DETAILED ACTION
Claims 2-21 are presented for examination. A preliminary amendment filed 3/5/25 cancelled claim 1 and added claims 2-21.
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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 3/26/25 has been considered by the examiner.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 2-21 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No.12,261,872. Although the claims at issue are not identical, they are not patentably distinct from each other because the claims of the parent patent anticipate those of the instant application. For illustration, consider claim 2 of the instant application compared to claim 1 of the ‘872 patent:
Claim 2 of the instant application
Claim 1 of the ‘872 patent
A computer-implemented method comprising:
by a system of one or more computers, accessing a plurality of datasets storing customer information comprising, at least, a plurality of transactions associated with a plurality of customers, each transaction indicating a plurality of features;
generating, based on application of one or more machine learning models, individual risk scores for the plurality of customers based on the customer information, wherein generating the risk scores is based on occurrences of scenario definitions that individually specify expressions utilizing one or more object types and a subset of the features, the object types being defined in accordance with an ontology; and
causing presentation, via a user device, of an interactive user interface, wherein the interactive user interface enables an investigation into whether a particular customer is exhibiting risky behavior.
A computer-implemented method comprising:
by a system of one or more computers, accessing a plurality of datasets storing customer information comprising, at least, a plurality of transactions associated with a plurality of customers, each transaction indicating a plurality of features;
generating individual risk scores for the plurality of customers based on the customer information, wherein generating the risk scores comprises: identifying occurrences of scenario definitions, wherein each scenario definition specifies one or more object types of a plurality of object types and expressions utilizing the object types and one or more of the features, wherein a data pipeline is applied to the datasets, wherein the data pipeline causes extraction of object types in accordance with an ontology,
wherein an occurrence of a scenario definition indicates satisfaction of the specified expression with respect to customer information, and providing the identified occurrences and customer information as input to one or more machine learning models, wherein the machine learning models assign respective risk scores to the customers; and
causing presentation, via a user device, of an interactive user interface, wherein the interactive user interface: presents summary information associated with the risk scores, wherein the interactive user interfaces enables an investigation into whether a particular customer is exhibiting risky behavior, and responds to user input indicating feedback usable to update the one or more machine learning models or scenario definitions, wherein the feedback triggers updating of the machine learning models.
As can be seen, all limitations of the instant claim 2 are present in near-verbatim form to the corresponding claim of the ‘872 patent; thus, any invention that would infringe the ‘872 patent would also necessarily infringe the instant application, resulting in two patents on the same invention. Independent claims 9 & 16 of the instant application are likewise parallel to claims 8 & 15 of the ‘872 patent and are rejected for substantially similar reasons as discussed supra. Dependent claims 3-8, 10-15, & 17-21 of the instant application are likewise parallel to claims 2-8, 10-14, & 16-20 of the ‘872 patent and are rejected for substantially similar reasons as discussed supra.
Claim Rejections - 35 USC § 102
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.
Claims 2-21 are rejected under 35 U.S.C. 102(a)(1) and 35 U.S.C. 102(a)(2) as being anticipated by Lee (U.S. Patent Publication 2020/0265356).
Regarding claims 2, 9, and 16:
Lee discloses a computer-implemented method, system, and non-transitory computer storage medium comprising: by a system of one or more computers, accessing a plurality of datasets storing customer information comprising, at least, a plurality of transactions associated with a plurality of customers, each transaction indicating a plurality of features (paragraph 0103: “In example embodiments, relationships between underlying datasets (e.g., a feedback data set, customer database, account valuation, customer activity, or robo-advisor activity), AI algorithms (e.g., average/min/max/standard deviation algorithms, value chain analysis algorithms, click rate/acceptance rate algorithms, and robo-advisor performance algorithms), and key metrics (e.g., feedback score metrics, and customer action vs. robo-advisor recommended actions) are depicted (e.g., for a particular date or range of dates associated with a replay or simulation of a particular scenario)”; and paragraph 0110: “Some example deployments may leverage Insider Threat Prevent, Cybersecurity, or Anti-Money Laundering/Bank Secrecy Act (AML/BSA) technology for more effective transaction monitoring, utilizing sophisticated AI models to drive more efficient decision making in compliance and credit risk management.”; see also paragraphs 0150-0154 for more details regarding the invention’s use in transaction monitoring); generating, based on application of one or more machine learning models, individual risk scores for the plurality of customers based on the customer information, wherein generating the risk scores is based on occurrences of scenario definitions that individually specify expressions utilizing one or more object types and a subset of the features (e.g. paragraph 0175: “Instead of focusing on certain users' behavior, our solution is to establish a Risk Context (“Risk Context”) around every user that could be different from other people or business. The Risk Context for each of the users is established by 6 key holistic factors (e.g., Sub-Contexts). They are access behavior, communication behavior, financial status, performance, sentiments, external device access”; paragraph 0187: “The Final Probabilistic Risk Assessment (FPRA)=f.of (Rating from Algo-D, Rating from Algo-AI, Operational mode, Confidence booster actor)”, paragraph 0201: “The AI is applied to generate a probabilistic assessment (e.g., a risk rating for a user A given the context of changes in access behavior or changes in financial status)”, and paragraph 0202: “Results of applications of the multiple AIs are consolidated to generate a probabilistic assessment (e.g., a risk rating for a user A given the context of changes in access behavior or changes in financial status)”, the object types being defined in accordance with an ontology (paragraph 0079: “…a guard rail set reference id (e.g., for complex business rules, the expected or allowed data ontology may be stored in a separate database that is referenced from this data item) …”; see also paragraphs 0095-0096 and 0132) and causing presentation, via a user device, of an interactive user interface, wherein the interactive user interface enables an investigation into whether a particular customer is exhibiting risky behavior (Figures 9-14).
Regarding claims 3, 10, and 17: Lee further discloses wherein identifying an occurrence of a scenario definition comprises: accessing raw data associated with a customer, wherein the raw data is transformed via the ontology (paragraph 0215: “As depicted in FIG. 24B, the AI platform (e.g., via start.py module executing at a data layer), at 1, fetches input data (e.g., from one or more data sources). At 2, the input data is transformed (e.g., into a standardized format by transform.py module) for subsequent processing” see also paragraphs 0095-0096 regarding ontology); and analyzing, via an expression of the scenario definition, the transformed raw data (paragraph 0215: “At 5, output is generated (e.g., by a primary.py module)”).
Regarding claims 4, 11, and 18: Lee further discloses wherein the interactive user interface presents summary information associated with the risk scores (Figure 13, and paragraph 0104).
Regarding claims 5, 12, and 19: Lee further discloses wherein the interactive user interface responds to user input indicating feedback usable to update the one or more machine learning models (paragraphs 0100 & 0103; and Figures 9-12).
Regarding claims 6 and 13: Lee further discloses wherein the feedback is indicative of feedback regarding a particular scenario definition (Ibid; see also paragraph 0181).
Regarding claims 7, 14, and 20: Lee further discloses wherein the interactive user interface adjusts a definition of a particular scenario definition based on the investigation, and wherein the adjustment includes updating of a value included in a specified expression for the particular scenario definition, the value being determined by the system (paragraph 0080: “Playback of an entire state or scenario handled by an algorithm (e.g., including incoming messages, outgoing messages, data used, variables used, and so on) is another feature provided by AI Cronus”; and paragraph 0103: “In example embodiments, relationships between underlying datasets (e.g., a feedback data set, customer database, account valuation, customer activity, or robo-advisor activity), AI algorithms (e.g., average/min/max/standard deviation algorithms, value chain analysis algorithms, click rate/acceptance rate algorithms, and robo-advisor performance algorithms), and key metrics (e.g., feedback score metrics, and customer action vs. robo-advisor recommended actions) are depicted (e.g., for a particular date or range of dates associated with a replay or simulation of a particular scenario)”).
Regarding claims 8, 15, and 21: Lee further discloses wherein the feedback indicates that a risk score assigned to the particular customer was indicative of risky behavior, wherein an outcome of the investigation into the particular customer indicated false positive, and wherein the machine learning models are updated based on the outcome (paragraphs 0200-0203; false positives at 0094, 0118-0119, & 0178).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
U.S. Patent Publication 2022/0172211 (Muthuswamy)
U.S. Patent Publication 2020/0089848 (Abdelaziz)
U.S. Patent Publication 2019/0378050 (Edkin)
U.S. Patent Publication 2019/0311367 (Reddy)
U.S. Patent Publication 2016/0203575 (Madhu)
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Thomas A Gyorfi whose telephone number is (571)272-3849. The examiner can normally be reached 10:00am - 6:30pm.
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, Amir Mehrmanesh can be reached at 571-270-3351. 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.
THOMAS A. GYORFI
Examiner
Art Unit 2435
/THOMAS A GYORFI/Examiner, Art Unit 2435 5/30/2026
Prosecution Insights