DETAILED ACTION
1. Claims 1-20 are currently pending. The effective filing date of the present application is 5/15/2023.
Notice of Pre-AIA or AIA Status
2. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Objections
3. Claims 1-20 are objected to because of the following informalities:
Claim 1 (similarly claims 11 and 20) like “an device.”
Claim 5 (similarly claim 15) recites “an a language setting.”
Appropriate correction is required.
Claim Rejections - 35 USC § 102
4. The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
5. Claims 1, 4, 11, 14 and 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by U.S. Pat. Pub. No. 2023/0143458 to Park et al. (“Park”).
6. With regards to claim 1 (Similarly claims 11 and 20), Park disclosed the limitations of,
enveloping a message, of one or more federated learning messages, by a control message format(Al/ML split computing [federated learning] includes indicating services supported using a message field [enveloping message] by configuring a POU message field value [control message format]; para [0047]-[0048], [0063]), the control message format comprising a plurality of fields respectively indicating ones of an identifier of the message, a size of the message, a type of the message, and a body of the message (POU message includes a plurality of values [fields] to indicate a plurality of attributes including an identifier [identifier of message and session type to establish [type of message]; para [0015], [0062]-[0063]); and
controlling the AI/ML federated learning based on the message enveloped by the control message format Al/ML split computing configured based on POU message indicating type of session to establish; para [0036], [0050], [0062]-[0063]),
wherein the AI/ML federated learning comprises a server controlling a plurality of separate devices to implement federated portions the AI/ML federated learning (Al/ML includes implementing split computing [federated portions] using a server and a plurality of UE devices; para [0036], [0044], [0050]) and to respectively report results of implementing the federated portions from each of the plurality of separate devices to the server (data according to the ML training/inference may be fed back [report] from the UE [separate devices], performing ML training/inference, to the network [server], wherein the ML training/inference includes processing of split computing [federated portions]; para [0036]-[0037], [0050]), and
wherein the body of the message indicates at least one of an AI/ML federated learning synchronization among the plurality of separate devices, an device eligibility of the AI/ML federated learning, a model evaluation of the AI/ML federated learning, a model update of the AI/ML federated learning, and an error of the AI/ML federated learning to indicate that the POU session is session establishment for split computing [Al/ML federated learning], the UE may include a predesignated, separate indicator in the POU session establishment request message [body of the message] about the split computing application service [device eligibility]; para [0063]-[0064]).
7. With regards to claim 4 (Similarly claim 14), Park disclosed the limitations of,
wherein the body of the message indicates the device eligibility and one or more criteria of device eligibility for the AI/ML federated learning (to indicate that the POU session is session establishment for split computing [Al/ML federated learning], the UE may include a predesignated, separate indicator in the POU session establishment request message [body of the message] about the split computing application service [device eligibility] and an associated QoS parameter [criteria]; para [0063]-[0064]).
Claim Rejections - 35 USC § 103
8. 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.
9. Claims 2-3, 5-10, 12-13, 15 and 16-19 are rejected under 35 U.S.C. 103 as being unpatenable by Park in view of U.S. Pat. Pub. No. 2022/00344049 to Li et al. (“Li”).
10. With regards to claim 2 (Similarly claim 12), Park is silent on the limitations of,
wherein the AI/ML federated learning comprises rounds of federated learning each being an iteration back and forth between the server and the plurality of separate devices, and
wherein the AI/ML federated learning is implemented in parallel at the plurality of separate devices.
However, Li teaches wherein the Al/ML federated learning comprises rounds of federated learning each being an iteration back and forth between the server and the plurality of separate devices (master node [server] manages distributed learning of a plurality of student nodes [plurality of sperate devices] that exchange learning data between master node [server] and the plurality of student nodes [plurality of devices] in multiple iterations of several training rounds; para [0134]-[0136]), and wherein the Al/ML federated learning is implemented in parallel at the plurality of separate devices (distributed Al learning [federated learning] implemented in parallel between network nodes including master node [server] and student nodes [plurality of devices]; para [0010], [0011], [0276]).
It would have been obvious to one of ordinary skill in the art prior to modify the Al/ML federated learning system of Park by incorporating federation learning in a plurality of iterations including parallel processing of learning data, as suggested by Li, in order to optimize efficiency and reduce computational cost of processing a large datasets (para [0140], Li).
11. With regards to claim 3 (Similarly claim 13), Park discloses the limitations of,
wherein the body of the message indicates the AI/ML (Al/ML split computing configured based on POU message [body of message] indicating type of session to establish; para [0036], [0050], [0062]-[0063])
Park is silent on the limitations of,
federated learning synchronization among the plurality of devices and that, of a round of the rounds of the federated learning, the federated learning is to begin at a same time at each of the plurality of separated devices.
However, Li teacheas federated learning synchronization among the plurality of devices and that, of a round of the rounds of the federated learning, the federated learning is to begin at a same time at each of the plurality of separated devices (distributed Al learning for a plurality of slave nodes [plurality of devices] manages by a master node includes synchronization of data processing of training/learning data of the slave nodes using parallel data processing; para [0010]-[0011], [0276]).
It would have been obvious to one of ordinary skill in the art prior to modify the Al/ML federated learning system of Park by incorporating federation learning in synchronization between a plurality of devices, as suggested by Li, in order to optimize efficiency and reduce computational cost of processing a large datasets (para [0140], Li).
12. With regards to claim 5 (Similarly claim 15), Park is silent on the limitations of,
wherein the one or more criteria of device eligibility for the AI/ML federated learning is any of an operating system, a processor speed, an available memory, an available image library, a number of images, a geographical location, an a language setting.
However, LI teaches wherein the one or more criteria of device eligibility for the Al/ML federated learning is any of an operating system, a processor speed, an available memory, an available image library, a number of images, a geographical location, and a language setting (server nodes exchange updates of Al/ML federated training data between UE nodes [plurality of devices] and central server including indication of location data [geographical location]; para [00581).
It would have been obvious to one of ordinary skill in the art to modify the federated learning system of Park by exchanging location data between servers and associated network nodes, as suggested by LI, in order to reduce computational complexity and latency in exchanging federated data by exchanging critical network data (para [0060], LI).
13. With regards to claim 6 (Similarly claim 16), Park is silent on the limitations of,
wherein the model evaluation of the AI/ML federated learning instructs the plurality of separate devices to implement an evaluation of a model of the AI/ML federated learning.
However, Li teaches wherein the model evaluation of the Al/ML federated learning instructs the plurality of separate devices to implement an evaluation of a model of the Al/ML federated learning (a plurality of student nodes [separate device] are managed by a central server node to test a model using a distributed Al/ML learning process to assess accuracy of the model [model evaluation]; para [0114], [0121], [0124]).
It would have been obvious to one of ordinary skill in the art prior to modify the Al/ML federated learning system of Park by incorporating federation learning for model evaluation, as suggested by Li, in order to optimize efficiency and reduce computational cost of processing large datasets (para [0140], Li).
14. With regards to claim 7 (Similarly claim 17), Park is silent on the limitations of,
wherein the model evaluation of the AI/ML federated learning instructs the plurality of separate devices to implement an evaluation of a model separate from the AI/ML federated learning.
However, Li teaches wherein the model evaluation of the Al/ML federated learning instructs the plurality of separate devices to implement an evaluation of a model separate from the Al/ML federated learning (a plurality of student nodes [separate device] are managed by a central server node to test a model using a distributed Al/ML learning process to assess accuracy of the model [model evaluation]; para [0114], [0121], [0124]).
It would have been obvious to one of ordinary skill in the art prior to modify the Al/ML federated learning system of Park by incorporating federation learning for model evaluation, as suggested by Li, in order to optimize efficiency and reduce computational cost of processing a large datasets (para [0140], Li).
15. With regards to claim 8 (Similarly claim 18), Park is silent on the limitations of,
wherein the model update of the AI/ML federated learning instructs the plurality of separate devices to update parameters of a model of the AI/ML federated learning.
However, Li teaches wherein the model update of the Al/ML federated learning instructs the plurality of separate devices to update parameters of a model of the Al/ML federated learning (a plurality of student nodes [separate device] are managed by a central server node to test a model using a distributed Al/ML learning process to assess accuracy of the model [model evaluation] and report an update on the model evaluation; para [0109], [0114], [0121], [0124]).
It would have been obvious to one of ordinary skill in the art prior to modify the Al/ML federated learning system of Park by incorporating federation learning for model evaluation and updates, as suggested by Li, in order to optimize efficiency and reduce computational cost of processing a large datasets (para [0140], Li).
16. With regards to claim 9 (Similarly claim 19), Park is silent on the limitations of,
wherein the model update is an instruction from the server to the plurality of separate devices.
However, Li teaches wherein the model update is an instruction from the server to the plurality of separate devices (a plurality of student nodes [separate device] are managed by a central server node to test [instruct] a model using a distributed Al/ML learning process to assess accuracy of the model [model evaluation] and report an update on the model evaluation; para [0109], [0114], [0121], [0124]).
It would have been obvious to one of ordinary skill in the art prior to modify the Al/ML federated learning system of Park by incorporating federation learning for model evaluation and updates, as suggested by Li, in order to optimize efficiency and reduce computational cost of processing a large datasets (para [0140], Li).
17. With regards to claim 10, Park is silent on the limitations of,
wherein the model update is an instruction from the at least one of the plurality of separate devices to the se
However, Li teaches wherein the model update is an instruction from the at least one of the plurality of separate devices to the server (a plurality of student nodes [separate device] are managed by a central server node to test a model using a distributed Al/ML learning process to assess accuracy of the model [model evaluation] and report [instruct] an update on the model evaluation; para [0109], [0114], [0121], [0124]).
It would have been obvious to one of ordinary skill in the art prior to modify the Al/ML federated learning system of Park by incorporating federation learning for model evaluation and updates, as suggested by Li, in order to optimize efficiency and reduce computational cost of processing a large datasets (para [0140], Li).
18. With regards to claim 19, Park is silent on the limitations of,
wherein the model update is one of a first instruction, from the server to the plurality of separate devices, and a second instruction from the at least one of the plurality of separate devices to the server.
However, Li teaches wherein the model update is one of a first instruction, from the server to the plurality of separate devices (a plurality of student nodes [separate device] are managed by a central server node to test [instruct] a model using a distributed Al/ML learning process to assess accuracy of the model [model evaluation] and report an update on the model evaluation; para [0109], [0114], [0121], [0124]), and a second instruction from the at least one of the plurality of separate devices to the server (a plurality of student nodes [separate device] are managed by a central server node to test a model using a distributed Al/ML learning process to assess accuracy of the model [model evaluation] and report [instruct] an update on the model evaluation; para [0109], [0114], [0121], [0124]).
It would have been obvious to one of ordinary skill in the art prior to modify the Al/ML federated learning system of Park by incorporating federation learning for model evaluation and updates, as suggested by Li, in order to optimize efficiency and reduce computational cost of processing a large datasets (para [0140], Li).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. See Notice of References Cited, PTO form 892.
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/MICHAEL JARED WALKER/Primary Examiner, Art Unit 3627 Michael.walker@uspto.gov
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