DETAILED ACTION
Claims 1-20 have been examined and are pending.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 11809585 and U.S. Patent No. 12124599. Although the claims at issue are not identical, they are not patentably distinct from each other.
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 claim(s) recite(s) determining a risk test score and a similarity score. This judicial exception is not integrated into a practical application because the generically recited computer elements do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because they are well-understood, routine, conventional computer functions.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over US Pat. No. 7984500 to Khanna et al. and further in view of US Pub. No. 2019/0340614 to Hanis et al. (hereinafter “Hanis”).
As to Claim 1, Khanna discloses a system comprising: at least one memory configured to store instructions, a test database including risk test parameters, a pattern database including pattern parameters, and a profile database including a risk profile; and at least one processor configured to execute the instructions and cause the system to perform,
in response to receiving interaction parameters of an interaction (Column 17 lines 55-60 of Khanna discloses receives information associated with a request being analyzed),
identifying a test from the test database based on the risk test parameters and the interaction parameters (Column 17 lines 60-67 of Khanna disclose determines which assessment tests to apply. The assessment tests to apply may be the same for each request or may instead be selected dynamically (e.g., based on the type of electronically accessible resource requested, based on information received for the request, based on the IP address making the request, based on previous requests, etc.)),
determining a risk test score based on rules of the test and the interaction parameters (Column 18 lines 1-25 of Khanna discloses applies the selected test to the request being analyzed. Indicate the fraud assessment score),
adjusting the risk test score based on the risk profile (Column 13 line 65 – column 14 line 5 of Khanna discloses some fraud assessment tests may further involve obtaining additional information not contained in the request from one or more third-parties (e.g., using whois to determine the country the referring hostname is registered in or the identity of the registrant, verifying an IP address using a reverse DNS lookup, obtaining information from a third-party information source to be analyzed, etc.) and using the obtained information in applying the fraud assessment test. Figure 5 of Khanna discloses a plurality of tests used, each modifying the score),
identifying a pattern from the pattern database based on the pattern parameters and the interaction parameters (Column 13 lines 60-65 of Khanna disclose previous requests may be used to determine if a combination of multiple requests are likely to together represent fraudulent activities, such as based on repeated patterns of activities),
determining a similarity score between the interaction parameters and the identified pattern (Column 13 lines 60-65 of Khanna disclose previous requests may be used to determine if a combination of multiple requests are likely to together represent fraudulent activities, such as based on repeated patterns of activities), and
adjusting the similarity score based on the risk profile (Column 13 line 65 – column 14 line 5 of Khanna discloses some fraud assessment tests may further involve obtaining additional information not contained in the request from one or more third-parties (e.g., using whois to determine the country the referring hostname is registered in or the identity of the registrant, verifying an IP address using a reverse DNS lookup, obtaining information from a third-party information source to be analyzed, etc.) and using the obtained information in applying the fraud assessment test. Figure 5 of Khanna discloses a plurality of tests used, each modifying the score).
Hanis further discloses comparing to patterns.
Paragraph [0009] of Hanis discloses creating a pattern image for the series of historical events from the event pattern layer, assigning a risk score to the pattern image, storing the pattern image in association with the risk score in a pattern library, and using the pattern library to establish that a series of current events is potentially fraudulent. A current pattern image can similarly be generated for the series of current events, and the pattern library includes multiple historical pattern images each having an associated risk score and is used to train a cognitive system, so the cognitive system can provide a current risk score based on risk scores associated with one or more likely matches from the pattern library to the current pattern image, and the series of current events is determined to be potentially fraudulent responsive to a determination that the current risk score exceeds a predetermined threshold.
It would have been obvious to one of ordinary skill in the art before the effective filing of the invention to combine the fraud detection system as disclosed by Khanna, with using a pattern library as disclosed by Hanis. One of ordinary skill in the art would have been motivated to combine to apply a known technique to a known device ready for improvement to yield predictable results. Khanna and Hanis are directed toward fraud detection systems and as such it would be obvious to use the techniques of one in the other. Using the techniques of Hanis in Khanna would improve the fraud detection capabilities.
As to Claim 2, Khanna-Hanis discloses the system of claim 1, wherein the memory further stores a threshold database including at least one risk test threshold and the system is further caused to perform: categorizing a level of risk of the risk test score based on the risk test score and the at least one risk test threshold (Column 4 lines 35-45 of Khanna disclose compared to one or more fraud score thresholds (e.g., a constant threshold for all requests, or a threshold that is selected for the request being evaluated, such as in a manner specific to a type of the request)).
As to Claim 3, Khanna-Hanis discloses the system of claim 2, wherein the categorizing categorizes the risk test score as low risk if the risk test score is less than a first risk test threshold (Column 4 lines 40-50 of Khanna disclose If the total fraud assessment score exceeds the selected threshold, the request may at least potentially reflect fraudulent activity).
As to Claim 4, Khanna-Hanis discloses the system of claim 3, wherein the categorizing categorizes the risk test score as moderate risk if the risk test score is greater than the first risk test threshold and less than a second risk test threshold (Column 4 lines 50-60 of Khanna disclose in some embodiments multiple degrees of likely fraudulence may be associated with a request based on the associated total fraud assessment score, such as based on multiple thresholds used with a particular request (e.g., with more extensive or severe types of corresponding actions taken for higher degrees of likely fraudulence)).
As to Claim 5, Khanna-Hanis discloses the system of claim 4, wherein the categorizing categorizes the risk test score as high risk if the risk test score is greater than the second risk test threshold (Column 4 lines 40-50 of Khanna disclose If the total fraud assessment score exceeds the selected threshold, the request may at least potentially reflect fraudulent activity).
As to Claim 6, Khanna-Hanis discloses the system of claim 5, wherein the system is further caused to perform, in response to the risk test score being categorized as moderate risk or high risk, generating and transmitting an alert (Column 4 lines 40-50 of Khanna disclose If the total fraud assessment score exceeds the selected threshold, the request may at least potentially reflect fraudulent activity).
As to Claim 7, Khanna-Hanis discloses the system of claim 1, wherein the memory further stores a threshold database including at least one similarity threshold and the system is further caused to perform: categorizing a level of similarity of the similarity score based on the similarity score and at least one similarity threshold (Column 4 lines 35-45 of Khanna disclose compared to one or more fraud score thresholds (e.g., a constant threshold for all requests, or a threshold that is selected for the request being evaluated, such as in a manner specific to a type of the request)).
As to Claim 8, Khanna-Hanis discloses the system of claim 7, wherein the categorizing categorizes the similarity score as low risk if the similarity score is less than a first similarity threshold (Column 4 lines 40-50 of Khanna disclose If the total fraud assessment score exceeds the selected threshold, the request may at least potentially reflect fraudulent activity).
As to Claim 9, Khanna-Hanis discloses the system of claim 8, wherein the categorizing categorizes the similarity score as moderate risk if the similarity score is greater than the first similarity threshold and less than a second similarity threshold (Column 4 lines 50-60 of Khanna disclose in some embodiments multiple degrees of likely fraudulence may be associated with a request based on the associated total fraud assessment score, such as based on multiple thresholds used with a particular request (e.g., with more extensive or severe types of corresponding actions taken for higher degrees of likely fraudulence)).
As to Claim 10, Khanna-Hanis discloses the system of claim 9, wherein the categorizing categorizes the similarity score as high risk if the similarity score is greater than the second similarity threshold (Column 4 lines 40-50 of Khanna disclose If the total fraud assessment score exceeds the selected threshold, the request may at least potentially reflect fraudulent activity).
As to Claim 11, Khanna-Hanis discloses the system of claim 10, wherein the system is further caused to perform, in response to the similarity score being categorized as moderate risk or high risk, generating and transmitting an alert (Column 4 lines 40-50 of Khanna disclose If the total fraud assessment score exceeds the selected threshold, the request may at least potentially reflect fraudulent activity).
As to Claim 12, Khanna-Hanis discloses the system of claim 10, wherein the system is further caused to perform in response to the similarity score being categorized as high risk and receiving interaction feedback indicating the interaction is fraudulent, updating known patterns of the pattern database based on a machine learning algorithm guided by the interaction parameters (Column 11 lines 10-15 of Khanna disclose automated learning based on updating tests to reflect current requests that are assessed).
As to Claim 13, Khanna-Hanis discloses the system of claim 12, wherein the known patterns are constructed from historical interaction parameters of interactions (Column 11 lines 10-15 of Khanna disclose automated learning based on updating tests to reflect current requests that are assessed).
As to Claim 14, Khanna-Hanis discloses the system of claim 12, wherein the known patterns are constructed by the machine learning algorithm and the machine learning algorithm is trained using historical interactions confirmed as being high risk (Column 11 lines 10-15 of Khanna disclose automated learning based on updating tests to reflect current requests that are assessed).
As to Claim 15, Khanna-Hanis discloses the system of claim 1, wherein the system is further caused to perform updating the risk profile to incorporate the risk test score and the similarity score (Column 11 lines 10-15 of Khanna disclose automated learning based on updating tests to reflect current requests that are assessed).
As to Claim 16, Khanna-Hanis discloses the system of claim 1, wherein the system is further caused to perform: storing the interaction and the interaction parameters in a review database for manual review; and pausing the interaction until the manual review is complete (Column 5 lines 45-50 of Khanna disclose the actions may also include providing information about the suspect third-party information sources or suspect senders to one or more humans for manual review).
As to Claim 17, Khanna-Hanis discloses the system of claim 16 wherein the interaction parameters include metadata that indicates a unique device that initiated a transaction and a geographical location of the unique device upon initiation of the transaction (Column 13 line 65 – column 14 line 5 of Khanna discloses some fraud assessment tests may further involve obtaining additional information not contained in the request from one or more third-parties (e.g., using whois to determine the country the referring hostname is registered in or the identity of the registrant, verifying an IP address using a reverse DNS lookup, obtaining information from a third-party information source to be analyzed, etc.) and using the obtained information in applying the fraud assessment test. Column 8 lines 50-55 of Khanna disclose a number of geographically specific domains).
As to Claim 18, Khanna-Hanis discloses the system of claim 17, wherein the system is further caused to perform reviewing the interaction parameters for a plurality of interactions to identify one or more anomalies among the plurality of interactions (Column 13 lines 60-65 of Khanna disclose previous requests may be used to determine if a combination of multiple requests are likely to together represent fraudulent activities, such as based on repeated patterns of activities).
As to Claim 19, Khanna-Hanis discloses the system of claim 1, wherein the risk profile includes a user risk score for one or more categories of risk (Column 10 lines 10-25 of Khanna disclose this example includes a fraud assessment test with a negative weight that is used to recognize legitimate domain names in which a user is trying to read email, such as for hostnames with "mail" or "webmail" as a subdomain (e.g., to prevent false positives for a domain name such as "mail.merchantLLL.com" or "mail.ispXXX.com", in which the incoming requests are based on a user viewing or otherwise interacting with a legitimate email sent by the target party to the user).
As to Claim 20, Khanna-Hanis discloses the system of claim 19, wherein each of the user risk scores is determined from historical user interactions (Column 11 lines 10-15 of Khanna disclose automated learning based on updating tests to reflect current requests that are assessed).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Kevin S Mai whose telephone number is (571)270-5001. The examiner can normally be reached Monday to Friday 9AM to 5PM.
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, Philip Chea can be reached at 5712723951. 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.
/KEVIN S MAI/Primary Examiner, Art Unit 2499
Prosecution Insights