The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This Office action is in response to communications filed on 3/10/2025.
Claims 20-25 have been cancelled.
Claims 1-19 and 26 are pending.
DETAILED ACTION
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.
Claim(s) 12-13, 15-17, and 26 is/are rejected under 35 U.S.C. 102(A)(1) as being anticipated by Cheung et al. (US 20220036363 A1, hereinafter Cheung).
Regarding claim 12, Cheung discloses a method for identifying anomaly patterns for card transactions within a consumer banking environment, the method comprising:
retrieving, card transaction data (¶[0032], "receive and store data from the transaction monitoring devices 120. The data includes data associated with each of a plurality of payment terminals 105. In some embodiments, the data is directly received from payment terminals 105 that could be considered transaction monitoring devices");
filtering and storing the card transaction data as raw data (¶[0043], "aggregate anomalous transaction data at one or more different aggregation levels");
processing the raw data in real-time, by detecting card transaction declines within the raw data (¶[0043], "processor 215 may determine a percentage of the overall transactions processed by a given payment terminal 105 or by the payment terminals 105 associated with a given merchant over a predetermined time period (e.g., one month) that were identified as anomalous according to each of the above-noted example identifications of anomalous transactions"; ¶[0038], "data fields that include transactional authorization information may include one or more of a transaction number/identification, a date and/or time of the transaction, a type of transaction (e.g., contactless, magnetic stripe, chip, whether the transaction was online or offline, and the like), a success indication (e.g., whether the transaction was successful/approved or unsuccessful/rejected)"; ¶[0050], "the electronic processor 215 may identify the merchant 110 as anomalous despite the overall transaction approval rate of the merchant 110 being above the respective threshold and/or the amount of payment terminals 105 owned and/or operated by the merchant 110 that did not process a contactless transaction being below the respective threshold" - that is, unprocessed transactions are identified);
identifying in real-time, via a machine learning model, anomaly patterns associated with the card transaction declines detected (¶[0047], "when the contactless acceptance approval rate of a payment terminal 105 and/or merchant 110 is below the threshold, the electronic processor 215 determines that the payment terminal 105 and/or merchant 110 has exhibited a pattern of anomalous behavior"; ¶[0026], "Using one or more of the approaches described above, a computer program can ingest, parse, and understand data and progressively refine algorithms for data analytics. In some examples, the machine learning performed by the payment terminal evaluation device 130 in executing the functionality described herein is an ensemble machine learning model named XGBoost (eXtreme Gradient Boosting trees), a gradient boosting algorithm implemented for speed and performance. This learning model utilizes many (for example, thousands) of independent trees whose results are aggregated or otherwise combined (e.g. via voting) to produce a final prediction value. In some embodiments, the electronic processor 215 engages in machine learning to determine new anomaly patterns in addition to the below-described example anomaly patterns"); and
generating at least one graphical illustration associated with the anomaly patterns identified, to be accessible via a user interface (Fig. 4, ¶[0062], "a dashboard 400 that is displayed on the acquirer/merchant communication device 145 in response to receiving health information from the payment terminal evaluation device 130"; ¶[0063], "dashboard 400 includes a map interface 405 and an anomaly pattern interface 410. The map interface 405 may visually represent anomaly information"; ¶[0064], "or example, using the anomaly pattern interface 410, the user (e.g., an employee of the acquirer 115 and/or merchant 110) may adjust thresholds and weighting of individual anomalies and/or anomalous patterns that were discussed previously herein").
Regarding claim 13, Cheung discloses the method of claim 12, wherein the method is performed in a cloud environment (¶[0032], "the data warehouse 135 is configured to receive and store data from the transaction monitoring devices 120"; Fig. 1, data warehouse 135 is remote from monitoring devices 120, connected by a WAN (see ¶[0019])).
Regarding claim 15, Cheung discloses the method of claim 14, wherein the anomaly patterns include historical anomaly data and active anomaly data (¶[0039], "the electronic processor 215 identifies an anomalous merchant and/or anomalous payment terminal by identifying a pattern of anomalous transactions that have occurred on a given payment terminal").
Regarding claim 16, Cheung discloses the method of claim 13, wherein the stored data is sent through a data pipeline for the data to be consumed by the machine learning model in real-time once placed in a model database of the model (¶[0039], "the data warehouse 135 stores the data received during the execution of block 305. At block 315, the electronic processor 215 of the payment terminal evaluation device 130 identifies, based on the data, at least one of (i) a merchant of the plurality of merchants 110 as an anomalous merchant and (ii) a payment terminal of the plurality of payment terminals 105 as an anomalous payment terminal. The electronic processor 215 is configured to identify the at least one of the anomalous merchant and the anomalous payment terminal by identifying one or more patterns of anomalous data associated with a merchant 110 or a payment terminal 105"; ¶[0026], machine learning is used).
Regarding claim 17, Cheung discloses the method of claim 16, wherein an alert is sent to the user at the user interface in real-time when anomaly data is written to the model database (¶[0060], "the payment terminal evaluation device 130 transmits health information (e.g., anomaly information) to one or more acquirer/merchant communication devices 145 to cause the acquirer/merchant communication device 145 to display the health information"; ¶[0063], "dashboard 400 includes a map interface 405 and an anomaly pattern interface 410. The map interface 405 may visually represent anomaly information. For example, in FIG. 4, the dots on the map interface 405 represent an amount of identified anomalies, a percentage of identified anomalies, an anomaly score of payment terminals 105 and/or merchants 110, and/or the like. Larger dots in California and New York may represent more identified anomalies than smaller dots in Washington or Wyoming. For example, the large dots in California and New York may represent a high anomaly level for payment terminals 105 and/or merchants 110 in California and New York. The medium dots in Washington and Illinois may represent a medium anomaly level for payment terminals 105 and/or merchants 110 in Washington and Illinois. The small dots in Wyoming and Delaware may represent a low anomaly level for payment terminals 105 and/or merchants 110 in Wyoming and Delaware. In some embodiments, the map interface 405 (or another interface) displays the locations of payment terminals 105 and/or merchants 110 in different colors to indicate health information of each of the payment terminals 105 and/or merchants 110. For example, low anomaly level payment terminals 105 and/or merchants 110 may be displayed with green dots. Medium anomaly level payment terminals 105 and/or merchants 110 may be displayed with yellow dots. High anomaly level payment terminals 105 and/or merchants 110 may be displayed with red dots"; ¶[0049], flagging anomalous data (writing)).
Regarding claim 26, Cheung discloses a tangible computer-readable medium having stored thereon, computer executable instructions that, if executed by a computing device, cause the computing device to perform a method (¶[0023], "program storage area may store the instructions executed by the electronic processor 215 during performance of the method 300 of FIG. 3 explained below").
The remaining limitations of claim 26 are similar in scope to those of claim 12. Therefore, claim 26 is rejected for the same reasons as set forth in the rejection of claim 12, above.
Claim Rejections - 35 USC § 103
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.
Claim(s) 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Cheung (US 20220036363 A1) in view of Chis et al. (US 12388720 B1, hereinafter Chis).
Regarding claim 18, Cheung discloses the method of claim 17.
Cheung does not disclose that detected anomalies are validated by the user via the user interface.
Chis discloses that detected anomalies may validated by the user via the user interface (col. 1, lines 9-10, " machine learning models configured to detect or classify anomalies in performance indicator data of a communication network"; col. 11, lines 24-33, "Anomalies detected/classified by FSAD 206 may be validated by user 200 during inference phase. For example, user 200 may provide validation of an anomaly detected/classified by FSAD 206 via the user interface. User 200 may also provide to the training system, via the user interface, an indication of incorrect labelling by FSAD 206. Based on the feedback from user 200, for example the indication of incorrect labelling or a ratio between validly and incorrectly labelled anomalies, as indicated by user 200, the training system may determine to re-train FSAD 206").
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to combine the teachings of Cheung and Chis to arrive at a system in which detected anomalies are validated by the user via the user interface.
One of ordinary skill in the art would have been motivated because it would improve anomaly detection (Chis col. 11, lines 33-34).
Allowable Subject Matter
Claims 1-11 are allowed.
Claims 14 and 19 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
REASONS FOR ALLOWANCE
The following is an examiner’s statement of reasons for allowance:
The prior art of record fails to teach, neither singly nor in combination, the claimed limitations of “a transaction collecting module configured to receive the card transaction data and read selected card transactions through a datahose application; a data pipeline data lake configured to receive the datahose application written in real-time; a bridge configured to perform a scanning operation to detect when raw data has been input into a data pipeline data lake, and to trigger a partitioning operation when detected” as recited in claim 1. These limitations, in conjunction with other limitations in the independent claim(s), are not specifically disclosed or remotely suggested in the prior art of record. A review of claim(s) 1-11 indicates claim(s) 1-11 are allowable over the prior art of record.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
US 20190129821 A1, which discloses "Example metrics can include a number of transactions performed using a particular type of payment method (e.g., credit card, debit card, etc.), a number of transactions performed using a particular transaction network (e.g., Visa, MasterCard, etc.), a number of declined transactions, and so forth)" (¶[0075]), "anomalies in a dataset may be detected by platform 150 without advance notice of or information related to whether the data in the dataset pertains to metrics regarding requests sent to or received by computer systems 104a, 104b, operations performed by or on the computer systems 104a, 104b, or data storage on computer systems 104a, 104, among other types of metrics. In some cases, anomalies can be identified by determining a forecast value (or range of forecast values) of a particular performance metric at a given time. If the measured value of the performance metric at that time deviates from the forecast value (or range of forecast values), the platform 150 determines that an anomaly has occurred" (¶[0082]), and "platform 150 can identify one or more patterns and/or trends of the performance metric that occurred during in the past, and use these patterns and/or trends to predict the forecast range 302 for some time in the future" (¶[0085]).
US 20210232603 A1, which discloses " data ingestion service 202 may be configured to receive data from one or more data sources. For instance, data may be provided to the data ingestion service 202 via a change data capture bus or other such service. The data ingestion service 202 may then distinguish between data records that are new and updates to existing data records. New data records may be provided to the ingestion pipeline 204, while mutated records may be provided to the mutation pipeline 206. Such pipelines may be used to maintain read and write streams of data, like a messaging service, for consumption by one or more downstream jobs spawned by the data storage engine 208. In some configurations, such pipelines may be implemented via Apache Kafka" (¶[0031]), and "the data storage engine 208 may identify an appropriate data partition for a data entry. Then, the data storage engine 208 may append ingestion data to the appropriate data partition" (¶[0032]).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BORIS D GRIJALVA LOBOS whose telephone number is (571)272-0767. The examiner can normally be reached M-F 10:30AM to 6:30PM EST.
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, Jorge L Ortiz-Criado can be reached at 571-272-7624. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/BORIS D GRIJALVA LOBOS/ Primary Patent Examiner, Art Unit 2496
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