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 § 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 1-11, 13-24 and 26 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite mental processes of judgement and evaluation. This judicial exception is not integrated into a practical application or amount to significantly mare as the additional elements are links to technologic field in combination of generic computer functions that are implemented to perform the disclosed abstract idea above. See the below analysis for further details.
Claims 1 and 14
Step 1: The claim recites a method and system, therefore, it falls into the statutory category of a method and apparatus.
Step 2A Prong 1: The claim recites, inter alia:
(b) Comparing performance of the machine learning system on the training data set to performance of the machine learning system on a development data set, (This amounts to a mental process of judgment and evaluation wherein a user compares the performance or result of two systems.)
(c) upon identification of a difference beyond a threshold between performance of the machine learning system on the training data set and performance of the machine learning system on the development data set, increasing a hyperparameter used in machine learning system, wherein the regularization hyperparameter weights an auxiliary objective that is evaluated on a latent representation; (This amounts to a mental process of observation, judgment and evaluation wherein a user compares the difference between the results of two systems to a threshold and increases a parameter if it is above the threshold.)
(d) validating performance of the machine learning system (This amounts to a mental process of judgment and evaluation wherein a user validates system results and being correct or accurate.)
repeating steps (b)-(d) iteratively until a stopping criterion is satisfied. (This is a mental step wherein a user repeats the process of comparing results of two systems, and when they are different adjusted a parameter of the system until they match, at which point the user stops.)
Step 2A Prong 2:
This judicial exception is no integrated into a practical application. Aside from the limitations above, the claim recites:
training, by a programmed computer system comprising one or more processor cores, the machine learning system, through machine learning, on a training data set; (This amounts to using a generic computer hardware (computer, processors, and machine learning models) to implement the abstract idea, see MPEP 2106.05(f). This is the case because the machine learning models and training is cited at high level of generality.)
wherein the development data set is distinct from the training data set; (This is amounts to linking the abstract idea to technological field, see MPEP 2106.05(h).)
re-training, by the programmed computer system, the machine learning system, wherein re-training the machine learning system comprises a regularization hyperparameter evaluated on during training; (This amounts to using a generic computer hardware (computer, processors, and machine learning models) to implement the abstract idea, see MPEP 2106.05(f). This is the case because the machine learning models and training is cited at high level of generality.)
wherein the validation data set is distinct from both the training data set and the development data set; and (this amounts to extra-solution activity of a particular type of data to used and/or manipulated, see MPEP 2106.05(g) and linking to particular technological field (machine learning), see MPEP 2106.05(h).)
one or more processor cores; and computer memory in communication the one or more processor cores, wherein the computer memory stores instructions that when executed by the one or more processor cores causes the one or more process cores; (claim 14) (This amount to using generic computer hardware to implement the abstract idea, see MPEP 2106.05(f).)
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are mere insignificant extra solution activity in combination of generic computer functions that are implemented to perform the disclosed abstract idea above.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements of: “training, by a programmed computer system comprising one or more processor cores, the machine learning system, through machine learning, on a training data set; and re-training, by the programmed computer system, the machine learning system, wherein re-training the machine learning system comprises a regularization hyperparameter evaluated on during training;” amounts to using a generic computer hardware (computer, processors, and machine learning models) to implement the abstract idea, see MPEP 2106.05(f). This is the case because the machine learning models and training is cited at high level of generality. The additional limitations of “wherein the development data set is distinct from the training data set; and wherein the validation data set is distinct from both the training data set and the development data set;” amounts to linking the abstract idea to technological field, as training data, development data and validation sets are common in the machine learning technology, see MPEP 2106.05(h).
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are links to technologic field in combination of generic computer functions that are implemented to perform the disclosed abstract idea above.
Claims 2 and 15
Step 2A Prong 1: The claim recites, inter alia:
Claim 2 and 15 inherit the abstract idea of claims 1 and 14.
Step 2A Prong 2:
This judicial exception is no integrated into a practical application. Aside from the limitations above, the claim recites:
wherein increasing the regularization hyperparameter used in the re-training the machine learning system comprises additionally increasing a L1 regularization hyperparameter used in the re-training the machine learning system. (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: high level recitation of training a machine learning model by changing a model parameter.)
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are mere insignificant extra solution activity in combination of generic computer hardware performing generic functions that are implemented to perform the disclosed abstract idea above.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional element are: “wherein increasing the regularization hyperparameter used in the re-training the machine learning system comprises additionally increasing a L1 regularization hyperparameter used in the re-training the machine learning system” which are cited at high level of generality and result in using the machine learning model a tool to implement the abstract idea, see MPEP 2106.05(f).
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are well, understood, routine and conventional activity as disclosed in combination of generic computer hardware performing generic functions that are implemented to perform the disclosed abstract idea above.
Claim 15 is the system embodiment of claim 2 with similar limitations and thus rejected using the reasoning found in claim 2.
Claims 3 and 16
Step 2A Prong 1: The claim recites, inter alia:
Claim 3 and 16 inherit the abstract idea of claims 1 and 14.
Step 2A Prong 2:
This judicial exception is no integrated into a practical application. Aside from the limitations above, the claim recites:
wherein increasing the regularization hyperparameter used in the re-training the machine learning system comprises additionally increasing a L2 regularization hyperparameter used in the re-training the machine learning system. (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: high level recitation of training a machine learning model by changing a model parameter.)
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are mere insignificant extra solution activity in combination of generic computer hardware performing generic functions that are implemented to perform the disclosed abstract idea above.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional element are: “wherein increasing the regularization hyperparameter used in the re-training the machine learning system comprises additionally increasing a L2 regularization hyperparameter used in the re-training the machine learning system” which are cited at high level of generality and result in using the machine learning model a tool to implement the abstract idea, see MPEP 2106.05(f).
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are well, understood, routine and conventional activity as disclosed in combination of generic computer hardware performing generic functions that are implemented to perform the disclosed abstract idea above.
Claim 16 is the system embodiment of claim 3 with similar limitations and thus rejected using the reasoning found in claim 3.
Claims 4 and 17
Step 2A Prong 1: The claim recites, inter alia:
Claim 4 and 17 inherit the abstract idea of claims 1 and 14.
Step 2A Prong 2:
This judicial exception is no integrated into a practical application. Aside from the limitations above, the claim recites:
the machine learning system comprises a plurality of asynchronous agents, where each of the plurality of asynchronous agents utilizes a feature vector, and wherein each of the plurality of asynchronous agents is trained with respect to performance objective; (this amounts to using generic hardware to execute the abstract idea, wherein the asynchronous agents are generic computer components, see MPEP 2106.05(f).)
the latent representation of the machine learning model comprises the feature vectors; (This amount to linking the abstract idea to a particular field of technology, see MPEP 2106.05(h).)
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are mere insignificant extra solution activity in combination of generic computer hardware performing generic functions that are implemented to perform the disclosed abstract idea above.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional element are: “the machine learning system comprises a plurality of asynchronous agents, where each of the plurality of asynchronous agents utilizes a feature vector, and wherein each of the plurality of asynchronous agents is trained with respect to performance objective” which amounts to using generic hardware to execute the abstract idea, wherein the asynchronous agents are generic computer components, see MPEP 2106.05(f). Also, the limitation of “the latent representation of the machine learning model comprises the feature vectors;” amount to linking the abstract idea to a particular field of technology, see MPEP 2106.05(h).
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as generic computer hardware performing generic functions that are implemented to perform the disclosed abstract idea above.
Claim 17 is the system embodiment of claim 4 with similar limitations and thus is rejected using the same reasoning as claim 4. Claim 17 also has the added limitations of a computer memory storing instructions that are executed by a one or more processors. These addition elements amount to using generic computer hardware to execute the abstract idea, see MPEP 2106.05(f). The combination of the elements do not amount to signification more than the abstract idea nor integrate it into a practical application as they are mere insignificant extra solution activity in combination of generic computer functions being implemented with generic computer elements in a high level of generality to perform the disclosed abstract idea above.
Claims 5 and 18
Step 2A Prong 1: The claim recites, inter alia:
increasing the regularization hyperparameter used in the re-training the machine learning system comprises increasing, for each asynchronous agent, a relative weighting between a performance objective for the asynchronous agent and a feature vector stabilization objective for the asynchronous agent. (This is a mental process of observation, evaluation and judgement wherein based on the performance of machine learning systems on data, the user determines whether and how to increase the weighting between performance and stabilization goals.)
Step 2A Prong 2:
This judicial exception is no integrated into a practical application. Aside from the limitations above, the claim recites:
the machine learning system comprises a plurality of asynchronous agents, where each of the plurality of asynchronous agents utilizes a feature vector; (this amounts to using generic hardware to execute the abstract idea, wherein the asynchronous agents are generic computer components, see MPEP 2106.05(f).)
the latent representation of the machine learning model comprises the feature vectors; (This amount to linking the abstract idea to a particular field of technology, see MPEP 2106.05(h).)
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are mere insignificant extra solution activity in combination of generic computer hardware performing generic functions that are implemented to perform the disclosed abstract idea above.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional element are: “the machine learning system comprises a plurality of asynchronous agents, where each of the plurality of asynchronous agents utilizes a feature vector;” amounts to using generic hardware to execute the abstract idea, wherein the asynchronous agents are generic computer components, see MPEP 2106.05(f); and the limitation of “ the latent representation of the machine learning model comprises the feature vectors;” amount to linking the abstract idea to a particular field of technology, see MPEP 2106.05(h).
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as generic computer hardware performing generic functions in combination with linking to technological field that are implemented to perform the disclosed abstract idea above.
Claim 18 is the system embodiment of claim 5 with similar limitations and thus is rejected using the same reasoning as claim 5. Claim 18 also has the added limitations of a computer memory storing instructions that are executed by a one or more processors. These addition elements amount to using generic computer hardware to execute the abstract idea, see MPEP 2106.05(f). The combination of the elements do not amount to signification more than the abstract idea nor integrate it into a practical application as they are mere insignificant extra solution activity in combination of generic computer functions being implemented with generic computer elements in a high level of generality to perform the disclosed abstract idea above.
Claims 6 and 19
Step 2A Prong 1: The claim recites, inter alia:
Inherits the mental process of claim 5.
Step 2A Prong 2:
This judicial exception is no integrated into a practical application. Aside from the limitations above, the claim recites:
each asynchronous agent comprises a classifier, and the performance objective for each asynchronous agent comprises a classification objective. This amounts to using generic hardware to execute the abstract idea, wherein the asynchronous agents are generic computer components, see MPEP 2106.05(f).
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are mere insignificant extra solution activity in combination of generic computer hardware performing generic functions that are implemented to perform the disclosed abstract idea above.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional element are: “each asynchronous agent comprises a classifier, and the performance objective for each asynchronous agent comprises a classification objective.” this amounts to using generic hardware to execute the abstract idea, wherein the asynchronous agents are generic computer components, see MPEP 2106.05(f).
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as generic computer hardware performing generic functions in combination with linking to technological field that are implemented to perform the disclosed abstract idea above.
In regards to claim 19, it is the system embodiment of claim 6 with similar limitations and thus rejected using the same reasoning in claim 6.
Claims 7 and 20
Step 2A Prong 1: The claim recites, inter alia:
increasing the regularization hyperparameter used in the re-training the machine learning system comprises increasing a relative weighting between a performance objective for the machine learning system and an autoencoding objective for the sparse feature vector. (This is a mental process of observation, evaluation and judgement wherein based on the performance of machine learning systems on data, the user determines whether and how to increase the weighting between performance and autoencoding goals.)
Step 2A Prong 2:
This judicial exception is no integrated into a practical application. Aside from the limitations above, the claim recites:
the machine learning system utilizes a sparse feature vector; (This amount to linking the abstract idea to technological field, machine learning, see MPEP 2106.05(h).
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as it merely links the abstract idea to technological field.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional element are: “the machine learning system utilizes a sparse feature vector” amounts to linking the abstract idea to technological field, machine learning, see MPEP 2106.05(h).
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as it merely links the abstract idea to technological field.
Claim 20 is the system embodiment of claim 7 with similar limitations and thus is rejected using the same reasoning as claim 7. Claim 20 also has the added limitations of a computer memory storing instructions that are executed by a one or more processors. These addition elements amount to using generic computer hardware to execute the abstract idea, see MPEP 2106.05(f). The combination of the elements do not amount to signification more than the abstract idea nor integrate it into a practical application as they are mere generic computer hardware in combination with linking to a technological field.
Claims 8 and 21
Step 2A Prong 1: The claim recites, inter alia:
increasing the regularization hyperparameter used in the re-training the machine learning system comprises increasing a relative weighting between a performance objective for the machine learning system and a clustering objective for the sparse feature vector. (This is a mental process of observation, evaluation and judgement wherein based on the performance of machine learning systems on data, the user determines whether and how to increase the weighting between performance and autoencoding goals.)
Step 2A Prong 2:
This judicial exception is no integrated into a practical application. Aside from the limitations above, the claim recites:
the machine learning system utilizes a sparse feature vector; (This amount to linking the abstract idea to technological field, machine learning, see MPEP 2106.05(h).
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as it merely links the abstract idea to technological field.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional element are: “the machine learning system utilizes a sparse feature vector” amounts to linking the abstract idea to technological field, machine learning, see MPEP 2106.05(h).
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as it merely links the abstract idea to technological field.
Claim 21 is the system embodiment of claim 8 with similar limitations and thus is rejected using the same reasoning as claim 8. Claim 21 also has the added limitations of a computer memory storing instructions that are executed by a one or more processors. These addition elements amount to using generic computer hardware to execute the abstract idea, see MPEP 2106.05(f). The combination of the elements do not amount to signification more than the abstract idea nor integrate it into a practical application as they are mere insignificant extra solution activity in combination of generic computer functions being implemented with generic computer elements in a high level of generality to perform the disclosed abstract idea above.
Claims 9 and 22
Step 2A Prong 1: The claim recites, inter alia:
increasing the regularization hyperparameter used in the re-training the machine learning system comprises increasing a temperature hyperparameter in the temperature-dependent sigmoid activation of the first node. (This amounts to a mental process of judgement and evaluation wherein a user decides the what value a temperature parameter should be increased to.)
Step 2A Prong 2:
This judicial exception is no integrated into a practical application. Aside from the limitations above, the claim recites:
the machine learning system comprises a first node with a temperature-dependent sigmoid activation function; (This amount to linking the abstract idea to technological field, machine learning, see MPEP 2106.05(h).
the latent representation of the machine learning model comprises activation values generated by the temperature-dependent sigmoid activation function; (This amount to linking the abstract idea to a particular field of technology, see MPEP 2106.05(h).)
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as it merely links the abstract idea to technological field.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional element are: “the machine learning system comprises a first node with a temperature-dependent sigmoid activation function; the latent representation of the machine learning model comprises activation values generated by the temperature-dependent sigmoid activation function;” amount to linking the abstract idea to a particular field of technology, see MPEP 2106.05(h).)
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as it merely links the abstract idea to technological field.
Claim 22 is the system embodiment of claim 9 with similar limitations and thus is rejected using the same reasoning as claim 9. Claim 22 also has the added limitations of a computer memory storing instructions that are executed by a one or more processors. These addition elements amount to using generic computer hardware to execute the abstract idea, see MPEP 2106.05(f). The combination of the elements do not amount to signification more than the abstract idea nor integrate it into a practical application as they are mere insignificant extra solution activity in combination of generic computer functions being implemented with generic computer elements in a high level of generality to perform the disclosed abstract idea above.
Claims 10 and 23
Step 2A Prong 1: The claim recites, inter alia:
identifying, a significant difference between performance of the machine learning system on the training data set and performance of the machine learning system on the development data set, wherein applying a predefined criterion to determine significance” is a mental process of judgement and evaluation wherein a user compares the two performance results to see if there is a significant difference wherein significant is number value or threshold.
Step 2A Prong 2:
This judicial exception is no integrated into a practical application. Aside from the limitations above, the claim recites:
Using a second machine learning; (This is cited a high level of generality and result in using the generic computer hardware to execute the abstract idea, see MPEP 2106.05(f).)
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as it merely using generic hardware to execute the abstract idea.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional element are: “Using a second machine learning;” amount using generic computer hardware to execute the abstract idea, see MPEP 2106.05(f).
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as it merely using generic hardware to execute the abstract idea.
Claim 23 is the system embodiment of claim 10 with similar limitations and thus is rejected using the same reasoning as claim 10. Claim 23 also has the added limitations of a computer memory storing instructions that are executed by a one or more processors. These addition elements amount to using generic computer hardware to execute the abstract idea, see MPEP 2106.05(f). The combination of the elements do not amount to signification more than the abstract idea nor integrate it into a practical application as they are mere insignificant extra solution activity in combination of generic computer functions being implemented with generic computer elements in a high level of generality to perform the disclosed abstract idea above.
Claims 11 and 24
Step 2A Prong 1: The claim recites, inter alia:
Claim 11 inherits the abstract idea of claims 1 and 10.
Step 2A Prong 2:
This judicial exception is no integrated into a practical application. Aside from the limitations above, the claim recites:
wherein the predefined criterion comprises convergence of the machine learning system during re-training. (This amounts to training a machine learning model wherein training has to being and stop when whatever stopping criteria or point is reached. As this limitation is cited at high level of generality, it result in using a tool to execute the abstract idea, See MPEP 2106.05(f).)
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as it merely using generic hardware to execute the abstract idea.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional element are: “wherein the predefined criterion comprises convergence of the machine learning system during re-training”, amounts to training a machine learning model wherein training has to being and stop when whatever stopping criteria or point is reached. As this limitation is cited at high level of generality, it result in using a tool to execute the abstract idea, see MPEP 2106.05(f).
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as it merely using generic hardware to execute the abstract idea.
Claim 24 is the system embodiment of claim 11 with similar limitations and thus is rejected using the same reasoning as claim 11.
Claims 13 and 26
Step 2A Prong 1: The claim recites, inter alia:
Claim 11 inherits the abstract idea of claims 1.
Step 2A Prong 2:
This judicial exception is no integrated into a practical application. Aside from the limitations above, the claim recites:
wherein the programmed computer system comprises at least two graphical processing units (GPUs); (This amounts to using generic computer hardware to execute the abstract idea, see MPEP 2106.05(f).)
training the machine learning system comprises processing data items in the training data set in parallel across the at least two GPUs. (This amounts to using generic computer hardware to execute the abstract idea, see MPEP 2106.05(f).)
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as it merely using generic hardware to execute the abstract idea.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional element are: “the programmed computer system comprises at least two graphical processing units (GPUs); and training the machine learning system comprises processing data items in the training data set in parallel across the at least two GPUs.”, these limitations amount to using generic computer hardware to execute the abstract, see MPEP 2106.05(f).
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as it merely using generic hardware to execute the abstract idea.
Claim 26 is the system embodiment of claim 13 with similar limitations and thus is rejected using the same reasoning as claim 13.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-11, 13-24 and 26 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 has the term “auxiliary objective” which renders claim 1 as indefinite. When looking to the instant specification for the meaning of the term “auxiliary objective”, the examiner could not find a mention of it. Also, as there is an “auxiliary objective”, it would indicate there is a primary or main objection which also I not defined in the specification and claims. As such it not clear to examiner what is meant or what is need to meet the limitation so the term “auxiliary objective”, and thus the claim is found to be indefinite. Also, independent claim 14 has the issues with the term “auxiliary objective. As claims 1 and 14 are found to be indefinite are too are all the claims dependent on them which are claims 2-11, 13, 15-24 and 26.
Response to Arguments
Applicant's arguments filed 10 February 2026 have been fully considered but they are not persuasive.
The applicant argues that claims do not recite a mathematical concept, but instead recite a specific machine implemented training control. Applicant further argues that the abstract of mathematical concept is integrated into a practical application as the claimed feedback-control architecture integrates any mathematical computation into a practical application that improves how the machine learning system trains and generalizes. Applicant also argues the claims do not include a mental process as training neural network cannot be practically performed in human mind, and that the amended claims overcome the rejection under 35 USC 103.
The examiner respectfully traverses the applicant’s arguments for the following reason: The examiner maintains that claims do disclose an abstract idea of a mental process and points out that the abstract idea of a mathematical concept was not stated by the examiner. For claim 1 the abstract idea is a mental process of observation, evaluation and judgment. The abstract ideas in claim1 and 14 are:
((b) Comparing performance of the machine learning system on the training data set to performance of the machine learning system on a development data set, (This amounts to a mental process of judgment and evaluation wherein a user compares the performance or result of two systems.)
(c) upon identification of a difference beyond a threshold between performance of the machine learning system on the training data set and performance of the machine learning system on the development data set, increasing a hyperparameter used in machine learning system, wherein the regularization hyperparameter weights an auxiliary objective that is evaluated on a latent representation; (This amounts to a mental process of observation, judgment and evaluation wherein a user compares the difference between the results of two systems to a threshold and increases a parameter if it is above the threshold.)
(d) validating performance of the machine learning system (This amounts to a mental process of judgment and evaluation wherein a user validates system results and being correct or accurate.)
repeating steps (b)-(d) iteratively until a stopping criterion is satisfied. (This is a mental step wherein a user repeats the process of comparing results of two systems, and when they are different adjusted a parameter of the system until they match, at which point the user stops.)
These limitations are mental processes of observation, judgment and evaluation of comparing the performance of system on a training data and development data, comparing the different to a threshold, increasing a hyperparameter when the threshold is exceeded, and validating the performance. All of these steps can be performed by a user, and the act of repeatedly doing it can also be done by a user. Thus, the examiner maintains that the claims do contain an abstract idea. Applicant further argues that claims are an improvement the functioning of computer but the examiner respectfully traverses this argument as the claims are not an improvement the functioning of a computer but an abstract idea being implemented using generic computer hardware. The remaining limitations of claim do not integrate the abstract into a practical application. The remaining limitations are:
training, by a programmed computer system comprising one or more processor cores, the machine learning system, through machine learning, on a training data set; (This amounts to using a generic computer hardware (computer, processors, and machine learning models) to implement the abstract idea, see MPEP 2106.05(f). This is the case because the machine learning models and training is cited at high level of generality.)
wherein the development data set is distinct from the training data set; (This is amounts to linking the abstract idea to technological field, see MPEP 2106.05(h).)
re-training, by the programmed computer system, the machine learning system, wherein re-training the machine learning system comprises a regularization hyperparameter evaluated on during training; (This amounts to using a generic computer hardware (computer, processors, and machine learning models) to implement the abstract idea, see MPEP 2106.05(f). This is the case because the machine learning models and training is cited at high level of generality.)
wherein the validation data set is distinct from both the training data set and the development data set; and (this amounts to extra-solution activity of a particular type of data to used and/or manipulated, see MPEP 2106.05(g) and linking to particular technological field (machine learning), see MPEP 2106.05(h).)
one or more processor cores; and computer memory in communication the one or more processor cores, wherein the computer memory stores instructions that when executed by the one or more processor cores causes the one or more process cores; (claim 14) (This amount to using generic computer hardware to implement the abstract idea, see MPEP 2106.05(f).)
All of the above limitations do not integrate do not integrate the judicial exception into practical application as they are mere insignificant extra solution activity in combination of generic computer functions that are implemented to perform the disclosed abstract idea. For these the reason the examiner maintains the rejection under 35 USC 101 for being abstract idea.
The rejection under 35 USC 103 should be withdrawn in light of the amendment to the claims as none of the references disclose the amended limitation. The examiner agrees that prior art references do not disclose claim 1 and 14 as amended and withdraws the rejection under 35 USC 103.
Conclusion
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 PAULINHO E SMITH whose telephone number is (571)270-1358. The examiner can normally be reached Mon-Fri. 10AM-6PM CST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Abdullah Kawsar can be reached at 571-270-3169. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/PAULINHO E SMITH/Primary Examiner, Art Unit 2127