claim 14, the rejection of claim 11 is incorporated herein. Further, the limitations in this claim are taught by Misler in view of Barker and further in view of Wang for the reasons set forth in the rejection of claim 5.
Regarding claim 15, the rejection of claim 11 is incorporated herein. Further, the limitations in this claim are taught by Misler in view of Barker and further in view of Wang for the reasons set forth in the rejection of claim 6.
Claim(s) 10, 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Misler in view of Barker and further in view of Laprise et al. (US 2024/0419907 A1 – Using Large Language Models for Similarity Determinations in Content Generation Systems and Applications, hereinafter referred to as “Laprise”).
Regarding claim 10, Misler in view of Barker teaches all of the limitations of the system of claim 9 as noted above. While Misler in view of Barker teaches using natural language processing for feature extraction, Misler in view of Barker does not explicitly teach wherein at least one of the first and second set of features are generated using large language model.
Laprise teaches wherein at least one of the first and second set of features are generated using large language model (Laprise, [0054] – teaches using LLMs for feature extraction and generation).
It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to modify Misler in view of Barker with the teachings of Laprise in order to generate more accurate features in the field of feature extraction using language models (Laprise, [0046] – “Approaches in accordance with at least one embodiment can provide a versatile approach to processing information about such an environment ..., as may include geospatial and semantic information. In at least one embodiment, a deep learning model can be used that encapsulates domain-specific (or agnostic) knowledge about how objects in an environment are structured and related. An example deep learning model is a large language model (LLM) that can be trained to generate a textual description of an environment that retains semantic understanding of an environment in addition to providing information about the categories and locations of objects in the environment. In at least one embodiment, an LLM can generate a tokenized text string as a representation of an environment, where objects in the environment are represented as tokens in the string. There can also be a set of token descriptors in the string, and associated with specific tokens, that provide semantic and/or relationship information with respect to the various tokens of the string. In addition to generating a compact yet thorough representation of an environment, for example, an advantage of using a model such as an LLM is that the LLM can fill in gaps in the sensor data or otherwise make corrections where needed to provide a more accurate representation of the environment. For example, training an LLM to predict the next token in the text string (corresponding to a next object in an object graph, for example) can help the LLM to learn to establish correct relationships between objects in the environment. This can include, for example, identifying or correcting mistakes or gaps in environment representations, creating environment representations (e.g., maps or object graphs) from a photo or video stream of environment data, creating environment representations from aerial or satellite images, and creating environment descriptions from textual descriptions, among other such tasks.”).
Regarding claim 19, the rejection of claim 18 is incorporated herein. Further, the limitations in this claim are taught by Misler in view of Barker and further in view of Laprise for the reasons set forth in the rejection of claim 10.
Conclusion
Any inquiry concerning this communication or earlier communication from the examiner should be directed to MARSHALL WERNER whose telephone number is (469) 295-9143. The examiner can normally be reached on Monday – Thursday 7:30 AM – 4:30 PM ET.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kamran Afshar, can be reached at (571) 272-7796. The fax number for the organization where this application or proceeding is assigned is (571) 273-8300.
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/MARSHALL L WERNER/ Primary Examiner, Art Unit 2125
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