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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on August 19, 2026 was filed after the mailing date of the Non-Final Office Action on June 25, 2026. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Response to Arguments
Applicant’s arguments with respect to claim(s) 1-7, 9-17, and 19-20 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 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.
Claim(s) 1-7, 9-17, and 19-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhang et al. (hereinafter “Zhang”, US 2023/0217310) in view of Salkintzis et al. (hereinafter “Salkintzis”, US 2024/0334219), and further in view of Jian et al. (hereinafter “Jian”, US 2023/0300671).
Regarding claims 1 and 11, Zhang discloses a user equipment (UE) (i.e., a UE 115-a as shown in Fig. 2, or UE 605 as shown in Fig. 6) and a method comprising:
an access performance prediction circuit (i.e., a communications manager 620 includes availability prediction manager 630), arranged to predict performance of a 3rd generation partnership project (3GPP) access and performance of a non-3GPP access (i.e., the parameters that are associated with the cellular network and non-cellular network are used to predict the availability of the cellular network and the non-cellular network as described in paragraphs 0021, 0027, 0054, 0083, 0086, 0094-0095, 0200-0201 and Abstract, as shown in Fig. 7); and
a wireless communication circuit (i.e., a transmitter 615 or a receiver 610), arranged to take action in response to predicted performance of the 3GPP access and predicted performance of the non-3GPP access (i.e., the UE takes actions based on the predicted availability as described in paragraphs 0095 and 0100-0101),
wherein the wireless communication circuit refers to both of the predicted performance of the 3GPP access and the predicted performance of the non-3GPP access to steer traffic by using the non-3GPP access (i.e., one or more algorithms predicts the availability status of at least one of the cellular network, the non-cellular network, or both. If the algorithm predicts that one of the networks may become unavailable (e.g., in the future), prompting one or both of the networks to adjust the ATSSS mode of the UE accordingly as described in paragraphs 0021, 0027 and 0054. Adjusting the ATSSS mode in which uplink/downlink traffic may be served by a cellular network, a non-cellular network, or both (via splitting) as described in paragraphs 0081 and 0084).
Zhang, however, does not expressly disclose:
wherein each of the predicted performance of the 3GPP access and the predicted performance of the non-3GPP access is a prediction result of a future network environment rather than a measurement result of a current network environment, the prediction result of the future network environment comprises at least one of predicted round-trip time (RTT) and predicted congestion, and computation of the least one of predicted RTT and predicted congestion is performed locally on the UE.
In a similar endeavor, Salkintzis discloses round trip time determination based on analytics. Salkintzis also discloses wherein each of the predicted performance of the 3GPP access and the predicted performance of the non-3GPP access is a prediction result of a future network environment rather than a measurement result of a current network environment, the prediction result of the future network environment comprises at least one of predicted round-trip time (RTT) and predicted congestion (i.e., a predicted RTT for each access type and is derived by the NWDAF based on historical RTT measurement as described in paragraphs 0054-0059),
Therefore, it would have been obvious to one of ordinary skilled in the art to modify the teachings of the cited references and arrive at the present invention.
The motivation/suggestion for doing so would have been to effectively estimate the performance of the networks.
The combination of Zhang and Salkintzis does not expressly disclose:
computation of the at least one of predicted RTT and predicted congestion is performed locally on the UE.
Furthermore, Jian discloses downlink congestion control optimization. Jian also discloses:
computation of the at least one of predicted RTT and predicted congestion is performed locally on the UE (i.e., the UE predicts modified connection parameters that reduces the packet congestion based on the estimated packet congestion as described in paragraphs 0040, 0098, and 0127).
Therefore, it would have been obvious to one of ordinary skilled in the art to modify the teachings of the cited references and arrive at the present invention.
The motivation/suggestion for doing so would have been to reduce the packet congestion.
Regarding claims 2 and 12, Zhang, Salkintzis, and Jian disclose all limitations as recited within claims as described above. Zhang also discloses wherein the access performance prediction circuit is arranged to obtain the predicted performance of the 3GPP access and the predicted performance of the non-3GPP access through machine learning (i.e., using machine learning to predict availability as described in paragraph 0095).
Regarding claims 3 and 13, Zhang, Salkintzis, and Jian disclose all limitations as recited within claims as described above. Zhang also discloses wherein the access performance prediction circuit comprises:
a radio-frequency (RF) feature extraction circuit, arranged to receive RF signal information of the 3GPP access and RF signal information of the non-3GPP access, and convert the RF signal information of the 3GPP access and the RF signal information of the non-3GPP access into feature metrics of the 3GPP access and the non-3GPP access (i.e., RF signature database as described in paragraphs 0097-0099);
an environment classification circuit, arranged to classify environments of the 3GPP access and the non-3GPP access according to the feature metrics of the 3GPP access and the non-3GPP access (i.e., training online/offline based on the generic patterns/behaviors as described in paragraphs 0097-0099); and
an action circuit, arranged to obtain the predicted performance of the 3GPP access and the predicted performance of the non-3GPP access according to a classification result of the environments of the 3GPP access and the non-3GPP access (i.e., outputting a prediction of network availability and reporting the change of network as described in paragraphs 0100);
wherein at least one of the RF feature extraction circuit and the environment classification circuit employs a neural-network (NN) model (i.e., the parameters are input into an algorithm (e.g., a neural-network) as described in paragraph 0054).
Regarding claims 4 and 14, Zhang, Salkintzis, and Jian disclose all limitations as recited within claims as described above. Zhang also discloses wherein the environment classification circuit employs adaptive machine learning (i.e., neural networks, AI modules, machine-learning modules as described in paragraph 0095).
Regarding claims 5 and 15, Zhang, Salkintzis, and Jian disclose all limitations as recited within claims as described above. Zhang also discloses wherein the RF feature extraction circuit employs a pre-trained NN model (i.e., neural networks, AI modules, machine-learning modules as described in paragraphs 0095 and 0097).
Regarding claims 6 and 16, Zhang, Salkintzis, and Jian disclose all limitations as recited within claims as described above. Zhang also discloses wherein the environment classification circuit is further arranged to provide feedbacks to the RF feature extraction circuit for adaption to environmental changes (i.e., providing feedback as described in paragraphs 0097, 0100 and 0104).
Regarding claims 7 and 17, Zhang, Salkintzis, and Jian disclose all limitations as recited within claims as described above. Zhang also discloses wherein the wireless communication circuit is further arranged to receive neural-network (NN) parameters transmitted from a network (i.e., the UE inputs parameters of the cellular network and non-cellular network into a neural network as described in paragraph 0054), and the access performance prediction circuit uses an NN model indicated by the NN parameters to obtain the predicted performance of the 3GPP access and the predicted performance of the non-3GPP access (i.e., predicting the availability based on the algorithm as described in paragraphs 0054 and 0095).
Regarding claims 9 and 19, Zhang, Salkintzis, and Jian disclose all limitations as recited within claims as described above. Zhang also discloses wherein the wireless communication circuit refers to the predicted performance of the 3GPP access and the predicted performance of the non-3GPP access to deal with traffic steering across the 3GPP access and the non-3GPP access under a steering mode of access traffic steering, switching and splitting (ATSSS) (i.e., dealing with ATSSS as described in paragraphs 0004, 0053-0055, 0101 and in Abstract).
Regarding claims 10 and 20, Zhang, Salkintzis, and Jian disclose all limitations as recited within claims as described above. Zhang also discloses wherein the wireless communication circuit reports the predicted performance of the 3GPP access and the predicted performance of the non-3GPP access to a network (i.e., reporting as described in paragraph 0100).
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 WAYNE CAI whose telephone number is (571)272-7798. The examiner can normally be reached Monday-Thursday, 7:00 AM-5:00 PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, KATHY WANG-HURST can be reached on (571)270-5371. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/Wayne H Cai/Primary Examiner, Art Unit 2644
Prosecution Insights