DETAILED ACTION
This action is in response to the Applicant Response filed 23 June 2026 for application 18/228,569 filed 31 July 2023.
Claim(s) 1, 8, 15 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 .
Response to Arguments
Applicant's arguments regarding the objections to the claims have been fully considered and, in light of the amendments to the claims, are persuasive.
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(n) computer-readable medium, 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 storing a set of parameters for a multitask model.
The limitation of initializing the multitask model comprising a plurality of layers of a multitask neural network, wherein the plurality of layers comprises a set of shared layers and a plurality of sets of branch layers, wherein each of the sets of branch layers corresponds to one of a plurality of tasks to be predicted by the multitask model, and wherein an output layer of the set of shared layers is connected to an input layer of each of set of branch layers, 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 an output score corresponding to each of the plurality of tasks ..., 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 computing a loss score for each of the plurality of tasks based on the corresponding output score, the label of the training example, the plurality of task indicators of the training example, and a corresponding conditional loss function for the task, wherein a conditional loss function for a task is a loss function that computes a loss score of zero when the plurality of task indicators of a training example do not indicate that the training example is relevant to the task of the conditional loss function, as drafted, is a process that, under its broadest reasonable interpretation, covers a mathematical concept. The limitation encompasses calculating a loss score.
The limitation of for each of the loss scores, backpropagating through the corresponding set of branch layers and the set of shared layers using the loss score to update a set of parameters of the set of branch layers and the set of shared layers, wherein each of the plurality of sets of branch layers is backpropagated through using the corresponding loss score computed using the corresponding conditional loss function, as drafted, is a process that, under its broadest reasonable interpretation, covers a mathematical concept. The limitation encompasses calculating parameter updates.
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. 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 additional element(s) – computer-readable medium. 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) – multitask model, multitask neural network. 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 ... by applying the multitask model to the input features of the training example 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)).
The claim recites accessing a set of training examples, wherein each training example comprises a set of input features, a plurality of task indicators, and a label, wherein each task indicator corresponds to one of the plurality of tasks and indicates whether the training example is relevant to the corresponding task; storing the sets of parameters of the set of shared layers and the plurality of sets of branch layers as the set of parameters for the multitask model, which is simply acquiring and storing 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:
computer-readable medium amount(s) to no more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(b))
applying a model amount(s) to no more than mere instructions to apply the exception (MPEP 2106.05(f))
acquiring and storing 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))
multitask model, multitask neural network 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 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(n) computer-readable medium, 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 storing a set of parameters for a multitask model.
The limitation of compute a loss score reflecting a performance of the multitask model in computing an output score when the task indicators of a training example indicate that the training example is relevant to the task of the conditional loss function, as drafted, is a process that, under its broadest reasonable interpretation, covers a mathematical concept. The limitation encompasses calculating a loss score.
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 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(n) computer-readable medium, 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 storing a set of parameters for a multitask model.
The limitation of applying mean squared error, binary cross-entropy loss, categorical cross-entropy loss, Hinge loss, or KL divergence to compute the loss score, as drafted, is a process that, under its broadest reasonable interpretation, covers a mathematical concept. The limitation encompasses calculating a loss score.
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(n) computer-readable medium, 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 storing a set of parameters for a multitask model. The Step 2A Prong One Analysis for claim 1 is applicable here since claim 4 carries out the computer-readable medium of claim 1 but for the recitation of additional element(s) of wherein each task indicator of the plurality of task indicators is an indicator bit.
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 task indicators 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 task indicators 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(n) computer-readable medium, 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 storing a set of parameters for a multitask model.
The limitation of multiplying an indicator bit for a corresponding task of the conditional loss function by a loss score computed by a loss subfunction, as drafted, is a process that, under its broadest reasonable interpretation, covers a mathematical concept. The limitation encompasses calculating a loss score.
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(n) computer-readable medium, 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 storing a set of parameters for a multitask model. The Step 2A Prong One Analysis for claim 1 is applicable here since claim 6 carries out the computer-readable medium of claim 1 but for the recitation of additional element(s) of wherein the process is performed using a machine-learning programming library or a machine-learning platform.
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 process is performed using a machine-learning programming library or a machine-learning platform which is simply additional information regarding the method, and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)).
The claim recites additional element(s) – machine-learning programming library, machine-learning platform. 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:
machine-learning programming library, machine-learning platform 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 method 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(n) computer-readable medium, 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 storing a set of parameters for a multitask model. The Step 2A Prong One Analysis for claim 1 is applicable here since claim 7 carries out the computer-readable medium of claim 1 but for the recitation of additional element(s) of wherein each training example of the set of training examples comprises a set of labels, wherein each of the set of labels corresponds to a task indicator of the set of task indicators that indicates that the training examples is relevant to a corresponding task.
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 data 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 data 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(n) method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) method for training a multitask neural network.
The limitation of initializing a multitask model comprising a plurality of layers of a multitask neural network, wherein the plurality of layers comprises a set of shared layers and a plurality of sets of branch layers, wherein each of the sets of branch layers corresponds to one of a plurality of tasks to be predicted by the multitask model, and wherein an output layer of the set of shared layers is connected to an input layer of each of set of branch layers, 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 an output score corresponding to each of the plurality of tasks ..., 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 computing a loss score for each of the plurality of tasks based on the corresponding output score, the label of the training example, the plurality of task indicators of the training example, and a corresponding conditional loss function for the task, wherein a conditional loss function for a task is a loss function that computes a loss score of zero when the plurality of task indicators of a training example do not indicate that the training example is relevant to the task of the conditional loss function, as drafted, is a process that, under its broadest reasonable interpretation, covers a mathematical concept. The limitation encompasses calculating a loss score.
The limitation of for each of the loss scores, backpropagating through the corresponding set of branch layers and the set of shared layers using the loss score to update a set of parameters of the set of branch layers and the set of shared layers, wherein each of the plurality of sets of branch layers is backpropagated through using the corresponding loss score computed using the corresponding conditional loss function, as drafted, is a process that, under its broadest reasonable interpretation, covers a mathematical concept. The limitation encompasses calculating parameter updates.
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. 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 additional element(s) – computer system, processor, computer-readable medium. 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) – multitask neural network, multitask 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 ... by applying the multitask model to the input features of the training example 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)).
The claim recites accessing a set of training examples, wherein each training example comprises a set of input features, a plurality of task indicators, and a label, wherein each task indicator corresponds to one of the plurality of tasks and indicates whether the training example is relevant to the corresponding task; storing the sets of parameters of the set of shared layers and the plurality of sets of branch layers as the set of parameters for the multitask model, which is simply acquiring and storing 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:
computer system, processor, computer-readable medium amount(s) to no more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(b))
applying a model amount(s) to no more than mere instructions to apply the exception (MPEP 2106.05(f))
acquiring and storing 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))
multitask neural network, multitask 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 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(n) method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) method for training a multitask neural network.
The limitation of compute a loss score reflecting a performance of the multitask model in computing an output score when the task indicators of a training example indicate that the training example is relevant to the task of the conditional loss function, as drafted, is a process that, under its broadest reasonable interpretation, covers a mathematical concept. The limitation encompasses calculating a loss score.
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 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(n) method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) method for training a multitask neural network.
The limitation of applying mean squared error, binary cross-entropy loss, categorical cross-entropy loss, Hinge loss, or KL divergence to compute the loss score, as drafted, is a process that, under its broadest reasonable interpretation, covers a mathematical concept. The limitation encompasses calculating a loss score.
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 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(n) method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) method for training a multitask neural network. The Step 2A Prong One Analysis for claim 8 is applicable here since claim 11 carries out the method of claim 8 but for the recitation of additional element(s) of wherein each task indicator of the plurality of task indicators is an indicator bit.
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 task indicators 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 task indicators 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 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(n) method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) method for training a multitask neural network.
The limitation of multiplying an indicator bit for a corresponding task of the conditional loss function by a loss score computed by a loss subfunction, as drafted, is a process that, under its broadest reasonable interpretation, covers a mathematical concept. The limitation encompasses calculating a loss score.
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(n) method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) method for training a multitask neural network. The Step 2A Prong One Analysis for claim 8 is applicable here since claim 13 carries out the method of claim 8 but for the recitation of additional element(s) of wherein the method is performed using a machine-learning programming library or a machine-learning platform.
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 method is performed using a machine-learning programming library or a machine-learning platform which is simply additional information regarding the method, and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)).
The claim recites additional element(s) – machine-learning programming library, machine-learning platform. 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:
machine-learning programming library, machine-learning platform 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 method 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(n) method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) method for training a multitask neural network. The Step 2A Prong One Analysis for claim 8 is applicable here since claim 14 carries out the method of claim 8 but for the recitation of additional element(s) of wherein each training example of the set of training examples comprises a set of labels, wherein each of the set of labels corresponds to a task indicator of the set of task indicators that indicates that the training examples is relevant to a corresponding task.
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 data 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 data 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 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(n) computer-readable medium, 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.
The limitation of initializing a multitask model comprising a plurality of layers of a multitask neural network, wherein the plurality of layers comprises a set of shared layers and a plurality of sets of branch layers, wherein each of the sets of branch layers corresponds to one of a plurality of tasks to be predicted by the multitask model, and wherein an output layer of the set of shared layers is connected to an input layer of each of set of branch layers, 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 an output score corresponding to each of the plurality of tasks ..., 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 computing a loss score for each of the plurality of tasks based on the corresponding output score, the label of the training example, the plurality of task indicators of the training example, and a corresponding conditional loss function for the task, wherein a conditional loss function for a task is a loss function that computes a loss score of zero when the plurality of task indicators of a training example do not indicate that the training example is relevant to the task of the conditional loss function, as drafted, is a process that, under its broadest reasonable interpretation, covers a mathematical concept. The limitation encompasses calculating a loss score.
The limitation of for each of the loss scores, backpropagating through the corresponding set of branch layers and the set of shared layers using the loss score to update a set of parameters of the set of branch layers and the set of shared layers, wherein each of the plurality of sets of branch layers is backpropagated through using the corresponding loss score computed using the corresponding conditional loss function, as drafted, is a process that, under its broadest reasonable interpretation, covers a mathematical concept. The limitation encompasses calculating parameter updates.
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. 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 additional element(s) – computer-readable medium, 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) – multitask model, multitask neural network. 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 ... by applying the multitask model to the input features of the training example 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)).
The claim recites accessing a set of training examples, wherein each training example comprises a set of input features, a plurality of task indicators, and a label, wherein each task indicator corresponds to one of the plurality of tasks and indicates whether the training example is relevant to the corresponding task; storing the sets of parameters of the set of shared layers and the plurality of sets of branch layers as the set of parameters for the multitask model, which is simply acquiring and storing 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:
computer-readable medium, instructions, processor amount(s) to no more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(b))
applying a model amount(s) to no more than mere instructions to apply the exception (MPEP 2106.05(f))
acquiring and storing 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))
multitask model, multitask neural network 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 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(n) computer-readable medium, 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.
The limitation of compute a loss score reflecting a performance of the multitask model in computing an output score when the task indicators of a training example indicate that the training example is relevant to the task of the conditional loss function, as drafted, is a process that, under its broadest reasonable interpretation, covers a mathematical concept. The limitation encompasses calculating a loss score.
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 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(n) computer-readable medium, 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.
The limitation of applying mean squared error, binary cross-entropy loss, categorical cross-entropy loss, Hinge loss, or KL divergence to compute the loss score, as drafted, is a process that, under its broadest reasonable interpretation, covers a mathematical concept. The limitation encompasses calculating a loss score.
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 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(n) computer-readable medium, 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. The Step 2A Prong One Analysis for claim 15 is applicable here since claim 18 carries out the computer-readable medium of claim 15 but for the recitation of additional element(s) of wherein each task indicator of the plurality of task indicators is an indicator bit.
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 task indicators 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 task indicators 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 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(n) computer-readable medium, 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.
The limitation of multiplying an indicator bit for a corresponding task of the conditional loss function by a loss score computed by a loss subfunction, as drafted, is a process that, under its broadest reasonable interpretation, covers a mathematical concept. The limitation encompasses calculating a loss score.
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 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(n) computer-readable medium, 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. The Step 2A Prong One Analysis for claim 15 is applicable here since claim 20 carries out the computer-readable medium of claim 15 but for the recitation of additional element(s) of wherein the process is performed using a machine-learning programming library or a machine-learning platform.
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 process is performed using a machine-learning programming library or a machine-learning platform which is simply additional information regarding the method, and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)).
The claim recites additional element(s) – machine-learning programming library, machine-learning platform. 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:
machine-learning programming library, machine-learning platform 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 method 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) 1-6, 8-13, 15-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Finotello et al. (US 2025/0378343 A1 – Method for Validating the Predictions of a Supervised Model for Multivariate Quantitative Analysis of Spectral Data, hereinafter referred to as “Finotello”) in view of Tang et al. (Progressive Layered Extraction (PLE): A Novel Multi-Task Learning (MTL) Model for Personalized Recommendations, hereinafter referred to as “Tang”).
Regarding claim 1, Finotello teaches a non-transitory computer-readable medium (Finotello further) storing a set of parameters for a multitask model (Finotello, [0068]-[0072] - teaches a multitask model; see also Finotello, Fig. 3), wherein the set of parameters are produced by a process comprising:
initializing the multitask model comprising a plurality of layers of a multitask neural network, wherein the plurality of layers comprises a set of shared layers and a plurality of sets of branch layers, wherein each of the sets of branch layers corresponds to one of a plurality of tasks to be predicted by the multitask model, and wherein an output layer of the set of shared layers is connected to an input layer of each of set of branch layers (Finotello, [0068]-[0078] - teaches initializing a multitask neural network with a plurality of shared layers and a plurality of task branches, each task branch having a plurality of task layers and the output of the shared layers is input to each of the task branches; see also Finotello, Fig. 3);
accessing a set of training examples, wherein each training example comprises a set of input features … and a label (Finotello, [0045]-[0047] - teaches input data separated into training and evaluation data wherein each data element has spectral feature data and a label) …;
for each of the set of training examples:
generating an output score corresponding to each of the plurality of tasks by applying the multitask model to the input features of the training example (Finotello, [0070]-[0072] - teaches generating outputs for each task branch given an input using a multitask neural network);
computing a loss score for each of the plurality of tasks based on the corresponding output score, the label of the training example, … and a corresponding conditional loss function for the task (Finotello, [0064] – teaches supervised training using a loss function comprising a linear weighted linear combination of teach task loss) …; and
for each of the loss scores, backpropagating through the corresponding set of branch layers and the set of shared layers using the loss score to update a set of parameters of the set of branch layers and the set of shared layers (Finotello, [0047] – teaches training and optimization; Finotello, [0062]-[0064] - teaches training the model using a loss function with supervised training; see also Finotello, Fig. 2), wherein each of the plurality of sets of branch layers is backpropagated through using the corresponding loss score computed using the corresponding conditional loss function (Finotello, [0062]-[0064] - teaches training the model using a loss function for each output branch with supervised training; Finotello, [0066]-[0067] - teaches using neural networks as the models); and
storing the sets of parameters of the set of shared layers and the plurality of sets of branch layers as the set of parameters for the multitask model (Finotello, [0019] - teaches using the training model to make predictions; Finotello, [0044] - teaches a use phase of the trained model; Finotello, [0049] - teaches applying new data after training; see also Finotello, [0079], Fig. 4 - teaches results of the model [Using a trained model on new data requires storing the trained model]).
While Finotello teaches a weighted linear combination of task losses, Finotello does not explicitly teach that the weight is a task indicator of whether the data applies to a given task.
Tang teaches
accessing a set of training examples, wherein each training example comprises a set of input features, a plurality of task indicators, and a label, wherein each task indicator corresponds to one of the plurality of tasks and indicates whether the training example is relevant to the corresponding task (Tang, section 4.3 - teaches determining the loss as a linear combination of individual task loss functions based on training samples, prediction outputs, ground truths [label], and a binary indicator of whether the sample lies in the sample space of a particular task);
for each of the set of training examples:
generating an output score corresponding to each of the plurality of tasks by applying the multitask model to the input features of the training example (Tang, section 4.3 - teaches determining the loss as a linear combination of individual task loss functions based on training samples and prediction outputs);
computing a loss score for each of the plurality of tasks based on the corresponding output score, the label of the training example, the plurality of task indicators of the training example, and a corresponding conditional loss function for the task (Tang, section 4.3 - teaches determining the loss as a linear combination of individual task loss functions based on training samples, prediction outputs, ground truths [label], and a binary indicator of whether the sample lies in the sample space of a particular task), wherein a conditional loss function for a task is a loss function that computes a loss score of zero when the plurality of task indicators of a training example do not indicate that the training example is relevant to the task of the conditional loss function (Tang, section 4.3 - teaches a binary indicator of whether the sample lies in the sample space of a particular task where the indicator is zero if the sample is outside of the task sample space); and
for each of the loss scores, backpropagating through the corresponding set of branch layers and the set of shared layers using the loss score to update a set of parameters of the set of branch layers and the set of shared layers (Tang, section 4.3 – teaches jointly training the shared and task layers using supervised training), wherein each of the plurality of sets of branch layers is backpropagated through using the corresponding loss score computed using the corresponding conditional loss function (Tang, section 4.3 – teaches jointly training the shared and task layers using supervised training based on branch losses).
It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to modify Finotello with the teachings of Tang in order to improve joint optimization in multi-task learning by addressing heterogeneous sample and task relevance in the field of multi-task learning (Tang, section 4.3 – “However, there exist several issues, making joint optimization of MTL models challenging in practice. In this paper, we optimize the joint loss function to address two critical ones encountered in real-world recommender systems. The first problem is the heterogeneous sample space due to sequential user actions. For instance, users can only share or comment on an item after clicking it, which leads to different sample space of different tasks... To train these tasks jointly, we consider the union of sample space of all tasks as the whole training set, and ignore samples out of its own sample space when calculating the loss of each individual task... The second problem is that the performance of an MTL model is sensitive to the choice of loss weight in the training process ..., as it determines the relative importance of each task on the joint loss. In practice, it is observed that each task may have different importance at different training phases. Therefore, we consider the loss weight for each task as a dynamic weight instead of a static one.”).
Regarding claim 2, Finotello in view of Tang teaches all of the limitations of the computer-readable medium of claim 1 as noted above. Tang further teaches wherein the conditional function is further configured to:
compute a loss score reflecting a performance of the multitask model in computing an output score when the task indicators of a training example indicate that the training example is relevant to the task of the conditional loss function (Tang, section 4.3 - teaches determining the loss as a linear combination of individual task loss functions based on training samples, prediction outputs, ground truths [label], and a binary indicator of whether the sample lies in the sample space of a particular task [relevant task]).
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 Finotello and Tang in order to calculate loss based on relevant tasks to improve joint optimization in multi-task learning by addressing heterogeneous sample and task relevance (Tang, section 4.3).
Regarding claim 3, Finotello in view of Tang teaches all of the limitations of the computer-readable medium of claim 2 as noted above. Tang further teaches wherein computing the score reflecting the performance of the multitask model comprises:
applying mean squared error, binary cross-entropy loss, categorical cross-entropy loss, Hinge loss, or KL divergence to compute the loss score (Tang, section 5.1.3 – teaches using MSE for regression tasks and binary cross-entropy for binary classification tasks).
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 Finotello and Tang in order to calculate loss based on relevant tasks to improve joint optimization in multi-task learning by addressing heterogeneous sample and task relevance (Tang, section 4.3).
Regarding claim 4, Finotello in view of Tang teaches all of the limitations of the computer-readable medium of claim 1 as noted above. Tang further teaches wherein each task indicator of the plurality of task indicators is an indicator bit (Tang, section 4.3 - teaches a binary indicator of whether the sample lies in the sample space of a particular task).
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 Finotello and Tang in order to calculate loss based on relevant tasks to improve joint optimization in multi-task learning by addressing heterogeneous sample and task relevance (Tang, section 4.3).
Regarding claim 5, Finotello in view of Tang teaches all of the limitations of the computer-readable medium of claim 4 as noted above. Tang further teaches wherein the conditional function is configured to compute the loss score by:
multiplying an indicator bit for a corresponding task of the conditional loss function by a loss score computed by a loss subfunction (Tang, section 4.3 - teaches determining the loss as a linear combination of individual task loss functions based on training samples, prediction outputs, ground truths [label], and a binary indicator of whether the sample lies in the sample space of a particular task).
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 Finotello and Tang in order to calculate loss based on relevant tasks to improve joint optimization in multi-task learning by addressing heterogeneous sample and task relevance (Tang, section 4.3).
Regarding claim 6, Finotello in view of Tang teaches all of the limitations of the computer-readable medium of claim 1 as noted above. Tang further teaches wherein the process is performed using a machine-learning programming library or a machine-learning platform (Tang, section 5.1.6 – teaches using a C++ based deep learning framework).
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 Finotello and Tang in order to use a machine learning platform to improve joint optimization in multi-task learning by addressing heterogeneous sample and task relevance (Tang, section 4.3).
Regarding claim 8, 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. Finotello further teaches a method for training a multitask neural network (Finotello, [0062]-[0064] - teaches training the model; Finotello, [0068]-[0072] - teaches a multitask model; see also Finotello, Fig. 3), performed by a computer system comprising a processor and a computer-readable medium, comprising (Finotello, [0082]-[0083] - teaches a processor executing instructions stored in memory) …
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 Finotello and Tang for the same reasons as disclosed in claim 1 above.
Regarding claim 9, the rejection of claim 8 is incorporated herein. Further, the limitations in this claim are taught by Finotello in view of Tang for the reasons set forth in the rejection of claim 2.
Regarding claim 10, the rejection of claim 9 is incorporated herein. Further, the limitations in this claim are taught by Finotello in view of Tang for the reasons set forth in the rejection of claim 3.
Regarding claim 11, the rejection of claim 8 is incorporated herein. Further, the limitations in this claim are taught by Finotello in view of Tang for the reasons set forth in the rejection of claim 4.
Regarding claim 12, the rejection of claim 11 is incorporated herein. Further, the limitations in this claim are taught by Finotello in view of Tang for the reasons set forth in the rejection of claim 5.
Regarding claim 13, the rejection of claim 8 is incorporated herein. Further, the limitations in this claim are taught by Finotello in view of Tang for the reasons set forth in the rejection of claim 6.
Regarding claim 15, it is the computer-readable medium embodiment of claim 1 with similar limitations to claim 1 and is rejected using the same reasoning found in claim 1. Finotello further teaches a non-transitory computer-readable medium storing instructions that, when executed by a processor, cause the processor to perform operations comprising (Finotello, [0082]-[0083] - teaches a processor executing instructions stored in memory) …
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 Finotello and Tang for the same reasons as disclosed in claim 1 above.
Regarding claim 16, the rejection of claim 15 is incorporated herein. Further, the limitations in this claim are taught by Finotello in view of Tang for the reasons set forth in the rejection of claim 2.
Regarding claim 17, the rejection of claim 16 is incorporated herein. Further, the limitations in this claim are taught by Finotello in view of Tang for the reasons set forth in the rejection of claim 3.
Regarding claim 18, the rejection of claim 15 is incorporated herein. Further, the limitations in this claim are taught by Finotello in view of Tang for the reasons set forth in the rejection of claim 4.
Regarding claim 19, the rejection of claim 18 is incorporated herein. Further, the limitations in this claim are taught by Finotello in view of Tang for the reasons set forth in the rejection of claim 5.
Regarding claim 20, the rejection of claim 15 is incorporated herein. Further, the limitations in this claim are taught by Finotello in view of Tang for the reasons set forth in the rejection of claim 6.
Claim(s) 7, 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Finotello in view of Tang and further in view of Foggia et al. (Multi-Task Learning on the Edge for Effective Gender, Age, Ethnicity and Emotion Recognition, hereinafter referred to as “Foggia”).
Regarding claim 7, Finotello in view of Tang teaches all of the limitations of the computer-readable medium of claim 1 as noted above. However, Finotello in view of Tang does not explicitly teach wherein each training example of the set of training examples comprises a set of labels, wherein each of the set of labels corresponds to a task indicator of the set of task indicators that indicates that the training examples is relevant to a corresponding task.
Foggia teaches wherein each training example of the set of training examples comprises a set of labels, wherein each of the set of labels corresponds to a task indicator of the set of task indicators that indicates that the training examples is relevant to a corresponding task (Foggia, secti8on 4.2 – teaches label masking wherein the loss function for a task is set to zero for a task if a label is not available).
It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to modify Finotello in view of Tang with the teachings of Foggia in order to solve the problems of missing labels, dataset imbalance and loss function imbalance through label masking, batch balancing and a custom weighted loss function in the field of multi-task learning (Foggia, Abstract – “More and more real-world applications, especially in cognitive robotics, require the running of different computer vision algorithms in parallel on board of embedded devices with limited GPU and memory resources. Multi-task learning, namely the usage of the same model to perform multiple classification and/or regression tasks by learning a shared low level representation, revealed to be a valid solution to reduce computation and required memory space while preserving the accuracy. In this paper, we propose a solution for real time user profiling based on a multi-task convolutional neural network (CNN) for gender, age, ethnicity and emotion recognition from face images. To find the best trade-off between accuracy and processing time, we evaluate three different architectures, specifically designed for the purpose, and backbones, based on MobileNet, ResNet and SENet, which include convolutional layers, residual blocks and attention modules that already demonstrated great potential in face analysis. We trained the multi-task neural network with a custom learning procedure, which solves the problems of missing labels, dataset imbalance and loss function imbalance through label masking, batch balancing and a custom weighted loss function; there are no other multi-task neural networks for face analysis that address all these challenges simultaneously. The proposed solution demonstrated its effectiveness in the comparison with the corresponding single-task CNNs in terms of accuracy, processing time and memory space; in fact, the multi-task CNNs achieved a processing speed-up between 2.5 and 4 times and a reduction of the memory space between 2 and 4 times, while preserving the accuracy. Moreover, the useful insights that arise from the experiments allow to choose a solution for face analysis easily integrable into real applications on smart cameras and embedded systems and most suited for the specific application constraints in terms of computational resources.”).
Regarding claim 14, the rejection of claim 8 is incorporated herein. Further, the limitations in this claim are taught by Finotello in view of Tang and further in view of Foggia for the reasons set forth in the rejection of claim 7.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier 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.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/MARSHALL L WERNER/ Primary Examiner, Art Unit 2125
Prosecution Insights