DETAILED ACTION
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 .
Status of Claims
The present application is being examined under the claims filed 11/06/2025.
Claims 1-20 are pending.
Response to Amendment
This Office Action is in response to Applicant’s communication filed 11/06/2025 in response to office action mailed 08/06/2025. The Applicant’s remarks and any amendments to the claims or specification have been considered with the results that follow.
Response to Arguments
Regarding 35 U.S.C. 101
In Remarks page 7, Argument 1
(Examiner summarizes Applicant’s arguments) Applicant argues that the claims are not directed to an abstract idea, but rather a practical application of reinforcement learning to battery charging because the claims now require a machine learning model output, which is not an abstract idea.
Examiner’s response to Argument 1
Examiner does not portray the claims as being directed entirely to an abstract idea. However, the additional elements apart from the abstract idea limitations are not sufficient to integrate the judicial exception into a practical application (step 2A prong 2) nor amount to significantly more than the judicial exception alone (step 2B). Merely outputting a machine learning model amounts to mere data gathering (see MPEP 2106.05(g)) and sending/receiving information over a network (see MPEP 2106.05(d)). Examiner notes that neither the claims nor the specification make any mention of reinforcement learning and battery charging. Thus the claims cannot reflect any improvement to battery charging.
In Remarks page 7-8, Argument 2
(Examiner summarizes Applicant’s arguments) Applicant argues that Examiner’s characterization of claim elements as mental processes is incorrect because they require high-dimensional computations using machine learning models, matrices, embeddings and weight parameters, citing case law. Applicant further argues that fusing the data-driven similarity graph with an expert-derived similarity graph to guide label propagation is not abstract data manipulation.
Examiner’s response to Argument 2
Examiner disagrees. According to MPEP 2106.04(a)(2) III recites “Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions.” Moreover, any matrix calculations allegedly required by applicant would fall under the mathematical process grouping of abstract ideas. For example, computing latent representation spaces amounts to evaluating a data point to determine a set of possible related data points (an embedding). This does not require any operation of a computer, but could instead be performed by a human using pen and paper (for example, some algorithm or mathematical function to obtain the set/space). Furthermore, “identifying a k-nearest neighbor similarity graph” can be performed by observation of a set of data points and drawing a picture of nodes and edges to obtain a graph, or imagining the graph in the human mind. Again, these operations are not inherently linked to computer technology, nor do they require any computer memory-based technologies, but instead algorithmic steps that are deemed to be abstract ideas. Moreover, combining the data-driven similarity graph with the expert-derived similarity can be easily performed by observing the data points in the graph and combining the two graphs using pen and paper as a tool. This process does not inherently require a computer.
In Remarks page 8, Argument 3
(Examiner summarizes Applicant’s arguments) Applicant argues that the combined similarity graph architecture and semi-supervised retraining provides an improvement by reducing the need for labeled data and increasing model accuracy, an improvement to training neural networks. Applicant argues that a targeted improvement to computer functionality constitutes a practical application and that the battery management system and physical battery are tangible and useful, resulting in significantly more than an abstract idea.
Examiner’s response to Argument 3
Examiner reiterates that neither the specification nor the claims recite any details about a battery or vehicle battery management. Though figs. 6 and 9 do depict vehicles, they do not contain any information about batteries, nor are the disclosed details present in the claims. Moreover, the machine learning training provided in the claims is generic and does little more than provide instructions to apply a judicial exception. MPEP 2106.05(a) reads “That is, the claim must include the components or steps of the invention that provide the improvement described in the specification.” Applicant fails to describe how the specific training steps claimed would provide the disclosed improvement:
initially training the machine learning model using the labeled dataset
subsequently training the machine learning model using the labeled dataset and the unlabeled dataset having samples propagated with labels using the combined similarity graph
The training steps amount to generic supervised and unsupervised learning and fail to provide any improvements to machine learning training in particular. The limitations instead merely apply the mental process of generating a combined similarity graph to an ordinary machine learning training process. That is, the claimed additional elements do not include the components or steps that provide an improvement.
Regarding 35 U.S.C. 103
In Remarks page 8-9, Argument 4
(Examiner summarizes Applicant’s arguments) Applicant argues that the combination of Bahrami and Bo fails to teach the limitation of “generating a combined similarity graph by augmenting the k-nearest neighbor similarity graph by using an expert-derived similarity graph” and that the rejection regarding this limitation is deficient.
Examiner’s response to Argument 4
Applicant’s arguments are convincing. Accordingly, the rejections under 35 U.S.C. 103 are withdrawn.
Claim Objections
Claims 1, 11, and 20 are objected to because of the following informalities: “the training utilizing the labeled dataset and the labeled dataset” should read “the training utilizing the labeled dataset and the unlabeled dataset”. Appropriate correction is required.
Allowable Subject Matter
Claims 1-20 are indicated under allowable subject matter under 35 U.S.C. 103. However, Examiner notes that Applicant must overcome eligibility rejections under 35 U.S.C. 101 prior to allowance.
The following is a statement of reasons for the indication of allowable subject matter: Applicant’s arguments regarding 103 rejections are convincing. Further search revealed NPL reference Pivovarov et al. “A hybrid knowledge-based and data-driven approach to identifying semantically similar concepts” which teaches combining a knowledge-based graph and a data-driven graph. However, Pivovarov does not teach “gerating a combined similarity graph by augmenting the k-nearest neighbor similarity graph using an expert-derived similarity graph”, which has the same deficiencies of other prior art references previously made of record.
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.
Claims 1-20 are rejected under 35 U.S.C. 101 for containing an abstract idea without significantly more.
Regarding Claim 1:
Step 1 – Is the claim to a process, machine, manufacture, or composition of matter?
Yes, the claim is to a process.
Step 2A – Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes, the claim recites the abstract ideas of:
computing, using the initially trained machine learning model, latent representation spaces for each respective sample of the plurality of labeled samples and each respective sample of the plurality of the unlabeled samples — This limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using pen and paper (see MPEP 2106.04(a)(2) III. C.). The limitation is directed to a mental process because it amounts to evaluating given data using known parameters.
generating a k-nearest neighbor similarity graph based on the latent representation spaces for each respective sample of the plurality of labeled samples and each respective sample of the plurality of unlabeled samples — This limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using pen and paper (see MPEP 2106.04(a)(2) III. C.). The limitation is directed to a mental process because it amounts to performing a judgement of given data to determine how similar the data is.
generating a combined similarity graph by augmenting the k-nearest neighbor similarity graph using an expert-derived similarity graph — This limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using pen and paper (see MPEP 2106.04(a)(2) III. C.). The limitation is directed to a mental process because it amounts to performing evaluating a set of steps for combining two sets of given data (for example, merely drawing a picture of the two sets together using pen and paper).
propagating, using the combined similarity graph, labels to each respective sample of the plurality of unlabeled samples — This limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using pen and paper (see MPEP 2106.04(a)(2) III. C.). The limitation is directed to a mental process because it amounts to evaluating given data using known parameters.
Step 2A – Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, the claim does not recite additional elements that integrate the judicial exception into a practical application. The additional elements:
A method for label propagation of training data used for training a machine learning model, the method comprising: receiving a labeled dataset that includes a plurality of labeled samples — This limitation is directed to mere data gathering and outputting which has been recognized by the courts (as per Ultramercial, 772 F.3d at 715, 112 USPQ2d at 1754) as insignificant extra-solution activity (see MPEP 2106.05(g)).
initially training the machine learning model using the labeled dataset — This limitation is directed to mere instructions to apply a judicial exception. Using machine learning training to apply a judicial exception (see MPEP 2106.05(f)) is insufficient to integrate the judicial exception into a practical application. Even if the machine learning training is implemented on a generic computer (see MPEP 2106.05(f)(2), 2106.04(d)), the limitation does not integrate the judicial exception into a practical application.
receiving an unlabeled dataset that includes a plurality of unlabeled samples — This limitation is directed to mere data gathering and outputting which has been recognized by the courts (as per Ultramercial, 772 F.3d at 715, 112 USPQ2d at 1754) as insignificant extra-solution activity (see MPEP 2106.05(g)).
and subsequently training the machine learning model using the labeled dataset and the unlabeled dataset having samples propagated with labels using the combined similarity graph — This limitation is directed to mere instructions to apply a judicial exception. Using machine learning training to apply a judicial exception (see MPEP 2106.05(f)) is insufficient to integrate the judicial exception into a practical application. Even if the machine learning training is implemented on a generic computer (see MPEP 2106.05(f)(2), 2106.04(d)), the limitation does not integrate the judicial exception into a practical application.
and in response to meeting a convergence criterion based on the training utilizing the labeled dataset and the labeled dataset, outputting a trained machine learning model — This limitation is directed to mere data gathering and outputting which has been recognized by the courts (as per Ultramercial, 772 F.3d at 715, 112 USPQ2d at 1754) as insignificant extra-solution activity (see MPEP 2106.05(g)).
Step 2B – Does the claim recite additional elements that amount to significantly more than the abstract idea itself?
No, the claim does not recite additional elements which amount to significantly more than the abstract idea itself. The additional elements as identified in step 2A prong 2:
A method for label propagation of training data used for training a machine learning model, the method comprising: receiving a labeled dataset that includes a plurality of labeled samples — This limitation is recited at a high level of generality and amounts to mere data gathering of transmitting and receiving data over a network, which is well-understood, routine, and conventional activity (see MPEP 2106.05(d) II.), which cannot amount to significantly more than the judicial exception.
initially training the machine learning model using the labeled dataset — Mere instructions to apply a judicial exception (see MPEP 2106.05(f)) and using a generic computer as a tool (see MPEP 2106.05(f)(2), 2106.05(d)) cannot amount to significantly more than the judicial exception itself.
receiving an unlabeled dataset that includes a plurality of unlabeled samples — This limitation is recited at a high level of generality and amounts to mere data gathering of transmitting and receiving data over a network, which is well-understood, routine, and conventional activity (see MPEP 2106.05(d) II.), which cannot amount to significantly more than the judicial exception.
and subsequently training the machine learning model using the labeled dataset and the unlabeled dataset having samples propagated with labels using the combined similarity graph — Mere instructions to apply a judicial exception (see MPEP 2106.05(f)) and using a generic computer as a tool (see MPEP 2106.05(f)(2), 2106.05(d)) cannot amount to significantly more than the judicial exception itself.
and in response to meeting a convergence criterion based on the training utilizing the labeled dataset and the labeled dataset, outputting a trained machine learning model — This limitation is recited at a high level of generality and amounts to mere data gathering of transmitting and receiving data over a network, which is well-understood, routine, and conventional activity (see MPEP 2106.05(d) II.), which cannot amount to significantly more than the judicial exception.
Regarding Claim 2
Claim 2 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim is dependent on claim 1 which included an abstract idea (see rejection for claim 1). The claim recites the additional limitations:
Step 2A Prong 2:
wherein initially training the machine learning model includes initially training a feature extractor of the machine learning model and one or more predictor networks of the machine learning model — This limitation is directed to mere instructions to apply a judicial exception. Using machine learning training to apply a judicial exception (see MPEP 2106.05(f)) is insufficient to integrate the judicial exception into a practical application. Even if the machine learning training is implemented on a generic computer (see MPEP 2106.05(f)(2), 2106.04(d)), the limitation does not integrate the judicial exception into a practical application.
Thus, the judicial exception is not integrated into a practical application (see MPEP 2106.04(d) I.), failing step 2A prong 2.
Step 2B:
The additional elements as identified in step 2A prong 2:
wherein initially training the machine learning model includes initially training a feature extractor of the machine learning model and one or more predictor networks of the machine learning model — Mere instructions to apply a judicial exception (see MPEP 2106.05(f)) and using a generic computer as a tool (see MPEP 2106.05(f)(2), 2106.05(d)) cannot amount to significantly more than the judicial exception itself.
Thus, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under step 2B.
Regarding Claim 3
Claim 3 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim is dependent on claim 1 which included an abstract idea (see rejection for claim 1). The claim recites the additional limitations:
Step 2A Prong 2:
wherein initially training the machine learning model includes using a fully supervised learning technique — This limitation is directed to merely limiting a judicial exception to a particular field of use (see MPEP 2106.05(h)) as it merely limits the field of the machine learning training.
Thus, the judicial exception is not integrated into a practical application (see MPEP 2106.04(d) I.), failing step 2A prong 2.
Step 2B:
The additional elements as identified in step 2A prong 2:
wherein initially training the machine learning model includes using a fully supervised learning technique — Merely limiting a judicial exception to a particular field of use (see MPEP 2106.05(h)) cannot amount to significantly more than the judicial exception.
Thus, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under step 2B.
Regarding Claim 4
Claim 4 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim is dependent on claim 1 which included an abstract idea (see rejection for claim 1). The claim recites the additional limitations:
Step 2A Prong 2:
wherein subsequently training the machine learning model includes using a semi-supervised learning technique — This limitation is directed to merely limiting a judicial exception to a particular field of use (see MPEP 2106.05(h)) as it merely limits the field of the machine learning training.
Thus, the judicial exception is not integrated into a practical application (see MPEP 2106.04(d) I.), failing step 2A prong 2.
Step 2B:
The additional elements as identified in step 2A prong 2:
wherein subsequently training the machine learning model includes using a semi-supervised learning technique — Merely limiting a judicial exception to a particular field of use (see MPEP 2106.05(h)) cannot amount to significantly more than the judicial exception.
Thus, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under step 2B.
Regarding Claim 5
Claim 5 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim is dependent on claim 1 which included an abstract idea (see rejection for claim 1). The claim merely recites the additional abstract idea:
Step 2A Prong 1:
wherein subsequently training the machine learning model using the labeled dataset and the unlabeled dataset having samples propagated with labels using the combined similarity graph includes optimizing a summation of a squared error for each sample of the plurality of labeled samples and a summation of a weighted squared error for each sample of the plurality of unlabeled samples — This limitation is directed to the abstract idea of a mathematical process, and mathematical calculations in particular (MPEP 2106.04(a)(2) I. C.). The claim describes the mathematical operations of calculating a sum and selecting the values which minimize the sum (for example by gradient descent).
Thus, the judicial exception is not integrated into a practical application (see MPEP 2106.04(d) I.), failing step 2A prong 2. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under step 2B.
Regarding Claim 6
Claim 6 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim is dependent on claim 1 which included an abstract idea (see rejection for claim 1). The claim merely recites a further abstract idea:
Step 2A Prong 1:
wherein subsequently training the machine learning model using the labeled dataset and the unlabeled dataset having samples propagated with labels using the combined similarity graph includes determining whether to continue training the machine learning model based on at least one convergence criterion — This limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using pen and paper (see MPEP 2106.04(a)(2) III. C.). The limitation is directed to a mental process because it amounts to forming an opinion on whether to continue an operation or not based on given information.
Thus, the judicial exception is not integrated into a practical application (see MPEP 2106.04(d) I.), failing step 2A prong 2. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under step 2B.
Regarding Claim 7
Claim 7 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim is dependent on claim 1 which included an abstract idea (see rejection for claim 1). The claim recites the additional limitations:
Step 2A Prong 2:
wherein a first node of the combined similarity graph is connected to a second node of the combined similarity graph in response to the first node and the second node being connected in at least one of the k-nearest neighbor similarity graph and the expert-derived similarity graph — This limitation is directed to merely limiting a judicial exception to a particular field of use (see MPEP 2106.05(h)) as it merely limits the field of the similarity graph.
Thus, the judicial exception is not integrated into a practical application (see MPEP 2106.04(d) I.), failing step 2A prong 2.
Step 2B:
The additional elements as identified in step 2A prong 2:
wherein a first node of the combined similarity graph is connected to a second node of the combined similarity graph in response to the first node and the second node being connected in at least one of the k-nearest neighbor similarity graph and the expert-derived similarity graph — Merely limiting a judicial exception to a particular field of use (see MPEP 2106.05(h)) cannot amount to significantly more than the judicial exception.
Thus, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under step 2B.
Regarding Claim 8
Claim 8 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim is dependent on claim 1 which included an abstract idea (see rejection for claim 1). The claim merely recites a further abstract idea:
Step 2A Prong 1:
wherein propagating, using the combined similarity graph, the labels to each respective sample of the plurality of unlabeled samples includes generating, for each respective sample of the plurality of unlabeled samples, a label and a confidence level — This limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using pen and paper (see MPEP 2106.04(a)(2) III. C.). The limitation is directed to a mental process because it amounts to evaluating given data using known parameters to determine a label and a confidence level.
Thus, the judicial exception is not integrated into a practical application (see MPEP 2106.04(d) I.), failing step 2A prong 2. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under step 2B.
Regarding Claim 9
Claim 9 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim is dependent on claim 1 which included an abstract idea (see rejection for claim 1). The claim recites the additional limitations:
Step 2A Prong 2:
wherein the machine learning model is configured to perform at least one classification task — This limitation is directed to merely limiting a judicial exception to a particular field of use (see MPEP 2106.05(h)) as it merely limits the field of the machine learning model.
Thus, the judicial exception is not integrated into a practical application (see MPEP 2106.04(d) I.), failing step 2A prong 2.
Step 2B:
The additional elements as identified in step 2A prong 2:
wherein the machine learning model is configured to perform at least one classification task — Merely limiting a judicial exception to a particular field of use (see MPEP 2106.05(h)) cannot amount to significantly more than the judicial exception.
Thus, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under step 2B.
Regarding Claim 10
Claim 10 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim is dependent on claim 1 which included an abstract idea (see rejection for claim 1). The claim recites the additional limitations:
Step 2A Prong 2:
wherein the machine learning model is configured to perform at least one regression task — This limitation is directed to merely limiting a judicial exception to a particular field of use (see MPEP 2106.05(h)) as it merely limits the field of the machine learning model.
Thus, the judicial exception is not integrated into a practical application (see MPEP 2106.04(d) I.), failing step 2A prong 2.
Step 2B:
The additional elements as identified in step 2A prong 2:
wherein the machine learning model is configured to perform at least one regression task — Merely limiting a judicial exception to a particular field of use (see MPEP 2106.05(h)) cannot amount to significantly more than the judicial exception.
Thus, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under step 2B.
Regarding Claim 11
Independent claim 11 is a computer system claim corresponding to method claim 1, which was directed to an abstract idea, therefore the same rejection and rationale applies. The only difference is that claim 11 recites the following additional elements treated under step 2A prong 2 and step 2B:
Step 2A Prong 2:
A system for label propagation of training data used for training a machine learning model, the system comprising: a processor; and a memory including instructions that, when executed by the processor, cause the processor to — This limitation is directed to merely applying an abstract idea using a generic computer as a tool (see MPEP 2106.05(f)(2), 2106.04(d)).
Thus, the judicial exception is not integrated into a practical application (see MPEP 2106.04(d) I.), failing step 2A prong 2.
Step 2B:
A system for label propagation of training data used for training a machine learning model, the system comprising: a processor; and a memory including instructions that, when executed by the processor, cause the processor to — Using a generic computer as a tool (see MPEP 2106.05(f)(2), 2106.05(d)) cannot amount to significantly more than the judicial exception itself.
Thus, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under step 2B.
Regarding Claim 12
Dependent claim 12 is a computer system claim corresponding to method claim 2, which was directed to an abstract idea, therefore the same rejection and rationale applies.
Regarding Claim 13
Dependent claim 13 is a computer system claim corresponding to method claim 3, which was directed to an abstract idea, therefore the same rejection and rationale applies.
Regarding Claim 14
Dependent claim 14 is a computer system claim corresponding to method claim 4, which was directed to an abstract idea, therefore the same rejection and rationale applies.
Regarding Claim 15
Dependent claim 15 is a computer system claim corresponding to method claim 5, which was directed to an abstract idea, therefore the same rejection and rationale applies.
Regarding Claim 16
Dependent claim 16 is a computer system claim corresponding to method claim 6, which was directed to an abstract idea, therefore the same rejection and rationale applies.
Regarding Claim 17
Dependent claim 17 is a computer system claim corresponding to method claim 7, which was directed to an abstract idea, therefore the same rejection and rationale applies.
Regarding Claim 18
Dependent claim 18 is a computer system claim corresponding to method claim 8, which was directed to an abstract idea, therefore the same rejection and rationale applies.
Regarding Claim 19
Claim 19 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim is dependent on claim 11 which included an abstract idea (see rejection for claim 11). The claim recites the additional limitations:
Step 2A Prong 2:
wherein the machine learning model is configured to perform at least one of at least one classification task and at least one regression task — This limitation is directed to merely limiting a judicial exception to a particular field of use (see MPEP 2106.05(h)) as it merely limits the field of the machine learning model.
Thus, the judicial exception is not integrated into a practical application (see MPEP 2106.04(d) I.), failing step 2A prong 2.
Step 2B:
The additional elements as identified in step 2A prong 2:
wherein the machine learning model is configured to perform at least one of at least one classification task and at least one regression task — Merely limiting a judicial exception to a particular field of use (see MPEP 2106.05(h)) cannot amount to significantly more than the judicial exception.
Thus, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under step 2B.
Regarding Claim 20:
Step 1 – Is the claim to a process, machine, manufacture, or composition of matter?
Yes, the claim is to a machine.
Step 2A – Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes, the claim recites the abstract ideas of:
compute, using the initially trained machine learning model, latent representation spaces for each respective sample of the plurality of labeled samples and each respective sample of the plurality of the unlabeled samples — This limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using pen and paper (see MPEP 2106.04(a)(2) III. C.). The limitation is directed to a mental process because it amounts to evaluating given data using known parameters.
generate a k-nearest neighbor similarity graph based on the latent representation spaces for each respective sample of the plurality of labeled samples and each respective sample of the plurality of unlabeled samples — This limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using pen and paper (see MPEP 2106.04(a)(2) III. C.). The limitation is directed to a mental process because it amounts to performing a judgement of given data to determine how similar the data is.
generate a combined similarity graph by augmenting the k-nearest neighbor similarity graph using an expert-derived similarity graph — This limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using pen and paper (see MPEP 2106.04(a)(2) III. C.). The limitation is directed to a mental process because it amounts to performing evaluating a set of steps for combining two sets of given data (for example, merely drawing a picture of the two sets together using pen and paper).
propagate, using the combined similarity graph, labels to each respective sample of the plurality of unlabeled samples — This limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using pen and paper (see MPEP 2106.04(a)(2) III. C.). The limitation is directed to a mental process because it amounts to evaluating given data using known parameters.
Step 2A – Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, the claim does not recite additional elements that integrate the judicial exception into a practical application. The additional elements:
An apparatus for label propagation of training data used for training a machine learning model, the apparatus comprising: a processor; and a memory including instructions that, when executed by the processor, cause the processor to: — This limitation is directed to merely applying an abstract idea using a generic computer as a tool (see MPEP 2106.05(f)(2), 2106.04(d)).
receive a labeled dataset that includes a plurality of labeled samples — This limitation is directed to mere data gathering and outputting which has been recognized by the courts (as per Ultramercial, 772 F.3d at 715, 112 USPQ2d at 1754) as insignificant extra-solution activity (see MPEP 2106.05(g)).
initially train, using a fully supervised learning technique, the machine learning model using the labeled dataset — This limitation is directed to mere instructions to apply a judicial exception. Using to apply a judicial exception (see MPEP 2106.05(f)) is insufficient to integrate the judicial exception into a practical application. Even if the machine learning training is implemented on a generic computer (see MPEP 2106.05(f)(2), 2106.04(d)), the limitation does not integrate the judicial exception into a practical application.
receive an unlabeled dataset that includes a plurality of unlabeled samples — This limitation is directed to mere data gathering and outputting which has been recognized by the courts (as per Ultramercial, 772 F.3d at 715, 112 USPQ2d at 1754) as insignificant extra-solution activity (see MPEP 2106.05(g)).
and subsequently train, using a semi-supervised learning technique, the machine learning model using the labeled dataset and the unlabeled dataset having samples propagated with labels using the combined similarity graph — This limitation is directed to mere instructions to apply a judicial exception. Using to apply a judicial exception (see MPEP 2106.05(f)) is insufficient to integrate the judicial exception into a practical application. Even if the machine learning training is implemented on a generic computer (see MPEP 2106.05(f)(2), 2106.04(d)), the limitation does not integrate the judicial exception into a practical application.
and in response to meeting a convergence criterion based on the training utilizing the labeled dataset and the labeled dataset, outputting a trained machine learning model — This limitation is directed to mere data gathering and outputting which has been recognized by the courts (as per Ultramercial, 772 F.3d at 715, 112 USPQ2d at 1754) as insignificant extra-solution activity (see MPEP 2106.05(g)).
Step 2B – Does the claim recite additional elements that amount to significantly more than the abstract idea itself?
No, the claim does not recite additional elements which amount to significantly more than the abstract idea itself. The additional elements as identified in step 2A prong 2:
An apparatus for label propagation of training data used for training a machine learning model, the apparatus comprising: a processor; and a memory including instructions that, when executed by the processor, cause the processor to: — Using a generic computer as a tool (see MPEP 2106.05(f)(2), 2106.05(d)) cannot amount to significantly more than the judicial exception itself.
receive a labeled dataset that includes a plurality of labeled samples — This limitation is recited at a high level of generality and amounts to mere data gathering of transmitting and receiving data over a network, which is well-understood, routine, and conventional activity (see MPEP 2106.05(d) II.), which cannot amount to significantly more than the judicial exception.
initially train, using a fully supervised learning technique, the machine learning model using the labeled dataset — Mere instructions to apply a judicial exception (see MPEP 2106.05(f)) and using a generic computer as a tool (see MPEP 2106.05(f)(2), 2106.05(d)) cannot amount to significantly more than the judicial exception itself.
receive an unlabeled dataset that includes a plurality of unlabeled samples — This limitation is directed to mere data gathering and outputting which has been recognized by the courts (as per Ultramercial, 772 F.3d at 715, 112 USPQ2d at 1754) as insignificant extra-solution activity (see MPEP 2106.05(g)).
and subsequently train, using a semi-supervised learning technique, the machine learning model using the labeled dataset and the unlabeled dataset having samples propagated with labels using the combined similarity graph —Mere instructions to apply a judicial exception (see MPEP 2106.05(f)) and using a generic computer as a tool (see MPEP 2106.05(f)(2), 2106.05(d)) cannot amount to significantly more than the judicial exception itself.
and in response to meeting a convergence criterion based on the training utilizing the labeled dataset and the labeled dataset, outputting a trained machine learning model — This limitation is recited at a high level of generality and amounts to mere data gathering of transmitting and receiving data over a network, which is well-understood, routine, and conventional activity (see MPEP 2106.05(d) II.), which cannot amount to significantly more than the judicial exception.
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.
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/E.J.B./Examiner, Art Unit 2126
/DAVID YI/Supervisory Patent Examiner, Art Unit 2126