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 .
DETAILED ACTION
Status of the Claims
Claims 1, 3 and 5-11 are pending in the application.
Claims 1, 3, 10 and 11 have been amended
Claims 2 and 4 have been cancelled
The rejection under 35 USC 101 is maintained
Response to Applicant Remarks
Applicant’s well-articulated remarks have been considered but are unpersuasive for the reasons below.
Regarding the rejection under 35 USC 101, Applicant argues that the claimed warning feature cannot be performed in the human mind. (Applicant’s 10/8/25 remarks, p.12, “As an initial matter, Applicant respectfully submits that at least “generating a warning against the potential accounting fraud in response to determination of the risk class; and transmitting the warning to one or more user terminals, the warning being configured to be displayed on the one or more user terminal, the warning comprising a display arrangement of one or more accounting fraud patterns detected and the risk class associated with a respective accounting fraud pattern,” are not limitations that can practically performed in the human mind.”). The examiner respectfully disagrees.
The examiner respectfully suggests that although the claim recites transmission and display, this only requires the abstract idea be applied in a technological environment. A human analyst could mentally determine a potential fraud pattern, draw up a report and share it with a colleague. (see e.g. the Shaw reference).
Applicant also argues that the periodic nature and data volume of the invention for a human to perform the operation. (Applicant’s 10/8/25 remarks, p.12, “Due to the complexity and abundance of data required in these operations, it is practically impossible for the human mind to perform the claimed operations, 1.c., collect all statements input to the external device within the preset period of time, apply all statements to each of a plurality of analyzing function, determine an accounting fraud pattern among the plurality of accounting fraud functions, determine a risk class in response to each accounting fraud pattern”) The examiner respectfully disagrees.
The examiner respectfully suggests that a human is capable of manually aggregating information and making a fraud determination. (See. E.g., the Shaw reference). Although increasing the data volume and decreasing the time allowed to make a fraud determination would at some point be impractical for a human to process efficiently, the examiner suggests that an improvement of this nature does not typically confer eligibility. (See e.g., Trinity Info Media, LLC v. Covalent, Inc., (Fed. Cir. 2023), The Federal Circuit declared ineligible patent claims that were directed to the abstract idea of matching users based on comparing answers provided by the users in response to questions. The court found that matching in this manner was effectively a mental step, and compared the claims to other inventions found ineligible relating to the collection and analysis of data or information. Furthermore, the court found that there was no inventive step simply in performing tasks more quickly or efficiently with the use of generic computer equipment.”) Accordingly, performing the fraud determination task more efficiently or quickly with a computer is not understood to involve a patent eligible technological improvement.
Regarding the rejection under 35 USC 103, Applicant argues that the Schrage reference does not disclose limitations of the invention. (Applicant’s 10/8/25 remarks, p.17, “Applicant respectfully submits, and as the Examiner agreed during the interview, the cited references, alone or in combination, fail to disclose each and every limitation of claim 1. For example, the cited references fail to disclose or suggest at least “applying the data regarding the plurality of statements to each of a plurality of analyzing functions corresponding to a plurality of accounting fraud patterns, according to an application cycle of the respective analyzing function, to generate result data for each of the plurality of analyzing functions, each application cycle being one of daily, monthly, or quarterly,” as recited in claim 1. For example, Schrage merely discloses that the data set 102 is applied to all of the machines 108(1,1)-108(J,K) at the same time, or that the set of rules is applied at the same time when the training data set is made. Indeed, Schrage does not even imply at all that the application period varies from one machine to another or the application period varies from one rule to another. In addition, Schrage focuses on a narrow area of embezzlement, 1.e., payroll frauds, and it is difficult to contemplate how customer data is involved in committing/detecting a payroll fraud. Thus, even assuming arguendo that Shaw discloses customer data as alleged in the rejection, there is no motivation or suggestion to use such customer data in detecting payroll frauds in Schrage. This is another reason for recognizing the inventiveness of the present invention over the cited references.”) The examiner respectfully disagrees.
Schrage discloses that analysis could be periodic, for example monthly or annually. (Schrage para 0136, “[0136] As shown, system 800 collects/receives a data set 802 that can include, for each of a number of employees, salary change information for that employee (e.g., old salary and new salary) on a month to month basis over some time period (e.g., last twelve months). Data set 802 is fed into a projection 804 that is configured to transform the data and output a salary increase percentage per employee per month. ”). Presumably, given that Schrage discloses periodic analysis, choosing a different arbitrary period would a obvious design choice based upon the business needs of the interested entity.
Further as written, it is not clear that “the application period varies from one machine to another or the application period varies from one rule to another”. As Schrage discloses a neural network capable of analyzing multiple fraud patterns, presumably it could determine multiple matched fraud patterns for a particular application period. Or the process could be run multiple times for multiple application periods.
Applicant’s further amendments are addressed by the newly cited art.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1, 3 and 5-11 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Regarding independent claims 1,10,11 the claimed invention recites an abstract idea without significantly more. The claims recites the abstract idea of detecting fraud which is a mental process. Other than reciting a transceiver, memory, processor nothing in the claims precludes the steps from being performed mentally. But for the transceiver, memory and processor the limitations on collecting data input within a preset time, applying data to analyzing functions, determining matching pattern, determining risk, generating warning, display warning pattern, repeat on cycle is a process that under its broadest reasonable interpretation could be performed by mentally but for the recitation of generic computer elements. If claim limitations, under the broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Further the above limitations related to detecting fraud stripped of the identified additional and insignificant elements could also be considered a “Method of Organizing Human Activity” relating to the managing human behavior and interactions (a fundamental economic practice). Thus, the claims recite an abstract idea.
The judicial exception is not integrated into a practical application. The computers are recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using generic computer components. The additional element(s) does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Simply implementing the abstract idea on a generic computer environment is not a practical application of the abstract idea and does not take the claim out of the mental process or method of organizing human activity grouping.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above, with respect to integration of the abstract idea into a practical application, the additional element a transceiver, memory and processor amounts to no more than mere instructions to apply the exception using a generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive conceptCollecting, analyzing and displaying information, and receiving and transmitting over a network are conventional in the computing arts. (MPEP 2106.05h; See also MPEP 2106.05, Alice v. CLS, “. Nearly every computer will include a ‘communications controller’ and ‘data storage unit’ capable of performing the basic calculation, storage, and transmission functions required by the method claims.”).] The claims are not patent eligible.
Regarding the dependent claims, these claims are directed to limitations which serve to limit the fraud detection steps. The subject matter of 3 (detecting modification of data over time), 5 (normalizing data and grouping accounts), 6 (determining non matching credits and debits), 7 (different analyzing functions), 8 (upgrading and downgrading risk), 9 (generate a report) appear to add additional steps to the abstract idea, implemented by generic computers. These claims neither introduce a new abstract idea nor additional limitations which are significantly more than an abstract idea. They provide descriptive details that offer helpful context, but have no impact on statutory subject matter eligibility.
Therefore the limitations on the invention, when viewed individually and in ordered combination are directed to in-eligible subject matter.
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 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.
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.
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 of this title, 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 1,3,5,6,9-11 are rejected under 35 U.S.C. 103 as being unpatentable over Schrage 20210004915 in view of Paul Shaw, “Preventing Corporate Embezzlement”, 2000, https://books.google.com/books?hl=en&lr=&id=T27eZ53F91AC in view of Griffin 20110131131
Regarding Claim 1,
a transceiver;
one or more processors; and
one or more memories configured to store instructions that cause, when executed by the one or more processors, the one or more processors to perform operations,
wherein the operations comprise operations of:
Schrage is directed to a machine learning system for detecting payroll fraud. (Schrage, abstract). Schrage discloses a machine learning system that processes data transmitted from external sources. (Schrage, para 0033, “[0033] Starting with block 502, system 100 can collect a variety of different types of data that may be relevant to payroll fraud from data sources 104(1)-(N) and temporarily store this collected data in some storage component in the form of data set 102. As mentioned previously, the data collected by system 100 from data sources 104(1)-(N) can include, e.g., employee data, family data, banking data, and so on.”)
collecting data including data regarding a plurality of statements of a company to be audited, … , and data regarding personnel records of the company to be audited from an external device through the transceiver;
(Schrage, para 0013, “[0013] FIG. 1 depicts the general architecture of a ML-based payroll fraud detection system 100 according to certain embodiments of the present disclosure. As shown, system 100 receives a data set 102 from a number of external data sources 104(1)-(N) (e.g., external payroll systems, external HR systems, external banking systems, etc.). Data set 102 can comprise many different types of data, each of which may be relevant to payroll fraud detection. Examples of such data include (but are not limited to): [0014] Employee data [0015] Family data (i.e., family members of employees) [0016] Employee bank data [0017] Payments data (i.e., payments made to employees) [0018] Payments over time [0019] Employee timesheets”)
applying the data regarding the plurality of statements to each of a plurality of analyzing functions corresponding to a plurality of accounting fraud patterns, according to an application cycle of the respective analyzing function, to generate result data for each of the plurality of analyzing functions, each application cycle being one of daily, monthly or quarterly;
determining that a pattern shown in one or more statements among the plurality of statements matches one accounting fraud pattern among the plurality of accounting fraud patterns based on the result data;
(Schrage, para 0042, “[0042] To address the foregoing issues, FIG. 6 depicts a workflow 600 that may be executed by system 100 of FIG. 1 to initially train final evaluation engine 112 using an indirect training approach according to certain embodiments. One advantage of this approach is that it automatically labels data points for training using a set of rules, thereby creating a training data set without requiring the time-consuming task of manual labeling. More importantly, the set of rules that are used in this approach are chosen in a manner that enables final evaluation engine 112 to learn broader data patterns, or in other words data patterns that are not specifically encoded in the rules. This allows engine 112 to identify and flag employees that do not strictly conform to those rules but nonetheless appear suspicious by virtue of certain correlations between their data and the data in the training data set.”; para 0136, “[0136] As shown, system 800 collects/receives a data set 802 that can include, for each of a number of employees, salary change information for that employee (e.g., old salary and new salary) on a month to month basis over some time period (e.g., last twelve months). Data set 802 is fed into a projection 804 that is configured to transform the data and output a salary increase percentage per employee per month. ”)
in response to determining that the pattern shown in the one or more statements matches the one accounting fraud pattern,
determining a risk class of potential accounting fraud represented by the one or more statements, … and the data regarding personnel records; and
generating a warming against the potential accounting fraud in response to determination of the risk class.
(Schrage, para 0037, “[0037] At block 512, the outputs of the terminal machines can be fed into final evaluation engine 112 (note that in some cases, a final projection/normalization component may be inserted between the terminal machines and final evaluation engine 112 as described in the example use cases below). Final evaluation engine 112 can use its neural network to process these inputs and generate, for each employee to which data set 102 pertains, an indicator of whether that employee is suspicious (i.e., has likely committed payroll fraud) (block 514). In one set of embodiments, the output generated by final evaluation engine 112 may take the form of a binary flag (e.g., T or F) or bit value (e.g., 0 or 1) that indicates “non-suspicious” or “suspicious.” In other embodiments, the output generated by final evaluation engine 112 may take the form of a continuous probability value (e.g., [0 . . . 1]) indicating the likelihood that the employee is suspicious.”)
wherein the data regarding the plurality of statements comprises data regarding all statements input to the external device within a preset period of time based on each collection time.
(Schrage, para 0136, “[0136] As shown, system 800 collects/receives a data set 802 that can include, for each of a number of employees, salary change information for that employee (e.g., old salary and new salary) on a month to month basis over some time period (e.g., last twelve months). Data set 802 is fed into a projection 804 that is configured to transform the data and output a salary increase percentage per employee per month. ”)
Schrage does not explicitly disclose
… based at least in part on the data regarding one or more customers
… data regarding one or more customers of the company to be audited
Shaw is a textbook directed to techniques for detecting corporate embezzlement. (Shaw, preface). Shaw discloses that embezzlement may entail use of customer data and that an embezzlement auditor can detect this pattern. (Shaw, p.11, “The second point is that each embezzler has a pattern of theft that is somewhat unique but discernible to an experienced fraud auditor… a favorite customer supplier or contractor whose account balance gets manipulated… Fraudulent pattern recognition is the unique skill of a … fraud auditor. Current efforts to design audit software to duplicate that unique skill, by way of artificial intelligence, are the best hope we now have…” ). It would have been obvious to one of ordinary skill in the art before the filing date of the invention to combine Schrage with the customer data of Shaw with the motivation of detecting embezzlement fraud perpetrated by company employees. Id.
Schrage does not explicitly disclose
transmitting the warning to one or more user terminals, the warning being configured to be displayed on the one or more user terminal, the warning comprising a display arrangement of one or more accounting fraud patterns detected and the risk class associated with a respective accounting fraud pattern,
wherein the one or more processors are configured to repeatedly perform the operations in a preset cycle, and
Griffin is directed to a system for identifying risk patterns. (Griffin, abstract) Griffin discloses reporting a detected risk patterns to interested parties. (Griffin, para 0135, “In addition to pattern reporting, risk pattern analysis logic/routine 118 may be further configured to prompt generation and communication of risk pattern alerts to designated entities who can then take appropriate action. For example, if risk pattern data shows high correlation of fraud activity coming from customers who take out cash advances against credit cards while concurrently overdrawing their checking accounts, designated entities may receive an alert/report outlining the new risk pattern and, in some embodiments, the probability of loss and/or recoverability associated with this risk pattern.”). Griffin discloses evaluating risk on a periodic basis or cycle. (Griffin, para 0134, “Additionally, the risk evaluating module 400, according to some embodiments, also includes previously described third party query logic/routine 115. The third party query logic/routine 115 is configured to receive deviation queries, risk score queries or behavioral baseline score queries from third parties and determine whether behaviors or events exhibited by customers at the third party are deviations from the norm (i.e., deviations from the behavioral baseline score) or determine the current risk score or behavioral baseline score and, based on the determination, communicate query responses back to the third party. In other embodiments, the third party query logic/routine 115 is configured to look at the customer data 260 and 360 and the negative file data 270 and 370 to confirm the customer's personal information and is legitimate. In some embodiments, the third party query logic/routine 115 also sets up and executes ongoing refreshes of risk scores and behavioral baseline scores on a periodic basis to third parties.”) It would have been obvious to one of ordinary skill in the art before the filing date of the invention to combine Schrage and Shaw with the periodic evaluation and reporting of Griffin with the motivation of alerting parties to abnormal behavior. Id.
Regarding Claim 3, Schrage, Shaw and Griffin disclose the server of claim 1.
wherein the operations further comprise operations of:
comparing data regarding the plurality of statements of the company to be audited collected at a first collection time with data regarding the plurality of statements of the company to be audited collected at a second collection time, which is immediately one cycle after the first collection time, to determine that at least a part of the data regarding the plurality of statements of the company to be audited has been modified between the first collection time and the second collection time; and
in response to determining that at least a part of the data regarding the plurality of statements of the company to be audited has been modified between the first collection time and the second collection time, generating a warming against potential accounting fraud according to the modification.
See prior art rejection of claim 1. Month to month changes in charges could be indicative of fraud. (See prior art rejection of claim 1 regarding Schrage; see also Schrage para 0136, “[0136] As shown, system 800 collects/receives a data set 802 that can include, for each of a number of employees, salary change information for that employee (e.g., old salary and new salary) on a month to month basis over some time period (e.g., last twelve months).”)
Regarding Claim 6, Schrage, Shaw and Griffin disclose the server of claim 1.
Schrage does not explicitly disclose
wherein the analyzing function corresponding to the one accounting fraud pattern is configured to determine, for one statement, a case to be true if an account title on the debit side is account payable, an account title on the credit side is advance payment, and a customer name on the debit side is different from a customer name on the credit side.
Shaw discloses that a common type of embezzlement is lapping, which is when an employee steals money from one customer’s incoming payment. The employee subsequently takes money from another customer’s payment and applies it to the first customer’s account to cover the theft. (Shaw, p.86). The examiner interprets this technique to entail different customers on the debit and credit side. Further, Shaw cautions that “emergency” condition such as cash advances are highly suspect for embezzlement, because they may an excuse to circumvent corporate controls for expenditures. (Shaw, p.26, “A fabricated invoice… may be all it takes to get an offline check issued to a spurious provider… if the story accompanying the request is some sort of feigned emergency, for example the vendor needs an advance to buy materials…”). It would have been obvious to one of ordinary skill in the art before the filing date of the invention to combine Schrage and Griffin with the fraud pattern of Shaw with the motivation of detecting fraud. Id.
Regarding Claim 7, Schrage, Shaw and Griffin disclose the server of claim 1.
Schrage does not explicitly disclose
wherein the plurality of analyzing functions comprises:
one or more analyzing functions of a first type, which are configured to determine, for one statement, a case to be true if an account title on the debit side and an account title on the credit side respectively satisfy preset criteria and one or more pieces of information recorded in the statement satisfy preset criteria;
one or more analyzing functions of a second type, which are configured to determine, for each of two or more statements, a case to be true if an account title on the debit side and an account title on the credit side respectively satisfy preset criteria and one or more pieces of information recorded in the statement satisfy preset criteria;
one or more analyzing functions of a third type, which are configured to determine, for a statement whose posting date satisfies preset criteria, a case to be true if an account title on the debit side and an account title on the credit side respectively satisfy preset criteria and one or more pieces of information recorded in the statement satisfy preset criteria; and
Shaw discloses a variety of patterns involving movement of funds which may be indicative of embezzlement. (Shaw, p.86)
one or more analyzing functions of a fourth type, which are configured to determine a case to be true if a balance of a designated account title does not meet preset criteria for a preset period of time.
Shaw discloses that a common embezzlement method is to continuing paying an employee despite the employee having left the company on an actual severance date. Id. It would have been obvious to one of ordinary skill in the art before the filing date of the invention to combine Schrage with the fraud pattern of Shaw with the motivation of detecting fraud. Id.
Regarding Claim 9, Schrage and Shaw disclose the server of claim 1.
wherein the operations further comprise operations of:
generating a report indicating one or more warnings against potential accounting fraud, wherein the one or more warnings occur within a preset period of time
and wherein the report comprises, for each of the one or more warnings… , a risk class, and data regarding one or more related statements; and
transmitting the report to one or more user terminals.
(Schrage, para 0037, “[0037] At block 512, the outputs of the terminal machines can be fed into final evaluation engine 112 (note that in some cases, a final projection/normalization component may be inserted between the terminal machines and final evaluation engine 112 as described in the example use cases below). Final evaluation engine 112 can use its neural network to process these inputs and generate, for each employee to which data set 102 pertains, an indicator of whether that employee is suspicious (i.e., has likely committed payroll fraud) (block 514). In one set of embodiments, the output generated by final evaluation engine 112 may take the form of a binary flag (e.g., T or F) or bit value (e.g., 0 or 1) that indicates “non-suspicious” or “suspicious.” In other embodiments, the output generated by final evaluation engine 112 may take the form of a continuous probability value (e.g., [0 . . . 1]) indicating the likelihood that the employee is suspicious.
; para 0038, “[0038] Finally, at block 516, the output of final evaluation engine 112 can be provided to one or more human auditors for review/investigation, and/or provided to one or more downstream systems. In the latter case, the downstream system(s) can evaluate the engine output and automatically trigger one or more actions as appropriate. For example, if the downstream system is a computerized audit system, it can identify the employees that have been flagged as suspicious by system 100 (or whose likelihood of fraud exceeds some threshold) and automatically kick off one or more workflows for initiating payroll audits of those employees (e.g., download appropriate records from the HR and payroll systems, inform auditors, inform legal, etc.). As another example, if the downstream system is a computerized reporting system, it can automatically generate a report summarizing the results for one or more stakeholders in the organization.”)
Schrage does not explicitly disclose
, a matching accounting fraud pattern
Shaw discloses different embezzlement fraud patterns that an auditor could detect. (Shaw, p.86). As Schrage discloses a report summarizing results of an audit, the examiner respectfully suggests it would be obvious to include in such a report the fraud that was detected. All the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination would have yielded predictable results to one of ordinary skill in the art at the time of the invention.
Regarding claims 10,11
See prior art rejection of claim 1.
Claims 5 are rejected under 35 U.S.C. 103 as being unpatentable overSchrage 20210004915 in view of Paul Shaw, “Preventing Corporate Embezzlement”, 2000, https://books.google.com/books?hl=en&lr=&id=T27eZ53F91AC
In view of Grffin In view of Recce 20090248465
Regarding Claim 5, Schrage, Shaw and Griffin disclose the server of claim 1.
wherein the operations further comprise an operation of pre- processing the collected data, and
wherein the pre-processing operation comprises operations of:
normalizing the collected data to conform to a preset database format;
Schrage discloses that information input into a machine learning model could be previously normalized. (Schrage, para 0060, “[0060] The output of the salary-based evaluation chain, as well as the outputs of projections 704-714, are then provided to a projection/normalization component 720 that is configured normalize all of these values before feeding them as input to final evaluation engine 722. Finally, final evaluation engine 722 is configured to output an indication of likely fraud per employee.”)
Schrage does not explicitly disclose
normalizing a customer name among the data regarding one or more customers according to a preset rule; and
Recce is directed to a system for evaluating risk when dealing with a customer. (Recce, abstract). Recce discloses that customer names may be normalized to help identify customers. (Recce, para 0047, “[0047] To best identify business entities KYC component 132 uses data normalization and transformation methods to process features of the presented characteristics being used as part of processing. These normalization and transformation methods are designed to deal with orthographic (cognitive misspellings, oral transmission, spelling variants) typographic (keyboard entry errors, or seriality differences) and syntactic (formatting, variant usage) noise that may be present. Such normalization and transformation methods allow synonym replacement, and other name, address and zipcode methods may be used to test alternate combinations of the presented characteristics (e.g., a customer called `Dick Smith` would be checked against both `Dick Smith`, `Richard Smith` and `Richard Smythe`, a zipcode is transformed into an address or vice-versa prior to testing).”)
grouping account titles among the data regarding the plurality of statements.
Recce discloses grouping accounts according to groups to evaluate risk. (Recce, para 0178, “[0178] Customer risk monitor 100 can be employed so that a KYC check defines the peer associations for a customer. The KYC check risk score or risk banding is used by profile generator 142 to associate the business entity scored by the check with a particular peer group. This peer group is subsequently used by transaction monitor 134 as part of event, infraction and alert generation processes. This allows peer groups for an account, customer or other business entity to be sub-divided in terms of the risk associated with characteristics of that business entity. A regular automated KYC check against customer details then allows customers to be re-allocated to different peer groups based on their degree of risk. Alerts may be generated according to the degree of change associated with peers.”) It would have been obvious to one of ordinary skill in the art before the filing date of the invention to combine Schrage and Shaw with the account data processing of Recce with the motivation of evaluating customer risk. Id.
Allowable Subject Matter
Claim 8 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims AND the rejection under 35 USC 101 is overcome.
Conclusion
THIS ACTION IS MADE FINAL. 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 ALLEN C CHEIN whose telephone number is (571)270-7985. The examiner can normally be reached Monday-Friday 8am -5pm.
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/ALLEN C CHEIN/Primary Examiner, Art Unit 3627