DETAILED ACTION
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
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.
Claims 1-3, 6, 8-12, 15-17, 21-24, and 28-29 are rejected under 35 U.S.C. 103 as being unpatentable over Norrman et al. WO/2021/032497 (US Patent Publication 2022/0321423 relied upon for translation).
Regarding independent claim 1, Norrman et al. teaches a computer-implemented method, performed by a first node, for handling an ongoing distributed machine-learning or federated learning process for which the first node acts an aggregator of data or analytics from a first group of second nodes,
(page 8, lines 1-8, " The local model updates are collated at an aggregator network entity (such as the NWDAF) and combined to obtain a combined model update.")
the first node operating in a communications system, (Figure 3)
the method comprising: obtaining, one or more first indications about one or more third nodes ("candidate network entities") operating in the communications system, the one or more first indications comprising respective information about the one or more third nodes, the respective information indicating that the one or more third nodes are eligible to be selected to participate in the ongoing distributed machine-learning or federated learning process, wherein the one or more first indications are obtained during the ongoing distributed machine-learning or federated learning process, and
(page 3, lines 10-20, Summary, " a method performed by a network entity is provided. The method comprises obtaining identification information for a plurality of candidate network entities in the communications network, wherein the identification information indicates that each of the candidate network entities is configured to participate in collaborative learning. The network entity sends a request for each of the candidate network entities, the request comprising one or more selection criteria. The network entity receives one or more response messages comprising an indication of which of the candidate network entities satisfy the one or more selection criteria; and based on the indication in the one or more response messages, selects one or more of the plurality of candidate network entities to participate in a collaborative learning process to train a model using a machine learning algorithm.")
(page 20, line 32, page 21, line 2, "The third response message 410 may comprise an indication of which of the two or more candidate network entities satisfy the one or more second selection criteria. The NWDAF308 may thus, for example, select the candidate network entities indicated as satisfying the one or more second selection criteria to participate in the collaborative learning process.", Hence, the NWDAF is both aggregator as well as selector)
(page 9, 15-19, "For example, the NWDAF 308 may send the combined model update to each of the network entities 302-306. In particular examples, the combined model update may be transmitted to one or more further network entities in addition to the network entities 302-306 used to train the model.", Hence, the machine learning in is ongoing)
providing, to a fourth node operating in the communications system, an output of the ongoing distributed machine-learning or federated learning process based on the obtained one or more first indications.
Norrman et al. is silent on a fourth node part of the communication system 100 and Norrman et al. is also silent on this output being delivered by the first node.
However, as in the application the first node is the NWDAF which acts as an aggregator in Norrman et al. This is a very strong hint that the output the ML process is aggregated by the aggregating NWDAF to be subsequently reported to some further entity.
In any case the effect of the difference is that the fourth is informed regarding some output. The skilled would effortlessly implement the sending of the desired information to a further node if prompted by some business requirement.
It would have been obvious to one of ordinary skill in the art before the effective filing date to include the other nodes based on the teachings of Norrman et al. The rationale to combine would be that mere duplication of parts has no patentable significance unless a new and unexpected result is produced, rendering the combination to be obvious. In re Harza, 274 F.2d 669, 124 USPQ 378 (CCPA 1960).
Regarding claim 2, Norrman et al. teaches the computer-implemented method according to claim 1, wherein that the output is based on the obtained one or more first indications comprises:
selecting, based on the received respective one or more first indications, from the first group of second nodes and the one or more third nodes, one or more selected nodes to continue the ongoing distributed machine-learning or federated learning process, wherein the ongoing distributed machine-learning or federated learning process is continued using the one or more selected nodes, and wherein the output is based on the ongoing distributed machine-learning or federated learning process, continued using the one or more selected nodes, and
(page 21, lines 16-22, "The NWDAF 308 may select one or more of the candidate network entities to participate in the collaborative learning process based on the one or more participation criteria.")
sending a respective second indication to the one or more selected nodes, the respective second indication indicating to the one or more selected nodes that they have been selected to continue the ongoing distributed machine-learning or federated learning.
(page 14, lines 1-9, "For example, the NWDAF 308 may send the combined model update to each of the network entities 302-306. In particular examples, the combined model update may be transmitted to one or more further network entities in addition to the network entities 302-306 used to train the model.")
Regarding claim 3, Norrman et al. teaches the computer-implemented method according to claim 2, wherein that the output is based on the obtained one or more first indications comprises: determining, based on the obtained respective one or more first indications, whether the ongoing distributed machine-learning or federated learning process is to be continued with any of the one or more selected nodes, and wherein the output is based on a result of the determination. (ibidem)
Regarding claim 6, Norrman et al. teaches the computer-implemented method according to claim 1, wherein the respective information comprised in the one or more first indications indicates, one or more of:
a respective willingness to join the ongoing distributed machine-learning or federated learning process,
a respective first capability to complete one or more training tasks of the ongoing distributed machine-learning or federated learning process,
(page 15, lines 8-18, "In particular embodiments, the at least one query relating to the configuration of a candidate network entity may relate to computational resources available at the candidate network entity. In this context, computational resources may include, for example, memory (e.g. random-access memory or storage) available at the network entity, processing power at the network entity, and/or any other suitable computational resources.")
one or more respective characteristics of available data to a respective third node,
(page 16, lines 4-8, "The at least one query for a candidate network entity may relate to an availability of training data at the candidate network entity.")
a respective supported machine-learning framework,
(page 11, line 27- page 12, line 2, "That is, the first request message 400 may specify that each network entity selected as a candidate network entity is capable of providing a particular service. For example, the first request message 400 may specify that each network entity selected as a candidate network entity is capable of providing subscriber authentication data, or a particular type of collaborative learning.")
(page 13, lines 1-5, "The capability information may comprise an indication of whether or not the network entity is capable of performing collaborative (e.g. federated) learning. Thus, the NRF 310 may use the profiles for the plurality of network entities registered at the network registration entity to identify network entities that are capable of performing collaborative learning.")
a respective time availability to participate in the ongoing distributed machine-learning or federated learning process.
("For example, the metrics may comprise an indicator of the resources used at the candidate network entity to train the preliminary model (memory, processing power etc.) and/or the time taken (e.g. processor time, wall clock time etc.) ")
Hence, all options in the list are disclosed by Norrman et al. Willingness is considered an exercised option.
Regarding claim 8, Norrman et al. teaches the computer-implemented method according to claim 1, wherein the obtaining is performed one of: directly from, respectively, the one or more third nodes, and via a fifth node operating in the communications system the one or more third nodes have previously registered with.
(page 22, lines 1-8, "In the embodiment described in relation to Figure 4, the NWDAF queries the candidate network entities by sending a query to an intermediate network entity (in this case, the OAM 312). However, those skilled in the art will appreciate that, in some embodiments, the NWDAF 308 may query the candidate network entities directly by transmitting a second request message 404 to each candidate network entity and receiving one or more responses from one or more of the candidate network entities. In such embodiments, the selection of a subset of candidate network entities at the OAM 312 may be omitted.")
Regarding claim 9, Norrman et al. teaches the computer-implemented method according to claim 8, further comprising: registering with the fifth node first information indicating the ongoing distributed machine-learning or federated learning process, wherein the first information indicates at least one of: an identifier of the ongoing distributed machine-learning or federated learning process, and second information about the first group of second nodes used for the ongoing distributed machine-learning or federated learning process, and wherein the obtaining (703) of the one or more first indications is based on the registered first information.
(Claim 9 only makes sense when being dependent on the second option of claim 8, but claim 8 is not limited as such.
Nevertheless, if the fifth node (mapped to OAM of Norrman et al.) is used to provide information about the third nodes as is disclosed in Norrman et al. as well, the OAM must have some administration. The implementation requires some identification which is considered obvious based on the given teachings.)
Regarding claim 10, Norrman et al. teaches the computer-implemented method according to claim 1, further comprising: sending a prior indication to the fifth node, the prior indication requesting the one or more first indications, and wherein the obtaining of the one or more first indications is based on the sent prior indication.
(page 17, lines 1-8, "The NWDAF 308 transmits the second request message 404 comprising the at least one query for additional information to the OAM 312. The NWDAF 308 may thus transmit a single second request message 404 to the OAM 312 for all of the candidate network entities. Alternatively, the NWDAF 308 may transmit two or more second request messages to the OAM 312 (e.g. one second request message per candidate network entity).")
Regarding claim 11, Norrman et al. teaches the computer-implemented method according to claim 8, wherein the communications system is a Fifth Generation, 5G, network, and wherein:
the first node is a server Network Data Analytics Function, NWDAF,
(page 20, line 32, page 21, line 2, "The third response message 410 may comprise an indication of which of the two or more candidate network entities satisfy the one or more second selection criteria. The NWDAF308 may thus, for example, select the candidate network entities indicated as satisfying the one or more second selection criteria to participate in the collaborative learning process.", Hence, the NWDAF is both aggregator as well as selector)
the first group of second nodes are client NWDAFs,
(page 20, line 32, page 21, line 2, "The third response message 410 may comprise an indication of which of the two or more candidate network entities satisfy the one or more second selection criteria")
the one or more third nodes are other client NWDAFs,
(page 20, line 32, page 21, line 2, "The third response message 410 may comprise an indication of which of the two or more candidate network entities satisfy the one or more second selection criteria")
the fourth node is a NWDAF service consumer, and
the fifth node is a distributed machine-learning or federated learning control function, DLCF.
Norrman et al. does not disclose the further characterizations of the fourth and fifth node. However, these further characterizations have no technical effect as they merely indicate a name. There is no difference defined in terms of method steps, rendering the features to be obvious in view of the teachings.
It would have been obvious to one of ordinary skill in the art before the effective filing date to include the other nodes based on the teachings of Norrman et al. The rationale to combine would be that mere duplication of parts has no patentable significance unless a new and unexpected result is produced, rendering the combination to be obvious. In re Harza, 274 F.2d 669, 124 USPQ 378 (CCPA 1960).
Regarding claim 12, Norrman et al. teaches a computer-implemented method, performed by a third node (Figure 3, 302,304,306 "NFA", "NFB", "NFC"), for handling an ongoing distributed machine-learning or federated learning process, the third node operating in a communications system, (Figure 3)
the method comprising: providing a first indication about the third node to one of a first node and a fifth node operating in the communications system,
(page 22, lines 1-8, "In the embodiment described in relation to Figure 4, the NWDAF queries the candidate network entities by sending a query to an intermediate network entity (in this case, the OAM 312). However, those skilled in the art will appreciate that, in some embodiments, the NWDAF 308 may query the candidate network entities directly by transmitting a second request message 404 to each candidate network entity and receiving one or more responses from one or more of the candidate network entities. In such embodiments, the selection of a subset of candidate network entities at the OAM 312 may be omitted.")
wherein the first node acts an aggregator of data or analytics from a first group of second nodes in the ongoing distributed machine-learning or federated learning process, the first indication comprising respective information about the third node, the respective information indicating that the third node is eligible to be selected to participate in the ongoing distributed machine-learning or federated learning process,
(Not limiting the third node, nevertheless the features are disclosed per mapping for the first node)
and wherein the first indication is provided during the ongoing distributed machine-learning or federated learning process.
(page 9, 15-19, "For example, the NWDAF 308 may send the combined model update to each of the network entities 302-306. In particular examples, the combined model update may be transmitted to one or more further network entities in addition to the network entities 302-306 used to train the model.", Hence, the machine learning in Norrman et al. is ongoing).
Regarding claim 15, Norrman et al. teaches the computer-implemented method according to claim 12, wherein the first group of second nodes is used as a first group of clients and wherein the third nodes is selected to be used as part of a second group of clients to continue the ongoing distributed machine-learning or federated learning process.
(ibidem, Calling nodes clients does not limit the claimed subject-matter further in technical sense, it merely defines a label.)
Regarding claim 16, Norrman et al. teaches the computer-implemented method according to claim 12, wherein the respective information indicates one or more of:
a respective willingness to join the ongoing distributed machine-learning or federated learning process,
a respective first capability to complete one or more training tasks of the ongoing distributed machine-learning or federated learning process,
(Norrman et al., page 15, lines 8-18, "In particular embodiments, the at least one query relating to the configuration of a candidate network entity may relate to computational resources available at the candidate network entity. In this context, computational resources may include, for example, memory (e.g. random-access memory or storage) available at the network entity, processing power at the network entity, and/or any other suitable computational resources.")
one or more respective characteristics of available data to the third node (113),
(page 16, lines 4-8, "The at least one query for a candidate network entity may relate to an availability of training data at the candidate network entity.")
a respective supported machine-learning framework,
(page 11, line 27- page 12, line 2, "That is, the first request message 400 may specify that each network entity selected as a candidate network entity is capable of providing a particular service. For example, the first request message 400 may specify that each network entity selected as a candidate network entity is capable of providing subscriber authentication data, or a particular type of collaborative learning.")
(page 13, lines 1-5, "The capability information may comprise an indication of whether or not the network entity is capable of performing collaborative (e.g. federated) learning. Thus, the NRF 310 may use the profiles for the plurality of network entities registered at the network registration entity to identify network entities that are capable of performing collaborative learning.")
a respective time availability to participate in the ongoing distributed machine-learning or federated learning process.
("For example, the metrics may comprise an indicator of the resources used at the candidate network entity to train the preliminary model (memory, processing power etc.) and/or the time taken (e.g. processor time, wall clock time etc.) ")
Hence, all options in the list are disclosed by Norrman et al. Willingness is considered an exercised option.
Regarding claim 17, Norrman et al. teaches the computer-implemented method according to claim 16, wherein providing the first indication comprises registering the respective information with the fifth node, and wherein the receiving of the respective second indication is based on the registered respective information.
(page 22, lines 1-8, "In the embodiment described in relation to Figure 4, the NWDAF queries the candidate network entities by sending a query to an intermediate network entity (in this case, the OAM 312). However, those skilled in the art will appreciate that, in some embodiments, the NWDAF 308 may query the candidate network entities directly by transmitting a second request message 404 to each candidate network entity and receiving one or more responses from one or more of the candidate network entities. In such embodiments, the selection of a subset of candidate network entities at the OAM 312 may be omitted." And if the fifth node (mapped to OAM of Norrman et al.) is used to provide information about the third nodes as is disclosed in Norrman et al. as well, the OAM must have some administration. The implementation requires some identification which is considered obvious based on the given teachings.)
Regarding claim 21, Norrman et al. teaches the computer-implemented method according to claim 12, wherein the communications system is a Fifth Generation, 5G, network, and wherein:
the first node is a server Network Data Analytics Function, NWDAF,
(page 20, line 32, page 21, line 2, "The third response message 410 may comprise an indication of which of the two or more candidate network entities satisfy the one or more second selection criteria. The NWDAF308 may thus, for example, select the candidate network entities indicated as satisfying the one or more second selection criteria to participate in the collaborative learning process.", Hence, the NWDAF is both aggregator as well as selector)
the first group of second nodes are client NWDAFs,
(page 20, line 32, page 21, line 2, "The third response message 410 may comprise an indication of which of the two or more candidate network entities satisfy the one or more second selection criteria")
the third node is another client NWDAFs, and
(page 20, line 32, page 21, line 2, "The third response message 410 may comprise an indication of which of the two or more candidate network entities satisfy the one or more second selection criteria")
the fifth node is a distributed machine-learning or federated learning control function, DLCF.
Norrman et al. does not disclose the further characterizations of the fourth and fifth node. However, these further characterizations have no technical effect as they merely indicate a name. There is no difference defined in terms of method steps, rendering the features to be obvious in view of the teachings.
It would have been obvious to one of ordinary skill in the art before the effective filing date to include the other nodes based on the teachings of Norrman et al. The rationale to combine would be that mere duplication of parts has no patentable significance unless a new and unexpected result is produced, rendering the combination to be obvious. In re Harza, 274 F.2d 669, 124 USPQ 378 (CCPA 1960).
Regarding independent claim 22, Norrman et al. teaches a computer-implemented method, performed by a fifth node, for handling an ongoing distributed machine-learning or federated learning process, the fifth node operating in a communications system, the method comprising:
obtaining one or more first indications from one or more third nodes operating in the communications system, the one or more first indications comprising respective information indicating that the one or more third nodes are eligible to be selected to participate in the ongoing distributed machine-learning or federated learning process, wherein the one or more first indications are obtained during the ongoing distributed machine-learning or federated learning process, and
providing the one or more first indications to a first node operating in the communications system, wherein the first node acts an aggregator of data or analytics from a first group of second nodes for the ongoing distributed machine-learning or federated learning process, and wherein the one or more first indications are provided during the ongoing distributed machine-learning or federated learning process.
(page 22, lines 1-8, "In the embodiment described in relation to Figure 4, the NWDAF queries the candidate network entities by sending a query to an intermediate network entity (in this case, the OAM 312). However, those skilled in the art will appreciate that, in some embodiments, the NWDAF 308 may query the candidate network entities directly by transmitting a second request message 404 to each candidate network entity and receiving one or more responses from one or more of the candidate network entities. In such embodiments, the selection of a subset of candidate network entities at the OAM 312 may be omitted.")
Regarding claim 23, Norrman et al. teaches the computer-implemented method according to claim 22, wherein the respective information indicates, one or more of:
a respective willingness to join the ongoing distributed machine-learning or federated learning process,
a respective first capability to complete one or more training tasks of the ongoing distributed machine-learning or federated learning process,
(Norrman et al., page 15, lines 8-18, "In particular embodiments, the at least one query relating to the configuration of a candidate network entity may relate to computational resources available at the candidate network entity. In this context, computational resources may include, for example, memory (e.g. random-access memory or storage) available at the network entity, processing power at the network entity, and/or any other suitable computational resources.")
one or more respective characteristics of available data to a respective third node,
(page 16, lines 4-8, "The at least one query for a candidate network entity may relate to an availability of training data at the candidate network entity.")
a respective supported machine-learning framework,
(page 11, line 27- page 12, line 2, "That is, the first request message 400 may specify that each network entity selected as a candidate network entity is capable of providing a particular service. For example, the first request message 400 may specify that each network entity selected as a candidate network entity is capable of providing subscriber authentication data, or a particular type of collaborative learning.")
(page 13, lines 1-5, "The capability information may comprise an indication of whether or not the network entity is capable of performing collaborative (e.g. federated) learning. Thus, the NRF 310 may use the profiles for the plurality of network entities registered at the network registration entity to identify network entities that are capable of performing collaborative learning.")
a respective time availability to participate in the ongoing distributed machine-learning or federated learning process.
("For example, the metrics may comprise an indicator of the resources used at the candidate network entity to train the preliminary model (memory, processing power etc.) and/or the time taken (e.g. processor time, wall clock time etc.) ")
Hence, all options in the list are disclosed by Norrman et al. Willingness is considered an exercised option.
Regarding claim 24, Norrman et al. teaches the computer-implemented method according to claim 22, wherein obtaining the one or more first indications comprises registering the respective information from the one or more third nodes, and wherein the providing of the one or more first indications is based on the registered respective information.
(page 22, lines 1-8, "In the embodiment described in relation to Figure 4, the NWDAF queries the candidate network entities by sending a query to an intermediate network entity (in this case, the OAM 312). However, those skilled in the art will appreciate that, in some embodiments, the NWDAF 308 may query the candidate network entities directly by transmitting a second request message 404 to each candidate network entity and receiving one or more responses from one or more of the candidate network entities. In such embodiments, the selection of a subset of candidate network entities at the OAM 312 may be omitted." And if the fifth node (mapped to OAM of Norrman et al.) is used to provide information about the third nodes as is disclosed in Norrman et al. as well, the OAM must have some administration. The implementation requires some identification which is considered obvious based on the given teachings.)
Regarding claim 28, Norrman et al. teaches the computer-implemented method according to claim 22, wherein the communications system is a Fifth Generation, 5G, network, and wherein:
the first node is a server Network Data Analytics Function, NWDAF,
(page 20, line 32, page 21, line 2, "The third response message 410 may comprise an indication of which of the two or more candidate network entities satisfy the one or more second selection criteria. The NWDAF308 may thus, for example, select the candidate network entities indicated as satisfying the one or more second selection criteria to participate in the collaborative learning process.", Hence, the NWDAF is both aggregator as well as selector)
the first group of second nodes are client NWDAFs,
(page 20, line 32, page 21, line 2, "The third response message 410 may comprise an indication of which of the two or more candidate network entities satisfy the one or more second selection criteria")
the one or more third nodes are other client NWDAFs, and
(page 20, line 32, page 21, line 2, "The third response message 410 may comprise an indication of which of the two or more candidate network entities satisfy the one or more second selection criteria")
the fifth node is a distributed machine-learning or federated learning control function, DLCF.
Norrman et al. does not disclose the further characterizations of the fourth and fifth node. However, these further characterizations have no technical effect as they merely indicate a name. There is no difference defined in terms of method steps, rendering the features to be obvious in view of the teachings.
It would have been obvious to one of ordinary skill in the art before the effective filing date to include the other nodes based on the teachings of Norrman et al. The rationale to combine would be that mere duplication of parts has no patentable significance unless a new and unexpected result is produced, rendering the combination to be obvious. In re Harza, 274 F.2d 669, 124 USPQ 378 (CCPA 1960).
Regarding independent claim 29, Norrman et al. teaches a first node, for handling a distributed machine-learning or federated learning process configured to be ongoing, for which the first node is configured to act an aggregator of data or analytics from a first group of second nodes,
(page 8, lines 1-8, " The local model updates are collated at an aggregator network entity (such as the NWDAF) and combined to obtain a combined model update.")
the first node being further configured to operate in a communications system, (Figure 3) the first node being further configured to:
obtain, one or more first indications about one or more third nodes ("candidate network entities") configured to operate in the communications system, the one or more first indications being configured to comprise respective information about the one or more third nodes, the respective information being configured to indicate that the one or more third nodes are eligible to be selected to participate in the distributed machine-learning or federated learning process configured to be ongoing, wherein the one or more first indications are configured to be obtained during the distributed machine-learning or federated learning process configured to be ongoing, and
(page 3, lines 10-20, Summary, " a method performed by a network entity is provided. The method comprises obtaining identification information for a plurality of candidate network entities in the communications network, wherein the identification information indicates that each of the candidate network entities is configured to participate in collaborative learning. The network entity sends a request for each of the candidate network entities, the request comprising one or more selection criteria. The network entity receives one or more response messages comprising an indication of which of the candidate network entities satisfy the one or more selection criteria; and based on the indication in the one or more response messages, selects one or more of the plurality of candidate network entities to participate in a collaborative learning process to train a model using a machine learning algorithm.")
(page 20, line 32, page 21, line 2, "The third response message 410 may comprise an indication of which of the two or more candidate network entities satisfy the one or more second selection criteria. The NWDAF308 may thus, for example, select the candidate network entities indicated as satisfying the one or more second selection criteria to participate in the collaborative learning process.", Hence, the NWDAF is both aggregator as well as selector)
(page 9, 15-19, "For example, the NWDAF 308 may send the combined model update to each of the network entities 302-306. In particular examples, the combined model update may be transmitted to one or more further network entities in addition to the network entities 302-306 used to train the model.", Hence, the machine learning in is ongoing)
provide, to a fourth node configured to operate in the communications system, an output of the distributed machine-learning or federated learning process configured to be ongoing, based on the one or more first indications configured to be obtained.
Norrman et al. is silent on a fourth node part of the communication system 100 and Norrman et al. is also silent on this output being delivered by the first node.
However, as in the application the first node is the NWDAF which acts as an aggregator in Norrman et al. This is a very strong hint that the output the ML process is aggregated by the aggregating NWDAF to be subsequently reported to some further entity.
In any case the effect of the difference is that the fourth is informed regarding some output. The skilled would effortlessly implement the sending of the desired information to a further node if prompted by some business requirement.
It would have been obvious to one of ordinary skill in the art before the effective filing date to include the other nodes based on the teachings of Norrman et al. The rationale to combine would be that mere duplication of parts has no patentable significance unless a new and unexpected result is produced, rendering the combination to be obvious. In re Harza, 274 F.2d 669, 124 USPQ 378 (CCPA 1960).
Allowable Subject Matter
Claims 13-14 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.
The following is a statement of reasons for the indication of allowable subject matter: none of the prior art, taken individually or in combination, teaches the specifics of the claims, especially “wherein the third node is selected, from the first group of second nodes and one or more third nodes comprising the third node, based on the provided first indication, to be comprised in one or more selected nodes to continue the ongoing distributed machine- learning or federated learning process, and wherein the method further comprises: receiving a respective second indication from the first node, the respective second indication indicating the third node has been selected to continue the ongoing distributed machine-learning or federated learning” as recited in claim 13. Claim 14 is allowable as being dependent on an allowable base claim.
Claims 40 and 50 are allowed.
The following is an examiner’s statement of reasons for allowance: none of the prior art, taken individually or in combination, teaches the specifics of the claims, especially “the distributed machine-learning or federated learning process configured to be ongoing, and wherein the … first indication(s)…configured to be provided during the distributed machine-learning or federated learning process configured to be ongoing.” as recited in independent claims 40 and 50.
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
Conclusion
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/PARUL H GUPTA/Primary Examiner, Art Unit 2627
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