DETAILED ACTION
This action is in response to the Applicant Response filed 17 February 2026 for application 17/977,880 filed 31 October 2022.
Claim(s) 1, 10, 19 is/are currently amended.
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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 17 February 2026 has been entered.
Response to Arguments
Applicant’s arguments regarding the 35 U.S.C. 101 rejection of the claims are based on the newly amended subject matter. All arguments are addressed in the 35 U.S.C. 101 rejection of the claims below.
Applicant’s arguments regarding the 35 U.S.C. 102 and/or 35 U.S.C. 103 rejections of the claims are based on the newly amended subject matter. All arguments are addressed in the 35 U.S.C. 102 and/or 35 U.S.C. 103 rejections of the claims below.
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 method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) method.
The limitation of encoding ... a set of features extracted from computer-readable data associated with an object, wherein the set of features describes one or more predetermined aspects of the object, 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 ... a set of attribute predictions based on the 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.
The limitation of mapping ... the set of attribute predictions to a set of predetermined attributes corresponding to one of a plurality of predetermined classes, 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 outputting ... a classification of the object based on the mapping the set of predetermined attributes corresponding to the one of the plurality of predetermined classes, 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 hardware. 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) – machine learning model, attributes-level loss function. 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 the set of attribute predictions is determined by a machine learning model that is capable of generating predictions for unseen attributes ... which is simply applying the 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)).
The claim recites ... wherein the set of attribute predictions is determined by a machine learning model ... that is trained using an attributes-level loss function that includes an unseen attributes loss component that is computed only with respect to the unseen attributes, wherein the unseen attributes loss component penalizes at least some of predictions of zero probability score for the unseen attributes which is simply generic training to perform the abstract idea of model calibration and amounts to mere instructions to apply the exception (MPEP 2106.05(f)).
The claim recites wherein the set of attribute predictions comprises prediction scores, wherein at least one of the prediction scores comprises a value corresponding to a likelihood that the object is characterized by an attribute associated with the at least one of the prediction scores which is simply additional information regarding the attribute 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:
computer hardware amount(s) to no more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(b))
applying the model and generic training to perform the abstract idea amount(s) to no more than mere instructions to apply the exception (MPEP 2106.05(f))
machine learning model, attributes-level loss function 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 attribute predictions 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 method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) method.
The limitation of wherein the attributes-level loss function measures prediction errors in training the machine learning model by summing the seen attributes loss component and the unseen attributes loss component, as drafted, is a process that, under its broadest reasonable interpretation, covers a mathematical concept. The limitation encompasses summing values.
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 attributes-level loss function includes a seen attributes loss component that is computed only with respect to seen attributes which is simply additional information regarding the attributes-level loss function, 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 attributes-level loss function 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 method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) method.
The limitation of wherein prior to summing the seen attributes loss component and the unseen attributes loss component, the unseen attributes loss component is multiplied by a weighting coefficient selected to mitigate an imbalance among a set of training examples used to train the machine learning model, as drafted, is a process that, under its broadest reasonable interpretation, covers a mathematical concept. The limitation encompasses multiplying values.
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 method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) method. The Step 2A Prong One Analysis for claim 2 is applicable here since claim 4 carries out the method of claim 2 but for the recitation of additional element(s) of wherein the unseen attributes loss component is an entropy-based loss, and wherein the seen attributes loss component is a binary cross-entropy loss.
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 unseen attributes loss component is an entropy-based loss, and wherein the seen attributes loss component is a binary cross-entropy loss which is simply additional information regarding the attributes-level loss function, and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)).
The claim recites additional element(s) – entropy-based loss, binary cross-entropy loss. 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:
entropy-based loss, binary cross-entropy loss 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 attributes-level loss function 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 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 method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) method. The Step 2A Prong One Analysis for claim 2 is applicable here since claim 5 carries out the method of claim 2 but for the recitation of additional element(s) of wherein the unseen attributes loss component is an entropy-based loss, and wherein the seen attributes loss component is a mean squared error (MSE).
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 unseen attributes loss component is an entropy-based loss, and wherein the seen attributes loss component is a mean squared error (MSE) which is simply additional information regarding the attributes-level loss function, and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)).
The claim recites additional element(s) – entropy-based loss, mean squared error (MSE). 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:
entropy-based loss, mean squared error (MSE) 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 attributes-level loss function 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 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 method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) method. The Step 2A Prong One Analysis for claim 1 is applicable here since claim 6 carries out the method of claim 1 but for the recitation of additional element(s) of wherein the mapping the set of attribute predictions to the set of predetermined attributes corresponding to the one of the plurality of predetermined classes is based on a distance between a vector representation of the set of attribute predictions and a binary vector corresponding to the one of the plurality of predetermined classes.
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 mapping 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 mapping 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 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 method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) method. The Step 2A Prong One Analysis for claim 1 is applicable here since claim 7 carries out the method of claim 1 but for the recitation of additional element(s) of wherein the mapping the set of attribute predictions to the set of predetermined attributes corresponding to the one of the plurality of predetermined classes is performed using an additional machine learning 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 wherein the mapping the set of attribute predictions to the set of predetermined attributes corresponding to the one of the plurality of predetermined classes is performed using an additional machine learning model which is simply additional information regarding the mapping, and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)).
The claim recites additional element(s) – additional machine learning 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)).
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 machine learning 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))
additional information regarding the mapping 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 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 method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) method.
The limitation of outputting a notification that the object corresponds to a new class, wherein the notification is generated in response to determining that the object does not correspond to any of the plurality of predetermined classes, 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 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 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 method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) method.
The limitation of outputting one or more identities of attributes of the new class, 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 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 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.
The limitation of encoding a set of features extracted from computer-readable data associated with an object, wherein the set of features describes one or more predetermined aspects of the object, 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 a set of attribute predictions based on the 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.
The limitation of mapping the set of attribute predictions to a set of predetermined attributes corresponding to one of a plurality of predetermined classes, 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 outputting a classification of the object based on the mapping the set of attribute predictions to the set of predetermined attributes corresponding to the one of the plurality of predetermined classes, 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. 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) – machine learning model, attributes-level loss function. 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 the set of attribute predictions is determined by a machine learning model that is capable of generating predictions for unseen attributes ... which is simply applying the 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)).
The claim recites ... wherein the set of attribute predictions is determined by a machine learning model ... that is trained using an attributes-level loss function that includes an unseen attributes loss component that is computed only with respect to the unseen attributes, wherein the unseen attributes loss component penalizes at least some of predictions of zero probability score for the unseen attributes which is simply generic training to perform the abstract idea of model calibration and amounts to mere instructions to apply the exception (MPEP 2106.05(f)).
The claim recites wherein the set of attribute predictions comprises prediction scores, wherein at least one of the prediction scores comprises a value corresponding to a likelihood that the object is characterized by an attribute associated with the at least one of the prediction scores which is simply additional information regarding the attribute 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:
system, processor amount(s) to no more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(b))
applying the model and generic training to perform the abstract idea amount(s) to no more than mere instructions to apply the exception (MPEP 2106.05(f))
machine learning model, attributes-level loss function 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 attribute predictions 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 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 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.
The limitation of wherein the attributes-level loss function measures prediction errors in training the machine learning model by summing the seen attributes loss component and the unseen attributes loss component, as drafted, is a process that, under its broadest reasonable interpretation, covers a mathematical concept. The limitation encompasses summing values.
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 attributes-level loss function includes a seen attributes loss component that is computed only with respect to seen attributes which is simply additional information regarding the attributes-level loss function, 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 attributes-level loss function 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 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.
The limitation of wherein prior to summing the seen attributes loss component and the unseen attributes loss component, the unseen attributes loss component is multiplied by a weighting coefficient selected to mitigate an imbalance among a set of training examples used to train the machine learning model, as drafted, is a process that, under its broadest reasonable interpretation, covers a mathematical concept. The limitation encompasses multiplying values.
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 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 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. The Step 2A Prong One Analysis for claim 11 is applicable here since claim 13 carries out the system of claim 11 but for the recitation of additional element(s) of wherein the unseen attributes loss component is an entropy-based loss, and wherein the seen attributes loss component is a binary cross-entropy loss.
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 unseen attributes loss component is an entropy-based loss, and wherein the seen attributes loss component is a binary cross-entropy loss which is simply additional information regarding the attributes-level loss function, and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)).
The claim recites additional element(s) – entropy-based loss, binary cross-entropy loss. 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:
entropy-based loss, binary cross-entropy loss 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 attributes-level loss function 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 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 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. The Step 2A Prong One Analysis for claim 11 is applicable here since claim 14 carries out the system of claim 11 but for the recitation of additional element(s) of wherein the unseen attributes loss component is an entropy-based loss, and wherein the seen attributes loss component is a mean squared error (MSE).
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 unseen attributes loss component is an entropy-based loss, and wherein the seen attributes loss component is a mean squared error (MSE) which is simply additional information regarding the attributes-level loss function, and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)).
The claim recites additional element(s) – entropy-based loss, mean squared error (MSE). 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:
entropy-based loss, mean squared error (MSE) 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 attributes-level loss function 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 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 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. The Step 2A Prong One Analysis for claim 10 is applicable here since claim 15 carries out the system of claim 10 but for the recitation of additional element(s) of wherein the mapping the set of attribute predictions to the set of predetermined attributes corresponding to the one of the plurality of predetermined classes is based on a distance between a vector representation of the attribute predictions and a binary vector corresponding to the one of the plurality of predetermined classes.
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 mapping 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 mapping 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 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 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. The Step 2A Prong One Analysis for claim 10 is applicable here since claim 16 carries out the system of claim 10 but for the recitation of additional element(s) of wherein the mapping the set of attribute predictions to the set of predetermined attributes corresponding to the one of the plurality of predetermined classes is performed using an additional machine learning 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 wherein the mapping the set of attribute predictions to the set of predetermined attributes corresponding to the one of the plurality of predetermined classes is performed using an additional machine learning model which is simply additional information regarding the mapping, and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)).
The claim recites additional element(s) – additional machine learning 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)).
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 machine learning 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))
additional information regarding the mapping 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 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 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.
The limitation of outputting a notification that the object corresponds to a new class, wherein the notification is generated in response to determining that the object does not correspond to any of the plurality of predetermined classes, 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 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 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 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.
The limitation of outputting identities of attributes of the new class, 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 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 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 computer program product, which is directed to an article of manufacture, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) computer program product.
The limitation of encoding a set of features extracted from computer-readable data associated with an object, wherein the set of features describes one or more predetermined aspects of the object, 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 a set of attribute predictions based on the 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.
The limitation of mapping the set of attribute predictions to a set of predetermined attributes corresponding to one of a plurality of predetermined classes, 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 outputting a classification of the object based on the mapping the set of attribute predictions to the set of predetermined attributes corresponding to the one of the plurality of predetermined classes, 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 program product, one or more computer-readable storage media, program instructions, processor. 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) – machine learning model, attributes-level loss function. 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 the set of attribute predictions is determined by a machine learning model that is capable of generating predictions for unseen attributes ... which is simply applying the 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)).
The claim recites ... wherein the set of attribute predictions is determined by a machine learning model ... that is trained using an attributes-level loss function that includes an unseen attributes loss component that is computed only with respect to the unseen attributes, wherein the unseen attributes loss component penalizes at least some of predictions of zero probability score for the unseen attributes which is simply generic training to perform the abstract idea of model calibration and amounts to mere instructions to apply the exception (MPEP 2106.05(f)).
The claim recites wherein the set of attribute predictions comprises prediction scores, wherein at least one of the prediction scores comprises a value corresponding to a likelihood that the object is characterized by an attribute associated with the at least one of the prediction scores which is simply additional information regarding the attribute 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:
computer program product, one or more computer-readable storage media, program instructions, processor amount(s) to no more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(b))
applying the model and generic training to perform the abstract idea amount(s) to no more than mere instructions to apply the exception (MPEP 2106.05(f))
machine learning model, attributes-level loss function 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 attribute predictions 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 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 program product, which is directed to an article of manufacture, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) computer program product.
The limitation of wherein the attributes-level loss function measures prediction errors in training the machine learning model by summing the seen attributes loss component and the unseen attributes loss component, as drafted, is a process that, under its broadest reasonable interpretation, covers a mathematical concept. The limitation encompasses summing values.
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 attributes-level loss function includes a seen attributes loss component that is computed only with respect to seen attributes which is simply additional information regarding the attributes-level loss function, 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 attributes-level loss function 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, 6-13, 15-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Saini et al. (Disentangling Visual Embeddings for Attributes and Objects, hereinafter referred to as “Saini”) in view of Singh et al. (US 2023/0342884 A1 – Diverse Image Inpainting Using Contrastive Learning, hereinafter referred to as “Singh”).
Regarding claim 1 (Currently Amended), Saini teaches a method, comprising:
encoding, with computer hardware (Saini, Abstract – teaches code, models and datasets are available on Github [The software necessarily requires a computer to run]), a set of features extracted from computer-readable data associated with an object, wherein the set of features describes one or more predetermined aspects of the object (Saini, section 3.2 – teaches encoding an image into a set of features associated with an object using at least an image encoder; see also Saini, Figure 2);
generating, using the computer hardware, a set of attribute predictions based on the set of features (Saini, section 3.2 – teaches generating attribute predictions from features using at least an Attribute Affinity Network; see also Saini, Figure 2), wherein the set of attribute predictions is determined by a machine learning model that is capable of generating predictions for unseen attributes (Saini, section 3.2 – teaches generating attribute predictions from features for seen and unseen attributes using at least an Attribute Affinity Network [machine learning model]; see also Saini, Figure 2) and that is trained using an attributes-level loss function that includes an unseen attributes loss component that is computed only with respect to the unseen attributes (Saini, section 3.3 – teaches training OADis end-to-end [including AAN] using attribute loss comprising a seen loss component and an unseen loss component), …, and wherein the set of attribute predictions comprises prediction scores, wherein at least one of the prediction scores comprises a value corresponding to a likelihood that the object is characterized by an attribute associated with the at least one of the prediction scores (Saini, section 3.2 – teaches the similarity score for the attribute features);
mapping, using the computer hardware, the set of attribute predictions to a set of predetermined attributes corresponding to one of a plurality of predetermined classes (Saini, section 3.2 – teaches mapping attributes to classes using a cosine classifier; see also Saini, Figure 2); and
outputting, using the computer hardware, a classification of the object based on the mapping the set of attribute predictions to the set of predetermined attributes corresponding to the one of the plurality of predetermined classes (Saini, section 3.2 – teaches mapping attributes to classes using a cosine classifier; see also Saini, section 4 – demonstrates experiments and outputs generated using OADis).
While Saini teaches weights associated with the attribute loss components, Saini does not explicitly teach penalizing predictions of zero probability score for unseen attributes.
Singh teaches wherein the unseen attributes loss component penalizes at least some of predictions of zero probability score for the unseen attributes (Singh, [0107]-[0108] – teaches penalizing unknown loss component of zero probability for unknown attributes).
It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to modify Saini with the teachings of Singh in order to disentangle known and unknown factors of an object in the field of attribute-based classification (Singh, [0025] – “To address these conventional shortcomings, an image inpainting system is described that implements a diverse image inpainting model to generate one or more synthesized images from a masked input image. The image inpainting system advantageously enables control over at least one known factor characterizing a visual appearance of an object depicted in the inpainted region of the synthesized image. To enable this control over an object's visual appearance, training the diverse image inpainting model involves disentangling latent space to learn separate codes for different factors. The diverse image inpainting model is trained to learn a known code that represents the one or more factors characterizing the visual appearance of the object in a latent space and an unknown code that represents factors apart from the one or more factors represented by the known code in the latent space. As an example, in an implementation where the diverse image inpainting model is trained to inpaint a human face at a masked region of an input image, the known code represents a facial expression of the human face and the unknown code represents a visual appearance of the human face apart from the facial expression. Different combinations of a known space code and an unknown space code are then provided as input with a masked input image to output a diverse range of synthesized images that each depict a different version of an object in the masked region of the input image.”).
Regarding claim 2 (Previously Presented), Saini in view of Singh teaches all of the limitations of the method of claim 1 as noted above. Saini further teaches wherein the attributes-level loss function includes a seen attributes loss component that is computed only with respect to seen attributes (Saini, section 3.3 – teaches an attribute loss comprising a seen loss component and an unseen loss component), and wherein the attributes-level loss function measures prediction errors in training the machine learning model by summing the seen attributes loss component and the unseen attributes loss component (Saini, section 3.3 – teaches training OADis end-to-end [including AAN] using attribute loss comprising a seen loss component and an unseen loss component).
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 Saini and Singh for the same reasons as disclosed in claim 1 above.
Regarding claim 3 (Original), Saini in view of Singh teaches all of the limitations of the method of claim 2 as noted above. Saini further teaches wherein prior to summing the seen attributes loss component and the unseen attributes loss component, the unseen attributes loss component is multiplied by a weighting coefficient selected to mitigate an imbalance among a set of training examples used to train the machine learning model (Saini, section 3.3 – teaches multiplying seen and unseen loss functions by a weight).
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 Saini and Singh for the same reasons as disclosed in claim 2 above.
Regarding claim 4 (Previously Presented), Saini in view of Singh teaches all of the limitations of the method of claim 2 as noted above. Saini further teaches wherein the unseen attributes loss component is an entropy-based loss, and wherein the seen attributes loss component is a binary cross-entropy loss (Saini, Appendix C – all loss functions use cross entropy loss [Seen loss and unseen loss are both binary]).
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 Saini and Singh for the same reasons as disclosed in claim 2 above.
Regarding claim 6 (Previously Presented), Saini in view of Singh teaches all of the limitations of the method of claim 1 as noted above. Saini further teaches wherein the mapping the set of attribute predictions to the set of predetermined attributes corresponding to the one of the plurality of predetermined classes is based on a distance between a vector representation of the set of attribute predictions and a binary vector corresponding to the one of the plurality of predetermined classes (Saini, section 3.2 – teaches mapping using cosine similarity of visual embeddings and word embeddings [binary class vector]).
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 Saini and Singh for the same reasons as disclosed in claim 1 above.
Regarding claim 7 (Previously Presented), Saini in view of Singh teaches all of the limitations of the method of claim 1 as noted above. Saini further teaches wherein the mapping the set of attribute predictions to the set of predetermined attributes corresponding to the one of the plurality of predetermined classes is performed using an additional machine learning model (Saini, section 3.2 – teaches mapping with a cosine classifier [ML model]).
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 Saini and Singh for the same reasons as disclosed in claim 1 above.
Regarding claim 8 (Original), Saini in view of Singh teaches all of the limitations of the method of claim 1 as noted above. Saini further teaches outputting a notification that the object corresponds to a new class, wherein the notification is generated in response to determining that the object does not correspond to any of the plurality of predetermined classes (Saini, section 4.1 – teaches reporting attribute and object accuracy for unseen pairs; see also Saini, Figure 4).
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 Saini and Singh for the same reasons as disclosed in claim 1 above.
Regarding claim 9 (Original), Saini in view of Singh teaches all of the limitations of the method of claim 8 as noted above. Saini further teaches outputting one or more identities of attributes of the new class (Saini, Figure 4 – teaches outputting object and attribute for new classifications).
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 Saini and Singh for the same reasons as disclosed in claim 8 above.
Regarding claim 10 (Currently Amended), it is the system embodiment of claim 1 with similar limitations to claim 1 and is rejected using the same reasoning found in claim 1. Saini further teaches a system, comprising:
a processor configured to initiate operations including (Saini, Abstract – teaches code, models and datasets are available on Github [The software necessarily requires a computer to run]) …
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 Saini and Singh for the same reasons as disclosed in claim 1 above.
Regarding claim 11 (Previously Presented), the rejection of claim 10 is incorporated herein. Further, the limitations in this claim are taught by Saini in view of Singh for the reasons set forth in the rejection of claim 2.
Regarding claim 12 (Original), the rejection of claim 11 is incorporated herein. Further, the limitations in this claim are taught by Saini in view of Singh for the reasons set forth in the rejection of claim 3.
Regarding claim 13 (Previously Presented), the rejection of claim 11 is incorporated herein. Further, the limitations in this claim are taught by Saini in view of Singh for the reasons set forth in the rejection of claim 4.
Regarding claim 15 (Previously Presented), the rejection of claim 10 is incorporated herein. Further, the limitations in this claim are taught by Saini in view of Singh for the reasons set forth in the rejection of claim 6.
Regarding claim 16 (Previously Presented), the rejection of claim 10 is incorporated herein. Further, the limitations in this claim are taught by Saini in view of Singh for the reasons set forth in the rejection of claim 7.
Regarding claim 17 (Original), the rejection of claim 10 is incorporated herein. Further, the limitations in this claim are taught by Saini in view of Singh for the reasons set forth in the rejection of claim 8.
Regarding claim 18 (Original), the rejection of claim 17 is incorporated herein. Further, the limitations in this claim are taught by Saini in view of Singh for the reasons set forth in the rejection of claim 9.
Regarding claim 19 (Currently Amended), it is the computer program product embodiment of claim 1 with similar limitations to claim 1 and is rejected using the same reasoning found in claim 1. Saini further teaches a computer program product, the computer program product comprising:
one or more computer-readable storage media and program instructions collectively stored on the one or more computer-readable storage media, the program instructions executable by a processor to cause the processor to initiate operations including (Saini, Abstract – teaches code, models and datasets are available on Github [The software necessarily requires a computer to run]) …
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 Saini and Singh for the same reasons as disclosed in claim 1 above.
Regarding claim 20 (Previously Presented), the rejection of claim 19 is incorporated herein. Further, the limitations in this claim are taught by Saini in view of Singh for the reasons set forth in the rejection of claim 2.
Claim(s) 5, 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Saini in view of Singh and further in view of An et al. (Content-Attribute Disentanglement for Generalized Zero-Shot Learning, hereinafter referred to as “An”).
Regarding claim 5 (Original), Saini in view of Singh teaches all of the limitations of the method of claim 2 as noted above. Saini further teaches wherein the unseen attributes loss component is an entropy-based loss (Saini, Appendix C – all loss functions use cross entropy loss).
However, Saini in view of Singh does not explicitly teach wherein the seen attributes loss component is a mean squared error (MSE).
An teaches wherein the seen attributes loss component is a mean squared error (MSE) (An, section III.B.(2) – teaches using MSE for loss of seen data).
It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to modify Saini in view of Singh with the teachings of An in order to improve attribute representation in the field of zero-shot attribute-based classification (An, section II.B – “Unlike the existing works, we define the style of an image as a set of attributes. Thereby, we focus on the impact of the content-attribute disentanglement architecture using an encoder-decoder network equipped with AdaIN to improve the attribute representation.”).
Regarding claim 14 (Original), the rejection of claim 11 is incorporated herein. Further, the limitations in this claim are taught by Saini in view of Singh and further in view of An for the reasons set forth in the rejection of claim 5.
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