DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
All Information Disclosure Statements received as of 12/01/2025 have been considered by examiner.
Response to Amendment
Claims 1-6, 8-15, and 17-20 remain pending. Claims 7 and 16 have been cancelled. Amendments to specification and drawings have overcome all previously held objections.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitations are:
“a communication interface (nonce) … configured to … (functional language)” in claim 2
“an operation information acquisition unit (nonce) …configured to …. (functional language)” in claim 5.
“a communication interface (nonce) … configured to … (functional language)” in claim 11
“an operation information acquisition unit (nonce) …configured to …. (functional language)” in claim 14.
Because these claim limitations are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, they are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof. For claims 5 and 14, paragraph 0117 provides the following: Fig. 12 is a flowchart for explaining the operations of the processor 81 as the operation information acquisition section 223. For claims 2 and 11, paragraph 0077 provides the following: The communication interface 86 is an interface for performing data communication conforming to a communication protocol with the self-service POS terminal 11, the POS server 12, and the display control device 13 or the like.”
If applicant does not intend to have these limitations interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitations to them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitations recites sufficient structure to perform the claimed function so as to avoid them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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.
Claims 1-3, and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Dubucean (US 20210216785 A1) in view of Adjaoute (US 20160086185 A1)
With respect to claim 1, Debucean teaches a fraudulent act detection device, comprising:a camera interface configured to connect to a camera positioned to acquire images of a purchaser at a settlement terminal (“In an aspect of the present disclosure, there is provided a system for detecting scan and non-scan events in a self-check out (SCO) process that includes a scanner for scanning one or more objects brought up in a scanning region, and generating point of sale (POS) data based on the scanning. The system may further include a video camera disposed perpendicularly to the scanner and in an abutting arrangement with respect to the scanner for generating a stream of image frames of the scanning region.” Paragraph 0007); and a processor (“The system 300 further includes a processing unit 305 that may be implemented locally at a local computing device, or at a remote processing server. In the context of the present disclosure, the processing unit 305 may include an AI based processor, a graphical processing unit (GPU) for processing video/image data, a memory for storing one or more instructions.” paragraph 0039) configured to:recognize a product registration-related action of the purchaser at the settlement terminal from an image acquired from the camera via the camera interface (“The system may further include one or more proximity sensors disposed proximally to the video camera and operable to define an Area of Action (AoA) disposed above the scanner, wherein the video camera is configured to start capturing the scanning region, when the one or more objects enter the AoA, and the POS data includes one or more non-zero values.” Paragraph 0007); acquire a degree of reliability for the recognition of the product reqistration- related action of the purchaser (“The system may further include an artificial neural network (ANN) for receiving an image frame from the video camera, and generating one or more values, each indicating a probability of classification of the image frame into one or more classes respectively. The system may further include a processing unit for receiving and processing the POS data, and one or more probabilities of one or more classes to detect a correlation between video data generated by the video camera, and POS data, detect one of: scan and non-scan event in the image frame based on the correlation, and generate an alert upon detection of the non-scan event.” Paragraph 0007); and comparing the acquired degree of reliability for the recognition of the product reqistration-related action of the purchaser to the set value for the degree of reliability threshold in determining whether a fraudulent act of the purchaser has been recognized in acquired images of the purchaser at the settlement terminal (“The system may further include an artificial neural network (ANN) for receiving an image frame from the video camera, and generating one or more values, each indicating a probability of classification of the image frame into one or more classes respectively. The system may further include a processing unit for receiving and processing the POS data, and one or more probabilities of one or more classes to detect a correlation between video data generated by the video camera, and POS data, detect one of: scan and non-scan event in the image frame based on the correlation, and generate an alert upon detection of the non-scan event.” Paragraph 0007)
Debucean does not teach acquiring a member ID of the purchaser from the settlement terminal; determining whether a change condition for changing a degree of reliability threshold has been met; setting a value for the degree of reliability threshold based on whether or not the change condition has been met; wherein the change condition is at least one of:the acquired member ID of the purchaser indicates the purchaser is a reqistered member of a rewards point proqram,a transaction time associated with the product reqistration-related action of the purchaser is within a particular time of day range,the settlement terminal is located in a particular store location, anda number of previous visits to the store by the purchaser exceeds a certain number.
Adjaoute teaches acquiring a member ID of the purchaser from a settlement terminal “…merchant branded cards (Sears, Macy's, etc.)…” paragraph 0293 and determining whether a change condition for changing a degree of reliability threshold has been met (“FIG. 10 represents for the first time in machine learning an apparatus that allows a different threshold for each customer. It further enables different thresholds for the same customer based on the context” paragraph 0210); setting a value for the degree of reliability threshold based on whether or not the change condition has been met (“FIG. 10 represents for the first time in machine learning an apparatus that allows a different threshold for each customer. It further enables different thresholds for the same customer based on the context” paragraph 0210); wherein the change condition is at least one of:the acquired member ID of the purchaser indicates the purchaser is a reqistered member of a rewards point proqram (“A relatively large number of predictive models 3206-3219 are individually trained and updated with training data selected and ordered according to what region of the world it comes from and what type of payment instrument was involved. “ and “…merchant branded cards (Sears, Macy's, etc.)…” paragraph 0293 and “The selected one predictive model 3206-3219 will produce a decision 3220 that will be output, e.g., as transaction request approved/declined messages to a payments processor. Such decisions 3220 are used to update smart agent profiles 3222 . . . 3224. They are also accumulated channel-by-channel and region-by-region according to accountholders 3226 and merchants 3228. These accumulations build up 360-degree views of what is occurring with each individual accountholder, merchant, and other entities.” Paragraph 0294), and a transaction time associated with the product reqistration-related action of the purchaser is within a particular time of day range (“a Threshold-4 during holidays, a Threshold-5 for nights, a Threshold-6 during business hours” paragraph 0210).
Adjaoute is analogous art in the same field of endeavor as the claimed invention. Adjaoute is directed towards risk based fraud detection for transaction related fraud (“The present invention relates to methods of operating artificial intelligence machines and more specifically to using such machines in multi-channel fraud detection so as to limit financial business losses.” Paragraph 0002 and “FIG. 10 represents for the first time in machine learning an apparatus that allows a different threshold for each customer. It further enables different thresholds for the same customer based on the context” paragraph 0210). A person of ordinary skill before the effective filing sate of the claimed invention would have found it obvious to combine the teachings of Debucean with Adjaoute by utilizing Adjaoute’s teachings of risked based fraud thresholds and smart agent in combination with Debucean’s fraud assessment system, with the expectation that doing so would lead to better fraud prevention (“The present invention relates to methods of operating artificial intelligence machines and more specifically to using such machines in multi-channel fraud detection so as to limit financial business losses.”paragraph 0002 and “A 360-degree view of cross-channel user activity is essential if such fraudulent activity is going to be detected and stopped in progress. Conventional methods limit themselves, and their perspectives to dealing with a single-channel, silo-approach. Detecting fraudulent activity can be near impossible when the fraud builds incrementally across online banking channels, account opening and transfers, bill pay, person-to-person payments, image-enabled ATMs, and other channels and applications. Fraudsters are now getting very adept at leveraging bits of customer information they collect here and there for account takeovers. So, such fraud, if it is to be stopped cold, must be tracked with real-time detection capabilities that operate at the customer level or end-user device level.” Paragraph 0007).
With respect to claim 2, Debucean and Adjaoute teach the fraudulent act detection device according to claim 1, but do not teach the further limitations. Debucean further teaches the fraudulent act detection device of claim 1, further comprising: a communication interface (“the processor 307…” paragraph 0056) configured to communicate with an external apparatus (“In an embodiment of the present invention, the processor 307 is further configured to generate an alert when a non-scan event occurs. The alert may include at least one of: a pre-defined audio played through a speaker, a pre-defined video displayed on a display device, an instant message to a store operator, and an email/SMS to the store operator.” Paragraph 0056), wherein the processor is further configured to output a notification to the external apparatus (“In an embodiment of the present invention, the processor 307 is further configured to generate an alert when a non-scan event occurs. The alert may include at least one of: a pre-defined audio played through a speaker, a pre-defined video displayed on a display device, an instant message to a store operator, and an email/SMS to the store operator.” Paragraph 0056)via the communication interface (“the processor 307…” paragraph 0056)after the fraudulent act of the purchaser has been recognized (“In an embodiment of the present invention, the processor 307 is further configured to generate an alert when a non-scan event occurs. The alert may include at least one of: a pre-defined audio played through a speaker, a pre-defined video displayed on a display device, an instant message to a store operator, and an email/SMS to the store operator.” Paragraph 0056).
With respect to claim 3, Debucean and Adjaoute teach the fraudulent act detection device according to claim 2. Debucean further teaches the fraudulent act detection device according to claim 2, wherein the external apparatus is an attendant terminal (“In an embodiment of the present invention, the processor 307 is further configured to generate an alert when a non-scan event occurs. The alert may include at least one of: a pre-defined audio played through a speaker, a pre-defined video displayed on a display device, an instant message to a store operator, and an email/SMS to the store operator.” Paragraph 0056).
With respect to claim 18, Debucean and Adjaoute render obvious all claim limitations in consideration of claim 1, due to their substantial similarity.
With respect to claim 19, Debucean and Adjaoute teach the method according to claim 18 and further render obvious all claim limitations in consideration of claim 2, due to their substantial similarity.
With respect to claim 20, Debucean and Adjaoute teach the method according to claim 19 and further render obvious all claim limitations in consideration of claim 3, due to their substantial similarity.
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Debucean and Adjaoute as applied to claim 2 above, and further in view of Chen (US 20190371134 A1).
With respect to claim 4, Debucean and Adjaoute teach the fraudulent act device according to claim 2, but do not teach the fraudulent act detection device according to claim 2, wherein the external apparatus is the settlement terminal.
Chen teaches wherein an external apparatus is the settlement terminal (“The abnormal checkout behavior detection process performs an abnormal checkout behavior detection based on the customer image to obtain an abnormal behavior detection result. When the abnormal behavior detection result is verified as an abnormal behavior, an abnormal behavior notification is sent to thereby adjust the abnormal behavior” page 16 col. 2 paragraph 0007 lines 8-14).
Chen is analogous art in the same field of endeavor as the claimed invention. Chen is directed towards a self-checkout system that can detect abnormal customer behavior (“The self-checkout system in one of the exemplary examples of the disclosure includes a platform, a product identification device and a customer abnormal behavior detection device.” Page 16 col. 1 paragraph 0005 lines 1-4). A person of ordinary skill in the art before the effective filing date of the claimed invention would have found it obvious to combine Debucean, Adjaoute and Chen by using Chen’s settlement terminal notification scheme to add an explicit notification to the user in place of the implicit notification strategy of Debucean, with the expectation that doing so would lead to less need for manual staff action by notifying the user automatically allowing them to adjust their suspicious behavior (“At present ... The computer vision based self-checkout system can only identify products on a platform and cannot detect whether the customer really did put all the products on the platform and settle accounts accordingly. When the products cannot be identified as expected, staffs would be conducted for troubleshooting manually.” Page 16 col. 1 paragraph 0003 And “The abnormal checkout behavior detection process performs an abnormal checkout behavior detection based on the customer image to obtain an abnormal behavior detection result. When the abnormal behavior detection result is verified as an abnormal behavior, an abnormal behavior notification is sent to thereby adjust the abnormal behavior” page 16 col. 2 paragraph 0007 lines 8-14).
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Debucean and Adjaoute as applied to claim 1 above, and further in view of Ghafoor (US 20160351022 A1).
With respect to claim 5, Debucean and Adjaoute teach the fraudulent act detection device according to claim 1, but do not teach the fraudulent act detection device according to claim 1, further comprising: an operation information acquisition unit configured to acquire operation information of the settlement terminal indicating a present operating state of the settlement terminal, wherein the operation information is used in determining whether the fraudulent act of the purchaser has been recognized.
Ghafoor teaches an operation information acquisition unit (“The SST includes a memory and a processor executing instructions to monitor for abnormal activity on the SST. The processor evaluates activity of at least two operational parameters of the SST in combination. When abnormal activity is detected, the processors execute a responsive action.” Page 6 col. 1 paragraph 0007 lines 2-7, processor) configured to acquire operation information of the settlement terminal indicating a present operating state of the settlement terminal (“The SST includes a memory and a processor executing instructions to monitor for abnormal activity on the SST. The processor evaluates activity of at least two operational parameters of the SST in combination. When abnormal activity is detected, the processors execute a responsive action.” Page 6 col. 1 paragraph 0007 lines 2-7, processor), wherein the operation information is used in determining whether the fraudulent act of the purchaser has been recognized (“For example, some embodiments monitor for more than one reset or system reboot in a period of time, in combination with one or more other operational parameters. In an example, a potential fraud is identified when two reboots or resets of the SST (or a system in or coupled to the SST) occur in ten minutes.” Page 6 col. 2 paragraph 0017 lines 4-7 and page 7 col. 1 paragraph 0017 lines 1-3).
Ghafoor is analogous art in the same field of endeavor as the claimed invention. Ghafoor is directed towards “detection of abnormal operation of a Self-Service Terminal (SST)” (page 6 col. 1 paragraph 0004 lines 2-3). A person of ordinary skill in the art before the effective filing date of the claimed invention would have found it obvious to combine Debucean, Adjaoute and Ghafoor by incorporating the teachings of the necessity and behavior detection improvements garnered by taking into account operational statuses, introducing such metrics into the system of Debucean and Adjaoute, with the expectation that doing so would lead to “prevention or interruption of a fraud perpetrated using a SST, as opposed to mere post-fraud information gathering” (page 6 col. 2 paragraph 0012 lines 5-7).
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Debucean and Adjaoute as applied to claim 1 above, and further in view of Basch (US 6119103 A).
With respect to claim 6, Debucean and Adjaoute teach the fraudulent act detection device according to claim 1. Adjaoute further teaches taking into account acquired member ID during the fraud detection process (“A relatively large number of predictive models 3206-3219 are individually trained and updated with training data selected and ordered according to what region of the world it comes from and what type of payment instrument was involved. “ and “…merchant branded cards (Sears, Macy's, etc.)…” paragraph 0293 and “The selected one predictive model 3206-3219 will produce a decision 3220 that will be output, e.g., as transaction request approved/declined messages to a payments processor. Such decisions 3220 are used to update smart agent profiles 3222 . . . 3224. They are also accumulated channel-by-channel and region-by-region according to accountholders 3226 and merchants 3228. These accumulations build up 360-degree views of what is occurring with each individual accountholder, merchant, and other entities.” Paragraph 0294), but does not explicitly teach wherein the value for the degree of reliability threshold is set to a higher level than a default reference value when the acquired member ID of the purchaser indicates the purchaser is the registered member.
Basch teaches wherein the value for the degree of reliability threshold is set to a higher level (“For example, raising the threshold tends to reduce the number of alerts received. Alert thresholds may be set for the account-level score…” page 19 col. 13 lines 33-35) than a default reference value (“For example, alerts to an account issuer may be triggered by an account-level score which exceeds the account issuer's predefined account score threshold. In general, the threshold controls the volume of alerts an account issuer receives. For example, raising the threshold tends to reduce the number of alerts received. Alert thresholds may be set for the account-level score, the consolidated (account holder-level) score” page 19 col. 13 lines 29-36) when the purchaser is the registered member (“For example, raising the threshold tends to reduce the number of alerts received. Alert thresholds may be set for the account-level score…” page 19 col. 13 lines 33-35, account holder as register member).
Basch is analogous art in the same field of endeavor as the claimed invention. Basch is directed towards using a threshold to determine fraud based on a risk score generated by machine learning (“The invention relates, in one embodiment, to a computer-implemented method for predicting financial risk, which includes receiving transaction data pertaining to a plurality of transactions for a financial account, the transaction data including one of a transaction type and a transaction amount for each of the plurality of transactions. The method further includes scoring the transaction data, including a transaction pattern ascertained from the transaction data, based on a preexisting model to form a score for the financial account. The method further includes transmitting, if the score is below a predefined financial risk threshold, the score to an account issuer of the financial account.” Page 14 col. 3 lines 51-63). A person of ordinary skill in the art before the effective filing date of the claimed invention would have found it obvious to combine Debucean, Adjaoute, and Basch by incorporating the teachings of risks associated with customer participation in membership programs and associated fraud indicators to the fraud detection system and scheme of Debucean and Adjaoute, with the expectation that doing so would lead to quicker fraud alerts allowing the receiver to better protect themselves from financial losses (“The improved financial risk prediction system preferably employs data that facilitates timely warnings of potential financial risks to the account issuers to enable the account issuers to take steps in time to minimize further financial losses” page 14 col.3 lines 36-41) .
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Debucean and Adjaoute as applied to claim 1 above, and further in view of Otsuki (JP 2018018150 A) and Basch (US 6119103 A).
With respect to claim 8, Debucean and Adjaoute teach the fraudulent act detection device according to claim 1, but do not teach further limitations. Basch teaches the value for the degree of reliability threshold is set to a higher level (“For example, raising the threshold tends to reduce the number of alerts received. Alert thresholds may be set for the account-level score…” page 19 col. 13 lines 33-35) than a default reference value (“For example, alerts to an account issuer may be triggered by an account-level score which exceeds the account issuer's predefined account score threshold. In general, the threshold controls the volume of alerts an account issuer receives. For example, raising the threshold tends to reduce the number of alerts received. Alert thresholds may be set for the account-level score, the consolidated (account holder-level) score” page 19 col. 13 lines 29-36) due to a certain change condition.
Basch is analogous art in the same field of endeavor as the claimed invention. Basch is directed towards using a threshold to determine fraud based on a risk score generated by machine learning (“The invention relates, in one embodiment, to a computer-implemented method for predicting financial risk, which includes receiving transaction data pertaining to a plurality of transactions for a financial account, the transaction data including one of a transaction type and a transaction amount for each of the plurality of transactions. The method further includes scoring the transaction data, including a transaction pattern ascertained from the transaction data, based on a preexisting model to form a score for the financial account. The method further includes transmitting, if the score is below a predefined financial risk threshold, the score to an account issuer of the financial account.” Page 14 col. 3 lines 51-63). A person of ordinary skill in the art before the effective filing date of the claimed invention would have found it obvious to combine Debucean, Adjaoute, and Basch by incorporating the teachings of risks associated with customer participation in membership programs and associated fraud indicators to the fraud detection system and scheme of Debucean and Adjaoute, with the expectation that doing so would lead to quicker fraud alerts allowing the receiver to better protect themselves from financial losses (“The improved financial risk prediction system preferably employs data that facilitates timely warnings of potential financial risks to the account issuers to enable the account issuers to take steps in time to minimize further financial losses” page 14 col.3 lines 36-41) .
Otsuki teaches wherein fraud risk is related to when the purchaser has more than the certain number of previous visits (“Shoplift addicts usually visit the store for a preview. Therefore, the number of visits may be recorded, and if the number of visits is greater than or equal to a predetermined number (for example, 2 times), it may be determined that there is a possibility of a person being warned.” Page 11 paragraph 4).
Otsuki is analogous art in the same field of endeavor as the claimed invention. Otsuki is directed towards theft prevention in stores (“The present invention relates to a crime prevention system, a crime prevention method, and a robot for preventing shoplifting in a store.” Page 1 Technical Field). A person of ordinary skill in the art before the effective filing date of the claimed invention would have found it obvious to combine the system of Debucean, Adjaoute, and Basch with the teachings of Otsuki by adjusting the fraud detection threshold of the combined system according to the teachings of Otsuki, using the teachings of Basch, with the expectation that doing so would help prevent retail related crime (“The present invention relates to a crime prevention system, a crime prevention method, and a robot for preventing shoplifting in a store.” Page 1 Technical Field).
Claims 9-12 are rejected under 35 U.S.C. 103 as being unpatentable over Debucean and Adjaoute as applied to claim 1 above, and further in view of Sumpter (US 11501301 B2).
With respect to claim 9, Debucean and Adjaoute teach the fraudulent act detection device according to claim 1, but do not explicitly teach the further limitations. Sumpter further teaches the fraudulent act detection device according to claim 1, wherein the camera interface (“fraud agent 116 streams and stores the images/video to a directory or data store accessible to image/video manager 123” page 8 col. 3 lines 47-49, fraud agent) is further configured to connect to a plurality of cameras (“The system 100 includes transaction terminal(s)” page 7 col.2 lines 60-61 and FIG. 5 Individual Terminal Camera), each camera positioned to acquire images of purchasers at a different one of a plurality of settlement terminals (“The system 100 includes transaction terminal(s)” page 7 col.2 lines 60-61 and FIG. 5 Individual Terminal Camera).
Sumpter is analogous art in the same field of endeavor as the claimed invention. Sumpter is directed towards fraud detection based on image/video (“When fraud is detected, at 233, fraud manager 125 determines from the image/video, at 250, based on comparison of a fraud score calculated and compared to a fraud threshold, at 251, to notify fraud agent 116, at 260, on ATM 110. Fraud agent 116 disables the transaction or the ATM 110 at 261 and sends alerts to bank personnel or bank devices” page 9 col. 6 lines 14-20). A person of ordinary skill before effective filing date of the claimed invention would have found it obvious to combine the teachings of Debucean and Adjaoute with Sumpter, by utilizing Sumpter’s teaching of multiple cameras directed at multiple terminals to propagate the system of Debucean throughout a commercial environment, with the expectation that doing so would allow fraud detection and fraud alerts at multiple terminals (“The system 100 includes transaction terminal(s)” page 7 col.2 lines 60-61 and FIG. 5 Individual Terminal Camera and “When fraud is detected, at 233, fraud manager 125 determines from the image/video, at 250, based on comparison of a fraud score calculated and compared to a fraud threshold, at 251, to notify fraud agent 116, at 260, on ATM 110. Fraud agent 116 disables the transaction or the ATM 110 at 261 and sends alerts to bank personnel or bank devices” page 9 col. 6 lines 14-20).
With respect to claim 10, Debucean and Adjaoute render obvious all claim limitations, with respect to the substantially similar claim 1. Additional limitations are taught by Sumpter. Sumpter teaches a fraudulent act detection system (“The system 100 includes transaction terminal(s)” page 7 col.2 lines 60-61 and FIG. 5 Individual Terminal Camera and “When fraud is detected, at 233, fraud manager 125 determines from the image/video, at 250, based on comparison of a fraud score calculated and compared to a fraud threshold, at 251, to notify fraud agent 116, at 260, on ATM 110. Fraud agent 116 disables the transaction or the ATM 110 at 261 and sends alerts to bank personnel or bank devices” page 9 col. 6 lines 14-20), comprising:a plurality of settlement terminals (“The system 100 includes transaction terminal(s)” page 7 col.2 lines 60-61 and FIG. 5 Individual Terminal Camera);a plurality of cameras positioned to capture images of purchasers at each of the plurality of settlement terminals (“The system 100 includes transaction terminal(s)” page 7 col.2 lines 60-61 and FIG. 5 Individual Terminal Camera); and a fraudulent act detection device including:a camera interface configured to connect to the plurality of cameras and acquire images from the cameras (“The system 100 includes transaction terminal(s)” page 7 col.2 lines 60-61 and FIG. 5 Individual Terminal Camera and “When fraud is detected, at 233, fraud manager 125 determines from the image/video, at 250, based on comparison of a fraud score calculated and compared to a fraud threshold, at 251, to notify fraud agent 116, at 260, on ATM 110. Fraud agent 116 disables the transaction or the ATM 110 at 261 and sends alerts to bank personnel or bank devices” page 9 col. 6 lines 14-20)
Sumpter is analogous art in the same field of endeavor as the claimed invention. Sumpter is directed towards fraud detection based on image/video (“When fraud is detected, at 233, fraud manager 125 determines from the image/video, at 250, based on comparison of a fraud score calculated and compared to a fraud threshold, at 251, to notify fraud agent 116, at 260, on ATM 110. Fraud agent 116 disables the transaction or the ATM 110 at 261 and sends alerts to bank personnel or bank devices” page 9 col. 6 lines 14-20). A person of ordinary skill before effective filing date of the claimed invention would have found it obvious to combine the teachings of Debucean and Adjaoute with Sumpter, by utilizing Sumpter’s teaching of multiple cameras directed at multiple terminals to propagate the system of Debucean throughout a commercial environment, with the expectation that doing so would allow fraud detection and fraud alerts at multiple terminals (“The system 100 includes transaction terminal(s)” page 7 col.2 lines 60-61 and FIG. 5 Individual Terminal Camera and “When fraud is detected, at 233, fraud manager 125 determines from the image/video, at 250, based on comparison of a fraud score calculated and compared to a fraud threshold, at 251, to notify fraud agent 116, at 260, on ATM 110. Fraud agent 116 disables the transaction or the ATM 110 at 261 and sends alerts to bank personnel or bank devices” page 9 col. 6 lines 14-20).
With respect to claim 11, Debucean, Adjaoute and Sumpter teach the fraudulent act detection system according to claim 10. Sumpter further teaches the fraudulent act detection system according to claim 10, wherein the fraudulent act detection device further includes: a communication interface (“the processor 307…” paragraph 0056) configured to communicate with an external apparatus (“In an embodiment of the present invention, the processor 307 is further configured to generate an alert when a non-scan event occurs. The alert may include at least one of: a pre-defined audio played through a speaker, a pre-defined video displayed on a display device, an instant message to a store operator, and an email/SMS to the store operator.” Paragraph 0056), wherein the processor is further configured to output a notification to the external apparatus (“In an embodiment of the present invention, the processor 307 is further configured to generate an alert when a non-scan event occurs. The alert may include at least one of: a pre-defined audio played through a speaker, a pre-defined video displayed on a display device, an instant message to a store operator, and an email/SMS to the store operator.” Paragraph 0056)via the communication interface (“the processor 307…” paragraph 0056)after the fraudulent act of the purchaser has been recognized (“In an embodiment of the present invention, the processor 307 is further configured to generate an alert when a non-scan event occurs. The alert may include at least one of: a pre-defined audio played through a speaker, a pre-defined video displayed on a display device, an instant message to a store operator, and an email/SMS to the store operator.” Paragraph 0056).
With respect to claim 12, Debucean, Adjaoute and Sumpter teach the fraudulent act detection system according to claim 11. Debucean further teaches the fraudulent act detection system according to claim 11, wherein the external apparatus is an attendant terminal (“In an embodiment of the present invention, the processor 307 is further configured to generate an alert when a non-scan event occurs. The alert may include at least one of: a pre-defined audio played through a speaker, a pre-defined video displayed on a display device, an instant message to a store operator, and an email/SMS to the store operator.” Paragraph 0056).
Claim 13 are rejected under 35 U.S.C. 103 as being unpatentable over Sumpter and Tussy as applied to claim 11 above, respectfully, and further in view of Chen (US 20190371134 A1).
With respect to claim 13, Debucean, Adjaoute, and Sumpter teach the fraudulent act detection system of claim 11, but do not teach the fraudulent act detection system according to claim 11, wherein the external apparatus is the settlement terminal.
Chen teaches wherein an external apparatus is the settlement terminal (“The abnormal checkout behavior detection process performs an abnormal checkout behavior detection based on the customer image to obtain an abnormal behavior detection result. When the abnormal behavior detection result is verified as an abnormal behavior, an abnormal behavior notification is sent to thereby adjust the abnormal behavior” page 16 col. 2 paragraph 0007 lines 8-14).
Chen is analogous art in the same field of endeavor as the claimed invention. Chen is directed towards a self-checkout system that can detect abnormal customer behavior (“The self-checkout system in one of the exemplary examples of the disclosure includes a platform, a product identification device and a customer abnormal behavior detection device.” Page 16 col. 1 paragraph 0005 lines 1-4). A person of ordinary skill in the art before the effective filing date of the claimed invention would have found it obvious to combine Debucean, Adjaoute, Sumpter and Chen by using Chen’s settlement terminal notification scheme to add an explicit notification to the user in place of the implicit notification strategy of Debucean, with the expectation that doing so would lead to less need for manual staff action by notifying the user automatically allowing them to adjust their suspicious behavior (“At present ... The computer vision based self-checkout system can only identify products on a platform and cannot detect whether the customer really did put all the products on the platform and settle accounts accordingly. When the products cannot be identified as expected, staffs would be conducted for troubleshooting manually.” Page 16 col. 1 paragraph 0003 And “The abnormal checkout behavior detection process performs an abnormal checkout behavior detection based on the customer image to obtain an abnormal behavior detection result. When the abnormal behavior detection result is verified as an abnormal behavior, an abnormal behavior notification is sent to thereby adjust the abnormal behavior” page 16 col. 2 paragraph 0007 lines 8-14).
Claim 14 are rejected under 35 U.S.C. 103 as being unpatentable over Debucean, Adjaoute, and Sumpter as applied to claim 10 above, respectfully, and further in view of Ghafoor (US 20160351022 A1).
With respect to claim 14, Debucean, Adjaoute, and Sumpter teach the fraudulent act detection system according to claim 10, but do not teach the fraudulent act detection system according to claim 10, wherein the fraudulent act detection device further includes: an operation information acquisition unit configured to acquire operation information of the settlement terminal indicating a present operating state of the settlement terminal, and the operation information is used in determining whether the fraudulent act of the purchaser has been recognized.
Ghafoor teaches an operation information acquisition unit (“The SST includes a memory and a processor executing instructions to monitor for abnormal activity on the SST. The processor evaluates activity of at least two operational parameters of the SST in combination. When abnormal activity is detected, the processors execute a responsive action.” Page 6 col. 1 paragraph 0007 lines 2-7, processor) configured to acquire operation information of the settlement terminal indicating a present operating state of the settlement terminal (“The SST includes a memory and a processor executing instructions to monitor for abnormal activity on the SST. The processor evaluates activity of at least two operational parameters of the SST in combination. When abnormal activity is detected, the processors execute a responsive action.” Page 6 col. 1 paragraph 0007 lines 2-7, processor), wherein the operation information is used in determining whether the fraudulent act of the purchaser has been recognized (“For example, some embodiments monitor for more than one reset or system reboot in a period of time, in combination with one or more other operational parameters. In an example, a potential fraud is identified when two reboots or resets of the SST (or a system in or coupled to the SST) occur in ten minutes.” Page 6 col. 2 paragraph 0017 lines 4-7 and page 7 col. 1 paragraph 0017 lines 1-3).
Ghafoor is analogous art in the same field of endeavor as the claimed invention. Ghafoor is directed towards “detection of abnormal operation of a Self-Service Terminal (SST)” (page 6 col. 1 paragraph 0004 lines 2-3). A person of ordinary skill in the art before the effective filing date of the claimed invention would have found it obvious to combine Debucean, Adjaoute and Ghafoor by incorporating the teachings of the necessity and behavior detection improvements garnered by taking into account operational statuses, introducing such metrics into the system of Debucean and Adjaoute, with the expectation that doing so would lead to “prevention or interruption of a fraud perpetrated using a SST, as opposed to mere post-fraud information gathering” (page 6 col. 2 paragraph 0012 lines 5-7).
Claim 15 is rejected under 35 U.S.C. 103 as being unpatentable over Debucean, Adjaoute, and Sumpter as applied to claim 10 above, respectfully, and further in view of Basch (US 6119103 A).
With respect to claim 15, Debucean, Adjaoute, and Sumpter teach the fraudulent act detection device according to claim 10. Adjaoute further teaches taking into account acquired member ID during the fraud detection process (“A relatively large number of predictive models 3206-3219 are individually trained and updated with training data selected and ordered according to what region of the world it comes from and what type of payment instrument was involved. “ and “…merchant branded cards (Sears, Macy's, etc.)…” paragraph 0293 and “The selected one predictive model 3206-3219 will produce a decision 3220 that will be output, e.g., as transaction request approved/declined messages to a payments processor. Such decisions 3220 are used to update smart agent profiles 3222 . . . 3224. They are also accumulated channel-by-channel and region-by-region according to accountholders 3226 and merchants 3228. These accumulations build up 360-degree views of what is occurring with each individual accountholder, merchant, and other entities.” Paragraph 0294), but does not explicitly teach wherein the value for the degree of reliability threshold is set to a higher level than a default reference value when the acquired member ID of the purchaser indicates the purchaser is the registered member.
Basch teaches wherein the value for the degree of reliability threshold is set to a higher level (“For example, raising the threshold tends to reduce the number of alerts received. Alert thresholds may be set for the account-level score…” page 19 col. 13 lines 33-35) than a default reference value (“For example, alerts to an account issuer may be triggered by an account-level score which exceeds the account issuer's predefined account score threshold. In general, the threshold controls the volume of alerts an account issuer receives. For example, raising the threshold tends to reduce the number of alerts received. Alert thresholds may be set for the account-level score, the consolidated (account holder-level) score” page 19 col. 13 lines 29-36) when the purchaser is the registered member (“For example, raising the threshold tends to reduce the number of alerts received. Alert thresholds may be set for the account-level score…” page 19 col. 13 lines 33-35, account holder as register member).
Basch is analogous art in the same field of endeavor as the claimed invention. Basch is directed towards using a threshold to determine fraud based on a risk score generated by machine learning (“The invention relates, in one embodiment, to a computer-implemented method for predicting financial risk, which includes receiving transaction data pertaining to a plurality of transactions for a financial account, the transaction data including one of a transaction type and a transaction amount for each of the plurality of transactions. The method further includes scoring the transaction data, including a transaction pattern ascertained from the transaction data, based on a preexisting model to form a score for the financial account. The method further includes transmitting, if the score is below a predefined financial risk threshold, the score to an account issuer of the financial account.” Page 14 col. 3 lines 51-63). A person of ordinary skill in the art before the effective filing date of the claimed invention would have found it obvious to combine Debucean, Adjaoute, Sumpter and Basch by incorporating the teachings of risks associated with customer participation in membership programs and associated fraud indicators to the fraud detection system and scheme of Debucean, Adjaoute, and Sumpter, with the expectation that doing so would lead to quicker fraud alerts allowing the receiver to better protect themselves from financial losses (“The improved financial risk prediction system preferably employs data that facilitates timely warnings of potential financial risks to the account issuers to enable the account issuers to take steps in time to minimize further financial losses” page 14 col.3 lines 36-41) .
Claims 17 are rejected under 35 U.S.C. 103 as being unpatentable over Debucean, Adjaoute, and Sumpter as applied to claim 10 above, respectfully, and further in view of Adams (US 20080172316 A1) and Otsuki (JP 2018018150 A).
With respect to claim 17, Debucean, Adjaoute, and Sumpter teach the fraudulent act detection system according to claim 10, but do not teach wherein the value for the degree of reliability threshold is set to a higher level than a default reference value when the purchaser has more than the certain number of previous visits.
Basch teaches the value for the degree of reliability threshold is set to a higher level (“For example, raising the threshold tends to reduce the number of alerts received. Alert thresholds may be set for the account-level score…” page 19 col. 13 lines 33-35) than a default reference value (“For example, alerts to an account issuer may be triggered by an account-level score which exceeds the account issuer's predefined account score threshold. In general, the threshold controls the volume of alerts an account issuer receives. For example, raising the threshold tends to reduce the number of alerts received. Alert thresholds may be set for the account-level score, the consolidated (account holder-level) score” page 19 col. 13 lines 29-36) due to a certain change condition.
Basch is analogous art in the same field of endeavor as the claimed invention. Basch is directed towards using a threshold to determine fraud based on a risk score generated by machine learning (“The invention relates, in one embodiment, to a computer-implemented method for predicting financial risk, which includes receiving transaction data pertaining to a plurality of transactions for a financial account, the transaction data including one of a transaction type and a transaction amount for each of the plurality of transactions. The method further includes scoring the transaction data, including a transaction pattern ascertained from the transaction data, based on a preexisting model to form a score for the financial account. The method further includes transmitting, if the score is below a predefined financial risk threshold, the score to an account issuer of the financial account.” Page 14 col. 3 lines 51-63). A person of ordinary skill in the art before the effective filing date of the claimed invention would have found it obvious to combine Debucean, Adjaoute, and Basch by incorporating the teachings of risks associated with customer participation in membership programs and associated fraud indicators to the fraud detection system and scheme of Debucean and Adjaoute, with the expectation that doing so would lead to quicker fraud alerts allowing the receiver to better protect themselves from financial losses (“The improved financial risk prediction system preferably employs data that facilitates timely warnings of potential financial risks to the account issuers to enable the account issuers to take steps in time to minimize further financial losses” page 14 col.3 lines 36-41) .
Otsuki teaches wherein fraud risk is related to when the purchaser has more than the certain number of previous visits (“Shoplift addicts usually visit the store for a preview. Therefore, the number of visits may be recorded, and if the number of visits is greater than or equal to a predetermined number (for example, 2 times), it may be determined that there is a possibility of a person being warned.” Page 11 paragraph 4).
Otsuki is analogous art in the same field of endeavor as the claimed invention. Otsuki is directed towards theft prevention in stores (“The present invention relates to a crime prevention system, a crime prevention method, and a robot for preventing shoplifting in a store.” Page 1 Technical Field). A person of ordinary skill in the art before the effective filing date of the claimed invention would have found it obvious to combine the system of Debucean, Adjaoute, and Basch with the teachings of Otsuki by adjusting the fraud detection threshold of the combined system according to the teachings of Otsuki, using the teachings of Basch, with the expectation that doing so would help prevent retail related crime (“The present invention relates to a crime prevention system, a crime prevention method, and a robot for preventing shoplifting in a store.” Page 1 Technical Field).
Response to Arguments
Applicant’s arguments filed 10/20/2025 have been fully considered.
With regards to pages 12- 19, Applicant argues the 112(f) interpretation of claims 1, 5,10, and 14, and associated 112(a) and 112(b) rejections. For claims 1 and 10, the applicant asserts that both “camera” and “interface” are terms that denote structure and further references supporting structure present within the drawings. While the examiner believes that the term “interface”, independently, lacks the structure necessary to overcome the 112(f) interpretation, the examiner sides with the applicant and agrees that the term “camera” is not a nonce and thus withdraws the corresponding 112(a) and 112(b) rejections for claims 1, 10, and any associated dependents.
For claims 5 and 14, applicant asserts that “an operation information acquisition unit” holds sufficient definite structural meaning (referencing Williamson v. Citrix Online, LLC), and should be considered similar to terms such as “eyeglass hanger member”, “detent mechanism”, or “sealing connected joints”. Applicant further asserts that “an operation information acquisition unit” necessitates specific structure. The examiner disagrees. The term “an operation information acquisition unit”, taken in plain meaning carries no structural meaning and is a nonce term. In comparison the term “eyeglass hanger member” features the words “eyeglass” and “hanger” both of which denote structure, the term “detent mechanism” features “detent” which denotes structure and the term “sealing connected joints” features “sealing” and “joints” which both denote structure. Accordingly, the examiner maintains the 112(f) interpretation for claims 5 and 14. Furthermore The examiner also disagrees with the assertion that the prior office action presented a bare assertion without support or considered reasoning and points to the fact that applicant goes on to acknowledge that means-plus-function analysis was performed (see page 16).
Applicant further claims that term is adequately described within the written description, specifically citing the drawings and specification. The examiner agrees that this fulfils the written description requirement and withdraws the associated 112(a) and 112(b) rejections for claims 5, 14, and any depending claims.
Furthermore, the examiner also disagrees with the assertion that the prior office action presented a bare assertion without support or considered reasoning and points to the fact that applicant goes on to acknowledge that means-plus-function analysis was performed (see pages 14 and 16).
Applicant’s arguments with respect to claims 1, 10, and 18 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument (see above claim mapping). Applicant’s further arguments regarding their dependents have also been considered and rendered moot (see above claim mapping).
Conclusion
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 REBECCA C WILLIAMS whose telephone number is (571)272-7074. The examiner can normally be reached M-F 7:30am - 4:00pm.
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/REBECCA COLETTE WILLIAMS/Examiner, Art Unit 2677
/ANDREW W BEE/Supervisory Patent Examiner, Art Unit 2677