DETAILED ACTION
This office action is in response to Applicants amendment filed 1/16/26.
The 35 U.S.C 101 Rejection is being maintained and the details are addressed below.
Response to Applicants amendment
The Applicants arguments have been fully considered, however are not found persuasive.
Response to Applicants remarks
With respect to claim 1 on page 16, the Applicant argues the combination of the references Mani et al. and Chopra et al. do not teach "simulating, by the computing device, the plurality of potential IT incident resolutions in a virtual environment configured with the set of system details."
The Examiner respectfully disagrees with the statements, and points to (column 5, lines 37-67), wherein the content analysis may include parsing the upgrade issue report to identify the content, analyzing each portion of the content to determine whether: (i) a majority of the content is relevant to the classification, and (ii) the content is formatted in a manner favorable for training. If either (i) or (ii) is not met, then preprocessing may be required. If pre-processing is required, the method proceeds to step 206; otherwise, the method proceeds to step 210. Thus, pre-processing may be required, and the paragraph also states pre-processing may further include re-formatting the content in the upgrade issue report such that the classification training may be applied. The re-formatting may be performed on the upgrade issue report if such action is necessary for the feature extraction discussed in step 208. The result of the upgrade issue report is a pre-processed upgrade issue report. Thus it is obvious that simulation or training can be done here, in a virtual environment, with tasks or a set of details. Even further as stated, the relevant features may include identifying unique portions of the selected upgrade issue report and attributing a unique score (e.g., a numerical value) to the unique portion. Each unique portion and the attributed unique score may collectively be included in the set of features generated during the feature extraction. Thus, the Chopra et al. reference teaches recites "simulating, by the computing device, the plurality of potential IT incident resolutions in a virtual environment configured with the set of system details" (column 5, lines 37-67).
With respect to claim 20 on pages 17-18, the Applicant argues the combination of the references Mani et al. and Chopra et al. do not teach identifying resolutions "within channel data" using a BHUVI model, simulating those resolutions, and specifically "detect[ing] errors within the simulation."
The Examiner respectfully disagrees with the statements, and points to (column 5, lines 37-67) of the Chopra et al. reference as indicated above, with same or similar reasoning. However, "within channel data" using a BHUVI model, the Examiner would like to point to (column 6, lines 17-32), wherein a trained classification model is generated using the self-healing training database, the training may include applying a z-score to each upgrade issue report based on the corresponding set of features and a specification about whether the upgrade issue was previously determined to be self-healable. For example, a first set of upgrade issue reports that are self-healable may each be associated with a z-score that is similar to other upgrade issue reports in the same set. The z-score may be generated by applying any combination of the unique scores, mean scores of the unique scores, variances of unique scores, and/or any other attributes of the upgrade issue report. Thus, it is obvious that within a channel or a training database, a classification model or a BHUVI model with ranking or scoring may be implemented.
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.
Claim 1 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding claim 1, the claim(s) recite(s) obtaining, by a computing device, an information technology (IT) incident report; obtaining, by the computing device, a set of system details of a system experiencing an IT incident, the IT incident being described by the IT incident report; identifying, by the computing device, a plurality of potential IT incident resolutions; simulating, by the computing device, the plurality of potential IT incident resolutions in a virtual environment configured with the set of system details; scoring, by the computing device, the plurality of potential IT incident resolutions using a machine learning model, the machine learning model configured to determine a success of the plurality of potential IT incident resolutions; and generating and transmitting, by the computing device and according to the scoring, the plurality of potential IT incident resolutions. For step 2A eligibility prong one analysis, scoring, by the computing device, the plurality of potential IT incident resolutions using a machine learning model, the machine learning model configured to determine a success of the plurality of potential IT incident resolutions; and generating and transmitting, by the computing device and according to the scoring, the plurality of potential IT incident resolutions.
For step 2A eligibility prong two analysis, this judicial exception is not integrated into a practical application because it is not tied to any particular computer problem. The other limitations are merely gathering data (obtaining, by the computing device, a set of system details of a system experiencing an IT incident, the IT incident being described by the IT incident report; identifying, by the computing device, a plurality of potential IT incident resolutions and simulating, by the computing device, the plurality of potential IT incident resolutions in a virtual environment configured with the set of system details), or is well-understood, routine, conventional activity (a machine learning module to produce to determine a success of the plurality of potential IT incident resolutions). Using machine learning modules to generate scores is well-understood in the error detection field. Further, it has to be shown that the limitations actually do something useful or improve a technology. For example, just capturing or gathering data, such as technology incident report, potential IT incident resolutions or simulation data are general practice in the art without any concrete or real-world result. The limitation that limits the score to incidents occurring within a computing environment is still so general that it could apply to any/every computing environment and does not integrate the claim to a practical application. These limitations do not meaningfully limit the claim by going beyond generally linking the use of the judicial exception to a particular technological environment.
For step 2B eligibility (Whether a Claim Amounts to Significantly More), The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional limitations are either gathering data (obtaining, by the computing device, a set of system details of a system experiencing an IT incident, the IT incident being described by the IT incident report; identifying, by the computing device, a plurality of potential IT incident resolutions and simulating a plurality of potential IT incident resolutions in virtual environment with details ), or is well-understood, routine, conventional activity (a machine learning module to produce an output value that comprises a risk score that indicates a likelihood of a potential malfunctioning occurring within the computing environment). Using machine learning modules to generate scores is well-understood in the error detection field. These limitations do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Independent claim 10 and 20 are also rejected under 35 U.S.C. 101 for being directed to non-statutory subject matter. The same or similar reasoning is given as applied above for claim 1.
The Dependent claims 2-3, 5-9, 11-18 and 21-22 are rejected under 35 U.S.C. 101 for being directed to non-statutory subject matter as they fail to remedy the independent claims.
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, 5-7 and 20 are rejected under 35 U.S.C 103 as being unpatentable over Mani et al. (US Pub no. 2014/0325254) in view of Chopra et al. (US Patent no. 11,398,960).
With respect to claims 1, the Mani et al. reference teaches obtaining, by a computing device, an information technology (IT) incident report ([0025] – arrangements for automatically analyzing problem tickets to discover groups of problems being reported in them and to provide meaningful, descriptive labels to help interpret such groups); obtaining, by the computing device, a set of system details of a system experiencing an IT incident, the IT incident being described by the IT incident report ([0002] – each application maintenance team collects a rich repository of problem tickets wherein the ticket represents a submission of a specific problem for resolution. Further, the information about the problem is typically embedded in the unstructured text of these tickets); identifying, by the computing device, a plurality of potential IT incident resolutions (see figure 10, 1008) and ([0063]).
The Mani et al. reference does not teach simulating, by the computing device, the plurality of potential IT incident resolutions in a virtual environment configured with the set of system details (column 5, lines 37-67); scoring, by the computing device, the plurality of potential IT incident resolutions using a machine learning model, the machine learning model configured to determine a success of the plurality of potential IT incident resolutions (column 5, line 58 – column 6, line 7 – relevant features may include identifying unique portions of the selected upgrade issue report and attributing a unique score (e.g., a numerical value) to the unique portion. Each unique portion and the attributed unique score may collectively be included in the set of features generated during the feature extraction. Further, a self-healing training database is populated, wherein, the self-healing training database is a data structure that, when fully populated, specifies the set of upgrade issue notifications and the features of the upgrade issue notifications); and generating and transmitting, by the computing device and according to the scoring, the plurality of potential IT incident resolutions (column 6, lines 25-31 - a first set of upgrade issue reports that are self-healable may each be associated with a z-score that is similar to other upgrade issue reports in the same set. The z-score may be generated by applying any combination of the unique scores, mean scores of the unique scores, variances of unique scores, and/or any other attributes of the upgrade issue report).
The Chopra et al. reference teaches scoring, by the computing device, the plurality of potential IT incident resolutions using a machine learning model, the machine learning model configured to determine a success of the plurality of potential IT incident resolutions (column 5, line 58 – column 6, line 7 – relevant features may include identifying unique portions of the selected upgrade issue report and attributing a unique score (e.g., a numerical value) to the unique portion. Each unique portion and the attributed unique score may collectively be included in the set of features generated during the feature extraction. Further, a self-healing training database is populated, wherein, the self-healing training database is a data structure that, when fully populated, specifies the set of upgrade issue notifications and the features of the upgrade issue notifications); and generating and transmitting, by the computing device and according to the scoring, the plurality of potential IT incident resolutions (column 6, lines 25-31 - a first set of upgrade issue reports that are self-healable may each be associated with a z-score that is similar to other upgrade issue reports in the same set. The z-score may be generated by applying any combination of the unique scores, mean scores of the unique scores, variances of unique scores, and/or any other attributes of the upgrade issue report).
Thus, it would have been obvious at a time prior to the effective filing date of Applicant’s claimed invention to have combined the references Mani et al. and Chopra et al. to incorporate the limitations of scoring, by the computing device, the plurality of potential IT incident resolutions using a machine learning model, the machine learning model configured to determine a success of the plurality of potential IT incident resolutions; and generating and transmitting, by the computing device and according to the scoring, the plurality of potential IT incident resolutions into the claimed invention.
One skilled in the art would have been motivated to by the proposed combination of the Mani et al. and Chopra et al. references for improving resolving issues (column, lines 41 – 43- Chopra et al.).
With respect to claims 2, all of the limitations of claim 1 have been addressed.
The Mani et al. reference does not teach wherein the obtaining the information technology (IT) incident report comprises obtaining the IT incident report from an external system, and the identifying the plurality of potential IT incident resolutions comprise using a best hedging, utilization, and validation of information (BHUVI) model comprising a ranking layer to identify potential IT incident resolutions within channel data.
The Chopra et al. reference teaches wherein the obtaining the information technology (IT) incident report comprises obtaining the IT incident report from an external system, and the identifying the plurality of potential IT incident resolutions comprise using a best hedging, utilization, and validation of information (BHUVI) model comprising a ranking layer to identify potential IT incident resolutions within channel data (column 7, lines 4-21 – The upgrade issue report may be serviced (e.g., the upgrade issue may be attempted to be resolved) by the administrative system. The administrative system may send a response to the application upgrade management system based on the servicing. The response may specify the details of the resolution).
Thus, it would have been obvious at a time prior to the effective filing date of Applicant’s claimed invention to have combined the references Mani et al. and Chopra et al. to incorporate the limitations of wherein the obtaining the information technology (IT) incident report comprises obtaining the IT incident report from an external system, and the identifying the plurality of potential IT incident resolutions comprise using a best hedging, utilization, and validation of information (BHUVI) model comprising a ranking layer to identify potential IT incident resolutions within channel data into the claimed invention.
One skilled in the art would have been motivated to by the proposed combination of the Mani et al. and Chopra et al. references for improving resolving issues (column, lines 41 – 43- Chopra et al.).
With respect to claims 3, all of the limitations of claims 2 have been addressed.
The Mani et al. reference does not teach wherein the scoring the plurality of potential IT incident resolutions using a machine learning model comprises using the BHUVI model comprising the ranking layer to generate a success score indicative of sentiment of the channel data.
The Chopra et al. reference teaches wherein the scoring the plurality of potential IT incident resolutions using a machine learning model comprises using the BHUVI model comprising the ranking layer to generate a success score indicative of sentiment of the channel data resolutions (column 5, line 58 – column 6, line 7 – relevant features may include identifying unique portions of the selected upgrade issue report and attributing a unique score (e.g., a numerical value) to the unique portion. Each unique portion and the attributed unique score may collectively be included in the set of features generated during the feature extraction. Further, a self-healing training database is populated, wherein, the self-healing training database is a data structure that, when fully populated, specifies the set of upgrade issue notifications and the features of the upgrade issue notifications).
Thus, it would have been obvious at a time prior to the effective filing date of Applicant’s claimed invention to have combined the references Mani et al. and Chopra et al. to incorporate the limitations of wherein the scoring the plurality of potential IT incident resolutions using a machine learning model comprises using the BHUVI model comprising the ranking layer to generate a success score indicative of sentiment of the channel data resolutions into the claimed invention.
One skilled in the art would have been motivated to by the proposed combination of the Mani et al. and Chopra et al. references for improving resolving issues (column, lines 41 – 43- Chopra et al.).
With respect to claim 5, all of the limitations of claim 4 have been addressed.
The Mani et al. reference does not teach not teach further comprising detecting errors within a simulation of the potential IT incident resolution.
The Chopra et al. reference teaches further comprising detecting errors within a simulation of the potential IT incident resolution (column 5, lines 37-47 – analyzing each portion of the content to determine whether: (i) a majority of the content is relevant to the classification, and (ii) the content is formatted in a manner favorable for training).
Thus, it would have been obvious at a time prior to the effective filing date of Applicant’s claimed invention to have combined the references Mani et al. and Chopra et al. to incorporate the limitations of detecting errors within a simulation of the potential IT incident resolution into the claimed invention.
One skilled in the art would have been motivated to by the proposed combination of the Mani et al. and Chopra et al. references for improving resolving issues (column, lines 41 – 43- Chopra et al.).
With respect to claims 6, all of the limitations of claim 5 have been addressed.
The Mani et al. reference does not teach , wherein the scoring the plurality of potential IT incident resolutions using the machine learning model utilizes relative success of the simulation of the potential IT incident resolution.
The Chopra et al. reference teaches , wherein the scoring the plurality of potential IT incident resolutions using the machine learning model utilizes relative success of the simulation of the potential IT incident resolution (column 5, line 58 – column 6, line 7 – relevant features may include identifying unique portions of the selected upgrade issue report and attributing a unique score (e.g., a numerical value) to the unique portion. Each unique portion and the attributed unique score may collectively be included in the set of features generated during the feature extraction. Further, a self-healing training database is populated, wherein, the self-healing training database is a data structure that, when fully populated, specifies the set of upgrade issue notifications and the features of the upgrade issue notifications).
Thus, it would have been obvious at a time prior to the effective filing date of Applicant’s claimed invention to have combined the references Mani et al. and Chopra et al. to incorporate the limitations of , wherein the scoring the plurality of potential IT incident resolutions using the machine learning model utilizes relative success of the simulation of the potential IT incident resolution into the claimed invention.
One skilled in the art would have been motivated to by the proposed combination of the Mani et al. and Chopra et al. references for improving resolving issues (column, lines 41 – 43- Chopra et al.).
With respect to claim 7, all of the limitations of claim 6 have been addressed.
The Mani et al. reference does not teach standardizing, by the computing device, the IT incident report and the set of system details for the system; generating a success score of each of the incident resolutions within the plurality of potential IT incident resolutions; and updating the success score of each of the incident resolutions based on a relative success of an implementation each of the incident resolution.
The Chopra et al. reference teaches standardizing, by the computing device, the IT incident report and the set of system details for the system (column 7, lines 4-11 – the response may specify the details of the resolution); generating a success score of each of the incident resolutions within the plurality of potential IT incident resolutions (column 5, line 58 – column 6, line 7 – relevant features may include identifying unique portions of the selected upgrade issue report and attributing a unique score (e.g., a numerical value) to the unique portion. Each unique portion and the attributed unique score may collectively be included in the set of features generated during the feature extraction. Further, a self-healing training database is populated, wherein, the self-healing training database is a data structure that, when fully populated, specifies the set of upgrade issue notifications and the features of the upgrade issue notifications); and updating the success score of each of the incident resolutions based on a relative success of an implementation each of the incident resolutions (column 6, lines 25-31 - a first set of upgrade issue reports that are self-healable may each be associated with a z-score that is similar to other upgrade issue reports in the same set. The z-score may be generated by applying any combination of the unique scores, mean scores of the unique scores, variances of unique scores, and/or any other attributes of the upgrade issue report).
Thus, it would have been obvious at a time prior to the effective filing date of Applicant’s claimed invention to have combined the references Mani et al. and Chopra et al. to incorporate the limitations of teaches standardizing, by the computing device, the IT incident report and the set of system details for the system; generating a success score of each of the incident resolutions within the plurality of potential IT incident resolutions; and updating the success score of each of the incident resolutions based on a relative success of an implementation each of the incident resolution into the claimed invention.
One skilled in the art would have been motivated to by the proposed combination of the Mani et al. and Chopra et al. references for improving resolving issues (column, lines 41 – 43- Chopra et al.).
With respect to claim 20, the Mani et al. reference teaches obtain an information technology (IT) incident report ([0025] – arrangements for automatically analyzing problem tickets to discover groups of problems being reported in them and to provide meaningful, descriptive labels to help interpret such groups); obtain a set of system details of a system experiencing an IT incident, the IT incident being described by the IT incident report ([0002] – each application maintenance team collects a rich repository of problem tickets wherein the ticket represents a submission of a specific problem for resolution. Further, the information about the problem is typically embedded in the unstructured text of these tickets); standardize the IT incident report and the set of system details for the system ([00 47] – The aggregation component 120 may be configured to plot intensity of types of data invalidations and present, through a GUI, a graphical representation of intensity of data invalidations. Other types of telemetric analysis, resulting from execution of machine learning modeling, may also be provided to developers such as in a report form presented through a GUI or sent via other types of modalities (e.g., email, message).
The Mani et al. reference does not teach identify a plurality of potential IT incident resolutions within channel data using a best hedging, utilization, and validation of information (BHUVI) model comprising a ranking layer; simulate the plurality of potential IT incident resolutions in a virtual environment configured with the set of system details; detect errors within the simulation of the plurality of potential IT incident resolutions; determine a simulation success score for each of the plurality of potential IT incident resolutions based on a severity or number of identified errors in the simulation; and generate and transmit the plurality of potential IT incident resolutions.
The Chopra et al. reference teaches identify a plurality of potential IT incident resolutions within channel data using a best hedging, utilization, and validation of information (BHUVI) model comprising a ranking layer (column 6, lines 17-32); simulate the plurality of potential IT incident resolutions in a virtual environment configured with the set of system details (column 5, lines 37-57); detect errors within the simulation of the plurality of potential IT incident resolutions; (column 5, lines 48-67); determine a simulation success score for each of the plurality of potential IT incident resolutions based on a severity or number of identified errors in the simulation (column 5, lines 58-67) and (column 6, lines 17-32); and generate and transmit the plurality of potential IT incident resolutions (column 6, lines 25-31 - a first set of upgrade issue reports that are self-healable may each be associated with a z-score that is similar to other upgrade issue reports in the same set. The z-score may be generated by applying any combination of the unique scores, mean scores of the unique scores, variances of unique scores, and/or any other attributes of the upgrade issue report).
Thus, it would have been obvious at a time prior to the effective filing date of Applicant’s claimed invention to have combined the references Mani et al. and Chopra et al. to incorporate the limitations of identify a plurality of potential IT incident resolutions within channel data using a best hedging, utilization, and validation of information (BHUVI) model comprising a ranking layer; simulate the plurality of potential IT incident resolutions in a virtual environment configured with the set of system details; detect errors within the simulation of the plurality of potential IT incident resolutions; determine a simulation success score for each of the plurality of potential IT incident resolutions based on a severity or number of identified errors in the simulation; and generate and transmit the plurality of potential IT incident resolutions into the claimed invention.
One skilled in the art would have been motivated to by the proposed combination of the Mani et al. and Chopra et al. for improving resolving issues (column, lines 41 – 43- Chopra et al.).
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 Enam Ahmed whose telephone number is 571-270-1729. The examiner can normally be reached on Mon-Fri from 8:30 A.M. to 5:30 P.M.
If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, Albert Decady, can be reached on 571-272-3819.
The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ENAM AHMED/
Examiner, Art Unit 2112
5/14/26
/ESAW T ABRAHAM/
Primary Examiner, Art Unit 2112
Prosecution Insights