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 .
Response to Arguments
Regarding first argument:
Applicant has amended independent claims and asserts that amended claims overcome previously cited prior art.
Applicant’s arguments with respect to claim(s) 1-19 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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) 1-4, 6-8, 10-13, and 15-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bortz in view of Maeda (US 20220012116 A1) hereafter Maeda.
Regarding Claim 1:
Bortz discloses:
A method ([Abstract] method ) of a collaboration server for identifying cells of a mobile network ([Abstract] telecommunication network ) for machine learning collaboration, the mobile network having a plurality of cells, wherein each cell of the plurality of cells is associated with different edge nodes of the mobile network and with a respective machine learning model for each cell, ([Pg 1, ¶2] Cells in a cellular or mobile telecommunication network) the method comprising: managing collection of features for the plurality of cells to generate at least one feature vector for each of the plurality of cells; ([Pg 2, ¶4] comparing cells or cell vectors associated with the cells) wherein the features are extracted at the respective machine learning model at each edge node; determining a cluster of cells within the plurality of cells based on similarity in feature vectors between at least two cells in the plurality of cells; ([Pg 5, ¶4] compare cells or cell vectors associated with the cells to each determined centroid such that the clustering module clusters together cells which substantially match a particular centroid/s) sending cluster information to each cell of the cluster; ([Pg 8, ¶3] transmit and receive information to and from the cells) receiving cluster pre-check information from each cell of the cluster; ([Pg 8, ¶3] transmit and receive information to and from the cells) and determining, based on the received pre-check information, at least a first cell and a second cell in the cluster to exchange information for machine learning model utilized by the first cell and machine learning model utilized by the second cell. ([Pg 28, ¶6-Pg29, ¶1] Because mobile telecommunication networks are dynamic, the vectors are sent into the network at 104. For example, thirty vectors are sent into each cluster of homogeneous cells. After a while, one of the vectors will get a better response, or a few of them will be close. Then, after a predetermined period of time has elapsed, say for example a month, only the top three vectors which gave the best responses in that cluster of cells are sent to that cluster.)
Bortz does not disclose:
wherein the features are extracted at the respective machine learning model at each edge node
Maeda discloses:
wherein the features are extracted at the respective machine learning model at each edge node ([¶0059] wherein the edge computer 102 extracts the desired features )
Regarding Claim 2:
Bortz in view of Maeda discloses the limitations of parent claims.
Bortz discloses:
wherein the managing collection of features, further comprises: extracting clutter features for each cell of the plurality of cells to form a clutter feature vector for each cell. ([Pg 3, ¶12] determining cell vectors)
Regarding Claim 3:
Bortz in view of Maeda discloses the limitations of parent claims.
Bortz discloses:
wherein the managing collection of features, further comprises: receiving feature vectors for training data set meta-features from each cell of the plurality of cells. ([Pg 8, ¶3] transmit and receive information to and from the cells)
Regarding Claim 4:
Bortz in view of Maeda discloses the limitations of parent claims.
Bortz discloses:
wherein the managing collection of features, further comprises: receiving feature vectors for model hyper-parameters from each cell of the plurality of cells. ([Pg 8, ¶3] transmit and receive information to and from the cells)
Regarding Claim 6:
Bortz discloses:
A network device ([Pg 8, ¶2] an apparatus) for executing a collaboration client for identifying cells of a mobile network ([Abstract] telecommunication network) for machine learning collaboration, the mobile network ([Pg 1, ¶2] Cells in a cellular or mobile telecommunication network) having a plurality of cells, wherein each cell of the plurality of cells is associated with different edge nodes of the mobile network and with a respective machine learning model for each cell, the network device comprising: a non-transitory computer-readable medium having stored therein a collaboration client; and a processor coupled to the non-transitory computer-readable medium, the processor to execute the collaboration client, ([Pg 17, ¶3] a processor 124. It is to be understood that the processor 124 may be one or more microprocessors, controllers, or any other suitable computing device, resource, hardware, software, or embedded logic.) the collaboration client to receive clustering information from a collaboration server, ([Pg 8, ¶3] transmit and receive information to and from the cells) which collaboration server collects features for the plurality of cells to generate at least one feature vector for each of the plurality of cells, wherein t([Pg 8, ¶3] transmit and receive information to and from the cells), and to exchange information with one or more cells identified in the collaboration information, the exchanged information for machine learning models utilized by the network device and the one or more cells. ([Pg 28, ¶6-Pg29, ¶1] Because mobile telecommunication networks are dynamic, the vectors are sent into the network at 104. For example, thirty vectors are sent into each cluster of homogeneous cells. After a while, one of the vectors will get a better response, or a few of them will be close. Then, after a predetermined period of time has elapsed, say for example a month, only the top three vectors which gave the best responses in that cluster of cells are sent to that cluster.)
Bortz does not disclose:
wherein the features are extracted at the respective machine learning model at each edge node
Maeda discloses:
wherein the features are extracted at the respective machine learning model at each edge node ([¶0059] wherein the edge computer 102 extracts the desired features )
Bortz and Maeda are analogous as they both pertain to wireless communications. The machine learning model at each edge node as described in the claim language is only claimed to extract features and not perform any inference actions. Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Bortz to extract features at the edge node as taught by Maeda in order to manage and configure the mobile network. (Instant Application [¶0002])
Regarding Claim 7:
Bortz in view of Maeda discloses the limitations of parent claims.
Bortz discloses:
The network device of claim 6, wherein the collaboration client is further to calculate feature vectors for training data set meta-features. ([Pg 3, ¶12] determining cell vectors)
Regarding Claim 8:
Bortz in view of Maeda discloses the limitations of parent claims.
Bortz discloses:
The network device of claim 6, wherein the collaboration client is further to calculate feature vectors for model hyper-parameters from each cell. ([Pg 3, ¶12] determining cell vectors)
Regarding Claim 10:
Bortz discloses:
An electronic device ([Pg 8, ¶2] an apparatus) to execute a plurality of virtual machines, the plurality of virtual machines to execute a method ([Pg 8, ¶4] The apparatus 10 comprises a plurality of modules. It is to be noted that, for the purposes of this specification, the term "module" includes an identifiable portion of code, computational or executable instructions, data, or computational object to achieve a particular function, operation, processing, or procedure. A module need not be implemented in software; a module may be implemented in software, hardware, or a combination of software and hardware. Further, a module need not be incorporated in the apparatus 10 but may be provided or may reside in the network to which the apparatus 10 is attached or connectable to such that the apparatus 10 may be operable to use the functionality provided by a module from within the network.) of a collaboration server for identifying cells of a mobile network for machine learning collaboration, the mobile network having a plurality of cells, wherein each cell of the plurality of cells is associated with different edge nodes of the mobile network and with a respective machine learning model for each cell, the electronic device comprising: a non-transitory computer-readable medium having stored therein a collaboration server; and a processor to execute the plurality of virtual machines, one of the plurality of virtual machines ([Pg 17, ¶3] a processor 124. It is to be understood that the processor 124 may be one or more microprocessors, controllers, or any other suitable computing device, resource, hardware, software, or embedded logic.) to execute the collaboration server, the collaboration server to manage collection of features for the plurality of cells to generate at least one feature vector for each of the plurality of cells, ([Pg 2, ¶4] comparing cells or cell vectors associated with the cells) cells based on similarity in feature vectors between at least two cells in the plurality of cells, ([Pg 5, ¶4] compare cells or cell vectors associated with the cells to each determined centroid such that the clustering module clusters together cells which substantially match a particular centroid/s) to send cluster information to each cell of the cluster, to receive cluster pre-check information from each cell of the cluster, ([Pg 8, ¶3] transmit and receive information to and from the cells) and to determine, based on the received pre-check information, at least a first cell and a second cell in the cluster to exchange information for machine learning model utilized by the first cell and the machine learning model utilized by second cell. ([Pg 28, ¶6-Pg29, ¶1] Because mobile telecommunication networks are dynamic, the vectors are sent into the network at 104. For example, thirty vectors are sent into each cluster of homogeneous cells. After a while, one of the vectors will get a better response, or a few of them will be close. Then, after a predetermined period of time has elapsed, say for example a month, only the top three vectors which gave the best responses in that cluster of cells are sent to that cluster.)
Bortz does not disclose:
wherein the features are extracted at the respective machine learning model at each edge node
Maeda discloses:
wherein the features are extracted at the respective machine learning model at each edge node ([¶0059] wherein the edge computer 102 extracts the desired features )
Bortz and Maeda are analogous as they both pertain to wireless communications. The machine learning model at each edge node as described in the claim language is only claimed to extract features and not perform any inference actions. Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Bortz to extract features at the edge node as taught by Maeda in order to manage and configure the mobile network. (Instant Application [¶0002])
Regarding Claim 11:
Bortz in view of Maeda discloses the limitations of parent claims.
Bortz discloses:
wherein the managing collection of features, further includes extracting clutter features for each cell of the plurality of cells to form a clutter feature vector for each cell. ([Pg 3, ¶12] determining cell vectors)
Regarding Claim 12:
Bortz in view of Maeda discloses the limitations of parent claims.
Bortz discloses:
wherein the managing collection of features, further includes receiving feature vectors for training data set meta-features from each cell of the plurality of cells. ([Pg 8, ¶3] transmit and receive information to and from the cells)
Regarding Claim 13:
Bortz in view of Maeda discloses the limitations of parent claims.
Bortz discloses:
wherein the managing collection of features, further includes receiving feature vectors for model hyper-parameters from each cell of the plurality of cells. ([Pg 8, ¶3] transmit and receive information to and from the cells)
Regarding Claim 15:
Bortz discloses:
A computing device ([Pg 8, ¶2] an apparatus) to implement a control plane of a software defined networking network, the computing device to execute a method of a collaboration server for identifying cells of a mobile network for machine learning collaboration, ([Pg 8, ¶4] The apparatus 10 comprises a plurality of modules. It is to be noted that, for the purposes of this specification, the term "module" includes an identifiable portion of code, computational or executable instructions, data, or computational object to achieve a particular function, operation, processing, or procedure. A module need not be implemented in software; a module may be implemented in software, hardware, or a combination of software and hardware. Further, a module need not be incorporated in the apparatus 10 but may be provided or may reside in the network to which the apparatus 10 is attached or connectable to such that the apparatus 10 may be operable to use the functionality provided by a module from within the network.) the mobile network having a plurality of cells, ([Pg 1, ¶2] Cells in a cellular or mobile telecommunication network) wherein each cell of the plurality of cells is associated with different edge nodes of the mobile network and with a respective machine learning model for each cell, the computing device comprising: a non-transitory computer readable medium having stored therein a collaboration server; and a processor coupled to the non-transitory computer readable medium, the processor to execute the collaboration server, ([Pg 17, ¶3] a processor 124. It is to be understood that the processor 124 may be one or more microprocessors, controllers, or any other suitable computing device, resource, hardware, software, or embedded logic.) the collaboration server to manage collection of features for the plurality of cells to generate at least one feature vector for each of the plurality of cells, ([Pg 2, ¶4] comparing cells or cell vectors associated with the cells) wherein the features are extracted at the respective machine learning model at each edge node, to determine a cluster of cells within the plurality of cells based on similarity in feature vectors between at least two cells in the plurality of cells, ([Pg 5, ¶4] compare cells or cell vectors associated with the cells to each determined centroid such that the clustering module clusters together cells which substantially match a particular centroid/s) to send cluster information to each cell of the cluster, receive cluster pre-check information from each cell of the cluster, and to determine, based on the received cluster pre-check information, a first cell and a second cell in the cluster to exchange information for machine learning model utilized by the first cell and machine learning model utilized by the second cell. ([Pg 28, ¶6-Pg29, ¶1] Because mobile telecommunication networks are dynamic, the vectors are sent into the network at 104. For example, thirty vectors are sent into each cluster of homogeneous cells. After a while, one of the vectors will get a better response, or a few of them will be close. Then, after a predetermined period of time has elapsed,
Bortz does not disclose:
wherein the features are extracted at the respective machine learning model at each edge node
Maeda discloses:
wherein the features are extracted at the respective machine learning model at each edge node ([¶0059] wherein the edge computer 102 extracts the desired features )
Bortz and Maeda are analogous as they both pertain to wireless communications. Thus, it Bortz and Maeda are analogous as they both pertain to wireless communications. The machine learning model at each edge node as described in the claim language is only claimed to extract features and not perform any inference actions. Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Bortz to extract features at the edge node as taught by Maeda in order to manage and configure the mobile network. (Instant Application [¶0002])
Regarding Claim 16:
Bortz in view of Maeda discloses the limitations of parent claims.
Bortz discloses:
wherein the managing collection of features, further includes extracting clutter features for each cell of the plurality of cells to form a clutter feature vector for each cell. ([Pg 3, ¶12] determining cell vectors)
Regarding Claim 17:
Bortz in view of Maeda discloses the limitations of parent claims.
Bortz discloses:
wherein the managing collection of features, further includes receiving feature vectors for training data set meta-features from each cell of the plurality of cells. ([Pg 8, ¶3] transmit and receive information to and from the cells)
Regarding Claim 18:
Bortz in view of Maeda discloses the limitations of parent claims.
Bortz discloses:
wherein the managing collection of features, further includes receiving feature vectors for model hyper-parameters from each cell of the plurality of cells. ([Pg 8, ¶3] transmit and receive information to and from the cells)
Claim(s) 5, 9, 14, and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bortz in view of Maeda and further in view of Chapelle (US 20130290223 A1) hereafter Chapelle
Regarding Claim 5:
Bortz in view of Maeda discloses the limitations of parent claims.
Bortz and Maeda does not disclose:
wherein the managing collection of features, further comprises: aggregating feature vectors for each cell from at least the feature vectors for training data set meta-features and feature vectors for model hyper-parameters.
Chapelle discloses:
wherein the managing collection of features, further comprises: aggregating feature vectors for each cell from at least the feature vectors for training data set meta-features and feature vectors for model hyper-parameters. ([¶0010] An aggregated parameter is generated by merging local parameters calculated in each of the plurality of operation nodes in accordance with the network topology.)
Bortz, Maeda and Chapelle are analogous as they both pertain to Wireless Communications. Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Bortz in view of Maeda to aggregate parameters as taught by Chapelle in order to improve the operation of mobile networks (Instant Application [¶0025]).
Regarding Claim 9:
Bortz in view of Maeda discloses the limitations of parent claims.
Bortz and Maeda does not disclose:
wherein the collaboration client is further to aggregate feature vectors to send to the collaboration server.
Chapelle discloses:
wherein the collaboration client is further to aggregate feature vectors to send to the collaboration server. ([¶0010] An aggregated parameter is generated by merging local parameters calculated in each of the plurality of operation nodes in accordance with the network topology.)
Bortz, Maeda and Chapelle are analogous as they both pertain to Wireless Communications. Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Bortz in view of Maeda to aggregate parameters as taught by Chapelle in order to improve the operation of mobile networks (Instant Application [¶0025]).
Regarding Claim 14:
Bortz in view of Maeda discloses the limitations of parent claims.
Bortz does not disclose:
wherein the managing collection of features, further includes aggregating feature vectors for each cell from at least the feature vectors for training data set meta-features and feature vectors for model hyper-parameters.
Chapelle discloses:
wherein the managing collection of features, further includes aggregating feature vectors for each cell from at least the feature vectors for training data set meta-features and feature vectors for model hyper-parameters. ([¶0010] An aggregated parameter is generated by merging local parameters calculated in each of the plurality of operation nodes in accordance with the network topology.)
Bortz, Maeda and Chapelle are analogous as they both pertain to Wireless Communications. Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Bortz in view of Maeda to aggregate parameters as taught by Chapelle in order to improve the operation of mobile networks (Instant Application [¶0025]).
Regarding Claim 19:
Bortz in view of Maeda discloses the limitations of parent claims.
Bortz does not disclose:
wherein the managing collection of features, further includes aggregating feature vectors for each cell from at least the feature vectors for training data set meta-features and feature vectors for model hyper-parameters.
Chapelle discloses:
wherein the managing collection of features, further includes aggregating feature vectors for each cell from at least the feature vectors for training data set meta-features and feature vectors for model hyper-parameters. ([¶0010] An aggregated parameter is generated by merging local parameters calculated in each of the plurality of operation nodes in accordance with the network topology.)
Bortz, Maeda and Chapelle are analogous as they both pertain to Wireless Communications. Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Bortz in view of Maeda to aggregate parameters as taught by Chapelle in order to improve the operation of mobile networks (Instant Application [¶0025]).
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HUGH MARK ASHLEY whose telephone number is (571)272-0199. The examiner can normally be reached M-F 8-430.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Asad Nawaz can be reached at (571) 272-3988. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/HUGH MARK ASHLEY/Examiner, Art Unit 2463
/ASAD M NAWAZ/Supervisory Patent Examiner, Art Unit 2463