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 .
Response to Amendment/Remarks
This communication is considered fully responsive to the amendment filed on 05/22/2026.
Claims 1-10, 12-20 are pending and are examined in this office action.
Claims 3-10, 13-20 have been amended.
No new claim has been added and claims 11 have been canceled.
In view of the applicant’s argument and claims amendment, objection to the claims have been withdrawn.
In view of the claims amendment, the rejection under 35 USC § 101 to the claims have been withdrawn.
Response to Arguments
Applicant’s arguments, filed 05/22/2026 , with respect to the rejection(s) of claim(s) under have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of PATEROMICHELAKIS et al. (US 20230345292 A1; hereinafter as “PATEROMICHELAKIS ”) in view of SZABO et al. (WO 2019238215 A1 ;; hereinafter as “SZABO”).
.
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 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 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over PATEROMICHELAKIS et al. (US 20230345292 A1; hereinafter as “PATEROMICHELAKIS ”) in view of SZABO et al. (WO 2019238215 A1 ; hereinafter as “SZABO”).
Examiner’s note: in what follows, references are drawn to PATEROMICHELAKIS unless otherwise mentioned.
With respect to independence claim:
Regarding claim 12, PATEROMICHELAKIS teaches
A network node (==Edge Data Network 121 in fig. 1B)
(Edge P-QOS Appl in Fig. 2 with AI function which control UE 105 in fig. 2: ),
the network node (==Edge Data Network 121 )
obtain said messages during one or more communication phases communicated when an initial first communication policy is applied for controlling a Quality of Service, QoS, mode in said communication, wherein the QoS mode is adapted to set to one of at least two predefined QoS modes having different levels of QoS for each of said one or more communication phases (see fig. 7 element 705, 710 “ FIG. 7 depicts one embodiment of a method 700 for edge enabled AI/ML-assisted QoS profile configuration, according to embodiments of the disclosure. In various embodiments, the method 700 is performed by a network equipment apparatus 600, described above. In some embodiments, the method 700 is performed by a processor, such as a microcontroller, a microprocessor, a CPU, a GPU, an auxiliary processing unit, a FPGA, or the like.”: [0198]; “ receives 705 at least one of actual and expected characteristics of a user equipment (“UE”) that describes a context of the UE. The method 700 obtains 710 a data analytics model describing at least one expected network condition of a radio access network (“RAN”) node based on the characteristics of the UE.”: [0199] ) ,
train a machine learning model based on said messages and the first communication policy, produce a second communication policy based on the machine learning model, wherein the second communication policy comprises at least one adjusted QoS mode for at least one of the one or more communication phases ( “ The QoS adaptation pattern may include a sequence of QoS profiles to be associated with a QoS flow for the UE over a time interval. The method 700 communicates 720 the expected QoS adaptation pattern for the QoS flow of the UE to the UE and/or at least one network node associated with the QoS flow, and the method 700 ends.”: [0200]; “ enabled AI/ML-assisted QoS profile configuration, according to embodiments of the disclosure”: [0204] ),
determine a performance score for the second communication policy in the one or more communication phases based on the radio resources used when communicating using the second communication policy and further based on a reduced operation precision when said one or more communication phases are communicated using the adjusted QoS mode ( “ updates 815 at least one local QoS policy at the communication part of the UE based on the expected QoS adaptation pattern. The method 800 sends 820 a packet data unit (“PDU”) session modification request to a core network. The request may include the expected QoS adaption pattern. The method 800 sends 825 the expected QoS adaptation pattern to a gNB over radio resource control (“RRC”), and the method 800 ends.”: [0203]) .
While PATEROMICHELAKIS teaches, “determine a performance score for the second communication policy in the one or more communication phases based on the radio resources used when communicating using the second communication policy and further based on a reduced operation precision when said one or more communication phases are communicated using the adjusted QoS mode” ;
PATEROMICHELAKIS does not expressively disclose:
SZABO, in the same field of endeavor, discloses:
when the determined performance score indicates a performance exceeding a predetermined performance, apply the second communication policy to said communication between the network node and the control node ( “ The control tolerance setting determined for execution of the command may belong to one of multiple control tolerance settings. In a similar manner, the determined QoC level may belong to one of multiple QoC levels. In such an implementation, a 15 control tolerance setting pertaining to a higher control tolerance may be associated with a QoC level pertaining to a lower QoC, and vice versa. The QoC level may generally be indicative of a key performance indicator (KPI) requirement for execution of the associated command by the robotic device (e.g., for 20 a movement of the robotic device). In this regard, a QoC level associated with a higher QoC may be associated with a higher KPI requirement, and vice versa. As such, the obtained command may belong to at least one of multiple QoC levels associated with different KPI requirement levels for the robotic device. Exemplary KPI requirements include movement accuracy (e.g., in regard to a target position or a 25 target path), movement speed, and so on”: [page 3 lines 11-34]; Performance Score : Page 10 lines 16-30; page 11 lines 16-25).
Therefore, 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 teaching of PATEROMICHELAKIS to include the above recited limitations as taught by SZABO. The suggestion/motivation would be to provide maximize wireless resource usage (SZABO; [Page 1 lines 28-37]).
Regarding claim 1, the claim is interpreted and rejected for the same reason as set forth in claim 12.
Regarding claim 10, the claim is interpreted and rejected for the same reason as set forth in claim 1.
With respect to dependent claims:
Regarding claims 2, PATEROMICHELAKIS in view of SZABO teaches the invention of claim 1 as set forth above. Further, PATEROMICHELAKIS teaches, The method according to claim 1 wherein said messages comprises a status indication received from the control node and control operations sent to the control node for controlling the remotely controlled device and wherein applying the second communication policy to said communication between the network node and the control node comprises sending the control operations to the control node and receiving the status indication from the control node using the second communication (see fig. 1B: “ Step 2a: The P-QoS MEC application 185 interacts (see block 325) with the AI model designer (which can be an external entity) or the corresponding AI database 181 (assuming that a database is storing the UE and RAN related analytics), which can be other MEC applications of the same or different cloud/MEC platform, to fetch the data needed to perform AI model inference. In this step, the model is trained and the model can be related to the UE expected behavior (e.g., expected location, traffic demand, sequence of handovers, or the like) and/or the expected status of the RAN node 303 (e.g., expected performance downgrade, expected UL/DL traffic demand, expected backhaul conditions, expected DRB load, or the like) for a given time frame and geographical area.
[0108] Step 2b: The P-QoS MEC application 185 translates the trained AI model to an expected QoS adaptation pattern for the respective QoS flow (see block 330), considering the list of QoS profiles and their priorities, the expected UE 301 and/or RAN node 303 behavior, the hysteresis threshold (which may impact the number of allowed transitions), the expected accuracy of the prediction, which may be configured per area within the cell, and/or the like.
”: [0107]-[0108] ).
Regarding claim 3, PATEROMICHELAKIS in view of SZABO teaches the invention of claim 1 as set forth above. Further, PATEROMICHELAKIS teaches,, The method according to claim 1 wherein determining a performance score for the second communication policy further comprises computing the performance score for the second communication policy based on an intermediate reward for selecting a high level or low level QoS mode for the at least one adjusted QoS mode and further based on an end reward for a change in operation precision caused by said selection ( “ The P-QoS MEC application 185 has as main functionality to provide the expected QoS adaptation pattern for the respective QoS flow(s) of the target UE 301. T”; [0088]).
Regarding claim 4, PATEROMICHELAKIS in view of SZABO teaches the invention of claim 1 as set forth above. Further, PATEROMICHELAKIS teaches,, The method according to claim 1 wherein determining a performance score for the second communication policy comprises any of simulating or measuring the communication performed between the network node and the control node using the second communication policy (“updating at least one local QoS policy at the communication part of the UE based on the expected QoS adaptation pattern.”: [0203]; [0228] ).
Regarding claim 5, PATEROMICHELAKIS in view of SZABO teaches the invention of claim 1 as set forth above. Further, PATEROMICHELAKIS teaches,, The method according to claim to claim 1 wherein training the machine learning model is further based on a first performance score of the first communication policy ([0228]-[0230]).
Regarding claim 6, PATEROMICHELAKIS in view of SZABO teaches the invention of claim 1 as set forth above. Further, PATEROMICHELAKIS teaches,, The method according to claim 5 wherein the machine learning model is further trained based on a third communication policy, second messages communicated between the network node and the control node using the third communication policy, and a third performance score associated with the third communication policy ([0228]-[0230]).
Regarding claim 7, PATEROMICHELAKIS in view of SZABO teaches the invention of claim 1 as set forth above. Further, PATEROMICHELAKIS teaches,, The method according to claim 1 wherein the at least one adjusted QoS mode is changed from a high level QoS to a low level QoS ([0039]).
Regarding claim 8, PATEROMICHELAKIS in view of SZABO teaches the invention of claim 1 as set forth above. Further, PATEROMICHELAKIS teaches,, The method according to claim 1 wherein a high level QoS mode comprises the network node demanding Ultra-Reliable Low-Latency Communication, URLLC, for communicating with the control ([0039], [0036]).
Regarding claim 9, PATEROMICHELAKIS in view of SZABO teaches the invention of claim 1 as set forth above. Further, PATEROMICHELAKIS teaches,, The method according to claim 1 wherein applying the second communication policy requires the determined performance score to indicate a performance exceeding a predefined performance by a predefined threshold ( “ Based on this translation, the P-QoS MEC application 185 determines the QoS flow to expected QoS adaptation pattern mapping. This may also include a tuning of the hysteresis threshold for a given time or area (e.g., in a tunnel) to capture some deep QoS downgrades within a pre-defined hysteresis.”: [109]-[0111]).
Regarding claims 13-20, the claim is interpreted and rejected for the same reason as set forth in claims 2-9.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to M MOSTAZIR RAHMAN whose telephone number is (571)272-4785. The examiner can normally be reached 8:30am-5:00pm PST.
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, Derrick Ferris can be reached at 571-272-3123. 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.
/M Mostazir Rahman/Examiner, Art Unit 2411
/DERRICK W FERRIS/Supervisory Patent Examiner, Art Unit 2411
Prosecution Insights