DETAILED ACTION
Response to Amendment
The amendment filed on February 26, 2026 has been entered. Applicant has amended claim 2. Claims 2-12 remain 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 . 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 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.
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 Tunnell et al. (US 2018/0012227 A1) (hereinafter “Tunnell”).
Regarding Claim 2: Shinjo discloses a method, comprising:
identifying from a first image an 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) an 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 the 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) the 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]).);
flagging the second transaction as potential fraud based on factors associated with the operator, the first transaction, and the second transaction (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 associated with the operator (e.g., the face image), the first transaction (e.g., the first card number), and the second transaction (e.g., the second card number).);
generating a fraud score from the face print and behaviors of the operator observed during the first transaction and the second transaction, and adjusting the fraud score based on transaction details for the first transaction and the second transaction to perform the flagging (See at least Shinjo [0043]; [0045]; [0052-0054]. Shinjo discloses generating a fraud score (i.e., a caution level) from the face print (i.e., facial information) and behaviors of the operator (e.g., peeping behaviors, such as being peeped and/or peeping another person) observed during the first transaction and the second transaction, and adjusting the fraud score (i.e., increasing or decreasing the rank of the caution level) based on transaction details for the first transaction and the second transaction (e.g., based on transaction details such as if an identical person used different cards/accounts for different transactions) to perform the flagging (i.e., to identify the transaction as suspicious).); 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 an operator (i.e., user) is identified from a first image (e.g., an image of a face) and 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: identifying an operator by extracting pixel features for a face of the operator from the first image and creating a face print that uniquely identifies the operator (emphasis added); or identifying the operator by extracting pixel features for the face of the operator from the second image and comparing the extracted pixel features against the face print created from the first image (emphasis added).
Niu, on the other hand, teaches:
identifying from a first image an operator by extracting pixel features for a face of the operator from the first image and creating a face print that uniquely identifies the operator (See at least Niu [0007]; [0021-0024]; [0032]; [0035]. Niu teaches identifying from a first image (i.e., a registered face image) an operator (i.e., a user) by extracting pixel features (i.e., face print features, e.g., pixels) for a face of the operator (i.e., of the user) from the first image (i.e., from the registered face image) and creating (i.e., generating) a face print (i.e., face print/face print code) that uniquely identifies the operator.); and
identifying from a second image the operator by extracting pixel features for the face of the operator from the second image and comparing the extracted pixel features against the face print created from the first image (See at least Niu [0007]; [0026]; [0028-0030]; [0032]; [0035]. Niu teaches identifying from a second image (i.e., a current user’s facial image) the operator (i.e., the user) by extracting pixel features (i.e., face print features, e.g., pixels) for the face of the operator (i.e., of the user) from the second image (i.e., from the current user’s facial image) and comparing the extracted pixel features against the face print created from the first image (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).). 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 identifying an operator (i.e., user) from a first image (e.g., an image of a face) and a second image (e.g., an image of a face), to include the teachings of Niu, 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 discloses generating a fraud score (i.e., caution level) from various attributes (e.g., facial information, behavior, etc.). Shinjo [0043]; [0045]; [0052-0054]. Shinjo differs, in part, from the claimed invention because Shinjo does not explicitly disclose generating a fraud score from facial expressions of the operator.
Tunnell, on the other hand, teaches generating a fraud score from facial expressions of the operator (See at least Tunnell Abstract; [0032]; [0103]; [0105-0107]; [0126]; [0158]; Fig. 5. Tunnell teaches generating a fraud score (i.e., risk score) from facial expressions of the operator (i.e., of the user).).
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 generating a fraud score (i.e., caution level) from various attributes (e.g., facial information, behavior, etc.) to include the teachings of Tunnell. One of skill in the art would have been motivated to include such features in order to generate a risk score (e.g., a caution level) based on one or more extracted features, frames, states, or dimensions (Tunnell [0142]).
Regarding Claim 3: The combination of Shinjo, Niu and Tunnell 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 Tunnell 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 Tunnell discloses the method of claim 2. Shinjo further discloses wherein identifying 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 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 Tunnell discloses the method of claim 6. Shinjo further discloses wherein identifying 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 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 Tunnell discloses the method of claim 2. Shinjo further discloses wherein flagging further includes using first features of the operator from the first image and second features of the operator from the second image to confirm that the operator is a same individual that 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 operator from the first image and second features (e.g., facial features) of the operator from the second image to confirm that the operator (i.e., user/person) is a same individual (i.e., identical person) that 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 Tunnell, 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 Tunnell 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.). 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 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, 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]).
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Shinjo in view of Niu in view of Tunnell, as applied above, and further in view of Roumeliotis (US 9,786,015 B1).
Regarding Claim 9: The combination of Shinjo, Niu and Tunnell discloses the method of claim 2. 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]. However, Shinjo does not explicitly disclose wherein identifying from the first image further includes setting a timer, wherein identifying from the second image further includes comparing an elapsed time of the timer to a threshold, and using the elapsed time compared to the threshold as one of the factors during the flagging. Roumeliotis, on the other hand, teaches setting a timer, comparing an elapsed time of the timer to a threshold, and using the elapsed time compared to the threshold as one of the factors during the flagging (See at least Roumeliotis Col. 9 line 7 – Col. 10 line 19; Fig. 3 items 234 and 240. Roumeliotis 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).). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include wherein identifying from the first image further includes setting a timer, wherein identifying from the second image further includes comparing an elapsed time of the timer to a threshold, and using the elapsed time compared to the threshold as one of the factors during the flagging, as taught/suggest by Roumeliotis, 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 detect potentially fraudulent activity by measuring the velocity of money outflows from multiple accounts (Roumeliotis Col. 9 lines 29-31).
Claims 10 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Shinjo in view of Niu in view of Tunnell in view of Roumeliotis, as applied above, and further in view of Crowell et al. (US 2015/0066764 A1) (“Crowell”).
Regarding Claim 10: The combination of Shinjo, Niu, Tunnell and Roumeliotis discloses the method of claim 9. 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 from the first image further includes identifying a first behavior of the operator from the first image, wherein identifying from the second image further includes identifying a second behavior of the 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 from the first image further includes identifying a first behavior of the operator from the first image, wherein identifying from the second image further includes identifying a second behavior of the 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 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 operator from the first image, wherein identifying 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 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).). 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 preventing illegal transactions based on detecting suspicious acts, to include the teachings of Crowell, 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, Tunnell, Roumeliotis and Crowell discloses the method of claim 10. Shinjo further discloses identifying from the first image further includes identifying first transaction details for the first transaction, wherein identifying 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 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 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 Tunnell in view of Roumeliotis 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, Tunnell, Roumeliotis 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, Roumeliotis 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 or suggest generating a fraud score that is derived from the specific combination of a face print, facial expressions of the operator, and behaviors of the operator, nor does Shinjo disclose or suggest adjusting such a fraud score based on transaction details to perform flagging. Amendment, p. 6. Examiner agrees in part. Examiner contends that Shinjo discloses generating a fraud score (i.e., a caution level) from the face print (i.e., facial information) and behaviors of the operator (e.g., peeping behaviors, such as being peeped and/or peeping another person) observed during the first transaction and the second transaction, and adjusting the fraud score (i.e., increasing or decreasing the rank of the caution level) based on transaction details for the first transaction and the second transaction (e.g., based on transaction details such as if an identical person used different cards/accounts for different transactions) to perform the flagging (i.e., to identify the transaction as suspicious). Shinjo [0043]; [0045]; [0052-0054]. Shinjo differs, in part, from the claimed invention because Shinjo does not explicitly disclose generating a fraud score from facial expressions of the operator. Examiner has added an additional reference, Tunnell, to the prior art rejection to teach that it was known in the art to generate a fraud score (i.e., risk score) from facial expressions of the operator (i.e., of the user) and/or from other features such as gestures, body movements, voice prints, sound excerpts, etc. Tunnell Abstract; [0032]; [0103]; [0105-0107]; [0126]; [0158]; Fig. 5. Examiner contends that the combination of Shinjo, Niu and Tunnell renders claim 2 obvious in view of the prior art.
Applicant argues that Niu does not disclose or suggest generating a fraud score from the combination of a face print, facial expressions, and operator behaviors, nor adjusting such a fraud score based on transaction details. Amendment, p. 7. Examiner agrees. However, it is noted that Niu was not, and is not, used to teach any of these particular features.
Applicant argues that there would be no motivation to incorporate the feature of generating a fraud score that is derived from the specific combination of a face print, facial expressions of the operator, and behaviors of the operator into the teachings of Shinjo and Niu. Amendment, p. 7. Examiner respectfully disagrees. The examiner recognizes that obviousness may be established by combining or modifying the teachings of the prior art to produce the claimed invention where there is some teaching, suggestion, or motivation to do so found either in the references themselves or in the knowledge generally available to one of ordinary skill in the art. See In re Fine, 837 F.2d 1071, 5 USPQ2d 1596 (Fed. Cir. 1988), In re Jones, 958 F.2d 347, 21 USPQ2d 1941 (Fed. Cir. 1992), and KSR International Co. v. Teleflex, Inc., 550 U.S. 398, 82 USPQ2d 1385 (2007). In this case, Shinjo discloses generating a fraud score (i.e., a caution level) from the face print (i.e., facial information) and behaviors of the operator (e.g., peeping behaviors, such as being peeped and/or peeping another person) observed during the first transaction and the second transaction, and adjusting the fraud score (i.e., increasing or decreasing the rank of the caution level) based on transaction details for the first transaction and the second transaction (e.g., based on transaction details such as if an identical person used different cards/accounts for different transactions) to perform the flagging (i.e., to identify the transaction as suspicious). Shinjo [0043]; [0045]; [0052-0054]. Shinjo only differs from the claimed invention because Shinjo does not explicitly disclose that the fraud score is generated from facial expressions of the operator. The newly added reference of Tunnell teaches that it was known in the art to generate a fraud score (i.e., risk score) from facial expressions of the operator (i.e., of the user) and/or from other features such as gestures, body movements, voice prints, sound excerpts, etc. Tunnell Abstract; [0032]; [0103]; [0105-0107]; [0126]; [0158]; Fig. 5. Examiner contends that 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 generating a fraud score (i.e., caution level) from various attributes (e.g., facial information, behavior, etc.) to include the teachings of Tunnell. One of skill in the art would have been motivated to include such features in order to generate a risk score (e.g., a caution level) based on one or more extracted features, frames, states, or dimensions (Tunnell [0142]).
Applicant argues that the amended claim reflects a specific technological implementation not present in the references. Amendment, p. 8. Examiner respectfully disagrees. Examiner contends that the combination of Shinjo, Niu and Tunnell discloses, or at least renders obvious, all of the features recited in amended claim 2.
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.
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.
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].
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JASON FENSTERMACHER whose telephone number is (571)270-3511. The examiner can normally be reached Monday - Friday 9:00 AM to 5:30 PM ET, Alternate Fridays Off.
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, Patrick McAtee can be reached at 571-272-7575. 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.
/J.F./Examiner, Art Unit 3698
/PATRICK MCATEE/Supervisory Patent Examiner, Art Unit 3698
Prosecution Insights