DETAILED ACTION
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on September 3, 2026, has been entered.
Response to Amendment
The amendment filed on September 3, 2026, has been entered. Applicant has amended claims 2, 6-9 and 11; and canceled claim 10. Claims 2-9, 11 and 12 are now pending, have been examined and currently stand rejected.
Priority
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged.
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 .
Claim Rejections - 35 USC § 103
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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
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.
Claims 2-4 and 6-8 are rejected under 35 U.S.C. 103 as being unpatentable over Shinjo et al. (EP 1635307 A1) (hereinafter “Shinjo”) in view of Niu et al. (US 2014/0380446 A1) (hereinafter “Niu”) in view of Scragg (US 2011/0016052 A1).
Regarding Claim 2: Shinjo discloses a method, comprising:
identifying from a first image a first operator at a first terminal performing a first transaction utilizing a first account (See at least Shinjo [0043]; Fig. 1 step 130; Fig. 8 steps 810-820. Shinjo discloses identifying from a first image (e.g., an image of a face) a first operator (i.e., user) at a first terminal (i.e., ATM) performing a first transaction utilizing a first account (i.e., utilizing a first card number).);
identifying from a second image, a second operator at a second terminal performing a second transaction utilizing a second account (See at least Shinjo [0043]; Fig. 1 steps 130 and 140; Fig. 8 follow “Yes” path at step 820. Shinjo discloses identifying from a second image (e.g., an image of a face), a second operator (i.e., the user/the identical person) at a second terminal (i.e., ATM) performing a second transaction utilizing a second account (i.e., using a second/different card number [of a plurality of cards]).);
determining that the first operator and the second operator are a same operator (See at least Shinjo [0017]; [0043]; [0045]; [0053]. Shinjo discloses determining that the first operator and the second operator are a same operator (i.e., an identical person).);
determining that the second account is different from the first account (See at least Shinjo [0017]; [0022]; [0043]; [0061]. Shinjo discloses determining that the second account is different from the first account (i.e., different cards from the plural of cards).);
flagging the second transaction as potential fraud based on factors comprising the determination that the first operator and the second operator are the same operator, [and] the determination that the second account is different from the first account (See at least Shinjo [0017]; [0037-0039]; [0043]; [0045]; Fig. 1 step 150; Fig. 6; Fig. 8. Shinjo discloses flagging the second transaction as potential fraud (i.e., considering the second transaction as suspicious) based on factors comprising the determination that the first operator and the second operator are the same operator (i.e., are an identical person), [and] the determination that the second account is different from the first account (i.e., the identical person is using a plurality of cards).); and
suspending the second transaction at the second terminal when the second transaction is flagged as potential fraud (See at least Shinjo [0017]; [0038-0039]; [0043]; [0058]; Fig. 6 steps 140, 152 and 600. Shinjo discloses suspending (i.e., halting) the second transaction at the second terminal (i.e., ATM) when the second transaction is flagged as potential fraud (i.e., when the second transaction is considered suspicious).).
Shinjo discloses where a first operator (i.e., user) is identified from a first image (e.g., an image of a face) and where a second operator (i.e., user) is identified from a second image (e.g., an image of a face). Shinjo [0043]; Fig. 1 steps 130 and 140; Fig. 8. Shinjo further discloses that, upon using an ATM, face detection processing detects a face area in an image. Shinjo [0042]. When a face is detected, a face picture and other information about a user in a transaction are registered in a biometrics information database. Shinjo [0042]. Shinjo also discloses identifying various facial features (e.g., internal canthus, the corners of the eyes, eyebrow, and wings of nose, lips, contour, dispersion of pixels of an area, brightness and shade of pixels, etc.) and calculating a vector for these features. Shinjo [0065-0067]. Shinjo indicates that the vectorized information is then registered in a biometrics information database. Shinjo [0067]. Shinjo differs, in part, from the claimed invention because Shinjo does not explicitly disclose: wherein identifying the first operator comprises extracting, from the first image, first pixel features associated with a face of the first operator and creating, from the first pixel features, a face print that uniquely identifies the first operator; wherein identifying the second operator comprises extracting second pixel features associated with a face of the second operator from the second image; or where the determining that the first operator and the second operator are the same operator is by comparing the second pixel features to the face print.
Niu, on the other hand, teaches:
wherein identifying the first operator comprises extracting, from the first image, first pixel features associated with a face of the first operator and creating, from the first pixel features, a face print that uniquely identifies the first operator (See at least Niu [0007]; [0021-0024]; [0032]; [0035]. Niu teaches wherein identifying the first operator (i.e., the user) comprises extracting, from the first image (i.e., from a registered face image), first pixel features (i.e., face print features, e.g., pixels) associated with a face of the first operator (i.e., of the user) and creating (i.e., generating), from the first pixel features, a face print (i.e., face print/face print code) that uniquely identifies the first operator.);
wherein identifying the second operator comprises extracting second pixel features associated with a face of the second operator from the second image (See at least Niu [0007]; [0026]; [0028-0030]; [0032]; [0035]. Niu teaches wherein identifying the second operator (i.e., the user) comprises extracting second pixel features (i.e., face print features, e.g., pixels) associated with a face of the second operator from the second image (i.e., a current user’s facial image).); and
determining, by comparing the second pixel features to the face print, that the first operator and the second operator are a same operator (See at least Niu [0007]; [0026]; [0028-0030]; [0032]; [0035]. Niu teaches determining, by comparing the second pixel features to the face print, that the first operator and the second operator are a same operator (i.e., using facial recognition to determine whether the current user's facial image and the registered user's face image bear the same face print features).). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Shinjo’s method of identifying a first operator (i.e., user) from a first image (e.g., an image of a face) and second operator (i.e., user) from a second image (e.g., an image of a face) to include the teachings of Niu. One of skill in the art would have been motivated to include such features in order to determine whether a current user’s facial image has the same face print as that of a registered face image in a database (Niu [0035]).
Shinjo expresses a desire to prevent illegal transactions by using face recognition. Shinjo [0001]. Shinjo further discloses that crimes such as illegal withdrawals of deposits and use of counterfeit bills are often conducted concentratedly in a short period of time by an identical person. Shinjo [0009]; [0046]. However, Shinjo does not explicitly disclose: starting a timer upon completion of the first transaction; determining, from a current value of the timer at a start of the second transaction, that an elapsed time between completion of the first transaction and the start of the second transaction is within a preconfigured period of time; or where the flagging is based on the determination that the elapsed time is within the preconfigured period of time. Scragg, on the other hand, teaches:
starting a timer upon completion of the first transaction (See at least Scragg [0040-0041]. This feature is implicit since Scragg discloses that the number of minutes between authorizations can be tracked.);
determining, from a current value of the timer at a start of the second transaction, that an elapsed time between completion of the first transaction and the start of the second transaction is within a preconfigured period of time (See at least Scragg [0040-0041]. Scragg teaches determining, from a current value of the timer at a start of the second transaction (i.e., at the start of a second/subsequent authorization), that an elapsed time between completion of the first transaction and the start of the second transaction (i.e., minutes since the last authorization) is within a preconfigured period of time (i.e., is less than 20 minutes).); and
where the flagging is based on the determination that the elapsed time is within the preconfigured period of time (See at least Scragg [0040-0041]. Scragg teaches where the flagging (i.e., fraud indication) is based on the determination that the elapsed time (i.e., minutes since the last authorization) is within the preconfigured period of time (i.e., is within 20 minutes).).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate starting a timer upon completion of the first transaction; determining, from a current value of the timer at a start of the second transaction, that an elapsed time between completion of the first transaction and the start of the second transaction is within a preconfigured period of time; and where the flagging is based on the determination that the elapsed time is within the preconfigured period of time, as taught/suggest by Scragg, into Shinjo’s method of preventing illegal transactions which could occur in a short period of time. One of ordinary skill in the art would have been motivated to include such features in order to trigger a fraud indication when authorizations are exceeding a certain velocity (Scragg [0040-0041]).
Regarding Claim 3: The combination of Shinjo, Niu and Scragg discloses the method of claim 2. Shinjo further discloses preventing the second transaction from completing based on the flagging (See at least Shinjo [0017]; [0038-0039]; [0043]; [0058]; Fig. 6 steps 140, 152 and 600. Shinjo discloses preventing (i.e., halting) the second transaction from completing based on the flagging (i.e., based on considering the second transaction as suspicious).).
Regarding Claim 4: The combination of Shinjo, Niu and Scragg discloses the method of claim 2. Shinjo further discloses wherein flagging further includes sending the factors to a remote terminal for determining the flagging (See at least Shinjo [0017]; [0037]; [0045]; Fig. 6. Shinjo discloses wherein flagging (i.e., wherein considering the second transaction as suspicious) further includes sending (i.e., transferring) the factors (e.g., a signal indicating abnormality, the face image of a suspicious person inputted from a camera, biometric information, attribute information such as card number(s)) to a remote terminal (e.g., to other apparatuses connected through a network) for determining the flagging.).
Regarding Claim 6: The combination of Shinjo, Niu and Scragg discloses the method of claim 2. Shinjo further discloses wherein identifying the first operator from the first image further includes capturing, by a camera, the first image when a first card is inserted into the first terminal for the first transaction, wherein the first card is associated with the first account (See at least Shinjo [0017]; [0029]; [0033]; [0043]; Fig. 8. Shinjo discloses wherein identifying the first operator from the first image (e.g., the image of a face) further includes capturing, by a camera, the first image (i.e., taking/inputting, by a camera, the face picture) when a first card (i.e., card) is inserted into the first terminal (i.e., inserted into the ATM) for the first transaction, wherein the first card is associated with the first account (i.e., first card number).).
Regarding Claim 7: The combination of Shinjo, Niu and Scragg discloses the method of claim 6. Shinjo further discloses wherein identifying the second operator from the second image further includes capturing, by another camera, the second image when a second card is inserted into the second terminal for the second transaction, wherein the second card is associated with the second account (See at least Shinjo [0017]; [0029]; [0033]; [0043]; Fig. 8. Shinjo discloses wherein identifying the second operator from the second image (e.g., the image of a face) further includes capturing, by another camera, the second image (i.e., taking/inputting, by a camera, the face picture, e.g., the picture of the suspicious person) when a second card (i.e., card) is inserted into the second terminal (i.e., inserted into the ATM) for the second transaction, wherein the second card is associated with the second account (i.e., second/different card number).).
Regarding Claim 8: The combination of Shinjo, Niu and Scragg discloses the method of claim 2. Shinjo further discloses wherein flagging further includes using first features of the first operator from the first image and second features of the second operator from the second image to confirm that the first operator and the second operator are a same operator who performed the first transaction at the first terminal and is attempting to perform the second transaction at the second terminal (See at least Shinjo [0017]; [0041-0043]; [0065-0068]; Fig. 12. Shinjo discloses wherein flagging further includes using first features (e.g., facial features) of the first operator from the first image and second features (e.g., facial features) of the second operator from the second image to confirm that the first operator (i.e., user/person) and the second operator (i.e., user/person) are a same operator (i.e., identical person) who performed the first transaction at the first terminal and is attempting to perform the second transaction at the second terminal.).
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Shinjo in view of Niu in view of Scragg, as applied above, and further in view of Manapat et al. (US 2023/0230090 A1) (“Manapat”).
Regarding Claim 5: The combination of Shinjo, Niu and Scragg discloses the method of claim 2. Shinjo further discloses providing (i.e., transferring) the factors (e.g., a signal indicating abnormality, the face image of a suspicious person inputted from a camera, biometric information, attribute information such as card number(s)) to another entity and/or device (e.g., a watchmen, bank clerk, other apparatuses in the network, etc.). Shinjo [0017]; [0037]; [0045]; [0058]; Fig. 6. However, Shinjo does not explicitly disclose wherein flagging further includes providing the factors to a machine-learning algorithm as input and determining the flagging based on output from the machine-learning algorithm. Manapat, on the other hand, teaches wherein flagging further includes providing the factors to a machine-learning algorithm as input and determining the flagging based on output from the machine-learning algorithm (See at least Manapat [0032]; [0034]; [0061-0062]; [0067]; [0071]; [0113]; Fig. 9. Manapat teaches wherein flagging further includes providing the factors (i.e., features/attributes) to a machine-learning algorithm (i.e., machine learning model) as input and determining the flagging (i.e., flagged as likely fraudulent) based on output (i.e., based on a score) from the machine-learning algorithm.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Shinjo’s method of transferring factors (e.g., a signal indicating abnormality, the face image of a suspicious person inputted from a camera, biometric information, attribute information such as card number(s)) to other apparatus in the network to include the teachings of Manapat. One of skill in the art would have been motivated to include such features in order to use machine learning to assess the risk of each attempted transaction and automatically block those transactions predicted to have an excessive risk of fraud (Manapat [0032]). Claims 9 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Shinjo in view of Niu in view of Scragg, as applied above, and further in view of Crowell et al. (US 2015/0066764 A1) (“Crowell”).
Regarding Claim 9: The combination of Shinjo, Niu and Scragg discloses the method of claim 2. Shinjo expresses a desire to prevent illegal transactions by using face recognition. Shinjo [0001]. Shinjo further discloses a desire to detect persons who conduct suspicious acts, such as the peeping of a personal identification number when others are using an ATM. Shinjo [0013]. However, Shinjo, as modified, does not explicitly disclose wherein identifying the first operator from the first image further includes identifying a first behavior of the first operator from the first image, wherein identifying the second operator from the second image further includes identifying a second behavior of the second operator from the second image and using the first behavior and the second behavior as additional ones of the factors during the flagging. Crowell, on the other hand, teaches wherein identifying the first operator from the first image further includes identifying a first behavior of the first operator from the first image, wherein identifying the second operator from the second image further includes identifying a second behavior of the second operator from the second image and using the first behavior and the second behavior as additional ones of the factors during the flagging (See at least Crowell [0007]; [0015]; [0020]; Crowell Claim 14. Crowell teaches wherein identifying the first operator from the first image (i.e., an image/frame of the continuously monitored images) further includes identifying a first behavior (i.e., emotion) of the first operator from the first image, wherein identifying the second operator from the second image (i.e., an image/frame of the continuously monitored images) further includes identifying a second behavior (i.e., emotion) of the second operator from the second image and using the first behavior and the second behavior (i.e., using the emotions) as additional ones of the factors during the flagging (i.e., during the alerting).). In view of the teachings provided by Crowell it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate wherein identifying the first operator from the first image further includes identifying a first behavior of the first operator from the first image, wherein identifying the second operator from the second image further includes identifying a second behavior of the second operator from the second image and using the first behavior and the second behavior as additional ones of the factors during the flagging into Shinjo’s method of preventing illegal transactions based on detecting suspicious acts. One of skill in the art would have been motivated to include such features in order to provide enhanced security when authenticating a user attempting to perform a financial transaction by implementing facial recognition and emotion detection algorithms to thwart fraudulent activity (Crowell [0023]).
Regarding Claim 11: The combination of Shinjo, Niu, Scragg and Crowell discloses the method of claim 9. Shinjo further discloses identifying the first operator from the first image further includes identifying first transaction details for the first transaction, wherein identifying the second operator from the second image further includes identifying second transaction details for the second transaction and using the first transaction details and the second transaction details as a further ones of the factors during the flagging (See at least Shinjo [0017]; [0037-0039]; [0043]; [0045]; Fig. 1 step 130; Fig. 6 step 130; Fig. 8 step 820. Shinjo discloses identifying the first operator from the first image (e.g., an image of a face) further includes identifying first transaction details for the first transaction (e.g., who the user is conducting the transaction, what card number is being used for the transaction), wherein identifying the second operator from the second image (e.g., an image of a face) further includes identifying second transaction details for the second transaction (e.g., who the user is conducting the transaction, what card number is being used for the transaction) and using the first transaction details and the second transaction details as a further ones of the factors during the flagging (i.e., as factors during the considering the transaction as suspicious).).
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Shinjo in view of Niu in view of Scragg in view of Crowell, as applied above, and further in view of Manapat et al. (US 2023/0230090 A1) (“Manapat”).Regarding Claim 12: The combination of Shinjo, Niu, Scragg and Crowell discloses the method of claim 11. Shinjo further discloses generating a fraud score (i.e., caution level) and adjusting the processing of the transaction based on the caution level. Shinjo [0045]; [0052-0054]. However, the combination of Shinjo, Niu, Scragg and Crowell does not explicitly disclose wherein flagging further includes generating a fraud score based on the factors and comparing the fraud score against a threshold score causing the second transaction to be halted at the second terminal. Manapat, on the other hand, teaches wherein flagging further includes generating a fraud score based on the factors and comparing the fraud score against a threshold score causing the second transaction to be halted at the second terminal (See at least Manapat [0032]; [0034]; [0055]; [0061-0062]; [0067]; Fig. 9. Manapat teaches wherein flagging (i.e., flagging, e.g., flagging for further review) further includes generating a fraud score based (i.e., fraud likelihood score (or "fraud score")) on the factors (i.e., features/attributes) and comparing the fraud score against a threshold score (i.e., permissible threshold) causing the second transaction (i.e., transaction) to be halted (i.e., blocked) at the second terminal (i.e., system).). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Shinjo’s method of adjusting the transaction processing based on a caution level, to include the teachings of Manapat, in order to block/stop transactions that have a high likelihood of fraud (Manapat [0055]).
Response to Arguments
Claim Rejections – 35 U.S.C. § 103
Applicant argues that Shinjo does not disclose the claimed timer-based correlation of the first and second transactions. In particular, applicant argues that Shinjo does not disclose starting a timer upon completion of the first transaction. Shinjo also does not disclose obtaining a current value of that timer at the start of the second transaction or determining from that current value whether an elapsed time between completion of the first transaction and the start of the second transaction is within a preconfigured period of time. Amendment, pp. 6-7. Examiner agrees. Examiner has added a new reference, Scragg, to the prior art rejection to teach the timer-based features. Examiner contends that the combination of Shinjo, Niu and Scragg renders claim 2 obvious.
Applicant argues that Niu does not create a registered face print from first pixel features extracted from an image of a first operator performing a first transaction. Applicant also alleges that Niu does not extract second pixel features from an image of a second operator performing a second transaction and compare those second pixel features with the first-transaction face print to determine that the first and second operators are the same operator. Amendment, p. 7. Examiner acknowledges that the facial recognition and face print features disclosed by Niu are not in the context of performing a transaction (e.g., a first transaction and/or a second transaction), however the primary reference, Shinjo, already discloses that it was known in the art to identify from a first image (e.g., an image of a face) a first operator (i.e., user) at a first terminal (i.e., ATM) and to identify from a second image (e.g., an image of a face), a second operator (i.e., the user/the identical person) at a second terminal (i.e., ATM). Niu is merely used to teach the particular type of image recognition and comparison that is utilized. Examiner notes that one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).
Applicant argues that Tunnell does not disclose creating a face print from first pixel features captured during a first transaction and comparing second pixel features obtained during a second transaction with that face print to determine that the first and second operators are the same operator. Amendment, pp. 7-8. Applicant also argues that Tunnell does not disclose starting a timer upon completion of the first transaction, obtaining the timer's current value at the start of the second transaction, determining that the second account differs from the first account, or flagging the second transaction based on the three determinations recited in claim 2. Amendment, pp. 7-8. Examiner agrees, however it is noted that Tunnell was not, and is not, used to teach any of these particular features. As indicated in applicant’s remarks (see Amendment at p. 7), the features Tunnell was used to teach in the prior office action have been deleted from claim 2.
Applicant argues that Roumeliotis does not disclose the timer operations recited in claim 2. Amendment, p. 8. Examiner agrees. While Roumeliotis discloses tracking the amount of time in between transactions and using the elapsed time as one of the factors in flagging a card (see e.g., Roumeliotis Col. 9 line 7 – Col. 10 line 19; Fig. 3 items 234 and 240), Examiner agrees that Roumeliotis does not explicitly start a timer in response to completion of the first transaction and obtaining a current value of that timer at the start of the second transaction, where the current value is then used to determine the elapsed time between the completion and start events.
Applicant also argues that Roumeliotis does not identify an observed operator across two transactions by creating a face print from pixel features extracted during the first transaction and comparing pixel features extracted during the second transaction with that face print. Amendment, pp. 8-9. Examiner agrees. However, Roumeliotis was not, and is not, used to teach these particular features.
Applicant argues that the cited teachings do not produce claim 2 as a whole. Amendment, pp. 9-10. Examiner contends that Shinjo and Niu continue to disclose/teach/suggest a significant portion of claim 2, however, as indicated above, Shinjo and Niu do not explicitly disclose the newly added timer related elements recited in amended claim 2. However, Examiner contends that the combination of Shinjo, Niu and the newly added reference of Scragg renders claim 2 obvious.
With respect to claim 9, Applicant argues that neither Tunnell nor Crowell discloses using respective behaviors identified from the first and second transaction images as additional factors in the claimed transaction-to-transaction correlation. Amendment, pp. 10-11. Examiner respectfully disagrees. Examiner contends that Crowell teaches wherein identifying the first operator from the first image (i.e., an image/frame of the continuously monitored images) further includes identifying a first behavior (i.e., emotion) of the first operator from the first image, wherein identifying the second operator from the second image (i.e., an image/frame of the continuously monitored images) further includes identifying a second behavior (i.e., emotion) of the second operator from the second image and using the first behavior and the second behavior (i.e., using the emotions) as additional ones of the factors during the flagging (i.e., during the alerting). Crowell [0007]; [0015]; [0020]; Crowell Claim 14.
With respect to claim 9, Applicant also alleges that neither Tunnell nor Crowell discloses a method in which the behaviors are combined with: a determination, based on comparing second pixel features with a first transaction face print, that the operators are the same; a determination that the accounts differ; and a determination made from the current value of a timer started upon completion of the first transaction. Amendment, p. 11. Examiner agrees.
With respect to claim 11, Applicant argues that Shinjo does not disclose the claimed use of first and second transaction details as further factors together with the image-derived behaviors and the determinations recited in claim 2. Amendment, p. 11. Examiner respectfully disagrees. Examiner contends that Shinjo discloses identifying the first operator from the first image (e.g., an image of a face) further includes identifying first transaction details for the first transaction (e.g., who the user is conducting the transaction, what card number is being used for the transaction), wherein identifying the second operator from the second image (e.g., an image of a face) further includes identifying second transaction details for the second transaction (e.g., who the user is conducting the transaction, what card number is being used for the transaction) and using the first transaction details and the second transaction details as a further ones of the factors during the flagging (i.e., as factors during the considering the transaction as suspicious. Shinjo [0017]; [0037-0039]; [0043]; [0045]; Fig. 1 step 130; Fig. 6 step 130; Fig. 8 step 820.
With respect to claim 12, Applicant argues that Manapat does not teach generating the claimed fraud score from the factors produced by the sequence inherited from claims 2, 9, and 11. Amendment, pp. 11-12. Examiner acknowledges that Manapat does not teach claims 2, 9 or 11 as a whole, however, as previously noted, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).
For the above reasons, and for those set forth in the 35 U.S.C. § 103 rejection seen above, the 35 U.S.C. § 103 rejection is maintained.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure is cited in the Notice of References Cited (PTO-892). The additional cited art further establishes the state of the art prior to the effective filling date of Applicant’s claimed invention.
O’Reilly et al. (US 2014/0067679 A1) discloses where an ATM stores transaction data every time a card user inserts their card and performs a transaction. The transaction data includes a start and end time that a transaction took place. O’Reilly [0075]. O’Reilly also discloses the use of a timer or other trigger that is used to retrieve a certain number of transactions. The start and end time of these transactions is used to retrieve the corresponding video for the transactions. O’Reilly [0079].
Paliga et al. (US 2014/0232863 A1) discloses a video analytics system that stores searchable time-stamped transactional data indicative of activity within a monitored system and stores time-stamped video metadata. A rule based correlation server compares the time-stamped transaction data with the time-stamped video metadata in order to identify events indicating potential activity of interest. Paliga Abstract; [0052]. Paliga also discloses where various data is used to determine if the same person, or the same license plate does more than one transaction in a row using the same card reader, but different cards. Paliga [0044]; [0099].
Acuna-Rohter (US 2015/0142595 A1) discloses that if a transaction is flagged as potentially fraudulent, the transaction is either blocked (e.g., a denial flag is generated by one or more computers) or forwarded to another module or system for automated or manual review. If a transaction is ultimately not flagged as potentially fraudulent, the transaction is flagged as allowable at (e.g., an authorization flag is generated). Acuna-Rohter [0113].
Rhee et al. (US 2019/0286884 A1) discloses generating feature information associated with a user face, based on a 3D shape information and pixel color values of a normalized 2D input image, and determining whether the user face included in the 2D input image correlates with a stored 2D image of an enrolled user face. Rhee [0007].
Xu et al. (US 2017/0352015 A1) discloses where body gestures can be analyzed in order to look for suspicious behavior in both a user of an ATM and people in the background. A body gesture model database can be built and trained using a body gesture model of many different behaviors. The body gesture model database is trained with sequences of images of a large number of different body gestures, with some of the body gestures in the body gesture model database indicating normal behavior and other body gestures in the body gesture model database 32 indicating suspicious behavior.
Roumeliotis (US 9,786,015 B1) teaches setting a timer (this is implied since Roumeliotis tracks the amount of time between transactions), comparing an elapsed time of the timer (i.e., a time since the last transaction) to a threshold (i.e., a particular time period (e.g., a half-hour, 2 hours, etc.)), and using the elapsed time compared to the threshold as one of the factors during the flagging (i.e., as one of the factors to flag the cards). Roumeliotis Col. 9 line 7 – Col. 10 line 19; Fig. 3 items 234 and 240.
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/J.F./Examiner, Art Unit 3698
/PATRICK MCATEE/Supervisory Patent Examiner, Art Unit 3698
