DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
The following is a final office action in response to the application filed 16 September 2025.
Applicant’s amendments to Claims 1, 3-4, 9-11, and 15-16 , cancellation of Claims 8 and13 and addition of Claims 21 and 22 have been received and are acknowledged.
Claims 1-7, 9-12, and 14-22 have been examined and are pending.
Response to Arguments
Applicant's arguments filed 16 September 2025 have been fully considered but they are not persuasive.
Examiner note: Applicant’s “Statement Regarding Substance of Interview” includes the statement:
“ The Examiner indicated that that the proposed claim amendments would overcome the 101 rejection and help advance prosecution towards allowance but stated that she would have to make a final determination after this response is filed.
Supervisory Examiner Vyas assured Applicant's representative that he and the Examiner will try their best to work with Applicant's representative to resolve any remaining issues if they do not think that the application is in condition for allowance after this response is filed…” (Applicant’s response, 9)
Examiner refers Applicant to the Interview Summary of 9/17/2025, and notes that
“ No agreement was reached” on any of the matters discussed. As noted previously, though phone communications may be used in the interest of compact prosecution for specific issues, phone communications are not appropriate to “ resolve any remaining issues.” Prosecution/examination of applications must be conducted in writing. (See MPEP 2002.02).
With regard to the rejections under 35 USC 101, Applicant : (1) Asserts that
“ …..Regarding Prong One of Step 2A, the Office Action makes conclusory statements….the Office Action states: "anomaly detection in the context of customer data is not a technical endeavor but a business challenge." …analysis is contrary to USPTO's August 4, 2025 Memorandum … "[e]xaminers should be careful to distinguish claims that recite an exception (which require further eligibility analysis) from claims that merely involve an exception (which are eligible and do not require further eligibility analysis)"-(emphasis added). Even assuming that amended claim 1 involves "a business challenge," as alleged, that does not mean that amended claim 1 recites an exception…The Office Action also alleges that "instant claims are not similar to the patent eligible claims of Example 39 because the transformation steps and associated computer elements recited in the instant claims are recited at a high level of generality and the computer elements are merely invoked as tools to perform an abstract idea." However, the Office Action fails to explain why that is the case and why this is even relevant for the analysis under Step 2A, Prong One based on which the claim of Example 39 is patent eligible. …since amended claim 1 does not recite any identified abstract idea, amended claim 1 is patent eligible under Step 2A, Prong One.” (Applicant’s response, 11)
(2) Applicant further re-asserts that:
“…the claims integrate the alleged abstract idea into a practical application.
… representative claim 1 as a whole integrates into a practical application because it is directed to improvements in the technical field of machine learning by first determining whether an anomaly is detectable using on the distribution of values before invoking a machine learning model for detecting the anomaly…, Fig. 6 and paragraphs 59, 61, and 68-72 of the specification. Since invoking the machine learning model utilizes significantly more resources than not invoking the machine learning model, a process that allows to bypass invoking the machine learning model when that's not necessary conserves resources necessary for invoking the machine learning model. And, the claims themselves reflect the disclosed improvement…The Office Action fails to fully address or respond to the above argument. Based on the above, the claims provide an improvement to a technology or technical field and, therefore, integrate the alleged abstract idea into a practical application and are patent-eligible under 35 U.S.C. § 101. …” (Applicant’s response, 12)
Examiner respectfully disagrees. As noted in the previous rejection and the rejection below and stated previously, the instant recited claims are directed to anomaly detection which falls into the category of mathematical concepts and organizing human activity (commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations). As previously stated, anomaly detection in the context of customer data is not a technical endeavor but a business challenge. The accuracy of anomaly detection in customer data is not an technological improvement. Applicant’s argument that involving a “business challenge” does not mean that ‘an exception’ is recited is unavailing in the instant case. The use of generically recited supervised/unsupervised machine learning models to process data to determine outlier/anomalies is also abstract albeit a less specific abstract idea than anomaly detection of customer data (i.e. a specific data set). The instant recitations include the specific data set scenario as well. The instant claims do not recite an improvement to technology rather what is a recited is a use of technological/computing elements recited at a high level of generality to process data; the computing elements are merely tools used to perform the abstract idea (See MPEP 2106(f) ). At most the instant claims recite an improvement to an abstract idea.
Simply implementing the abstract idea on a generic computer elements is not a practical application of the abstract idea nor are these recitations ‘significantly more’ than the abstract idea. Further, Applicant’s assertions as to the improvement of utilization of resources is not supported. Applicant’s arguments as such are not commensurate with the scope of the recited claims. Further, as such, Applicant’s arguments are not persuasive. (Applicant’s arguments, 1 and 2).
With regard to the rejections under 35 USC 103, Examiner withdraws the rejections in view of Applicant’s amendments.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-7, 9-12, and 14-22 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for pre-AIA the inventor(s), at the time the application was filed, had possession of the claimed invention. Claims 1, 11 and 16 recite:
Claim 1
…determining, that an anomaly is not detectable using the distribution of values based on whether values of one or parameters in the unstructured data are within an outlier threshold calculated for the distribution of values;
based on determining that the anomaly is not detectable using the distribution of values and based on whether a supervised machine learning model reaches a threshold level of accuracy after an unsupervised machine learning model is used, invoking one or more of the of a supervised machine learning model or the unsupervised machine learning model for detecting the anomaly based on at least the portion of the unstructured data…
Claim 11
based on determining that the anomaly is not detectable using the, distribution of values and based on a supervised machine learning model not reaching a threshold level of accuracy invoke a an unsupervised machine learning model for detecting the anomaly;
Claim 16
based on determining that the anomaly is not detectable using the, distribution of values and based on a supervised machine learning model not reaching a threshold level of accuracy, invoke the supervised machine learning model for predicting an anomaly for the customer detecting the anomaly
Applicant is requested to provide reference from the original disclosure to support the amendments of:
Claim 1….
determining, that an anomaly is not detectable using the distribution of values based on whether values of one or parameters in the unstructured data are within an outlier threshold calculated for the distribution of values;
based on determining that the anomaly is not detectable using the distribution of values and based on whether a supervised machine learning model reaches a threshold level of accuracy after an unsupervised machine learning model is used, invoking one or more of the of a supervised machine learning model or the unsupervised machine learning model for detecting the anomaly based on at least the portion of the unstructured data
Claim 11
based on determining that the anomaly is not detectable using the, distribution of values and based on a supervised machine learning model not reaching a threshold level of accuracy invoke a an unsupervised machine learning model for detecting the anomaly;
Claim 16
based on determining that the anomaly is not detectable using the, distribution of values and based on a supervised machine learning model not reaching a threshold level of accuracy, invoke the supervised machine learning model for predicting an anomaly for the customer detecting the anomaly
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 1-7, 9-12, and 14-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
When considering subject matter eligibility under 35 U.S.C. 101, (1) it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter. If the claim does fall within one of the statutory categories, (2a) it must then be determined whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea), and if so (2b), it must additionally be determined whether the claim is a patent-eligible application of the exception. If an abstract idea is present in the claim, any element or combination of elements in the claim must be sufficient to ensure that the claim amounts to significantly more than the abstract idea itself. Examples of abstract ideas include fundamental economic practices; certain methods of organizing human activities; an idea itself; and mathematical relationships/formulas. Alice Corporation Pty. Ltd. v. CLS Bank International, et al., 573 U.S. ____ (2014).
The claimed invention is directed to a judicial exception (i.e. a law of nature, a natural phenomenon, or an abstract idea) without significantly more. In the instant case, the claim(s) as a whole, considering all claim elements both individually and in combination, do not amount to significantly more than an abstract idea.
(1) In the instant case, the claims are directed towards a method, non-transitory computer readable medium, and the system of anomaly detection. In the instant case, Claims 1-7, 9-10 and 21 are directed to a process. Claims 11-12, 14-15 and 22 are directed to a system. Claims 16-20 are directed to a non-transitory computer readable medium.
(2a) Prong 1: Anomaly detection is categorized in/akin to the abstract idea subject matter grouping of: mathematical concepts and methods of organizing human activity [organizing human activity (commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations)]. As such, the claims include an abstract idea.
The specific limitations of the invention are (a) identified to encompass the abstract idea include:
1. (Currently Amended) A method for conserving resources used for anomaly detection, comprising: ;
transforming, into a structured format, unstructured data that is an ….;
identifying a segment for a portion of the unstructured data based on values identified by the structured format;
identifying a distribution of values for the segment;
determining, that an anomaly is not detectable using the distribution of values based on whether values of one or parameters in the unstructured data are within an outlier threshold calculated for the distribution of values;
based on determining that the anomaly is not detectable using the distribution of values an based on whether a supervised … model reaches a threshold level of accuracy after an unsupervised …model is used, invoking one or more of …model or …model for detecting the anomaly based on at least the portion of the unstructured data;
flagging at least the portion of the unstructured data as the anomaly based on invoking one or more the … model or … model for detecting the anomaly; and
training or retraining the … model based at least the portion of the unstructured data being flagged as the anomaly.
11. (Currently Amended) A… comprising:
…; and
..:
identify data generated for a customer;
transform the data from an unstructured format to a structured format;
identify a first set of features associated with the customer based on the transformed data;
determine, that an anomaly is not detectable using a distribution of values based on whether a value associated with the first set of features satisfies an outlier threshold calculated for the distribution of values;
based on determining that the anomaly is not detectable using the distribution of values and based on a supervised … model not reaching a threshold level of accuracy, invoke an unsupervised…model for detecting the anomaly; and
trigger an action for addressing the anomaly based on invoking the unsupervised …model for detecting the anomaly.
16. (Currently Amended) A …, cause the.. to:
identify data generated for a customer;
transform the data from an unstructured format to a structured format;
identify a first set of features associated with the customer based on the transformed data;
identify a customer segment based on the first set of features, ,
identify a distribution of values for the customer segment;
determine, that an anomaly is not detectable using the distribution of values based on whether a value associated with the first set of features satisfies an outlier threshold calculated for the distribution of values;
based on determining that the anomaly is not detectable using the distribution of values and based on a supervised … model not reaching a threshold level of accuracy, invoke the supervised … model for predicting an anomaly for the customer detecting the anomaly; and
retraining the …model based invoking the supervised... model for detecting the anomaly.
As stated above, this abstract idea falls into the (b) subject matter grouping of: methods of organizing human activity.
Prong 2: When considered individually and in combination, the instant claims are do not integrate the exception into a practical application because the steps of identify…transforming… identifying segment… identifying a distribution of values… … identifying a first set of features….. determining… invoking…flagging…training or retraining…trigger an action/transmit a notification do not apply, rely on, or use the judicial exception in a manner that that imposes a meaningful limitation on the judicial exception (i.e. the abstract idea).
The instant recited claims including additional elements (i.e. transmitting…) do not improve the functioning of the computer or improve another technology or technical field nor do they recite meaningful limitations beyond generally linking the use of an abstract idea to a particular technological environment. The limitations merely recite: “apply it” (or an equivalent) or merely include instructions to implement an abstract idea on a computer or merely uses a computer a as tool to perform an abstract idea or merely uses generic computing elements to perform well known, routine, and conventional functions or generally link the use of the judicial exception to a particular technological environment or field of use (See MPEP 2106.05 (d), and (f))
(2b) In the instant case, Claims 1-7, 9-10 and 21 are directed to a process. Claims 11-12, 14-15 and 22 are directed to a system. Claims 16-20 are directed to a non-transitory computer readable medium.
Additionally, the claims (independent and dependent) do not include additional elements that individually or in combination are sufficient to amount to significantly more than the judicial exception of abstract idea (i.e. provide an inventive concept). As discussed above with respect to integration of the abstract idea into a practical application, the additional element(s) of: ( supervised/unsupervised machine learning model, processor, memory, non-transitory computer readable medium) merely uses a computer a as tool to perform an abstract idea or merely uses generic computing elements to perform well known, routine, and conventional functions. (See MPEP 2106.05 (d) and (f)) (Specification,[19] machine learning model, [77] processors, general purpose or special purpose central …general-purpose hardware…non-transitory storage medium (e.g. memory) )
The dependent claims have also been examined and do not correct the deficiencies of the independent claims.
It is noted that claim (2-10, 12-15, and 17-22) introduces the additional elements of wherein clauses further describing claim elements ( the anomaly (Claims 2, 12, and 17), set of features (Claim 3, ), customer segment (Claim 4, 18) threshold (Claim 5), distribution of values (Claim 6, 14, and 19), pricing parameter (Claims 7, 15, and 20), making ..prediction… wherein… (Claim 9) training…detecting…generating… associating.. including (data)(Claim 10); … transition …(Claims 21 and 22). These elements are not a practical application of the judicial exception because the limitations merely recite: “apply it” (or an equivalent) or merely include instructions to implement an abstract idea on a computer or merely uses a computer a as tool to perform an abstract idea or merely uses generic computing elements to perform well known, routine, and conventional functions or generally link the use of the judicial exception to a particular technological environment or field of use (See MPEP 2106.05 (d) and (f)) Further these limitations taken alone or in combination with the abstract do not amount to significantly more than the abstract idea alone because the elements amount to mere use of a computer a as tool to perform an abstract idea or merely uses generic computing elements to perform well known, routine, and conventional functions. (See MPEP 2106.05 (d) and (f)) (Specification,[19] machine learning model, [77] processors, general purpose or special purpose central …general-purpose hardware…non-transitory storage medium (e.g. memory) )
Therefore, Claims 1-7, 9-12, and 14-22 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 2020/0351283 Al
US 2023/0196420 Al ( Anomaly detection for bill generation)
US 2023/0359706 Al (ANOMALY DETECTION AND ANOMALOUS PATTERNS IDENTIFICATION; See Fig. 3 Phase 2 supervised anomaly detection > Phase II unsupervised anomalous patter identification)
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ASHA PUTTAIA H whose telephone number is (571)270-1352. The examiner can normally be reached M-F 9 am to 5:30 pm.
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, Abhishek Vyas can be reached at 571-270-1836. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/ASHA PUTTAIA H/ Primary Examiner, Art Unit 3691