DETAILED ACTION
This Final Office Action is in response to the arguments and amendments filed June 25, 2026.
Claims 1, 5, 7, 8, 12, 14, 15, and 19 have been amended.
Claims 6, 13, and 20 have been cancelled.
Claims 1-5, 7-12, and 14-19 are currently pending and have been considered below.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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 1-5, 7-12, and 14-19 are rejected under 35 U.S.C. 101 because the claimed invention is directed towards non-eligible subject matter.
In terms of step 1, claims 1-5, 7-12, and 14-19 are directed towards one of the four categories of statutory subject matter.
In terms of step 2(a)(1), independent claims 1, 8, and 15 are directed towards (as represented by claim 1), “receiving a device identifier corresponding to a device; identifying a plurality of segments of device data associated with the device; assembling resource usage data, using one or more models, for the device by: generating one or more resource usage data segments for one or more of the plurality of segments of the device data; and generating the resource usage data for the device from the one or more resource usage data segments; generating, based on the resource usage data and device lifespan data, lifecycle efficiency prediction for each solution of a plurality of solutions for at least one defect with the device; selecting a first solution of the plurality of solutions based on the lifecycle efficiency prediction for each solution, wherein the first solution comprises a resource usage per unit time less than any other of the plurality of solutions; initiate replacement of at least one component of the device with at least one replacement component; and triggering a subsequent use period of the device by directing disposition of the device, wherein the subsequent use period comprises linking the device with a subsequent user account”. The claims are describing a collection of information, high level of analysis, and displaying the results based on the analysis. The claim collects device information/data, analyzes in terms of the resource usage data, and providing the results for a second subsequent use period. The claims further describe the collection and analysis in terms of providing a replacement component based on the defect and chosen solution. The replacement analysis is high level that a person would be able to collect and analyze such information to provide the replacement component for the device. As such, the claims are directed towards an abstract idea under the mental process grouping.
The claims are also describing a business/commercial interaction in terms of describing a prediction for component replacement initiation based on device usage. This is a business/commercial interaction in terms of device repair and replacement. As such, the claims are also directed towards an abstract idea under certain method of organizing human activity.
Step 2(a)(II) considers the additional elements in terms of being transformative into a practical application. The additional elements of the independent claims are, “a computer-implemented method comprising (claim 1), An apparatus comprising one or more processors, and at least one non-transitory memory comprising instructions that, with the one or more processors, cause the apparatus to (claim 8), at least one non-transitory computer readable medium having computer coded instructions configured to, when executed by at least one processor (claim 15); causing application of the first solution to the device to resolve the at least one defect with the device by transmitting a signal configured to”. The additional elements are described in the originally filed specification [88-102]. The additional elements are merely describing computer elements as tools to implement the identified abstract idea. The specification does not describe the additional elements in terms of being technical improvements. In terms of the signal and application configured to provide the replacement component, the additional element is described in the originally filed specification [166]. The additional element is merely describing generic technology to implement the abstract idea. The control signal provides the display based on the analysis. Further, there is no discussion or description beyond an idea of a solution in terms of providing the signal to initiate replacement which falls within merely displaying the result for a person to implement. As such, the claims are not directed towards additional elements that are transformative into a practical application. Refer to MPEP 2106.05(f).
Step 2(b) considers the additional elements in terms of being significantly more than the identified abstract idea. The additional elements of the independent claims are, “a computer-implemented method comprising (claim 1), An apparatus comprising one or more processors, and at least one non-transitory memory comprising instructions that, with the one or more processors, cause the apparatus to (claim 8), at least one non-transitory computer readable medium having computer coded instructions configured to, when executed by at least one processor (claim 15) causing application of the first solution to the device to resolve the at least one defect with the device by transmitting a signal configured to”. The additional elements are described in the originally filed specification [88-102]. The additional elements are merely describing computer elements as tools to implement the identified abstract idea. The specification does not describe the additional elements in terms of being technical improvements. In terms of the signal and application configured to provide the replacement component, the additional element is described in the originally filed specification [166]. The additional element is merely describing generic technology to implement the abstract idea. The control signal provides the display based on the analysis. Further, there is no discussion or description beyond an idea of a solution in terms of providing the signal to initiate replacement which falls within merely displaying the result for a person to implement. As such, the claims are not directed towards additional elements that are significantly more than the identified abstract idea. Refer to MPEP 2106.05(f).
Dependent claims 2-5, 7, 9-12, 14, and 16-19 are further describing the abstract idea without additional elements beyond those identified above. The claims are directed towards, “wherein the one or more models are trained using aggregated device data sets from a plurality of devices that comprise two or more different devices”, “wherein the aggregated device data sets comprise data retrieved from a plurality of data sources along the lifecycle of each of the plurality of devices”, “wherein the one or more resource usage data segments comprises at least two resource usage data segments, wherein a first resource usage data segment of the at least two resource usage data segments is generated using a different model that is trained using different aggregated device data set relative to a second resource usage data segment”, “where the first solution comprises triggering the subsequent use period”, and “wherein generating the lifecycle efficiency prediction comprises: generating one or more lifecycle efficiency prediction segments corresponding to the one or more resource usage data segments, and assembling the one or more lifecycle efficiency prediction segments into the lifecycle efficiency prediction”. The claims are further describing the identified abstract idea in terms of the collection, high level analysis, and display. This further includes the consideration with respect to the trained models, as the models are not describing a specific modeling technique or algorithm, but rather high-level analysis. In terms of the display, the claims provide aspects of generating efficiency and prediction elements for the lifecycle/usage. The claims are further describing the identified abstract idea. The claims are not directed towards additional elements beyond those identified above. As such, the claims, individually and as a combination, are directed towards an abstract idea without additional elements that are significantly more or transformative into a practical application. Refer to MPEP 2106.05(f).
The claimed invention is directed towards an abstract idea without additional elements that are significantly more or transformative into a practical application. Therefore, claims 1-20 are rejected under 35 USC 101 for being directed towards non-eligible subject matter.
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 (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-5, 7-12, and 14-19 are rejected under 35 U.S.C. 103 as being unpatentable over Ionescu et al [2021/0248618], hereafter Ionescu, in view of Morrison et al [2023/0185348], hereafter Morrison.
Regarding claim 1, Ionescu discloses a computer-implemented method comprising: receiving a device identifier corresponding to a device; identifying a plurality of segments of device data associated with the device (Paragraphs [106-108 and 119]; Ionescu discloses a device diagnostic system that provides device identifier information (data set) that includes multiple segments of the device’s systems.);
assembling resource usage data, using one or more models, for the device by: generating one or more resource usage data segments for one or more of the plurality of segments of the device data; and generating the resource usage data for the device from the one or more resource usage data segments (Paragraphs [120-125]; Ionescu discloses assembling usage data including for systems and sub-systems to determine diagnostic information for the device. In terms of the model aspect, Ionescu discloses [149-155] machine learning and AI used to make predictions regarding the usage data.);
Ionescu discloses a usage data modeling and diagnostic system based on a user device, however, Ionescu does not specifically disclose triggering a disposition based on the usage and linking with a subsequent user;
Morrison teaches generating, based on the resource usage data and device lifespan data, lifecycle efficiency prediction for each solution of a plurality of solutions for at least one defect with the device; selecting a first solution of the plurality of solutions based on the lifecycle efficiency prediction for each solution, wherein the first solution comprises a resource usage per unit time less than any other of the plurality of solutions; (Fig 19 and paragraphs [78-79]; Morrison teaches a similar diagnostic system that provides repair and disposition determinations. This further includes threshold determinations based on user usage discussed in [57-58 and 63-65].);
causing application of the first solution to the device to resolve the at least one defect with the device by transmitting a signal configured to initiate replacement of at least one component of the device with at least one replacement component; and triggering a subsequent use period of the device by directing disposition of the device, wherein the subsequent use period comprises linking the device with a subsequent user account (Fig 19 and paragraphs [57-58 and 78-79]; Morrison teaches a similar diagnostic system that provides repair and disposition determinations. This further includes providing a system failure and coordinating the system swap based on the usage data (interpreted as resolve by transmitting a signal to initiate replacement). This is further included with respect to the subsequent user that the disposition includes refurbishing a device for a new user based on the current user’s usage data.).
Ionescu discloses a device diagnostic system that is based on usage data, however, Ionescu does not specifically teach initiating replacement and disposition in terms of a subsequent user.
Morrison teaches a similar diagnostic device system based on usage data that specifically provides initiating replacement and disposition including providing the device for a second user.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention for the device diagnostic system that is based on usage data of Ionescu the ability to include a similar diagnostic device system based on usage data that specifically provides initiating replacement and disposition including providing the device for a second user as taught by Morrison since the claimed invention is merely a combination of prior art elements and in the combination each element would have performed the same function as it did separately and one of ordinary skill in the art would have recognized the results of the combination were predictable.
Regarding claim 2, the combination teaches the above-enclosed limitations of the computer-implemented method of claim 1,
Ionescu further discloses wherein the one or more models are trained using aggregated device data sets from a plurality of devices that comprise two or more different devices (Paragraphs [141-145 and 154-156]; Ionescu discloses that the machine learning model is trained based on aggregated data set.).
Regarding claim 3, the combination teaches the above-enclosed limitations of the computer-implemented method of claim 2,
Ionescu further discloses wherein the aggregated device data sets comprise data retrieved from a plurality of data sources along the lifecycle of each of the plurality of devices (Paragraphs [141-145 and 158-161]; Ionescu discloses that the machine learning model is trained based on aggregated data set. The determination includes aggregated data comparison modeling based on average lifecycle for the device/component.).
Regarding claim 4, the combination teaches the above-enclosed limitations of the computer-implemented method of claim 1,
Ionescu further discloses wherein the one or more resource usage data segments comprises at least two resource usage data segments, wherein a first resource usage data segment of the at least two resource usage data segments is generated using a different model that is trained using different aggregated device data set relative to a second resource usage data segment (Paragraphs [145-150]; Ionescu discloses that the machine learning model is trained based on aggregated data set which includes source-specific classifiers. This determination is further disclosed in paragraphs [155-159] in terms of the diagnostic lifecycle for the usage data for the source/component.).
Regarding claim 5, the combination teaches the above-enclosed limitations of the computer-implemented method of claim 1,
Morrison teaches the first solution comprises triggering the subsequent use period (Fig 19 and paragraphs [78-79]; Morrison teaches a similar diagnostic system that provides repair and disposition determinations. This further includes threshold determinations based on user usage discussed in [63-65].).
Ionescu discloses a device diagnostic system that is based on lifecycle efficiency data, however, Ionescu does not specifically teach disposition for subsequent use.
Morrison teaches a similar diagnostic device system based on usage data that specifically provides disposition including providing the device for a second use.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention for the device diagnostic system that is based on lifecycle efficiency usage data of Ionescu the ability to include a similar diagnostic device system based on usage data that specifically provides disposition including providing the device for a second period as taught by Morrison since the claimed invention is merely a combination of prior art elements and in the combination each element would have performed the same function as it did separately and one of ordinary skill in the art would have recognized the results of the combination were predictable.
Regarding claim 7, the combination teaches the above-enclosed limitations of the computer-implemented method of claim 1,
Ionescu further discloses wherein generating the lifecycle efficiency prediction data comprises: generating one or more lifecycle efficiency prediction segments corresponding to the one or more resource usage segments, and assembling the one or more lifecycle efficiency prediction segments into the lifecycle efficiency prediction (Paragraphs [154-156]; Ionescu discloses prediction based on the usage data for the user device. Further, within the combination, Morrison teaches [63-65] anticipated usage information based on the data.).
Regarding claim 8, Ionescu discloses an apparatus comprising one or more processors, and at least one non-transitory memory comprising instructions that, with the one or more processors, cause the apparatus to: receive a device identifier corresponding to a device; identify a plurality of segments of device data associated with the device (Paragraphs [106-108 and 119]; Ionescu discloses a device diagnostic system that provides device identifier information (data set) that includes multiple segments of the device’s systems.);
assemble resource usage data for the device by: generating, using one or more models, one or more resource usage data segments for one or more of the plurality of segments of the device data; and generating the resource usage data for the device from the one or more resource usage data segments (Paragraphs [120-125]; Ionescu discloses assembling usage data including for systems and sub-systems to determine diagnostic information for the device. In terms of the model aspect, Ionescu discloses [149-155] machine learning and AI used to make predictions regarding the usage data.);
Ionescu discloses a usage data modeling and diagnostic system based on a user device, however, Ionescu does not specifically disclose triggering a disposition based on the usage and linking with a subsequent user;
Morrison teaches generate, based on the resource usage data and device lifespan data, lifecycle efficiency prediction for each solution of a plurality of solutions for at least one defect with the device; select a first solution of the plurality of solutions based on the lifecycle efficiency prediction for each solution, wherein the first solution comprises a resource usage per unit time less than any other of the plurality of solutions; (Fig 19 and paragraphs [78-79]; Morrison teaches a similar diagnostic system that provides repair and disposition determinations. This further includes threshold determinations based on user usage discussed in [57-58 and 63-65].);
cause application of the first solution to the device to resolve the at least one defect with the device by transmitting a signal configured to initiate replacement of at least one component of the device with at least one replacement component; and triggering a subsequent use period of the device by directing disposition of the device, wherein the subsequent use period comprises linking the device with a subsequent user account (Fig 19 and paragraphs [57-58 and 78-79]; Morrison teaches a similar diagnostic system that provides repair and disposition determinations. This further includes providing a system failure and coordinating the system swap based on the usage data (interpreted as resolve by transmitting a signal to initiate replacement). This is further included with respect to the subsequent user that the disposition includes refurbishing a device for a new user based on the current user’s usage data.).
Ionescu discloses a device diagnostic system that is based on usage data, however, Ionescu does not specifically teach initiating replacement and disposition in terms of a subsequent user.
Morrison teaches a similar diagnostic device system based on usage data that specifically provides initiating replacement and disposition including providing the device for a second user.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention for the device diagnostic system that is based on usage data of Ionescu the ability to include a similar diagnostic device system based on usage data that specifically provides initiating replacement and disposition including providing the device for a second user as taught by Morrison since the claimed invention is merely a combination of prior art elements and in the combination each element would have performed the same function as it did separately and one of ordinary skill in the art would have recognized the results of the combination were predictable.
Regarding claim 9, the combination teaches the above-enclosed limitations of the apparatus of claim 8,
Ionescu further discloses wherein the one or more models are trained using aggregated device data sets from a plurality of devices that comprise two or more different devices (Paragraphs [141-145 and 154-156]; Ionescu discloses that the machine learning model is trained based on aggregated data set.).
Regarding claim 10, the combination teaches the above-enclosed limitations of the apparatus of claim 9,
Ionescu further discloses wherein the aggregated device data sets comprise data retrieved from a plurality of data sources along the lifecycle of each of the plurality of devices (Paragraphs [141-145 and 158-161]; Ionescu discloses that the machine learning model is trained based on aggregated data set. The determination includes aggregated data comparison modeling based on average lifecycle for the device/component.).
Regarding claim 11, the combination teaches the above-enclosed limitations of the apparatus of claim 8,
Ionescu further discloses wherein the one or more resource usage data segments comprises at least two resource usage data segments, wherein a first resource usage data segment of the at least two resource usage data segments is generated using a different model that is trained using different aggregated device data set relative to a second resource usage data segment (Paragraphs [145-150]; Ionescu discloses that the machine learning model is trained based on aggregated data set which includes source-specific classifiers. This determination is further disclosed in paragraphs [155-159] in terms of the diagnostic lifecycle for the usage data for the source/component.).
Regarding claim 12, the combination teaches the above-enclosed limitations of the apparatus of claim 8,
Morrison teaches where the first solution comprises triggering the subsequent use period (Fig 19 and paragraphs [78-79]; Morrison teaches a similar diagnostic system that provides repair and disposition determinations. This further includes threshold determinations based on user usage discussed in [63-65].).
Ionescu discloses a device diagnostic system that is based on lifecycle efficiency data, however, Ionescu does not specifically teach disposition for subsequent use.
Morrison teaches a similar diagnostic device system based on usage data that specifically provides disposition including providing the device for a second use.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention for the device diagnostic system that is based on lifecycle efficiency usage data of Ionescu the ability to include a similar diagnostic device system based on usage data that specifically provides disposition including providing the device for a second period as taught by Morrison since the claimed invention is merely a combination of prior art elements and in the combination each element would have performed the same function as it did separately and one of ordinary skill in the art would have recognized the results of the combination were predictable.
Regarding claim 14, the combination teaches the above-enclosed limitations of the apparatus of claim 8,
Ionescu further discloses wherein the at least one non-transitory memory, with the one or more processors, cause the apparatus to generate the lifecycle efficiency prediction by: generating one or more lifecycle efficiency prediction segments corresponding to the one or more resource usage segments, and assembling the one or more lifecycle efficiency prediction segments into the lifecycle efficiency prediction (Paragraphs [154-156]; Ionescu discloses prediction based on the usage data for the user device. Further, within the combination, Morrison teaches [63-65] anticipated usage information based on the data.).
Regarding claim 15, Ionescu discloses at least one non-transitory computer readable medium having computer coded instructions configured to, when executed by at least one processor: receive a device identifier corresponding to a device; identify a plurality of segments of device data associated with the device (Paragraphs [106-108 and 119]; Ionescu discloses a device diagnostic system that provides device identifier information (data set) that includes multiple segments of the device’s systems.);
assemble resource usage data for the device by: generating, using one or more models, one or more resource usage data segments for one or more of the plurality of segments of the device data; and generating the resource usage data for the device from the one or more resource usage data segments (Paragraphs [120-125]; Ionescu discloses assembling usage data including for systems and sub-systems to determine diagnostic information for the device. In terms of the model aspect, Ionescu discloses [149-155] machine learning and AI used to make predictions regarding the usage data.);
Ionescu discloses a usage data modeling and diagnostic system based on a user device, however, Ionescu does not specifically disclose triggering a disposition based on the usage and linking with a subsequent user;
Morrison teaches generate, based on the resource usage data and device lifespan data, lifecycle efficiency prediction for each solution of a plurality of solutions for at least one defect with the device; select a first solution of the plurality of solutions based on the lifecycle efficiency prediction for each solution, wherein the first solution comprises a resource usage per unit time less than any other of the plurality of solutions (Fig 19 and paragraphs [78-79]; Morrison teaches a similar diagnostic system that provides repair and disposition determinations. This further includes threshold determinations based on user usage discussed in [57-58 and 63-65].);
cause application of the first solution to the device to resolve the at least one defect with the device by transmitting a signal configured to initiate replacement of at least one component of the device with at least one replacement component; and trigger a subsequent use period of the device by directing disposition of the device based on the resource usage data, wherein the subsequent use period comprises linking the device with a subsequent user account (Fig 19 and paragraphs [57-58 and 78-79]; Morrison teaches a similar diagnostic system that provides repair and disposition determinations. This further includes providing a system failure and coordinating the system swap based on the usage data (interpreted as resolve by transmitting a signal to initiate replacement). This is further included with respect to the subsequent user that the disposition includes refurbishing a device for a new user based on the current user’s usage data.).
Ionescu discloses a device diagnostic system that is based on usage data, however, Ionescu does not specifically teach initiating replacement and disposition in terms of a subsequent user.
Morrison teaches a similar diagnostic device system based on usage data that specifically provides initiating replacement and disposition including providing the device for a second user.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention for the device diagnostic system that is based on usage data of Ionescu the ability to include a similar diagnostic device system based on usage data that specifically provides initiating replacement and disposition including providing the device for a second user as taught by Morrison since the claimed invention is merely a combination of prior art elements and in the combination each element would have performed the same function as it did separately and one of ordinary skill in the art would have recognized the results of the combination were predictable.
Regarding claim 16, the combination teaches the above-enclosed limitations of the at least one non-transitory computer readable medium of claim 15,
Ionescu further discloses wherein the one or more models are trained using aggregated device data sets from a plurality of devices that comprise two or more different devices (Paragraphs [141-145 and 154-156]; Ionescu discloses that the machine learning model is trained based on aggregated data set.).
Regarding claim 17, the combination teaches the above-enclosed limitations of the at least one non-transitory computer readable medium of claim 16,
Ionescu further teaches wherein the aggregated device data sets comprise data retrieved from a plurality of data sources along the lifecycle of each of the plurality of devices (Paragraphs [141-145 and 158-161]; Ionescu discloses that the machine learning model is trained based on aggregated data set. The determination includes aggregated data comparison modeling based on average lifecycle for the device/component.).
Regarding claim 18, the combination teaches the above-enclosed limitations of the at least one non-transitory computer readable medium of claim 15,
Ionescu further discloses wherein the one or more resource usage data segments comprises at least two resource usage data segments, wherein a first resource usage data segment of the at least two resource usage data segments is generated using a different model that is trained using resource usage data for a different aggregated device data set than a second resource usage data segment (Paragraphs [145-150]; Ionescu discloses that the machine learning model is trained based on aggregated data set which includes source-specific classifiers. This determination is further disclosed in paragraphs [155-159] in terms of the diagnostic lifecycle for the usage data for the source/component.).
Regarding claim 19, the combination teaches the above-enclosed limitations of the at least one non-transitory computer readable medium of claim 15,
Morrison teaches where the first solution comprises triggering the subsequent use period (Fig 19 and paragraphs [78-79]; Morrison teaches a similar diagnostic system that provides repair and disposition determinations. This further includes threshold determinations based on user usage discussed in [63-65].).
Ionescu discloses a device diagnostic system that is based on lifecycle efficiency data, however, Ionescu does not specifically teach disposition for subsequent use.
Morrison teaches a similar diagnostic device system based on usage data that specifically provides disposition including providing the device for a second use.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention for the device diagnostic system that is based on lifecycle efficiency usage data of Ionescu the ability to include a similar diagnostic device system based on usage data that specifically provides disposition including providing the device for a second period as taught by Morrison since the claimed invention is merely a combination of prior art elements and in the combination each element would have performed the same function as it did separately and one of ordinary skill in the art would have recognized the results of the combination were predictable.
Response to Arguments
In response to the arguments filed June 25, 2026 on pages 8-11 regarding the 35 USC 101 rejection, specifically that the amended claim limitations are directed towards eligible subject matter.
Examiner respectfully disagrees.
The arguments allege that the amended claim limitations provide a technical improvement with respect to the automated signal for initiating the replacement component for the device based on the analysis. Based on the specification, the control signal is merely utilizing generic technology to implement. This further includes the idea of a solution as the specification does not provide specific technical description towards the improvement. The initiation of the replacement includes high level ideas in terms of any means to accomplishing the initiation that includes both technical elements and elements that would invoke the abstract idea (such as a display for a person to initiate based on displayed information). As such, the amended claim limitations are not directed towards additional elements that are significantly more or transformative into a practical application. Refer to MPEP 2106.05(f). Lacking any further arguments, claims 1, 8, and 15 are maintaining the 35 USC 101 rejection, as considered above in light of the amended claim limitations.
Lacking any further arguments, claims 1-5, 7-12, and 14-19 are maintaining the 35 USC 101 rejection, as considered above in light of the amended claim limitations.
In response to the arguments filed June 25, 2026 on pages 11-13 regarding the 35 USC 103 rejection, specifically that the amended claim limitations are not taught by the cited prior art.
Examiner respectfully disagrees.
Based on further consideration, the previously cited prior art of Morrison teaches the amended claim limitations. The arguments allege that Morrison does not teach a plurality of solutions, however, Morrison provides a plurality in terms of the binary threshold analysis. Further, the claims are not providing elements that are specific that the prediction and usage analysis of Morrison are beyond the interpretation. Morrison provides a threshold analysis for component swaps and other disposition that incorporates a plurality of solutions based on the threshold analysis that then utilizes the analysis for disposition of the device and component replacement. As such, based on the further considered prior art, claims 1, 8, and 15 are maintaining the 35 USC 103 rejection, as considered above in light of the amended claim limitations.
Lacking any further arguments, claims 1-5, 7-12, and 14-19 are maintaining the 35 USC 103 rejection, as considered above in light of the amended claim limitations.
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 ANDREW CHASE LAKHANI whose telephone number is (571)272-5687. The examiner can normally be reached M-F 730am - 5pm (EST).
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/ANDREW CHASE LAKHANI/Primary Examiner, Art Unit 3629
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