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 . Claims 1-10 have been submitted for examination.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 3, 5-6, 10-12, 14, 16-17 and 20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Khashman et al (hereinafter Khashman) US Patent No 12242445.
As per claim 1, Khashman teaches:
One or more non-transitory computer readable media comprising instructions which, when executed by one or more hardware processors, cause performance of operations comprising:
presenting, in a graphical user interface (GUI) a first set of event records and a second set of event records;
(Abstract column 1, lines 49-67 and column 2, lines 1-19 and column 4, lines 1-28, column 11, lines 41-67 and column 12, lines 3-67)
detecting generation of a first remediation record in the second set of event records to reconcile a first discrepancy associated with the first set of event records and the second set of event records;
(Abstract column 1, lines 49-67 and column 2, lines 1-19 and column 11, lines 41-67 and column 12, lines 3-67)
in response to the generation of the first remediation record, evaluating, in real-time, the first remediation record at least by:
generating a set of prompt content comprising:
the first remediation record;
(Abstract column 1, lines 49-67 and column 2, lines 1-19 and column 11, lines 41-67 and column 12, lines 3-67 and column 17, lines 1-38)
and contextual data associated with at least one of the first set of event records and the second set of event records;
(Abstract column 1, lines 49-67 and column 2, lines 1-19 and column 11, lines 41-67 and column 12, lines 3-67 and column 17, lines 1-38, wherein record domains is the contextual data)
and providing the prompt content to a generative artificial intelligence (AI) model trained to detect anomalies in remediation records;
(Abstract column 1, lines 49-67 and column 2, lines 1-19 and column 3, lines 61-67 and column 4, lines 1-20 and column 19, lines 12-27 and column 20, lines 15-57, wherein the accuracy/confidence metric of the resolution is the anomaly as cited in paragraph [0116] of the instant specification)
obtaining, from the generative AI model, a first output identifying a first anomaly associated with the first remediation record;
(Abstract column 1, lines 49-67 and column 2, lines 1-19 and column 3, lines 61-67 and column 4, lines 1-20 and column 19, lines 12-27 and column 20, lines 15-57, wherein the accuracy/confidence metric of the resolution is the anomaly as cited in paragraph [0116] of the instant specification)
remediating the first anomaly associated with the first remediation record to generate a second remediation record;
(Column 17, lines 63-67 and column 18, lines 1-5 and column 25, lines 37-53)
and modifying the second set of event records to include the second remediation record.
(Column 17, lines 63-67 and column 18, lines 1-5 and column 25, lines 37-53)
As per claim 3, Khashman teaches:
The one or more non-transitory computer readable media of claim 1, wherein the operations further comprise:
training the generative AI model to detect remediation record anomalies at least by:
generating a training data set comprising (a) historical datasets, (b) historical discrepancies associated with pairs of the historical datasets, (c) historical remediation records added to the historical datasets to remediate the historical discrepancies;
(column 13, lines 54-60 and column 17, lines 57-62)
(d) historical anomalies associated with the historical remediation records; and training the generative AI model based on the training data set.
(column 13, lines 54-60 and column 17, lines 57-62)
As per claim 5, Khashman teaches:
The one or more non-transitory computer readable media of claim 1, wherein remediating the first anomaly in the first remediation record comprises presenting, in the GUI, a recommendation to modify the first remediation record to remediate the first anomaly, wherein the operations further comprise:
based on the recommendation, detecting a user selection to modify the first remediation record to remediate the first anomaly;
(Column 12, lines 3-13 and Column 17, lines 63-67 and column 18, lines 1-5 and column 25, lines 37-53)
based on the user selection, generating a training record;
(Column 12, lines 3-13 and Column 17, lines 63-67 and column 18, lines 1-5 and column 25, lines 37-53)
modifying a training dataset to include the training record to generate a modified training data set;
(Column 17, lines 63-67 and column 18, lines 1-5 and column 25, lines 37-53)
and retraining the generative AI model with the modified training data set.
(column 13, lines 54-60 and column 17, lines 57-62)
As per claim 6, Khashman teaches:
The one or more non-transitory computer readable media of claim 1, wherein the operations further comprise:
presenting, in the GUI, a recommendation to modify a third remediation record to remediate a second anomaly;
(Column 17, lines 63-67 and column 18, lines 1-5 and column 25, lines 15-53)
detecting a user selection to refrain from modifying the third remediation record in a manner recommended in the recommendation;
(Column 17, lines 63-67 and column 18, lines 1-5 and column 25, lines 15-53)
based on the user selection, generating a training record;
(column 13, lines 54-60 and column 17, lines 57-62)
modifying a training dataset to include the training record to generate a modified training data set;
(column 13, lines 54-60 and column 17, lines 57-62)
and retraining the generative AI model with the modified training data set.
(column 13, lines 54-60 and column 17, lines 57-62)
As per claim 10, Khashman teaches:
The one or more non-transitory computer readable media of claim 1, wherein reconciling the first discrepancy improves consistency associated with the first set of event records and the second set of event records.
(Column 17, lines 63-67 and column 18, lines 1-5 and column 25, lines 37-53)
As per claim 11, Khashman teaches:
The one or more non-transitory computer readable media of claim 1, wherein the operations further comprise:
receiving a request to access a target set of records corresponding to a target set of events recorded in the second set of event records;
(column 9, lines 4-11 and column 21, lines 20-32)
and responsive to receiving the request to access the target set of records corresponding to the target set of events: returning a modified second set of event records that includes the second remediation record instead of returning the second set of event records.
(column 9, lines 4-11 and column 21, lines 20-32)
Claims 12, 14 and 16-17 are method claims respectively corresponding to non-transitory computer readable media claims 1, 3 and 5-6 and they are rejected under the same rational ad claims 1, 3 and 5-6.
Claim 20 is a system claim corresponding to non-transitory computer readable media claim 1 and it is rejected under the same rational ad claim 1.
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.
Claims 2 and 13 are rejected under 35 U.S.C. 103(a) as being unpatentable over Khashman in view of Kishimoto et al (hereinafter Kishimoto) US Publication No. 20230054582.
As per claim 2, Khashman does not explicitly teach modifying the second set of event records to include the second remediation record includes replacing the first remediation record with the second remediation record, however in analogous art of data management, Kishimoto teaches:
modifying the second set of event records to include the second remediation record includes replacing the first remediation record with the second remediation record.
(Abstract and paragraphs [0003]-[0005] and [0053])
Therefore, it would have been obvious to a person in the ordinary skill in the art at the time of the filling of the invention to combine Khashman and Kishimoto by incorporating the teaching of Kishimoto into the method of Khashman. One having ordinary skill in the art would have found it motivated to use the content management of Kishimoto into the system of Khashman for the purpose of comparing solutions and providing optimized solution.
Claim 2 is a method claim corresponding to non-transitory computer readable media claim 2 and it is rejected under the same rational ad claim 2.
Claims 4 and 15 are rejected under 35 U.S.C. 103(a) as being unpatentable over Khashman in view of Gaur et al (hereinafter Gaur) US Publication No. 20250391547.
As per claim 4, Khashman does not explicitly teach modifying the second set of event records to include the second remediation record includes replacing the first remediation record with the second remediation record, however in analogous art of data management, Gaur teaches
training the generative AI model to detect remediation record anomalies at least by:
obtaining a pre-trained transformer-type machine learning model;
(Paragraphs [0044], [0067]-[0068])
generating a neural network layer to receive an output from the pre-trained transformer-type machine learning model;
(Paragraphs [0044], [0067]-[0068])
obtaining the training data set;
(Paragraphs [0044], [0067]-[0068])
and applying the training data set to a machine learning algorithm to determine first parameters for the generative AI model at least by:
freezing second parameters of the pre-trained transformer-type machine learning model while modifying third parameters of the neural network layer based on an error function.
(Paragraphs [0044], [0067]-[0068] and claim 5)
Therefore, it would have been obvious to a person in the ordinary skill in the art at the time of the filling of the invention to combine Khashman and Gaur by incorporating the teaching of Gaur into the method of Khashman. One having ordinary skill in the art would have found it motivated to use the content management of Gaur into the system of Khashman for the purpose of retraining learning model and providing fine-tuning model.
Claim 15 is a method claim corresponding to non-transitory computer readable media claim 4 and it is rejected under the same rational ad claim 4.
Claims 7 and 18 are rejected under 35 U.S.C. 103(a) as being unpatentable over Khashman in view of Natalie Koenig (hereinafter Koenig) US Publication No. 20260037361.
As per claim 7, Khashman teaches:
The one or more non-transitory computer readable media of claim 1, wherein the operations further comprise:
presenting, in the GUI, a recommendation to modify a third remediation record to remediate a second anomaly identified by the generative AI model;
(Column 17, lines 63-67 and column 18, lines 1-5 and column 25, lines 37-53)
Khashman does not explicitly teach modifying the second set of event records to include the second remediation record includes replacing the first remediation record with the second remediation record, however in analogous art of data management, Kishimoto teaches:
detecting a user selection to refrain from modifying the third remediation record in a manner recommended in the recommendation;
(Paragraphs [0113] and [0118]-[0119])
based on the user selection, generating a second set of prompt content including the recommendation as a negative example;
(Paragraphs [0113] and [0118]-[0119])
providing the second set of prompt content to the generative AI model;
(Paragraphs [0113] and [0118]-[0119])
and responsive to providing the second set of prompt content to the generative AI model, obtaining from the generative AI model a second output that does not identify the second anomaly in the third remediation record.
(Paragraphs [0113] and [0118]-[0119])
Therefore, it would have been obvious to a person in the ordinary skill in the art at the time of the filling of the invention to combine Khashman and Koenig by incorporating the teaching of Koenig into the method of Khashman. One having ordinary skill in the art would have found it motivated to use the content management of Koenig into the system of Khashman for the purpose of managing user’s feedback and improving model accuracy.
Claim 18 is a method claim corresponding to non-transitory computer readable media claim 7 and it is rejected under the same rational ad claim 7.
Claim 8 is rejected under 35 U.S.C. 103(a) as being unpatentable over Khashman in view of Kumari et al (hereinafter Kumari) US Publication No. 20230297956.
As per claim 8, Khashman does not explicitly teach detecting, in the user selection, an indicator that the user selection is an exception; and based on detecting the indicator that the user selection is an exception, refraining from generating a training record corresponding to the user selection for retraining the generative AI model, however in analogous art of data management, Kumari teaches:
based on the recommendation, detecting a user selection to modify the first remediation record to remediate the first anomaly;
(Abstract and paragraphs [0004], [0094]-[0095] and [0116])
detecting, in the user selection, an indicator that the user selection is an exception;
(paragraphs [0114] and [0116])
and based on detecting the indicator that the user selection is an exception, refraining from generating a training record corresponding to the user selection for retraining the generative AI model.
(paragraphs [0114] and [0116])
Therefore, it would have been obvious to a person in the ordinary skill in the art at the time of the filling of the invention to combine Khashman and Kumari by incorporating the teaching of Kumari into the method of Khashman. One having ordinary skill in the art would have found it motivated to use the content management of Kumari into the system of Khashman for the purpose of managing user’s feedback and improving model accuracy. (Kumari: paragraph [0017])
Claim 9 is rejected under 35 U.S.C. 103(a) as being unpatentable over Khashman in view of Singh et al (hereinafter Singh) US Publication No. 20230222113.
As per claim 9, Khashman does not explicitly teach detecting, based on a user interaction with the GUI, a selection to (a) generate the second remediation record and (b) include the second remediation record in the second set of event records, however in analogous art of data management, Singh teaches:
generating, by the generative AI, a recommendation to remediate the first anomaly, wherein the recommendation includes the second remediation record, wherein remediating the first anomaly in the first remediation record to generate a second remediation record comprises:
detecting, based on a user interaction with the GUI, a selection to (a) generate the second remediation record and (b) include the second remediation record in the second set of event records.
(Paragraphs [0018], [0035]-[0036], [0070], [0095], [0114] and [0119])
Therefore, it would have been obvious to a person in the ordinary skill in the art at the time of the filling of the invention to combine Khashman and Singh by incorporating the teaching of Singh into the method of Khashman. One having ordinary skill in the art would have found it motivated to use the content management of Singh into the system of Khashman for the purpose of managing user’s feedback and improving solution suggestion.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Tarek Chbouki whose telephone number is 571-2703154. The examiner can normally be reached on Mon-Fri 9:00 am to 6:00 pm EST. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Aleksandr Kerzhner can be reached at 571-2701760. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/TAREK CHBOUKI/Primary Examiner, Art Unit 2165
6/20/2026
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