DETAILED ACTION
This action is in response to the amended filing on 9/01/2026. Claims 1-20 are pending for examination.
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 .
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Cella et al. (US 20250054008 A1) in view of Bradley et al. (US 12488524 B2) in additional view of Humphreys et al. (US 20240386100 A1).
Cella and Bradley were cited in the previous Office Action.
Regarding Claim 1, Cella teaches a method for automatically augmenting a machine training dataset (Paragraph [0525] In some embodiments, the machine learning model 3000 may automatically increase or decrease collection rates, processing, storage, sampling rates, bandwidth allocation, bitrates, and other attributes of sensor data collection to achieve or better achieve the modeling goal. Paragraph [0928] In embodiments, the reconfiguring/retraining an executive agent may include removing an input that is the source of the error, reconfiguring a set of nodes of the artificial intelligence system, reconfiguring a set of weights of the artificial intelligence system, reconfiguring a set of outputs of the artificial intelligence system, reconfiguring a processing flow within the artificial intelligence system, and/or augmenting the set of inputs to the artificial intelligence system.), the method comprising:
using at least one sufficiency criteria, determining that the training dataset is insufficient for training the neural network NN; (Paragraph [0525] In some embodiments, a user of the information technology system may input a modeling goal into the machine learning model 3000. The machine learning model 3000 may learn to analyze training data to output suggestions to the user of the information technology system regarding which types of sensor data are most relevant to achieving the modeling goal, such as one or more types of sensors positioned in, on, or near a value chain entity or a plurality of value chain entities that is relevant to the achievement of the modeling goal is and/or are not sufficient for achieving the modeling goal, and how a different configuration of the types of sensors, such as by adding, removing, or repositioning sensors, may better facilitate achievement of the modeling goal by the machine learning model 3000 and the digital twin system 1700. (Analyzing training data to determine if a defined outcome is feasible. If not, determining that the training data is insufficient))
selecting a generative convolutional neural network GCNN for which the existing training dataset is sufficient to get trained; (Paragraph [0616] Examples of learning algorithms/models include (e.g., deep neural networks, convolution neural networks, and many others as described throughout this disclosure), statistical models (e.g., regression-based models and many others), decision trees and other decision models, random/hidden forests, Hidden Markov models, Bayesian models, and the like. Paragraph [0772] At 5202, a plurality of streams of machine related data from multiple data sources are received at the machine twin 1770. This includes machine specifications like mechanical properties, data from maintenance records, operating data collected from the sensors, historical data including failure data from multiple machines running at different times and under different operating conditions and so on. At 5205, the raw data is cleaned by removing any missing or noisy data, which may occur due to any technical problems in the machine at the time of collection of data. At 5208, one or more models are selected for training by machine twin 1770. The selection of model is based on the kind of data available at the machine twin 1770 and the desired outcome of the model. For example, there may be cases where failure data from machines is not available, or only a limited number of failure datasets exist because of regular maintenance being performed. Classification or regression models may not work well for such cases and clustering models may be most suitable. As another example, if the desired outcome of the model is determining current condition of the machine and detecting any faults, then fault detection models may be selected, whereas if the desired outcome is predicting future failures then remaining useful life prediction model may be selected. At 5210, the one or more models are trained using training dataset and tested for performance using testing dataset. At 5212, the trained model is used for detecting faults and predicting future failure of the machine on production data. Paragraph [0774] Machine twin 1770 then coordinates with artificial intelligence system to select one or more of models based on the kind and quality of available data and the desired answers or outcomes. For example, physical models 5320 may be selected if the intended use of machine twin 1770 is to simulate what-if scenarios and predict how the machine will behave under such scenarios. (Since the selection of the model to be trained is determined based on the type of data available, this is considered to be equivalent to selecting a model for which there is sufficient data to be trained.))
Cella does not teach gaining access to a training dataset for training a neural network NN, wherein the training dataset comprises a plurality of items in at least a partial numeric sequence, wherein each of the plurality of items is represented by a sequence of zeros and ones that can be interpreted as a number; training the generative convolutional neural network GCNN using the training dataset or a subset of the training dataset;
In the same field of endeavor, Bradley teaches gaining access to a training dataset for training a neural network NN, wherein the training dataset comprises a plurality of items in at least a partial numeric sequence, wherein each of the plurality of items is represented by a sequence of zeros and ones that can be interpreted as a number; (Col 8 Lines 7-16 Returning to the discussion of FIG. 2, training engine 122 trains transformer 200 using training data 214 that includes performance captures 226 and sampled geometries 228 from performance captures 226. Performance captures 226 include 3D representations of movements that are related to synthesized sequences to be generated by transformer 200. For example, performance captures 226 could include sequences of blendshape coefficients, 3D meshes, and/or other geometric representations of facial performances, dances, or other types of movements. Col 9. Lines 29-32 As shown, in step 402, training engine 122 trains an encoder neural network and a decoder neural network based on a training dataset that includes multiple sequences of geometries. (In order to use a dataset for training, intuitively access must already be granted. Furthermore, the training data comprises performance captures that can be in the form of a "sequence of blendshape coefficients". Blendshape coefficients are numeric values that control how much a shape or facial expression is changed in a 3D model. Finally, the training engine is a computer, meaning all data that the computer interacts with is represented as either a zero or one due to its binary code nature. Understanding this, the sequence of coefficients must comprise a binary sequence that the computer then interprets as a number or numbers))
training the generative convolutional neural network GCNN using the training dataset or a subset of the training dataset; (Col. 13 Lines 56-65 After parameters of discriminator 510 have been updated over one or more epochs, training engine 132 can train generator 500 based on a generator loss 518 that is calculated based on the frequency with which discriminator 510 incorrectly classifies training images 508 from generator 500 as coming from discriminator training data 516. After parameters of generator 500 have been updated over one or more epochs, training engine 132 can resume training discriminator 510 using additional training images 508 produced by generator 500. (Training generator))
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have incorporated the concept of gaining access to a training data set and training a neural network on that dataset or a subset of that dataset as taught by Bradley into Cella, as both are in the same field of machine learning model training and doing so would improve the method of Cella as collecting large amounts of relevant real data can be time and resource intensive, and implementation of this would expedite the process. Certain types of data may not be collected in a quick or efficient manner, and the creation of synthetic data allows for the supplementation of such datasets (Bradely Col. 1 Lines 31-57).
The combination of Cella and Bradley does not teach in response to determining that the training dataset is insufficient for training the neural network NN, generating, by the GCNN trained on the existing training dataset, an additional item by generating at least one number missing in the partial numeric sequence; and adding the additional item to the training dataset by inserting the at least one number to the partial numeric sequence
In the same field of endeavor, Humphreys teaches in response to determining that the training dataset is insufficient for training the neural network NN, generating, by the GCNN trained on the existing training dataset, an additional item by generating at least one number missing in the partial numeric sequence; and (Paragraph [0038] For example, the enterprise information storage system 106 may store records of financial transactions, consumer account information, consumer credit histories, and/or consumer information (e.g., address and/or phone number). Paragraph [0060] Based on the one or more data sample criteria not being met, the computing system may request additional data samples for processing. Paragraph [0061] At step 420, a computing system may, based on the data meeting the one or more data sample criteria, train, a plurality of discriminative machine learning models based on training using the synthetic data and non-synthetic data. Paragraph [0079] At step 510, a computing system may generate, based on inputting the non-synthetic training data into one or more generative machine learning models, synthetic training data comprising a plurality of synthetic training data samples. For example, the synthetic data analysis and validation platform 102 may comprise a generative adversarial network that includes discriminative machine learning models and generative machine leaning models that are configured and/or trained to generate synthetic training data (Training data such as customer information like a phone number (paragraph [0038]) is evaluated for sufficiency. If deemed to be insufficient, new data of the same type (i.e. a phone number) is generated by a trained generative adversarial network (GAN) trained on the existing dataset of both synthetic and real examples. GAN's contain convolutional neural networks within.))
adding the additional item to the training dataset by inserting the at least one number to the partial numeric sequence (Paragraph [0030] Training the machine learning models may be carried out using gradient descent. Paragraph [0079] At step 510, a computing system may generate, based on inputting the non-synthetic training data into one or more generative machine learning models, synthetic training data comprising a plurality of synthetic training data samples. (A data item comprising a number (i.e. phone number) is added to the training set. The model is trained using an iterative (i.e. sequential) gradient descent training method, meaning any synthetic data must be added to this sequence as well))
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have incorporated the concepts of generation of synthetic numerical data by a trained network in response to determination that the existing dataset is insufficient and adding that additional synthetic data to the training set as taught by Humphreys into the combination of Cella and Bradley as all three references are in the same field of machine learning model training and doing so would present a clear improvement in the state of the art by allowing for expedited training of these models through the use of synthetic data, as synthetic data may sometimes produce invalid or false results and verifying it as taught by Humphreys would allow for alleviation of this issue (Humphreys Paragraph [0003]).
Regarding Claim 2, the combination of Cella, Bradley, and Humphreys teaches the invention as claimed in Claim 1 including: wherein the training dataset comprises items, item labels, and other meta-information corresponding to each of the items. (Bradley Col. 13 Lines 6 -23 Returning to the discussion of FIG. 5, training engine 132 trains generator 500 using generator training data 514 that includes training texture maps 528 and training segmentation masks 530 associated with a number of synthetic geometries 526. Synthetic geometries 526 include 3D models of synthetic objects that are similar to objects for which images 540 are to be generated. For example, synthetic geometries 526 could include full-head 3D models of synthetic faces. Training engine 132 and/or another component could generate each synthetic face by randomizing the identity, expression, hairstyle, and/or pose of a parametric face model, such as the face model of FIG. 6B. The component could then generate one or more training texture maps 528 and/or one or more training segmentation masks 530 for each synthetic face by posing and rendering the corresponding face model, as described above with respect to FIGS. 6B-6C. (Training data which has a corresponding element, in this case synthetic faces corresponding to a face model))
Regarding Claim 3, the combination of Cella, Bradley, and Humphreys teaches the invention as claimed in Claim 1 including: identifying if the training dataset is sufficient for training the neural network NN based on a sufficiency criterion (Cella Paragraph [0525] In some embodiments, a user of the information technology system may input a modeling goal into the machine learning model 3000. The machine learning model 3000 may learn to analyze training data to output suggestions to the user of the information technology system regarding which types of sensor data are most relevant to achieving the modeling goal, such as one or more types of sensors positioned in, on, or near a value chain entity or a plurality of value chain entities that is relevant to the achievement of the modeling goal is and/or are not sufficient for achieving the modeling goal, and how a different configuration of the types of sensors, such as by adding, removing, or repositioning sensors, may better facilitate achievement of the modeling goal by the machine learning model 3000 and the digital twin system 1700. (Analyzing training data to determine if a defined outcome is feasible. If not, determining that the training data is insufficient))
Regarding Claim 4, the combination of Cella, Bradley, and Humphreys teaches the invention as claimed in Claim 1 including: wherein the GCNN has the same degree of freedom as NN (Cella Paragraph [0531] In some embodiments, the machine learning model 3000 may be and/or include an artificial neural network, e.g., a connectionist system configured to “learn” to perform tasks by considering examples and without being explicitly programmed with task-specific rules. One or more of the units and/or nodes and connections therebetween may have one or more numerical “weights” assigned. The assigned weights may be configured to facilitate learning, i.e., training, of the machine learning model 3000. The weights assigned weights may increase and/or decrease one or more signals between one or more units and/or nodes, and in some embodiments may have one or more thresholds associated with one or more of the weights. The one or more thresholds may be configured such that a signal is only sent between one or more units and/or nodes, if a signal and/or aggregate signal crosses the threshold. (Degrees of freedom are analogous to weights.))
Regarding Claim 5, the combination of Cella, Bradley, and Humphreys teaches the invention as claimed in Claim 1 including: wherein the degrees of freedom of the GCNN is decreased from the degree of freedom of NN to require fewer items in a GCNN training set to make the existing training set sufficient for the GCNN training. (Cella Paragraph [0531] In some embodiments, the machine learning model 3000 may be and/or include an artificial neural network, e.g., a connectionist system configured to “learn” to perform tasks by considering examples and without being explicitly programmed with task-specific rules. One or more of the units and/or nodes and connections therebetween may have one or more numerical “weights” assigned. The assigned weights may be configured to facilitate learning, i.e., training, of the machine learning model 3000. The weights assigned weights may increase and/or decrease one or more signals between one or more units and/or nodes, and in some embodiments may have one or more thresholds associated with one or more of the weights. The one or more thresholds may be configured such that a signal is only sent between one or more units and/or nodes, if a signal and/or aggregate signal crosses the threshold. (Degrees of freedom (DoF) in neural networks generally refer to the number of independent parameters (weights and biases) that can be adjusted to minimize error, representing the model's flexibility to fit data.))
Regarding Claim 6, the combination of Cella, Bradley, and Humphreys teaches the invention as claimed in Claim 5 including: wherein different parameters comprising degrees of freedom are omitted in the GCNN to generate consecutive new items. (Cella Paragraph [0746] At 5106, one or more stress scenarios may be simulated by changing one or more parameters beyond the normal operating values. The simulating of stress scenarios overcome the limitation of any analysis based only on historical data and helps analyze the network performance across a range of hypothetical yet plausible stress conditions. The simulation involves varying (shocking) one or more parameters while keeping the other parameters as fixed to analyze the impact of such variations on value chain network. In embodiments, a single parameter may be varied while keeping remaining parameters as fixed. In other embodiments, multiple parameters may be varied simultaneously. At 5108, the outcomes of stress scenario simulations are determined, and the performance of value chain network and its different subsystems is estimated across various scenarios. At 5110, the data, parameters and outcomes are fed into a machine learning process in the artificial intelligence system 1160 for further analysis. Paragraph [0747] An advantage of generating data through simulations and then training machine learning algorithms on this data is the control this approach provides on the features in the data as well as volume and frequency of data. (Degrees of freedom (DoF) in neural networks generally refer to the number of independent parameters (weights and biases) that can be adjusted to minimize error, representing the model's flexibility to fit data. As these are independent parameters that can be adjusted, they qualify. An example of a variation in these parameters would be being made equal or equivalent to zero, therefore equivalent to an omission))
Regarding Claim 7, the combination of Cella, Bradley, and Humphreys teaches the invention as claimed in Claim 6 including: wherein the omitted degrees of freedom in the GCNN are determined based on statistical parameters related to different parameters of items within the training set. (Cella Paragraph [0745] At 5102, all historical and current data related to the value chain network are received. The data may include information related to various operating parameters of the value chain network over a particular historical time period, say last 12 months. Paragraph [0746] At 5106, one or more stress scenarios may be simulated by changing one or more parameters beyond the normal operating values. The simulating of stress scenarios overcome the limitation of any analysis based only on historical data and helps analyze the network performance across a range of hypothetical yet plausible stress conditions. The simulation involves varying (shocking) one or more parameters while keeping the other parameters as fixed to analyze the impact of such variations on value chain network. Paragraph [0748] In embodiments, the platform may include a system for learning on a training set of outcomes, parameters, and data collected from data sources relating to a set of value chain network activities to train an artificial intelligence/machine learning system to perform stress tests on a physical object using a digital twin that represents a set of value chain entities. (The adjusted parameters relate to differing parameters in the training set, as the adjustments are meant to test the effects on other parameters, therefore forming a relationship))
Regarding Claim 8, the combination of Cella, Bradley, and Humphreys teaches the invention as claimed in Claim 1 including: checking the augmented training dataset for sufficiency to train the NN after adding the at least one additional item to the original training set. (Cella Paragraph [2889] In embodiments, the DPLF may be or include the continued process retention of one or more training datasets and/or memories stored in the memory over time. The DPLF thereby allows the ANN to apply existing neural functions and draw upon sets of past events (including ones that are intentionally varied and/or curated for distinct purposes), such as to frame understanding of and behavior within present, recent, and/or new scenarios, including in simulations, during training processes, and in fully operational deployments of the ANN. The DPLF may provide the ANN with a framework by which the ANN may analyze, evaluate, and/or manage data, such as data related to the past, present and future. As such, the DPLF plays a crucial role in training and retraining the ANN via the training system and the retraining system. (Analysis, evaluation, and/or management of training data for retraining purposes.))
Regarding Claim 9, the combination of Cella, Bradley, and Humphreys teaches the invention as claimed in Claim 8 including: when checking the augmented training dataset for sufficiency to train the NN, and determining, based on the checking, that the augmented training dataset is insufficient to train the NN, executing the method of claim 1. (Cella Paragraph [2890] In embodiments, the DPLF is configured to perform a dual-process operation to manage existing training processes and is also configured to manage and/or perform new training processes, i.e., retraining processes. In embodiments, each instance of the ANN is trained via the training system and configured to be retrained via the retraining system. The ANN encodes training and/or retraining datasets, stores the datasets, and retrieves the datasets during both training via the training system and retraining via the retraining system. The DPANN system 20000 may recognize whether a dataset (the term dataset in this context optionally including various subsets, supersets, combinations, permutations, elements, metadata, augmentations, or the like, relative to a base dataset used for training or retraining), storage activity, processing operation and/or output, has characteristics that natively favor the training system versus the retraining system based on its respective inputs, processing (e.g., based on its structure, type, models, operations, execution environment, resource utilization, or the like) and/or outcomes (including outcome types, performance requirements (including contextual or dynamic requirements), and the like. For example, the DPANN system 20000 may determine that poor performance of the training system on a classification task may indicate a novel problem for which the training of the ANN was not adequate (e.g., in type of data set, nature of input models and/or feedback, quantity of training data, quality of tagging or labeling, quality of supervision, or the like), for which the processing operations of the ANN are not well-suited (e.g., where they are prone to known vulnerabilities due to the type of neural network used, the type of models used, etc.), and that may be solved by engaging the retraining system to retrain the model to teach the model to learn to solve the new classification problem (e.g., by feeding it many more labeled instances of correctly classified items). (Recognizing sufficiency and retraining of model))
Regarding Claim 10, the combination of Cella, Bradley, and Humphreys teaches the invention as claimed in Claim 8 including: when checking the augmented training dataset for sufficiency to train the NN, and determining, based on the checking, that the augmented training dataset is sufficient to train the NN, training the NN. (Cella Paragraph [2890] In embodiments, the DPLF is configured to perform a dual-process operation to manage existing training processes and is also configured to manage and/or perform new training processes, i.e., retraining processes. In embodiments, each instance of the ANN is trained via the training system and configured to be retrained via the retraining system. The ANN encodes training and/or retraining datasets, stores the datasets, and retrieves the datasets during both training via the training system and retraining via the retraining system. The DPANN system 20000 may recognize whether a dataset (the term dataset in this context optionally including various subsets, supersets, combinations, permutations, elements, metadata, augmentations, or the like, relative to a base dataset used for training or retraining), storage activity, processing operation and/or output, has characteristics that natively favor the training system versus the retraining system based on its respective inputs, processing (e.g., based on its structure, type, models, operations, execution environment, resource utilization, or the like) and/or outcomes (including outcome types, performance requirements (including contextual or dynamic requirements), and the like. For example, the DPANN system 20000 may determine that poor performance of the training system on a classification task may indicate a novel problem for which the training of the ANN was not adequate (e.g., in type of data set, nature of input models and/or feedback, quantity of training data, quality of tagging or labeling, quality of supervision, or the like), for which the processing operations of the ANN are not well-suited (e.g., where they are prone to known vulnerabilities due to the type of neural network used, the type of models used, etc.), and that may be solved by engaging the retraining system to retrain the model to teach the model to learn to solve the new classification problem (e.g., by feeding it many more labeled instances of correctly classified items). (Recognizing sufficiency and retraining of model))
Regarding Claim 20, the combination of Cella, Bradley, and Humphreys teaches the invention as claimed in Claim 1 including: wherein the at least one sufficiency criteria is based on a known system similar to the system in which the neural network NN is applied, and (Cella Paragraph [0399] In embodiments, an artificial intelligence system 1160 may provide an information routing recommendation based on goals, such as goals of a value chain network, goals of information routing, and the like. Goal-based information routing recommendations may include routing goals, such as Quality of Service routing goals, routing reliability goals (which may be measured based on a transmission failure rate and the like). (The goal of the model ( and therefore its sufficiency criteria as outlined above) is based upon its application to systems (i.e. known systems) for which the model is trained))
wherein the at least one sufficiency criteria is based on a number of degrees of freedom of a neural network of the known system. (Cella Paragraph [0531] One or more of the units and/or nodes and connections therebetween may have one or more numerical “weights” assigned. The assigned weights may be configured to facilitate learning, i.e., training, of the machine learning model 3000. The weights assigned weights may increase and/or decrease one or more signals between one or more units and/or nodes, and in some embodiments may have one or more thresholds associated with one or more of the weights. Paragraph [0928] The report may be used to reconfigure/retrain the executive agent. In embodiments, the reconfiguring/retraining an executive agent may include removing an input that is the source of the error, reconfiguring a set of nodes of the artificial intelligence system, reconfiguring a set of weights of the artificial intelligence system (The sufficiency of the model is judged based on its output, and therefore its training data. Since the weights (analogous to degrees of freedom) are included in this training data, this means that the sufficiency of the model is at least in part based on these weights))
Regarding Claims 11 - 19, they are system claims that correspond to the method claims 1 – 10 above. Therefore, they are rejected for the same reasons as method claims 1 - 10 above.
Response to Arguments
Applicants’ arguments filed on 09/01/2026 on the remark (sheet 1) with respect to Claims 2 and 12 under objections have been fully considered and are persuasive. Therefore, objections to Claims 2 and 12 are withdrawn.
Applicant’s arguments filed 09/01/2026 on the remark (page 10), with respect to claims 9 and 10 under 35 U.S.C 112(b) rejection have been fully considered and are persuasive. Therefore, the rejection of Claims 9 and 10 under 35 U.S.C 112(b) is withdrawn.
Applicant’s arguments filed 09/01/2026 on the remark (page 10), with respect to Claims 11, 13, and 18 under 35 U.S.C 112(f) interpretation have been fully considered and are considered persuasive. Therefore, the present interpretation of Claims 11, 13, and 18 are withdrawn in light of amendments filed.
Applicant’s arguments filed 09/01/2026 on the remark (pages 10-13), with respect to Claims 1-19 under 35 U.S.C 101 rejection have been fully considered and are persuasive. Therefore, the rejection of Claims 1-19 under 35 U.S.C 101 is withdrawn.
Applicant’s arguments filed on 09/01/2026 on the remark (pages 13-15) with respect to Claims 1-29 under 35 U.S.C 103 have been considered but are moot because the new ground of rejection (see rejection above).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Bouton et al. (US 20250242150 A1) discusses training in machine learning models and a base dataset consisting of, in part, degrees of freedom in an observed rotational integrated circuit.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JUSTIN A CARDOSO whose telephone number is (571)272-8512. The examiner can normally be reached M-F 7:30 - 5:00, alternate Friday's off.
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/JUSTIN CARDOSO/
Patent Examiner, Art Unit 2143
/JENNIFER N WELCH/Supervisory Patent Examiner, Art Unit 2143
