DETAILED ACTION
This Office action is in response to the amendment filed 26 August 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 .
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 in a wireless communication system; a device in a wireless communication system, comprising: memory storing instructions; communication circuitry; and at least one processor; and computer readable storage medium 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.
Allowable Subject Matter
Claims 6 and 16 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.
Response to Arguments
The amendment filed 26 August 2026 has been entered.
The double patenting rejections are withdrawn in light of the approved terminal disclaimer filed 26 August 2026.
Applicant’s arguments with respect to the rejections under 35 USC 103 have been fully considered, but are not persuasive. Claims other than 6 and 16 remain rejected under 35 USC 103.
For the McMahan reference, the filing date of 16 October 2015 of the provision application 62/242771 is a valid date under 35 USC 102(a)(2). The provisional application provides support for the cited portions of the McMahan reference. A copy of the provisional application is provided herewith. Please see paragraphs 19-20, 23, 32-33, and Claims 5 and 8 of the provisional, which describe providing to a remote device locally learned data and biased or proportional application thereof in the global model.
Thus, the sections provide support for “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; 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”.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Lin et al. (US 8364613) teaches biasing or weighting a model based on timing of training data. Koymans et al. (US 2020/0234829) teaches updating weightings and biases for a machine learning model based on feedback.
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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/CASSANDRA L DECKER/Examiner, Art Unit 2466 9/18/2026
/FARUK HAMZA/Supervisory Patent Examiner, Art Unit 2466
