DETAILED ACTION
This Office action is in response to the RCE filed 16 April 2026. Claims 1, 2, 5-9, 11, 12, and 15-25 are pending in this application.
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 .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 16 April 2026 has been entered.
Claim Rejections - 35 USC § 103
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claim(s) 1, 2, 7, 11, 12, 17, and 20-25 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kwan (US 2017/0026888) in view of McMahan et al. (US 2020/0004801) and Luo et al. (US 2015/0016434).
For Claims 1, 11, and 20, Kwan teaches a method performed by a device of a base station (BS); a device of a base station (BS), comprising communication circuitry and a processor; and an electronic device comprising memory configured to store instructions (see paragraph 162); the method comprising:
obtaining local data and a state model for local learning to predict a state of the device (see paragraph 43: manage system in base station; abstract, paragraph 11: model, current data for prediction);
performing the local learning to update the state model using the local data based on a machine learning algorithm (see paragraph 16: continuously update model with current data; paragraphs 78, 97: machine learning);
obtaining information of at least one parameter of the updated state model, based on the local learning (see paragraphs 51-52: training, weighting); and
transmitting, to a remote device through communication circuitry of the device, first state related information including the information of the at least one parameter of the updated state model (see paragraphs 138, 143, 147: exchange of measurement data among BSs).
Though the system of Kwan does use weighting, Kwan as applied above is not explicit as to, but McMahan teaches obtaining weight information of at least one parameter of the updated state model, based on the local learning, wherein the weight information indicates a degree to which the at least one parameter is used for a determination of the state of the device in the updated state model (see paragraph 53, claims 21 and 33); and
transmitting, to a remote device through communication circuitry of the device, first state related information including the weight information of the at least one parameter of the updated state model (see paragraphs 15, 53, 56-57, 63).
Thus it would have been obvious to one of ordinary skill in the art at the time the application was filed to employ the weight information as in McMahan when implementing the system of Kwan. The motivation would be to ensure the proportional consideration of data when updating state models.
Though Kwan does teach exchanging weighted values with a remote device and aspects of the state of the BS including loading, resource utilization, and power (see paragraphs 44, 47), the references as applied above are not explicit as to, but Luo teaches the method wherein the state of the device is associated with at least one of a power consumption of the device, a resource usage of the device related to frequency or time resources, an abnormal operation of the device for detecting at least one error occurring in the device, or a network throughput performance of at least one terminal (see paragraphs 44-45: load information, wireless resource usage, weighting; paragraphs 44, 103: transmitting information).
Thus it would have been obvious to one of ordinary skill in the art at the time the application was filed to provide parameters as in Luo when implementing the method of Kwan. The motivation would be to ensure that nodes acquire information needed for network optimization.
For Claims 2 and 12, Kwan teaches the method, comprising: receiving, from the remote device through the communication circuitry of the device, second state related information for predicting the state of the device (see paragraphs 138, 143, 147: exchange of information among BSs for data analysis and prediction),
wherein the local learning to update the state model is performed based on the second state related information (see paragraphs 16, 51-52: training model; paragraphs 138, 143, 147).
For Claims 7 and 17, Kwan and McMahan as applied above are not explicit as to, but Luo teaches the method, wherein the transmitting of the first state related information comprises:
determining whether the remote device belongs to a shared group for the device (see paragraphs 43, 98-102); and
in case that the remote device belongs to the shared group, transmitting the first state related information to the remote device (see paragraphs 43-45, 98-103).
Thus it would have been obvious to one of ordinary skill in the art at the time the application was filed to employ grouping as in Luo when implementing the method of Kwan and Luo. The motivation would be to ensure the appropriate information is directed to devices in a more efficient manner.
For Claim 21, Kwan as applied above is not explicit as to, but McMahan teaches the method,
wherein the remote device is connected to a plurality of network devices including the device (see abstract: user devices on a network), and
wherein the first state related information transmitted to the remote device is used for the determination of the state of the device without using the local data obtained at the device in the updated state model (see paragraphs 15, 56-57, 63: updated information, weighting provided without data).
Thus it would have bene obvious to one of ordinary skill in the art at the time the application was field for devices to exchange information as in McMahan when communicating as in Kwan. The motivation would be to reduce overhead and conserve network resources.
For Claims 22 and 24, Kwan as applied above is not explicit as to, but McMahan teaches the method, wherein the weight information of the at least one parameter is transmitted from the device to the remote device without transmitting the local data used for the local learning (see paragraphs 15, 56-57, 63: updated information, weighting provided without data).
Thus it would have bene obvious to one of ordinary skill in the art at the time the application was filed for devices to exchange information as in McMahan when communicating as in Kwan. The motivation would be to reduce overhead and conserve network resources.
For Claims 23 and 25, Kwan as applied above is not explicit as to, but McMahan teaches the method, wherein the weight information and the at least one parameter are used to generate an updated state model in the remote device (see paragraph 53, Claims 21 and 33).
Thus it would have been obvious to one of ordinary skill in the art at the time the application was filed to employ the weight information as in McMahan when implementing the system of Kwan. The motivation would be to ensure the proportional consideration of data when updating state models.
Claim(s) 5 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kwan (US 2017/0026888), McMahan et al. (US 2020/0004801), and Luo et al. (US 2015/0016434) as applied to claims 1 and 11 above, and further in view of Kakadia et al. (US 2014/0241159).
For Claims 5 and 15, the references as applied above are not explicit as to, but Kakadia teaches the method, wherein the number of one or more parameters included in the at least one parameter of the updated state model is smaller than a total number of parameters included in the state model (see paragraphs 17, 21, 51, and 56).
Thus it would have been obvious to one of ordinary skill in the art at the time the application was filed to use some number of the parameters as in Kakadia in the optimization process of Kwan and Luo. The motivation would be further optimizing the optimization process by using an appropriate combination of the parameters.
Claim(s) 8 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kwan (US 2017/0026888), McMahan et al. (US 2020/0004801), and Luo et al. (US 2015/0016434) as applied to claims 1 and 11 above, and further in view of Watanabe (US 2012/0252440).
For Claims 8 and 18, the references as applied above are not explicit as to, but Watanabe teaches the method, wherein the at least one error comprises at least one of a communication error, a memory error, a fan error, a memory full error, a central processing unit (CPU) full error, or a digital signal processing (DSP) error (see paragraphs 93, 111: failures).
Thus it would have been obvious to one of ordinary skill in the art at the time the application was filed to consider errors as in Watanabe when modeling performance as in Kwan and Luo. The motivation would be to provide a model considering aspects of reliability from a user device perspective.
Claim(s) 9 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kwan (US 2017/0026888), McMahan et al. (US 2020/0004801), and Luo et al. (US 2015/0016434) as applied to claim 1 above, and further in view of Yang (US 2018/0132196).
For Claims 9 and 19, the references as applied above are not explicit as to, but Yang teaches the method, wherein the state model before the local learning in the device is received from the remote device (see paragraphs 211, 160-161), and
wherein the local data comprises sensor data obtained by a sensor of the device (see paragraphs 211, 160-161).
Thus it would have been obvious to one of ordinary skill in the art at the time the application was filed to provide models as in Yang when enabling the devices to train and update models as in Kwan, McMahan, and Luo. The motivation would be to ensure that the devices are operating with the same models when they analyze the collected data.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
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Claims 1, 2, 5, 6, 8, 11, 12, 15, 16, 18, and 20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-3, 6-12, 15, and 17-19 of U.S. Patent No. 11758415. Although the claims at issue are not identical, they are not patentably distinct from each other because each teaches matter found in the other.
For Claim 1, Claim 1 of 11758415 teaches a method performed by a device in a wireless communication system, the method comprising:
obtaining local data and a state model for local learning to predict a state of the device;
performing the local learning to update the state model using the local data based on a machine learning algorithm;
obtaining weight information of at least one parameter of the updated state model, based on the local learning; and
transmitting, to a remote device through communication circuitry of the device, first state related information including the weight information of the at least one parameter of the updated state model,
wherein the state of the device is associated with at least one of a power consumption of the device, a resource usage of the device related to frequency or time resources, an abnormal operation of the device for detecting at least one error occurring in the device, or a network throughput performance of at least one terminal.
Claim 1 of 11758415 is not explicit as to, but Claim 9 of 11758415 teaches wherein the weight information indicates a degree to which the at least one parameter is used for a determination of the state of the device in the updated state model. Thus it would have been obvious to one of ordinary skill in the art at the time the application was filed to employ the weight as in claim 9 when implementing claim 1. The motivation would be to improve the accuracy of the state model.
For Claim 2, Claims 2 and 3 of 11758415 teach the method of claim 1, comprising:
receiving, from the remote device through the communication circuitry of the device, second state related information for predicting the state of the device,
wherein the local learning to update the state model is performed based on the second state related information.
For Claim 5, Claim 8 of 11758415 teaches the method of claim 1, wherein the number of one or more parameters included in the at least one parameter of the updated state model is smaller than a total number of parameters included in the state model.
For Claim 6, Claims 1 and 7 of 11758415 teach the method of claim 1, wherein the at least one parameter and the weight information are used to determine the state of the device based on the updated state model in the remote device.
For Claim 8, Claim 6 of 11758415 teach the method of claim 1, wherein the at least one error comprises at least one of a communication error, a memory error, a fan error, a memory full error, a central processing unit (CPU) full error, or a digital signal processing (DSP) error.
For Claim 11, Claim 10 of 11758415 teaches a device in a wireless communication system, comprising:
communication circuitry; and
a processor configured to:
obtain local data and a state model for local learning to predict a state of the device,
perform the local learning to update the state model using the local data based on a machine learning algorithm,
obtain weight information of at least one parameter of the updated state model based on the local learning, and
control the communication circuitry to transmit, to a remote device, first state related information including the weight information of the at least one parameter of the updated state model,
wherein the state of the device is associated with at least one of a power consumption of the device, a resource usage of the device related to frequency or time resources, an abnormal operation of the device for detecting at least one error occurring in the device, or a network throughput performance of at least one terminal.
Claim 10 of 11758415 is not explicit as to, but Claim 18 of 11758415 teaches wherein the weight information indicates a degree to which the at least one parameter is used for a determination of the state of the device in the updated state model. Thus it would have been obvious to one of ordinary skill in the art at the time the application was filed to employ the weight as in claim 9 when implementing claim 1. The motivation would be to improve the accuracy of the state model.
For Claim 12, Claims 11 and 12 of 11758415 teach the device of claim 11,
wherein the processor is configured to control the communication circuitry to receive, from the remote device, second state related information for predicting the state of the device, and
wherein the local learning to update the state model is performed based on the second state related information.
For Claim 15, Claim 17 of 11758415 teaches the device of claim 11, wherein the number of one or more parameters included in the at least one parameter of the updated state model is smaller than a total number of parameters included in the state model.
For Claim 16, Claim 18 of 11758415 teaches the device of claim 11, wherein the at least one parameter and the weight information are used to determine the state of the device based on the updated state model in the remote device.
For Claim 18, Claim 15 of 11758415 teaches the device of claim 11, wherein the at least one error comprises at least one of a communication error, a memory error, a fan error, a memory full error, a central processing unit (CPU) full error, or a digital signal processing (DSP) error.
For Claim 20, Claim 19 of 11758415 teaches an electronic device comprising:
memory configured to store instructions, wherein, when the instructions are executed on a device of a base station (BS), the instructions cause the device to:
obtain local data and a state model for local learning to predict a state of the device,
perform the local learning to update the state model using the local data based on a machine learning algorithm,
obtain weight information of at least one parameter of the updated state model based on the local learning, and
transmit, to a remote device through communication circuitry of the device, first state related information including the weight information of the at least one parameter of the updated state model, and
wherein the state of the device is associated with at least one of a power consumption of the device, a resource usage of the device related to frequency or time resources, an abnormal operation of the device for detecting at least one error occurring in the device, or a network throughput performance of at least one terminal.
Claim 19 of 11758415 is not explicit as to, but Claim 18 of 11758415 teaches wherein the weight information indicates a degree to which the at least one parameter is used for a determination of the state of the device in the updated state model. Thus it would have been obvious to one of ordinary skill in the art at the time the application was filed to employ the weight as in claim 9 when implementing claim 1. The motivation would be to improve the accuracy of the state model.
Response to Arguments
The amendment filed 16 April 2026 has been entered.
With regards to the double patenting rejection, please note that this is not a provisional double patenting rejection.
Applicant’s arguments with respect to rejections under 35 USC 103 have been fully considered, but are moot in view of the new grounds of rejection introduced herein. Claims 6 and 16 are not rejected over prior art, but remaining claims remain rejected under 35 USC 103.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Newnham et al. (US 2013/0024405) teach a system using weighted values when adjusting a local model.
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 CASSANDRA L DECKER whose telephone number is (571)270-3946. The examiner can normally be reached 7:30 am - 4:00 pm.
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/CASSANDRA L DECKER/Examiner, Art Unit 2466 5/7/2026
/FARUK HAMZA/Supervisory Patent Examiner, Art Unit 2466
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