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 .
DETAILED ACTION
This action is responsive to the Application filed on 6/9/2023. Claims 1-15 are pending in the case. Claims 1, and 15 are independent claims.
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed on 7/17/2023.
Claim Rejections - 35 U.S.C. § 101
35 U.S.C. § 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-15 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more.
When considering subject matter eligibility under 35 U.S.C. 101, it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter (Step 1). If the claim does fall within one of the statutory categories, the second step in the analysis is to determine whether the claim is directed to a judicial exception (Step 2A). The Step 2A analysis is broken into two prongs. In the first prong (Step 2A, Prong 1), it is determined whether or not the claims recite a judicial exception (e.g., mathematical concepts, mental processes, certain methods of organizing human activity). If it is determined in Step 2A, Prong 1 that the claims recite a judicial exception, the analysis proceeds to the second prong (Step 2A, Prong 2), where it is determined whether or not the claims integrate the judicial exception into a practical application. If itis determined at step 2A, Prong 2 that the claims do not integrate the judicial exception into a practical application, the analysis proceeds to determining whether the claim is a patent-eligible application of the exception (Step 2B). If an abstract idea is present in the claim, any element or combination of elements in the claim must be sufficient to ensure that the claim integrates the judicial exception into a practical application, or else amounts to significantly more than the abstract idea itself. Applicant is advised to consult the 2019 PEG for more details of the analysis.
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Claims 1-14 are drawn to a method, claim 15 is drawn to a system, therefore each of these claim groups falls under one of four categories of statutory subject matter (machine/products/apparatus, process/method, manufactures and compositions of mater; Step 1). Nonetheless, the claims are directed to a judicially recognized exception of an abstract idea without significant more (Step 2A, see below). Independent claims 1 and 15 are non-verbatim but similar in claim construction, hence share the same rationale that the claimed inventions are directed to non-statutory subject matter as follows:
As to claim 1:
Claim 1 recites “A computer-implemented method for customising a pre-trained machine learning model which has been installed on a user device and which has a set of basic parameters which have been learnt using a labelled training dataset, the method comprising:
adding at least one adapter module to the pre-trained machine learning model to create a local machine learning model, wherein each adapter module has a set of adapter parameters;
storing a dataset of user data, wherein the user dataset comprises unlabelled data; and
customising the local machine learning model by: fixing the set of basic parameters and using an unsupervised loss function on the stored user dataset to learn the set of adapter parameters.”
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
Yes, the limitation “customising the local machine learning model by: fixing the set of basic parameters and using an unsupervised loss function on the stored user dataset to learn the set of adapter parameters” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Yes, the limitation “customising the local machine learning model by: … using an unsupervised loss function on the stored user dataset to learn the set of adapter parameters” is the abstract idea of a mathematical formula or equation, as directed to “a claim that recites a numerical formula or equation will be considered as falling within the "mathematical concepts" grouping. In addition, there are instances where a formula or equation is written in text format that should also be considered as falling within this grouping”. See MPEP § 2106.04(a)(2)(I)(B).
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
No, this limitation “customising a pre-trained machine learning model which has been installed on a user device”, and “adding at least one adapter module to the pre-trained machine learning model to create a local machine learning model” are additional elements that amounts to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “pre-trained machine learning model”, adapter module” and “local machine learning model” are used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d).
No, this limitation “computer-implemented” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process, and as such is deemed insufficient to transform the judicial exception to a patentable invention. See MPEP §§ 2106.04(d), 2106.05(f)(2).
No, this limitation “a set of basic parameters which have been learnt using a labelled training dataset” amounts to mere data gathering. It is necessary to acquire the data in order to use the recited judicial exception to perform “learnt”. Therefore, the additional limitation is insignificant extra-solution activity to the judicial exception, and as such is deemed insufficient to transform the judicial exception to a patentable invention. See MPEP §§ 2106.04(d), 2106.05(g).
No, this limitation “wherein each adapter module has a set of adapter parameters” amounts to mere data gathering. It is necessary to acquire the data in order to use the recited judicial exception. Therefore, the additional limitation is insignificant extra-solution activity to the judicial exception, and as such is deemed insufficient to transform the judicial exception to a patentable invention. See MPEP §§ 2106.04(d), 2106.05(g).
No, this limitation “storing a dataset of user data, wherein the user dataset comprises unlabelled data” amounts to mere data gathering. It is necessary to acquire the data in order to use the recited judicial exception. Therefore, the additional limitation is insignificant extra-solution activity to the judicial exception, and as such is deemed insufficient to transform the judicial exception to a patentable invention. See MPEP §§ 2106.04(d), 2106.05(g).
This limitation “wherein each adapter module has a set of adapter parameters” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use, and as such is deemed insufficient to transform the judicial exception to a patentable invention. See MPEP §§ 2106.04(d), 2106.05(h).
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea when considered as an ordered combination and as a whole.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
First, the additional elements directed to generally linking the use of a judicial exception to a particular technological environment or field of use are deemed insufficient to transform the judicial exception to a patentable invention because the claimed limitations generally link the judicial exception to the technology environment, see MPEP 2106.05(h). However, they are included below for the sake of completeness.
Second, the additional elements mere application of the abstract idea or mere instructions to implement an abstract idea on a computer are deemed insufficient to transform the judicial exception to a patentable invention because the limitations generally apply the use of a generic computer and/or process with the judicial exception. See MPEP 2106.05(f). However, they are included below for the sake of completeness.
No, this limitation “customising a pre-trained machine learning model which has been installed on a user device”, and “adding at least one adapter module to the pre-trained machine learning model to create a local machine learning model” are additional elements that amounts to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “pre-trained machine learning model”, adapter module” and “local machine learning model” are used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d).
No, this limitation “computer-implemented” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process, and as such is deemed insufficient to transform the judicial exception to a patentable invention. See MPEP §§ 2106.04(d), 2106.05(f)(2).
No, this limitation “a set of basic parameters which have been learnt using a labelled training dataset” amounts to mere data gathering. It is necessary to acquire the data in order to use the recited judicial exception to perform “learnt”. Therefore, the additional limitation is insignificant extra-solution activity to the judicial exception, and as such is deemed insufficient to transform the judicial exception to a patentable invention. See MPEP §§ 2106.04(d), 2106.05(g). Furthermore the additional element is directed to receiving or transmitting data over a network / performing repetitive calculations / electronic recordkeeping / storing and retrieving information in memory / electronically scanning or extracting data from a physical document, which the courts have recognized as well‐understood, routine, and conventional when they are claimed in a generic manner. See MPEP § 2106.05(d)(II).
No, this limitation “wherein each adapter module has a set of adapter parameters” amounts to mere data gathering. It is necessary to acquire the data in order to use the recited judicial exception. Therefore, the additional limitation is insignificant extra-solution activity to the judicial exception, and as such is deemed insufficient to transform the judicial exception to a patentable invention. See MPEP §§ 2106.04(d), 2106.05(g). Furthermore the additional element is directed to receiving or transmitting data over a network / performing repetitive calculations / electronic recordkeeping / storing and retrieving information in memory / electronically scanning or extracting data from a physical document, which the courts have recognized as well‐understood, routine, and conventional when they are claimed in a generic manner. See MPEP § 2106.05(d)(II).
No, this limitation “storing a dataset of user data, wherein the user dataset comprises unlabelled data” amounts to mere data gathering. It is necessary to acquire the data in order to use the recited judicial exception. Therefore, the additional limitation is insignificant extra-solution activity to the judicial exception, and as such is deemed insufficient to transform the judicial exception to a patentable invention. See MPEP §§ 2106.04(d), 2106.05(g). Furthermore the additional element is directed to receiving or transmitting data over a network / performing repetitive calculations / electronic recordkeeping / storing and retrieving information in memory / electronically scanning or extracting data from a physical document, which the courts have recognized as well‐understood, routine, and conventional when they are claimed in a generic manner. See MPEP § 2106.05(d)(II).
This limitation “wherein each adapter module has a set of adapter parameters” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use, and as such is deemed insufficient to transform the judicial exception to a patentable invention. See MPEP §§ 2106.04(d), 2106.05(h).
Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. The claims are not eligible subject matter.
Therefore, in examining elements as recited by the limitations individually and as an ordered combination, as a whole the independent claim limitations do not recite what have the courts have identified as “significantly more”.
As to claim 15:
Claim 15 recites “A system for customising a machine learning model, the system comprising: a server comprising: a processor for training a machine learning model to learn a set of basic parameters using a labelled training dataset; and an electronic user device comprising: memory for storing the pre-trained machine learning model which is received from the server and for storing a dataset of user data, wherein the user dataset comprises unlabelled data; and at least one processor coupled to memory and arranged to: add at least one adapter module to the pre-trained machine learning model to create a local machine learning model, wherein each adapter module has a set of adapter parameters; and customise the local machine learning model by fixing the set of basic parameters and using an unsupervised loss function on the stored user dataset to learn the set of adapter parameters.”
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
Yes, the limitation “customising the local machine learning model by: fixing the set of basic parameters and using an unsupervised loss function on the stored user dataset to learn the set of adapter parameters” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Yes, the limitation “customising the local machine learning model by: … using an unsupervised loss function on the stored user dataset to learn the set of adapter parameters” is the abstract idea of a mathematical formula or equation, as directed to “a claim that recites a numerical formula or equation will be considered as falling within the "mathematical concepts" grouping. In addition, there are instances where a formula or equation is written in text format that should also be considered as falling within this grouping”. See MPEP § 2106.04(a)(2)(I)(B).
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
No, this limitation “customising a machine learning model”, and “training a machine learning model”. “add at least one adapter module to the pre-trained machine learning model to create a local machine learning model” are additional elements that amounts to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “pre-trained machine learning model”, adapter module” and “local machine learning model” are used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d).
No, this limitation “a server” and “a processor”, “an electronic user device” and “memory” are additional elements that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process, and as such is deemed insufficient to transform the judicial exception to a patentable invention. See MPEP §§ 2106.04(d), 2106.05(f)(2).
No, this limitation “learn a set of basic parameters using a labelled training dataset” amounts to mere data gathering. It is necessary to acquire the data in order to use the recited judicial exception. Therefore, the additional limitation is insignificant extra-solution activity to the judicial exception, and as such is deemed insufficient to transform the judicial exception to a patentable invention. See MPEP §§ 2106.04(d), 2106.05(g).
No, this limitation “storing the pre-trained machine learning model”, “storing a dataset of user data, wherein the user dataset comprises unlabelled data” amounts to mere data gathering. It is necessary to acquire the data in order to use the recited judicial exception. Therefore, the additional limitation is insignificant extra-solution activity to the judicial exception, and as such is deemed insufficient to transform the judicial exception to a patentable invention. See MPEP §§ 2106.04(d), 2106.05(g).
This limitation ““wherein each adapter module has a set of adapter parameters” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use, and as such is deemed insufficient to transform the judicial exception to a patentable invention. See MPEP §§ 2106.04(d), 2106.05(h).
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea when considered as an ordered combination and as a whole.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
First, the additional elements directed to generally linking the use of a judicial exception to a particular technological environment or field of use are deemed insufficient to transform the judicial exception to a patentable invention because the claimed limitations generally link the judicial exception to the technology environment, see MPEP 2106.05(h). However, they are included below for the sake of completeness.
Second, the additional elements mere application of the abstract idea or mere instructions to implement an abstract idea on a computer are deemed insufficient to transform the judicial exception to a patentable invention because the limitations generally apply the use of a generic computer and/or process with the judicial exception. See MPEP 2106.05(f). However, they are included below for the sake of completeness.
No, this limitation “customising a machine learning model”, and “training a machine learning model”. “add at least one adapter module to the pre-trained machine learning model to create a local machine learning model” are additional elements that amounts to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “pre-trained machine learning model”, adapter module” and “local machine learning model” are used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d).
No, this limitation “a server” and “a processor”, “an electronic user device” and “memory” are additional elements that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process, and as such is deemed insufficient to transform the judicial exception to a patentable invention. See MPEP §§ 2106.04(d), 2106.05(f)(2).
No, this limitation “learn a set of basic parameters using a labelled training dataset” amounts to mere data gathering. It is necessary to acquire the data in order to use the recited judicial exception. Therefore, the additional limitation is insignificant extra-solution activity to the judicial exception, and as such is deemed insufficient to transform the judicial exception to a patentable invention. See MPEP §§ 2106.04(d), 2106.05(g). Furthermore the additional element is directed to receiving or transmitting data over a network / performing repetitive calculations / electronic recordkeeping / storing and retrieving information in memory / electronically scanning or extracting data from a physical document, which the courts have recognized as well‐understood, routine, and conventional when they are claimed in a generic manner. See MPEP § 2106.05(d)(II).
No, this limitation “storing the pre-trained machine learning model”, “storing a dataset of user data, wherein the user dataset comprises unlabelled data” amounts to mere data gathering. It is necessary to acquire the data in order to use the recited judicial exception. Therefore, the additional limitation is insignificant extra-solution activity to the judicial exception, and as such is deemed insufficient to transform the judicial exception to a patentable invention. See MPEP §§ 2106.04(d), 2106.05(g). Furthermore the additional element is directed to receiving or transmitting data over a network / performing repetitive calculations / electronic recordkeeping / storing and retrieving information in memory / electronically scanning or extracting data from a physical document, which the courts have recognized as well‐understood, routine, and conventional when they are claimed in a generic manner. See MPEP § 2106.05(d)(II).
This limitation ““wherein each adapter module has a set of adapter parameters” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use, and as such is deemed insufficient to transform the judicial exception to a patentable invention. See MPEP §§ 2106.04(d), 2106.05(h). Furthermore the additional element is directed to receiving or transmitting data over a network / performing repetitive calculations / electronic recordkeeping / storing and retrieving information in memory / electronically scanning or extracting data from a physical document, which the courts have recognized as well‐understood, routine, and conventional when they are claimed in a generic manner. See MPEP § 2106.05(d)(II).
Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. The claims are not eligible subject matter.
Therefore, in examining elements as recited by the limitations individually and as an ordered combination, as a whole the independent claim limitations do not recite what have the courts have identified as “significantly more”.
Furthermore, regarding dependent claims 2-14 which are dependent on claim 1, the claims are directed to a judicial exception without significantly more as highlighted below in the claim limitations by evaluating the claim limitations under Step 2A and 2B:
Dependent claim 2
Incorporates the rejection of independent claim
Step 2A Prong 1: does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1). Incorporates the abstract idea of independent claim.
Step 2A prong 2: the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d). this limitation “adding the at least one adapter module comprises adding at least one parallel adapter module, at least one serial adapter module, and/or at least one transformer adapter module” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “adapter module” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d).
Step 2B: the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05. and Is the additional element recognized as well-understood, routine, and conventional? No.
Dependent claim 3
Incorporates the rejection of independent claim
Step 2A Prong 1: does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1). Incorporates the abstract idea of independent claim.
Step 2A prong 2: the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d) No. this limitation “adding the at least one adapter module comprises adding a plurality of adapter modules” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “adapter module” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d).
Step 2B: the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05. and Is the additional element recognized as well-understood, routine, and conventional? No.
Dependent claim 4
Incorporates the rejection of independent claim
Step 2A Prong 1: does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1). this limitation “the machine learning model is a neural network model comprising a plurality of layers and wherein adding the at least one adapter module comprises associating an adapter module with a layer for at least some of the plurality of layers” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “adapter module” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d).
Step 2A prong 2: the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d) No.
Step 2B: the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05. and Is the additional element recognized as well-understood, routine, and conventional? No.
Dependent claim 5
Incorporates the rejection of independent claim
Step 2A Prong 1: does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1). Yes, the limitation is the abstract idea of a mathematical formula or equation, as directed to “a claim that recites a numerical formula or equation will be considered as falling within the "mathematical concepts" grouping. In addition, there are instances where a formula or equation is written in text format that should also be considered as falling within this grouping”. See MPEP § 2106.04(a)(2)(I)(B).
Step 2A prong 2: the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d) No.
Step 2B: the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05. and Is the additional element recognized as well-understood, routine, and conventional? No.
Dependent claim 6
Incorporates the rejection of independent claim
Step 2A Prong 1: does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1). Incorporates the abstract idea of independent claim.
Step 2A prong 2: the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d) No. this limitation “the plurality of adapter modules comprise sets of adapter modules with each adapter module in the set of adapter modules having adapter parameters associated with an adaptation environment” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “adapter module” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d).
Step 2B: the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05. and Is the additional element recognized as well-understood, routine, and conventional? No.
Dependent claim 7
Incorporates the rejection of independent claim
Step 2A Prong 1: does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1). Yes, the limitation “select one of the adapter modules from the set of adapter modules and which has a set of switch parameters” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Step 2A prong 2: the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d) No, this limitation “adding a switching module which is configured to…” and “which are learnt when customising the local machine learning model” are additional elements that amounts to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “machine learning” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d).
Step 2B: the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05. and Is the additional element recognized as well-understood, routine, and conventional? No.
Dependent claim 8
Incorporates the rejection of independent claim
Step 2A Prong 1: does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1). Yes, the limitation is the abstract idea of a mathematical formula or equation, as directed to “a claim that recites a numerical formula or equation will be considered as falling within the "mathematical concepts" grouping. In addition, there are instances where a formula or equation is written in text format that should also be considered as falling within this grouping”. See MPEP § 2106.04(a)(2)(I)(B).
Step 2A prong 2: the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d) No.
Step 2B: the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05. and Is the additional element recognized as well-understood, routine, and conventional? No.
Dependent claim 9
Incorporates the rejection of independent claim
Step 2A Prong 1: does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1). Incorporate abstract idea of independent claim.
Step 2A prong 2: the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d) No.
Step 2B: the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05. and Is the additional element recognized as well-understood, routine, and conventional? No.
Dependent claim 10
Incorporates the rejection of independent claim
Step 2A Prong 1: does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1). Yes, the limitation “determined automatically when customising the model and comprises defining a weighted sum of adapter modules; defining a set of weighting parameters with each weighting parameter being associated with one of the adapter modules in the weighted sum” and “whereby the learnt set of weighting parameters determine which adapter modules are to be added” are the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III). the limitation “determined automatically when customising the model and comprises defining a weighted sum of adapter modules; defining a set of weighting parameters with each weighting parameter being associated with one of the adapter modules in the weighted sum” and “whereby the learnt set of weighting parameters determine which adapter modules are to be added” are the abstract idea of a mathematical calculation, as directed to “a claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping. A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number”. See MPEP § 2106.04(a)(2)(I)(C).
Step 2A prong 2: the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d) No, this limitation “adding the at least one adapter module …” and “learning the set of weighting parameters when customising the local machine learning model” are additional elements that amounts to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “adapter module” and “machine learning” are used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d).
Step 2B: the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05. and Is the additional element recognized as well-understood, routine, and conventional?
Dependent claim 11
Incorporates the rejection of independent claim
Step 2A Prong 1: does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1). Incorporate abstract idea of independent claim.
Step 2A prong 2: the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d) No.
Step 2B: the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05. and Is the additional element recognized as well-understood, routine, and conventional? No.
Dependent claim 12
Incorporates the rejection of independent claim
Step 2A Prong 1: does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1). Yes, the limitation “inferring a first prediction from the sample…; performing at least one verification step; when the verification is successful, outputting the first prediction, and when the verification is not successful, outputting a second prediction which is inferred from the sample” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Step 2A prong 2: the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d) No, this limitation “receiving a sample to be analysed by the customised machine learning model” amounts to mere data gathering. It is necessary to acquire the data in order to use the recited judicial exception. Therefore, the additional limitation is insignificant extra-solution activity to the judicial exception, and as such is deemed insufficient to transform the judicial exception to a patentable invention. See MPEP §§ 2106.04(d), 2106.05(g). this limitation “using the customised machine learning model”, “using the pre-trained machine learning model” are additional elements that amounts to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “machine learning” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d).
Step 2B: the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05. and Is the additional element recognized as well-understood, routine, and conventional? No, this limitation “using the customised machine learning model”, “using the pre-trained machine learning model” are additional elements that amounts to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “machine learning” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d). this limitation “receiving a sample to be analysed by the customised machine learning model” amounts to mere data gathering. It is necessary to acquire the data in order to use the recited judicial exception. Therefore, the additional limitation is insignificant extra-solution activity to the judicial exception, and as such is deemed insufficient to transform the judicial exception to a patentable invention. See MPEP §§ 2106.04(d), 2106.05(g). Furthermore the additional element is directed to receiving or transmitting data over a network / performing repetitive calculations / electronic recordkeeping / storing and retrieving information in memory / electronically scanning or extracting data from a physical document, which the courts have recognized as well‐understood, routine, and conventional when they are claimed in a generic manner. See MPEP § 2106.05(d)(II).
Dependent claim 13
Incorporates the rejection of independent claim
Step 2A Prong 1: does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1). Yes, the limitation “the at least one verification step comprises at least one of verifying a likelihood of the sample itself and verifying an entropy value” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Step 2A prong 2: the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d) No, this limitation “associated with the model or the prediction” are additional elements that amounts to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “model” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d).
Step 2B: the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05. and Is the additional element recognized as well-understood, routine, and conventional? No.
Dependent claim 14
Incorporates the rejection of independent claim
Step 2A Prong 1: does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1). Incorporates the rejection of independent claim.
Step 2A prong 2: the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d) No.
Step 2B: the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05. and Is the additional element recognized as well-understood, routine, and conventional? No.
The dependent claims as analyzed above, do not recite limitations that integrated the judicial exception into a practical application. In addition, the claim limitations do not include additional elements that are sufficient to amount to significantly more than the judicial exception (Step 2B). Therefore, the claims do not recite any limitations, when considered individually or as a whole, that recite what the courts have identified as “significantly more”, see MPEP 2106.05; and therefore, as a whole the claims are not patent eligible.
As shown above, the dependent claims do not provide any additional elements that when considered individually or as an ordered combination, amount to significantly more than the abstract idea identified. Therefore, as a whole the dependent claims do not recite what the courts have identified as “significantly more” than the recited judicial exception.
Therefore, claims 1-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception and does not recite, when claim elements are examined individually and as a whole, elements that the courts have identified as “significantly more” than the recited judicial exception.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-8, 10, and 14-15 are rejected under 35 U.S.C. 103 as being unpatentable over “User-specific adaptive fine-tuning for cross-domain recommendations”, Chen et al, 6/18/2021 in view of Le Groux et al (US 20220383857 A1).
Referring to claims 1 and 15, Chen discloses a computer-implemented method for customising a pre-trained machine learning model which has been installed on a user device and which has a set of basic parameters, (fig. 1 and page 2 of Chen, a pre-trained model with pre-trained parameters associated with the user devices) the method comprising:
adding at least one adapter module to the pre-trained machine learning model to create a local machine learning model, wherein each adapter module has a set of adapter parameters; (Figs. 1-2 and pages 2 and 3 of Chen, fine-tuning module is added on to the pre-trained model to create user specific model/local model, where the fine tuning module has the tuning parameters)
storing a dataset of user data, wherein the user dataset comprises unlabelled data; (page 7 of Chen, source dataset includes both new s and video interactions, which is raw data/unlabelled data)
and customising the local machine learning model by: fixing the set of basic parameters and using an unsupervised loss function on the stored user dataset to learn the set of adapter parameters. (pages 5-7 of Chen, loss function does feed into the tuning model, eqn. (8) is to determine which layers to fixed and which layers to fine-tuning and eqn. (14) is to reward policy using fewer blocks to fine-tun)
Chen does not specifically disclose a set of basic parameters “which have been learnt using a labelled training dataset”.
However, Le Croux discloses a set of basic parameters which have been learnt using a labelled training dataset ([0015] of Le Croux, global model is pre-trained with labelled dataset).
Chen and Le Croux are analogous art because both references concern customize client data model. Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Chen’s fine tuning a pre-trained model with training model with labelled data as taught by Le Croux. The motivation for doing so would have been to customizing local training model based on a pre-trained model.
Referring to claim 2, Chen in view of Le Croux disclose the method as claimed in claim 1 wherein adding the at least one adapter module comprises adding at least one parallel adapter module, at least one serial adapter module, and/or at least one transformer adapter module. (Figs. 1-2 and pages 2 and 3 of Chen, fine-tuning module is added on to the pre-trained model to create user specific model/local model, where the fine tuning module has the tuning parameters)
Referring to claim 3, Chen in view of Le Croux disclose the method as claimed in claim 1 wherein adding the at least one adapter module comprises adding a plurality of adapter modules. (Figs. 1-2 and pages 2 and 3 of Chen, fine-tuning module is added on to the pre-trained model to create user specific model/local model, where the fine tuning module has the tuning parameters)
Referring to claim 4, Chen in view of Le Croux disclose the method of claim 3, wherein the machine learning model is a neural network model comprising a plurality of layers and wherein adding the at least one adapter module comprises associating an adapter module with a layer for at least some of the plurality of layers. (Figs. 1-2 and pages 2 and 3 of Chen, fine-tuning module is added on to the pre-trained model to create user specific model/local model, where the fine tuning module has the tuning parameters)
Referring to claim 6, Chen in view of Le Croux disclose the method of claim 3, wherein the plurality of adapter modules comprise sets of adapter modules with each adapter module in the set of adapter modules having adapter parameters associated with an adaptation environment. (Figs. 1-2 and pages 2 and 3 of Chen, fine-tuning module is added on to the pre-trained model to create user specific model/local model, where the fine tuning module has the tuning parameters)
Referring to claim 7, Chen in view of Le Croux disclose the method of claim 6, wherein the method further comprises adding a switching module which is configured to select one of the adapter modules from the set of adapter modules and which has a set of switch parameters which are learnt when customising the local machine learning model. (Figs. 1-2 and pages 2 and 3 of Chen, fine-tuning module is added on to the pre-trained model to create user specific model/local model, where the fine tuning module has the tuning parameters as well as fixing parameter modules)
Referring to claim 10, Chen in view of Le Croux disclose the method of claim 1, wherein adding the at least one adapter module is determined automatically when customising the model and comprises defining a weighted sum of adapter modules; (page 7 of Chen, weighted-sum of the BPR loss and SCST loss) defining a set of weighting parameters with each weighting parameter being associated with one of the adapter modules in the weighted sum; (page 6 of Chen, weight distribution of the fine-tuning policy) and learning the set of weighting parameters when customising the local machine learning model whereby the learnt set of weighting parameters determine which adapter modules are to be added. (page 5 of Chen, fine-tuned parameter are added to pre-trained parameters)
Referring to claim 14, Chen in view of Le Croux disclose the non-transitory data carrier carrying code which, when implemented on a processor, causes the processor to carry out the method of claim 1. (Fig. 6 of Le Croux)
Claims 9, 11-13 are rejected under 35 U.S.C. 103 as being unpatentable over “User-specific adaptive fine-tuning for cross-domain recommendations”, Chen et al, 6/18/2021 in view of Le Groux et al (US 20220383857 A1) in further view of Xu et al (US 20220171947 A1).
Referring to claim 9, Chen in view of Le Croux disclose the method of claim 1. Chen in view of Le Croux do not specifically disclose wherein the unsupervised loss function is selected from the group comprising an entropy loss function, an infomax loss function, a self-supervised masked prediction function, and a stochastic classifier disagreement loss which minimises a difference between two sampled predictions made by the local machine learning model.
However, Xu discloses a cross entropy loss model. ([0058] of Xu).
Chen and Le Croux and Xu are analogous art because both references concern customize client data model. Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Chen’s fine tuning a pre-trained model with training model with labelled data as taught by Le Croux and cross-entropy loss model as taught by Xu. The motivation for doing so would have been to customizing local training model based on a pre-trained model.
Referring to claim 11, Chen in view of Le Croux disclose the method of claim 1. Chen in view of Le Croux do not specifically disclose further comprising: after customising the local machine learning model, verifying the customized local machine learning model; when the customized local machine learning model is verified, causing the user device to implement the customized local machine learning model and when the customized local machine learning model is not verified, disabling the set of adaptation parameters whereby the local machine learning model is reset to the set of basic parameters.
However, Xu discloses after customising the local machine learning model, verifying the customized local machine learning model; when the customized local machine learning model is verified, causing the user device to implement the customized local machine learning model and when the customized local machine learning model is not verified, disabling the set of adaptation parameters whereby the local machine learning model is reset to the set of basic parameters ([0094] of Xu, verifying the model. Note: The claim does not disclose how to verify the model, hence it is interpreted as either using the adaptation parameter or the basic parameters. pages 5-7 of Chen, loss function does feed into the tuning model, eqn. (8) is to determine which layers to fixed and which layers to fine-tuning and eqn. (14) is to reward policy using fewer blocks to fine-tun. Chen discloses a method of either using the pre-trained/basic parameters, or using fine-tuned parameters)
Chen and Le Croux and Xu are analogous art because both references concern customize client data model. Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Chen’s fine tuning a pre-trained model with training model with labelled data as taught by Le Croux and cross-entropy loss model as taught by Xu. The motivation for doing so would have been to customizing local training model based on a pre-trained model.
Referring to claim 12, Chen in view of Le Croux disclose the method of implementing a local machine learning model which has been customised as set out in claim 1. Chen in view of Le Croux do not specifically disclose the method comprising: receiving a sample to be analysed by the customised machine learning model, inferring a first prediction from the sample using the customised machine learning model; performing at least one verification step; when the verification is successful, outputting the first prediction, and when the verification is not successful, outputting a second prediction which is inferred from the sample using the pre-trained machine learning model.
However, Xu discloses receiving a sample to be analysed by the customised machine learning model, inferring a first prediction from the sample using the customised machine learning model; performing at least one verification step; when the verification is successful, outputting the first prediction, and when the verification is not successful, outputting a second prediction which is inferred from the sample using the pre-trained machine learning model. ([0094] of Xu, verifying the model. Note: The claim does not disclose how to verify the model, hence it is interpreted as either using the adaptation parameter or the basic parameters. pages 5-7 of Chen, loss function does feed into the tuning model, eqn. (8) is to determine which layers to fixed and which layers to fine-tuning and eqn. (14) is to reward policy using fewer blocks to fine-tun. Chen discloses a method of either using the pre-trained/basic parameters, or using fine-tuned parameters)
Chen and Le Croux and Xu are analogous art because both references concern customize client data model. Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Chen’s fine tuning a pre-trained model with training model with labelled data as taught by Le Croux and cross-entropy loss model as taught by Xu. The motivation for doing so would have been to customizing local training model based on a pre-trained model.
Referring to claim 13, Chen in view of Le Croux and Xu disclose the method of claim 12, wherein the at least one verification step comprises at least one of verifying a likelihood of the sample itself and verifying an entropy value associated with the model or the prediction. ([0138] of Xu).
Allowable Subject Matter
Claims 5 and 8 are allowed. 101 rejections still remains.
Reason for Allowance:
Examiner has considered the claims in view of the searched arts and NPLs provided by the Applicant. After reviewing the art and performing an detailed search, the examiner finds that no combination of prior art reads on the claim as a whole. Specifically, no prior arts, either alone or in combination make the independent claims as a whole novel.
The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure:
“Communication-Memory-Efficient Decentralized Learning for Audio Representation”, Li et al , 7/22/2021. Smartphones and wearable devices produce a wealth of audio data, which cannot be accumulated in a centralized repository for learning supervised models due to privacy and bandwidth limitation. Federated learning provides a solution for learning model from decentralized data. But conventionally, it assumes the availability of labeled samples, whereas on-device data are generally unlabeled. For solving these issues, in this paper we propose the self-supervised learning approach in a federated manner without moving the unlabeled audio data. We try the audio albert as the self-supervised model, which achieves comparable performance to other pre-trained model but with smaller model size. The federated self-supervised framework has tremendous communication cost during training, and the transformer architecture utilized in audio albert has the problem of memory footprint, which are practical in loT devices. To address the first issue, we propose the Gradient Compression and CSR Encoding (GCE) to reduce communication requires each round. Furthermore, we apply the reversible idea to the transformer, which does not need to store the activation in each layer thus reduce the memory footprint. Moreover, we evaluate the quality of the self-supervised pre-training model under the federated setting, and the model achieves considerable performance in the downstream tasks by fine-tuning.
Dhanyamraju et al (US 20180373988 A1): The present disclosure relates to system(s) and method(s) for tuning an analytical model. The system builds a global analytical model based on modelling data received from a user. Further, the system analyses a target eco-system to identify a set of target eco-system parameters. The system further selects a sub-set of model parameters, corresponding to the set of target eco-system parameters, from a set of model parameters. Further, the system generates a local analytical model based on updating the global analytical model, based on the sub-set of model parameters and one or more PMML wrappers. The system further deploys the local analytical model at each node, from a set of nodes, associated with the target eco-system. Further, the system gathers test results from each node based on executing the local analytical model. The system further tunes the sub-set of model parameters associated with the local analytical model using federated learning algorithms.
Applicant is required under 37 C.F.R. § 1.111(c) to consider these references fully when responding to this action.
It is noted that any citation to specific pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck, 699 F.2d 1331, 1332-33, 216 U.S.P.Q. 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 U.S.P.Q. 275, 277 (C.C.P.A. 1968)).
In the interests of compact prosecution, Applicant is invited to contact the examiner via electronic media pursuant to USPTO policy outlined MPEP § 502.03. All electronic communication must be authorized in writing. Applicant may wish to file an Internet Communications Authorization Form PTO/SB/439. Applicant may wish to request an interview using the Interview Practice website: http://;www.uspto.gov/patent/laws-and-regulations/interview-practice.
Applicant is reminded Internet e-mail may not be used for communication for matters under 35 U.S.C. § 132 or which otherwise require a signature. A reply to an Office action may NOT be communicated by Applicant to the USPTO via Internet e- mail. If such a reply is submitted by Applicant via Internet e-mail, a paper copy will be placed in the appropriate patent application file with an indication that the reply is NOT ENTERED. See MPEP § 502.03(II).
Conclusion
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/HAIMEI JIANG/Primary Examiner, Art Unit 2142
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