DETAILED ACTION
This Office action is in response to Applicant's amendment and request for
reconsideration filed on January 25, 2026.
Claims 1-11, 13-23, and 25 are pending.
Response to Arguments
Applicant’s amendments overcome the previous rejections under 35 U.S.C. § 112 and §101.
Applicant’s remarks with respect to the previous rejection of claims 1-25 under 35 U.S.C. §103 have been considered but are moot in view of the new ground of rejection based on Applicant’s amended claims. Nevertheless, with respect to Applicant’s argument, see pp. 18, “…the model in Yang only outputs network actions to be performed. By performing the network action, the configuration of the network device or the UE, and a manner in which the network device communicates with the UE may be modified. That is, Yang only discloses that the model can output an action to be performed. However, Yang says nothing about ‘the timing to perform the action, the timing to release the action or a combination or order of the actions’, even if so, however, the claim is not limited to “the timing to perform the action, the timing to release the action or a combination or order of the actions”, but broadly only requires “…choosing a best combination and an order of OTH processes” (i.e., “…including at least one of: predicting a best point in time to start an 0TH action; estimating when to release an 0TH process and when to return to normal operation; and/or choosing a best combination and an order of OTH processes"), which Yang clearly teaches (see ¶0040, where based the output of the model, “the network device may select two or more network actions to perform and may determine an order for performing the two or more network actions based on their scores”).
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-6, 13-18 and 25 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Yang et al. (US 2021/0135963)(“Yang”).
As per claim 1, Yang teaches a method for mitigating an undesired environmental condition in a communication network radio, the method comprising:
determining, by a network node (i.e., network device 102), a solution to mitigate (e.g., “a first network action”) the undesired environmental condition using a machine-learning model (see ¶0034); and
mitigating, by the network node (i.e., network device 102), the undesired environmental condition based at least on the determined solution (i.e., “performing the first network action”, see ¶0044),
wherein the machine-learning model is trained based on a plurality of data sources (i.e., “thousands or millions of data items”, see ¶0034), wherein the plurality of data sources include data associated with at least one of over temperature handling, OTH, process parameters (see ¶0035, e.g., “historical data related to network actions taken to mitigate elevated temperatures of the UEs”), radio internal measurements (see ¶0015 and ¶0026, e.g., a temperature value from one or more sensors), … ;
wherein the determined solution is a model-based OTH (i.e., “a model for use in thermal mitigation”, see ¶0035) including at least one of: … choosing a best combination and an order of OTH processes (see ¶0040, where based the output of the model, “the network device may select two or more network actions to perform and may determine an order for performing the two or more network actions based on their scores”); and
and wherein the mitigating the undesired environmental condition comprises … optimizing scheduling (see ¶0055, e.g., “reducing an amount of time slots being used to monitor a PDCCH”).
As per claim 2, Yang further teaches, wherein the plurality of data sources include data associated with at least one of: …
radio internal measurements including at least one of a temperature reading from a temperature sensor (see ¶0015 and ¶0026, e.g., a temperature value from one or more sensors).
As per claim 3, Yang further teaches wherein the selected machine learning model includes at least supervised learning, SL, models (i.e., supervised training, see ¶0039).
As per clam 4, Yang further teaches wherein the SL models include at least:
a model training feature, the model training feature including a supervised machine learning process for training and validation (i.e., supervised training, see ¶0039) based at least on one of a feature engineering (see ¶0034, i.e., “analyzing thousands or millions of data items”, which implies feature engineering for selecting the data items for analysis) and a feature generation (e.g., preprocessing historical data to remove non-ASCII characters, white spaces, confidential data, and/or the like, see ¶0036), the feature engineering and the feature generation being based on data from the data sources (i.e., data items, see ¶0034 and ¶0036, which are impliedly from data sources, e.g., UEs and/or historical databases/data sources); and
a deployment feature, the deployment feature including at least a predictive OTH model based at least on one of a feature engineering and a feature generation (i.e., perform temperature mitigation predictions based on the ML model, see ¶0038 and ¶0040).
As per claim 5, Yang further teaches wherein the predictive OTH model is further based on the supervised machine learning process for training and validation (i.e., supervised training, see ¶0039).
As per claim 6, Yang further teaches wherein the determined solution to mitigate the undesired environmental condition is determined by the predictive OTH model (i.e., perform temperature mitigation predictions based on the ML model, see ¶0038 and ¶0040), the determined solution being a model-based OTH including at least one of: …
estimating optimum parameters for at least a selected OTH process (see ¶0040, i.e., output data, read as optimum parameters, relating to one or more network services that are to be performed, alternatively see ¶0044, where the outputted actions may include modification to configurations (i.e., optimum parameters) at the network device 102).
Claims 13-18 and 25 are rejected under the same rationale as claims 1-6 since they recite substantially identical subject matter. Any differences between the claims do not result in patentably distinct claims and all of the limitations are taught by the above cited art.
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 of this title, 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 7-9, 11, 19-21 and 23 are rejected under 35 U.S.C. 103 as being unpatentable over Yang, in further view of Devulapalli et al. (US 2019/0042979)(“Devulapalli”).
As per claim 7, Yang does not expressly teach wherein the selected machine learning model includes at least reinforcement learning, RL, models.
Nevertheless, reinforcement machine leaning models were well known in the art, prior to the earliest effective filing date of the claimed invention. For example, Devulapalli teaches the use of reinforced learning in the context of thermal mitigation (see abstract and ¶0016).
It would have been obvious to a person having ordinary skill in the art, prior to the earliest effective filing date of the claimed invention, to modify the teachings of Yang with the use of reinforced learning, RL, models. The obvious motivation for doing so would have been to provide an improved or optimal cooling solution that requires little or no user intervention (see for example, Devulapalli, ¶0028).
As per claim 8, Yang further teaches wherein determining the solution to mitigate the undesired environmental condition is further based on a current state obtained from the data from at least one of the plurality of data sources (see for example ¶0052, where the data sources/inputs include, e.g., “a battery state of the first UE 104” and/or see ¶0052, i.e., “the first UE 104 is in a high temperature state 144”).
As per claim 9, Yang further anticipates a positive resulting action (e.g., “resulting in a temperature decrease of 8 degrees Celsius for the UE”, see ¶0016) for thermal mitigating associated with one of an internal and an external environment of the communication network radio (see ¶0055, e.g., “modify a configuration of the first UE and/or a manner in which the first UE 104 communicates with the network device”).
As per claim 9, Yang does not however teach wherein determining the solution to mitigate the undesired environmental condition is further based on a reward associated with one of an internal and an external environment of the communication network radio.
Nevertheless, the use of reinforced learning, which assigns a reward to a past or historic positive action, was well known in the art prior to the earliest effective filing date of the claimed invention (see for example, Devulapalli, ¶0032, i.e., “based on rewards and/or penalty information”).
The same motivation for combining Yang and Devulapalli for utilizing reinforced learning, RL, models for thermal mitigation in claim 7, applies equally well to claim 9.
As per claim 11, Yang further teaches wherein determining the solution to mitigate the undesired environmental condition includes determining an action including at least an OTH process (see ¶0044, wherein the thermal mitigation or OTH process/action include configuration modifications) and at least one parameter associated with the OTH process (i.e., configuration parameters/data, see ¶0044).
Claims 19-21 and 23 are rejected under the same rationale as claims 7-9 and 11 since they recite substantially identical subject matter. Any differences between the claims do not result in patentably distinct claims and all of the limitations are taught by the above cited art.
Claims 10 and 22 are rejected under 35 U.S.C. 103 as being unpatentable over Yang and Devulapalli, in further view of CHI et al. (US 2020/0366557)(“CHI”).
As per claims 10 and 22, Yang further teaches an indicating a temperature of the communication network radio is reduced (e.g., “resulting in a temperature decrease of 8 degrees Celsius for the UE”, see ¶0016).
Yang, however, does not expressly teach assigning a positive reward to the action.
Nevertheless, the use of reinforced learning, which assigns a reward to a past or historic positive action, was well known in the art prior to the earliest effective filing date of the claimed invention (see for example, Devulapalli, ¶0032, i.e., “based on rewards and/or penalty information”).
The same motivation for combining Yang and Devulapalli for utilizing reinforced learning, RL, models for thermal mitigation in claim 7, applies equally well to claim 10.
In addition, the combination of Yang, Li and Devulapalli does not expressly teach the further goal of maintaining KPI.
Nevertheless, in the same art of wireless network management, CHI teaches performing parameters adjustments by using a reinforcement learning method while maintaining KPI (see abstract, ¶0007, ¶0014, ¶0019, ¶0023, and ¶0027).
It would have been obvious to a person having ordinary skill in the art, prior to the earliest effective filing date of the claimed invention, based on the teachings of CHI, to modify the reward of the reinforcement learning model taught by the combination of Yang and Devulapalli, to further take into consideration an acceptable KPI. The obvious motivation for doing so would have been to ensure that KPI deterioration does not occur when performing the temperature mitigation action in Yang (see for example, CHI ¶0019).
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 BRENDAN HIGA whose telephone number is (571)272-5823. The examiner can normally be reached Monday - Friday 8:30 AM - 5:00 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, James Hwang can be reached at (571) 272-4036. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/BRENDAN Y HIGA/Primary Examiner, Art Unit 2441
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