DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
2. Acknowledgment is made of the present application claims priority to PCT Application No. PCT/EP2021/073764 filed on August 27, 2021.
Information Disclosure Statement
3. Acknowledgment is made of Applicant’s submission of information disclosure statement (IDS), dated on February 6, 2024. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Examiner's Notes
4. Applicant is encouraged to submit a written authorization for Internet communications (PTO/SB/439, http://www.uspto.gov/sites/default/files/documents/sb0439.pdf) in the instant patent application to authorize the examiner to communicate with the applicant via email. The authorization will allow the examiner to better practice compact prosecution. The written authorization can be submitted via one of the following methods only: (1) Central Fax which can be found in the Conclusion section of this Office action; (2) regular postal mail; (3) EFS WEB; or (4) the service window on the Alexandria campus. EFS web is the recommended way to submit the form since this allows the form to be entered into the file wrapper within the same day (system dependent). Written authorization submitted via other methods, such as direct fax to the examiner or email, will not be accepted. See MPEP § 502.03.
Application Status
5. Acknowledgment is made of Applicant’s submission of the preliminary amendment on February 6, 2024. Claims 4-10, 22-27, 29-30 and 40 have been amended; claims 11-21, 31-39, 41-80, 83-84, and 86 have been cancelled.
Upon entering the amendment, claims 1-10, 22-30, 40, 81-82, and 85 are pending. This communication is considered fully responsive and sets forth below.
Claim Rejections - 35 USC § 102
6. 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.
7. 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.
(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.
8. Claims 1-10, 23-30, 40, 81-82, and 85 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Elshafie et al. (US 2024/0187906).
Regarding claim 1, Elshafie et al. teach the method comprising:
transmitting an indication of support of learning reporting information (paragraph [0026] lines 1-18; Examiner’s Notes: the information, e.g., feedback report on machine learning in the prior art teaches the limitation of “learning reporting information;”
in fact, UE transmitting the indication on information, e.g., feedback report on machine learning in the prior of machine learning in the prior art teaches the limitation of “transmitting an indication of support of learning reporting information” in the instant application);
receiving a configuration related to the learning of the reporting information (paragraph [0026] lines 1-18; Examiner’s Notes: the transmission configuration in the prior art teaches the limitation of “a configuration;”
in fact, UE receiving the transmission configuration regards to feedback report/information on machine learning in the prior art teaches the limitation of “receiving a configuration related to the learning of the reporting information” in the instant application),
the configuration comprising at least one parameter related to exploration of the reporting information (paragraph [0026] lines 1-18; Examiner’s Notes: the desired reward parameter in the prior art teaches the limitation of “one parameter related to exploration of the reporting information;”
in fact, the transmission configuration including/indicating the desired reward parameter regards to feedback report on machine learning in the prior art teaches the limitation of “the configuration comprising at least one parameter related to exploration of the reporting information” in the instant application);
initiating the exploration of the reporting information to activate the learning of the reporting information (paragraph [0026] lines 6-18; Examiner’s Notes: taking action on machine learning in the prior art teaches the limitation of “to activate the learning;”
in fact, indicating/taking action regards to feedback report on machine learning in the prior art teaches the limitation of “initiating the exploration of the reporting information to activate the learning of the reporting information” in the instant application);
varying at least one value of the reporting information, based on the at least one parameter, to learn the at least one value of the reporting information (paragraphs [0026] lines 1-6 & [0027] lines 1-20; Examiner’s Notes: the element, e.g., a state index, regards to the feedback report in the prior art teaches the limitation of “one value of the reporting information;”
in fact, changing/varying an element, e.g., a state index, regards to the feedback report based on the desired reward parameter in machine learning in the prior art teaches the limitation of “varying at least one value of the reporting information, based on the at least one parameter, to learn the at least one value of the reporting information” in the instant application); and
reporting the at least one value of the reporting information during the learning or following a deactivation of the learning of the at least one value of the reporting information (paragraph [0026] lines 6-18; Examiner’s Notes: feedbacking/reporting the element, e.g., a state index, during the machine learning in the prior art teaches the limitation of “reporting the at least one value of the reporting information during the learning” in the instant application;
in fact, the cited art teaches the limitation of “reporting the at least one value of the reporting information during the learning or following a deactivation of the learning of the at least one value of the reporting information” in the instant application as well).
Regarding claim 2, Elshafie et al. teach the method, further comprising receiving exploration permission to explore learning of the at least one value of the reporting information (paragraph [0080] lines 1-14; Examiner’s Notes: the policy on machine learning in the prior art teaches the limitation of “exploration permission” in the instant application;
in fact, receiving the policy via learning element/value regards to feedback report in the prior art teaches the limitation of “receiving exploration permission to explore learning of the at least one value of the reporting information” in the instant application).
Regarding claim 3, Elshafie et al. teach the method, further comprising requesting exploration permission to initiate the exploration of the reporting information prior to receiving exploration permission to explore learning of the at least one value of the reporting information (paragraph [0080] lines 1-14; Examiner’s Notes: UE requesting the learning policy/permission before receiving the permission regards to the element/information on feedback report of machine learning in the prior art teaches the limitation of “requesting exploration permission to initiate the exploration of the reporting information prior to receiving exploration permission to explore learning of the at least one value of the reporting information” in the instant application).
Regarding claim 4, Elshafie et al. further teach the method, wherein the exploration permission and the configuration are received with a user equipment from a base station (paragraph [0053] lines 1-15; Examiner’s Notes: UE 120a depicted in FIG. 5 in the prior art teaches the limitation of “a user equipment;”
BS 110a depicted in FIG. 5 in the prior art teaches the limitation of “a base station;”
in fact, UE 120a receiving the policy and the configuration from BS 110a, as illustrated in step 502 in FIG. 5 in the prior art teaches the limitation of “wherein the exploration permission and the configuration are received with a user equipment from a base station” in the instant application).
Regarding claim 5, Elshafie et al. further teach the method, wherein the initiating of the exploration of the reporting information occurs upon receiving the exploration permission (paragraph [0080] lines 1-14; Examiner’s Notes: activating/initiating feedback report based on the policy/permission in the prior art teaches the limitation of “wherein the initiating of the exploration of the reporting information occurs upon receiving the exploration permission” in the instant application).
Regarding claim 6, Elshafie et al. further teach the method, wherein the exploration permission is valid until exploration is deactivated (paragraph [0075] lines 1-17; Examiner’s Notes: the learning is within a finite set of actions in the prior art teaches the limitation of “valid until exploration is deactivated;”
in fact, the learning permission/policy is within a finite set of actions in the prior art teaches the limitation of “wherein the exploration permission is valid until exploration is deactivated” in the instant application).
Regarding claim 7, Elshafie et al. further teach the method, wherein the exploration is deactivated with a network or with a user equipment (paragraphs [0075] lines 1-17 & [0080] lines 1-14; Examiner’s Notes: network 605 depicted in FIG. 6 in the prior art teaches the limitation of “a network;”
in fact, the learning is within a finite set of actions in network 605, as illustrated in FIG. 6 in the prior art teaches the limitation of “wherein the exploration is deactivated with a network or with a user equipment” in the instant application).
Regarding claim 8, Elshafie et al. further teach the method, wherein the reporting information comprises channel state information or a subset of the channel state information (paragraph [0088] lines 1-17; Examiner’s Notes: the feedback report including CSI in the prior art teaches the limitation of “wherein the reporting information comprises channel state information or a subset of the channel state information” in the instant application),
wherein the subset of the channel state information comprises a channel quality indicator (paragraph [0088] lines 1-17; Examiner’s Notes: the CSI report/information indicating a CQI in the prior art teaches the limitation of “wherein the subset of the channel state information comprises a channel quality indicator” in the instant application).
Regarding claim 9, Elshafie et al. further teach the method, wherein the at least one parameter comprises a time window during which the exploration of the reporting information is to occur (paragraph [0049] lines 1-13; Examiner’s Notes: the predetermined time duration in the prior art teaches the limitation of “a time window” in the instant application;
in fact, the information/parameter including the predetermined time duration regards to the machine learning in the prior art teaches the limitation of “wherein the at least one parameter comprises a time window during which the exploration of the reporting information is to occur” in the instant application).
Regarding claim 10, Elshafie et al. further teach the method, wherein the at least one parameter comprises one or more consecutive transmissions during which the exploration of the reporting information is to occur (paragraph [0049] lines 1-13; Examiner’s Notes: the repeat transmissions, e.g., HARQ ACKs in the prior art teaches the limitation of “consecutive transmissions;”
in fact, the information/parameter including the repeat transmissions, e.g., HARQ ACKs, regards to the feedback report of the machine learning in the prior art teaches the limitation of “wherein the at least one parameter comprises one or more consecutive transmissions during which the exploration of the reporting information is to occur” in the instant application).
Regarding claim 23, Elshafie et al. further teach the method, wherein the at least one value is reported together with other information of the reporting information, or the at least one value comprises multiple values that are reported together (paragraph [0026] lines 1-18; Examiner’s Notes: the feedback report including the element, e.g., a state index, and other parameters/configurations in the prior art teaches the limitation of “wherein the at least one value is reported together with other information of the reporting information;”
consequently, the cited prior art teaches the limitation of “wherein the at least one value is reported together with other information of the reporting information, or the at least one value comprises multiple values that are reported together” in the instant application).
Regarding claim 24, Elshafie et al. further teach the method, wherein the at least one value is reported separately from other information of the reporting information, or the at least one value comprises multiple values that are reported separately (paragraph [0026] lines 1-18; Examiner’s Notes: the feedback report including the element, e.g., a state index, out from other parameters/configurations in the prior art teaches the limitation of “wherein the at least one value is reported separately from other information of the reporting information;”
consequently, the cited prior art teaches the limitation of “wherein the at least one value is reported separately from other information of the reporting information, or the at least one value comprises multiple values that are reported separately” in the instant application).
Regarding claim 25, Elshafie et al. further teach the method, wherein a channel quality indicator offset is calculated within a reported channel quality indicator index value (paragraph [0101] lines 1-17; Examiner’s Notes: calculating/reporting the CQI parameter/offset withing a feedback report on the CQI parameter, e.g., a channel state index value, in the prior art teaches the limitation of “wherein a channel quality indicator offset is calculated within a reported channel quality indicator index value” in the instant application).
Regarding claim 26, Elshafie et al. further teach the method, wherein a channel quality indicator offset is reported separately with a separate offset index (paragraph [0101] lines 1-17; Examiner’s Notes: calculating/reporting the CQI parameter/offset individually in the prior art teaches the limitation of “wherein a channel quality indicator offset is reported separately with a separate offset index” in the instant application).
Regarding claim 27, Elshafie et al. further teach the method, wherein a user equipment is in one of at least one reporting state (paragraph [0101] lines 1-17; Examiner’s Notes: UE in providing feedback state in the prior art teaches the limitation of “wherein a user equipment is in one of at least one reporting state” in the instant application),
the at least one reporting state comprising a normal state, an exploration state, and a learning state (paragraph [0101] lines 1-17; Examiner’s Notes: providing feedback including a feedback reporting state, a observing state and marching learning state in the prior art teaches the limitation of “the at least one reporting state comprising a normal state, an exploration state, and a learning state” in the instant application).
Regarding claim 28, Elshafie et al. further teach the method, wherein when the user equipment is in the exploration state, the user equipment varies the at least one value of the reporting information during reporting of the reporting information to learn the at least one value of the reporting information (paragraphs [0026] lines 1-6 & [0027] lines 1-20; Examiner’s Notes: the element, e.g., a state index, regards to the feedback report in the prior art teaches the limitation of “one value of the reporting information;”
in fact, UE changing/varying an element, e.g., a state index, regards to the feedback report based on the desired reward parameter in machine learning in the prior art teaches the limitation of “wherein when the user equipment is in the exploration state, the user equipment varies the at least one value of the reporting information during reporting of the reporting information to learn the at least one value of the reporting information” in the instant application).
Regarding claim 29, Elshafie et al. further teach the method, wherein when the user equipment is in the learning state, the user equipment continues learning and does not vary the at least one value of the reporting information during reporting of the reporting information (paragraphs [0026] lines 1-18; Examiner’s Notes: the element, e.g., a state index, regards to the feedback report in the prior art teaches the limitation of “one value of the reporting information;”
in fact, while in the learning state, UE not changing state index during reporting feedback in the prior art teaches the limitation of “wherein when the user equipment is in the learning state, the user equipment continues learning and does not vary the at least one value of the reporting information during reporting of the reporting information” in the instant application).
Regarding claim 30, Elshafie et al. further teach the method, comprising receiving separate data allocations for learning, said separate data allocations utilizing a user equipment reported channel quality indicator with an explored offset for selection of a modulation and coding scheme (paragraphs [0058] lines 1-16; Examiner’s Notes: determining/selecting the learning regards to MCS in the prior art teaches the limitation of “selection of a modulation and coding scheme;”
in fact, receiving information/elements in CQI for determining/selecting the learning regards to MCS in the prior art teaches the limitation of “receiving separate data allocations for learning, said separate data allocations utilizing a user equipment reported channel quality indicator with an explored offset for selection of a modulation and coding scheme” in the instant application).
Regarding claim 40, Elshafie et al. further teach the method, wherein: a subset of one or more user equipments of a plurality of user equipments are determined to benefit most after performing learning exploration (paragraphs [0035] lines 1-17; Examiner’s Notes: during the learning with BS 110a, UEs 120a and 120 withing cell 102a as illustrated in FIG. 1 in the prior art teaches the limitation of “wherein: a subset of one or more user equipments of a plurality of user equipments are determined to benefit most after performing learning exploration” in the instant application); and
wherein the subset of the one or more user equipments is selected for learning exploration (paragraphs [0035] lines 1-17; Examiner’s Notes: BS 110a identifying/selecting UEs 120a and 120 withing cell 102a for marching learing, as illustrated in FIG. 1, in the prior art teaches the limitation of “wherein the subset of the one or more user equipments is selected for learning exploration” in the instant application).
Regarding claim 81, Elshafie et al. teach the apparatus (paragraph [0106] lines 1-24; Examiner’s Notes: processing system 1102 illustrated in FIG. 11 in the prior art teaches the limitation of “apparatus” in the instant application) comprising:
at least one processor (paragraph [0106] lines 1-24; Examiner’s Notes: processor 1104 in processing system 1102 illustrated in FIG. 11 in the prior art teaches the limitation of “processor” in the instant application); and
at least one memory (paragraph [0106] lines 1-24; Examiner’s Notes: memory 1112 in processing system 1102 illustrated in FIG. 11 in the prior art teaches the limitation of “memory” in the instant application) including computer program code; wherein the at least one memory and the computer program code are configured to, with the at least one processor, cause the apparatus at least to:
transmit an indication of support of learning reporting information (paragraph [0026] lines 1-18; Examiner’s Notes: the information, e.g., feedback report on machine learning in the prior art teaches the limitation of “learning reporting information;”
in fact, UE transmitting the indication on information, e.g., feedback report on machine learning in the prior of machine learning in the prior art teaches the limitation of “transmit an indication of support of learning reporting information” in the instant application);
receive a configuration related to the learning of the reporting information (paragraph [0026] lines 1-18; Examiner’s Notes: the transmission configuration in the prior art teaches the limitation of “a configuration;”
in fact, UE receiving the transmission configuration regards to feedback report/information on machine learning in the prior art teaches the limitation of “receive a configuration related to the learning of the reporting information” in the instant application),
the configuration comprising at least one parameter related to exploration of the reporting information (paragraph [0026] lines 1-18; Examiner’s Notes: the desired reward parameter in the prior art teaches the limitation of “one parameter related to exploration of the reporting information;”
in fact, the transmission configuration including/indicating the desired reward parameter regards to feedback report on machine learning in the prior art teaches the limitation of “the configuration comprising at least one parameter related to exploration of the reporting information” in the instant application);
initiate the exploration of the reporting information to activate the learning of the reporting information (paragraph [0026] lines 6-18; Examiner’s Notes: taking action on machine learning in the prior art teaches the limitation of “to activate the learning;”
in fact, indicating/taking action regards to feedback report on machine learning in the prior art teaches the limitation of “initiate the exploration of the reporting information to activate the learning of the reporting information” in the instant application);
vary at least one value of the reporting information, based on the at least one parameter, to learn the at least one value of the reporting information (paragraphs [0026] lines 1-6 & [0027] lines 1-20; Examiner’s Notes: the element, e.g., a state index, regards to the feedback report in the prior art teaches the limitation of “one value of the reporting information;”
in fact, changing/varying an element, e.g., a state index, regards to the feedback report based on the desired reward parameter in machine learning in the prior art teaches the limitation of “vary at least one value of the reporting information, based on the at least one parameter, to learn the at least one value of the reporting information” in the instant application); and
report the at least one value of the reporting information during the learning or following a deactivation of the learning of the at least one value of the reporting information (paragraph [0026] lines 6-18; Examiner’s Notes: feedbacking/reporting the element, e.g., a state index, during the machine learning in the prior art teaches the limitation of “report the at least one value of the reporting information during the learning” in the instant application;
in fact, the cited art teaches the limitation of “report the at least one value of the reporting information during the learning or following a deactivation of the learning of the at least one value of the reporting information” in the instant application as well).
Regarding claim 82, Elshafie et al. teach the apparatus (paragraph [0106] lines 1-24; Examiner’s Notes: processing system 1102 illustrated in FIG. 11 in the prior art teaches the limitation of “apparatus” in the instant application) comprising:
at least one processor (paragraph [0106] lines 1-24; Examiner’s Notes: processor 1104 in processing system 1102 illustrated in FIG. 11 in the prior art teaches the limitation of “processor” in the instant application); and
at least one memory (paragraph [0106] lines 1-24; Examiner’s Notes: memory 1112 in processing system 1102 illustrated in FIG. 11 in the prior art teaches the limitation of “memory” in the instant application) including computer program code; wherein the at least one memory and the computer program code are configured to, with the at least one processor, cause the apparatus at least to:
receive an indication of support of learning reporting information (paragraph [0026] lines 1-18; Examiner’s Notes: the information, e.g., feedback report on machine learning in the prior art teaches the limitation of “learning reporting information;”
in fact, BS receiving the indication on information, e.g., feedback report on machine learning in the prior of machine learning in the prior art teaches the limitation of “receive an indication of support of learning reporting information” in the instant application);
transmit a configuration related to the learning of the reporting information (paragraph [0026] lines 1-18; Examiner’s Notes: the transmission configuration in the prior art teaches the limitation of “a configuration;”
in fact, BS transmitting the transmission configuration regards to feedback report/information on machine learning in the prior art teaches the limitation of “transmit a configuration related to the learning of the reporting information” in the instant application),
the configuration comprising at least one parameter related to exploration of the reporting information (paragraph [0026] lines 1-18; Examiner’s Notes: the desired reward parameter in the prior art teaches the limitation of “one parameter related to exploration of the reporting information;”
in fact, the transmission configuration including/indicating the desired reward parameter regards to feedback report on machine learning in the prior art teaches the limitation of “the configuration comprising at least one parameter related to exploration of the reporting information” in the instant application);
wherein the exploration of the reporting information is initiated to activate the learning of the reporting information (paragraph [0026] lines 6-18; Examiner’s Notes: taking action on machine learning in the prior art teaches the limitation of “to activate the learning;”
in fact, indicating/taking action regards to feedback report on machine learning in the prior art teaches the limitation of “wherein the exploration of the reporting information is initiated to activate the learning of the reporting information” in the instant application);
wherein at least one value of the reporting information is varied, based on the at least one parameter, to learn the at least one value of the reporting information (paragraphs [0026] lines 1-6 & [0027] lines 1-20; Examiner’s Notes: the element, e.g., a state index, regards to the feedback report in the prior art teaches the limitation of “one value of the reporting information;”
in fact, changing/varying an element, e.g., a state index, regards to the feedback report based on the desired reward parameter in machine learning in the prior art teaches the limitation of “wherein at least one value of the reporting information is varied, based on the at least one parameter, to learn the at least one value of the reporting information” in the instant application); and
receive reporting of the at least one value of the reporting information during the learning or following a deactivation of the learning of the at least one value of the reporting information (paragraph [0026] lines 6-18; Examiner’s Notes: receiving the element, e.g., a state index, during the machine learning in the prior art teaches the limitation of “receive reporting of the at least one value of the reporting information during the learning” in the instant application;
in fact, the cited art teaches the limitation of “receive reporting of the at least one value of the reporting information during the learning or following a deactivation of the learning of the at least one value of the reporting information” in the instant application as well).
Regarding claim 85, Elshafie et al. teach the non-transitory program storage device readable by a machine (paragraph [0106] lines 1-24; Examiner’s Notes: processing system 1102 illustrated in FIG. 11 in the prior art teaches the limitation of “a machine;” memory 1112 in processing system 1102 illustrated in FIG. 11 in the prior art teaches the limitation of “non-transitory program storage device” in the instant application), tangibly embodying a program of instructions executable with the machine for performing operations, the operations comprising:
transmitting an indication of support of learning reporting information (paragraph [0026] lines 1-18; Examiner’s Notes: the information, e.g., feedback report on machine learning in the prior art teaches the limitation of “learning reporting information;”
in fact, UE transmitting the indication on information, e.g., feedback report on machine learning in the prior of machine learning in the prior art teaches the limitation of “transmitting an indication of support of learning reporting information” in the instant application);
receiving a configuration related to the learning of the reporting information (paragraph [0026] lines 1-18; Examiner’s Notes: the transmission configuration in the prior art teaches the limitation of “a configuration;”
in fact, UE receiving the transmission configuration regards to feedback report/information on machine learning in the prior art teaches the limitation of “receiving a configuration related to the learning of the reporting information” in the instant application),
the configuration comprising at least one parameter related to exploration of the reporting information (paragraph [0026] lines 1-18; Examiner’s Notes: the desired reward parameter in the prior art teaches the limitation of “one parameter related to exploration of the reporting information;”
in fact, the transmission configuration including/indicating the desired reward parameter regards to feedback report on machine learning in the prior art teaches the limitation of “the configuration comprising at least one parameter related to exploration of the reporting information” in the instant application);
initiating the exploration of the reporting information to activate the learning of the reporting information (paragraph [0026] lines 6-18; Examiner’s Notes: taking action on machine learning in the prior art teaches the limitation of “to activate the learning;”
in fact, indicating/taking action regards to feedback report on machine learning in the prior art teaches the limitation of “initiating the exploration of the reporting information to activate the learning of the reporting information” in the instant application);
varying at least one value of the reporting information, based on the at least one parameter, to learn the at least one value of the reporting information (paragraphs [0026] lines 1-6 & [0027] lines 1-20; Examiner’s Notes: the element, e.g., a state index, regards to the feedback report in the prior art teaches the limitation of “one value of the reporting information;”
in fact, changing/varying an element, e.g., a state index, regards to the feedback report based on the desired reward parameter in machine learning in the prior art teaches the limitation of “varying at least one value of the reporting information, based on the at least one parameter, to learn the at least one value of the reporting information” in the instant application); and
reporting the at least one value of the reporting information during the learning or following a deactivation of the learning of the at least one value of the reporting information (paragraph [0026] lines 6-18; Examiner’s Notes: feedbacking/reporting the element, e.g., a state index, during the machine learning in the prior art teaches the limitation of “reporting the at least one value of the reporting information during the learning” in the instant application;
in fact, the cited art teaches the limitation of “reporting the at least one value of the reporting information during the learning or following a deactivation of the learning of the at least one value of the reporting information” in the instant application as well).
Claim Rejections - 35 USC § 103
9. 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 of this title, 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.
10. Claim 22 is rejected under 35 U.S.C. 103 as being unpatentable over Elshafie et al. (US 2024/0187906) in view of Turcanu et al. (US 2024/0064061).
Regarding claim 22, Elshafie et al. teach the method without explicitly teaching implementing an exploration averaging window.
Turcanu et al. from the same or similar field of endeavor teach implementing fairness of the method, wherein the at least one parameter comprises an exploration averaging window during which an explored offset value remains constant during exploration (paragraph [0105] lines 1-12; Examiner’s Notes: the time-average spectral efficiency loss in the prior art teaches the limitation of “an exploration averaging window;”
In fact, the learning element including the time-average spectral efficiency loss during which the highest spectral efficiency keeps the same value in the prior art teaches the limitation of “wherein the at least one parameter comprises an exploration averaging window during which an explored offset value remains constant during exploration” in the instant application).
Thus, it would have been obvious before the effective filing date of the claimed invention to one of ordinary skill in art to implement the method of Turcanu et al. in the system of Elshafie et al.
The motivation for implementing an exploration averaging window, is to further enhance the mechanism of a method for learning a set of trajectories based on a first set of observations received from a wireless device, a trajectory including a subset of the first set of observations, wherein the method further includes adjusting an update period for receiving future observations from the first WD based on the assigned trajectory.
Conclusion
11. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Mondal et al. (US 2021/0168779) is cited to set physical downlink shared channel (PDSCH) default beam behavior for single transmission-reception point (TRP), single downlink control information (DCI) multi-TRP and multi-DCI multi-TRP operation, as well as physical downlink control channel (PDCCH) prioritization based on quasi-colocation (QCL) Type-D for multi-panel reception and single panel reception;
Xiong et al. (US 10,666,334) is cited to show an apparatus of an e-NodeB (eNB) capable to establish a communication connection with a user equipment (UE) in a communication network, the eNB comprising processing circuitry to transmit a downlink (DL) beamforming training reference signal (BF-TRS) to a user equipment (UE) using transmit beamforming weights.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to WEI ZHAO whose telephone number is (571)270-5672. The examiner can normally be reached from 8:00AM to 5:00PM Monday through Friday.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, JAE Y. LEE can be reached on 571-270-3936. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/WEI ZHAO/ Primary Examiner
Art Unit 2479
Prosecution Insights