DETAILED ACTION
Applicant’s amendment and arguments filed September 11, 2025 is acknowledged.
Claims 1, 12, 25, and 28 have been amended.
Claims 4, 8, 9, 16, 19, 27, and 30 are cancelled.
Claims 1-3, 5-7, 10-15, 17, 18, 20-26, 28, 29, 31, and 32 are currently pending.
Claim Objections
Claims 1, 12, 25, and 28 are objected to because of the following informalities: in the body of the claims, replace “a size of the resource allocation or a configuration of the resource allocation” with -- a size of a resource allocation or a configuration of a resource allocation-- and “an uplink grant for a resource allocation” with --an uplink grant for the resource allocation-- or a different appropriate correction based on insufficient antecedent basis for the limitation (“resource allocation”) in the claims. Appropriate correction is required.
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 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.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
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.
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.
Claims 1, 2, 5-7, 10-13, 15, 17, 18, 20-22, 24, 25, 28, and 29 are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. (hereinafter Wang) (U.S. Patent Application Publication # 2019/0373554 A1) in view of Peng et al. (hereinafter Peng) (U.S. Patent Application Publication # 2019/0324805 A1), and further in view of WANG et al. (hereinafter Wang2) (U.S. Patent Application Publication # 2020/0229265 A1).
Regarding claims 1 and 25, Wang teaches a method and apparatus for wireless communications at a user equipment (UE; figure 1), comprising: one or more memories (110, figure 1); and one or more processors (105, figure 1) coupled to the one or more memories, the one or more processors configured to:
transmit a scheduling request (scheduling request, SR) that includes information associated with an artificial intelligence module (data associated with an application, such as the learning application, 135, figure 1) of the user equipment ([0002]; “…the UE may transmit a scheduling request (SR) to the network. In response, the network may transmit an uplink grant to the UE…”; [0025]; “…the UE may transmit a SR prior to the transmission of the data related to the application…”; [0029]; teaches transmitting a scheduling request (SR) indicating information associated with a learning application of the UE), wherein a scheduling request resource used to transmit the scheduling request indicates the artificial intelligence module (learning application, 135, figure 1) ([0025]; [0029]; [0032]; “…functionality provided by the self-learning application 135 and the SR power saving application 140 may be performed by a single application…”; [0043]; “…self-learning mode of operation may include collecting statistics related to the transmission of a SR…”; teaches the scheduling request (SR) indicates the learning and power saving application based in part on the resources used to transmit the SR); and
receive an uplink grant (uplink grant) for a resource allocation that is based at least in part on the artificial intelligence module ([0002]; [0025]; [0029]; teaches receiving an uplink grant based in part on the learning application of the UE; [0091]).
However, Wang may not explicitly disclose artificial intelligence information corresponding to an artificial intelligence module, wherein the artificial information indicates at least one of one or more weights or one or more parameters for the artificial intelligence module; and receive an uplink grant for a resource allocation that is based at least in part on the artificial intelligence information (although Wang does suggest information is included in the SR related to the learning application of the UE).
Nonetheless, in the same field of endeavor, Peng teaches and suggests a scheduling request (scheduling request, figure 2) that includes artificial intelligence information (parameters of the deep learning task; [0028]) corresponding to an artificial intelligence module (prediction model; figure 2; [0028]), wherein the artificial information indicates at least one of one or more weights or one or more parameters for the artificial intelligence module (parameters of the deep learning task; [0028]) ([0028]; “…the processing parameter(s) of the deep learning task 220 may be sent by the user terminal 210 to the resource scheduler 230 within or together with the scheduling request 214…the resource prediction model 240, and the resource prediction model 240 may obtain corresponding information if required…”; [0032]; teaches the user terminal transmitting a scheduling request that includes information, such as processing parameters, related to a deep learning task/prediction model in relation to resource allocation); and receive an uplink grant for a resource allocation that is based at least in part on the artificial intelligence information (resource allocation; [0018]) ([0018]; [0025]; [0032]; teaches receiving resources allocation from a resource pool based on the parameters of the deep learning task).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to incorporate transmitting a scheduling request that includes information, such as processing parameters, related to a deep learning task/prediction model in relation to resource allocation as taught by Peng with the method and apparatus for transmitting a scheduling request and receiving an uplink grant as disclosed by Wang for the purpose of providing resource allocation and scheduling, as suggested by Peng.
However, Wang, as modified by Peng, may not explicitly disclose wherein a size of the resource allocation or a configuration of the resource allocation is based at least in part on the artificial intelligence information, and wherein a scheduling request resource used to transmit the scheduling request indicates the artificial intelligence module, from a set of artificial intelligence modules.
Nonetheless, in the same field of endeavor, Wang2 teaches and suggests wherein a size of the resource allocation or a configuration of the resource allocation is based at least in part on the artificial intelligence information (parameters) ([0009]; [0010]; “…a resource request message sent by the user equipment based on the dedicated resource configuration information, where the resource request message is used to request the first network device to allocate an uplink resource; sending, by the first network device, uplink grant information to the user equipment, where the uplink grant information is used to indicate the uplink resource used by the user equipment…”; [0290] [0292]; teaches configuration of the allocation of uplink resource allocation based on the parameters associated with the modules of the UE), and wherein a scheduling request resource used to transmit the scheduling request indicates the artificial intelligence module, from a set of artificial intelligence modules (modules; figure 7) ([0010]; “…a resource request message sent by the user equipment based on the dedicated resource configuration information, where the resource request message is used to request the first network device to allocate an uplink resource; sending, by the first network device, uplink grant information to the user equipment, where the uplink grant information is used to indicate the uplink resource used by the user equipment…”; [0290] [0292]; teaches resources used to send a scheduling request for resources based on the parameters associated with the plurality of modules of the UE) .
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to incorporate configuration of the allocation of uplink resource allocation based on the parameters associated with the modules of the UE and resources used to send a scheduling request for resources based on the parameters associated with the plurality of modules of the UE as taught by Wang2 with the learning applications/modules and method and apparatus for transmitting a scheduling request and receiving an uplink grant as disclosed by Wang, as modified by Peng, for the purpose of improve resource utilization efficiency, as suggested by Wang2 ([0004]).
Regarding claims 2 and 15, Wang, as modified by Peng and Wang2, further teaches wherein the artificial intelligence module is one of a plurality of artificial intelligence modules associated with the user equipment, and wherein the artificial intelligence module is selected by the user equipment ([0029]; [0031]; [0032]; teaches the UE comprises a plurality of learning applications).
Regarding claims 5 and 20, Wang discloses transmitting an SR and receiving an uplink grant based on the SR, but may not explicitly disclose wherein a phase of a transmission of the scheduling request indicates the artificial intelligence information.
Nonetheless, in the same field of endeavor, Peng further teaches and suggests wherein a phase of a transmission of the scheduling request indicates the artificial intelligence information ([0005]; [0024]; [0029]; [0034]; teaches a completion time of the deep learning task included in the scheduling request).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to incorporate a completion time of the deep learning task included in the scheduling request as taught by Peng with the method and apparatus for transmitting a scheduling request and receiving an uplink grant as disclosed by Wang, as modified by Peng and Wang2, for the purpose of indicating information within a scheduling request.
Regarding claims 6 and 21, Wang discloses transmitting an SR and receiving an uplink grant based on the SR, but may not explicitly disclose wherein the artificial intelligence information comprises one or more bits of the scheduling request.
Nonetheless, in the same field of endeavor, Peng further teaches and suggests wherein the artificial intelligence information comprises one or more bits of the scheduling request ([0028]; teaches the scheduling request comprises bits of information related to an indication of the information in the scheduling request).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the scheduling request comprises an indication of the information in the scheduling request as taught by Peng with the method and apparatus for transmitting a scheduling request and receiving an uplink grant as disclosed by Wang, as modified by Peng and Wang2, for the purpose of indicating information within a scheduling request.
Regarding claim 7, Wang, as modified by Peng and Wang2, further teaches wherein the resource allocation is based at least in part on which scheduling request resource is used to transmit the scheduling request ([0002]; [0025]; [0038]; teaches transmitting a scheduling request (SR) using resources to transmit the SR during a subframe).
Regarding claims 10 and 17, Wang, as modified by Peng and Wang2, further teaches wherein the one or more processors are configured to: perform an artificial intelligence operation based at least in part on the artificial intelligence module; and provide second information determined using the artificial intelligence module on the resource allocation ([0002]; [0025]; [0029]; teaches the UE comprises a plurality of learning applications and performing the application operations using the allocated resources).
Regarding claims 11 and 18, Wang, as modified by Peng and Wang2, further teaches wherein the artificial intelligence module is a preferred artificial intelligence module selected by the user equipment ([0002]; [0025]; [0029]; teaches the UE comprises a plurality of learning applications).
Regarding claims 12 and 28, Wang teaches a method and apparatus for wireless communications at a base station (eNB; [0005]), comprising: a memory (inherent component of an eNB); and one or more processors (inherent component of an eNB) coupled to the memory, the one or more processors configured to:
receive a scheduling request (scheduling request, SR) that includes information associated with an artificial intelligence module (learning application, 135, figure 1) of a user equipment (UE; figure 1) ([0002]; [0025]; [0029]; teaches the eNB receiving a scheduling request (SR) indicating information associated with a learning application of the UE), wherein a scheduling request resource used to transmit the scheduling request indicates the artificial intelligence module (learning application, 135, figure 1) ([0025]; [0029]; [0032]; “…functionality provided by the self-learning application 135 and the SR power saving application 140 may be performed by a single application…”; [0043]; “…self-learning mode of operation may include collecting statistics related to the transmission of a SR…”; teaches the scheduling request (SR) indicates the learning and power saving application based in part on the resources used to transmit the SR); and
transmit, to the user equipment, an uplink grant (uplink grant) for a resource allocation that is based at least in part on the artificial intelligence module ([0002]; [0025]; [0029]; teaches transmitting an uplink grant based in part on the learning application of the UE; [0091]).
However, Wang may not explicitly disclose a scheduling request that includes artificial intelligence information corresponding to an artificial intelligence module, wherein the artificial information indicates at least one of one or more weights or one or more parameters for the artificial intelligence module; and transmitting an uplink grant for a resource allocation that is based at least in part on the artificial intelligence information (although Wang does suggest information is included in the SR related to the learning application of the UE).
Nonetheless, in the same field of endeavor, Peng teaches and suggests a scheduling request (scheduling request, figure 2) that includes artificial intelligence information (parameters of the deep learning task; [0028]) corresponding to an artificial intelligence module (prediction model; figure 2; [0028]), wherein the artificial information indicates at least one of one or more weights or one or more parameters for the artificial intelligence module (parameters of the deep learning task; [0028]) ([0028]; “…the processing parameter(s) of the deep learning task 220 may be sent by the user terminal 210 to the resource scheduler 230 within or together with the scheduling request 214…the resource prediction model 240, and the resource prediction model 240 may obtain corresponding information if required…”; [0032]; teaches the user terminal transmitting a scheduling request that includes information, such as processing parameters, related to a deep learning task/prediction model in relation to resource allocation); and transmitting an uplink grant for a resource allocation that is based at least in part on the artificial intelligence information (resource allocation; [0018]) ([0018]; [0025]; [0032]; teaches transmitting resources allocation from a resource pool based on the parameters of the deep learning task).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to incorporate transmitting a scheduling request that includes information, such as processing parameters, related to a deep learning task/prediction model in relation to resource allocation as taught by Peng with the method and apparatus for transmitting a scheduling request and receiving an uplink grant as disclosed by Wang for the purpose of providing resource allocation and scheduling, as suggested by Peng.
However, Wang, as modified by Peng, may not explicitly disclose wherein a size of the resource allocation or a configuration of the resource allocation is based at least in part on the artificial intelligence information, and wherein a scheduling request resource used to transmit the scheduling request indicates the artificial intelligence module, from a set of artificial intelligence modules.
Nonetheless, in the same field of endeavor, Wang2 teaches and suggests wherein a size of the resource allocation or a configuration of the resource allocation is based at least in part on the artificial intelligence information (parameters) ([0009]; [0010]; “…a resource request message sent by the user equipment based on the dedicated resource configuration information, where the resource request message is used to request the first network device to allocate an uplink resource; sending, by the first network device, uplink grant information to the user equipment, where the uplink grant information is used to indicate the uplink resource used by the user equipment…”; [0290] [0292]; teaches configuration of the allocation of uplink resource allocation based on the parameters associated with the modules of the UE), and wherein a scheduling request resource used to transmit the scheduling request indicates the artificial intelligence module, from a set of artificial intelligence modules (modules; figure 7) ([0010]; “…a resource request message sent by the user equipment based on the dedicated resource configuration information, where the resource request message is used to request the first network device to allocate an uplink resource; sending, by the first network device, uplink grant information to the user equipment, where the uplink grant information is used to indicate the uplink resource used by the user equipment…”; [0290] [0292]; teaches resources used to send a scheduling request for resources based on the parameters associated with the plurality of modules of the UE) .
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to incorporate configuration of the allocation of uplink resource allocation based on the parameters associated with the modules of the UE and resources used to send a scheduling request for resources based on the parameters associated with the plurality of modules of the UE as taught by Wang2 with the learning applications/modules and method and apparatus for transmitting a scheduling request and receiving an uplink grant as disclosed by Wang, as modified by Peng, for the purpose of improve resource utilization efficiency, as suggested by Wang2 ([0004]).
Regarding claim 13 and 29, Wang, as modified by Peng and Wang2, further teaches determining a selected artificial intelligence module based at least in part on the artificial intelligence module indicated by the scheduling request ([0002]; [0025]; [0029]; teaches the UE comprises a plurality of learning applications); and performing an operation based at least in part on the selected artificial intelligence module ([0002]; [0025]; [0029]; teaches the UE comprises a plurality of learning applications and performing the application operations using the allocated resources).
Regarding claim 22, Wang discloses transmitting an SR and receiving an uplink grant based on the SR, but may not explicitly disclose the artificial intelligence information corresponding to the artificial intelligence module to another UE.
Nonetheless, in the same field of endeavor, Peng further teaches and suggests the artificial intelligence information corresponding to the artificial intelligence module to another UE (one of another user terminal; figures 1-2) ([0038]; [0047]; teaches communicating information with another user terminal of the network).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to incorporate communicating information with another user terminal of the network as taught by Peng with the method and apparatus for transmitting a scheduling request and receiving an uplink grant as disclosed by Wang, as modified by Peng and Wang2, for the purpose indicating information within a scheduling request.
Regarding claim 24, Wang, as modified by Peng and Wang2, further teaches wherein the scheduling request indicates the artificial intelligence module (learning application, 135, figure 1) based at least in part on a scheduling request resource used to transmit the scheduling request ([0025]; [0029]; [0032]; “…functionality provided by the self-learning application 135 and the SR power saving application 140 may be performed by a single application…”; [0043]; “…self-learning mode of operation may include collecting statistics related to the transmission of a SR…”; teaches the scheduling request (SR) indicates the learning and power saving application based in part on the resources used to transmit the SR).
Claims 3, 14, and 26 are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. (hereinafter Wang) (U.S. Patent Application Publication # 2019/0373554 A1) in view of Peng et al. (hereinafter Peng) (U.S. Patent Application Publication # 2019/0324805 A1) and WANG et al. (hereinafter Wang2) (U.S. Patent Application Publication # 2020/0229265 A1), and further in view of Zhao et al. (hereinafter Zhao) (U.S. Patent Application Publication # 2022/0046698 A1).
Regarding claims 3 and 26, Wang, as modified by Peng and Wang2, discloses transmitting an SR and receiving an uplink grant based on the SR, but may not explicitly disclose wherein the artificial intelligence module comprises a channel state information feedback encoding operation.
Nonetheless, in the same field of endeavor, Zhao teaches and suggests wherein the artificial intelligence module comprises a channel state information feedback encoding operation ([0195]; [0200]; teaches including CSI feedback information).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to incorporate CSI feedback information as taught by Zhao with the method and apparatus as disclosed by Wang, as modified by Peng and Wang2, for the purpose of indicating whether the receiving terminal needs to send channel state information to the transmitting terminal, as suggested by Zhao.
Regarding claim 14, Wang, as modified by Peng and Wang2, discloses transmitting an SR and receiving an uplink grant based on the SR, but may not explicitly disclose wherein the artificial intelligence module indicated by the scheduling request comprises a channel state information feedback encoding operation and the selected artificial intelligence module comprises a channel state information feedback decoding operation.
Nonetheless, in the same field of endeavor, Zhao teaches and suggests wherein the artificial intelligence module indicated by the scheduling request comprises a channel state information feedback encoding operation and the selected artificial intelligence module comprises a channel state information feedback decoding operation ([0195]; [0200]; teaches including CSI feedback information).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to incorporate CSI feedback information as taught by Zhao with the method and apparatus as disclosed by Wang, as modified by Peng and Wang2, for the purpose of indicating whether the receiving terminal needs to send channel state information to the transmitting terminal, as suggested by Zhao.
Claim 23 is rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. (hereinafter Wang) (U.S. Patent Application Publication # 2019/0373554 A1) in view of Peng et al. (hereinafter Peng) (U.S. Patent Application Publication # 2019/0324805 A1) and WANG et al. (hereinafter Wang2) (U.S. Patent Application Publication # 2020/0229265 A1), and further in view of Larsson et al. (hereinafter Larsson) (U.S. Patent Application Publication # 2015/0289287 A1).
Regarding claim 23, Wang, as modified by Peng and Wang2, discloses transmitting an SR and receiving an uplink grant based on the SR, but may not explicitly disclose wherein a size of a downlink resource allocation used to provide the artificial intelligence information corresponding to the artificial intelligence module to the other UE is based at least in part on the artificial intelligence information.
Nonetheless, in the same field of endeavor, Larsson further teaches and suggests wherein a size of a downlink resource allocation used to provide the artificial intelligence information corresponding to the artificial intelligence module to the other UE is based at least in part on the artificial intelligence information ([0005]; “…A grant is transmitted on the Physical Downlink Control Channel (PDCCH) and the UE responds with a transmission using the resources specified in the grant and with the size specified in the grant…”; [0030]; teaches a size of the resource allocation based on the information in the scheduling request).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to incorporate a size of the resource allocation based on the information in the scheduling request as taught by Larsson with the method and apparatus for transmitting a scheduling request and receiving an uplink grant as disclosed by Wang, as modified by Peng and Wang2, for the purpose of optimizing the use of radio resources, as suggested by Larsson.
Claims 31 and 32 are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. (hereinafter Wang) (U.S. Patent Application Publication # 2019/0373554 A1) in view of Peng et al. (hereinafter Peng) (U.S. Patent Application Publication # 2019/0324805 A1) and WANG et al. (hereinafter Wang2) (U.S. Patent Application Publication # 2020/0229265 A1), and further in view of O’Shea (U.S. Patent Application Publication # 2023/0055213 A1).
Regarding claims 31 and 32, Wang, as modified by Peng and Wang2, discloses transmitting an SR and receiving an uplink grant based on the SR, but may not explicitly disclose determining the at least one of the one or more weights or the one or more parameters for the artificial intelligence module based at least in part on an encoder network.
Nonetheless, in the same field of endeavor, O’Shea teaches and suggests determining the at least one of the one or more weights or the one or more parameters for the artificial intelligence module based at least in part on an encoder network ([0100]; teaches determining network weights and parameters for machine-learning model based in part on an encoder network).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to incorporate determining network weights and parameters for machine-learning model based in part on an encoder network as taught by O’Shea with the method and apparatus as disclosed by Wang, as modified by Peng and Wang2, for the purpose of updating a machine learning model based an encoder network, as suggested by O’Shea.
Response to Arguments
Applicant's arguments with respect to claims 1-3, 5-7, 10-15, 17, 18, 20-26, 28, 29, 31, and 32 have been considered but are moot in view of a new ground(s) of rejection. Upon a quality review of the application and further consideration of the claims based on the broadest reasonable interpretation, the new ground(s) of rejection has been made as indicated in the above rejections.
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicant’s disclosure.
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/Suk Jin Kang/
Examiner, Art Unit 2477
December 30, 2025