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 .
Claims 1-9, 11-18 are pending. Claim 10 is cancelled.
Amendments of 02/20/2026 are entered.
Response to Arguments
Applicant's arguments filed with the RCE of 02/20/2026 have been fully considered but they are moot as being directed to the new limitations to the claims and are addressed by the new ground of rejections below.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-9, 11-18 is/are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Independent claims 1 and 11 are amended to recite: “herein the evaluation identifies services of high importance or strict requirements associated with services provided by the distributed node, and wherein the one or more exploration parameters are determined to achieve a reduced exploration in the presence of the identified services of high importance or strict requirements”
The claims are indefinite because of subjective terminologies, namely “high importance” and “strict requirements”. A claim term that requires the exercise of subjective judgment without restriction may render the claim indefinite. In re Musgrave, 431 F.2d 882, 893, 167 USPQ 280, 289 (CCPA 1970). The claims fail to establish a standard to judge the concept of “high importance” vs. lower levels of importance, and similarly, no criteria to define a requirement as being “strict”. Nor does the Specification provide any suggestion or guidance.
Moreover, the recitation of “the one or more exploration parameters are determined to achieve a reduced exploration in the presence of the identified services of high importance or strict requirements” merely states a desired goal (i.e. a reduced exploration) without explicitly explaining or elaborating how such “reduced exploration” are achieved, leaving the guess work persons of ordinary skill the art as to the how and why. Numbers (parameters) are just numbers. There is no evidence or guidance on how such parameters achieving a reduced exploration. Thus the scope of the limitation is unclear.
For the purpose of examination, the examiner will perform best attempt at BRI until further clarifications.
The respective dependent claims of claims 1 and 11 fall together with the base claims as they fail to remedy the shortcoming above.
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. 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) 1-9, 11-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ghadimi et al. (WO 2020/174262) – 09/2020 in view of Vivanco et al. (US 2022/0060956).
As to claim 1:
Ghadimi discloses:
A method performed by a central node for controlling an exploration strategy associated to Reinforcement Learning (RL) (See at least Fig. 13, a control node 1310 that control configurations and policies) in one or more RL modules in a distributed node in a Radio Access Network (RAN) (0017, 0073 RL actor read as the RL modules implementing PL policies. RAN node (i.e. gNB) running the RL algorithm models, “actor may reside in an eNB/gNB that controls one or more co-located or non-co-located cell”), the method comprising:
evaluating a cost of actions performed for explorations in the one or more RL modules, and a performance of the one or more RL modules, (¶0072, 0057, obtaining interaction information feedback from actor(s), such as cost value (of tasks according to exploration strategy), and performance (reward, measured state of the network, KPI, performance measurement etc.), 0060-0064, evaluate the obtained feedback in view of one or more criteria to validate/training the policies to be used by actor)
based on the evaluation, determining one or more exploration parameters associated to the exploration strategy, (¶0057-0064, , evaluate the obtained feedback in view of one or more criteria to validate/training the policies to be used by actor, wherein policies are updated/retrained . See Table 1. 0099, policies are defined by parameters)
and, controlling the exploration strategy by configuring the one or more RL modules with the determined one or more exploration parameters to update its exploration strategy of the one or more RL modules, enforcing the respective one or more RL modules to act according to the updated exploration strategy to produce data samples for the one or more RL modules in the distributed node. (¶0067-0072, 0088, the updated/validated polices are transmitted to the actor(s) in RAN node(s) and configure them to modify their exploration strategy, i.e. changing RRM actions and/or their parameters. The adjustments are thus implemented on the actors, causing adjusted behaviors and measurements and record the interactions suitable for further rounds of feedback/update as necessary).
Regarding:
wherein the evaluation identifies services of high importance or strict requirements associated with services provided by the distributed node, and wherein the one or more exploration parameters are determined to achieve a reduced exploration in the presence of the identified services of high importance or strict requirement.
Ghadimi discloses in ¶0097, 0098, 0057-0064, determining updated policies having new parameters for network performance to optimize reward/cost, and per ¶00101, minimizing loss, logistic loss. Ghadimi describes providing network performance in general sense but are not specifically specifying services services of high importance or strict requirements associated with services provided by the distributed node.
However, it is implicit implied that parameters and KPI can’t not be deprived in vacuum but has to be associated with some services targeted for evaluation.
Vivanco, in a related field of network optimization learning via algorithm and collected performance indicators to update new network parameters, in particular ¶0058, 0039 high priority services like voice and video, or technology services such as LTE and 5G are identified, and evaluation of the services are strictly adhered to requirements (i.e. meeting threshold requirements per ¶0049)
It would have been obvious to one of ordinary skill in the art before the effective filing time of the invention that Ghadimi’s system performing cost/performance evaluation involves specifying services of high importance or strict requirements associated with services provided by the distributed node. Both Ghadami and Vivanco strive to optimize performance via analysis of network parameters. Selective service-specific analysis advantageously allow Ghadimi to perform optimization to the most relevant set of services, thus reducing workload on services while meeting evolving customer requirements (¶0082 of Vivanco).
As to claim 11:
A central node (See at least Fig. 13, a control node 1310 that control configurations and policies) for controlling an exploration strategy associated to Reinforcement Learning, RL, in one or more RL modules in a distributed node in a Radio Access Network, RAN, wherein the central node comprises a processor; and a memory storing instructions that, when executed by the processor (¶0017, 0073 RL actor read as the RL modules implementing PL policies. RAN node (i.e. gNB) running the RL algorithm models, “actor may reside in an eNB/gNB that controls one or more co-located or non-co-located cell”. ¶00111, processor/memory) wherein the central node is configured to: evaluate a cost of actions performed for explorations in the one or more RL modules, and a performance of the one or more RL modules, (¶0072, 0057, obtaining interaction information feedback from actor(s), such as cost value (of tasks according to exploration strategy), and performance (reward, measured state of the network, KPI, performance measurement etc.), 0060-0064, evaluate the obtained feedback in view of one or more criteria to validate/training the policies to be used by actor)
based on the evaluation, determine one or more exploration parameters associated to the exploration strategy, (¶0057-0064, , evaluate the obtained feedback in view of one or more criteria to validate/training the policies to be used by actor, wherein policies are updated/retrained . See Table 1. 0099, policies are defined by parameters)
and, control the exploration strategy by configuring the one or more RL modules with the determined one or more exploration parameters to update its exploration strategy of the one or more RL modules, to enforce the respective one or more RL modules to act according to the updated exploration strategy to produce data samples for the one or more RL modules in the distributed node. (¶0067-0072, 0088, the updated/validated polices are transmitted to the actor(s) in RAN node(s) and configure them to modify their exploration strategy, i.e. changing RRM actions and/or their parameters. The adjustments are thus implemented on the actors, causing adjusted behaviors and measurements and record the interactions suitable for further rounds of feedback/update as necessary)
Ghadimi discloses in ¶0097, 0098, 0057-0064, determining updated policies having new parameters for network performance to optimize reward/cost, and per ¶00101, minimizing loss, logistic loss. Ghadimi describes providing network performance in general sense but are not specifically specifying services services of high importance or strict requirements associated with services provided by the distributed node.
However, it is implicit implied that parameters and KPI can’t not be deprived in vacuum but has to be associated with some services targeted for evaluation.
Vivanco, in a related field of network optimization learning via algorithm and collected performance indicators to update new network parameters, in particular ¶0058, 0039 high priority services like voice and video, or technology services such as LTE and 5G are identified, and evaluation of the services are strictly adhered to requirements (i.e. meeting threshold requirements per ¶0049)
It would have been obvious to one of ordinary skill in the art before the effective filing time of the invention that Ghadimi’s system performing cost/performance evaluation involves specifying services of high importance or strict requirements associated with services provided by the distributed node. Both Ghadami and Vivanco strive to optimize performance via analysis of network parameters. Selective service-specific analysis advantageously allow Ghadimi to perform optimization to the most relevant set of services, thus reducing workload on services while meeting evolving customer requirements (¶0082 of Vivanco).
As to claims 2 and 12:
Ghadimi in view of Vivanco disclose all limitations of claim 1/11, further being for controlling a training strategy associated to the RL in the one or more RL modules in the distributed node, the method further comprises: based on the evaluation, determining one or more training parameters, which one or more training parameters are associated to the training strategy, configuring the one or more RL modules with the determined one or more training parameters to update training strategy of the one or more RL modules, enforcing the respective one or more RL modules in the distributed node to act according to the updated training strategy to use the produced data samples to update an RL policy of one or more the RL modules. (Ghadimi, ¶ 0057-0064, 0067-0072, 0088, the feedback are used in a training strategy. The training strategy include progressive training for example, wherein policies are updated via being progressively trained . See Table 1. 0099, policies are defined by parameters. The updated polices configure the actors to modify their exploration strategy, i.e. changing RRM actions and/or their parameters. The adjustments are thus implemented on the actors, causing adjusted behaviors and measurements and record the interactions suitable for further rounds of feedback/update as necessary)
As to claims 3 and 13:
Ghadimi in view of Vivanco disclose all limitations of claim 1/11, wherein the one or more exploration parameters are determined for a specific cell or group of cells controlled by the distributed node (Ghadimi, See Fig. 13, 0073, a specific eNB/gNB the actor is embedded in and for. carrying out the exploration strategy)
As to claims 4 and 14:
Ghadimi in view of Vivanco disclose all limitations of claim 1/11, wherein the one or more exploration parameters are determined further based on any one or more out of: a performance of the RAN. (Ghadimi, ¶0067-0069, based on performance of network, KPIs)
As to claims 5 and 15:
Ghadimi in view of Vivanco disclose all limitations of claim 1/11, wherein the one or more exploration parameters comprises any one or more out of: an index indicating a type of the exploration strategy, and a value of the respective one or more exploration parameters. (Ghadimi, ¶0067, “e.g., RSRP, SINR, RSRQ, TA, resources assigned to a UE, throughput, spectral efficiency, etc.) either raw data or in aggregate form (such as averages, median, sum, max, min, variance, standard deviation, etc.)”)
As to claims 6 and 16:
Ghadimi in view of Vivanco disclose all limitations of claim 1/12, wherein the one or more training parameters are determined further based on any one or more out of: importance of services provided by the distributed node, requirements of services provided by the distributed node, a search policy at the central node, observed performance of the distributed node for a variety of Key Performance Indicators, KPIs. (Ghadimi, See ¶0072, “an actor records its interactions with the environment (i.e. , information such as state, task, reward, probability of chosen task according to exploration strategy, etc.) in some suitable format. This information may be collected and/or calculated at different moments and may be used later for training and validation purposes or provided as feedback to the target learner and/or source learner ”)
As to claims 7 and 17:
Ghadimi in view of Vivanco disclose all limitations of claim 2/12, wherein the one or more training parameters comprises any one or more out of: a discount factor for calculating the value of an action, a type of gradient and the corresponding one or more training parameters, and an index indicating a type of learning scheme. (Ghadimi, ¶0099, “gradient based methods may be used for updating the adaptive policy parameters. Training a base policy follows a similar procedure except that outputs from a base network (e.g., Q-value of the base network) are used to calculate the loss function and the gradient function of the base policy. Moreover, the gradient is calculated with respect to the base policy parameters.”)
As to claims 8 and 18:
Ghadimi in view of Vivanco disclose all limitations of claim 2/12, wherein any one or more out of: configuring one or more RL modules with the determined one or more exploration parameters is performed by sending the one or more exploration parameters in a first control message, and configuring one or more RL modules with the one or more training parameters, is performed by sending the one or more training parameters in a second control message. (Ghadimi, ¶0067-0072, 0088, the updated/validated polices are transmitted to the actor(s) in RAN node(s) and configure them to modify their exploration strategy, i.e. changing RRM actions and/or their parameters. The adjustments are thus implemented on the actors, causing adjusted behaviors and measurements and record the interactions suitable for further rounds of feedback/update as necessary. See Fig. 13 signaling of Actor Configuring and policy updates. Configuration/polices are control information carrying by signaling between network-side nodes to configure each other, i.e. control messaging)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 2021/0376895 - An example method generally includes receiving, from a network entity, a channel state information (CSI) prediction model for quantized CSI, calculating CSI based on downlink reference signal measurements, generating a quantized CSI difference value based a quantization of a difference between the calculated CSI and CSI predicted based on a CSI prediction model, and reporting, to the network entity, the calculated CSI and the quantized CSI difference value.
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/QUAN M HUA/ Primary Examiner, Art Unit 2645
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