DETAILED ACTION
This action is in response to the Applicant Response filed 13 March 2026 for application 18/169,699 filed 15 February 2023.
Claim(s) 1-8, 11-15, 18-20 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 35 U.S.C. 112(b) rejection(s) of claim(s) 11 have been fully considered and, in light of the amendments to the claims, are persuasive. The 35 U.S.C. 112(b) rejection(s) of claim(s) 11 has/have been withdrawn.
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 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 ... generate a first set of intermediate outputs based on the first set of values, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
The limitation of ... generate a second set of intermediate outputs based on the second set of value, 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 ... determine a first weight for the second set of intermediate outputs based on a first parameter value received from a gate of the neural network, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
The limitation of ... generate a combined output based on the first set of intermediate outputs, the second set of intermediate outputs, and the first weight, 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 processing the transaction based on the combined output obtained from the neural network, 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, memory, one or more hardware processors, instructions. The additional element(s) is/are recited at a high-level of generality (i.e., as generic computer components performing generic computer functions of executing instructions on the computers) such that it amounts to no more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(b)).
The claim recites additional element(s) – neural network, first domain expert, common expert, second domain expert, first domain aggregator, gate of the 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 providing the first set of values and the second set of values to the first domain expert and the common expert of the neural network, respectively 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 receiving a request for processing a transaction corresponding to a first domain; accessing a neural network for processing the request ...; obtaining a set of input values associated with the transaction ..., which is simply acquiring data recited at a high level of generality. This is nothing more than insignificant extra-solution activity (MPEP 2106.05(g)).
The claim recites ... wherein the neural network comprises a first domain expert corresponding to a first set of input features unique to the first domain, a common expert corresponding to a second set of input features shared by the first domain and a second domain different from the first domain, and a second domain expert corresponding to third set of input features unique to the second domain, wherein the common expert is trained based on training data associated with the second domain; … wherein the set of input values comprises a first set of values corresponding to the first set of input features and a second set of values corresponding to the second set of input features which is simply additional information regarding the model and 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:
system, memory, one or more hardware processors, instructions 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 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))
neural network, first domain expert, common expert, second domain expert, first domain aggregator, gate of the 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))
additional information regarding the model and the data do(es) not apply the exception in a meaningful way (MPEP 2106.05(e))
The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 2, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 2 is directed to a system with a processor, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) system. The Step 2A Prong One Analysis for claim 1 is applicable here since claim 2 carries out the system of claim 1 but for the recitation of additional element(s) of wherein the first domain expert of the neural network comprises (i) a first input layer configured to receive the first set of values corresponding to the first set of input features and (ii) a first set of hidden layers configured to generate the first set of intermediate outputs based on manipulating the first set of values.
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 first domain expert of the neural network comprises (i) a first input layer configured to receive the first set of values corresponding to the first set of input features and (ii) a first set of hidden layers configured to generate the first set of intermediate outputs based on manipulating the first set of values which is simply additional information regarding the neural network, and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)).
The claim recites wherein the first domain expert of the neural network comprises (i) a first input layer configured to receive the first set of values corresponding to the first set of input features and (ii) a first set of hidden layers configured to generate the first set of intermediate outputs based on manipulating the first set of values 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)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of:
applying the model amount(s) to no more than mere instructions to apply the exception (MPEP 2106.05(f))
additional information regarding the neural network do(es) not apply the exception in a meaningful way (MPEP 2106.05(e))
The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 3, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 3 is directed to a system with a processor, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) system. The Step 2A Prong One Analysis for claim 2 is applicable here since claim 3 carries out the system of claim 2 but for the recitation of additional element(s) of wherein the common expert of the neural network comprises (i) a second input layer configured to receive the second set of values corresponding to the second set of input features and (ii) a second set of hidden layers configured to generate the second set of intermediate outputs based on manipulating the second set of values.
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 common expert of the neural network comprises (i) a second input layer configured to receive the second set of values corresponding to the second set of input features and (ii) a second set of hidden layers configured to generate the second set of intermediate outputs based on manipulating the second set of values which is simply additional information regarding the neural network, and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)).
The claim recites wherein the common expert of the neural network comprises (i) a second input layer configured to receive the second set of values corresponding to the second set of input features and (ii) a second set of hidden layers configured to generate the second set of intermediate outputs based on manipulating the second set of values 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)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of:
applying the model amount(s) to no more than mere instructions to apply the exception (MPEP 2106.05(f))
additional information regarding the neural network 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 4, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 4 is directed to a system with a processor, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) system. The Step 2A Prong One Analysis for claim 1 is applicable here since claim 4 carries out the system of claim 1 but for the recitation of additional element(s) of wherein the first domain expert and the common expert operate independently of each other in the neural network.
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 model and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of additional information regarding the model do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Not applying the exception in a meaningful way does not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 5, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 5 is directed to a system with a processor, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) system. The Step 2A Prong One Analysis for claim 1 is applicable here since claim 5 carries out the system of claim 1 but for the recitation of additional element(s) of wherein the gate is configured to provide a second parameter value different from the first parameter value to the common expert for processing a second transaction corresponding to the second domain.
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 gate is configured to provide a second parameter value different from the first parameter value to the common expert for processing a second transaction corresponding to the second domain 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)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of:
applying the model amount(s) to no more than mere instructions to apply the exception (MPEP 2106.05(f))
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 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 1 is applicable here since claim 6 carries out the system of claim 1 but for the recitation of additional element(s) of wherein the first domain aggregator comprises one or more hidden layers.
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 model and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of additional information regarding the model do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). 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 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 ... modify the second set of intermediate outputs based on the first weight, 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 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 ... generate a first set of intermediate outputs based on the first set of values, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
The limitation of ... generate a second set of intermediate outputs based on the second set of values, 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 ... determine a weight for the second set of intermediate outputs based on a parameter value received from a gate of the machine learning model, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
The limitation of ... generate a combined output based on the first set of intermediate outputs, the second set of intermediate outputs, and the weight, 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 determining a risk associated with the transaction based the combined output, 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 processing the transaction based on the risk, 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) – machine learning model, first domain expert, common expert, second domain expert, first domain aggregator, gate of the 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)).
The claim recites providing the set of input values to a machine learning model configured to perform risk prediction associated with the first domain and the second domain ... 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 ... wherein the set of input values corresponds to input features related to the first domain, wherein the set of input values comprises a first set of values corresponding to a first set of input features unique to the first domain and a second set of values corresponding to a second set of input features shared by the first domain and a second domain; ... wherein the machine learning model comprises a first domain expert configured to receive the first set of values corresponding to the first set of input features unique to the first domain …, a common expert configured to receive the second set of values corresponding to the second set of input features shared by the first domain and the second domain …, and a second domain expert corresponding to third set of input features unique to the second domain … which is simply additional information regarding the model and the data, and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)).
The claim recites receiving a request for processing a transaction corresponding to a first domain; obtaining a set of input values associated with the transaction ..., which is simply acquiring 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:
applying a model amount(s) to no more than mere instructions to apply the exception (MPEP 2106.05(f))
acquiring 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))
machine learning model, first domain expert, common expert, second domain expert, first domain aggregator, gate of the 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 model and the data 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 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 Step 2A Prong One Analysis for claim 8 is applicable here since claim 9 carries out the method of claim 8 but for the recitation of additional element(s) of obtaining a first set of training data corresponding to the second domain; and prior to providing the set of input values to the machine learning model, training the machine learning model using the first set of training data.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application.
The claim recites obtaining a first set of training data corresponding to the second domain, which is simply acquiring data recited at a high level of generality. This is nothing more than insignificant extra-solution activity (MPEP 2106.05(g)).
The claim recites prior to providing the set of input values to the machine learning model, training the machine learning model using the first set of training data which is simply generic training to perform the abstract idea of transaction processing and amounts to mere instructions to apply the exception (MPEP 2106.05(f)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of:
generic training to perform the abstract idea amount(s) to no more than mere instructions to apply the exception (MPEP 2106.05(f))
acquiring data amount(s) to no more than insignificant extra-solution activity (MPEP 2106.05(g)), wherein the insignificant extra-solution activity is the well-understood routine and conventional activit(y/ies) of receiving or transmitting data over a network and/or storing and retrieving information in memory (MPEP 2016.05(d))
The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 10, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 10 is directed to a 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 9 is applicable here since claim 10 carries out the method of claim 9 but for the recitation of additional element(s) of wherein the training the machine learning model using the first set of training data comprises modifying the common expert, but not the first domain expert, of the machine learning model.
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 training 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 training 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 11, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 11 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) method.
The limitation of generating a merged set of training data based on merging the first set of training data with the second set of training data, 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 obtaining a first set of training data corresponding to the first domain; obtaining a second set of training data corresponding to the second domain, which is simply acquiring data recited at a high level of generality. This is nothing more than insignificant extra-solution activity (MPEP 2106.05(g)).
The claim recites training the machine learning model using the merged set of training data which is simply generic training to perform the abstract idea of transaction processing and amounts to mere instructions to apply the exception (MPEP 2106.05(f)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of:
generic training to perform the abstract idea amount(s) to no more than mere instructions to apply the exception (MPEP 2106.05(f))
acquiring data amount(s) to no more than insignificant extra-solution activity (MPEP 2106.05(g)), wherein the insignificant extra-solution activity is the well-understood routine and conventional activit(y/ies) of receiving or transmitting data over a network and/or storing and retrieving information in memory (MPEP 2016.05(d))
The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 12, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 12 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) method. The Step 2A Prong One Analysis for claim 8 is applicable here since claim 12 carries out the method of claim 8 but for the recitation of additional element(s) of wherein the first domain expert is further configured to manipulate the first set of values corresponding to the first set of input features, and wherein the common expert is further configured to manipulate the second set of values corresponding to the second set of input features.
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 first domain expert is further configured to manipulate the first set of values corresponding to the first set of input features, and wherein the common expert is further configured to manipulate the second set of values corresponding to the second set of input features which is simply additional information regarding the model, and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)).
The claim recites wherein the first domain expert is further configured to manipulate the first set of values corresponding to the first set of input features, and wherein the common expert is further configured to manipulate the second set of values corresponding to the second set of input features 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)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of:
applying the model amount(s) to no more than mere instructions to apply the exception (MPEP 2106.05(f))
additional information regarding the model do(es) not apply the exception in a meaningful way (MPEP 2106.05(e))
The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 13, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 13 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) method. The Step 2A Prong One Analysis for claim 12 is applicable here since claim 13 carries out the method of claim 12 but for the recitation of additional element(s) of wherein the first domain expert is further configured to manipulate the first set of values independent of the common expert.
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 first domain expert is further configured to manipulate the first set of values independent of the common expert which is simply additional information regarding the model, and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)).
The claim recites wherein the first domain expert is further configured to manipulate the first set of values independent of the common expert 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)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of:
applying the model amount(s) to no more than mere instructions to apply the exception (MPEP 2106.05(f))
additional information regarding the model do(es) not apply the exception in a meaningful way (MPEP 2106.05(e))
The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 14, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 14 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) method.
The limitation of ... modify the second set of intermediate outputs from the common expert based on the weight, 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 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) machine-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) machine-readable medium.
The limitation of ... generate an output based on the first set of intermediate values, the second set of intermediate values, and a weight associated with the second set of intermediate values, 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 processing the transaction based on an output obtained from the machine learning model, 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) – machine-readable medium, machine-readable instructions, machine. 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, first domain expert, common expert, second domain expert, first domain aggregator. 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 providing a set of input values associated with the transaction to the machine learning model, wherein the machine learning model is configured (i) to use the first domain expert to generate a first set of intermediate values based on processing a first subset of the set of input values corresponding to the first set of input features, (ii) to use the common expert to generate a second set of intermediate values based on processing a second subset of the set of input values corresponding to the second set of input features 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 receiving a request for processing a transaction corresponding to a first domain; accessing a machine learning model based on the request ..., which is simply acquiring data recited at a high level of generality. This is nothing more than insignificant extra-solution activity (MPEP 2106.05(g)).
The claim recites ... wherein the machine learning model comprises a first domain expert corresponding to a first set of input features unique to the first domain, a common expert corresponding to a second set of input features shared by the first domain and a second domain different from the first domain, and a second domain expert corresponding to third set of input features unique to the second domain which is simply additional information regarding the model, and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of:
machine-readable medium, machine-readable instructions, machine amount(s) to no more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(b))
applying the model amount(s) to no more than mere instructions to apply the exception (MPEP 2106.05(f))
acquiring 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))
machine learning model, first domain expert, common expert, second domain expert, first domain aggregator amount(s) to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h))
additional information regarding the model do(es) not apply the exception in a meaningful way (MPEP 2106.05(e))
The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 16, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 16 is directed to a(n) machine-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) machine-readable medium. The Step 2A Prong One Analysis for claim 15 is applicable here since claim 16 carries out the machine-readable medium of claim 15 but for the recitation of additional element(s) of obtaining a first set of training data corresponding to the second domain; and prior to providing the set of input values to the machine learning model, training the machine learning model using the first set of training data.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application.
The claim recites obtaining a first set of training data corresponding to the second domain, which is simply acquiring data recited at a high level of generality. This is nothing more than insignificant extra-solution activity (MPEP 2106.05(g)).
The claim recites prior to providing the set of input values to the machine learning model, training the machine learning model using the first set of training data which is simply generic training to perform the abstract idea of transaction processing and amounts to mere instructions to apply the exception (MPEP 2106.05(f)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of:
generic training to perform the abstract idea amount(s) to no more than mere instructions to apply the exception (MPEP 2106.05(f))
acquiring data amount(s) to no more than insignificant extra-solution activity (MPEP 2106.05(g)), wherein the insignificant extra-solution activity is the well-understood routine and conventional activit(y/ies) of receiving or transmitting data over a network and/or storing and retrieving information in memory (MPEP 2016.05(d))
The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 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) machine-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) machine-readable medium. The Step 2A Prong One Analysis for claim 16 is applicable here since claim 17 carries out the machine-readable medium of claim 16 but for the recitation of additional element(s) of wherein the training the machine learning model using the first set of training data comprises modifying the common expert, but not the first domain expert, of the machine learning model.
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 training 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 training 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 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) machine-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) machine-readable medium. The Step 2A Prong One Analysis for claim 15 is applicable here since claim 18 carries out the machine-readable medium of claim 15 but for the recitation of additional element(s) of wherein the first domain expert is not connected to the common expert in the machine learning model.
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 model and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of additional information regarding the model do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). 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) machine-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) machine-readable medium.
The limitation of ... manipulate the second set of intermediate values based on the weight, 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 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) machine-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) machine-readable medium. The Step 2A Prong One Analysis for claim 15 is applicable here since claim 20 carries out the machine-readable medium of claim 15 but for the recitation of additional element(s) of wherein the first domain aggregator comprises one or more hidden layers.
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 first domain aggregator 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 first domain aggregator 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.
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) 1-7, 15-16, 18-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over 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 (Currently Amended), Tang teaches a system, comprising:
a non-transitory memory (Tang, section 5 - teaches offline and online experiments using large datasets generating various computer generated results as demonstrated by tables and graphs [This would necessarily require the use of a computer]); and
one or more hardware processors coupled with the non-transitory memory and configured to read instructions from the non-transitory memory to cause the system to perform operations (Tang, section 5 - teaches offline and online experiments using large datasets generating various computer generated results as demonstrated by tables and graphs [This would necessarily require the use of a computer]) comprising:
receiving a request for processing a transaction corresponding to a first domain (Tang, section 5.1 – teaches processing video recommendation data for multiple tasks [domains], included VCP, CTR, VTR, SHR, CMR);
accessing a neural network for processing the request (Tang, section 5.1.3 – teaches a neural network MLP structure), wherein the neural network comprises a first domain expert corresponding to a first set of input features unique to the first domain (Tang, section 4.1 - teaches task-specific experts and shared experts; Tang, Figure 4 - teaches Expert A [first domain expert], Expert B [second domain expert] and Experts Shared [common expert]; see also Tang, sections 4, 4.2 - teaches task-specific and shared components are explicitly separated; see also Tang, Figure 4), a common expert corresponding to a second set of input features shared by the first domain and a second domain different from the first domain (Tang, section 4.1 - teaches task-specific experts and shared experts; Tang, Figure 4 - teaches Expert A [first domain expert], Expert B [second domain expert] and Experts Shared [common expert]; see also Tang, sections 4, 4.2 - teaches task-specific and shared components are explicitly separated; see also Tang, Figure 4), and a second domain expert corresponding to third set of input features unique to the second domain (Tang, section 4.1 - teaches task-specific experts and shared experts; Tang, Figure 4 - teaches Expert A [first domain expert], Expert B [second domain expert] and Experts Shared [common expert]; see also Tang, sections 4, 4.2 – teaches task-specific and shared components are explicitly separated; see also Tang, Figure 4), wherein the common expert is trained based on training data associated with the second domain (Tang, section 4.1 – teaches the shared expert inputs shared data [first and second domains] from the tasks);
obtaining a set of input values associated with the transaction, wherein the set of input values comprises a first set of values corresponding to the first set of input features and a second set of values corresponding to the second set of input features (Tang, section 4.1 – teaches applying input to the model in which the shared experts learn shared patterns and the task-specific experts learn specific tasks; see also Tang, Figure 4);
providing the first set of values and the second set of values to the first domain expert and the common expert of the neural network, respectively (Tang, section 4.1 – teaches applying input to the model in which the shared experts learn shared patterns and the task-specific experts learn specific tasks; see also Tang, Figure 4), wherein the first domain expert of the neural network is configured to generate a first set of intermediate outputs based on the first set of values (Tang, section 4.1 – teaches each tower network inputs values from the shared expert and the task specific expert [first set intermediate values]; see also Tang, Fig. 4), wherein the common expert is configured to generate a second set of intermediate outputs based on the second set of value (Tang, section 4.1 – teaches each tower network inputs values from the shared expert [second set intermediate values] and the task specific expert; see also Tang, Fig. 4), and wherein a first domain aggregator of the neural network (Tang, section 4.1 – teaches a task-specific gating network [aggregator] which weights and aggregates the outputs from the task-specific expert and the shared expert, wherein the output of the gating network [aggregator] is processed by a task-specific tower network; see also Tang, Figure 4) is configured to (i) determine a first weight for the second set of intermediate outputs based on a first parameter value received from a gate of the neural network (Tang, section 4.1 – teaches a task-specific gating network [aggregator] which weights and aggregates the outputs from the task-specific expert and the shared expert, wherein the output of the gating network [aggregator] is processed by a task-specific tower network; see also Tang, Figure 4) and (ii) generate a combined output based on the first set of intermediate outputs, the second set of intermediate outputs, and the first weight (Tang, section 4.1 – teaches a task-specific gating network [aggregator] which weights and aggregates the outputs from the task-specific expert and the shared expert, wherein the output of the gating network [aggregator] is processed by a task-specific tower network; see also Tang, Figure 4); and
processing the transaction based on the combined output obtained from the neural network (Tang, section 4.1 – teaches the shared expert and the task specific expert are combined through a gating network to generate an output; Tang, section 5.1 – teaches processing video recommendation data).
Regarding claim 2 (Currently Amended), Tang teaches all of the limitations of the system of claim 1 as noted above. Tang further teaches wherein the first domain expert of the neural network comprises (i) a first input layer configured to receive the first set of values corresponding to the first set of input features (Tang, section 4.1 – teaches input layer applying task-specific and shared inputs to task-specific and shared experts; see also Tang, Figure 4; see also Tang, sections 4, 4.2 - teaches task-specific and shared components are explicitly separated) and (ii) a first set of hidden layers configured to generate the first set of intermediate outputs based on manipulating the first set of values (Tang, section 4.1 – teaches the task-specific and shared experts manipulating data and sending intermediate outputs to the task-specific gating networks; Tang, section 5.1.3 – teaches single layer [hidden] experts; see also Tang, Figure 4).
Regarding claim 3 (Currently Amended), Tang teaches all of the limitations of the system of claim 2 as noted above. Tang further teaches wherein the common expert of the neural network comprises (i) a second input layer configured to receive the second set of values corresponding to the second set of input features (Tang, section 4.1 – teaches input layer applying task-specific and shared inputs to task-specific and shared experts; see also Tang, Figure 4; see also Tang, sections 4, 4.2 - teaches task-specific and shared components are explicitly separated) and (ii) a second set of hidden layers configured to generate the second set of intermediate outputs based on manipulating the second set of values (Tang, section 4.1 – teaches the task-specific and shared experts manipulating data and sending intermediate outputs to the task-specific gating networks; Tang, section 5.1.3 – teaches single layer [hidden] experts; see also Tang, Figure 4).
Regarding claim 4 (Currently Amended), Tang teaches all of the limitations of the system of claim 1 as noted above. Tang further teaches wherein the first domain expert and the common expert operate independently of each other in the neural network (Tang, sections 4, 4.2 – teaches task-specific and shared components are explicitly separated).
Regarding claim 5 (Currently Amended), Tang teaches all of the limitations of the system of claim 1 as noted above. Tang further teaches wherein the gate is configured to provide a second parameter value different from the first parameter value to the common expert for processing a second transaction corresponding to the second domain (Tang, section 4.1 – teaches the gating network generates a weighted sum [a different parameter for each vector] of the expert outputs).
Regarding claim 6 (Currently Amended), Tang teaches all of the limitations of the system of claim 1 as noted above. Tang further teaches wherein the first domain aggregator comprises one or more hidden layers (Tang, section 4.1 – teaches the gating network is a fully connected single layer feed forward network with softmax activation).
Regarding claim 7 (Currently Amended), Tang teaches all of the limitations of the system of claim 1 as noted above. Tang further teaches wherein the first domain aggregator of the neural network is further configured to modify the second set of intermediate outputs based on the first weight (Tang, section 4.1 – teaches a task-specific gating network [aggregator] which weights and aggregates the outputs from the task-specific expert and the shared expert, wherein the output of the gating network [aggregator] is processed by a task-specific tower network; see also Tang, Figure 4).
Regarding claim 15 (Currently Amended), Tang teaches a non-transitory machine-readable medium having stored thereon machine-readable instructions executable to cause a machine to perform operations (Tang, section 5 - teaches offline and online experiments using large datasets generating various computer generated results as demonstrated by tables and graphs [This would necessarily require the use of a computer]) comprising:
receiving a request for processing a transaction corresponding to a first domain (Tang, section 5.1 – teaches processing video recommendation data for multiple tasks [domains], included VCP, CTR, VTR, SHR, CMR);
accessing a machine learning model based on the request (Tang, section 4.1 – teaches a machine learning model structure), wherein the machine learning model comprises a first domain expert corresponding to a first set of input features unique to the first domain (Tang, section 4.1 - teaches task-specific experts and shared experts; Tang, Figure 4 - teaches Expert A [first domain expert], Expert B [second domain expert] and Experts Shared [common expert]; see also Tang, sections 4, 4.2 - teaches task-specific and shared components are explicitly separated; see also Tang, Figure 4), a common expert corresponding to a second set of input features shared by the first domain and a second domain different from the first domain (Tang, section 4.1 - teaches task-specific experts and shared experts; Tang, Figure 4 - teaches Expert A [first domain expert], Expert B [second domain expert] and Experts Shared [common expert]; see also Tang, sections 4, 4.2 - teaches task-specific and shared components are explicitly separated; see also Tang, Figure 4), and a second domain expert corresponding to third set of input features unique to the second domain (Tang, section 4.1 - teaches task-specific experts and shared experts; Tang, Figure 4 - teaches Expert A [first domain expert], Expert B [second domain expert] and Experts Shared [common expert]; see also Tang, sections 4, 4.2 - teaches task-specific and shared components are explicitly separated; see also Tang, Figure 4);
providing a set of input values associated with the transaction to the machine learning model (Tang, section 4.1 – teaches applying input to the model in which the shared experts learn shared patterns and the task-specific experts learn specific tasks; see also Tang, Figure 4), wherein the machine learning model is configured (i) to use the first domain expert to generate a first set of intermediate values based on processing a first subset of the set of input values corresponding to the first set of input features (Tang, section 4.1 – teaches each tower network inputs values from the shared expert and the task specific expert [first set intermediate values]; see also Tang, Fig. 4), (ii) to use the common expert to generate a second set of intermediate values based on processing a second subset of the set of input values corresponding to the second set of input features (Tang, section 4.1 – teaches each tower network inputs values from the shared expert [second set intermediate values] and the task specific expert; see also Tang, Fig. 4), to use a first domain aggregator to generate an output based on the first set of intermediate values, the second set of intermediate values, and a weight associated with the second set of intermediate values (Tang, section 4.1 – teaches a task-specific gating network [aggregator] which weights and aggregates the outputs from the task-specific expert and the shared expert, wherein the output of the gating network [aggregator] is processed by a task-specific tower network; see also Tang, Figure 4); and
processing the transaction based on the output (Tang, section 5.1 – teaches processing video recommendation data).
Regarding claim 16 (Original), Tang teaches all of the limitations of the machine-readable medium of claim 15 as noted above. Tang further teaches
obtaining a first set of training data corresponding to the second domain (Tang, section 4.3 – teaches acquiring a training dataset corresponding to each of the tasks and training using task specific loss functions based on task specific training data); and
prior to providing the set of input values to the machine learning model, training the machine learning model using the first set of training data (Tang, section 4.3 – teaches acquiring a training dataset corresponding to each of the tasks and training using task specific loss functions based on task specific training data).
Regarding claim 18 (Currently Amended), Tang teaches all of the limitations of the machine-readable medium of claim 15 as noted above. Tang further teaches wherein the first domain expert is not connected to the common expert in the machine learning model (Tang, sections 4, 4.2 – teaches task-specific and shared components are explicitly separated).
Regarding claim 19 (Currently Amended), Tang teaches all of the limitations of the machine-readable medium of claim 15 as noted above. Tang further teaches wherein the first domain aggregator is further configured to manipulate the second set of intermediate values based on the weight (Tang, section 4.1 – teaches a task-specific gating network [aggregator] which weights and aggregates the outputs from the task-specific expert and the shared expert, wherein the output of the gating network [aggregator] is processed by a task-specific tower network; see also Tang, Figure 4).
Regarding claim 20 (Currently Amended), Tang teaches all of the limitations of the machine-readable medium of claim 15 as noted above. Tang further teaches wherein the first domain aggregator comprises one or more hidden layers (Tang, section 4.1 – teaches the gating network is a fully connected single layer feed forward network with softmax activation).
Claim(s) 8-9, 11-14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Tang et al. (Progressive Layered Extraction (PLE): A Novel Multi-Task Learning (MTL) Model for Personalized Recommendations, hereinafter referred to as “Tang”) in view of Liang et al. (Credit Risk and Limits Forecasting in E-Commerce Consumer Lending Service via Multi-view-aware Mixture-of-experts Nets, hereinafter referred to as "Liang").
Regarding claim 8 (Currently Amended), Tang teaches a method, comprising:
receiving a request for processing a transaction corresponding to a first domain (Tang, section 5.1 – teaches processing video recommendation data for multiple tasks [domains], included VCP, CTR, VTR, SHR, CMR);
obtaining a set of input values associated with the transaction, wherein the set of input values corresponds to input features related to the first domain (Tang, section 4.1 – teaches applying input to the model in which the shared experts learn shared patterns and the task-specific experts learn specific tasks; see also Tang, Figure 4), wherein the set of input values comprises a first set of values corresponding to a first set of input features unique to the first domain (Tang, section 4.1 – teaches applying input to the model in which the shared experts learn shared patterns and the task-specific experts learn specific tasks; see also Tang, sections 4, 4.2 - teaches task-specific and shared components are explicitly separated; see also Tang, Figure 4) and a second set of values corresponding to a second set of input features shared by the first domain and a second domain (Tang, section 4.1 – teaches applying input to the model in which the shared experts learn shared patterns and the task-specific experts learn specific tasks; see also Tang, Figure 4);
providing the set of input values to a machine learning model … associated with the first domain and the second domain (Tang, section 4.1 – teaches applying input to the model in which the shared experts learn shared patterns and the task-specific experts learn specific tasks; see also Tang, Figure 4), wherein the machine learning model comprises a first domain expert configured to receive the first set of values corresponding to the first set of input features unique to the first domain (Tang, section 4.1 - teaches task-specific experts and shared experts; Tang, Figure 4 - teaches Expert A [first domain expert], Expert B [second domain expert] and Experts Shared [common expert]; see also Tang, sections 4, 4.2 - teaches task-specific and shared components are explicitly separated; see also Tang, Figure 4) and generate a first set of intermediate outputs based on the first set of values (Tang, section 4.1 – teaches each tower network inputs values from the shared expert and the task specific expert [first set intermediate values]; see also Tang, Fig. 4), a common expert configured to receive the second set of values corresponding to the second set of input features shared by the first domain and the second domain (Tang, section 4.1 - teaches task-specific experts and shared experts; Tang, Figure 4 - teaches Expert A [first domain expert], Expert B [second domain expert] and Experts Shared [common expert]; see also Tang, sections 4, 4.2 - teaches task-specific and shared components are explicitly separated; see also Tang, Figure 4) and generate a second set of intermediate outputs based on the second set of values (Tang, section 4.1 – teaches each tower network inputs values from the shared expert [second set intermediate values] and the task specific expert; see also Tang, Fig. 4), and a second domain expert corresponding to third set of input features unique to the second domain (Tang, section 4.1 - teaches task-specific experts and shared experts; Tang, Figure 4 - teaches Expert A [first domain expert], Expert B [second domain expert] and Experts Shared [common expert]; see also Tang, sections 4, 4.2 - teaches task-specific and shared components are explicitly separated; see also Tang, Figure 4), and wherein the machine learning model further comprises a first domain aggregator configured to (i) determine a weight for the second set of intermediate outputs based on a parameter value received from a gate of the machine learning model (Tang, section 4.1 – teaches a task-specific gating network [aggregator] which weights and aggregates the outputs from the task-specific expert and the shared expert, wherein the output of the gating network [aggregator] is processed by a task-specific tower network; see also Tang, Figure 4) and (ii) generate a combined output based on the first set of intermediate outputs, the second set of intermediate outputs, and the weight (Tang, section 4.1 – teaches a task-specific gating network [aggregator] which weights and aggregates the outputs from the task-specific expert and the shared expert, wherein the output of the gating network [aggregator] is processed by a task-specific tower network; see also Tang, Figure 4).
While Tang teaches processing transaction data, Tang does not explicitly teach that the transaction data is a risk prediction. Further, Tang does not explicitly teach determining a risk associated with the transaction based on the combined output; and processing the transaction based on the risk.
Liang teaches
providing the set of input values to a machine learning model configured to perform risk prediction associated with the first domain and the second domain (Liang, section 3 – teaches input data associated with credit risk in e-commerce; see also, Liang, section 4 – mixture-of-experts model; Liang, section 5) …
determining a risk associated with the transaction based on the combined output (Liang, section 3 – teaches controlling the risk of e-commerce lending; see also, Liang, section 4 – mixture-of-experts model; Liang, section 5); and
processing the transaction based on the risk (Liang, section 3 – teaches controlling the risk of e-commerce lending; see also, Liang, section 4 – mixture-of-experts model; Liang, section 5).
It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to modify Tang with the teachings of Liang in order to combine different fundamental online lending problems into a single model and generate more accurate performance for online lending methods in the field of mixture-of-experts models (Liang, Abstract – “Consumer lending service is escalating in E-Commerce platforms due to its capability in enhancing buyers’ purchasing power, improving average order value, and increasing revenue of the platforms. Credit risk forecasting and credit limits setting are two fundamental problems in E-Commerce/online consumer lending services. Currently, the majority of institutes rely on two-separate-step methods to resolve. First, build a rating model to evaluate credit risk, and then design heuristic strategies to set credit limits, which requires a large amount of prior knowledge and lacks theoretical justifications. In this paper, we propose an end-to-end multi-view and multitask learning based approach named MvMoE (Multi-view-aware Mixture-of-Experts network) to solve these two problems simultaneously. First, a multi-view network with a hierarchical attention mechanism is constructed to distill users’ heterogeneous financial information into shared hidden representations. Then, we jointly train these two tasks with a view-aware multi-gate mixture-of-experts network and a subsequent progressive network to improve their performances. With the real-world dataset contained 5.44 million users, we investigate the effectiveness of MvMoE. Experimental results exhibit that the proposed model is able to improve AP over 5.60% on credit risk forecasting and MAE over 9.52% on credit limits setting compared with conventional methods. Meanwhile, MvMoE has good interpretability, which better underpins the imperative demands in financial industries.”).
Regarding claim 9 (Original), Tang in view of Liang teaches all of the limitations of the method of claim 8 as noted above. Tang further teaches
obtaining a first set of training data corresponding to the second domain (Tang, section 4.3 – teaches acquiring a training dataset corresponding to each of the tasks and training using task specific loss functions based on task specific training data); and
prior to providing the set of input values to the machine learning model, training the machine learning model using the first set of training data (Tang, section 4.3 – teaches acquiring a training dataset corresponding to each of the tasks and training using task specific loss functions based on task specific training data).
It would have been obvious to one of ordinary skill in the art before the filing data of the claimed invention to combine the teachings of Tang and Liang for the same reasons as disclosed in claim 8 above.
Regarding claim 11 (Currently Amended), Tang in view of Liang teaches all of the limitations of the method of claim 8 as noted above. Tang further teaches
obtaining a first set of training data corresponding to the first domain (Tang, section 4.3 – teaches acquiring a training dataset corresponding to each of the tasks and training using task specific loss functions based on task specific training data);
obtaining a second set of training data corresponding to the second domain (Tang, section 4.3 – teaches acquiring a training dataset corresponding to each of the tasks and training using task specific loss functions based on task specific training data);
generating a merged set of training data based on merging the first set of training data with the second set of training data (Tang, section 4.3 – teaches a union of all tasks training data as the whole training set); and
training the machine learning model using the merged set of training data (Tang, section 4.3 – teaches joint loss optimization using the losses of each individual task based on the whole training set).
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 Tang and Liang for the same reasons as disclosed in claim 8 above.
Regarding claim 12 (Currently Amended), Tang in view of Liang teaches all of the limitations of the method of claim 8 as noted above. Tang further teaches wherein the first domain expert is further configured to manipulate the first set of values corresponding to the first set of input features (Tang, section 4.1 – teaches the task-specific and shared experts manipulating data and sending intermediate outputs to the task-specific gating networks; see also Tang, Figure 4), and wherein the common expert is further configured to manipulate the second set of values corresponding to the second set of input features (Tang, section 4.1 – teaches the task-specific and shared experts manipulating data and sending intermediate outputs to the task-specific gating networks; see also Tang, 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 Tang and Liang for the same reasons as disclosed in claim 8 above.
Regarding claim 13 (Currently Amended), Tang in view of Liang teaches all of the limitations of the method of claim 8 as noted above. Tang further teaches wherein the first domain expert is further configured to manipulate the first set of values independent of the common expert (Tang, sections 4, 4.2 – teaches task-specific and shared components are explicitly separated).
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 Tang and Liang for the same reasons as disclosed in claim 8 above.
Regarding claim 14 (Currently Amended), Tang in view of Liang teaches all of the limitations of the method of claim 12 as noted above. Tang further teaches wherein the first domain aggregator is further configured to modify the second set of intermediate outputs from the common expert based on the weight (Tang, section 4.1 – teaches a task-specific gating network [aggregator] which weights and aggregates the outputs from the task-specific expert and the shared expert, wherein the output of the gating network [aggregator] is processed by a task-specific tower network; see also Tang, 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 Tang and Liang for the same reasons as disclosed in claim 12 above.
Claim(s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Tang in view of Liang and further in view of Chen et al. (Task’s Choice: Pruning Based Feature Sharing (PBFS) for Multi-Task Learning, hereinafter referred to as “Chen”).
Regarding claim 10 (Original), Tang in view of Liang teaches all of the limitations of the method of claim 9 as noted above. However, Tang in view of Liang does not explicitly teach wherein the training the machine learning model using the first set of training data comprises modifying the common expert, but not the first domain expert, of the machine learning model.
Chen teaches wherein the training the machine learning model using the first set of training data comprises modifying the common expert, but not the first domain expert, of the machine learning model (Chen, section 3.3 – teaches, as part of training, modifying the shared expert through pruning, while keeping the task-specific experts fixed).
It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to modify Tang in view of Liang with the teachings of Chen in order to reduce storage requirements and improve computing performance without affecting accuracy in the field of mixture-of-experts models (Chen, section 3.3 – “For Hypothesis 1 Frankle J. et al. ... conducted a lot of experiments to prove that in an original base model, a more sparse pruned sub-model can always be found, and this sub-model can reach or even exceed the performance of the original model in roughly the same training process as the original model and the number of iterations. For Hypothesis 2 Sun T et al. ... warmed up the original model before pruning so that a specific subnet could be confirmed before pruning model parameters related to the task. Then, the cut parameters with IMP according to the neurons’ weight value for obtaining the subnet of the task will hardly be harmful to the model performance in the next training process. Moreover, the pruning mechanism of the neural network can reduce more than 90% of parameters in the trained network, thus cutting down storage requirements and improving computing performance without affecting accuracy...”).
Claim(s) 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Tang in view of Chen et al. (Task’s Choice: Pruning Based Feature Sharing (PBFS) for Multi-Task Learning, hereinafter referred to as “Chen”).
Regarding claim 17 (Original), Tang teaches all of the limitations of the machine-readable medium of claim 16 as noted above. However, Tang does not explicitly teach wherein the training the machine learning model using the first set of training data comprises modifying the common expert, but not the first domain expert, of the machine learning model.
Chen teaches wherein the training the machine learning model using the first set of training data comprises modifying the common expert, but not the first domain expert, of the machine learning model (Chen, section 3.3 – teaches, as part of training, modifying the shared expert through pruning, while keeping the task-specific experts fixed).
It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to modify Tang with the teachings of Chen in order to reduce storage requirements and improve computing performance without affecting accuracy in the field of mixture-of-experts models (Chen, section 3.3 – “For Hypothesis 1 Frankle J. et al. ... conducted a lot of experiments to prove that in an original base model, a more sparse pruned sub-model can always be found, and this sub-model can reach or even exceed the performance of the original model in roughly the same training process as the original model and the number of iterations. For Hypothesis 2 Sun T et al. ... warmed up the original model before pruning so that a specific subnet could be confirmed before pruning model parameters related to the task. Then, the cut parameters with IMP according to the neurons’ weight value for obtaining the subnet of the task will hardly be harmful to the model performance in the next training process. Moreover, the pruning mechanism of the neural network can reduce more than 90% of parameters in the trained network, thus cutting down storage requirements and improving computing performance without affecting accuracy...”).
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.
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/MARSHALL L WERNER/ Primary Examiner, Art Unit 2125
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