DETAILED ACTION
The following claims are pending in this office action: 1-20
Claims 1, 9 and 17 are independent claims.
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 .
Drawings
The drawings filed on 07/28/2025 are accepted.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-3, 9-11 and 17-19 are rejected under 35 U.S.C. §103 as being unpatentable over Lande et al.(US 20140074731 A1)[hereinafter “Lande”]in view of Liu et al. (US 10325177 B1) [hereinafter “Liu”] and view of McLaney et al (US 20210406920 A1) [hereinafter “McLaney”]
As per claim 1, Lande discloses a computer system for identifying anomalous data, the computer system comprising at least one processor in communication with at least one memory device,( Lande, [0021]” The system 150 includes a device 110; the device 110 in turn includes a data discrepancy application 100 constructed from program code that is stored on a memory 111 and executable by a central processing unit (CPU) 112”) wherein the at least one processor programmed to:
store a plurality of initial appraisals; ( Lande, [0026]” The compiling module 102 includes program code for analyzing historical data comprising of previously processed appraisals and generating a data set (i.e. comparison information)”).
receive at least one appraisal verification request including appraisal data; (Lande, [0043]” the application would receive an appraisal evaluation request and then subsequently analyze the subject and comparable characteristics for consistent descriptor parings”).
in response to the one or more Al models detecting one or more data anomalies within the at least one appraisal verification request, ( Lande, [0027]” The comparison module 103 may identify inconsistencies or contradictions by direct comparison or through statistical trends across geographic areas, specific categories, appraiser history, and other statistical dimensions. Further, the comparison module 103 may also search for identified contradictions and flag appraisals that possess these contradictions or flag appraisers that consistently contradict themselves or the field.”)
Lande does not disclose
access one or more artificial intelligence (AI) models trained to analyze appraisals to identify anomalies associated with the appraisals;
input the at least one appraisal verification request into the one or more Al models to determine whether the at least one appraisal verification request includes one or more data anomalies;
transmit one or more notifications to a user computing device including a message identifying the detected data anomaly.
However, Liu in the same field of endeavor discloses
input the at least one appraisal verification request into the one or more Al models to determine [whether the at least one appraisal verification request includes one or more data anomalies;] (Liu, [col.10 Ln45-50, col.8 ln5-15]” the data management engine 130 outputs information to the submitters 102 and/or reviewers 104 regarding whether or not any image anomalies have been detected” and “The image anomaly detection system 108 also includes one or more anomaly detection engines that perform various processes associated with detecting errors and potentially fraudulent appraisal reports”)
transmit one or more notifications to a user computing device including a message identifying the detected data anomaly. (Liu, [col.10 Ln45-50]” the data management engine 130 outputs information to the submitters 102 and/or reviewers 104 regarding whether or not any image anomalies have been detected”).
Therefore, it would have been obvious before the effective filing date of the claimed invention for one of ordinary skill in the art to modify Lande to include access one or more artificial intelligence (AI) models trained to analyze appraisals to identify anomalies associated with the appraisals;
input the at least one appraisal verification request into the one or more Al models to determine whether the at least one appraisal verification request includes one or more data anomalies;
transmit one or more notifications to a user computing device including a message identifying the detected data anomaly as suggested by Lui. One of ordinary skill in the art would have been motivated to do so because incorporating Lui’s anomaly detection and reporting functionality would have predictably improved Lande’s appraisal discrepancy system by enabling detected appraisal inconsistencies to be automatically identified and communicated to a user for further review.
The combination of Lande and Lui fails to disclose
access one or more artificial intelligence (AI) models trained to analyze appraisals to identify anomalies associated with the appraisals;
However, McLaney in the same field of endeavor discloses
access one or more artificial intelligence (AI) models trained to analyze appraisals to identify anomalies associated with the appraisals; (McLaney, [0011]” The disclosed appraisal system may use machine learning algorithms, such as digital image manipulation detection algorithms, to detect if the digital image of the asset has been altered.”).
Therefore, it would have been obvious before the effective filing date of the claimed invention for one of ordinary skill in the art to modify the system created by combination of Lande and Lui to include access one or more artificial intelligence (AI) models trained to analyze appraisals to identify anomalies associated with the appraisals as suggested by McLaney. One of ordinary skill in the art would have been motivated to do so because incorporating McLaney’s machine-learning-based manipulation detection technique would have constituted the use of a known technique to improve a similar appraisal-verification system by providing automated detection of altered appraisal data.
As per claim 2, the references as combined above disclose the computer system of claim 1. Lande further discloses wherein the at least one processor is further programmed to identify at least one of an item or an appraiser associated with the appraisal data.( Lande, [0058]” the process 400 identifies 402 the appraiser listed in the component data.”).
As per claim 3, the references as combined above disclose the computer system of Claim 2. Lande further discloses wherein the at least one processor is further programmed to validate an existence of the appraiser( Lande, [0058]” the process 400 may retrieve the identity of an appraiser by registration number or similar means”) associated with the appraisal data.
As per claim 9, Lande discloses a computer-implemented method for identifying anomalous data, the computer-implemented method implemented by a computer system including at least one memory device and at least one processor in communication with the at least one memory device and one or more user computer devices, ( Lande, [0021]” The system 150 includes a device 110; the device 110 in turn includes a data discrepancy application 100 constructed from program code that is stored on a memory 111 and executable by a central processing unit (CPU) 112”) the method comprising:
storing a plurality of initial appraisals; ( Lande, [0026]” The compiling module 102 includes program code for analyzing historical data comprising of previously processed appraisals and generating a data set (i.e. comparison information)”).
receiving at least one appraisal verification request including appraisal data; (Lande, [0043]” the application would receive an appraisal evaluation request and then subsequently analyze the subject and comparable characteristics for consistent descriptor parings”).
in response to the one or more AI models detecting one or more data anomalies within the at least one appraisal verification request, ( Lande, [0027]” The comparison module 103 may identify inconsistencies or contradictions by direct comparison or through statistical trends across geographic areas, specific categories, appraiser history, and other statistical dimensions. Further, the comparison module 103 may also search for identified contradictions and flag appraisals that possess these contradictions or flag appraisers that consistently contradict themselves or the field.”).
Lande does not disclose
accessing one or more artificial intelligence (AI) models trained to analyze appraisals to identify anomalies associated with the appraisals;
inputting the at least one appraisal verification request into the one or more AI models to determine whether the at least one appraisal verification request includes one or more data anomalies; and
transmitting one or more notifications to a user computing device of the one or more user computing devices including a message identifying the detected data anomaly.
However, Liu in the same field of endeavor discloses
inputting the at least one appraisal verification request into the one or more AI models to determine [whether the at least one appraisal verification request includes one or more data anomalies;] (Liu, [col.10 Ln45-50, col.8 ln5-15]”” the data management engine 130 outputs information to the submitters 102 and/or reviewers 104 regarding whether or not any image anomalies have been detected” and “The image anomaly detection system 108 also includes one or more anomaly detection engines that perform various processes associated with detecting errors and potentially fraudulent appraisal reports”).
transmitting one or more notifications to a user computing device of the one or more user computing devices including a message identifying the detected data anomaly. (Liu, [col.10 Ln45-50]” the data management engine 130 outputs information to the submitters 102 and/or reviewers 104 regarding whether or not any image anomalies have been detected”).
Therefore, it would have been obvious before the effective filing date of the claimed invention for one of ordinary skill in the art to modify Lande to include access one or more artificial intelligence (AI) models trained to analyze appraisals to identify anomalies associated with the appraisals;
input the at least one appraisal verification request into the one or more Al models to determine whether the at least one appraisal verification request includes one or more data anomalies;
transmit one or more notifications to a user computing device including a message identifying the detected data anomaly as suggested by Lui. One of ordinary skill in the art would have been motivated to do so because incorporating Lui’s anomaly detection and reporting functionality would have predictably improved Lande’s appraisal discrepancy system by enabling detected appraisal inconsistencies to be automatically identified and communicated to a user for further review.
The combination of Lande and Lui fails to disclose
accessing one or more artificial intelligence (AI) models trained to analyze appraisals to identify anomalies associated with the appraisals;
However, McLaney in the same field of endeavor discloses
accessing one or more artificial intelligence (AI) models trained to analyze appraisals to identify anomalies associated with the appraisals; (McLaney, [0011]” The disclosed appraisal system may use machine learning algorithms, such as digital image manipulation detection algorithms, to detect if the digital image of the asset has been altered.”).
Therefore, it would have been obvious before the effective filing date of the claimed invention for one of ordinary skill in the art to modify the system created by combination of Lande and Lui to include access one or more artificial intelligence (AI) models trained to analyze appraisals to identify anomalies associated with the appraisals as suggested by McLaney. One of ordinary skill in the art would have been motivated to do so because incorporating McLaney’s machine-learning-based manipulation detection technique would have constituted the use of a known technique to improve a similar appraisal-verification system by providing automated detection of altered appraisal data.
As per claim 10, the substance of the claimed invention is identical or substantially similar to that of claim 2. Accordingly, this claim is rejected under the same rationale.
As per claim 11, the substance of the claimed invention is identical or substantially similar to that of claim 3. Accordingly, this claim is rejected under the same rationale.
As per claim 17, Lande discloses at least one non-transitory computer-readable storage medium having computer-executable instructions embodied thereon, wherein when executed by at least one processor, the at least one processor in commnunication ( Lande, [0021]” The system 150 includes a device 110; the device 110 in turn includes a data discrepancy application 100 constructed from program code that is stored on a memory 111 and executable by a central processing unit (CPU) 112”) with at least one memory, the computer-executable instructions cause the at least one processor to:
store a plurality of initial appraisals; ( Lande, [0026]” The compiling module 102 includes program code for analyzing historical data comprising of previously processed appraisals and generating a data set (i.e. comparison information)”).
receive at least one appraisal verification request including appraisal data; (Lande, [0043]” the application would receive an appraisal evaluation request and then subsequently analyze the subject and comparable characteristics for consistent descriptor parings”).
in response to the one or more Al models detecting one or more data anomalies within the at least one appraisal verification request, ( Lande, [0027]” The comparison module 103 may identify inconsistencies or contradictions by direct comparison or through statistical trends across geographic areas, specific categories, appraiser history, and other statistical dimensions. Further, the comparison module 103 may also search for identified contradictions and flag appraisals that possess these contradictions or flag appraisers that consistently contradict themselves or the field.”)
Lande does not disclose
access one or more artificial intelligence (AI) models trained to analyze appraisals to identify anomalies associated with the appraisals;
input the at least one appraisal verification request into the one or more Al models to determine whether the at least one appraisal verification request includes one or more data anomalies;
transmit one or more notifications to a user computing device including a message identifying the detected data anomaly.
However, Liu in the same field of endeavor discloses
input the at least one appraisal verification request into the one or more Al models to determine [whether the at least one appraisal verification request includes one or more data anomalies;] (Liu, [col.10 Ln45-50, col.8 ln5-15]”” the data management engine 130 outputs information to the submitters 102 and/or reviewers 104 regarding whether or not any image anomalies have been detected” and “The image anomaly detection system 108 also includes one or more anomaly detection engines that perform various processes associated with detecting errors and potentially fraudulent appraisal reports”)
transmit one or more notifications to a user computing device including a message identifying the detected data anomaly. (Liu, [col.10 Ln45-50]” the data management engine 130 outputs information to the submitters 102 and/or reviewers 104 regarding whether or not any image anomalies have been detected”).
Therefore, it would have been obvious before the effective filing date of the claimed invention for one of ordinary skill in the art to modify Lande to include access one or more artificial intelligence (AI) models trained to analyze appraisals to identify anomalies associated with the appraisals;
input the at least one appraisal verification request into the one or more Al models to determine whether the at least one appraisal verification request includes one or more data anomalies;
transmit one or more notifications to a user computing device including a message identifying the detected data anomaly as suggested by Lui. One of ordinary skill in the art would have been motivated to do so because incorporating Lui’s anomaly detection and reporting functionality would have predictably improved Lande’s appraisal discrepancy system by enabling detected appraisal inconsistencies to be automatically identified and communicated to a user for further review.
The combination of Lande and Lui fails to disclose
access one or more artificial intelligence (AI) models trained to analyze appraisals to identify anomalies associated with the appraisals;
However, McLaney in the same field of endeavor discloses
access one or more artificial intelligence (AI) models trained to analyze appraisals to identify anomalies associated with the appraisals; (McLaney, [0011]” The disclosed appraisal system may use machine learning algorithms, such as digital image manipulation detection algorithms, to detect if the digital image of the asset has been altered.”).
Therefore, it would have been obvious before the effective filing date of the claimed invention for one of ordinary skill in the art to modify the system created by combination of Lande and Lui to include access one or more artificial intelligence (AI) models trained to analyze appraisals to identify anomalies associated with the appraisals as suggested by McLaney. One of ordinary skill in the art would have been motivated to do so because incorporating McLaney’s machine-learning-based manipulation detection technique would have constituted the use of a known technique to improve a similar appraisal-verification system by providing automated detection of altered appraisal data.
As per claim 18, the substance of the claimed invention is identical or substantially similar to that of claim 2. Accordingly, this claim is rejected under the same rationale.
As per claim 19, the substance of the claimed invention is identical or substantially similar to that of claim 3. Accordingly, this claim is rejected under the same rationale.
Claims 4-8, 12-16 and 20 are rejected under 35 U.S.C. §103 as being unpatentable over Lande et al.(US 20140074731 A1)[hereinafter “Lande”]in view of Liu et al. (US 10325177 B1) [hereinafter “Liu”] and view of McLaney et al (US 20210406920 A1) [hereinafter “McLaney”] as applied to claim 1 and further in view of Willard et al. (US 20150154663 A1) [hereinafter “Willard”].
As per claim 4, the references as combined above disclose the computer system of Claim 2. The combination fails to disclose wherein the at least one processor is further programmed to validate the appraiser associated with the appraisal data by comparing the appraisal data to information obtained from a plurality of known anomalous claims of the plurality of initial appraisals.
However, Willard in the same field of endeavor discloses
wherein the at least one processor is further programmed to validate the appraiser associated with the appraisal data(Willard, [0073]” In decision block 1710, it is determined whether or not there is a self-consensus. A self-consensus exists if there is a value in the set of corresponding data field entries that was used by the appraiser in question more often than any other value”) by comparing the appraisal data(Willard, [0059]” the appraisal evaluation module searches the data field entries of property appraisals that are stored in a database ("appraisal data field entries") and determines whether there are any erroneous values. The appraisal evaluation module identifies erroneous values by detecting discrepancies between corresponding data field entries”) to information obtained from a plurality of [known anomalous claims] (Willard, [0059]” The appraisal evaluation module identifies erroneous values by detecting discrepancies…A discrepancy score may be assigned to each erroneous data field that is identified,”) of the plurality of initial appraisals(Willard, [0058]” an appraisal evaluation module analyzes a pool of property appraisals and automatically determines a score for each appraisal (a "total discrepancy score").”).
Therefore, it would have been obvious before the effective filing date of the claimed invention for one of ordinary skill in the art to modify the system created by combination of Lande and Lui to include access one or more artificial intelligence (AI) models trained to analyze appraisals to identify anomalies associated with the appraisals as suggested by McLaney to further include wherein the at least one processor is further programmed to validate the appraiser associated with the appraisal data by comparing the appraisal data to information obtained from a plurality of known anomalous claims of the plurality of initial appraisals as taught by Willard. One of ordinary skill in the art would have been motivated to do so because incorporating Willard’s appraisal-evaluation technique of comparing appraisal data against corresponding data from multiple stored appraisals to identify discrepancies and erroneous values would improve the reliability and accuracy of validating appraisal information and the associated appraiser by using established appraisal history to detect anomalous entries.
As per claim 5, the references as combined above disclose the computer system of claim 2. The combination fails to disclose
wherein the at least one processor is further programmed to calculate an expected value for the item based upon the plurality of initial appraisals.
However, Willard in the same field of endeavor discloses
wherein the at least one processor is further programmed to calculate an expected value for the item( Willard, [0076]” A peer-consensus exists if a value is used by a predetermined proportion of peer data field entries…the deemed-correct value for the discrepancy in question may be the peer-consensus value.”) based upon the plurality of initial appraisals.( Willard, [0009]” access appraisal-data-field entries from a plurality of property appraisals, each of the appraisal-data-field entries indicating a value assigned to a property characteristic ”).
Therefore, it would have been obvious before the effective filing date of the claimed invention for one of ordinary skill in the art to modify the system created by combination of Lande and Lui to include access one or more artificial intelligence (AI) models trained to analyze appraisals to identify anomalies associated with the appraisals as suggested by McLaney to further include wherein the at least one processor is further programmed to calculate an expected value for the item based upon the plurality of initial appraisals as taught by Willard. One of ordinary skill in the art would have been motivated to do so because incorporating Willard’s peer-consensus approach provides a deemed-correct value derived from values used across multiple property appraisals, thereby improving the reliability and accuracy of the calculated expected value for the item.
As per claim 6, the references as combined above disclose the computer system of claim 5. Willard further discloses wherein the appraisal data includes an item valuation,( Willard, [0004]” A property appraisal is an opinion of the value of a given property based on certain facts.”) and the at least one processor is further programmed to [detect the one or more anomalies based upon comparing the expected value to the item valuation.] ( Willard, [0077]” the value that differs from the deemed correct value (i.e., the value that most inflates valuation) is determined to be the erroneous value.”)
As per claim 7, the references as combined above discloses the computer system of Claim 6. Willard further discloses wherein the at least one processor is further programmed to detect the one or more anomalies based upon [comparing] ( Willard, [0009]” detecting a discrepancy between the target entry and an appraisal-data-field entry corresponding to the target entry; ”) [a feature associated with the appraisal data and a feature associated with at least one similar appraisal from the plurality of initial appraisals.] ( Willard, [0009]” access appraisal-data-field entries from a plurality of property appraisals, each of the appraisal-data-field entries indicating a value assigned to a property characteristic of a property included in the respective property appraisal; perform an error detection operation for each of the accessed appraisal-data-field entries as a target entry, the error detection operation comprising detecting a discrepancy between the target entry and an appraisal-data-field entry corresponding to the target entry; ”)
As per claim 8, the references as combined above discloses the computer system of claim 1.The combination fails to disclose wherein the at least one processor is further programmed to, upon detection of an anomaly, perform at least one of: (i) generating a remediated valuation associated with the appraisal data, (ii) generating an insurance policy associated with the appraisal data, (iii) implementing one or more security measures, (iv) deploying one or more additional anomaly detection processes, (v) flagging the data as anomalous, or (vi) declining a claim associated with the data.
However, Willard discloses
wherein the at least one processor is further programmed to, upon detection of an anomaly, perform at least one of: (i) generating a remediated valuation associated with the appraisal data, (ii) generating an insurance policy associated with the appraisal data, (iii) implementing one or more security measures, (iv) deploying one or more additional anomaly detection processes, [(v) flagging the data as anomalous], ( Willard, [0009]” flag as erroneous each appraisal-data-field entry determined by the error detection operation to be erroneous”) or (vi) declining a claim associated with the data.
Therefore, it would have been obvious before the effective filing date of the claimed invention for one of ordinary skill in the art to modify the system created by combination of Lande and Lui to include access one or more artificial intelligence (AI) models trained to analyze appraisals to identify anomalies associated with the appraisals as suggested by McLaney to further include wherein the at least one processor is further programmed to, upon detection of an anomaly, perform at least one of: (i) generating a remediated valuation associated with the appraisal data, (ii) generating an insurance policy associated with the appraisal data, (iii) implementing one or more security measures, (iv) deploying one or more additional anomaly detection processes, (v) flagging the data as anomalous, or (vi) declining a claim associated with the data as suggested by Willard. One ordinary skill in the art would have been motivated to do so because incorporating Willard’s error detection and flagging technique would have predictably improved the combined system by automatically identifying and marking anomalous appraisal-data entries for further review or corrective action.
As per claim 12, the substance of the claimed invention is identical or substantially similar to that of claim 4. Accordingly, this claim is rejected under the same rationale.
As per claim 13, the substance of the claimed invention is identical or substantially similar to that of claim 5. Accordingly, this claim is rejected under the same rationale.
As per claim 14, the substance of the claimed invention is identical or substantially similar to that of claim 6. Accordingly, this claim is rejected under the same rationale.
As per claim 15, the substance of the claimed invention is identical or substantially similar to that of claim 7. Accordingly, this claim is rejected under the same rationale.
As per claim 16, the substance of the claimed invention is identical or substantially similar to that of claim 8. Accordingly, this claim is rejected under the same rationale.
As per claim 20, the substance of the claimed invention is identical or substantially similar to that of claim 4. Accordingly, this claim is rejected under the same rationale.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's
disclosure.
JOHNSON et al, (AU 2010246382 A1) discloses “Method And Apparatus For Valuation Of A Resource “.
HAO et al, (AU 2020200779 A1) discloses “crowd-sourced asset valuation system and method”
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Komi N. AMEVIGBE whose telephone number is (571)272-3381. The examiner can normally be reached Monday-Friday 2pm-10pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Carl Colin can be reached at (571) 272-3862. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/K.N.A./Examiner, Art Unit 2493
/CARL G COLIN/Supervisory Patent Examiner, Art Unit 2493
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