DETAILED ACTION
This Office Action is with regard to the most recent papers filed 6/18/2026.
Response to Arguments
On pages 9-11, Applicant argues the newly amended subject matter of “the one or more radio network nodes comprising renewable and/or non-renewable energy sources.” However, this limitation, as presented in the instant claim, is inherent, as no functionality is realized based on the type of energy source. Energy sources would all be either renewable or non-renewable, where the language would only require one of the two options to be taught as a whole, thus covering all types of energy sources. Further, it is noted that generally the use of renewable energy sources and non-renewable energy sources, in itself, would have no real impact on the functionality within the instant claim, as these could be provided by a power supplier with the components of the instant claim having no knowledge of the type of source. For such detail to have an impact on the functionality of the claimed invention in a substantial way, the components of the instant claims would need to have a knowledge of whether the energy source is renewable or non-renewable, then perform a functionality based on this (such as factoring this into the energy score.).
On page 11, Applicant requests evidentiary support of the findings of Official Notice. In accordance with MPEP 2144.03 C, such evidentiary support is only needed when the finding is adequately traversed, which would include stating why the noticed fact is not well-known. However, if Applicant provides a good faith statement that the noticed fact (e.g. “that the use of machine learning model, and the training of such machine learning model based on inputs and expected outputs for the inputs was well-known to one of ordinary skill in the art”) is not believed by Applicant to have been well-known to one of ordinary skill in the art at the time of filing, such evidentiary support would be provided. It is noted that such a statement should take into account the level of detail provided by the instant specification with regard to such subject matter, as the disclosure would need to provide a level of disclosure to enable a person of ordinary skill in the art who is not aware of the noticed fact.
Accordingly, the instant claims stand rejected for the reasons provided below.
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) 1-35 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 2017/0280435 (Egner) in view of US 2022/0174589 (Bellamkonda).
With regard to claim 1, Egner discloses a method for allocating computing tasks to one or more radio network nodes in a communication network, the method comprising:
obtaining node performance data indicative of operational performance of respective radio network node of the one or more radio network nodes (Egner: Paragraph [0023]);
based on the obtained node performance data, generating a node energy index for respective node of the one or more radio network nodes, the node energy index being indicative of energy efficiency of the respective node of one or more radio network nodes (Egner: Paragraph [0023]. An energy score is determined based on information relating to power consumption of each of the nodes.);
initiating allocation of a computing task to a radio network node of the one or more radio network nodes, wherein the allocation is based on the generated node energy index.
performance data indicative of operational performance (Egner: Figure 3, 58 and Paragraphs [0023] to [0024]. The score, such as the energy score, can be used to allocate nodes for inclusion in a transmission path, where the “computing task” would at least be being an intermediate node in the path.), the one or more radio network nodes comprising renewable and/or non-renewable energy sources (This limitation is inherent with any electronic device, as energy is required for such devices to function, so an energy source would be required. Further, “renewable and/or non-renewable” would cover every possible type of energy source, where the instant claim fails to recite that both are present or provide any functionality that is realized based on the type of energy source. Further, “energy efficiency,” as recited in conjunction with the energy index, does not need to take into account the type of energy source, but can merely refer to how the energy that is available is used.).
Egner fails to disclose, but Bellamkonda teaches that the node energy index is in a given time window (Bellamkonda: Paragraph [0021]. Energy consumption scores can be determined over time periods (windows).).
Accordingly, it would have been obvious to one of ordinary skill in the art at the time of filing to have the node energy index be determined in a given time window to allow for the period evaluation of the energy consumption information of Egner, thus enabling period evaluation and optimization of the network paths, ensuring that any changes to the nodes can be accounted for in subsequent time periods.
With regard to claim 2, Egner teaches wherein the node performance data comprises energy source data indicative of characteristics of energy source used to power the respective radio network node (Egner: Paragraph [0023]).
With regard to claim 3, Egner teaches wherein the energy source data comprises a battery and the characteristics comprise a battery capacity (Egner: Paragraph [0023]).
With regard to claim 4, Egner fails to teach, but Bellamkonda teaches wherein the node performance data comprises network traffic data associated with the respective radio network node (Bellamkonda: Paragraph [0022]. A weighted energy score may be determined based on QoS metrics, such as throughput, latency, jitter, etc.). Accordingly, it would have been obvious to one of ordinary skill in the art at the time of filing to have the performance data include network traffic data to allow the QoS information of the nodes to be taken into account when determining the energy efficiency of a node, such that a low powered node that provides poor QoS would not be selected based solely on the power consumption, thus improving the operation of the network while still providing power efficiency considerations.
With regard to claim 5, Egner teaches wherein the node performance data comprises configuration data associated with the respective radio network node (Bellamkonda: Paragraph [0023]. The presence of external or battery power as well as the energy consumption information would both constitute “configuration data” in as much detail as provided in the instant claim.).
With regard to claim 6, Egner fails to disclose expressly, but does teach wherein generating the node energy index comprises calculating the node energy index according to a formula obtained from a third party (Egner: Page 1, Assignee and Paragraph [0004]. Dell is the assignee of the Egner, where Dell was a known vendor. Further, Egner provides users (e.g. a first/second party) and businesses (e.g. the other of the first/second party), where the vendor who provides the software and hardware, including any calculation logic, would be a third party (e.g. Dell).). Accordingly, it would have been obvious to one of ordinary skill in the art at the time of filing to have the calculating being according to a formula obtained from a third party (neither user or service provider) to allow a company that provides hardware and software to sell the product to other entities (such as the service provider), thus allowing such companies to focus on producing the software and hardware and not provide the service. As a note, the instant claim does not provide how the obtaining occurs, nor does the instant claim appear to have significant impact on the actual functionality of the claimed method.
With regard to claim 7, Egner fails to teach, but Bellamkonda teaches generating the node energy index comprises providing the node performance data as input to an estimating machine learning model which estimates the node energy index based on the input (Bellamkonda: Paragraph [0014]. Bellamkonda teaches the use of AI/ML techniques to model energy usage information was known, where when such is applied to the generation of the score of Egner, such data would be input in the AI model to generate the score.). Accordingly, it would have been obvious to one of ordinary skill in the art at the time of filing to provide the performance data to an estimating ML model to estimate the energy index to allow more information to be taken into account for the generation of the index, including historic information based on feedback/training (as was common for machine learning systems), thus providing a more accurate evaluation of the energy score, especially over time as additional feedback/training occurs.
With regard to claim 8, Egner fails to teach, but Bellamkonda teaches obtaining node computational capacity data of respective radio network node of the one or more radio network nodes, and wherein the allocation is further based on the obtained node computational capacity data (Bellamkonda: Paragraph [0016]. Different types of performance metrics can be taken into account to weight energy consumption information, including bandwidth, latency, jitter, etc., which are all types of data that would constitute “computational capacity data,” as it represents the node’s actual ability to properly process traffic using its computational resources.). Accordingly, it would have been obvious to one of ordinary skill in the art at the time of filing to obtain computational capacity data (data at least indicative of the computational capacity of the node, such as its ability to process traffic) to allow such information of the nodes to be taken into account when determining the energy efficiency of a node, such that a low powered node that provides poor service would not be selected based solely on the power consumption, thus improving the operation of the network while still providing power efficiency considerations.
With regard to claim 9, Egner teaches wherein the allocation of the computing task comprises maximising a utility function for the one or more radio network nodes, wherein the utility function comprises one or more of: the energy index, computing task, estimated energy consumption of the computing task and node computational capacity (Egner: Figure 3, 58 and Paragraphs [0023] to [0024]. The score, such as the energy score, can be used to allocate nodes for inclusion in a transmission path, which would at least maximize a utility function (energy efficiency) comprising an energy index and energy consumption.).
With regard to claim 10, Egner teaches obtaining an intent representing aggregate operational goal to be reached by the communication network, wherein the utility function is obtained based on the intent (Egner: Figure 3, 58 and Paragraphs [0023] to [0024]. The intent would be to transmit traffic in an energy efficient manner, where information of the traffic is obtained to trigger the determining of a path (which would be an aggregate operational goal, as it would require multiple nodes to reach the goal to transmit the traffic).).
With regard to claim 11, Egner fails to disclose, but Egner does teach wherein the utility function is obtained from a third party (Egner: Page 1, Assignee and Paragraph [0004]. Dell is the assignee of the Egner, where Dell was a known vendor. Further, Egner provides users (e.g. a first/second party) and businesses (e.g. the other of the first/second party), where the vendor who provides the software and hardware, including any utility functions, would be a third party (e.g. Dell).). Accordingly, it would have been obvious to one of ordinary skill in the art at the time of filing to have utility function obtained from a third party (neither user or service provider) to allow a company that provides hardware and software to sell the product to other entities (such as the service provider), thus allowing such companies to focus on producing the software and hardware and not provide the service. As a note, the instant claim does not provide how the obtaining occurs, nor does the instant claim appear to have significant impact on the actual functionality of the claimed method.
With regard to claim 12, Egner fails to teach, but knowledge possessed by one of ordinary skill in the art teaches wherein generating the node energy index comprises: generating a first node energy index based on the obtained node performance data; training a predictive machine learning model, wherein the training is based on the first node energy index and the associated time window; and
generating the node energy index using the trained predictive machine learning model, wherein the given time window for the node energy index is a future time window (More specifically, Official Notice is taken that the use of machine learning model, and the training of such machine learning model based on inputs and expected outputs for the inputs was well-known to one of ordinary skill in the art, where such trained machine learning model would be used for a future time window.). Accordingly, it would have been obvious to one of ordinary skill in the art to train and use a machine learning model to efficiently provide decisions based on such training in a manner that can continue to improve with additional feedback and training in accordance with known practice with regard to such machine learning models.
With regard to claim 13, Egner in view of Bellamkonda fail to teach, but knowledge possessed by one of ordinary skill in the art at the time of filing teaches allocating a computing task comprises allocating a model training task of a plurality of model training tasks in a distributed machine learning system (More specifically, Official Notice is taken that distributed machine learning systems with a plurality of model training tasks were well-known to one of ordinary skill in the art at the time of filing.). Accordingly, it would have been obvious to allocate a model training tasks to enable the training and use of machine learning models to realize the known benefits of such models in a scalable manner, where the distributed tasks would allow tasks to be added or removed as needed according to the current training loads.
With regard to claims 14-15, the instant claims are similar to claim 1, and are rejected for similar reasons (where claims 14-15 merely provide an apparatus for performing the functions, such as in claim 1).
With regard to claim 16, Egner discloses a communication network comprising:
a node configured to generate respective node energy index indicative of energy efficiency of the respective node, wherein the node energy index is generated based on node performance data indicative of operational performance of respective radio network node (Egner: Paragraph [0023]);
an allocating node (Egner: Abstract, mesh network manager) configured to obtain the node energy index, wherein the allocating node is further configured to initiate allocation of a computing task to a radio network node of the one or more radio network nodes, wherein the allocation is based on the obtained node energy index (Egner: Figure 3, 58 and Paragraphs [0023] to [0024]. The score, such as the energy score, can be used to allocate nodes for inclusion in a transmission path, where the “computing task” would at least be being an intermediate node in the path.) and
wherein, the allocating node is further configured to send to a central node an indication of the radio network node allocated with the computing task, wherein the central node is configured to receive from the allocating node the indication of the radio network node allocated with the computing task, wherein the central node is further configured to send the computing task for execution to the indicated radio network node (Egner: Figure 1. The mesh network manager connects to the other components via the Information Handling System 10).
Egner fails to disclose, but Bellamkonda teaches that the node energy index is in a given time window (Bellamkonda: Paragraph [0021]. Energy consumption scores can be determined over time periods (windows).).
Accordingly, it would have been obvious to one of ordinary skill in the art at the time of filing to have the node energy index be determined in a given time window to allow for the period evaluation of the energy consumption information of Egner, thus enabling period evaluation and optimization of the network paths, ensuring that any changes to the nodes can be accounted for in subsequent time periods.
Egner fails to teach, but knowledge possessed by one of ordinary skill in the art teaches that one or more radio network nodes (Egner: Abstract) is configured to generate the respective node energy index; (More specifically, Official Notice is taken that the generation of energy efficiency scores by a device for itself was well-known to one of ordinary skill in the art at the time of filing, where when such is calculated at the different location, it would be sent instead of the information sent by the node.). Accordingly, it would have been obvious to one of ordinary skill in the art at the time of filing to have the radio network nodes determine their own energy scores to reduce the burden of such calculating on the mesh network manager.
With regard to claims 17-35, the instant claims are similar to claims 1-16, and are rejected for similar reasons.
Conclusion
THIS ACTION IS MADE FINAL. 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.
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SCOTT B. CHRISTENSEN
Examiner
Art Unit 2444
/SCOTT B CHRISTENSEN/Primary Examiner, Art Unit 2444
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