DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This is in response to Application 18/814139 filed on August 23, 2024 in which Claims 1-20 are presented for examination.
Status of Claims
Claims 2 and 13 have been cancelled. Claims 1, 3-12 and 14-20 are pending, of which claims 1, 3-12 and 14-20 are rejected under 103. Claims 1, 7, 11, 14, 16 and 19 are rejected under Alice 101.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1, 7, 11, 14, 16 and 19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to (an) abstract idea(s) without significantly more.
Claim 1 recites:
detect presence of an error condition at a first computing device of a plurality of computing devices
generate, using on a machine learning (ML) model, a non-technical natural language alert related to the error condition, wherein the non-technical natural language alert includes: a non-technical natural language summary of the error condition; and a non-technical natural language indication of a recommended action to remedy the error condition, wherein the recommended action is one of a plurality of pre-configured actions related to the error condition
provide, to a user of the electronic device via a user interface, the non-technical natural language alert that includes: the non-technical natural language summary of the error condition; and the non-technical natural language indication of the recommended action to remedy the error condition, wherein the recommended action is one of a plurality of pre-configured actions related to the error condition.
Step 1: Is the claim to a process, machine, manufacture, or composition of matter?
Yes (process).
Step 2A, Prong I: Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes: (an) abstract idea(s).
The ‘detecting’ limitation in # 1 above, as claimed, is a process that, under its broadest reasonable limitation, is a mental process that covers the performance of the limitation in the mind. For example, “detecting” in the context of this claim encompasses a person evaluating computing device performance data on paper or visually using a generic display.
The ‘generate’ limitation in # 2 above, as claimed, is a process that, under its broadest reasonable limitation, is a mental process that covers the performance of the limitation in the mind. For example, “generate” in the context of this claim encompasses the person flag erroneous computing device performance data.
The ‘provide’ limitation in # 3 above, as claimed, is a process that, under its broadest reasonable limitation, is a mental process that covers the performance of the limitation in the mind. For example, “provide” in the context of this claim encompasses the person showing data on paper or visually using a generic display.
Step 2A, Prong II: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No.
The ‘the non-technical natural language alert includes: a non-technical natural language summary of the error condition; and a non-technical natural language indication of a recommended action to remedy the error condition, wherein the recommended action is one of a plurality of pre-configured actions related to the error condition’ limitation in # 2 above, as claimed, is a process that, under its broadest reasonable limitation, is an additional element that is insignificant extra-solution activity. For example, “the non-technical natural language alert includes: a non-technical natural language summary of the error condition; and a non-technical natural language indication of a recommended action to remedy the error condition, wherein the recommended action is one of a plurality of pre-configured actions related to the error condition” in the context of this claim encompasses the person obtaining a certain type of data (see MPEP 2106.05(d)(II) and 2106.05(g)).
The ‘the non-technical natural language summary of the error condition; and the non-technical natural language indication of the recommended action to remedy the error condition, wherein the recommended action is one of a plurality of pre-configured actions related to the error condition’ limitation in # 3 above, as claimed, is a process that, under its broadest reasonable limitation, is an additional element that is insignificant extra-solution activity. For example, “the non-technical natural language summary of the error condition; and the non-technical natural language indication of the recommended action to remedy the error condition, wherein the recommended action is one of a plurality of pre-configured actions related to the error condition” in the context of this claim encompasses the person obtaining a certain type of data (see MPEP 2106.05(d)(II) and 2106.05(g)).
Additionally, the claim recites the following additional element:
an electronic device
a machine learning model
a user interface
These additional elements are recited at a high-level of generality (i.e. as generic computer components) such that they amount to no more than components comprising mere instructions to apply the exception. Accordingly, these additional elements do not integrate the abstract idea(s) into a practical application because they do not impose any meaningful limits on practicing the abstract idea(s).
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
No.
As discussed above with respect to integration of the abstract idea into a
practical application, the aforementioned additional element amounts to no more than
a component comprising mere instructions to apply the exception. Mere instructions to
apply an exception using one or more generic computer components cannot provide an inventive concept.
i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network);
iv. Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93; and
v. Electronically scanning or extracting data from a physical document, Content Extraction and Transmission, LLC v. Wells Fargo Bank, 776 F.3d 1343, 1348, 113 USPQ2d 1354, 1358 (Fed. Cir. 2014) (optical character recognition).
Claim 11 recites:
detect presence of an error condition at a first computing device of a plurality of computing devices
generate, using on a machine learning (ML) model, a non-technical natural language alert related to the error condition, wherein the non-technical natural language alert includes: a non-technical natural language summary of the error condition; and a non-technical natural language indication of a recommended action to remedy the error condition, wherein the recommended action is one of a plurality of pre-configured actions related to the error condition
wherein the indication of the recommended action is generated using the ML model
provide, to a user of the electronic device via a user interface, the non-technical natural language alert that includes: the non-technical natural language summary of the error condition; and the non-technical natural language indication of the recommended action to remedy the error condition, wherein the recommended action is one of a plurality of pre-configured actions related to the error condition
wherein the indication of the recommended action is generated using the ML model
Step 1: Is the claim to a process, machine, manufacture, or composition of matter?
Yes (process).
Step 2A, Prong I: Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes: (an) abstract idea(s).
The ‘detecting’ limitation in # 4 above, as claimed, is a process that, under its broadest reasonable limitation, is a mental process that covers the performance of the limitation in the mind. For example, “detecting” in the context of this claim encompasses a person evaluating computing device performance data on paper or visually using a generic display.
The ‘generate’ limitation in # 5 above, as claimed, is a process that, under its broadest reasonable limitation, is a mental process that covers the performance of the limitation in the mind. For example, “generate” in the context of this claim encompasses the person flagging erroneous computing device performance data.
The ‘provide’ limitation in # 7 above, as claimed, is a process that, under its broadest reasonable limitation, is a mental process that covers the performance of the limitation in the mind. For example, “provide” in the context of this claim encompasses the person showing data on paper or visually using a generic display.
The ‘generate’ limitation in # 6 and 8 above, as claimed, is a process that, under its broadest reasonable limitation, is a mental process that covers the performance of the limitation in the mind. For example, “generate” in the context of this claim encompasses the person determining a corrective to use.
Step 2A, Prong II: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No.
The ‘the non-technical natural language alert includes: a non-technical natural language summary of the error condition; and a non-technical natural language indication of a recommended action to remedy the error condition, wherein the recommended action is one of a plurality of pre-configured actions related to the error condition’ limitation in # 5 above, as claimed, is a process that, under its broadest reasonable limitation, is an additional element that is insignificant extra-solution activity. For example, “the non-technical natural language alert includes: a non-technical natural language summary of the error condition; and a non-technical natural language indication of a recommended action to remedy the error condition, wherein the recommended action is one of a plurality of pre-configured actions related to the error condition” in the context of this claim encompasses the person obtaining a certain type of data (see MPEP 2106.05(d)(II) and 2106.05(g)).
The ‘the non-technical natural language summary of the error condition; and the non-technical natural language indication of the recommended action to remedy the error condition, wherein the recommended action is one of a plurality of pre-configured actions related to the error condition’ limitation in # 7 above, as claimed, is a process that, under its broadest reasonable limitation, is an additional element that is insignificant extra-solution activity. For example, “the non-technical natural language summary of the error condition; and the non-technical natural language indication of the recommended action to remedy the error condition, wherein the recommended action is one of a plurality of pre-configured actions related to the error condition” in the context of this claim encompasses the person obtaining a certain type of data (see MPEP 2106.05(d)(II) and 2106.05(g)).
Additionally, the claim recites the following additional element:
an electronic device
a machine learning model
a user interface
These additional elements are recited at a high-level of generality (i.e. as generic computer components) such that they amount to no more than components comprising mere instructions to apply the exception. Accordingly, these additional elements do not integrate the abstract idea(s) into a practical application because they do not impose any meaningful limits on practicing the abstract idea(s).
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
No.
As discussed above with respect to integration of the abstract idea into a
practical application, the aforementioned additional element amounts to no more than
a component comprising mere instructions to apply the exception. Mere instructions to
apply an exception using one or more generic computer components cannot provide an inventive concept.
i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network);
iv. Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93; and
v. Electronically scanning or extracting data from a physical document, Content Extraction and Transmission, LLC v. Wells Fargo Bank, 776 F.3d 1343, 1348, 113 USPQ2d 1354, 1358 (Fed. Cir. 2014) (optical character recognition).
Claim 16 recites:
detecting presence of an error condition at a first computing device of a plurality of computing devices
wherein the error condition relates to presence of a virus on the first computing device
generating, using on a machine learning (ML) model, a non-technical natural language alert related to the error condition, wherein the non-technical natural language alert includes: a non-technical natural language summary of the error condition; and a non-technical natural language indication of a recommended action to remedy the error condition, wherein the recommended action is one of a plurality of pre-configured actions related to the error condition
providing, to a user of the electronic device via a user interface, the non-technical natural language alert that includes: the non-technical natural language summary of the error condition; and the non-technical natural language indication of the recommended action to remedy the error condition, wherein the recommended action is one of a plurality of pre-configured actions related to the error condition
Step 1: Is the claim to a process, machine, manufacture, or composition of matter?
Yes (process).
Step 2A, Prong I: Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes: (an) abstract idea(s).
The ‘detecting’ limitation in # 9 above, as claimed, is a process that, under its broadest reasonable limitation, is a mental process that covers the performance of the limitation in the mind. For example, “detecting” in the context of this claim encompasses a person evaluating computing device performance data on paper or visually using a generic display.
The ‘generate’ limitation in # 11 above, as claimed, is a process that, under its broadest reasonable limitation, is a mental process that covers the performance of the limitation in the mind. For example, “generate” in the context of this claim encompasses the person flagging erroneous computing device performance data.
The ‘provide’ limitation in # 12 above, as claimed, is a process that, under its broadest reasonable limitation, is a mental process that covers the performance of the limitation in the mind. For example, “provide” in the context of this claim encompasses the person showing data on paper or visually using a generic display.
Step 2A, Prong II: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No.
The ‘the non-technical natural language alert includes: a non-technical natural language summary of the error condition; and a non-technical natural language indication of a recommended action to remedy the error condition, wherein the recommended action is one of a plurality of pre-configured actions related to the error condition’ limitation in # 11 above, as claimed, is a process that, under its broadest reasonable limitation, is an additional element that is insignificant extra-solution activity. For example, “the non-technical natural language alert includes: a non-technical natural language summary of the error condition; and a non-technical natural language indication of a recommended action to remedy the error condition, wherein the recommended action is one of a plurality of pre-configured actions related to the error condition” in the context of this claim encompasses the person obtaining a certain type of data (see MPEP 2106.05(d)(II) and 2106.05(g)).
The ‘the non-technical natural language summary of the error condition; and the non-technical natural language indication of the recommended action to remedy the error condition, wherein the recommended action is one of a plurality of pre-configured actions related to the error condition’ limitation in # 12 above, as claimed, is a process that, under its broadest reasonable limitation, is an additional element that is insignificant extra-solution activity. For example, “the non-technical natural language summary of the error condition; and the non-technical natural language indication of the recommended action to remedy the error condition, wherein the recommended action is one of a plurality of pre-configured actions related to the error condition” in the context of this claim encompasses the person obtaining a certain type of data (see MPEP 2106.05(d)(II) and 2106.05(g)).
The ‘wherein the error condition relates to presence of a virus on the first computing device’ limitation in # 10 above, as claimed, is a process that, under its broadest reasonable limitation, is an additional element that is insignificant extra-solution activity. For example, “wherein the error condition relates to presence of a virus on the first computing device” in the context of this claim encompasses the person obtaining a certain type of data (see MPEP 2106.05(d)(II) and 2106.05(g)).
Additionally, the claim recites the following additional element:
an electronic device
a machine learning model
a user interface
These additional elements are recited at a high-level of generality (i.e. as generic computer components) such that they amount to no more than components comprising mere instructions to apply the exception. Accordingly, these additional elements do not integrate the abstract idea(s) into a practical application because they do not impose any meaningful limits on practicing the abstract idea(s).
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
No.
As discussed above with respect to integration of the abstract idea into a
practical application, the aforementioned additional element amounts to no more than
a component comprising mere instructions to apply the exception. Mere instructions to
apply an exception using one or more generic computer components cannot provide an inventive concept.
i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network);
iv. Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93; and
v. Electronically scanning or extracting data from a physical document, Content Extraction and Transmission, LLC v. Wells Fargo Bank, 776 F.3d 1343, 1348, 113 USPQ2d 1354, 1358 (Fed. Cir. 2014) (optical character recognition).
Claims 7, 14 and 19 recite:
provide, to the user via the user interface, one or more interactive elements related to the recommended action
Step 1: Is the claim to a process, machine, manufacture, or composition of matter?
Yes (process)
Step 2A, Prong I: Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes: (an) abstract idea(s).
The ‘provide’ limitation in # 13 above, as claimed, is a process that, under its broadest reasonable limitation, is a mental process that covers the performance of the limitation in the mind. For example, “provide” in the context of this claim encompasses the person showing data on paper or visually using a generic display.
Step 2A, Prong II: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
No.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
Claim(s) 1, 3-5 and 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Stollmeyer (US Patent Application 2024/0210963) in view of Srinivasan (US Patent Application 2022/0308943) in view of Paul (US Patent Application 2025/0258729) and further in view of Roussel (US Patent Application 2011/0271149).
Claim 1, Stollmeyer teaches one or more non-transitory computer-readable media (NTCRM) comprising instructions that, upon execution of the instructions by one or more processors of an electronic device, are to cause the electronic device to: detect presence of an error condition at a first computing device of a plurality of computing devices (View Stollmeyer ¶ 65, 85; fault detection), wherein the recommended action is one of a plurality of pre-configured actions related to the error condition (View Stollmeyer ¶ 56, 69, 95, 132, 143, 145; in-flight failure mitigation may be implemented locally through an API interface between the recommendation & intervention engine; a recommendation & intervention engine recommends a course of action to take for each type of failure, and automates an appropriate response), wherein the recommended action is one of a plurality of pre-configured actions related to the error condition (View Stollmeyer ¶ 56, 69, 95, 132, 143, 145; in-flight failure mitigation may be implemented locally through an API interface between the recommendation & intervention engine; a recommendation & intervention engine recommends a course of action to take for each type of failure, and automates an appropriate response).
Stollmeyer does not explicitly teach generate, using a machine learning (ML) model, a non-technical natural language alert related to the error condition, wherein the non-technical natural language alert includes: a non-technical natural language summary of the error condition; and a non-technical natural language indication of a recommended action to remedy the error condition; and provide, to a user of the electronic device via a user interface, the non-technical natural language alert that includes: the non-technical natural language summary of the error condition; and the non-technical natural language indication of the recommended action to remedy the error condition.
However, Srinivasan teaches generate, using a machine learning (ML) model, a non-technical natural language alert related to the error condition (View Srinivasan ¶ 7, 22, 29; system error in natural language; trained mode; actionable alert), wherein the non-technical natural language alert includes: a non-technical natural language summary of the error condition (View Srinivasan ¶ 7, 64, 65; processor inputs data relating to an alert that a system error occurred in a natural language format); and a non-technical natural language indication of a recommended action to remedy the error condition (View Srinivasan ¶ 64; automatically resolve the system error using machine learning based on natural language).
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to modify Stollmeyer with generate, using a machine learning (ML) model, a non-technical natural language alert related to the error condition, wherein the non-technical natural language alert includes: a non-technical natural language summary of the error condition; and a non-technical natural language indication of a recommended action to remedy the error condition since it is known in the art that system errors can be converted to natural language (View Srinivasan ¶ 7, 64). Such modification would have allowed an error alert to be generated using natural language.
Stollmeyer and Srinivasan do not explicitly teach provide, to a user of the electronic device via a user interface, the non-technical natural language alert that includes: the non-technical natural language summary of the error condition; and the non-technical natural language indication of the recommended action to remedy the error condition.
However, Paul teaches provide, to a user of the electronic device via a user interface, the non-technical natural language alert that includes: the non-technical natural language summary of the error condition (View Paul ¶ 58; user may be notified about potential error in natural language via user interface).
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to modify the combination of teachings with provide, to a user of the electronic device via a user interface, the non-technical natural language alert that includes: the non-technical natural language summary of the error condition since it is known in the art that data can be displayed on a user interface (View Paul ¶ 58). Such modification would have allowed an error condition to be displayed.
Stollmeyer, Srinivasan and Paul do not explicitly teach the non-technical natural language indication of the recommended action to remedy the error condition.
However, Roussel teaches the non-technical natural language indication of the recommended action to remedy the error condition (View Roussel ¶ 33, 34; explanatory information such as description of an error and how the problem can be corrected may be displayed in a hover-on (or similar) style information box upon user selection).
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to modify the combination of teachings with the non-technical natural language indication of the recommended action to remedy the error condition since it is known in the art that a recommended corrective action can be generated in natural language (View Roussel ¶ 33, 34). Such modification would have allowed a recommended corrective action to be described in natural language.
Claim 3, most of the limitations of this claim has been noted in the rejection of Claim 1. Stollmeyer further teaches the indication of the recommended action is generated using the ML model (View Stollmeyer ¶ 151; AI/ML training).
Claim 4, most of the limitations of this claim has been noted in the rejection of Claim 1. Stollmeyer further teaches the electronic device is separate from, and communicatively coupled with, the plurality of computing devices (View Stollmeyer Fig. 8, ¶ 182; network).
Claim 5, most of the limitations of this claim has been noted in the rejection of Claim 1. Stollmeyer further teaches the electronic device is the first computing device (View Stollmeyer ¶ 182; computer).
Claim 7, most of the limitations of this claim has been noted in the rejection of Claim 1. Srinivasan further teaches to provide, to the user via the user interface, one or more interactive elements related to the recommended action (View Srinivasan ¶ 40, 41; user interface, user can interact with actionable alert generation program).
Claim(s) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Stollmeyer (US Patent Application 2024/0210963) in view of Srinivasan (US Patent Application 2022/0308943) in view of Paul (US Patent Application 2025/0258729) in view of Roussel (US Patent Application 2011/0271149) and further in view of Shao (US Patent Application 2012/0137180).
Claim 6, most of the limitations of this claim has been noted in the rejection of Claim 1. The combination of teachings above does not explicitly teach the error condition relates to presence of a virus on the first computing device.
However, Shao teaches the error condition relates to presence of a virus on the first computing device (View Shao ¶ 4; virus).
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to modify the combination of teachings with the error condition relates to presence of a virus on the first computing device since it is known in the art that error data can be a virus (View Shao ¶ 4). Such modification would have allowed virus data to be displayed.
Claim(s) 8-10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Stollmeyer (US Patent Application 2024/0210963) in view of Srinivasan (US Patent Application 2022/0308943) in view of Paul (US Patent Application 2025/0258729) in view of Roussel (US Patent Application 2011/0271149) and further in view of Singh (US Patent Application 2014/0095144).
Claim 8, most of the limitations of this claim has been noted in the rejection of Claim 1. Srinivasan further teaches identify whether a previously-generated non-technical natural language alert related to the error condition is stored in an electronic database (View Srinavasan ¶ 23, 27, 30, 33, 35; chat data including historical actionable alerts stored in database).
The combination of teachings above does not explicitly teach responsive to identification that the previously-generated non-technical natural language alert is not stored in the electronic database, generate the non-technical natural language alert.
However, Singh teaches responsive to identification that the previously-generated non-technical natural language alert is not stored in the electronic database, generate the non-technical natural language alert (View Singh ¶ 27, 56; new and/or unreferenced alert).
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to modify the combination of teachings with responsive to identification that the previously-generated non-technical natural language alert is not stored in the electronic database, generate the non-technical natural language alert since it is known in the art that a new error alert can be generated (View Singh ¶ 27, 56). Such modification would have allowed a new error alert to be generated.
Claim 9, most of the limitations of this claim has been noted in the rejection of Claim 8. Srinivasan further teaches the instructions are further to cause the electronic device to store the generated non-technical natural language alert in the electronic database (View Srinivasan ¶ 60, 61; store actions in database).
Claim 10, most of the limitations of this claim has been noted in the rejection of Claim 8. Srinivasan further teaches responsive to identification that the previously generated non-technical natural language alert is stored in the electronic database, retrieve the previously-generated indication of the error condition if the previously-generated non-technical natural language alert from the electronic database (View Srinivasan ¶ 33, 47; historical incident reports; chatbot selects reply from a textual database); and provide the previously-generated non-technical natural language alert to the user via the user interface without generating a new non-technical natural language alert on the ML model (View Srinivasan ¶ 33, 41; user interface; chatbot).
Claim(s) 11, 12 and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Stollmeyer (US Patent Application 2024/0210963) in view of Srinivasan (US Patent Application 2022/0308943) in view of Paul (US Patent Application 2025/0258729) in view of Roussel (US Patent Application 2011/0271149) and further in view of Balasubramanian (US Patent Application 2023/0267074).
Claim 11, Stollmeyer teaches an electronic device comprising one or more processors (View Stollmeyer ¶ 60, 186; computer processing devices); and one or more non-transitory computer-readable media (NTCRM) comprising instructions that, upon execution of the instructions by one or more processors, are to cause the electronic device to: detect presence of an error condition at a first computing device of a plurality of computing devices (View Stollmeyer ¶ 65, 85; fault detection).
Stollmeyer does not explicitly teach generate, using a machine learning (ML) model, a non-technical natural language alert related to the error condition, wherein the non-technical natural language alert includes: a non-technical natural language summary of the error condition; and a non-technical natural language indication of a recommended action to remedy the error condition, wherein the indication of the recommended action is generated using the ML model; and provide, to a user of the electronic device via a user interface, the non-technical natural language alert that includes: the non-technical natural language summary of the error condition; and the non-technical natural language indication of the recommended action to remedy the error condition, wherein the indication of the recommended action is generated using the ML model;.
However, Srinivasan teaches generate, using a machine learning (ML) model, a non-technical natural language alert related to the error condition (View Srinivasan ¶ 7, 22, 29; system error in natural language; trained mode; actionable alert), wherein the non-technical natural language alert includes: a non-technical natural language summary of the error condition (View Srinivasan ¶ 7, 64, 65; processor inputs data relating to an alert that a system error occurred in a natural language format); and a non-technical natural language indication of a recommended action to remedy the error condition (View Srinivasan ¶ 64; automatically resolve the system error using machine learning based on natural language).
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to modify Stollmeyer with generate, using a machine learning (ML) model, a non-technical natural language alert related to the error condition, wherein the non-technical natural language alert includes: a non-technical natural language summary of the error condition; and a non-technical natural language indication of a recommended action to remedy the error condition since it is known in the art that system errors can be converted to natural language (View Srinivasan ¶ 7, 64). Such modification would have allowed an error alert to be generated using natural language.
Stollmeyer and Srinivasan do not explicitly teach wherein the indication of the recommended action is generated using the ML model; provide, to a user of the electronic device via a user interface, the non-technical natural language alert that includes: the non-technical natural language summary of the error condition; and the non-technical natural language indication of the recommended action to remedy the error condition, wherein the indication of the recommended action is generated using the ML model.
However, Paul teaches provide, to a user of the electronic device via a user interface, the non-technical natural language alert that includes: the non-technical natural language summary of the error condition (View Paul ¶ 58; user may be notified about potential error in natural language via user interface).
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to modify the combination of teachings with provide, to a user of the electronic device via a user interface, the non-technical natural language alert that includes: the non-technical natural language summary of the error condition since it is known in the art that data can be displayed on a user interface (View Paul ¶ 58). Such modification would have allowed an error condition to be displayed.
Stollmeyer, Srinivasan and Paul do not explicitly teach, wherein the indication of the recommended action is generated using the ML model; the non-technical natural language indication of the recommended action to remedy the error condition, wherein the indication of the recommended action is generated using the ML model.
However, Roussel teaches the non-technical natural language indication of the recommended action to remedy the error condition (View Roussel ¶ 33, 34; explanatory information such as description of an error and how the problem can be corrected may be displayed in a hover-on (or similar) style information box upon user selection).
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to modify the combination of teachings with the non-technical natural language indication of the recommended action to remedy the error condition since it is known in the art that a recommended corrective action can be generated in natural language (View Roussel ¶ 33, 34). Such modification would have allowed a recommended corrective action to be described in natural language.
Stollmeyer, Srinivasan, Paul and Roussel do not explicitly teach wherein the indication of the recommended action is generated using the ML model; wherein the indication of the recommended action is generated using the ML model.
However, Balasubramanian teaches wherein the indication of the recommended action is generated using the ML model (View Balasubramanian ¶ 153, 156; trained machine learning model may be used to generate a recommendation regarding corrective action to be taken based on a current operating status and corrective action that was taken in past); wherein the indication of the recommended action is generated using the ML model (View Balasubramanian ¶ 153, 156; trained machine learning model may be used to generate a recommendation regarding corrective action to be taken based on a current operating status and corrective action that was taken in past).
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to modify the combination of teachings with wherein the indication of the recommended action is generated using the ML model; wherein the indication of the recommended action is generated using the ML model since it is known in the art that a recommended corrective action can be generated by a machine learning model (View Balasubramanian ¶ 153, 156). Such modification would have allowed a recommended corrective action to be recommended by a machine learning model.
Claim 12, most of the limitations of this claim has been noted in the rejection of Claim 11. Stollmeyer further teaches the recommended action is one of a plurality of pre-configured actions related to the error condition (View Stollmeyer ¶ 69; recommendation).
Claim 14, most of the limitations of this claim has been noted in the rejection of Claim 11. Srinivasan further teaches to provide, to the user via the user interface, one or more interactive elements related to the recommended action (View Srinivasan ¶ 40, 41; user interface, user can interact with actionable alert generation program).
Claim(s) 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Stollmeyer (US Patent Application 2024/0210963) in view of Srinivasan (US Patent Application 2022/0308943) in view of Paul (US Patent Application 2025/0258729) in view of Roussel (US Patent Application 2011/0271149) in view of Balasubramanian (US Patent Application 2023/0267074) and further in view of Singh (US Patent Application 2014/0095144).
Claim 15, most of the limitations of this claim has been noted in the rejection of Claim 11. Srinivasan further teaches identify whether a previously-generated non-technical natural language alert related to the error condition is stored in an electronic database (View Srinavasan ¶ 23, 27, 30, 33, 35; chat data including historical actionable alerts stored in database).
The combination of teachings above does not explicitly teach responsive to identification that the previously-generated non-technical natural language alert is not stored in the electronic database, generate the non-technical natural language alert.
However, Singh teaches responsive to identification that the previously-generated non-technical natural language alert is not stored in the electronic database, generate the non-technical natural language alert (View Singh ¶ 27, 56; new and/or unreferenced alert).
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to modify the combination of teachings with responsive to identification that the previously-generated non-technical natural language alert is not stored in the electronic database, generate the non-technical natural language alert since it is known in the art that a new error alert can be generated (View Singh ¶ 27, 56). Such modification would have allowed a new error alert to be generated.
Claim(s) 16-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Stollmeyer (US Patent Application 2024/0210963) in view of Srinivasan (US Patent Application 2022/0308943) in view of Paul (US Patent Application 2025/0258729) in view of Roussel (US Patent Application 2011/0271149) and further in view of Morris (US Patent Application 2011/0113283).
Claim 16, Stollmeyer teaches a method to be performed by an electronic device (View Stollmeyer ¶ 60, 186; computer processing devices), wherein the method comprises: detecting presence of an error condition at a first computing device of a plurality of computing devices (View Stollmeyer ¶ 65, 85; fault detection).
Stollmeyer does not explicitly teach the error condition relates to presence of a virus on the first computing device; generating, using a machine learning (ML) model, a non-technical natural language alert related to the error condition, wherein the non-technical natural language alert includes: a non-technical natural language summary of the error condition; and a non-technical natural language indication of a recommended action to remedy the error condition; and providing, to a user of the electronic device via a user interface, the non-technical natural language alert that includes: the non-technical natural language summary of the error condition; and the non-technical natural language indication of the recommended action to remedy the error condition.
However, Srinivasan teaches generating, using a machine learning (ML) model, a non-technical natural language alert related to the error condition (View Srinivasan ¶ 7, 22, 29; system error in natural language; trained mode; actionable alert), wherein the non-technical natural language alert includes: a non-technical natural language summary of the error condition (View Srinivasan ¶ 7, 64, 65; processor inputs data relating to an alert that a system error occurred in a natural language format); and a non-technical natural language indication of a recommended action to remedy the error condition (View Srinivasan ¶ 64; automatically resolve the system error using machine learning based on natural language).
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to modify Stollmeyer with generating, using a machine learning (ML) model, a non-technical natural language alert related to the error condition, wherein the non-technical natural language alert includes: a non-technical natural language summary of the error condition; and a non-technical natural language indication of a recommended action to remedy the error condition since it is known in the art that system errors can be converted to natural language (View Srinivasan ¶ 7, 64). Such modification would have allowed an error alert to be generated using natural language.
Stollmeyer and Srinivasan do not explicitly teach the error condition relates to presence of a virus on the first computing device; providing, to a user of the electronic device via a user interface, the non-technical natural language alert that includes: the non-technical natural language summary of the error condition; and the non-technical natural language indication of the recommended action to remedy the error condition.
However, Paul teaches providing, to a user of the electronic device via a user interface, the non-technical natural language alert that includes: the non-technical natural language summary of the error condition (View Paul ¶ 58; user may be notified about potential error in natural language via user interface).
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to modify the combination of teachings with providing, to a user of the electronic device via a user interface, the non-technical natural language alert that includes: the non-technical natural language summary of the error condition since it is known in the art that data can be displayed on a user interface (View Paul ¶ 58). Such modification would have allowed an error condition to be displayed.
Stollmeyer, Srinivasan and Paul do not explicitly teach the error condition relates to presence of a virus on the first computing device; the non-technical natural language indication of the recommended action to remedy the error condition.
However, Roussel teaches the non-technical natural language indication of the recommended action to remedy the error condition (View Roussel ¶ 33, 34; explanatory information such as description of an error and how the problem can be corrected may be displayed in a hover-on (or similar) style information box upon user selection).
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to modify the combination of teachings with the non-technical natural language indication of the recommended action to remedy the error condition since it is known in the art that a recommended corrective action can be generated in natural language (View Roussel ¶ 33, 34). Such modification would have allowed a recommended corrective action to be described in natural language.
Stollmeyer, Srinivasan, Paul and Roussel do not explicitly teach the error condition relates to presence of a virus on the first computing device.
However, Morris teaches the error condition relates to presence of a virus on the first computing device (View Morris ¶ 52, 61; the detection module detects a computer virus operational anomaly).
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to modify the combination of teachings with the error condition relates to presence of a virus on the first computing device since it is known in the art that a computer virus can be detected (View Morris ¶ 52, 61). Such modification would have allowed a computer virus to be remediated.
Claim 17, most of the limitations of this claim has been noted in the rejection of Claim 16. Stollmeyer further teaches the recommended action is one of a plurality of pre-configured actions related to the error condition (View Stollmeyer ¶ 69; recommendation).
Claim 18, most of the limitations of this claim has been noted in the rejection of Claim 16. Stollmeyer further teaches the indication of the recommended action is generated using the ML model (View Stollmeyer ¶ 151; AI/ML training).
Claim 19, most of the limitations of this claim has been noted in the rejection of Claim 16. Srinivasan further teaches to provide, to the user via the user interface, one or more interactive elements related to the recommended action (View Srinivasan ¶ 40, 41; user interface, user can interact with actionable alert generation program).
Claim(s) 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Stollmeyer (US Patent Application 2024/0210963) in view of Srinivasan (US Patent Application 2022/0308943) in view of Paul (US Patent Application 2025/0258729) in view of Roussel (US Patent Application 2011/0271149) in view of Morris (US Patent Application 2011/0113283) and further in view of Singh (US Patent Application 2014/0095144).
Claim 20, most of the limitations of this claim has been noted in the rejection of Claim 16. Srinivasan further teaches identify whether a previously-generated non-technical natural language alert related to the error condition is stored in an electronic database (View Srinavasan ¶ 23, 27, 30, 33, 35; chat data including historical actionable alerts stored in database).
The combination of teachings above does not explicitly teach responsive to identification that the previously-generated non-technical natural language alert is not stored in the electronic database, generate the non-technical natural language alert.
However, Singh teaches responsive to identification that the previously-generated non-technical natural language alert is not stored in the electronic database, generate the non-technical natural language alert (View Singh ¶ 27, 56; new and/or unreferenced alert).
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to modify the combination of teachings with responsive to identification that the previously-generated non-technical natural language alert is not stored in the electronic database, generate the non-technical natural language alert since it is known in the art that a new error alert can be generated (View Singh ¶ 27, 56). Such modification would have allowed a new error alert to be generated.
Prior Art Made of Record
The prior art made of record and not relied upon is considered pertinent to Applicant’s disclosure:
Poola et al. (U.S. Patent Application 2021/0248024); teaches the system includes an error log engine that is stored in the memory and executable by the one or more processing devices and configured to receive indication of an error resulting from the database application evaluation engine, access a historical error resolution storage to identify a known solution for correcting the error, and execute the known solution to auto-correct the error.
Response to Arguments
Applicant’s arguments with respect to claim(s) 1 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
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/SARAI E BUTLER/Primary Examiner, Art Unit 2114
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