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 .
DETAILED ACTION
Claims 1-20 received on 9/27/2024 have been examined, of which claims 1, 9 and 16 are independent.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 3, 6, 10, 13, 18 and 20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
Claims 3, 10 and 18 recite “a candidate device”. It is unclear, if “a candidate device” is part of “M candidate devices” or how the limitation “a candidate device comprises a terminal” is related to claim 1 and 9 respectively.
Claims 6 and 13 recite “filtering information comprises an area of interest”. The term “area of interest” could be interpreted as physical location (coverage area) or any logical subject or topic. The metes and bounds of the term for claim interpretation is unclear.
Claim 20 recites a communication device implementing steps of device determining method according to claim 9. However, claim 9 recites a second communication device and a first communication device. It is unclear, if communication device of claim 20 is first communication device or second communication device. For the purpose of examination and comparison to claim 16, the examiner assumes “the communication device being the second communication device” for claim 20.
Claim Rejections - 35 USC § 102
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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1, 3, 5-6, 8-10, 12-13, 15-16, 18, 20 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Kim et al. (US 20230308903).
Regarding claim 1, Kim teaches a device determining method (abstract: a method for an AS (application server) to perform FL (federated learning) related communication; fig 8), wherein the method comprises:
sending, by a first communication device (AIML NF/NEF, fig 8), a first request message to a second communication device (step 3, fig 8; para 242: 3. AIML NF/NEF may transmit a request message requesting analytics information to NWDAF), wherein the first request message is used for requesting to obtain network performance analytics information (para 243-244: the analytics information may be information such as a list of candidate UEs with a recommended period that can satisfy the request of the AI/ML application server or a recommended area, AIML NF/NEF may request analytics information from NWDAF to collect the requested UE's analytics information (e.g., UE Communication analytics, UE Mobility analytics, User Data Congestion analytics, QoS Sustainability analytics)); and
receiving, by the first communication device, the network performance analytics information from the second communication device (step 6, fig 8; para 256: 6. A NWDAF may transmit a response message to AIML NF/NEF), wherein the network performance analytics information comprises network performance analytics information corresponding to M candidate devices, and M is a positive integer (step 5, 6, fig 8; para 247-256: AIML NF/NEF may use UE Mobility Analytics information to consider UEs that will be in a specific area in a preferred time period as UE candidates; para 243: the analytics information may be information such as a list of candidate UEs with a recommended period that can satisfy the request of the AI/ML application server or a recommended area); wherein
network performance analytics information corresponding to one candidate device comprises at least one of the following information: information about a time period in which the one candidate device has network coverage (para 250: AIML NF/NEF may use UE Mobility Analytics information to consider UEs that will be in a specific area in a preferred time period as UE candidates).
Regarding claim 9, Kim teaches a device determining method (abstract: a method for an AS (application server) to perform FL (federated learning) related communication; fig 8), wherein the method comprises:
receiving, by a second communication device (NWDAF, fig 8), a first request message from a first communication device (step 3, fig 8; para 242: 3. AIML NF/NEF may transmit a request message requesting analytics information to NWDAF), wherein the first request message is used for requesting to obtain network performance analytics information (para 243-244: the analytics information may be information such as a list of candidate UEs with a recommended period that can satisfy the request of the AI/ML application server or a recommended area, AIML NF/NEF may request analytics information from NWDAF to collect the requested UE's analytics information (e.g., UE Communication analytics, UE Mobility analytics, User Data Congestion analytics, QoS Sustainability analytics)); and
sending, by the second communication device, the network performance analytics information to the first communication device (step 6, fig 8; para 256: 6. A NWDAF may transmit a response message to AIML NF/NEF), wherein the network performance analytics information comprises network performance analytics information corresponding to M candidate devices, and M is a positive integer (step 5, 6, fig 8; para 247-256: AIML NF/NEF may use UE Mobility Analytics information to consider UEs that will be in a specific area in a preferred time period as UE candidates; para 243: the analytics information may be information such as a list of candidate UEs with a recommended period that can satisfy the request of the AI/ML application server or a recommended area); wherein
network performance analytics information corresponding to one candidate device comprises at least one of the following information: information about a time period in which the one candidate device has network coverage (para 250: AIML NF/NEF may use UE Mobility Analytics information to consider UEs that will be in a specific area in a preferred time period as UE candidates).
Regarding claim 16, Kim teaches a communication device, being a first communication device (AIML NF/NEF, fig 8), comprising a processor, a memory, and a program or an instruction stored in the memory and capable of running on the processor (para 325: the operation of a network node (e.g., UDM, NWDAF, AIML NF, NEF, AIML NF/NEF, AS, AI/ML AS etc.) implemented by the apparatus of FIGS. 1 to 3, processors 102 or 202, memories 104/204 and programs stored in memories), wherein the program or the instruction, when executed by the processor, causes the first communication device to perform (para 326: the instructions recorded in the storage medium are executed by one or more processors 102 or 202, so that the operations of a network node or base station are performed):
sending a first request message to a second communication device (step 3, fig 8; para 242: 3. AIML NF/NEF may transmit a request message requesting analytics information to NWDAF), wherein the first request message is used for requesting to obtain network performance analytics information (para 243-244: the analytics information may be information such as a list of candidate UEs with a recommended period that can satisfy the request of the AI/ML application server or a recommended area, AIML NF/NEF may request analytics information from NWDAF to collect the requested UE's analytics information (e.g., UE Communication analytics, UE Mobility analytics, User Data Congestion analytics, QoS Sustainability analytics)); and
receiving the network performance analytics information from the second communication device (step 6, fig 8; para 256: 6. A NWDAF may transmit a response message to AIML NF/NEF), wherein the network performance analytics information comprises network performance analytics information corresponding to M candidate devices, and M is a positive integer (step 5, 6, fig 8; para 247-256: AIML NF/NEF may use UE Mobility Analytics information to consider UEs that will be in a specific area in a preferred time period as UE candidates; para 243: the analytics information may be information such as a list of candidate UEs with a recommended period that can satisfy the request of the AI/ML application server or a recommended area);
wherein network performance analytics information corresponding to one candidate device comprises at least one of the following information: information about a time period in which the one candidate device has network coverage (para 250: AIML NF/NEF may use UE Mobility Analytics information to consider UEs that will be in a specific area in a preferred time period as UE candidates).
Regarding claim 3, 10 and 18, Kim further teaches wherein a candidate device comprises a terminal (candidate UE, para 250-253), and a granularity of the network performance analytics information is per terminal (para 249-250: AIML NF may collect one or more of the following Analytics information/data through NWDAF to process requests from AI/ML AS, UE Mobility Analytics: through UE Mobility Analytics, NWDAF can make historical statistics and predictions about the mobility of UEs, AI/ML Application Server may request Assistance Information for UE in a specific region and preferred FL operation time zone, AIML NF/NEF may use UE Mobility Analytics information to consider UEs that will be in a specific area in a preferred time period as UE candidates; thus, the network analytics including UE mobility analytics is per UE based).
Regarding claim 5 and 12, Kim further teaches wherein the first request message comprises reporting granularity indication information (para 244: AIML NF/NEF may request analytics information from NWDAF to collect the requested UE's analytics information (e.g., UE Communication analytics, UE Mobility analytics, User Data Congestion analytics, QoS Sustainability analytics)), and the reporting granularity indication information is used to indicate reporting network performance analytics information corresponding to a candidate device based on a granularity of device (para 253: for UEs requested by AI/ML Application Server, AIML NF, can select candidate UEs in consideration of UE mobility analytics and UEs in low-congestion time zones and regions with low congestion among the time zones that AI/ML Application Server prefers FL operation; para 249-250: AIML NF may collect one or more of the following Analytics information/data through NWDAF to process requests from AI/ML AS: UE Mobility Analytics: AIML NF/NEF may use UE Mobility Analytics information to consider UEs that will be in a specific area in a preferred time period as UE candidates; here, request and response correspond to per UE in the list of UEs).
Regarding claim 6 and 13, Kim further teaches wherein the first request message comprises filtering information (para 290-305: the request message may include one or more of the following information), and the filtering information comprises at least one of the following: an area of interest (para 304: area of Interest (FL operation for the UEs in the area of interest)).
Regarding claim 8 and 15, Kim further teaches wherein the first communication device comprises an application function (AF) (AIML NF/NEF, fig 8), and the second communication device comprises a network data analytics function (NWDAF) (NWDAF, fig 8).
Regarding claim 20, Kim teaches the device determining method according to claim 9.
Kim further teaches a communication device (NWDAF, fig 8), comprising a processor, a memory, and a program or an instruction stored in the memory and capable of running on the processor (para 325: the operation of a network node (e.g., UDM, NWDAF, AIML NF, NEF, AIML NF/NEF, AS, AI/ML AS etc.) implemented by the apparatus of FIGS. 1 to 3, processors 102 or 202, memories 104/204 and programs stored in memories), wherein when the program or the instruction is executed by the processor (para 326: the instructions recorded in the storage medium are executed by one or more processors 102 or 202, so that the operations of a network node or base station are performed), the steps of the device determining method according to claim 9 are implemented (fig 8).
Claim Rejections - 35 USC § 103
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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims, the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 2, 7, 14, 17 are rejected under 35 U.S.C. 103 as being unpatentable over Kim et al. (US 20230308903) in view of Mu et al. (US 20240023082)
Regarding claim 2 and 17, Kim teaches the limitations of parent claim.
Kim further teaches wherein after the receiving, by the first communication device, the network performance analytics information from the second communication device (steps 3-6, fig 8), the method further comprises:
determining, by the first communication device, N devices participating in the federated learning from the M candidate devices based on the network performance analytics information, wherein N is a positive integer less than or equal to M (para 268-269: based on the received the 5GS assistance information, the AI/ML Application Server can select and manage the group of UEs, and determine the start time for FL operation, the AI/ML AS may select a member for the FL operation based on the assistance information received from the 5GC).
Kim teaches the network analytics information transmission, candidate UE determination, and UE selection for the FL operation. However, the reference is silent regarding establishing connection to the selected devices to perform federated learning. Mu is directed to scheduling of UE for participating the federated learning based on the distribution characteristics of the local dataset.
Mu further teaches establishing, by the first communication device, connection to the N devices and performing federated learning (para 45-49: the base station may establish communication connections with a plurality of UE and schedule at least a portion of the UE for performing the federated learning, the base station can utilize the distribution characteristic of the local dataset of the UE to schedule the UE that can participate in the federated learning from a plurality of candidate UE that have established communication connections with the base station, and jointly perform the federated learning with the UE participating in the federated learning). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine network analysis for federated learning as taught by Kim with establishing connection with UE for the federated learning as taught by Mu for the benefit of improving training efficiency as taught by Mu in para 49.
Regarding claim 7, Kim fails to teach, Mu further teaches wherein before the determining, by the first communication device, N devices participating in the federated learning from the M candidate devices based on the network performance analytics information (para 49: the base station can utilize the distribution characteristic of the local dataset of the UE to schedule the UE that can participate in the federated learning from a plurality of candidate UE that have established communication connections with the base station, and jointly perform the federated learning with the UE participating in the federated learning), the method further comprises:
determining, by the first communication device, that the M candidate devices have a federated learning capability (para 62-66: obtaining capability information of the at least one UE, the capability of the at least one UE for participating in the federated learning may also be considered, and the target UE to be scheduled for participating in the federated learning may be jointly determined based on the distribution characteristic of the local dataset and the capability information described above). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine network analysis for federated learning as taught by Kim with establishing connection with UE for the federated learning as taught by Mu for the benefit of improving training efficiency as taught by Mu in para 49.
Regarding claim 14, Kim fails to teach, Mu further teaches wherein before the sending, by the second communication device, the network performance analytics information to the first communication device (para 49: the base station can utilize the distribution characteristic of the local dataset of the UE to schedule the UE that can participate in the federated learning from a plurality of candidate UE that have established communication connections with the base station, and jointly perform the federated learning with the UE participating in the federated learning), the method further comprises:
determining, by the second communication device, that the M candidate devices have a federated learning capability (para 62-66: obtaining capability information of the at least one UE, the capability of the at least one UE for participating in the federated learning may also be considered, and the target UE to be scheduled for participating in the federated learning may be jointly determined based on the distribution characteristic of the local dataset and the capability information described above). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine network analysis for federated learning as taught by Kim with establishing connection with UE for the federated learning as taught by Mu for the benefit of improving training efficiency as taught by Mu in para 49.
Claims 4, 11, 19 are rejected under 35 U.S.C. 103 as being unpatentable over Kim et al. (US 20230308903) in view of Akdeniz et al. (US 20230068386)
Regarding claim 4, 11 and 19, Kim teaches the limitations of parent claim. Kim fails to teach network analytics information for candidate includes network signal quality. Akdeniz is directed to distributed learning for wireless edge computing network.
Akdeniz further teaches wherein the network signal quality information comprises received signal strength indication (RSSI) (para 278-279: the MEC server 2608 is to select K clients/client devices 2604 from a pool of N clients 2604 to perform federated learning in a next training round, each client may be connected to the MEC server differently, the bandwidth available to users and their data rate can be dependent on the technology as well as their signal-to-noise ratio; here, signal to noise ratio is considered signal strength indication). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine network analysis for federated learning as taught by Kim with UE selection for the federated learning based on signal strength as taught by Akdeniz for the benefit of minimizing total training time as taught by Akdeniz in para 283.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RINA C PANCHOLI whose telephone number is (571)272-2679. The examiner can normally be reached M-F 7:30am-4pm.
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/RINA C PANCHOLI/Primary Examiner, Art Unit 2477 9/29/2026
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