FINAL ACTION
Response to Amendment
Applicant's amendments filed 2/05/2026 regarding the prior art rejections under USC 102 and 103 are addressed in updated rejections which cover the new elements, see below.
Claim Rejections - 35 USC § 112
Applicant’s arguments, see remarks, filed 2/05/2026, with respect to claims 1, 8 and 15 have been fully considered and are persuasive. The rejection under USC 112 has been withdrawn.
Claim Rejections - 35 USC § 101
Applicant's arguments filed 2/05/2026 regarding claims 1, 6, 8, 13, 15 and 20 have been fully considered but they are not persuasive. The amended elements do add the essential steps which made the claims indefinite, however they do not change the overall mental process and lack of significantly more. For all other claims, the rejection under USC 101 has been withdrawn.
Claims 1, 6, 8, 13, 15 and 20 are rejected under 35 U.S.C. 101 because they are directed to abstract ideas without significantly more.
Step 2A prong 1:
Claims 1, 8 and 15 recite monitoring of one or more users, monitoring is considered a mental process, a root cause determination is making a determination.. Claims 6, 13 and 20 also recite making a determination, the AI assistant is adapted to determine that a device configuration is part of an issue.
Step 2A prong 2:
For claims 1, 8 and 15, the use of an AI assistant that is adapted to troubleshoot issues are merely instructions to apply the judicial exception. Logging device metrics and providing a recommendation to a user are extra solution activity, mere data gathering and output. Receiving a request with specific metrics is also data gathering. Claims 6, 13 and 20 describe a type of metrics data that can be gathered, device configuration.
Step 2B:
The additional elements do not amount to significantly more. The use of AI to perform root cause analysis is recited at a high level of generality. An invocation request only serves the purpose of starting the process, it is still extra solution activity. The AI agent being adapted to troubleshoot issues only requires that it is able to accelerate the process of analyzing data with a computer, which is not considered a substantial improvement to the function of a computer (MPEP 2106.05(a)). Root cause analysis is still making a determination. Providing a remediation recommendation is displaying an alert or recommendation to a user, which is well understood, routine and conventional (MPEP 2106.05(d)). Metrics including configuration information and a determination being made based on that is the same concept, only with a type of data. Generating a report is displaying the result after performing analysis, and is not a sufficient improvement.
Even when considered in combination, these additional elements amount to mere instructions to apply the abstract idea on a computer or insignificant extra-solution activity. However, dependent claims as currently presented have elements that are agreed by the examiner to be significantly more.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1, 6-8, 13-15, and 20 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated b Ostrowski (US 20250156302).
Regarding claim 1, Ostrowski teaches A method comprising steps of: performing monitoring of one or more users via a cloud-based system and logging device-specific metrics based thereon (“Operation 210 can include performing one or more actions while monitoring interactions between a user device 100 and digital experience, such as through the digital experience server 150... The monitoring of the interactions can be performed using one or more aspects of the digital experience server instructions 160, the digital experience client instructions 112, the metrics instructions 162, other instructions or devices, or combinations thereof. In some examples, the monitoring occurs in real time as users interact with the server 150 via respective user devices 1 ” ¶46); wherein the device-specific metrics are associated with one or more devices of the one or more users; (“. In some examples, the metrics instructions 162 cause the creation or analysis of a log of interactions as part of the digital experience. In some examples, the metrics instructions include bespoke metrics or analysis functions” ¶41); providing an Artificial Intelligence (AI) assistant adapted to troubleshoot issues related to the one or more devices, a network, and one or more applications; receiving, by the cloud-based system, an invocation request for the AI assistant, the invocation request identifying a specific device of the one or more devices; (“In examples, the diagnostic platform uses behavior science and technology to improve the functioning of the digital experience by identifying requests from a user device (e.g., indicative of user input received at the user device 100) indicative of the user struggling with an aspect of the digital experience.” ¶22); responsive to receiving the invocation request, automatically retrieving, by the cloud-based system, device-specific metrics comprising at least device-related metrics and network-related metrics; performing, by the AI assistant, a root cause determination for one or more issues based on the automatically retrieved device-specific metrics; and responsive to receiving the invocation request, providing one or more remediation recommendation for one or more issues based on the device specific metrics and based on the root cause determination. (“The root cause detection server 702, the user device 704, and/or the host server 724 can include (e.g., each include) or utilize at least one processing unit or other logic devices such as a programmable logic array engine or a module configured to communicate with one another or other resources or databases.” ¶77); and responsive to receiving the invocation request, providing one or more remediation recommendation for one or more issues based on the device specific metrics and based on the root cause determination. (““In some examples, the diagnostic platform can use or leverage large language models. For instance, there can be a combination of a conversational artificial intelligence, an ecosystem of past user interactions and preferences (e.g., input to the diagnostic platform), which can then be used to build and deploy artificial intelligence models. In addition or instead, large language models can be prompted to provide output summarizing a user interaction with a digital experience or an output including a recommended change to the digital experience to ameliorate the struggle.” ¶24).
Regarding claim 6, Ostrowski teaches wherein the device-specific metrics comprise device configuration information, and wherein the AI assistant is adapted to determine that a device configuration is part of an issue and provide a remediation recommendation based thereon (“For example, root causes of forced struggle events can include errors in the configuration of the application or a lack of computer resources available for the digital experience. The computer can determine the root causes of such forced struggle events according to the templates that were satisfied to determine the root causes and remediate the struggle events” ¶73).
Regarding claim 7, Ostrowski teaches wherein the AI assistant is adapted to generate a report and fill out a support ticket based on the one or more issues. (“The alert can contain the user inputs, the system responses, and/or any other collected data regarding the struggle event. The data processing system can transmit the alert to an administrator computing device to enable an administrator to analyze the struggle event for immediate remediation and/or to avoid such struggle events in the future.” ¶116) wherein generating the report comprises including relevant device-specific metrics and user-AI conversation logs, and wherein filling out the support ticket comprises populating ticket fields using at least the relevant device-specific metrics and the user -AI conversation logs (“Large language models can be used to implement or enhance aspects described herein. As discussed above, replays, logs, or other data of user interactions with the digital experience can be captured. Such data can be provided as input to a large language model with a prompt to summarize what occurred. Such a summary can be provided as part of the remediation (e.g., to developers to better understand the problem).” ¶156).
Regarding claim 8, Ostrowski teaches A non-transitory computer-readable storage medium having computer-readable code stored thereon for programming one or more processors to perform steps of: performing monitoring of one or more users via a cloud-based system and logging device-specific metrics based thereon (“Operation 210 can include performing one or more actions while monitoring interactions between a user device 100 and digital experience, such as through the digital experience server 150... The monitoring of the interactions can be performed using one or more aspects of the digital experience server instructions 160, the digital experience client instructions 112, the metrics instructions 162, other instructions or devices, or combinations thereof. In some examples, the monitoring occurs in real time as users interact with the server 150 via respective user devices 1 ” ¶46); wherein the device-specific metrics are associated with one or more devices of the one or more users; (“. In some examples, the metrics instructions 162 cause the creation or analysis of a log of interactions as part of the digital experience. In some examples, the metrics instructions include bespoke metrics or analysis functions” ¶41); providing an Artificial Intelligence (AI) assistant adapted to troubleshoot issues related to the one or more devices, a network, and one or more applications; receiving, by the cloud-based system, an invocation request for the AI assistant, the invocation request identifying a specific device of the one or more devices; (“In examples, the diagnostic platform uses behavior science and technology to improve the functioning of the digital experience by identifying requests from a user device (e.g., indicative of user input received at the user device 100) indicative of the user struggling with an aspect of the digital experience.” ¶22); responsive to receiving the invocation request, automatically retrieving, by the cloud-based system, device-specific metrics comprising at least device-related metrics and network-related metrics; performing, by the AI assistant, a root cause determination for one or more issues based on the automatically retrieved device-specific metrics; and responsive to receiving the invocation request, providing one or more remediation recommendation for one or more issues based on the device specific metrics and based on the root cause determination. (“The root cause detection server 702, the user device 704, and/or the host server 724 can include (e.g., each include) or utilize at least one processing unit or other logic devices such as a programmable logic array engine or a module configured to communicate with one another or other resources or databases.” ¶77); and responsive to receiving the invocation request, providing one or more remediation recommendation for one or more issues based on the device specific metrics and based on the root cause determination. (““In some examples, the diagnostic platform can use or leverage large language models. For instance, there can be a combination of a conversational artificial intelligence, an ecosystem of past user interactions and preferences (e.g., input to the diagnostic platform), which can then be used to build and deploy artificial intelligence models. In addition or instead, large language models can be prompted to provide output summarizing a user interaction with a digital experience or an output including a recommended change to the digital experience to ameliorate the struggle.” ¶24).
Regarding claim 13, Ostrowski teaches The non-transitory computer-readable storage medium of claim 8, wherein the device-specific metrics comprise device configuration information, and wherein the AI assistant is adapted to determine that a device configuration is part of an issue and provide a remediation recommendation based thereon (“For example, root causes of forced struggle events can include errors in the configuration of the application or a lack of computer resources available for the digital experience. The computer can determine the root causes of such forced struggle events according to the templates that were satisfied to determine the root causes and remediate the struggle events” ¶73).
Regarding claim 14, Ostrowski teaches wherein the AI assistant is adapted to generate a report and fill out a support ticket based on the one or more issues. (“The alert can contain the user inputs, the system responses, and/or any other collected data regarding the struggle event. The data processing system can transmit the alert to an administrator computing device to enable an administrator to analyze the struggle event for immediate remediation and/or to avoid such struggle events in the future.” ¶116). wherein generating the report comprises including relevant device-specific metrics and user-AI conversation logs, and wherein filling out the support ticket comprises populating ticket fields using at least the relevant device-specific metrics and the user -AI conversation logs (“Large language models can be used to implement or enhance aspects described herein. As discussed above, replays, logs, or other data of user interactions with the digital experience can be captured. Such data can be provided as input to a large language model with a prompt to summarize what occurred. Such a summary can be provided as part of the remediation (e.g., to developers to better understand the problem).” ¶156).
Regarding claim 15, Ostrowski teaches A cloud-based system comprising: one or more processors; and memory storing computer-executable instructions that, when executed, cause the one or more processors to: performing monitoring of one or more users via a cloud-based system and logging device-specific metrics based thereon (“Operation 210 can include performing one or more actions while monitoring interactions between a user device 100 and digital experience, such as through the digital experience server 150... The monitoring of the interactions can be performed using one or more aspects of the digital experience server instructions 160, the digital experience client instructions 112, the metrics instructions 162, other instructions or devices, or combinations thereof. In some examples, the monitoring occurs in real time as users interact with the server 150 via respective user devices 1 ” ¶46); wherein the device-specific metrics are associated with one or more devices of the one or more users; (“. In some examples, the metrics instructions 162 cause the creation or analysis of a log of interactions as part of the digital experience. In some examples, the metrics instructions include bespoke metrics or analysis functions” ¶41); providing an Artificial Intelligence (AI) assistant adapted to troubleshoot issues related to the one or more devices, a network, and one or more applications receiving, by the cloud-based system, an invocation request for the AI assistant, the invocation request identifying a specific device of the one or more devices; (“In examples, the diagnostic platform uses behavior science and technology to improve the functioning of the digital experience by identifying requests from a user device (e.g., indicative of user input received at the user device 100) indicative of the user struggling with an aspect of the digital experience.” ¶22); responsive to receiving the invocation request, automatically retrieving, by the cloud-based system, device-specific metrics comprising at least device-related metrics and network-related metrics; performing, by the AI assistant, a root cause determination for one or more issues based on the automatically retrieved device-specific metrics; and responsive to receiving the invocation request, providing one or more remediation recommendation for one or more issues based on the device specific metrics and based on the root cause determination. (“The root cause detection server 702, the user device 704, and/or the host server 724 can include (e.g., each include) or utilize at least one processing unit or other logic devices such as a programmable logic array engine or a module configured to communicate with one another or other resources or databases.” ¶77); and responsive to receiving the invocation request, providing one or more remediation recommendation for one or more issues based on the device specific metrics and based on the root cause determination. (““In some examples, the diagnostic platform can use or leverage large language models. For instance, there can be a combination of a conversational artificial intelligence, an ecosystem of past user interactions and preferences (e.g., input to the diagnostic platform), which can then be used to build and deploy artificial intelligence models. In addition or instead, large language models can be prompted to provide output summarizing a user interaction with a digital experience or an output including a recommended change to the digital experience to ameliorate the struggle.” ¶24).
Regarding claim 20, Ostrowski teaches The cloud-based system of claim 15, wherein the device-specific metrics comprise device configuration information, and wherein the AI assistant is adapted to determine that a device configuration is part of an issue and provide a remediation recommendation based thereon (“For example, root causes of forced struggle events can include errors in the configuration of the application or a lack of computer resources available for the digital experience. The computer can determine the root causes of such forced struggle events according to the templates that were satisfied to determine the root causes and remediate the struggle events” ¶73).
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.
Claim(s) 2, 4, 5, 9, 11, 12, 16, 18, 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ostrowski in view of Bhatia (US 20240177119).
Regarding claim 2, Ostrowski teaches the method of claim 1. Ostrowski does not teach wherein the AI assistant is invoked via a device-specific Quick Response (QR) code.
Bhatia teaches wherein the AI assistant is invoked via a device-specific Quick Response (QR) code (“The AI personal assistant may also provide a quick response (QR) code that triggers the experience on a user's personal device if viewed or shared from another device or printed QR code.” ¶30) wherein the device-specific QR code resolves to a URL and comprises a unique key that identifies the specific device to the cloud-based system, and wherein the invocation request is generated responsive to scanning the device-specific QR code and accessing the URL (““The QR code may also provide links to websites in connection with responses to questions.” ¶30) a website link is an example of a URL. It would have been obvious for one of ordinary skill in the art prior to the filing of the claimed invention to combine the AI troubleshooting assistant methods of Ostrowski with the use of a QR code to launch it as taught by Bhatia. The QR code could provide links with more information to the user (Bhatia ¶30).
Regarding claim 4, Bhatia teaches The method of claim 2, wherein the one or more issues are associated with a first device and the device-specific QR code is scanned by a second device, (“The AI personal assistant may also provide a quick response (QR) code that triggers the experience on a user's personal device if viewed or shared from another device or printed QR code.” ¶30); and wherein the steps further comprise: launching the AI assistant via a web browser of the second device (“The QR code may also provide links to websites in connection with responses to questions.” ¶30, “The programs 4016 may include various applications, add-ons, etc. configured to provide end user functionality with the user device 4000. For example, example programs 4016 may include, but not limited to, a web browser” ¶185).
Regarding claim 5, Ostrowski and Bhatia teach The method of claim 4. Ostrowski teaches wherein responsive to launching the AI assistant, the steps further comprise: automatically performing root cause analysis of the one or more issues based on device-specific metrics associated with the first device (“The root cause detector 720 may comprise programmable instructions that, upon execution, cause the processor 710 to determine (e.g., automatically determine) the root causes of struggle events at different applications” ¶97).
Regarding claim 9, Ostrowski teaches The non-transitory computer-readable storage medium of claim 8, Bhatia teaches wherein the AI assistant is invoked via a device-specific Quick Response (QR) code (“The AI personal assistant may also provide a quick response (QR) code that triggers the experience on a user's personal device if viewed or shared from another device or printed QR code.” ¶30). ) wherein the device-specific QR code resolves to a URL and comprises a unique key that identifies the specific device to the cloud-based system, and wherein the invocation request is generated responsive to scanning the device-specific QR code and accessing the URL (““The QR code may also provide links to websites in connection with responses to questions.” ¶30)
Regarding claim 11, wherein the one or more issues are associated with a first device and the device-specific QR code is scanned by a second device, (“The AI personal assistant may also provide a quick response (QR) code that triggers the experience on a user's personal device if viewed or shared from another device or printed QR code.” ¶30); and wherein the steps further comprise: launching the AI assistant via a web browser of the second device (“The QR code may also provide links to websites in connection with responses to questions.” ¶30, “The programs 4016 may include various applications, add-ons, etc. configured to provide end user functionality with the user device 4000. For example, example programs 4016 may include, but not limited to, a web browser” ¶185).
Regarding claim 12, Ostrowski and Bhatia teach The non-transitory computer-readable storage medium of claim 11, wherein responsive to launching the AI assistant, the steps further comprise: automatically performing root cause analysis of the one or more issues based on device-specific metrics associated with the first device (“The root cause detector 720 may comprise programmable instructions that, upon execution, cause the processor 710 to determine (e.g., automatically determine) the root causes of struggle events at different applications” ¶97).
Regarding claim 16, Ostrowski teaches The cloud-based system of claim 15, Bhatia teaches wherein the AI assistant is invoked via a device-specific Quick Response (QR) code (“The AI personal assistant may also provide a quick response (QR) code that triggers the experience on a user's personal device if viewed or shared from another device or printed QR code.” ¶30). ) wherein the device-specific QR code resolves to a URL and comprises a unique key that identifies the specific device to the cloud-based system, and wherein the invocation request is generated responsive to scanning the device-specific QR code and accessing the URL (““The QR code may also provide links to websites in connection with responses to questions.” ¶30)
Regarding claim 18, wherein the one or more issues are associated with a first device and the device-specific QR code is scanned by a second device, (“The AI personal assistant may also provide a quick response (QR) code that triggers the experience on a user's personal device if viewed or shared from another device or printed QR code.” ¶30); and wherein the steps further comprise: launching the AI assistant via a web browser of the second device (“The QR code may also provide links to websites in connection with responses to questions.” ¶30, “The programs 4016 may include various applications, add-ons, etc. configured to provide end user functionality with the user device 4000. For example, example programs 4016 may include, but not limited to, a web browser” ¶185).
Regarding claim 19, Ostrowski and Bhatia teach The cloud-based system of claim 18, wherein responsive to launching the AI assistant, the steps further comprise: automatically performing root cause analysis of the one or more issues based on device-specific metrics associated with the first device (“The root cause detector 720 may comprise programmable instructions that, upon execution, cause the processor 710 to determine (e.g., automatically determine) the root causes of struggle events at different applications” ¶97).
Claim(s) 3, 10 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ostrowski and Bhatia in view of Han (US 20200036525).
Regarding claim 3, Ostrowski and Bhatia teach the method of claim 2. They do not teach wherein the device-specific QR code carries keys for authentication and authorization to the AI assistant.
Han teaches wherein the device-specific QR code carries keys for authentication and authorization to the AI assistant (“In addition, when the installation of the corresponding application is completed, the authorizer terminal displays the user interface to register the authorizer in the authentication server, and the authorizer inputs a QR code or a registration code value as the provided key value through the user interface to perform registration of the authorizer, that is, registration of the identification key in the authentication server.” ¶50). wherein the cloud-based system authenticates the user based on the keys prior to automatically retrieving the device specific metrics for the specific metrics for the specific device, (“Then, the authentication server may designate identification information such as a universally unique identifier (UUID) for the identification key of the registered authorizer and store the identification information in a database in response to the authorizer information.” ¶53) and wherein authorization based on the keys restricts access to log data and telemetry to the specific device identified by the unique key (“As a result, the authentication server rejects the approval performed by a third party or the like which is not an actual authorizer by hacking and the like, thereby improving security.” ¶76). It would have been obvious for one of ordinary skill in the art prior to the filing of the claimed invention to combine the AI assistant and use of QR codes taught by Ostrowski and Bhatia with the transmission of authentication keys as taught by Han. This would allow for the user to authorize access to URLs (Han ¶49), and Bhatia teaches accessing links to websites (Bhatia ¶30).
Regarding claim 10, Han teaches wherein the device-specific QR code carries keys for authentication and authorization to the AI assistant (“In addition, when the installation of the corresponding application is completed, the authorizer terminal displays the user interface to register the authorizer in the authentication server, and the authorizer inputs a QR code or a registration code value as the provided key value through the user interface to perform registration of the authorizer, that is, registration of the identification key in the authentication server.” ¶50). wherein the cloud-based system authenticates the user based on the keys prior to automatically retrieving the device specific metrics for the specific metrics for the specific device, (“Then, the authentication server may designate identification information such as a universally unique identifier (UUID) for the identification key of the registered authorizer and store the identification information in a database in response to the authorizer information.” ¶53) and wherein authorization based on the keys restricts access to log data and telemetry to the specific device identified by the unique key (“As a result, the authentication server rejects the approval performed by a third party or the like which is not an actual authorizer by hacking and the like, thereby improving security.” ¶76).
Regarding claim 17, Han teaches wherein the device-specific QR code carries keys for authentication and authorization to the AI assistant (“In addition, when the installation of the corresponding application is completed, the authorizer terminal displays the user interface to register the authorizer in the authentication server, and the authorizer inputs a QR code or a registration code value as the provided key value through the user interface to perform registration of the authorizer, that is, registration of the identification key in the authentication server.” ¶50). wherein the cloud-based system authenticates the user based on the keys prior to automatically retrieving the device specific metrics for the specific metrics for the specific device, (“Then, the authentication server may designate identification information such as a universally unique identifier (UUID) for the identification key of the registered authorizer and store the identification information in a database in response to the authorizer information.” ¶53) and wherein authorization based on the keys restricts access to log data and telemetry to the specific device identified by the unique key (“As a result, the authentication server rejects the approval performed by a third party or the like which is not an actual authorizer by hacking and the like, thereby improving security.” ¶76).
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 SEAN KEVIN MCNAMARA whose telephone number is (703)756-1884. The examiner can normally be reached Monday-Friday 7:30-5:00 EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Bryce Bonzo can be reached at 571-272-3655. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/SEAN KEVIN MCNAMARA/ Examiner, Art Unit 2113 /BRYCE P BONZO/Supervisory Patent Examiner, Art Unit 2113
Prosecution Insights