DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claims 1-19 and 21 are presented for examination.
Response to Amendment
The prior rejections under 35 U.S.C. 102 have been obviated by the amendments, thus these rejections are withdrawn.
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 1-19 and 21 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.
The term “relevant” in claims 1 and 21 is a relative term which renders the claim indefinite. The term “relevant” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. The limitation of “identify relevant external sources” has been rendered indefinite by the use of the term “relevant,” as it is unclear as to what would constitute a “relevant” source. Claims 2-19 are rejected due to dependency on claim 1.
The term “broader” in claim 21 is a relative term which renders the claim indefinite. The term “broader” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. The limitation of “identifying, by the processor, matching broader data sets” has been rendered indefinite by the use of the term “broader,” as it is unclear as to what the data sets are “broader” than, and what a “broader data set” is defined as.
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 1-19 and 21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The analysis of the claims will follow the 2019 Revised Patent Subject Matter Eligibility Guidance (“2019 PEG”).
Claim 1
Step 1: The claim recites a system comprising a processor, and therefore is directed to the statutory category of machines.
Step 2A Prong 1: The claim recites, inter alia:
“…process an incoming data set to generate a data output, an intrinsic characteristic output, an extrinsic characteristic output and a pre-model drift dataset”; This limitation encompasses mentally processing an incoming data set to generate a data output, an intrinsic characteristic output, an extrinsic characteristic output, and a pre-model drift dataset.
“…determine intrinsic characteristics of the incoming data set based on one or more algorithmic processes that identify data items of the incoming data set as objects, a type of file associated with a data item, and contents of the file”; This limitation encompasses mentally determining intrinsic characteristics of the incoming data set by identifying the data items as objects, identifying a type of file associated with each data item, and identifying the contents of the file.
“…derive extrinsic characteristics of the incoming data set by processing the incoming data set and the intrinsic characteristics to obtain extrinsic data from an external source based on one or more algorithmic processes that identify relevant external sources based on the incoming data set and the intrinsic characteristics”; This limitation encompasses mentally deriving extrinsic characteristics of the incoming data set by mentally processing the incoming data set and the intrinsic characteristic to identify relevant external sources, then mentally extracting extrinsic characteristics of the incoming data set from the sources.
“…generate an anomaly detection as a function of the incoming data set, the pre-model drift dataset, the intrinsic characteristic output, the extrinsic characteristic output and the data output, wherein the pre-model drift dataset is generated by comparing variations in tagged data of the incoming data set with variations in data used to create the AI model”; This limitation encompasses mentally generating an anomaly detection using the various data sets and outputs, and mentally generating a pre-model drift dataset by comparing variations in the tagged data with variations in data that was used to create an AI model.
“…generate post-AI model drift data by performing a post-model drift assessment that compares results of processing of the incoming data set by the AI model with predicted results, and identifying drift-causing characteristics of the incoming data set by analyzing tagged data and comparing the tagged data with tagged data of other data sets that experienced drift and tagged data of other data sets that did not experience drift”; This limitation encompasses mentally generating post-AI model drift data by mentally comparing results of processing the incoming data with predicted results, and mentally identifying drift-causing characteristics by comparing the tagged data with other tagged data that experienced drift and other tagged data that did not experience drift.
“…generate Al model anomaly correction data as a function of the pre-model drift dataset and the post-AI model drift data”; This limitation encompasses mentally generating AI anomaly correction data using the pre-model drift dataset and the post-AI model drift data.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites "A system for processing data," "an artificial intelligence (AI) model operating on a processor," "an AI model anomaly detection system operating on the processor," "an AI model anomaly analysis system operating on the processor," and "an AI model anomaly mitigation system operating on the processor," however, these limitations amount to mere instructions to apply an exception using a generic computer (MPEP 2106.05(f)). The claim further recites "receive the incoming data set, the intrinsic characteristic output, the extrinsic characteristic output, and the data output," "receive the anomaly detection and the incoming data set," and "receive the AI model anomaly data," however these limitations amount to the insignificant extra-solution activity of mere data gathering (MPEP 2106.05(g)).
Step 2B: The claim does not contain significantly more than the judicial exception. The receiving data limitations, in addition to reciting insignificant extra-solution activity, are also directed to the well-understood, routine, and conventional activity of receiving or transmitting data over a network (MPEP 2106.05(d)(II)(i) OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network)). Otherwise, the analysis at this step mirrors that of Step 2A Prong 2. As an ordered whole, the claim is directed to a mentally performable process of determining intrinsic characteristics of an incoming data set, deriving extrinsic characteristics of the incoming data set, generating a pre-model drift dataset, generating an anomaly detection, generating post-AI model drift data, and generating AI model anomaly correction data. Nothing in the claim provides significantly more than this. As such, the claim is not patent eligible.
Claim 2
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites, inter alia:
"…generate a request for characteristic data collection from an external data source on demand in response to the incoming data set"; This limitation encompasses mentally generating a request for characteristic data collection.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites "the AI model anomaly detection system comprises an on-demand data collection
system operating on the processor,” however, this limitation amounts to mere instructions to apply an exception using a generic computer (MPEP 2106.05(f)).
Step 2B: The claim does not contain significantly more than the judicial exception. The analysis at this step mirrors that of Step 2A Prong 2.
Claim 3
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites, inter alia:
"…generate perception classification data for the characteristic data output in
response to the incoming data set and processed sensor data"; This limitation encompasses mentally generating perception classification data.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites "the AI model anomaly detection system comprises a perception classification system operating on the processor,” however, this limitation amounts to mere instructions to apply an exception using a generic computer (MPEP 2106.05(f)).
Step 2B: The claim does not contain significantly more than the judicial exception. The analysis at this step mirrors that of Step 2A Prong 2.
Claim 4
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites, inter alia:
"…generate inference data for the characteristic data output to identify when data
presented to an inference model has changed from a training set and is changing am inference
probability of object detection"; This limitation encompasses mentally generating inference data.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites "the AI model anomaly detection system comprises an inference operations system operating on the processor," however, this limitation amounts to mere instructions to apply the judicial exception using a generic computer (MPEP 2106.05(f)). The claim further recites "receive perception classification data," however this limitation amounts to the insignificant extra-solution activity of mere data gathering (MPEP 2106.05(g)).
Step 2B: The claim does not contain significantly more than the judicial exception. The receiving data limitation, in addition to reciting insignificant extra-solution activity, is also directed to the well-understood, routine, and conventional activity of receiving or transmitting data over a network (MPEP 2106.05(d)(II)(i) OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network)). Otherwise, the analysis at this step mirrors that of Step 2A Prong 2.
Claim 5
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites, inter alia:
“…generate stationary monitoring output data for the characteristic data output”; This limitation encompasses mentally generating inference data.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites "the AI model anomaly detection system comprises a stationary monitoring system operating on the processor," however, this limitation amounts to mere instructions to apply the judicial exception using a generic computer (MPEP 2106.05(f)). The claim further recites "receive inference data," however this limitation amounts to the insignificant extra-solution activity of mere data gathering (MPEP 2106.05(g)).
Step 2B: The claim does not contain significantly more than the judicial exception. The receiving data limitation, in addition to reciting insignificant extra-solution activity, is also directed to the well-understood, routine, and conventional activity of receiving or transmitting data over a network (MPEP 2106.05(d)(II)(i) OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network)). Otherwise, the analysis at this step mirrors that of Step 2A Prong 2.
Claim 6
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites, inter alia:
“…generate causal event detection data for the characteristic data output”; This limitation encompasses mentally generating casual event detection data.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites "the AI model anomaly analysis system comprises a casual event detection system operating on the processor," however, this limitation amounts to mere instructions to apply the judicial exception using a generic computer (MPEP 2106.05(f)). The claim further recites "receive stationary monitoring data," however this limitation amounts to the insignificant extra-solution activity of mere data gathering (MPEP 2106.05(g)).
Step 2B: The claim does not contain significantly more than the judicial exception. The receiving data limitation, in addition to reciting insignificant extra-solution activity, is also directed to the well-understood, routine, and conventional activity of receiving or transmitting data over a network (MPEP 2106.05(d)(II)(i) OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network)). Otherwise, the analysis at this step mirrors that of Step 2A Prong 2.
Claim 7
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites, inter alia:
“…generate detailed logs anomaly tracking data for intrinsic characteristic data and extrinsic characteristic data”; This limitation encompasses mentally generating detailed logs anomaly tracking data.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites "the AI model anomaly analysis system comprises a detailed logs anomaly tracking system operating on the processor," however, this limitation amounts to mere instructions to apply the judicial exception using a generic computer (MPEP 2106.05(f)). The claim further recites "receive casual event detection data," however this limitation amounts to the insignificant extra-solution activity of mere data gathering (MPEP 2106.05(g)).
Step 2B: The claim does not contain significantly more than the judicial exception. The receiving data limitation, in addition to reciting insignificant extra-solution activity, is also directed to the well-understood, routine, and conventional activity of receiving or transmitting data over a network (MPEP 2106.05(d)(II)(i) OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network)). Otherwise, the analysis at this step mirrors that of Step 2A Prong 2.
Claim 8
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites, inter alia:
“…generate drift detection data”; This limitation encompasses mentally generating drift detection data.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites "the AI model anomaly analysis system comprises a drift detection engine operating on the processor," however, this limitation amounts to mere instructions to apply the judicial exception using a generic computer (MPEP 2106.05(f)). The claim further recites "receive casual event detection data," however this limitation amounts to the insignificant extra-solution activity of mere data gathering (MPEP 2106.05(g)).
Step 2B: The claim does not contain significantly more than the judicial exception. The receiving data limitation, in addition to reciting insignificant extra-solution activity, is also directed to the well-understood, routine, and conventional activity of receiving or transmitting data over a network (MPEP 2106.05(d)(II)(i) OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network)). Otherwise, the analysis at this step mirrors that of Step 2A Prong 2.
Claim 9
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites, inter alia:
“…generate analyst review data”; This limitation encompasses mentally generating analyst review data.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites "the AI model anomaly analysis system comprises an analyst review system operating on the processor," however, this limitation amounts to mere instructions to apply the judicial exception using a generic computer (MPEP 2106.05(f)). The claim further recites "receive drift decision block output data," however this limitation amounts to the insignificant extra-solution activity of mere data gathering (MPEP 2106.05(g)).
Step 2B: The claim does not contain significantly more than the judicial exception. The receiving data limitation, in addition to reciting insignificant extra-solution activity, is also directed to the well-understood, routine, and conventional activity of receiving or transmitting data over a network (MPEP 2106.05(d)(II)(i) OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network)). Otherwise, the analysis at this step mirrors that of Step 2A Prong 2.
Claim 10
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites, inter alia:
“…generate a drift decision output”; This limitation encompasses mentally generating a drift decision output.
“…generate analyst review data”; This limitation encompasses mentally generating analyst review data.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites "the AI model anomaly analysis system comprises a drift decision block operating on the processor” and “an analyst review system operating on the processor," however, these limitations amount to mere instructions to apply the judicial exception using a generic computer (MPEP 2106.05(f)). The claim further recites "receive drift detection data," and “receive the drift decision output data,” however these limitations amount to the insignificant extra-solution activity of mere data gathering (MPEP 2106.05(g)).
Step 2B: The claim does not contain significantly more than the judicial exception. The receiving data limitations, in addition to reciting insignificant extra-solution activity, are also directed to the well-understood, routine, and conventional activity of receiving or transmitting data over a network (MPEP 2106.05(d)(II)(i) OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network)). Otherwise, the analysis at this step mirrors that of Step 2A Prong 2.
Claim 11
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites, inter alia:
“…generate drift causal analysis output data”; This limitation encompasses mentally generating drift casual analysis output data.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites "the AI model anomaly analysis system comprises a drift casual analysis decision block operating on the processor," however, this limitation amounts to mere instructions to apply the judicial exception using a generic computer (MPEP 2106.05(f)). The claim further recites "receive drift decision output data," however this limitation amounts to the insignificant extra-solution activity of mere data gathering (MPEP 2106.05(g)).
Step 2B: The claim does not contain significantly more than the judicial exception. The receiving data limitations, in addition to reciting insignificant extra-solution activity, are also directed to the well-understood, routine, and conventional activity of receiving or transmitting data over a network (MPEP 2106.05(d)(II)(i) OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network)). Otherwise, the analysis at this step mirrors that of Step 2A Prong 2.
Claim 12
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites, inter alia:
“…generate drift causal analysis output data”; This limitation encompasses mentally generating drift causal analysis output data.
“…generate drift data analysis output data; This limitation encompasses mentally generating drift data analysis output data.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites "the AI model anomaly analysis system comprises a drift causal analysis decision block operating on the processor” and “a drift data analysis system operating on the processor," however, these limitations amount to mere instructions to apply the judicial exception using a generic computer (MPEP 2106.05(f)). The claim further recites "receive drift decision output data," and “receive the drift causal analysis output data,” however these limitations amount to the insignificant extra-solution activity of mere data gathering (MPEP 2106.05(g)).
Step 2B: The claim does not contain significantly more than the judicial exception. The receiving data limitations, in addition to reciting insignificant extra-solution activity, are also directed to the well-understood, routine, and conventional activity of receiving or transmitting data over a network (MPEP 2106.05(d)(II)(i) OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network)). Otherwise, the analysis at this step mirrors that of Step 2A Prong 2.
Claim 13
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites, inter alia:
“…generate intrinsic drift label analysis output data and extrinsic drift label analysis data”; This limitation encompasses mentally generating intrinsic drift label analysis output data and extrinsic drift label analysis data.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites "the AI model anomaly analysis system comprises a drift label analysis system operating on the processor," however, this limitation amounts to mere instructions to apply the judicial exception using a generic computer (MPEP 2106.05(f)). The claim further recites "receive drift causal analysis output data," however this limitation amounts to the insignificant extra-solution activity of mere data gathering (MPEP 2106.05(g)).
Step 2B: The claim does not contain significantly more than the judicial exception. The receiving data limitations, in addition to reciting insignificant extra-solution activity, are also directed to the well-understood, routine, and conventional activity of receiving or transmitting data over a network (MPEP 2106.05(d)(II)(i) OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network)). Otherwise, the analysis at this step mirrors that of Step 2A Prong 2.
Claim 14
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites, inter alia:
“…generate drift detection engine data”; This limitation encompasses mentally generating drift detection engine data.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites "the AI model anomaly mitigation system comprises an anomaly repository operating on the processor," however, this limitation amounts to mere instructions to apply the judicial exception using a generic computer (MPEP 2106.05(f)). The claim further recites "receive on-demand data collection," however this limitation amounts to the insignificant extra-solution activity of mere data gathering (MPEP 2106.05(g)).
Step 2B: The claim does not contain significantly more than the judicial exception. The receiving data limitations, in addition to reciting insignificant extra-solution activity, are also directed to the well-understood, routine, and conventional activity of receiving or transmitting data over a network (MPEP 2106.05(d)(II)(i) OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network)). Otherwise, the analysis at this step mirrors that of Step 2A Prong 2.
Claim 15
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites, inter alia:
“…generate inference operations output data that includes a new AI model”; This limitation encompasses mentally generating inference operations output data.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites "the AI model anomaly mitigation system comprises a model deployment system operating on the processor," however, this limitation amounts to mere instructions to apply the judicial exception using a generic computer (MPEP 2106.05(f)).
Step 2B: The claim does not contain significantly more than the judicial exception. The analysis at this step mirrors that of Step 2A Prong 2.
Claim 16
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites the same judicial exception as claim 1.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites "the AI model anomaly mitigation system comprises a model repository operating on the processor and configured to store a plurality of AI models," however this limitation amounts to the insignificant extra-solution activity of mere data gathering (MPEP 2106.05(g)).
Step 2B: The claim does not contain significantly more than the judicial exception. The “store a plurality of AI models” limitation, in addition to reciting insignificant extra-solution activity, is also directed to the well-understood, routine, and conventional activity of storing and retrieving information in memory (MPEP 2106.05(d)(II)(iv) Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015)). Otherwise, the analysis at this step mirrors that of Step 2A Prong 2.
Claim 17
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites, inter alia:
“…generate detailed log data”; This limitation encompasses mentally generating detailed log data.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites "the AI model anomaly mitigation system comprises a model validation system operating on the processor," however, this limitation amounts to mere instructions to apply the judicial exception using a generic computer (MPEP 2106.05(f)). The claim further recites "receive AI model data," however this limitation amounts to the insignificant extra-solution activity of mere data gathering (MPEP 2106.05(g)).
Step 2B: The claim does not contain significantly more than the judicial exception. The receiving data limitations, in addition to reciting insignificant extra-solution activity, are also directed to the well-understood, routine, and conventional activity of receiving or transmitting data over a network (MPEP 2106.05(d)(II)(i) OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network)). Otherwise, the analysis at this step mirrors that of Step 2A Prong 2.
Claim 18
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites, inter alia:
“…generate AI model training data”; This limitation encompasses mentally generating AI model training data.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites "the AI model anomaly mitigation system comprises a model training system operating on the processor," however, this limitation amounts to mere instructions to apply the judicial exception using a generic computer (MPEP 2106.05(f)). The claim further recites "receive AI model data," however this limitation amounts to the insignificant extra-solution activity of mere data gathering (MPEP 2106.05(g)).
Step 2B: The claim does not contain significantly more than the judicial exception. The receiving data limitations, in addition to reciting insignificant extra-solution activity, are also directed to the well-understood, routine, and conventional activity of receiving or transmitting data over a network (MPEP 2106.05(d)(II)(i) OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network)). Otherwise, the analysis at this step mirrors that of Step 2A Prong 2.
Claim 19
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites, inter alia:
“…generate mitigation strategy selection data”; This limitation encompasses mentally generating mitigation strategy selection data.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites "the AI model anomaly mitigation system comprises a mitigation strategy selection system operating on the processor," however, this limitation amounts to mere instructions to apply the judicial exception using a generic computer (MPEP 2106.05(f)). The claim further recites "receive drift data and label analysis data," however this limitation amounts to the insignificant extra-solution activity of mere data gathering (MPEP 2106.05(g)).
Step 2B: The claim does not contain significantly more than the judicial exception. The receiving data limitations, in addition to reciting insignificant extra-solution activity, are also directed to the well-understood, routine, and conventional activity of receiving or transmitting data over a network (MPEP 2106.05(d)(II)(i) OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network)). Otherwise, the analysis at this step mirrors that of Step 2A Prong 2.
Claim 21
Step 1: The claim recites a system comprising a processor, and therefore is directed to the statutory category of machines.
Step 2A Prong 1: The claim recites, inter alia:
“tagging…the data items in the incoming data set for drift analysis based on one or more algorithmic processes that identify tags from each data item as an object, a type of file associated with each data item, and contents of the file associated with each data item”; This limitation encompasses mentally tagging the data items by mentally identifying tags from each data item as an object, a type of file associated with each data item, and contents of the file associated with each data item.
“determining… intrinsic characteristics of the incoming data set based on one or more algorithmic processes that identify the data items of the incoming data set as objects, a type of file associated with each data item, and the contents of the file”; This limitation encompasses mentally determining intrinsic characteristics of the incoming data set by identifying the data items as objects, identifying a type of file associated with each data item, and identifying the contents of the file.
“deriving… extrinsic characteristics of the incoming data set by processing the incoming data set and the intrinsic characteristics to obtain extrinsic data from an external source based on one or more algorithmic processes that identify relevant external sources based on the incoming data set and the intrinsic characteristics”; This limitation encompasses mentally deriving extrinsic characteristics of the incoming data set by mentally processing the incoming data set and the intrinsic characteristic to identify relevant external sources, then mentally extracting extrinsic characteristics of the incoming data set from the sources.
“performing… a pre-model drift assessment by comparing variations in the tagged data of the incoming data set with variations in data that was used to create an artificial intelligence (AI) model to generate a pre-model drift dataset”; This limitation encompasses mentally comparing variations in the tagged data with variations in data that was used to create an AI model and using this comparison to mentally generate a pre-model drift dataset.
“processing… the incoming data set…to generate a data output”; This limitation encompasses mentally processing the incoming data set to generate a data output.
“performing… a post-model drift assessment by comparing results of processing of the incoming data set by the AI model with predicted results to generate post-AI model drift data”; This limitation encompasses mentally comparing results of processing the incoming data with predicted results to mentally generate post-AI model drift data.
“identifying…drift-causing characteristics of the incoming data set by analyzing the tagged data and comparing the tagged data with tagged data of other data sets that experienced drift and tagged data of other data sets that did not experience drift”; This limitation encompasses mentally analyzing the tagged data and comparing the tagged data with tagged data of other data sets that experienced drift and tagged data of other data sets that did not experience drift to mentally identify drift-causing characteristics.
“identifying… matching broader data sets based on the drift-causing characteristics for training and validation of the AI model”; This limitation encompasses mentally identifying matching broader data sets based on the drift-causing characteristics.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites “receiving, by the processor, an incoming data set comprising a plurality of data items,” however this limitation amounts to the insignificant extra-solution activity of mere data gathering (MPEP 2106.05(g)). The claim further recites “A system for processing data, comprising: a processor configured to execute a plurality of instructions stored in a memory coupled to the processor,” that each of the above-mentioned judicial exceptions are performed “by the processor,” and that the “processing, by the processor, the incoming data set” limitation is performed “using the AI model,” however, these limitations amount to mere instructions to apply an exception using a generic computer programmed with a generic class of computer algorithms (MPEP 2106.05(f)). The claim further recites “retraining, by the processor, the AI model using the matching broader data sets to correct the drift,” however, this limitation amounts to merely generally linking the use of the judicial exception to the technological environment of model training and the field of use of AI model drift correction (MPEP 2106.05(h)).
Step 2B: The claim does not contain significantly more than the judicial exception. The receiving an incoming data set limitation, in addition to reciting insignificant extra-solution activity, is also directed to the well-understood, routine, and conventional activity of receiving or transmitting data over a network (MPEP 2106.05(d)(II)(i) OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network)). Otherwise, the analysis at this step mirrors that of Step 2A Prong 2. As an ordered whole, the claim is directed to a mentally performable process of determining intrinsic characteristics of an incoming data set, deriving extrinsic characteristics of the incoming data set, performing a pre-model drift assessment, performing a post-model drift assessment, identifying drift-causing characteristics of the incoming data set, and identifying matching broader data sets based on the drift-causing characteristics. Nothing in the claim provides significantly more than this. As such, the claim is not patent eligible.
Response to Arguments
Applicant's arguments filed 5/11/2026 regarding the rejections under 35 U.S.C. 101 have been fully considered but they are not persuasive.
Applicant argues on page 7: "The added limitations include the processor performing specific algorithmic operations, such as determining intrinsic characteristics by analyzing data items as objects, by file type, and by file contents, and then deriving extrinsic characteristics by querying external sources based on those intrinsic characteristics. These are not steps a person could realistically perform mentally or with pen and paper, particularly the automated querying of external data sources and iterative comparison across multiple tagged data sets." Regarding the "determining intrinsic characteristics" step, Examiner respectfully disagrees with Applicant's assertion that this could not be realistically performed mentally. A human can mentally determine intrinsic characteristics of a dataset by observing data items of the dataset and identifying data items as objects, identifying file types of the data items, and identifying file contents of the data items. Regarding the "deriving extrinsic characteristics” step, the limitation as currently drafted encompasses mentally deriving extrinsic characteristics of the incoming data set by mentally processing the incoming data set and the intrinsic characteristics to identify relevant external sources, then mentally extracting extrinsic characteristics of the incoming data set from the identified sources, as analyzed in the 101 rejection above. The claims do not recite “automated querying of external data sources.” Regarding “iterative comparison across multiple tagged data sets,” Examiner submits that a human could mentally perform iterative comparison across multiple tagged data sets, given a reasonable number of data sets/iterations.
Applicant further argues on page 7 that the claims are tied to a technical improvement: “the autonomic lifecycle management of an AI model experiencing drift, including automated identification of drift-causing characteristics by comparing across drift/ non-drift data sets, and automated identification of matching broader data sets for retraining.” Examiner submits that this argument amounts to an assertion that the abstract ideas of identifying drift-causing characteristics by comparing across drift/non-drift data sets and identifying matching broader data sets provide the purported improvement, and the judicial exception alone cannot provide the improvement (see MPEP 2106.05(a)). The fact that these limitations are “automated” amounts to mere instructions to apply an exception on a generic computer and thus does not integrate the judicial exception into a practical application (see MPEP 2106.05(f)). Applicant further argues that “[t]he pre-model drift assessment via tagged data comparison, post-model drift assessment comparing actual vs predicted results and automated identification of retraining data represents a specific, non-conventional ordered combination of steps that improves the functioning of AI model systems,” however, these steps are all part of the judicial exception and thus cannot provide the improvement.
Applicant’s arguments filed 5/11/2026 regarding the rejections under 35 U.S.C. 102 are moot due to the withdrawal of that ground of rejection.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/G.A.D./Examiner, Art Unit 2125
/KAMRAN AFSHAR/Supervisory Patent Examiner, Art Unit 2125
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