Detailed Action
This Office Action is in response to the remarks entered on 05/26/2026. Amended claims 1, 19 and 20 have been entered. Claims 1-16 and 19-23 are presently pending.
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 1-16 and 19-23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding claim 1,
Step 1: The claim recites a method. Therefore, it falls into the statutory category of processes.
2A Prong 1:
based on the corresponding ranking scores, determining a relative contribution of each of the data records in the first group to the differentiation between the first group and a second group (a mental process, as it merely recites determining how each data contributes to the data records based on the ranking scores, which can be done in human mind, or with the aid of pen and paper)
determining that an anomaly in data records of at least one of the first group and the second group has occurred including by: determining a threshold based at least on a fixed number of bins that are updated in a linear pass to reduce resource consumption compared with multiple passes (mathematical concept. The specification paragraph 0066-0067 and 0079 indicates that the thresholds are calculated using mathematical calculations including a fixed percentile) and redistributing positions of the bins to restore an invariant such that an average count per bin is the same for all bins, and determining the anomaly in response to a data record exceeding the determined threshold (a mental process of evaluation – the limitation recites a process of equally redistributing data of bins [0068 and Fig. 7] and determining whether the anomaly exist based on the determined threshold, which can be done with the aid of pen and paper)
generating an explanation report associated with machine learning model monitoring and an explanation of causes of concept drift in response to the determined anomaly, wherein the explanation report includes a validation curve indicating an effect of at least one data record in the first group on lowering a signal associated with the first group and the second group wherein a data record in the first group exceeding a score threshold is removed from the first group; and (mental process of evaluation – writing an explanation report about effect of data records can be done with the aid of pen and paper)
denying a digital transaction in response to determining the anomaly. (mental process of judgment – determining whether the transaction is anomalous and denying the transaction can be done with the aid of pen and paper)
2A Prong 2:
Machine learning models to learn how to differentiate (mere instructions to apply an exception using a computer to perform an abstract idea MPEP 2106.05(f)).
obtaining model scores from a first machine learning model (amounts to mere data gathering which is an insignificant extra-solution activity MPEP 2106.05(g)).
training a second machine learning model (amounts to mere instructions to apply an exception on a generic computer MPEP 2106.05(f)).
the first group of is included in a target time window and the second group is included in a reference time window (a field of use and technological environment MPEP 2106.05(h), as the limitation is nothing more than explaining the data)
the target time window and the reference time window are at least one of: sequential or homologous (a field of use and technological environment MPEP 2106.05(h), as the limitation is nothing more than explaining the data)
applying the second machine learning model to each data record in a first group of data records
including by storing a fixed size object in computer memory (insignificant extra-solution activity MPEP 2106.05(g) of gathering statistics)
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, combination of generic computer functions that are restricted to field of use are implemented to perform the disclosed abstract idea above
2B:
Machine learning models to learn how to differentiate (are mere instructions to apply an exception using a computer MPEP 2106.05(f)).
obtaining model scores from a first machine learning model (was indicated as a mere data gathering MPEP 2106.05(g) in Step 2A Prong 2, thus re-evaluated as a well understood, routine, and conventional activity MPEP 2106.05(d)(iv) of presenting offers)
As shown in the 2A Prong 2, the limitation of training a second machine learning model (amounts to mere instructions to apply an exception on a generic computer MPEP 2106.05(f)).
the first group of is included in a target time window and the second group is included in a reference time window (is a field of use and technological environment MPEP 2106.05(h)).
the target time window and the reference time window are at least one of: sequential or homologous (is a field of use and technological environment MPEP 2106.05(h), as the limitation is nothing more than explaining the data)
applying the second machine learning model to each data record in a first group of data records
including by storing a fixed size object in computer memory (well understood routine and conventional MPEP 2106.05(d) of iv. Storing and retrieving information in memory)
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 functions and usage of elements that are restricted to field of use that are implemented to perform the disclosed abstract idea above.
Regarding claim 2,
Step 1: Processes, as above.
2A Prong 1: Incorporates the rejection of claim 1.
2A Prong 2: wherein at least a portion of features that are available for the data records of both groups are used (is merely a field of use and technological environment MPEP 2106.05(h)).
2B: wherein at least a portion of features that are available for the data records of both groups are used (is merely a field of use and technological environment MPEP 2106.05(h)).
Regarding claim 3,
Step 1: Processes, as above.
2A Prong 1: Incorporates the rejection of claim 1.
2A Prong 2: the reference time window includes data records occurring in a reference time period prior to a time period associated with the target time window (is a field of use and technological environment MPEP 2106.05(h)).
2B: the reference time window includes data records occurring in a reference time period prior to a time period associated with the target time window (is a field of use and technological environment MPEP 2106.05(h)).
Regarding claim 4,
Step 1: Processes, as above.
2A Prong 1: further comprising removing index-correlated features prior to training the second machine learning model (a mental process of evaluation, as it recites filtering index-correlated features before training, which can be done with the aid of pen and paper)
2A Prong 2: This judicial exception is not integrated into a practical application.
2B: The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Regarding claim 5,
Step 1: Processes, as above.
2A Prong 1: further comprising removing time-correlated features prior to training the second machine learning model (a mental process of evaluation, as it recites filtering time-correlated features before training, which can be done with the aid of pen and paper)
2A Prong 2: This judicial exception is not integrated into a practical application.
2B: The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Regarding claim 6,
Step 1: Processes, as above.
2A Prong 1: wherein removing time-correlated features prior to training the second machine learning model (a mental process of evaluation, as it merely recites filtering time-correlated features which can be done with the aid of pen and paper)
shuffling the data series randomly a predetermined number of times (a mental process, as it is about rearranging records which can be performed in human mind)
calculating corresponding values of a measure of correlation for each shuffle (a mathematical concept, as it recites a process of calculating the corresponding values based on an equation in [0120-0121] of the instant specification)
selecting a maximum observed value among the shuffles to be a threshold (a mental process of evaluation, as it merely recites comparing the values to select a maximum value which can be performed in human mind)
determining a value for the measure of correlation without shuffling (a mental process of judgment, as it can be performed in human mind)
removing a feature if the value for the measure of correlation without shuffling of the feature is larger than the threshold (a mental process of evaluation, as it merely recites filtering a feature by comparing it with the threshold which can be done in human mind)
2A Prong 2: This judicial exception is not integrated into a practical application.
obtaining a data series associated with a distribution of values that generated the data records (an insignificant extra-solution activity MPEP 2106.05(g) of gathering statistics)
2B: The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
obtaining a data series associated with a distribution of values that generated the data records (was indicated as an insignificant extra-solution activity MPEP 2106.05(g) in Step 2A Prong 2, thus it is re-evaluated as a well understood, routine, and conventional activity of gathering statistics MPEP 2106.05(d)(iv)).
Regarding claim 7,
Step 1: Processes, as above.
2A Prong 1: wherein the measure of correlation is sensitive to non-linear relations (a mathematical concept, as it recites giving higher weight to the non-linear relations compared to other types of relations)
2A Prong 2: This judicial exception is not integrated into a practical application.
2B: The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Regarding claim 8,
Step 1: Processes, as above.
2A Prong 1: wherein the measure of correlation includes a Maximal Information Coefficient (MIC) (a mathematical concept, as it recites using a mathematical function to measure correlations)
2A Prong 2: This judicial exception is not integrated into a practical application.
2B: The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Regarding claim 9,
Step 1: Processes, as above.
2A Prong 1: wherein shuffling the data series randomly a predetermined number of times includes choosing the predetermined number of times to ensure a statistical confidence above a threshold (a mental process of evaluation, as it merely recites shuffling the data randomly and sampling the data to ensure the samples are above the statistical confidence level which can be done with the aid of pen and paper)
2A Prong 2: This judicial exception is not integrated into a practical application.
2B: The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Regarding claim 10,
Step 1: Processes, as above.
2A Prong 1: Incorporates the rejection of claim 1.
2A Prong 2: wherein the second machine learning model includes a measure of feature importance for correlated features is a field of use and technological environment (MPEP 2106.05(h)).
2B: wherein the second machine learning model includes a measure of feature importance for correlated features is a field of use and technological environment (MPEP 2106.05(h)).
Regarding claim 11,
Step 1: Processes, as above.
2A Prong 1: Incorporates the rejection of claim 10.
2A Prong 2: wherein the second machine learning model is a Gradient Boosted Decision Trees (GBDT) model is merely a field of use or technological environment (MPEP 2106.05(h)).
2B: wherein the second machine learning model is a Gradient Boosted Decision Trees (GBDT) model is merely a field of use or technological environment (MPEP 2106.05(h)).
Regarding claim 12,
Step 1: Processes, as above.
2A Prong 1: Incorporates the rejection of claim 1.
2A Prong 2: outputting an explanation report (amounts to mere data gathering (presenting offer) which is an insignificant extra-solution activity MPEP 2106.05(g)).
2B: further comprising outputting an explanation report (amounts to well understood, routine, and conventional activity of presenting MPEP 2106.05(d)(II)(iv)).
Regarding claim 13,
Step 1: Processes, as above.
2A Prong 1: Incorporates the rejection of claim 1.
2A Prong 2: further comprising outputting an explanation report including window start and end timestamps (amounts to mere data gathering (presenting offer) which is an insignificant extra-solution activity MPEP 2106.05(g)).
2B: further comprising outputting an explanation report including window start and end timestamps (amounts to well understood, routine, and conventional activity of presenting MPEP 2106.05(d)(II)(iv)).
Regarding claim 14,
Step 1: Processes, as above.
2A Prong 1: Incorporates the rejection of claim 1.
2A Prong 2: further comprising outputting an explanation report including a feature importance ranking list based at least in part on the ranking scores (amounts to mere data gathering (presenting offer) which is an insignificant extra-solution activity MPEP 2106.05(g)).
2B: further comprising outputting an explanation report including a feature importance ranking list based at least in part on the ranking scores (amounts to well understood, routine, and conventional activity of presenting MPEP 2106.05(d)(II)(iv)).
Regarding claim 15,
Step 1: Processes, as above.
2A Prong 1: Incorporates the rejection of claim 1.
2A Prong 2: further comprising outputting an explanation report including a list of a predetermined number of top data records (amounts to mere data gathering (presenting offer) which is an insignificant extra-solution activity MPEP 2106.05(g)).
2B: further comprising outputting an explanation report including a list of a predetermined number of top data records (amounts to well understood, routine, and conventional activity of presenting MPEP 2106.05(d)(II)(iv)).
Regarding claim 16,
Step 1: Processes, as above.
2A Prong 1: Incorporates the rejection of claim 15.
2A Prong 2: wherein the list of a predetermined number of top data records includes feature values used by the second machine learning model (merely a field of use or technological environment MPEP 2106.05(h)).
2B: wherein the list of a predetermined number of top data records includes feature values used by the second machine learning model (merely a field of use or technological environment MPEP 2106.05(h)).
Regarding Claim 19,
Step 1: Claim 19 recites a system comprising: a processor. Therefore, it is directed to the statutory category of a machine.
2A Prong 1: Claim 19 is a system claim having similar limitation to the method claim 1. Therefore, it is rejected with the same rationale as the claim 1.
2A Prong 2: A system comprising: a processor configured to: (mere instructions to apply an exception using a computer MPEP 2106.05(f))
2B: A system comprising: a processor configured to: (mere instructions to apply an exception using a computer MPEP 2106.05(f))
Regarding Claim 20,
Step 1: Claim 20 recites a non-transitory computer readable storage medium. Therefore, it is directed to the statutory category of a machine.
2A Prong 1: Claim 20 is a non-transitory computer readable storage medium claim having similar limitation to the method claim 1. Therefore, it is rejected with the same rationale as the claim 1.
2A Prong 2: A computer program product embodied in a non-transitory computer readable storage medium and comprising computer instructions for: (mere instructions to apply an exception using a computer MPEP 2106.05(f))
2B: A computer program product embodied in a non-transitory computer readable storage medium and comprising computer instructions for: (mere instructions to apply an exception using a computer MPEP 2106.05(f))
Regarding claim 21,
Step 1: Processes, as above.
2A Prong 1: wherein the time-correlated features are determined and removed during an initialization period including by: filling at least one of the target time window and the reference time window to determine time-correlated features. (a mental process of evaluation. The broadest reasonable interpretation of the limitation encompasses a programmer identifying the features which look relevant and erasing the features from the original data)
2A Prong 2: This judicial exception is not integrated into a practical application.
2B: The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Regarding claim 22,
Step 1: Processes, as above.
2A Prong 1: Incorporates the rejection of claim 1.
2A Prong 2: wherein a model score of the obtained model scores indicates an aggregated view of a data record to enable ranking data records within the first group without using features (a field of use and technological environment MPEP 2106.05(h))
2B: wherein a model score of the obtained model scores indicates an aggregated view of a data record to enable ranking data records within the first group without using features (a field of use and technological environment MPEP 2106.05(h))
Regarding claim 23,
Step 1: Processes, as above.
2A Prong 1: The method of claim 1,
2A Prong 2: wherein training the second machine learning model to learn how to differentiate between the first group of data records associated with the model scores and the second group of data records associated with the model scores (mere instructions to apply an exception using a generic computer component MPEP 2106.05(f))
2B: wherein training the second machine learning model to learn how to differentiate between the first group of data records associated with the model scores and the second group of data records associated with the model scores (mere instructions to apply an exception using a generic computer component MPEP 2106.05(f))
Response to Arguments
Applicant's arguments filed 05/26/2026 have been fully considered but they are not persuasive.
Response to Arguments under 35 U.S.C. 101
Arguments: Applicant asserts that (a) the recited operations are computationally complex and tied to streaming digital architecture, (b) claim 1 as amended recites “determining a threshold based at least on a fixed number of bins that are updated in a linear pass to reduce resource consumption compared with multiple passes including by storing fixed size object in computer memory and redistributing positions of the bins to restore an invariant such that an average count per bin is the same for all bins” and this limitation is not a generic mathematical calculation, but a concrete, state-saving computer memory management technique [Remarks, page 8], (c) the claim integrate the unsupervised training of a second machine learning model and the resource-efficient adaptive thresholding mechanism to detect and explain concept drift without needing labels which provides a technological solution to a technical problem in ML system [Remarks, page 9], and (d) just as dropping potentially malicious packets in Example 47 remediates network danger, denying a digital transaction in response to an anomaly detected via real-time model drift monitoring is a proactive technological remediation step and not a mental process of judgment [Remarks, page 10].
Examiner’s Responses: Examiner respectfully disagrees. Regarding (a), first, examiner agrees that ‘training and apply a machine learning model’ to learn complex, multi-dimensional boundaries to differentiate between a target time window and a reference time window cannot practically be performed in the human mind. However, the training and applying is directed to an additional element of mere instructions to apply an exception using a computer MPEP 2106.05(f), not mental processes nor mathematical concept. Second, the limitations of ‘calculating ranking scores’ can practically be performed in one’s mind with the aid of pen and paper. Scoring and ranking process is not computationally complex and do constitute mental processes.
Regarding (b), examiner notes that “determining a threshold based at least on a fixed number of bins that are updated in a linear pass to reduce resource consumption compared with multiple passes” is directed to the mathematical calculation, because ‘determining a threshold’ is performed based on Tukey fence equation in the spec paragraph [0066]-[0067] and does not require any computer component including memory devices. The amended limitation of “redistributing positions of the bins to restore an invariant such that an average count per bin is the same for all bins” merely recites redistributing data included in each bin as shown in [Fig. 7], which also does not require a memory device. The limitation of “by storing fixed size object in computer memory” is explaining that objects are stored in a computer memory, which is a generic computer functioning.
Regarding (c), the examiner asserts that the “unsupervised training of a second machine learning model … without needing labels” is an additional element simply applied to perform the “determine a corresponding ranking score for each data record …”, “determining a relative contribution of each of the data records”, “determining that an anomaly in data records of at least one of the first group and the second group has occurred.” Also, the first machine learning model only performs score generations for each data record. The first machine learning model and the second machine learning model are merely invoked as a tool to perform the mental processes and mathematical calculations.
Regarding (d), the examiner asserts that the limitation of “denying a digital transaction in response to an anomaly” is broad and Example 47 and the instant application are distinguishable. Example 47 Claim 3 is eligible because steps (e) and (f) recites “dropping the one or more malicious network packets in real time; and blocking future traffic from the source address.” The example claim as presented performing real-time monitoring of network traffic and based on the determining anomaly in the network traffic, dropping network packets and blocking traffic from the source address as recited in the example claim provide specific computer solutions that use the output from the ANN to provide security solutions to the detected anomalies. The claim as recited does not have any correspondence or link or association with the data group being compared to the transaction being denied and thus fails to provide any specific computer solution that is tied to the function of the rest of the claim limitations. The claim as recited without any relation with rest of the claim elements can be broadly interpreted a person/expert denying transaction from a provided account/payment (Instant spec [01330-135]). Therefore, the limitation is directed to a mental process of evaluation – evaluating the transaction.
Accordingly, arguments regarding independent claims 1, 19 and 20 are not persuasive. Claims 2-16 and 21-23 depend from claim 1. Therefore, arguments regarding claims 2-16 and 21-23 are not persuasive.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US-20150286684-A1 (This prior art is pertinent because it discloses detecting anomalous transaction patterns using a machine learning algorithm)
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JUN KWON whose telephone number is (571)272-2072. The examiner can normally be reached Monday – Friday 8:00AM – 5:00PM ET.
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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/JUN KWON/Examiner, Art Unit 2127
/ABDULLAH AL KAWSAR/Supervisory Patent Examiner, Art Unit 2127
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