DETAILED ACTION
Status of Application
This action is a Non-Final Rejection. This action is in response to the request for continued examination filed on March 12, 2026.
Claims 1-41, 47, 54, and 61 have been canceled.
Claims 42-46, 48-53, and 55-60 have been amended.
Claims 62-64 have been added.
Claims 42-46, 48-53, 55-60, and 64 are rejected.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . 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.
Response to Arguments
Regarding the rejection under 35 U.S.C. 101, the rejection is withdrawn in light of Applicant’s amendments incorporating limitations previously designated as eligible.
Regarding the rejections under 35 U.S.C. 103, there are new grounds of rejection in light of Applicant’s amendments. Therefore, Applicant’s arguments are moot.
Allowable Subject Matter
Claims 62 and 63 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims. These claims are allowable because the limitations of these claims, as combined with the limitations of the independent claims, are not obvious in light of the prior art. Claim 64 is not designated as allowable because it is a method claim with contingent language. The limitations of claim 64 and the independent method claim are mutually exclusive because they cannot both be performed. Therefore, per the broadest reasonable interpretation, only one condition/result occurs. Applicant may consider amending the limitations in claim 64 to make them refer to an output that is distinct from the output in claim 56, thereby making it clear that each condition must separately occur.
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.
Claims 42-44, 49-51, and 56-58 are rejected under 35 U.S.C. 103 as being unpatentable over Yee et al., U.S. Patent Application Publication Number 2023/0042210 A; Ismail (Ismail, Mahmoud M. “A Machine Learning Approach for Energy-Efficient IoT Systems,” Journal of Intelligent Systems and Internet of Things (JISIoT), (2020), Vol. 01, No. 02, p. 61-69.); and Gupta et al., U.S. Patent Application Publication Number 2010/0070784 A1.
Claim 42:
Yee teaches:
interface circuitry; machine readable instructions; and at least one programmable circuit operable based on the machine readable instructions to: (see at least Yee, Figures 1A and 1B and associated text; paragraph 0057).
for a data element ingested [from an edge environment]: generate, based on execution of a machine learning model on the data element, a first output representative of criticality of the data element and a second output representative of quality of the data element; and based on the first output not satisfying a first threshold and the second output not satisfying a second threshold, cause deletion of the data element to reduce a resource utilization of the node (see at least Yee, paragraph 0057 (“The processed data can be aggregated across the one or more data sources. The AI engine 155 can continuously evaluate the aggregated data to determine whether the one or more conditions is met. To minimize system resources, the AI-based system 105 can store the compressed aggregated data in the data storage 145. The compressed aggregated data can include the associated conditionally relevant meaning verification and associated metadata necessary to establish proof of occurrence of the condition, rather than the raw received input data. Specifically, the AI-based system 105 can automatically determine whether data is consequential by evaluating whether the data is pertinent to determining whether a condition has been met and/or is associated with data that does not satisfy a condition. Inconsequential data can be purged by the AI-based system 105 to save storage space. For example, data which is not relevant to a particular user's preferences or relevant for a past event, or which is redundant of information already stored in data storage 145. Consequential data can include data of previously processed transactions, previous user activity data, and previous events for brands and/or certain products that can be utilized to predict a likelihood of future events. As such, this data that surrounds transactions (in time), which can be inferred to be consequential based on patterns, can also be stored in the data storage.”)).
Yee does not explicitly teach, but Ismail, however, does teach:
[for a data element ingested by a node] from an edge environment (see at least Ismail, Figure 1 (The server receives data from edge computing environments.)).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Ismail’s edge environment with Yee’s method of purging data. One of ordinary skill in the art would have been motivated to incorporate this feature for the purpose of analyzing data from devices in an edge environment in order to efficiently and centrally determine which data can be deleted. Paragraph 0053 of Yee states that the received data is from third party systems such as banks, other financial entities, and public data sources. While Yee does not refer to these as part of an edge environment, it appears that Yee is pulling data from computing systems that are at the edge of a network. However Ismail clearly shows that this type of environment is an edge environment.
Yee does not explicitly teach, but Gupta, however, does teach:
based on the resource utilization not satisfying a third threshold, cause the node to transition to a reduce power state (see at least Gupta, paragraph 0011 (“In particular, one embodiment is a method of reducing power consumption of a server cluster of host systems with virtual machines executing on the host systems, the method comprising: considering recommending host system power-on when there is a host system whose utilization is above a target utilization range, and considering recommending host system power-off when there is a host system whose utilization is below the target utilization range;”)).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Gupta’s method of powering off a system whose utilization is below a target utilization range with Yee’s method of purging data. One of ordinary skill in the art would have been motivated to incorporate this feature for the purpose of conserving energy when a device is not needed or has reduced needs.
Claim 43:
Yee further teaches:
wherein the criticality of the data element is based on at least one of a potential consequence if the data element is not subsequently processed or stored, a latency requirement associated with the data element, a number of nodes in the edge environment that are associated with the data element, a purpose of a workload associated with the data element, a size of the workload, a priority of the data element, or a regulatory requirement associated with the data element (see at least Yee, paragraph 0057 (“The processed data can be aggregated across the one or more data sources. The AI engine 155 can continuously evaluate the aggregated data to determine whether the one or more conditions is met. To minimize system resources, the AI-based system 105 can store the compressed aggregated data in the data storage 145. The compressed aggregated data can include the associated conditionally relevant meaning verification and associated metadata necessary to establish proof of occurrence of the condition, rather than the raw received input data. Specifically, the AI-based system 105 can automatically determine whether data is consequential by evaluating whether the data is pertinent to determining whether a condition has been met and/or is associated with data that does not satisfy a condition. Inconsequential data can be purged by the AI-based system 105 to save storage space. For example, data which is not relevant to a particular user's preferences or relevant for a past event, or which is redundant of information already stored in data storage 145. Consequential data can include data of previously processed transactions, previous user activity data, and previous events for brands and/or certain products that can be utilized to predict a likelihood of future events. As such, this data that surrounds transactions (in time), which can be inferred to be consequential based on patterns, can also be stored in the data storage.”)).
Claim 44:
Yee further teaches:
wherein the quality of the data element is based on at least one of an accuracy of the data element, a completeness of the data element, a consistency of the data element, a currency of the data element, a redundancy of the data element in the edge environment, a timeliness of the data element, or a validity of the data element (see at least Yee, paragraph 0057 (“The processed data can be aggregated across the one or more data sources. The AI engine 155 can continuously evaluate the aggregated data to determine whether the one or more conditions is met. To minimize system resources, the AI-based system 105 can store the compressed aggregated data in the data storage 145. The compressed aggregated data can include the associated conditionally relevant meaning verification and associated metadata necessary to establish proof of occurrence of the condition, rather than the raw received input data. Specifically, the AI-based system 105 can automatically determine whether data is consequential by evaluating whether the data is pertinent to determining whether a condition has been met and/or is associated with data that does not satisfy a condition. Inconsequential data can be purged by the AI-based system 105 to save storage space. For example, data which is not relevant to a particular user's preferences or relevant for a past event, or which is redundant of information already stored in data storage 145. Consequential data can include data of previously processed transactions, previous user activity data, and previous events for brands and/or certain products that can be utilized to predict a likelihood of future events. As such, this data that surrounds transactions (in time), which can be inferred to be consequential based on patterns, can also be stored in the data storage.”)).
Claim 49:
Claim 49 is rejected using the same rationale that was used for the rejection of claim 42.
Claim 50:
Claim 50 is rejected using the same rationale that was used for the rejection of claim 43.
Claim 51:
Claim 51 is rejected using the same rationale that was used for the rejection of claim 44.
Claim 56:
Claim 56 is rejected using the same rationale that was used for the rejection of claim 42.
Claim 57:
Claim 57 is rejected using the same rationale that was used for the rejection of claim 43.
Claim 58:
Claim 58 is rejected using the same rationale that was used for the rejection of claim 44.
Claim 64:
Claim 64 is rejected using the same rationale that was used for the rejection of claim 56. Claim 64 is a method claim that includes contingent limitations. For example, claim 64 recites “based on at least one of the first output satisfying the first threshold or the second output satisfying the second threshold, storing the data element in baseline data.” However, claim 56 recites the limitation of “based on the first output not satisfying a first threshold and the second output not satisfying a second threshold, deleting….” Both conditions cannot be satisfied for the same output. Therefore, this claim is interpreted as having the conditions of claim 56 satisfied. Therefore, the limitations of claim 64 are not given patentable weight.
Claims 45, 52, and 59 are rejected under 35 U.S.C. 103 as being unpatentable over Yee et al., U.S. Patent Application Publication Number 2023/0042210 A1; Ismail (Ismail, Mahmoud M. “A Machine Learning Approach for Energy-Efficient IoT Systems,” Journal of Intelligent Systems and Internet of Things (JISIoT), (2020), Vol. 01, No. 02, p. 61-69.); Gupta et al., U.S. Patent Application Publication Number 2010/0070784 A1; and Fakhraie et al. U.S. Patent Number 12,130,937 B1.
Claim 45:
Yee does not explicitly teach, but Fakhraie, however, does teach:
wherein one or more of the at least one programmable circuit is to tag the data element for at least one of discard or replacement with a symbolic representation (see at least Fakhraie, column 44, lines 20-43 (“In various embodiments, a control tower portal allows the customer to instruct entities (e.g., third party computing systems and applications, customer computing devices, etc.) to delete accounts, restrict the use or sharing of customer data, and/or to delete customer data (e.g., all data or data of a particular type). In some implementations, restrictions on customer data or deletion thereof may be accomplished by, for example, the financial institution computing system 110 and/or the financial institution client application 208 making an API call to third party systems 106 to have customer data restricted or deleted (or marked for subsequent restriction or deletion). The control tower portal may allow for multiple levels of deletion or scrubbing. For example, an account may be deleted, all accounts may be deleted, all accounts of a certain type may be deleted, certain customer data or customer data of particular types may be deleted, and/or all customer data may be deleted. In some implementations, only customer data received from the financial institution computing system 110 through access granted by the customer (and not received by the third party from other sources, such as directly from the customer) may be marked for deletion. Advantageously, the ability to choose to have account data deleted empowers the customer with the right to be partially or wholly forgotten.”)).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Fakhraie’s method of marking data for deletion with Yee’s method of purging data. One of ordinary skill in the art would have been motivated to incorporate this feature for the purpose of identifying data that is to be deleted so that it is properly deleted.
Claim 52:
Claim 52 is rejected using the same rationale that was used for the rejection of claim 45.
Claim 59:
Claim 59 is rejected using the same rationale that was used for the rejection of claim 45.
Claims 46, 48, 53, 55, and 60 are rejected under 35 U.S.C. 103 as being unpatentable over Yee et al., U.S. Patent Application Publication Number 2023/0042210 A1; Ismail (Ismail, Mahmoud M. “A Machine Learning Approach for Energy-Efficient IoT Systems,” Journal of Intelligent Systems and Internet of Things (JISIoT), (2020), Vol. 01, No. 02, p. 61-69.); Gupta et al., U.S. Patent Application Publication Number 2010/0070784 A1; and Guim Bernat et al., U.S. Patent Application Publication Number 2020/0167205 A1.
Claim 46:
Yee does not explicitly teach, but Guim Bernat, however, does teach:
wherein the node is a first node, the resource utilization is a first resource utilization, and one or more of the at least one programmable circuit is to: determine the first resource utilization of the first node; and reduce the first resource utilization of the first node by at least one of reducing at least one subsequent data element that is to be ingested by the first node or rerouting processing of the at least one subsequent data element from the first node to a second node, the second node having a second resource utilization that is less than the first resource utilization (see at least Guim Bernat, paragraph 0073 (“In the example of FIG. 2, when power and/or other ones of the resource(s) 210 meet and/or exceed a first threshold level of resources to allocate to orchestration, the orchestrator 202 can offload orchestration tasks to another computer to obtain coarse-grained orchestration results. In this case, the orchestrator 202 can receive, from the other computer, scheduling actions that describe what operating conditions services and/or workloads assigned to the edge platform 200 might require in the future and/or near future. The coarse-grained orchestration results provide directions to the orchestrator 202 that reduce the computational burden associated with processing orchestration tasks at the orchestrator 202 while allowing the orchestrator 202 to make more fine-grained orchestration decisions at the edge platform 200.”)).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Guim Bernat’s method of offloading tasks to another computer with Yee’s method of purging data. One of ordinary skill in the art would have been motivated to incorporate this feature for the purpose of optimizing the use of resources in order to increase efficiency and improve energy efficiency.
Claim 48:
Yee does not explicitly teach, but Guim Bernat, however, does teach:
determine an estimated value of environmental impact associated with an operation to be performed at the node; and based on the estimated value satisfying a third threshold, cause the node to perform the operation (see at least Guim Bernat, Figure 9, item 906 (“TEMPERATURE OF EDGE PLATFORM EXCEEDS THRESHOLD?”); item 908 (“INCREASE ACTIVE COOLING”); paragraph 0125 (“While the examples disclosed herein have been described in the context of power metrics (an important metric in green energy-based edge platforms), ….”); paragraph 0162 (“At block 904, the orchestrator interface 302, and/or, more generally, the orchestrator 202 monitors the temperature of the edge platform and/or the orchestration components. At block 906, the thermal controller 308, and/or, more generally, the orchestrator 202, determines whether the temperature of the edge platform and/or the orchestration components exceeds a threshold temperature. For example, the threshold temperature can be a temperature that has been classified as “extreme” by the edge service provider, an OEM, a silicon vendor, etc.”); paragraph 0163 (“Responsive to the thermal controller 308 determining that the temperature of the edge platform and/or the orchestration components exceeds a threshold temperature (block 906: YES), the machine readable instructions 900 proceed to block 908. At block 908, the thermal controller 308, and/or, more generally, the orchestrator 202, increases the active cooling at the edge platform. At block 910, the thermal controller 308, and/or, more generally, the orchestrator 202, reduces the computation conditions at the edge platform. For example, the thermal controller 308 can configure the orchestrator 202 and/or more generally, the edge platform 200 to operate at a lower clock speed (e.g., rate).”)).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Guim Bernat’s method of increasing active cooling when the temperature of the edge platform exceeds a threshold with Yee’s method of purging data. One of ordinary skill in the art would have been motivated to incorporate this feature for the purpose of improving energy efficiency.
Claim 53:
Claim 53 is rejected using the same rationale that was used for the rejection of claim 46.
Claim 55:
Claim 55 is rejected using the same rationale that was used for the rejection of claim 48.
Claim 60:
Claim 60 is rejected using the same rationale that was used for the rejection of claim 46.
Relevant Prior Art
The following references are relevant to Applicant’s invention:
Yamashita, U.S. Patent Application Publication Number 2019/0370233 A1. This reference teaches data cleansing according to a data quality requirement.
“Artificial Intelligence, Machine Learning, and Data Storage: The Paradox,” Arcserve, https://www.arcserve.com/blog/artificial-intelligence-machine-learning-and-data-storage-paradox (May 16, 2018). This reference teaches using machine learning to solve data storage problems.
Email Communications
Per MPEP 502.03, Applicant may authorize email communications by filing Form PTO/SB/439, available at https://www.uspto.gov/sites/default/files/documents/sb0439.pdf, via the USPTO patent electronic filing system.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ELIZABETH H ROSEN whose telephone number is (571) 270-1850 and email address is elizabeth.rosen@uspto.gov. The examiner can normally be reached Monday - Friday, 10 AM ET - 7 PM ET.
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/ELIZABETH H ROSEN/Primary Examiner, 3693
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