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 .
Status of Claims
This action is in reply to the communication(s) filed on 18 April 2024.
Claim(s) 8-9, 12, and 14 is/are amended in a preliminary amendment.
Claim(s) 1-20 is/are currently pending and have been examined.
Claim Interpretation
Examiner is interpreting the “total distance” metric recited Claims 2, 9 and 16 to be equivalent to Earth Movers Distance.
Claim Objections
Claim 15 is objected to because of the following informalities:
In Claim 15 “computer readable” should be “computer-readable” (hyphenated).
Appropriate correction is required.
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.
Step 1 of the 101 Analysis:
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 recites a method, system, and computer program product including a non-transitory computer readable medium for auto-profiling anomalies. These are a process, machine, and article of manufacture which are within the four categories of statutory subject matter.
Step 2A Prong 1 of the 101 Analysis:
The following limitations and/or similar versions are recited in claim(s) 1, 8, and 15:
Claim(s) 1, 8 and 15:
“receiving,… , a plurality of anomaly transactions identified as anomalies by an anomaly detection system within a plurality of transactions;”
“selecting,…, a subset of anomaly transactions of the plurality of anomaly transactions, wherein the subset of anomaly transactions is associated with a plurality of features;”
“generating,…, based on the plurality of features associated with the subset of anomaly transactions and a distribution of the plurality of features associated with the subset of anomaly transactions, a plurality of weights associated with the plurality of features associated with the subset of anomaly transactions;”
“segmenting,…,…, based on the plurality of features associated with the subset of anomaly transactions and the plurality of weights associated with the plurality of features associated with the subset of anomaly transactions, the subset of anomaly transactions into a plurality of segments of anomaly transactions;”
“labeling,…, a subset of segments of the plurality of segments with a feature profile including a feature from each segment of the subset of segments associated with a highest weight of the plurality of weights of the plurality of features of the anomaly transactions in that segment.”
These limitations, as drafted, are a process that, under its broadest reasonable interpretation, describes Fundamental Economic Principles or Practices but for the recitation of generic computer components. That is, other than reciting “at least one processor”, or “A computer program product including a non-transitory computer readable medium including program instructions which, when executed by at least one processor, cause the at least one processor to:” nothing in the claims’ elements precludes the steps from practically describing Fundamental Economic Principles or Practices. For example, but for the recited computer language, the limitations in the context of this claim describes Mitigating Risk. Mitigating Risk is described when analyzing and classifying an anomalous transaction. If a claim limitations, under their broadest reasonable interpretation, describes Fundamental Economic Principles or Practices but for the recitation of generic computer components, then it falls within the “Certain Methods of Organizing Activity” grouping of abstract ideas.
Accordingly, the independent claims recite an abstract idea.
Step 2A Prong 2 of the 101 Analysis:
This judicial exception is not integrated into a practical application. In particular, the independent claim(s) recite the following (or similar) additional elements:
Claim 1:
“…with at least one processor…”
“…with the at least one processor…”
“…with the at least one processor…”
“…with the at least one processor…”
“…using an unsupervised clustering algorithm…”
“…with the at least one processor…”
Claim 8:
“at least one processor configured to:”
Claim 15:
“A computer program product including a non-transitory computer readable medium including program instructions which, when executed by at least one processor, cause the at least one processor to:”
The computer components (processor and non-transitory computer-readable medium) are recited at a high level of generality (i.e. as a generic processor and generic storage.) such that it amounts to no more than mere instructions to implement the judicial exception on a computer or by using a computer merely as a tool to perform an existing process. These element(s) in combination do not add anything that is not already present when the steps are considered separately. Simply implementing an abstract idea on a computer as a tool to perform an existing process is not indicative of integration into a practical application (See MPEP § 2106.05(f).)
The use of an unsupervised clustering algorithm is implemented at a high level of generality (i.e. as simply using the technology) such that it amounts to no more than generally linking the use of the judicial exception to a particular technological environment or field of use. These element(s) in combination do not add anything that is not already pre-sent when the steps are considered separately. Generally linking the use of the judicial exception to a particular technological environment or field of use is not indicative of integration into a practical application (See MPEP § 2106.05(h).)
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
The independent claims are directed to an abstract idea.
Step 2B of the 101 Analysis:
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements identified in Step 2A Prong 2 (if any) amount to no more than mere instructions to implement the judicial exception on a computer or no more than mere data gathering or data outputting which only adds insignificant extra solution activity to the judicial exception. Accordingly, the Examiner in accordance with MPEP §2106.05(II):
• Carries over their identification of the additional element(s) in the claim from Step 2A Prong Two;
• Carries over their conclusions from Step 2A Prong Two on the considerations discussed in MPEP §§ 2106.05(a) - (c), (e) (f) and (h):
• Re-evaluates any additional element or combination of elements that was considered to be insignificant extra-solution activity per MPEP § 2106.05(g), because if such re-evaluation finds that the element is unconventional or otherwise more than what is well-understood, routine, conventional activity in the field, this finding may indicate that the additional element is no longer considered to be insignificant.
The claim elements which recite additional elements are:
Claim 1:
“…with at least one processor…”
“…with the at least one processor…”
“…with the at least one processor…”
“…with the at least one processor…”
“…using an unsupervised clustering algorithm…”
“…with the at least one processor…”
Claim 8:
“at least one processor configured to:”
Claim 15:
“A computer program product including a non-transitory computer readable medium including program instructions which, when executed by at least one processor, cause the at least one processor to:”
Examiner incorporates the corresponding rationale provided in Step 2A Prong Two herein by carrying over their conclusions from Step 2A Prong Two on the considerations discussed in MPEP §§ 2106.05(a) – (c), (e), (f) and (h).
These element(s) in combination do not add anything that is not already present when the steps are considered separately. Adding insignificant extra-solution activity cannot provide an inventive concept when the activities are well-understood routine and conventional. There are no additional elements that are classified as insignificant extra-solution activity.
The independent claims are not patent eligible.
Dependent Claim(s) 2-3, 5-7, 9-10, 12-14, 16-17, and 19-20 recite limitations that are similar to the abstract idea noted in the independent claims because they further narrow the independent claim(s) which recite one or more judicial exceptions. Accordingly, these claim elements do not serve to confer subject matter eligibility to the claims since they recite abstract ideas.
Claims 4, 11 and 18 further recite the clustering algorithm including a modular-transform based clustering algorithm. The use of a modular-transform based clustering algorithm is implemented at a high level of generality (i.e. as simply using the technology) such that it amounts to no more than generally linking the use of the judicial exception to a particular technological environment or field of use. These element(s) in combination do not add anything that is not already pre-sent when the steps are considered separately. Generally linking the use of the judicial exception to a particular technological environment or field of use is not indicative of integration into a practical application (See MPEP § 2106.05(h).)
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements identified in Step 2A Prong 2 (if any) amount to no more than mere instructions to implement the judicial exception on a computer or no more than mere data gathering or data outputting which only adds insignificant extra solution activity to the judicial exception. Accordingly, the Examiner in accordance with MPEP §2106.05(II):
• Carries over their identification of the additional element(s) in the claim from Step 2A Prong Two;
• Carries over their conclusions from Step 2A Prong Two on the considerations discussed in MPEP §§ 2106.05(a) - (c), (e) (f) and (h):
• Re-evaluates any additional element or combination of elements that was considered to be insignificant extra-solution activity per MPEP § 2106.05(g), because if such re-evaluation finds that the element is unconventional or otherwise more than what is well-understood, routine, conventional activity in the field, this finding may indicate that the additional element is no longer considered to be insignificant.
The claim elements which recite additional elements are:
Claim(s) 4, 11 and 18:
“…wherein the unsupervised clustering algorithm includes a modular-transform based clustering algorithm.”
Examiner incorporates the corresponding rationale provided in Step 2A Prong Two herein by carrying over their conclusions from Step 2A Prong Two on the considerations discussed in MPEP §§ 2106.05(a) – (c), (e), (f) and (h).
These element(s) in combination do not add anything that is not already present when the steps are considered separately. Adding insignificant extra-solution activity cannot provide an inventive concept when the activities are well-understood routine and conventional. There are no additional elements that are classified as insignificant extra-solution activity.
The claims are not patent eligible.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1, 4-6, 8, 11-13, 15 and 18-19 is/are rejected under 35 U.S.C. 102(a)(1) as being clearly anticipated by Zizzamia et al. (US 2014/0058763 A1 hereinafter Zizzamia).
Claim 1
A computer-implemented method, comprising:
receiving, with at least one processor, a plurality of anomaly transactions identified as anomalies by an anomaly detection system within a plurality of transactions; (Zizzamia discloses detecting fraud and anomaly on a set of transactions or claims, where new claims or transactions are received and analyzed using trained claims/transactions data to identify new claims/transactions as anomalous. See at least paragraphs [0044], [0046], [0085], [0307], and [0316]. Zizzamia discloses the invention embodied with a processor executing instructions on a non-transitory computer-readable medium. See at least paragraph [0487]. Zizzamia discloses system may be performed with transactions in a banking system. See at least paragraph [0046].)
selecting, with the at least one processor, a subset of anomaly transactions of the plurality of anomaly transactions, wherein the subset of anomaly transactions is associated with a plurality of features; (Zizzamia discloses a subset of randomly sampled claims or transactions are used for anomaly detection. See at least paragraphs [0053], [0383], and [0470]-[0472]. Zizzamia discloses a sample size of randomly sampled claims/transactions are obtained to analyze a plurality of anomaly variables and indicators (i.e. features). See at least paragraphs [0046], [0048], [0050], [0094], [0291]-[0300], and [0383].)
generating, with the at least one processor, based on the plurality of features associated with the subset of anomaly transactions and a distribution of the plurality of features associated with the subset of anomaly transactions, a plurality of weights associated with the plurality of features associated with the subset of anomaly transactions; (Zizzamia discloses a sample size of randomly sampled claims/transactions are obtained to analyze a plurality of anomaly variables and indicators, wherein the analysis for anomalous claims/transactions including performing clustering to detect an anomaly using the number of variables (i.e. features) and a probability distribution of the values. See at least paragraphs [0046], [0048], [0050], [0094], [0133], [0143], [0159], [0187]-[0188], [0291]-[0300] and [0383]. Zizzamia discloses the associated weights. See at least paragraphs [0014]-[0015], [0057]-[0058] and [0292]-[0300].)
segmenting, with the at least one processor, using an unsupervised clustering algorithm, based on the plurality of features associated with the subset of anomaly transactions and the plurality of weights associated with the plurality of features associated with the subset of anomaly transactions, the subset of anomaly transactions into a plurality of segments of anomaly transactions; and (Zizzamia discloses segmenting the claims/transactions using an unsupervised clustering algorithm. See at least paragraphs [0013], [0016]-[0018], [0166], and [0175]. Zizzamia discloses that the sample size of claims/transactions are processed and segmented using an unsupervised clustering algorithm and sorted in profiles based on the weighted anomaly indicators (i.e. features). See at least paragraphs [0015], [0057]-[0058], [0194] and [0292]-[0300].)
labeling, with the at least one processor, a subset of segments of the plurality of segments with a feature profile including a feature from each segment of the subset of segments associated with a highest weight of the plurality of weights of the plurality of features of the anomaly transactions in that segment. (Zizzamia discloses that the subset of sampled claims/transactions are segmented and classified (i.e. labeled) and sorted in profiles based on descending orders (i.e. highest first) of weighted anomaly indicators/variables (i.e. features), wherein different profilers are generated using the weighted anomaly indicators/variables such that the highest/top weighted variables/indicators are associated with a corresponding profile. See at least paragraphs [0057]-[0060], [0143]-[0159], [0191], [0291]-[0300], [0303], and [0378]-[0381].)
Claim 4
The computer-implemented method of claim 1, wherein the unsupervised clustering algorithm includes a modular-transform based clustering algorithm. (Zizzamia discloses using z-transforms (i.e. a modular-transform) for their clustering. See at least paragraphs [0130]-[0132].)
Claim 5
The computer-implemented method of claim 1, further comprising:
generating, with the at least one processor, using the anomaly detection system, during processing of the plurality of transactions in a transaction processing network, the plurality of anomaly transactions identified as anomalies within the plurality of transactions. (Zizzamia discloses network analysis for detecting fraud in a transaction processing network (i.e. during processing). See at least paragraphs [0109]-[0111] and [0204]-[0213]. Zizzamia discloses fraud and anomaly detection based on analyzing a network to identify a plurality of fraudulent and anomalous claims/transactions. See at least paragraphs [0204]-[0213].)
Claim 6
The computer-implemented method of claim 5, wherein the anomaly detection system includes a fraud detection model, and wherein the plurality of anomaly transactions is identified as fraudulent transactions. (Zizzamia discloses fraud detection model which identifies claims and transactions as being fraudulent/anomalous. See at least paragraphs [0045]-[0050], [0048]-[0050], [0060]-[0061], [0070]-[0073].)
Claim 8
A system comprising:
at least one processor configured to: (Zizzamia discloses the invention embodied with a processor executing instructions on a non-transitory computer-readable medium. See at least paragraph [0487].)
…
The remainder of Claim 8 is substantially similar to the corresponding elements in Claim 1 and is therefore rejected using similar reasoning.
Claim 11
Claim 11 is substantially similar to the corresponding elements in Claim 4 and is therefore rejected using similar reasoning.
Claim 12
Claim 12 is substantially similar to the corresponding elements in Claim 5 and is therefore rejected using similar reasoning.
Claim 13
Claim 13 is substantially similar to the corresponding elements in Claim 6 and is therefore rejected using similar reasoning.
Claim 15
A computer program product including a non-transitory computer readable medium including program instructions which, when executed by at least one processor, cause the at least one processor to: (Zizzamia discloses the invention embodied with a processor executing instructions on a non-transitory computer-readable medium. See at least paragraph [0487].)
…
The remainder of Claim 15 is substantially similar to the corresponding elements in Claim 1 and is therefore rejected using similar reasoning.
Claim 18
Claim 18 is substantially similar to the corresponding elements in Claim 4 and is therefore rejected using similar reasoning.
Claim 19
Claim 19 is substantially similar to the corresponding elements in Claim 5 and is therefore rejected using similar reasoning.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 2, 9, and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zizzamia et al. (US 2014/0058763 A1 hereinafter Zizzamia) in view of Wolters (“Technical Report: Sample Size for Estimating Multinomial Populations” hereinafter Wolters).
Claim 2
The computer-implemented method of claim 1, wherein selecting the subset of anomaly transactions of the plurality of anomaly transactions includes determining a sample size n of the subset of anomaly transactions based on a distance d of true values of a multinomial population of the plurality of anomaly transactions at a significance level α. (Zizzamia discloses a subset of randomly sampled claims or transactions are used for anomaly detection. See at least paragraphs [0053], [0383], and [0470]-[0472]. Zizzamia does not disclose determining a sample size in the manner disclosed by Claim 2.)
Although Zizzamia does not disclose determining a sample size in the manner disclosed by Claim 2, Wolters teaches determining a sample size N based on interval width d (i.e. true distance d) at a significance level α by using The Thompson Method. See at least pages 8-9 and Fig. 2.
It would be obvious to one of ordinary skill in the art before the effective filing date to use the Thompson Method as disclosed by Wolters for selecting a sample size of the sampled subset in Zizzamia because Wolters additionally teaches the motivation that this method lets only a single α value, the groupwise α, as needing to be specified. See at least page 8.
Also, using the Thompson Method as disclosed by Wolters for selecting a sample size of the sampled subset in Zizzamia is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claim 9
Claim 9 is substantially similar to the corresponding elements in Claim 2 and is therefore rejected using similar reasoning.
Claim 16
Claim 16 is substantially similar to the corresponding elements in Claim 2 and is therefore rejected using similar reasoning.
Claim(s) 1, 4-8, 11-15 and 18-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zizzamia et al. (US 2014/0058763 A1 hereinafter Zizzamia) in view of Yang et al. (US 2007/0106580 A1 hereinafter Yang).
Claim 7
The computer-implemented method of claim 5, further comprising:
receiving, with the at least one processor, a current transaction currently being processed in the transaction processing network; (Zizzamia discloses network analysis for detecting fraud in a transaction processing network. See at least paragraphs [0109]-[0111] and [0204]-[0213]. Zizzamia discloses fraud and anomaly detection based on analyzing a network to identify a plurality of fraudulent and anomalous claims/transactions. See at least paragraphs [0204]-[0213]. Zizzamia does not explicitly disclose receiving a transaction currently being processed (emphasis added).)
generating, with the at least one processor, using the anomaly detection system, a current anomaly transaction identified as a current anomaly; (Zizzamia discloses network analysis and applying thresholds to compare the new claims/transactions with trained/historical data to include new claims/transactions into either a normal or abnormal profile. See at least paragraphs [0109]-[0111], [0313]-[0318], [0345]-[0351], and [0378]-[0383]. Zizzamia does not explicitly disclose receiving a transaction currently being processed (emphasis added).)
automatically labeling, with the at least one processor, the current anomaly transaction by comparing one or more features associated with the current anomaly transaction to the feature profile, wherein the current anomaly transaction is labeled with the feature profile in response to a threshold number of the one or more features associated with the current anomaly transaction matching a threshold number of features in the feature profile; and (Zizzamia discloses identifying and classifying (i.e. labelling) new claims/transactions (i.e. current transactions) and applying thresholds to compare the new claims/transactions with trained/historical data of a profile (i.e. feature profile) to include new claims/transactions into either a normal or abnormal profile based on whether a comparison threshold between new claims/transactions and trained/historical data associated with a profile is exceeded. See at least paragraphs [0086], [0109]-[0111], [0159], [0313]-[0318], [0345]-[0351], and [0378]-[0383]. Zizzamia does not explicitly disclose receiving a transaction currently being processed (emphasis added).)
updating, with the at least one processor, based on the current anomaly transaction, the feature profile. (Zizzamia discloses updating the abnormal and normal profiles to include new data. See at least paragraphs [0045], [0058]-[0060], [0303], [0378]-[0383] and [0479].)
Although Zizzamia does not explicitly disclose receiving a transaction currently being processed, Yang teaches real-time processing of transactions for fraud detection so that a system may deny a transaction immediately upon determining that the transaction is likely fraudulent (i.e. currently being processed). See at least paragraph [0027]. It would be obvious to one of ordinary skill in the art before the effective filing date to implement the fraud detection of Zizzamia as a real-time process for currently processing transactions as taught by Yang because Yang additionally teaches the motivation that this lets a fraud system not make a determination after the transaction is complete and not waiting until the next transaction for a denial. See at least paragraph [0027].
Also, implementing the fraud detection of Zizzamia as a real-time process for currently processing transactions as taught by Yang is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claim 14
Claim 14 is substantially similar to the corresponding elements in Claim 7 and is therefore rejected using similar reasoning.
Claim 20
Claim 20 is substantially similar to the corresponding elements in Claim 7 and is therefore rejected using similar reasoning.
Examiner’s Note
Examiner notes a search was performed but did not result in a prior art rejection for Claims 3, 10 and 17. These claims still have an outstanding 101 rejection.
Examiner notes that Claim 10 does not contain the same amendment crossing out the phrase “programmed and/or” as Claims 9-10, 12 and 14 received in the preliminary amendment dated 18 April 2024. Examiner would appreciate clarification on if this omission was intentional or not.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Priess et al. (US 2015/0026027 A1) discloses estimation of an unknown multinomial probability distribution.
Gullikson et al. (WO 2023/064397 A1) discloses clustering techniques to label transaction data.
Fariha et al. (“Advanced fraud detection using machine learning models: enhancing financial transaction security”) discloses segmentation of transaction landscapes using K-means clustering.
Pontes Jesus (EP 4141693 A1) discloses usage of the Wasserstein metric and other similarity metrics for testing distributions.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ADAM J HILMANTEL whose telephone number is (571)272-8984. The examiner can normally be reached M-F 8:30AM-5:00PM.
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.
/ADAM HILMANTEL/Examiner, Art Unit 3691
Prosecution Insights