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 .
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119
(a)-(d). The certified copy has been filed in parent Application No. EP23305900.5, filed on
06/06/2023.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 06/06/2024 is in compliance
with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being
considered by the examiner.
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.
Claim 10 is 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.
Regarding claim 10, the phrase "preferably" renders the claim indefinite because it is unclear whether the limitation(s) following the phrase are part of the claimed invention. See MPEP § 2173.05(d). The claim will be interoperated as if the following phrase is part of the claimed invention.
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.
Regarding claim 1, in Step 1 of the 101 analyses set forth in MPEP 2106, the claim recites A method for configuring a data processing chain comprising. An method is one of the four statutory categories.
In Step 2a Prong 1 of the 101 analyses set forth in the MPEP 2106, the examiner has determined that the following limitations recite a process that, under the broadest reasonable interpretation, covers a [ mental process/mathematical concept] but for recitation of generic computer components:
selecting a dataset from a set of datasets, according to a reward associated with each data from said set of datasets; (A person can mentally select a data set from a set of datasets by a process of simply evaluating the datasets and making a judgement on what dataset has the highest reward.)
calculating an experimental accuracy score of the experimental artificial intelligence model, representative of a match between, on one hand, a second predicted state of the monitored environment, determined by the experimental artificial intelligence model from the test data, and, on another hand, the actual state of the monitored environment for the test data; (A person can mentally calculate an experimental accuracy score by a process of simply evaluating the predicted state and the actual state and making a judgement on how accurately the model predicted the current state.)
If claim limitations, under their broadest reasonable interpretation, covers performance of the limitations as a [ mental process/mathematical concept] but for the recitation of generic computer components, then it falls within the mental process grouping of abstract ideas. According, the claim “recites” an abstract idea.
In Step 2a Prong 2 of the 101 analyses set forth in MPEP 2106, the examiner has determined that the following additional elements do not integrate this judicial exception into a practical application:
a prediction stage implementing a current artificial intelligence model, previously trained based on a training dataset, to predict an anomaly in a monitored environment equipped with at least one sensor, (Generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h))).
from input data received from each sensor of said at least one sensor (Adding insignificant extra-solution activity (mere data gathering) to the judicial exception (MPEP 2106.05(g))).
the current artificial intelligence model being associated with a current accuracy score, representative of a match between, on one hand, a first predicted state of the monitored environment, determined by the current artificial intelligence model from test data dependent on the input data, and, on another hand, an actual state of the monitored environment for said test data, the method being computer-implemented and comprising: (Generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h))).
an implementation of a reinforcement algorithm comprising (Uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f))).
stored in a memory (Adding insignificant extra-solution activity (mere data storage) to the judicial exception (MPEP 2106.05(g))).
training an artificial intelligence model based on dataset that is selected to obtain an experimental artificial intelligence model; (Merely training a generic machine learning model constitutes “applying” the machine learning model (MPEP 2106.05(f))).
and based on a result of a comparison between the current accuracy score and the experimental accuracy score, either replacing or not replacing the current artificial intelligence model with the experimental artificial intelligence model in the prediction stage; (Generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h))).
and updating the reward associated with the dataset that is selected. (Generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h))).
Since the claim does not contain any other additional elements that are indicative of integration into a practical application, the claim is “directed” to an abstract idea.
In Step 2b of the 101 analyses set forth in the 2019 PEG, the examiner has determined that the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
a prediction stage implementing a current artificial intelligence model, previously trained based on a training dataset, to predict an anomaly in a monitored environment equipped with at least one sensor, (Generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h)) Furthermore, the additional element is directed to generally linking the use of the judicial exception to a particular technological environment or field of use, which is not indicative of significantly more.).
from input data received from each sensor of said at least one sensor (Adding insignificant extra-solution activity (mere data gathering) to the judicial exception (MPEP 2106.05(g)) Furthermore, the additional element is directed to receiving or transmitting data over a network / performing repetitive calculations / electronic recordkeeping / storing and retrieving information in memory / electronically scanning or extracting data from a physical document, which the courts have recognized as well‐understood, routine, and conventional when they are claimed in a generic manner. See MPEP § 2106.05(d)(II).).
the current artificial intelligence model being associated with a current accuracy score, representative of a match between, on one hand, a first predicted state of the monitored environment, determined by the current artificial intelligence model from test data dependent on the input data, and, on another hand, an actual state of the monitored environment for said test data, the method being computer-implemented and comprising: (Generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h)) Furthermore, the additional element is directed to generally linking the use of the judicial exception to a particular technological environment or field of use, which is not indicative of significantly more.).
an implementation of a reinforcement algorithm comprising (Uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)) Furthermore, the additional element is directed to application of a computer tool (machine learning model), which is not indicative of significantly more (MPEP 2106.05(f)).)
stored in a memory (Adding insignificant extra-solution activity (mere data storage) to the judicial exception (MPEP 2106.05(g)) Furthermore, the additional element is directed to receiving or transmitting data over a network / performing repetitive calculations / electronic recordkeeping / storing and retrieving information in memory / electronically scanning or extracting data from a physical document, which the courts have recognized as well‐understood, routine, and conventional when they are claimed in a generic manner. See MPEP § 2106.05(d)(II).).
training an artificial intelligence model based on dataset that is selected to obtain an experimental artificial intelligence model; (Merely training a generic machine learning model constitutes “applying” the machine learning model (MPEP 2106.05(f)) Furthermore, the additional element is directed to application of a computer tool (machine learning model), which is not indicative of significantly more (MPEP 2106.05(f)).)
and based on a result of a comparison between the current accuracy score and the experimental accuracy score, either replacing or not replacing the current artificial intelligence model with the experimental artificial intelligence model in the prediction stage; (Generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h)) Furthermore, the additional element is directed to generally linking the use of the judicial exception to a particular technological environment or field of use, which is not indicative of significantly more.).
and updating the reward associated with the dataset that is selected. (Generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h)) Furthermore, the additional element is directed to generally linking the use of the judicial exception to a particular technological environment or field of use, which is not indicative of significantly more.).
Claims 12 and 13 are rejected on the same grounds as Claim 1.
Regarding claim 2 it is dependent upon claim 1, and thereby incorporates the limitations of, and corresponding analysis applied to claim 1. Further, claim 2 recites wherein, if the experimental accuracy score is higher than the current accuracy score, the current artificial intelligence model is replaced by the experimental artificial intelligence mode; (In step 2A, prong 2, this recites generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h))). In step 2B, generally linking the use of the judicial exception to a particular technological environment is not indicative of significantly more.)
Regarding claim 3 it is dependent upon claim 1, and thereby incorporates the limitations of, and corresponding analysis applied to claim 1. Further, claim 3 recites wherein, if the experimental accuracy score is lower than the current accuracy score the current artificial intelligence model is not replaced by the experimental artificial intelligence model the updating of the reward associated with the dataset that is selected is a decrease of said reward. (In step 2A, prong 2, this recites generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h))). In step 2B, generally linking the use of the judicial exception to a particular technological environment is not indicative of significantly more.)
Regarding claim 4 it is dependent upon claim 1, and thereby incorporates the limitations of, and corresponding analysis applied to claim 1. Further, claim 4 recites wherein the dataset that is selected is a dataset associated with a maximum reward. (In step 2A, prong 2, this recites generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h))). In step 2B, generally linking the use of the judicial exception to a particular technological environment is not indicative of significantly more.)
Regarding claim 5 it is dependent upon claim 1, and thereby incorporates the limitations of, and corresponding analysis applied to claim 1. Further, claim 5 recites comparing a current predicted state of the monitored environment, predicted by the current artificial intelligence model from the input data with the actual state of the monitored environment; (In step 2A, prong 1, this recites a mental process without significantly more. A person can mentally make a comparison by a process of simply evaluating the predicted state and the current actual state and making a judgement on how close they are to one another. (MPEP 2106).)
Regarding claim 6 it is dependent upon claim 5, and thereby incorporates the limitations of, and corresponding analysis applied to claim 5. Further, claim 6 recites wherein the updating the test data comprises adding to the test data some or all of the input data from which the current predicted state was determined, together with a label representative of whether a prediction is correct or incorrect. (In step 2A, prong 2, this recites generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h))). In step 2B, generally linking the use of the judicial exception to a particular technological environment is not indicative of significantly more.)
Regarding claim 7 it is dependent upon claim 1, and thereby incorporates the limitations of, and corresponding analysis applied to claim 1. Further, claim 7 recites further comprising comparing a current predicted state of the monitored environment, predicted by the current artificial intelligence model from the input data with the actual state of the monitored environment in an event of a discrepancy between the current predicted state and the actual state of the monitored environment, (In step 2A, prong 1, this recites a mental process without significantly more. A person can mentally make a comparison by a process of simply evaluating the predicted state and the current actual state and making a judgement on how close they are to one another. (MPEP 2106).) creating an additional dataset by modifying the dataset based on which the current artificial intelligence model was trained, from the current predicted state and the actual state of the monitored environment; and storing the additional dataset that is created in said memory. (In step 2A, prong 2, this recites generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h))). In step 2B, generally linking the use of the judicial exception to a particular technological environment is not indicative of significantly more.)
Regarding claim 8 it is dependent upon claim 7, and thereby incorporates the limitations of, and corresponding analysis applied to claim 7. Further, claim 8 recites wherein the additional dataset that is created comprises data of the dataset based on which the current artificial intelligence model has been trained, to which has been added the input data from which the current predicted state has been determined, associated with a label representative of whether a prediction is correct or incorrect. (In step 2A, prong 2, this recites generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h))). In step 2B, generally linking the use of the judicial exception to a particular technological environment is not indicative of significantly more.)
Regarding claim 9 it is dependent upon claim 7, and thereby incorporates the limitations of, and corresponding analysis applied to claim 7. Further, claim 9 recites wherein, on creation, the additional dataset that is created is associated with a reward having a value greater than the value of the reward associated with each other dataset of the set of datasets stored in the memory. (In step 2A, prong 2, this recites generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h))). In step 2B, generally linking the use of the judicial exception to a particular technological environment is not indicative of significantly more.)
Regarding claim 10 it is dependent upon claim 1, and thereby incorporates the limitations of, and corresponding analysis applied to claim 1. Further, claim 10 recites teaches further comprising selecting at least some of the input data, preferably by implementing a feature selection process to generate at least one additional dataset; (In step 2A, prong 1, this recites a mental process without significantly more. A person can mentally select input data to add to a dataset by a process of simply evaluating the input data and making a judgement on what data should be included in the data set. (MPEP 2106).) and storing the at least one additional dataset that is generated in the memory. (In step 2A, prong 2, this recites insignificant extra solution activity of mere data storage, which is not indicative of integration into a practical application (MPEP 2106.05(g)). In step 2B, this recites storing and retrieving information in memory which is a well-understood, routine and conventional activity, which is not indicative of significantly more.)
Regarding claim 11 it is dependent upon claim 1, and thereby incorporates the limitations of, and corresponding analysis applied to claim 1. Further, claim 11 recites wherein the reinforcement algorithm is a Q-learning algorithm, a Deep Q-learning algorithm or a neural network. (In step 2A, prong 2, this recites generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h))). In step 2B, generally linking the use of the judicial exception to a particular technological environment is not indicative of significantly more.)
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-13 are rejected under 35 U.S.C. 103 as being unpatentable over Han et al. “Few-shot Anomaly Detection and Classification Through Reinforced Data Selection” 12/01/2022 in view of Hiltunen et al. Pub. Num.: US 20250103035 A1 and Maurice Patent Num.: US 11726862 B1.
Regarding claim 1 Han teaches A method for configuring a data processing chain comprising a prediction stage implementing a current artificial intelligence model, previously trained based on a training dataset, 1… the current artificial intelligence model being associated with a current accuracy score, representative of a match between, on one hand, a first predicted state of the monitored environment, determined by the current artificial intelligence model from test data dependent on the input data, and, on another hand, an actual state of the monitored environment for said test data, the method being computer-implemented and comprising: (Han, page 965 – 966, Section II – Rewards and Section III – A, teaches the use of an F! score that is used to evaluate the performance of the anomaly detection algorithms against previous or future algorithms by comparison)
an implementation of a reinforcement algorithm comprising selecting a dataset from a set of datasets stored in a memory, according to a reward associated with each data from said set of datasets; (Han, page 965 – 966, Section II and Section III, teaches a reinforcement learning algorithm that selects the data, based on a reward associated with the data, to be used in the training of an artificial intelligence model that will predict anomalies.)
training an artificial intelligence model based on dataset that is selected to obtain an experimental artificial intelligence model; (Han, page 965 – 966, Section II and Section III, teaches the training of an artificial intelligence model based on the data selected by the reinforcement learning model.)
calculating an experimental accuracy score of the experimental artificial intelligence model, representative of a match between, on one hand, a second predicted state of the monitored environment, determined by the experimental artificial intelligence model from the test data, and, on another hand, the actual state of the monitored environment for the test data; 2… (Han, page 965 – 966, Section II and Section III, teaches the calculation of an F1 score that is used to evaluate the accuracy of the artificial intelligence model and compare to the previous artificial intelligence models F1 score to see if the model is an improvement over the previous model.)
and updating the reward associated with the dataset that is selected. ((Han, page 965 – 966, Section II and Section III, teaches the updating of the rewards based on the selected data and if the model is better than the previous model)
Han does not teach …1 to predict an anomaly in a monitored environment equipped with at least one sensor, from input data received from each sensor of said at least one sensor, … However, Hiltunen teaches this limitation in analogous art (Hiltunen, paragraph 0057, teaches a neural network for detecting anomalies in sensor time series data, the sensors being sensors that are monitoring an environment meaning that the neural network is detecting anomalies in a monitored environment.)
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine Hiltunen’s teaching of detecting anomalies in monitored environments using sensors with Han’s teaching of using reinforcement learning to choose datasets for training an anomaly detection model. The motivation to do so would be to apply the anomaly detection algorithm to real world scenarios and wider ranges of data that is gathered in actual applications.
Further the combination of Han and Hiltunen does not teach 2… and based on a result of a comparison between the current accuracy score and the experimental accuracy score, either replacing or not replacing the current artificial intelligence model with the experimental artificial intelligence model in the prediction stage; … However, Maurice teaches this limitation in analogous art (Maurice, column 19, line 3- 24, teaches based on the evaluation of a model being used currently and a newly trained models accuracies, deciding whether to keep using the old model or to use the newly trained model.)
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine Maurice teaching of replacing an old model with a new model based on the comparison of both models’ performance with the combination of Han and Hiltunen teaching of using reinforcement learning to choose datasets for training an anomaly detection model. The motivation to do so would be to continue to improve the accuracy of the model that is in use in the system by constantly training a new model on new data in order to be able to predict the new data.
Claims 12 and 13 are rejected on the same grounds as Claim 1.
Regarding claim 2 The combination of Han, Hiltunen, and Maurice teaches The method according to claim 1, wherein, if the experimental accuracy score is higher than the current accuracy score, (Han, page 965 – 966, Section II and Section III, teaches the calculation of an F1 score that is used to evaluate the accuracy of the artificial intelligence model and compare to the previous artificial intelligence models F1 score to see if the model is an improvement over the previous model.)the current artificial intelligence model is replaced by the experimental artificial intelligence mode; (Maurice, column 19, line 3- 24, teaches based on the evaluation of a model being used currently and a newly trained models accuracies, deciding whether to keep using the old model or to use the newly trained model.) and the updating of the reward associated with the dataset that is selected is an increase of said reward. ((Han, page 965 – 966, Section II and Section III, teaches the updating of the reward based on the selected data and if the model is better then the previous model)
Regarding claim 3 The combination of Han, Hiltunen, and Maurice teaches The method according to claim 1, wherein, if the experimental accuracy score is lower than the current accuracy score the current artificial intelligence model is not replaced by the experimental artificial intelligence model (Maurice, column 19, line 3- 24, teaches based on the evaluation of a model being used currently and a newly trained models accuracies, deciding whether to keep using the old model or to use the newly trained model.) the updating of the reward associated with the dataset that is selected is a decrease of said reward. ((Han, page 965 – 966, Section II and Section III, teaches the updating of the rewards based on the selected data and if the model is better than the previous model if the model is worse than the previous model based on the F1 score the reward is decreased for that data.)
Regarding claim 4 The combination of Han, Hiltunen, and Maurice teaches The method according to claim 1, wherein the dataset that is selected is a dataset associated with a maximum reward. ((Han, page 965 – 966, Section II, teaches the selecting of datasets with the highest reward at the time (i.e. maximum reward).)
Regarding claim 5 The combination of Han, Hiltunen, and Maurice teaches The method according to claim 1 further comprising comparing a current predicted state of the monitored environment, predicted by the current artificial intelligence model from the input data with the actual state of the monitored environment; (Hiltunen, paragraph 0057, teaches a neural network for detecting anomalies in sensor time series data, the sensors being sensors that are monitoring an environment meaning that the neural network is detecting anomalies in a monitored environment.) and updating the test data based on a comparison result. ((Han, page 965 – 966, Section II and Section III, teaches the updating of the data based on the F1 score compared to the F1 score of the previous model to try and increase the performance of the model.)
Regarding claim 6 The combination of Han, Hiltunen, and Maurice teaches the method according to claim 5, wherein the updating the test data comprises adding to the test data some or all of the input data from which the current predicted state was determined, together with a label representative of whether a prediction is correct or incorrect. ((Han, page 965 – 966, Section II and Section III, teaches adding new data to the old prediction data and the old training data in order to create new datasets to train a new anomaly prediction model on.)
Regarding claim 7 The combination of Han, Hiltunen, and Maurice teaches The method according to claim 1, further comprising comparing a current predicted state of the monitored environment, (Hiltunen, paragraph 0057, teaches a neural network for detecting anomalies in sensor time series data, the sensors being sensors that are monitoring an environment meaning that the neural network is detecting anomalies in a monitored environment.)predicted by the current artificial intelligence model from the input data with the actual state of the monitored environment in an event of a discrepancy between the current predicted state and the actual state of the monitored environment, (Maurice, column 19, line 3- 24, teaches based on the evaluation of a model being used currently and a newly trained models accuracies, deciding whether to keep using the old model or to use the newly trained model.)creating an additional dataset by modifying the dataset based on which the current artificial intelligence model was trained, from the current predicted state and the actual state of the monitored environment; and storing the additional dataset that is created in said memory. ((Han, page 965 – 966, Section II and Section III, teaches adding new data to the old prediction data and the old training data in order to create new datasets to train a new anomaly prediction model on.)
Regarding claim 8 The combination of Han, Hiltunen, and Maurice teaches The method according to claim 7, wherein the additional dataset that is created comprises data of the dataset based on which the current artificial intelligence model has been trained, to which has been added the input data from which the current predicted state has been determined, associated with a label representative of whether a prediction is correct or incorrect. ((Han, page 965 – 966, Section II and Section III, teaches adding new data to the old prediction data and the old training data in order to create new datasets to train a new anomaly prediction model on the data that is added includes labels.)
Regarding claim 9 The combination of Han, Hiltunen, and Maurice teaches The method according to claim 7, wherein, on creation, the additional dataset that is created is associated with a reward having a value greater than the value of the reward associated with each other dataset of the set of datasets stored in the memory. ((Han, page 965 – 966, Section II and Section III, teaches the updating of the rewards based on the selected data and if the model is better than the previous model if the model is worse than the previous model based on the F1 score the reward is decreased for that data. If the model with the new data performs better than the previous models it will have a reward that is higher than all the other data)
Regarding claim 10 The combination of Han, Hiltunen, and Maurice teaches the method according to claim 1, further comprising selecting at least some of the input data, preferably by implementing a feature selection process to generate at least one additional dataset; and storing the at least one additional dataset that is generated in the memory. ((Han, page 965 – 966, Section II and Section III, teaches the reinforcement learning algorithm being able to determine features of the data in order to determine which samples to use in the new dataset and storing the newly created dataset in memory.)
Regarding claim 11 The combination of Han, Hiltunen, and Maurice teaches The method according to claim 1 wherein the reinforcement algorithm is a Q-learning algorithm, a Deep Q-learning algorithm or a neural network. ((Han, page 965 – 966, Section II and Section III, teaches the reinforcement model using neural networks.)
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to THOMAS B LANE whose telephone number is (571)272-1872. The examiner can normally be reached M-Th: 7:20am-5:20pm; F: Out of Office.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, MARIELA REYES can be reached at (571) 270-1006. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/THOMAS BERNARD LANE/Examiner, Art Unit 2142
/HAIMEI JIANG/Primary Examiner, Art Unit 2142
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