DETAILED ACTION
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 .
Status of Claims
This Office action is in response to correspondence received March 9, 2026.
Claims 1, 8, and 14-20 are amended. Claims 1-20 are pending and have been examined.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on March 9, 2026 has been entered.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claim(s) 1 recite(s):
A device refurbishment carbon dioxide (CO2) emissions minimization : a receive a incident file including indexed operational telemetry measurements , including measured power consumption levels, software application usage data, a first user geographic location, and output of a predictedCO2 emissions value the first client information handling system that will exceed a first CO2 usage threshold for a predicted CO2 monitoring period; a of the device refurbishment CO2 emissions minimization to identify changes in the predicted CO2 emissions values as a first input, changes in the operational telemetry measurements for the measured power consumption levels and software application usage data as a second input, and the first user geographic location as a third input, and to output a CO2 increase causative operational telemetry measurement as a cause of a transition to exceed the first CO2 usage threshold for the predicted CO2 monitoring period; determine that the first user geographic location has a first annual average temperature that is higher than a second annual average temperature for a second user geographic location of a second information handling system that isa fourth input; receive an updated indexed incident file indicating the first client information handling system with a determined CO2 emissions value that has exceeded the first CO2 usage threshold and to output a first CO2 increase causative operational telemetry measurement that caused a transition of the first information handling system from a predicted CO2 emissions eco-friendliness state to a non-eco-friendly state by exceeding the first CO2 usage value; and generate a recommendation instructions to replace the first client information handling system for the first user with the second client information handling system and transfer the first client information handling system to a second user.
Claim 8 recites:
A method of for minimizing carbon dioxide (CO2) emissions with recommended refurbishment and replacement of a first client information handling system comprising: receiving, a incident file including indexed operational telemetry measurements for a first client information handling system operated by a first user, including measured power consumption levels, software application usage data, a first user usage profile, and output of a predicted CO2 emissions value for a predicted CO2 monitoring period of time; to input changes in measured power consumption level and changes in the software application usage data and output a CO2 increase causative operational telemetry measurement contributing to the predicted CO2 emissions value increase that is dependent upon a workload placed on the first client information handling system from inputs of the received operational telemetry measurements ; determining that a second user usage profile associated with a second user of a second client information handling system as a lower processing workload and differs from the first user usage profile with respect to processing workload placed on the second client information handling system as ; to output a transition to a predicted non--eco-friendliness state based, in part, on a determination that the predicted CO2 emissions value under the processing workload placed in the first client information handling system by the first user usage profile causes the predicted CO2 emissions value to exceed a non-eco-friendly state transition threshold value; and transmitting, , generated recommendation instructions that the first client information handling system is to replace the second client information handling system for the second user based on the predicted CO2 emissions value to exceed the non-eco-friendly state transition threshold value with the first user.
Claim 14 recites:
A device refurbishment carbon dioxide (CO2) emissions minimization : receive a first incident file including indexed first operational telemetry measurements including first measured power consumption levels, first software application usage data, a first user usage profile, and output of a first predicted CO2 emissions value for a predicted CO2 monitoring period ; receive second JSON incident file including indexed second operational telemetry measurements for a second client information handling system operated by a second user, including second measured power consumption levels and second software application usage data at the second client information handling system; of the device refurbishment CO2 emissions minimization system to identify changes in the first measured power consumption levels and changes in the first software usage data as input, and output a CO2 increase causative operational telemetry measurement for the first client information handling system that contributes to the first predicted CO2 emissions value exceeding a non-eco-friendly state transition threshold value and that is dependent upon a processing workload placed on a hardware component having a first hardware type of the first client information handling system; the determine the first user at the first client information handling system previously rejected a recommendation to cap resources of the hardware component of the first hardware type consumed during execution of a software or firmware application at a maximum resource threshold value; determine the indexed second operational telemetry measurements indicate previous consumption of resources for the first hardware type during use by the second user of a second client information handling system falls below the maximum resource threshold value; and generate and transmit with the network interface device recommendation instructions for display recommending that the first client information handling system replace the second client information handling system for the second user.
Claims 1, 8, and 14 recite an abstract idea that is a mental process because the steps, as presented above, using applicant’s claim language, are steps of observation and judgement. Information is received and this information could be observed mentally by one looking at power bars or other widgets on a laptop, for example. Then there are determinations made which are mental processes like whether something increases CO_2 or not (like, for example, something that uses more power relatively to something else would increase CO_2, all things being equal). Finally a transfer or other action of recommendation is performed, wherein under a broadest reasonable interpretation transferring is merely putting someone else’s name on something, there is nothing physical and even if there were (mailing a device) that would be at best an additional element that would not alter the finding of a judicial exception. In other words, a transfer is accomplished by stating that Alan’s laptop is Bob’s laptop. Therefore, for these reasons Applicants independent claims are a mental process, and these represent the claims as a whole, with the recited additional elements that are applied to the abstract idea analyzed below.
This judicial exception is not integrated into a practical application. Applicant has recited broad additional elements such as systems, processors, and network interfaces that could be taught by a consumer computers given the mental process steps to perform. This is invoking a computer to perform an existing process, which here is determining CO_2 consumption and making recommendations. Further a neural network is recited however it is only described in terms of its functional result, see MPEP 2106.05(f)(1), and is further recited by an apply it synonym. The neural networks as well as classifier are pretrained and used, with inputs, but there is no detail as to how the neural network makes calculations, only that it is given inputs. JSON is a data object and is merely received. In combination the ordinary computers and the applied neural network are also applied as a neural network is necessarily run or accessed on a computer.
Claim 1 recites the following:
system executing on a unified endpoint management (UEM) platform information handling system comprising
Javascript Object Notification (JSON)
network interface device to
from a first client information handling system operated by a first user
from a first neural network algorithm executed at
hardware processor executing machine readable code instructions of a second trained neural network
system
the hardware processor to x 2
the hardware processor executing machine readable code instructions of the trained neural network algorithm
conduct a classifier algorithm to
Claim 8:
executing machine readable code instructions of a device refurbishment carbon dioxide (CO2) emissions minimization system
Javascript Object Notification (JSON)
, via a network interface device
from a first neural network algorithm executed at the first client information handling system
executing machine readable code instructions a second trained neural network of the device refurbishment CO2 emissions minimization system, via a hardware processor,
into the second trained neural network
another input into the second trained neural network
executing machine readable code instructions of the second trained neural network algorithm for a neural network classifier
via the network interface device
Claim 14:
system executing on a unified endpoint management (UEM) platform information handling system comprising
Javascript Object Notification (JSON)
a network interface device to x 2
from a first client information handling system operated by a first user,
from a first neural network algorithm executed at the first client information handling system
a hardware processor executing machine readable code instructions of a second trained neural network algorithm
executing a neural network classifier
the hardware processor to x many
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because for the reasons above, that under MPEP 2106.05(f) the additional elements are apply it both alone and in combination, they are also not significantly more. The reasoning is carried over.
Per the dependent claims:
Per claims 2-7, 8-13, and 15-20, the limitations further limit the abstract idea. Additional elements such as a network interface device transmitting information were previously analyzed in claim 1 and the limitations here are further abstract idea limitations (information receiving, determining, transmitting). Per claims 16-20 where hardware types are listed, they are a) common to generic computers and b) are the element used to determine their resource cap, and therefore are a further detail of the abstract idea. Therefore, these claims if integrated into the independent claims would not overcome the abstract idea.
Therefore, claims 1-20 are rejected under 35 USC 101.
Prior Art Made of Record
The following prior art is considered relevant to Applicant’s disclosure but is not relied upon in the above rejection:
Geffin et al., US PGPUB 20120053925 A1, teaches an emissions minimization system to receive information see par 41; indexed telemetry measurements from first/second devices with power consumption levels and software application usage in par 042, including geographic location, see par 043. Then the output of predicted emissions value is taught in par 045 with a threshold taught in par 081. Then first geographic first annual average temperature and second location taught in par 055 (“moved to other physical locations or to remote locations.”)
Geffin does not teach
JSON
Neural network
the hardware processor executing machine readable code instructions of the trained neural network algorithm receive an updated indexed incident file indicating the first client information handling system with a determined CO2 emissions value that has exceeded the first CO2 usage threshold and to conduct a classifier algorithm to output a first CO2 increase causative operational telemetry measurement that caused a transition of the first information handling system from a predicted CO2 emissions eco-friendliness state to a non-eco-friendly state by exceeding the first CO2 usage value
and the hardware processor to generate a recommendation instructions to replace the first client information handling system for the first user with the second client information handling system and transfer the first client information handling system to a second user
Therefore Geffin does not teach all the limitations of Applicant’s claims.
Russo, US 20240104476 A1 teaches for determining emissions, using JSON, see par 047, neural networks, a user device with the sensor that is determining temperature, see par 019, and using activity to classify and train neural network, see par 024. But Russo does not teach
a hardware processor executing machine readable code instructions of a second trained neural network of the device refurbishment CO2 emissions minimization system to identify changes in the predicted CO2 emissions values as a first input, changes in the operational telemetry measurements for the measured power consumption levels and software application usage data as a second input, and the first user geographic location as a third input, and to output a CO2 increase causative operational telemetry measurement as a cause of a transition to exceed the first CO2 usage threshold for the predicted CO2 monitoring period
and the hardware processor to generate a recommendation instructions to replace the first client information handling system for the first user with the second client information handling system and transfer the first client information handling system to a second user
Therefore Russo does not teach all of the limitations of Applicant’s claims.
Bachman et al., US PGPUB 20230359970 teaches using models to estimate emissions in software processes, see par 069. Bachman does not teach
a hardware processor executing machine readable code instructions of a second trained neural network of the device refurbishment CO2 emissions minimization system to identify changes in the predicted CO2 emissions values as a first input, changes in the operational telemetry measurements for the measured power consumption levels and software application usage data as a second input, and the first user geographic location as a third input, and to output a CO2 increase causative operational telemetry measurement as a cause of a transition to exceed the first CO2 usage threshold for the predicted CO2 monitoring period
and the hardware processor to generate a recommendation instructions to replace the first client information handling system for the first user with the second client information handling system and transfer the first client information handling system to a second user.
StackOverflow, “A Beginner’s Guide to JSON, the data format for the internet,” available at: < https://stackoverflow.blog/2022/06/02/a-beginners-guide-to-json-the-data-format-for-the-internet/ > archived on June 2, 2022. teaches JSON as a data object.
Response to arguments
Applicant argues that automatic telemetry reporting overcomes the previous interpretation that a mental process is being performed. Examiner disagrees as automated reporting, claimed as it is here, is an apply it element to send information from one device to another or to receive data. Sending/receiving data is understood as applying known computer technology per guidance, see MPEP 2106.05(f)(2), TLI Communications, Alice, Ultramercial. Therefore the 101 rejection is not overcome. Overall, the invention is understood (the claims as a whole) as the abstract idea of taking information about computer usage and other related information (location) and then transferring a computer or recommending something to a user. The applied elements to the claims as a whole are applied models and computing devices to process, send, or receive information. Taken together, the combination of additional elements is no more than generic computing technology and steps (process, send, receive, recited in their equivalent terms in the claims) and applied neural networks, which as they are recited, are performing in their ordinary capacity, see MPEP 2106.05(f)(2). Therefore, the 101 rejection is maintained.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RICHARD W. CRANDALL whose telephone number is (313)446-6562. The examiner can normally be reached M - F, 8:00 AM - 5:00 PM.
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/RICHARD W. CRANDALL/ Primary Examiner, Art Unit 3619
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