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 .
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite identifying, predicting and determining an anomaly. These steps are directed to a metal process (observation, evaluation and forming a judgment) and a mathematical relationship (metric range/threshold and training model). If a claim limitation, under its broadest reasonable interpretation this covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Metal Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application because the claim recites a processor and memory which are recited at a high-level of generality (i.e., as a generic processor performing a generic computer functions) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Data collection, prediction, and machine learning are well-understood, routine and conventional functions.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the use of a machine learning model for anomaly prediction and detection is well-understood, routine and conventional to one of ordinary skill in the art. Claims 1, 8 and 15 are not patent eligible.
Dependent claims 2-7, 9-14 and 18-20 only further limitations to the abstract idea in the independent claims (data gathering, classification) or insignificant extra-solution activity. They do not integrate the abstract idea into practical application or add significantly more than the judicial exception. As such claims are still directed to an abstract idea.
Response to Arguments
Applicant's arguments filed 2/5/2026 have been fully considered but they are not persuasive.
Applicant states:
Applicant respectfully submits that that claim 1 as amended does not recite a mental process because claim 1 cannot "practically be performed in the human mind." See MPEP § 2106.04 (a)(2)(III)(A). Claim 1 as amended recites in part (emphasis added): receive feedback data for the anomaly website event from a user interface, the feedback data indicating a correct indicator or an incorrect indicator for the anomaly type; and update the machine learning model based at least in part on the feedback data for the anomaly website event. Applicant notes that the human mind cannot practically "receive feedback data for the anomaly website event from a user interface" or "update the machine learning model."
Examiner respectfully disagrees. The claim under its broadest reasonable interpretations still recites steps of collecting, analyzing and classifying information, which are mental process. The additional limitations of receiving feedback and updating a machine learning model merely constitute further data processing and learning. These limitations directly relate to human evaluation and judgement. Using a computer does not remove them from the abstract idea category.
Applicant states:
Consider for example, the published USPTO examples 39, which illustrates claim limitations that merely involve an abstract idea, and 47, which shows limitations that recite an abstract idea. The claim limitation "training the neural network in a first stage using the first training set" of example 39 does not recite a judicial exception. Even though "training the neural network" involves a broad array of techniques and/or activities that may involve or rely upon mathematical concepts, the limitation does not set forth or describe any mathematical relationships, calculations, formulas, or equations using words or mathematical symbols. Contrast this with the limitation "training, by the computer, the ANN based on the input data and a selected training algorithm to generate a trained ANN, wherein the selected training algorithm includes a backpropagation algorithm and a gradient descent algorithm" of claim 2 of example 47.
This limitation requires specific mathematical calculations by referring to the mathematical calculations by name, i.e., a backpropagation algorithm and a gradient descent algorithm, and therefore recites a judicial exception, namely an abstract idea.
USPTO, Reminders on evaluating subject matter eligibility of claims under 35 U.S.C. 101, p. 3, (published August 4, 2025) (last accessed February 3, 2026) (available at https://www.uspto.gov/sites/default/files/documents/memo-101-20250804.pdf) (emphasis added). In addition to claim 1 being outside the scope of what can "practically be performed in the human mind," claim 1 also "does not set forth or describe any mathematical relationships, calculations, formulas, or equations using words or mathematical symbols." Therefore, Applicant submits that under Step 2A Prong I of the Alice/Mayo framework, claim 1 does not recite a judicial exception.
Examiner respectfully disagrees. The absence of mathematical equations does not remove the claim from the abstract idea category, the claims still recite evaluation, prediction and classification of information. The claim does not recite any improvement to the computer functionality or technology.
Applicant state:
First, Applicant respectfully submits that it is improper to broadly label "Data collection, prediction, and machine learning" as "well-understood, routine and conventional functions." The Office Action does not follow the requirements for making this determination (emphasis added):
In the Step 2B inquiry, if the examiner has concluded that particular claim limitations are well understood, routine, conventional activities (or elements) to those in the relevant field, the rejection should support this conclusion in writing with a factual determination in accordance with Subsection III below. See MPEP § 2106.05(d) for more information about well understood, routine, conventional activities and elements, and Subsection III below for more information about how to support a conclusion that a claim limitation is well understood, routine, conventional activity.
MPEP § 2106.07 (a)(II). Additionally, the MPEP provides a list of examples for what "well- understood, routine and conventional functions" could look like: "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," and "A Web browser's back and forward button functionality." MPEP § 2106.05 (d)(II). Applicant submits that "machine learning" is a poor fit for this category and is strikingly less "well-understood, routine and conventional" than "A Web browser's back and forward button functionality." Moreover, Applicant submits that reducing Applicant's claims to mere "Data collection, prediction, and machine learning" is inappropriate, as Applicant's claim language is far more specific and detailed than this overgeneralization.
Examiner respectfully disagrees. The identified additional elements of data collection, prediction, receiving feedback and updating a machine learning model as well understood, routine and conventional activities in the field of data analytics and machine learning. The specification does not describe these elements as anything other than generically implemented using conventional techniques. Reciting “machine learning model” and “prediction” does not render the claim nonconventional. No specific algorithm, architecture or technical improvement is claimed. Applicant has no provided evidence that the recited machine learning are unconventional.
Applicant states:
Thus, the embodiments of the present disclosure relate to various improvements related to detecting and identifying errors in websites that cannot be discovered by conventional digital analytics tools. For example, the embodiments of the present disclosure are directed to improved approaches that identify anomaly website events (e.g., potential website errors) from a website navigation sequence performed by a client device on a website and automate the identification of website errors from the detected anomaly website events (e.g., identifying technical website errors from other causes of the anomaly website events). In other examples, the embodiments are directed to improved approaches include display an interactive user interface for receiving a classification of the anomaly website event (e.g., classifying the cause of the anomaly website event, such as a technical website error, a promotional offer, a market trend, etc.) and using the classification to update one or more machine learning models used for detecting and classifying the anomaly website events and reconstruct website navigation sequences of various client devices interacting with a website from the clickstream data.
Applicant respectfully submits that the cited language outlines a clear improvement in the field of error detection. Additionally, this improvement is reflected in Applicant's claims. For example, claim 1 recites in part (emphasis added): determine an anomaly website event for an interaction of the client device with the website based on a measurement of the clickstream metric failing to reach a predefined range of the predicted clickstream metric; determine an anomaly type for the anomaly website event based at least in part on a machine learning model being trained with a plurality of previous anomaly website events identified from a plurality of previous instances of clickstream data; receive feedback data for the anomaly website event from a user interface, the feedback data indicating a correct indicator or an incorrect indicator for the anomaly type; and update the machine learning model based at least in part on the feedback data for the anomaly website event.
Applicant respectfully submits that the language of claim 1 maps to the improvements described in the specification. For at least this reason, Applicant submits that claim 1 as amended integrates the alleged judicial exception into a practical application.
Examiner respectfully disagrees. Applicant argues that the invention improves error detection in websites, the claims do not recite a specific technological improvement to a computer functionality. The claims are directed to collecting clickstream data, predicting metrics, detecting anomalies based on comparisons, and updating the machine learning model based on feedback. The alleged improvement is to the quality of information or analysis not the underlying technology. The claims do not recite any specific implementation details on how the computer performs these functions. The claims do not integrate the judicial exception into practical application.
Applicant states:
Applicant submits that using "a measurement of the clickstream metric" to "determine an anomaly website event for an interaction of the client device with the website" and determining "an anomaly type for the anomaly website event" using "a machine learning model being trained with a plurality of previous anomaly website events" is not "well- understood, routine, conventional activity previously known to the industry" and therefore "favors eligibility."
Examiner respectfully disagrees. The recited steps of using a measured metric to detect an anomaly based on deviation from and expected range and determining an anomaly type using a machine learning model trained on prior data are well known techniques in the field of data analysis and anomaly detection. Such methods of anomaly detection and machine learning trained model based historical data are well known, routine and conventional activities. The claim does not recite any specific or unconventional implementation of these techniques or any improvement to computer or technological functions. The claim applies know methods to a particular type of data being clickstream data.
Applicant states:
Dependent claims 2-7, 9-14 and 18-20 only further limitations to the abstract idea in the independent claims (data gathering, classification) or insignificant extra-solution activity. They do not integrate the abstract idea into practical application or add significantly more than the judicial exception. As such claims are still directed to an abstract idea. (Office Action, p. 3). Applicant submits that this analysis does not consider "each claim for eligibility separately." Instead, the analysis judges Applicant's claims "to automatically stand or fall with similar claims." Additionally, Applicant submits that claims 16 and 17 are not addressed under 35 U.S.C § 101 anywhere in the Office Action, beyond the general statement on page 2 that "[c]laims 1-20 are rejected under 35 U.S.C. 101." Therefore, Applicant is unable to properly respond to the rejections of claims 16 and 17 in this response. For at least these reasons, Applicant respectfully requests that the rejections of claims 2-7, 9-14, and 16-20 be withdrawn.
Examiner respectfully disagrees. The dependent claims have been considered and analyzed. Each claim was analyzed to see if it included additional elements that integrate the application into practical application or provide significantly more than the abstract idea. The additional limitations in the dependent claims (user interface display, feedback, confidence threshold, model updating, confidence levels, assigning identifiers, specifying anomaly type and collecting data) recite further data gathering, analysis and post solution activity, which are well understood, routine and conventional and do not amount to significantly more and fail to recite a technological improvement.
Examiner agrees with applicant that Bates (US 2021/0194751) fails to disclose all the limitations of the amended independent claims. As such the 102 rejection has been withdrawn.
Conclusion
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 CHARLES EHNE whose telephone number is (571)272-2471. The examiner can normally be reached 8:00-5:00 M-F.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Bryce Bonzo can be reached at 571-272-3655. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/CHARLES EHNE/ Primary Examiner, Art Unit 2113
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