DETAILED ACTION
This action is in response the amendment filed on 03/05/2026.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 03/05/2026 has been entered.
Response to Amendment
Applicant’s amendment filed on 03/05/2026 has been entered. Claims 1, 8, 9, 11, 14, 18, 19 and 20 have been amended. Claims 7 and 17 have been canceled. No claims have been added. Claims 1 – 6, 8 – 16 and 18 – 20 are still pending in this application, with claims 1, 11 and 20 being independent.
Claim 7 and 17 have canceled status indicator. When a claim is canceled, the proper indication includes the claim number and canceled, status indicator alone. For example,
7. (Canceled)
To further prosecution the claim amendments are being considered as filed. However, an updated claim set with the proper status indicators and text markings must be filed.
Allowable Subject Matter
Aside from the non-prior art rejections, it has been determined that the prior art fails to teach or suggest in reasonable combination the limitations recited in the independent claims 1, 11 and 20. The independent claims recite the following limitations:
retrieve the real-time monitoring service alert ,wherein the real-time monitoring service alert comprises a text string, including user generated content (UGC) text related to a reported problem; identify one or more UGC data components of the UGC text of the real-time monitoring service alert comprising personal identifiable data; replace each of the one or more UGC data components comprising the personal identifiable data with non-identifying placeholders based at least in part on a UGC type of each of the one or more UGC data components; generate an anonymized incident text list comprising each of the identifying placeholders; utilize a semantic parser to identify one or more first portions of the anonymized incident text list related to identifying the reported problem, and one or more second portions of the anonymized incident text list related to a description of the reported problem; compile the one or more first portions of the anonymized incident text list into an alert message problem component, and the one or more second portions of the anonymized incident text list into an alert auxiliary details component; and determine, based on the alert message problem component, the alert auxiliary details component, and using an alert message machine learning model trained based on UGC transformed alert data, an alert message category of the real-time monitoring service alert.
In addition to the prior art cited in the previous office actions, this newly uncovered prior art fails to teach all of the limitations recited in the independent claims. Palla et al. (US 2018/0321997) discloses a system and method of computing system problem detection (Abstract), comprising the following: receiving a real-time monitoring service alert related to a reported problem (The alert comprises an application report and diagnostic data, Fig.6A, 232, 236, 242, 252, 260, 270, 278, 288 and Fig.6B, 292; [0053 – 0055] [0057 – 0064]); identifying one or more UGC data components of the UGC text of the real-time monitoring service alert comprising personal identifiable data ([0050]); replacing each of the one or more UGC data components comprising the personal identifiable data with non-identifying placeholders ([0051] [0052]).
Yet, Palla fails to teach the remaining limitations recited in the independent claims. Palla teaches applying the alert to a model to categorize (identify a root cause) the alert ([0047] [0048] [0059 – 0061] [0076 -0080]) without performing the specific semantic parsing and compiling recited by the independent claims.
Claims 2 - 6, 8 – 10, 12 – 16, 18 and 19 are objected to as being dependent upon a rejected base claim.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1, 11 and 20 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 6 and 17 of copending Application No. 18/190,557 (reference application) in view of Krishnan et al. (US 2019/0361760) (”Krishnan”), and further in view of Singh et al. (US 2022/0172024) (“Singh”), and further in view of Bais et al. (US 2022/0131766) (“Bais”) and further in view of Shah et al. (US 2023/0306049) (“Shah”). Although the claims at issue are not identical, they are not patentably distinct from each other .
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
The claim mapping is as follows.
Current Application
1. (Currently Amended) An apparatus for categorizing a real-time monitoring service alert, the apparatus comprising at least one processor and at least one memory including program code, the at least one memory and the program code configured to, with the at least one processor, cause the apparatus to at least :retrieve the real-time monitoring service alert, wherein the real-time monitoring service alert comprises a text string, including user generated content (UGC) text related to a reported problem; identify one or more UGC data components of the UGC text of the real-time monitoring service alert comprising personal identifiable data; replace each of the one or more UGC data components comprising the personal identifiable data with [[a ]]non-identifying placeholders based at least in part on a UGC type of each of the one or more UGC data components; generate an anonymized incident text list comprising each of the non- identifying placeholders; utilize a semantic parser to identify one or more first portions of the anonymized incident text list related to identifying the reported problem, and one or more second portions of the anonymized incident text list related to a description of the reported problem; compile the one or more first portions of the anonymized incident text list into an alert message problem component, and the one or more second portions of the anonymized incident text list into an alert auxiliary details component; and determine, based on the alert message problem component, the alert auxiliary details component, and using an alert message machine learning model trained based on [[the ]]UGC transformed alert data, an alert message category of the real-time monitoring service alert.
11. (Currently Amended) A method for categorizing a real-time monitoring service alert, the method comprising: retrieving the real-time monitoring service alert, wherein the real-time monitoring service alert comprises a text string, including user generated content (UGC) text related to a reported problem; identifying one or more UGC data components of the UGC text of the real-time monitoring service alert comprising personal identifiable data; replacing each of the one or more UGC data components comprising the personal identifiable data with [[a ]]non-identifying placeholders based at least in part on a UGC type of each of the one or more UGC data components; generating an anonymized incident text list comprising each of the non- identifying placeholders; utilizing a semantic parser to identify one or more first portions of the anonymized incident text list related to identifying the reported problem, and one or more second portions of the anonymized incident text list related to a description of the reported problem; compiling the one or more first portions of the anonymized incident text list into an alert message problem component, and the one or more second portions of the anonymized incident text list into an alert auxiliary details component ; and determining, based on the alert message problem component, the alert auxiliary details component, and using an alert message machine learning model trained based on [[the ]]UGC transformed alert data, an alert message category of the real-time monitoring service alert.
20. (Currently Amended) A computer program product for categorizing a real-time monitoring service alert, the computer program product comprising at least one non-transitory computer-readable storage medium having computer-readable program code portions stored therein, the computer-readable program code portions configured to: retrieve the real-time monitoring service alert, wherein the real-time monitoring service alert comprises a text string, including user generated content (UGC) text related to a reported problem; identify one or more UGC data components of the UGC text of the real-time monitoring service alert comprising personal identifiable data; replace each of the one or more UGC data components comprising the personal identifiable data with [[a]] non-identifying placeholders based at least in part on a UGC type of each of the one or more UGC data components; generate an anonymized incident text list comprising each of the non- identifying placeholders; utilize a semantic parser to identify one or more first portions of the anonymized incident text list related to identifying the reported problem, and one or more second portions of the anonymized incident text list related to a description of the reported problem; compile the one or more first portions of the anonymized incident text list into an alert message problem component, and the one or more second portions of the anonymized incident text list into an alert auxiliary details component; and determine, based on the alert message problem component, the alert auxiliary details component, and using an alert message machine learning model trained based on [[the]]UGC transformed alert data, an alert message category of the real-time monitoring service alert.
Application no. 18/190,557
1. (Currently Amended) An apparatus for generating UGC transformed alert data from a monitoring service alert, the apparatus comprising at least one processor and at least one memory including program code, the at least one memory and the program code configured to, with the at least one processor, cause the apparatus to at least: retrieve the monitoring service alert, wherein the monitoring service alert comprises a text string, including user generated content (UGC) text; utilize a semantic parser to identify one or more first portions of the text string related to identifying a reported problem, and one or more second portions of the text string related to a description of the reported problem; compile the one or more first portions of the text string into an alert message problem component, and the one or more second portions of the text string into an alert auxiliary details component; generate an alert message problem embedding by applying a first feature extraction to the alert message problem component, wherein the first feature extraction utilizes a non-linear embedding technique to generate the alert message problem embedding; generate an alert message description embedding by applying a second feature extraction to the alert auxiliary details component, wherein the second feature extraction utilizes a second embedding technique to generate the alert message description embedding, and wherein the second embedding technique is different from the non-linear embedding technique; and generate the UGC transformed alert data based on the alert message problem embedding and the alert message description embedding.
6. (Previously Presented) The apparatus of Claim 1, wherein, segregating the monitoring service alert further comprises: identifying one or more UGC data components of the text string of the monitoring service alert corresponding to the UGC text; and replacing each of the one or more UGC data components with one or more generic data tokens based at least in part on a UGC type of each of the one or more UGC data components.
14. (Currently Amended) A method for generating UGC transformed alert data from a monitoring service alert, the method comprising: retrieving the monitoring service alert, wherein the monitoring service alert comprises a text string, including user generated content (UGC) text; utilizing a semantic parser to identify one or more first portions of the text string related to identifying a reported problem, and one or more second portions of the text string related to a description of the reported problem; compiling the one or more first portions of the text string into an alert message problem component, and the one or more second portions of the text string into an alert auxiliary details component; generating an alert message problem embedding by applying a first feature extraction to the alert message problem component, wherein the first feature extraction utilizes a non-linear embedding technique to generate the alert message problem embedding; generating an alert message description embedding by applying a second feature extraction to the alert auxiliary details component, wherein the second feature extraction utilizes a second embedding technique to generate the alert message description embedding, and wherein the second embedding technique is different from the non-linear embedding technique; and generating the UGC transformed alert data based on the alert message problem embedding and the alert message description embedding.
17. (Original) The method of Claim 14, wherein segregating the monitoring service alert further comprises: identifying one or more UGC data components of the text string of the monitoring service alert corresponding to the UGC text; and replacing each of the one or more UGC data components with one or more generic data tokens based at least in part on a UGC type of each of the one or more UGC data components.
As shown above, claim 6 of application no. 18/190,557 recites the limitations of claim 1 of the current application except for the following: identify one or more UGC data components of the UGC text of the real-time monitoring service alert comprising personal identifiable data; replace each of the one or more UGC data components comprising the personal identifiable data with a non-identifying placeholder based at least in part on a UGC type of each of the one or more UGC data components; generated an anonymized incident text list comprising each of the non-identifying placeholders; generate, at the alert data transformation module, UGC transformed alert data comprising each of the one or more UGC data components; and determine, based on the alert message problem component, the alert auxiliary details component, and using an alert message machine learning model trained based on UGC transformed alert data, an alert message category of the real-time monitoring service alert.
However, Krishnan discloses a method for categorizing a real time alert (Abstract) comprising the following: determining, based on the alert message problem component, and using an alert message machine learning model trained based on alert data, an alert message category of the real-time monitoring service alert (“the problem detection platform may utilize the machine learning module to process the text of the electronic issue ticket to identify an issue category and/or an issue sub-category for the electronic issue ticket”, [0032] [0075]).
Moreover, Singh discloses a system and method for assigning a service ticket to a queue for servicing (Abstract), comprising the following: the text of the service ticket is parsed to extract a summary and a description ([0044] [0063] [0064]); the summary is no more than 10 words, and the description is no more than 100 words ([0045] [0046]); and the summary and description are both input to a neural network ([0047 – 0056]) to determine further processing ([0002] [0011] [0012] [0022] [0024] [0065 – 0067] [0069]).
Furthermore, Bais discloses a system and method for analyzing and resolving tickets raised in an IT environment (Abstract), comprising the following: training a cognitive engine on transformed, user generated content text (“In this regard, the cognitive engine uses supervised machine learning models which are trained on historically available ticket data over a period. Description of ticket is primarily used to train a classification model. Typically, these ticket descriptions contain a lot of bad/junk data in real life. Therefore, few text pre-processing steps such as removing stop words, punctuation and normalization of texts are performed on raw texts”, [0032] [0043]); and accessing the alerts to be categorized further comprises retrieving the alerts from storage ([0033] [0035]).
Additionally, Shah discloses a messaging platform and method (Abstract), comprising the following: identify one or more data components of message text comprising personal identifiable data ([0050]); replace each of the one or more data components comprising the personal identifiable data with a non-identifying placeholder based at least in part on a type of each of the one or more data components to generate a transformed message text ([0050]).
Furthermore, tokenization of input text to perform language processing would have been well known and obvious at the time of applicant’s filing.
Therefore, it would have been obvious to one of ordinary skill in the art at the time of applicant’s filing to improve the invention recited by claim 6 of application no, 18/190,557 in the same way that Krishnan’s invention has been improved to achieve the following, predictable results for the purpose of efficiently tracking issues with services provided by an enterprise to prevent wasting time, resources, etc. resolving the issues from scratch each time (Krishnan, [0001] [0012]): determine, based on the alert message problem component and using an alert message machine learning model trained based on alert data, an alert message category of the real-time monitoring service alert.
Additionally, it would have been obvious to one of ordinary skill in the art at the time of applicant’s filing to improve the invention recited by claim 6 of application no, 18/190,557 and Krishnan in the same way that Singh’s invention has been improved to achieve the following, predictable results for the purpose of efficiently tracking issues with services provided by an enterprise to prevent wasting time, resources, etc. resolving the issues from scratch each time (Krishnan, [0001] [0012]): the segregation of the text string further generates an auxiliary alert data component which comprises a no more than 100 word description, wherein the alert message problem component further comprises a no more than 10 word description; and both the generated auxiliary alert data (long description) and alert message problem component (short description) are used as input to an alert message machine learning model to identify the alert message category (the short and long descriptions are input to a neural network to perform further processing).
Moreover, it would have been obvious to one of ordinary skill in the art at the time of applicant’s to improve the invention disclosed by the combination of the limitations recited by claim 6 of application no. 18/190,557, Krishnan and Singh in the same way that Bais’ invention has been improved to achieve the following, predictable results for the purpose of analyzing and efficiently resolving tickets raised in an IT environment (Krishnan, [0001] [0012]) (Bais, [0001]): the accessed alerts are further retrieved (from storage); and the alert message machine learning model (cognitive engine used to process ticket information) is further trained based on transformed data, e.g. UGC transformed alert data.
Furthermore, it would have been obvious to one of ordinary skill in the art at the time of applicant’s to improve the invention disclosed by the combination of the limitations recited by claim 6 of application no. 18/190,557, Krishnan, Singh and Bais in the same way that Shah’s invention has been improved to achieve the following, predictable results for the purpose of analyzing and efficiently resolving tickets raised in an IT environment (Krishnan, [0001] [0012]) (Bais, [0001]): identify one or more UGC data components of the UGC text of the real-time monitoring service alert comprising personal identifiable data; replace each of the one or more UGC data components comprising the personal identifiable data with a non-identifying placeholder based at least in part on a UGC type of each of the one or more UGC data components; and generate, at the alert data transformation module, the UGC transformed alert data comprising each of the one or more UGC data components.
In view of the obviousness of tokenization of text to perform natural language processing, claim 1 of the current application is an obvious variant of claim 6 of application no. 18/190,557.
As shown above, claim 17 of application no. 18/190,557 recites the limitations of claim 11 of the current application except for the following: identify one or more UGC data components of the UGC text of the real-time monitoring service alert comprising personal identifiable data; replace each of the one or more UGC data components comprising the personal identifiable data with a non-identifying placeholder based at least in part on a UGC type of each of the one or more UGC data components; generated an anonymized incident text list comprising each of the non-identifying placeholders; generate, at the alert data transformation module, UGC transformed alert data comprising each of the one or more UGC data components; and determine, based on the alert message problem component, the alert auxiliary details component, and using an alert message machine learning model trained based on UGC transformed alert data, an alert message category of the real-time monitoring service alert.
However, Krishnan discloses a method for categorizing a real time alert (Abstract) comprising the following: determining, based on the alert message problem component, and using an alert message machine learning model trained based on alert data, an alert message category of the real-time monitoring service alert (“the problem detection platform may utilize the machine learning module to process the text of the electronic issue ticket to identify an issue category and/or an issue sub-category for the electronic issue ticket”, [0032] [0075]).
Moreover, Singh discloses a system and method for assigning a service ticket to a queue for servicing (Abstract), comprising the following: the text of the service ticket is parsed to extract a summary and a description ([0044] [0063] [0064]); the summary is no more than 10 words, and the description is no more than 100 words ([0045] [0046]); and the summary and description are both input to a neural network ([0047 – 0056]) to determine further processing ([0002] [0011] [0012] [0022] [0024] [0065 – 0067] [0069]).
Furthermore, Bais discloses a system and method for analyzing and resolving tickets raised in an IT environment (Abstract), comprising the following: training a cognitive engine on transformed, user generated content text (“In this regard, the cognitive engine uses supervised machine learning models which are trained on historically available ticket data over a period. Description of ticket is primarily used to train a classification model. Typically, these ticket descriptions contain a lot of bad/junk data in real life. Therefore, few text pre-processing steps such as removing stop words, punctuation and normalization of texts are performed on raw texts”, [0032] [0043]); and accessing the alerts to be categorized further comprises retrieving the alerts from storage ([0033] [0035]).
Additionally, Shah discloses a messaging platform and method (Abstract), comprising the following: identify one or more data components of message text comprising personal identifiable data ([0050]); replace each of the one or more data components comprising the personal identifiable data with a non-identifying placeholder based at least in part on a type of each of the one or more data components to generate a transformed message text ([0050]).
Furthermore, tokenization of input text to perform language processing would have been well known and obvious at the time of applicant’s filing.
Therefore, it would have been obvious to one of ordinary skill in the art at the time of applicant’s filing to improve the invention recited by claim 17 of application no, 18/190,557 in the same way that Krishnan’s invention has been improved to achieve the following, predictable results for the purpose of efficiently tracking issues with services provided by an enterprise to prevent wasting time, resources, etc. resolving the issues from scratch each time (Krishnan, [0001] [0012]): determine, based on the alert message problem component and using an alert message machine learning model trained based on alert data, an alert message category of the real-time monitoring service alert.
Additionally, it would have been obvious to one of ordinary skill in the art at the time of applicant’s filing to improve the invention recited by claim 17 of application no, 18/190,557 and Krishnan in the same way that Singh’s invention has been improved to achieve the following, predictable results for the purpose of efficiently tracking issues with services provided by an enterprise to prevent wasting time, resources, etc. resolving the issues from scratch each time (Krishnan, [0001] [0012]): the segregation of the text string further generates an auxiliary alert data component which comprises a no more than 100 word description, wherein the alert message problem component further comprises a no more than 10 word description; and both the generated auxiliary alert data (long description) and alert message problem component (short description) are used as input to an alert message machine learning model to identify the alert message category (the short and long descriptions are input to a neural network to perform further processing).
Moreover, it would have been obvious to one of ordinary skill in the art at the time of applicant’s to improve the invention disclosed by the combination of the limitations recited by claim 17 of application no. 18/190,557, Krishnan and Singh in the same way that Bais’ invention has been improved to achieve the following, predictable results for the purpose of analyzing and efficiently resolving tickets raised in an IT environment (Krishnan, [0001] [0012]) (Bais, [0001]): the accessed alerts are further retrieved (from storage); and the alert message machine learning model (cognitive engine used to process ticket information) is further trained based on transformed data, e.g. UGC transformed alert data.
Furthermore, it would have been obvious to one of ordinary skill in the art at the time of applicant’s to improve the invention disclosed by the combination of the limitations recited by claim 17 of application no. 18/190,557, Krishnan, Singh and Bais in the same way that Shah’s invention has been improved to achieve the following, predictable results for the purpose of analyzing and efficiently resolving tickets raised in an IT environment (Krishnan, [0001] [0012]) (Bais, [0001]): identify one or more UGC data components of the UGC text of the real-time monitoring service alert comprising personal identifiable data; replace each of the one or more UGC data components comprising the personal identifiable data with a non-identifying placeholder based at least in part on a UGC type of each of the one or more UGC data components; and generate, at the alert data transformation module, the UGC transformed alert data comprising each of the one or more UGC data components.
In view of the obviousness of tokenization of text to perform natural language processing, claim 11 of the current application is an obvious variant of claim 17 of application no. 18/190,557.
As shown above, claim 6 of application no. 18/190,557 recites the limitations of claim 20 of the current application except for the following: identify one or more UGC data components of the UGC text of the real-time monitoring service alert comprising personal identifiable data; replace each of the one or more UGC data components comprising the personal identifiable data with a non-identifying placeholder based at least in part on a UGC type of each of the one or more UGC data components; generated an anonymized incident text list comprising each of the non-identifying placeholders; generate, at the alert data transformation module, UGC transformed alert data comprising each of the one or more UGC data components; determine, based on the alert message problem component, the alert auxiliary details component, and using an alert message machine learning model trained based on UGC transformed alert data, an alert message category of the real-time monitoring service alert; and a computer program product embodiment.
However, Krishnan discloses a method for categorizing a real time alert (Abstract) comprising the following: determining, based on the alert message problem component, and using an alert message machine learning model trained based on alert data, an alert message category of the real-time monitoring service alert (“the problem detection platform may utilize the machine learning module to process the text of the electronic issue ticket to identify an issue category and/or an issue sub-category for the electronic issue ticket”, [0032] [0075]).
Moreover, Singh discloses a system and method for assigning a service ticket to a queue for servicing (Abstract), comprising the following: the text of the service ticket is parsed to extract a summary and a description ([0044] [0063] [0064]); the summary is no more than 10 words, and the description is no more than 100 words ([0045] [0046]); and the summary and description are both input to a neural network ([0047 – 0056]) to determine further processing ([0002] [0011] [0012] [0022] [0024] [0065 – 0067] [0069]).
Furthermore, Bais discloses a system and method for analyzing and resolving tickets raised in an IT environment (Abstract), comprising the following: training a cognitive engine on transformed, user generated content text (“In this regard, the cognitive engine uses supervised machine learning models which are trained on historically available ticket data over a period. Description of ticket is primarily used to train a classification model. Typically, these ticket descriptions contain a lot of bad/junk data in real life. Therefore, few text pre-processing steps such as removing stop words, punctuation and normalization of texts are performed on raw texts”, [0032] [0043]); and accessing the alerts to be categorized further comprises retrieving the alerts from storage ([0033] [0035]).
Additionally, Shah discloses a messaging platform and method (Abstract), comprising the following: identify one or more data components of message text comprising personal identifiable data ([0050]); replace each of the one or more data components comprising the personal identifiable data with a non-identifying placeholder based at least in part on a type of each of the one or more data components to generate a transformed message text ([0050]).
Furthermore, tokenization of input text to perform language processing would have been well known and obvious at the time of applicant’s filing.
Therefore, it would have been obvious to one of ordinary skill in the art at the time of applicant’s filing to improve the invention recited by claim 6 of application no, 18/190,557 in the same way that Krishnan’s invention has been improved to achieve the following, predictable results for the purpose of efficiently tracking issues with services provided by an enterprise to prevent wasting time, resources, etc. resolving the issues from scratch each time (Krishnan, [0001] [0012]): determine, based on the alert message problem component and using an alert message machine learning model trained based on alert data, an alert message category of the real-time monitoring service alert.
Additionally, it would have been obvious to one of ordinary skill in the art at the time of applicant’s filing to improve the invention recited by claim 6 of application no, 18/190,557 and Krishnan in the same way that Singh’s invention has been improved to achieve the following, predictable results for the purpose of efficiently tracking issues with services provided by an enterprise to prevent wasting time, resources, etc. resolving the issues from scratch each time (Krishnan, [0001] [0012]): the segregation of the text string further generates an auxiliary alert data component which comprises a no more than 100 word description, wherein the alert message problem component further comprises a no more than 10 word description; and both the generated auxiliary alert data (long description) and alert message problem component (short description) are used as input to an alert message machine learning model to identify the alert message category (the short and long descriptions are input to a neural network to perform further processing).
Moreover, it would have been obvious to one of ordinary skill in the art at the time of applicant’s to improve the invention disclosed by the combination of the limitations recited by claim 6 of application no. 18/190,557, Krishnan and Singh in the same way that Bais’ invention has been improved to achieve the following, predictable results for the purpose of analyzing and efficiently resolving tickets raised in an IT environment (Krishnan, [0001] [0012]) (Bais, [0001]): the accessed alerts are further retrieved (from storage); and the alert message machine learning model (cognitive engine used to process ticket information) is further trained based on transformed data, e.g. UGC transformed alert data.
Furthermore, it would have been obvious to one of ordinary skill in the art at the time of applicant’s to improve the invention disclosed by the combination of the limitations recited by claim 6 of application no. 18/190,557, Krishnan, Singh and Bais in the same way that Shah’s invention has been improved to achieve the following, predictable results for the purpose of analyzing and efficiently resolving tickets raised in an IT environment (Krishnan, [0001] [0012]) (Bais, [0001]): identify one or more UGC data components of the UGC text of the real-time monitoring service alert comprising personal identifiable data; replace each of the one or more UGC data components comprising the personal identifiable data with a non-identifying placeholder based at least in part on a UGC type of each of the one or more UGC data components; and generate, at the alert data transformation module, the UGC transformed alert data comprising each of the one or more UGC data components.
Furthermore, a computer program product of an invention is an obvious variant of an apparatus embodiment of the invention. In view of this and the obviousness of tokenization of text to perform natural language processing, claim 20 of the current application is an obvious variant of claim 6 of application no. 18/190,557.
Response to Arguments
Applicant’s arguments with respect to claim(s) 1 – 6, 8 – 16 and 18 – 20 have been considered but are moot in view of the new ground(s) of rejection.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Goswami et al. (US 2026/0127515) which is related to the inventive concept of pre-processing incident text (normalizing and masking) to categorize the incident.
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/SONIA L GAY/Primary Examiner, Art Unit 2657
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