Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
In the amendment filed March 18, 2026, claims 1 - 20 are currently pending for examination.
Response to Arguments
Regarding 35 U.S.C. 102 and 103 applicant’s arguments, see page 7 paragraphs 5 - page 11 paragraph 1, filed March 18, 2026, with respect to claims 1 - 20 have been fully considered and are not persuasive.
Regarding claims 1 and 12, the applicant argued that, see page 8, “…Shoki is silent with respect to coordinating, via transmission of control signaling, execution of one or more operations for a machine learning model. … However, Shoki describes communication of data signals, not communication of control signals. That is, the signals of Shoki include an "output vector," but Shoki does not teach that such an output vector controls anything, much less functions to "coordinate ... execution of one or more operations for [a] machine learning model." Rather, a data signal (e.g., the output vector of Shoki) merely carries data to be processed or otherwise operated upon. Thus, because a data signal is not equivalent to a control signal, Shoki does not teach "coordinate, via transmission of control signaling by the entity to one or more of the user equipment or network entities in the wireless communications network, execution of one or more operations for the machine learning model using a set of sub-models from the plurality of sub- models, the one or more operations being based on the input prompt," as recited in Claim 1 and similar features recited in independent Claim 12. Applicant therefore submits that Claims 1 and 12, as well as claims dependent thereon, are allowable and respectfully requests withdrawal of this rejection.”
In response to applicant's argument, the examiner respectfully disagrees with the argument above.
The broadly claimed limitation recites “coordinate, via transmission of control signaling by the entity to one or more of the user equipment or network entities in the wireless communications network”, clearly the data signal (e.g., the output vector of Shoki) carries data coordinated/based on control signaling, to be processed or otherwise operated upon. It is clear, that for a signal to be transmitted, control signaling needs to be activated/be present and data is then transmitted.
Examiner Note: Claims 4, 14 and 20 and instant specification paragraph 0059; “The control signaling may include, (these limitation are not in the independent claims) for example, downlink control information (DCI) transmitted by the RU 240 to the UE 104, signaling carried in a medium access control (MAC) control element (CE), radio resource control (RRC) signaling, signaling carried on a dedicated control channel (DCCH), or the like. Generally, the transmission of DCI by the RU 240 to the UE may have lower latency than MAC CE signaling, RRC signaling, or DCCH signaling”.
Regarding claims 1 and 12, Shoki clearly teaches, coordinate (see page 4, column 2, lines 27-30, coordinating between the entity and network/server), via transmission of control signaling by the entity to one or more of the user equipment or network entities in the wireless communications network (page 5, column 1, lines 2-6, see also Table 1, the different types are transmitted/coordinated via control signals, see also Fig.2, page 3 column 1, last paragraph and column 2 paragraphs 1-2, a preparation phase of establishing/coordinating control signaling split), execution of one or more operations for the machine learning model using a set of sub-models from the plurality of sub-models, the one or more operations being based on the input prompt (see page 3, column 1, lines 24-30, using modern generative AI models than encompass intricate mechanisms such as branching, autoregression, and loops).
Under the broadest reasonable interpretation, the system as disclosed by Shoki reads upon “receive, at the entity, an input prompt for processing using a machine learning model including a plurality of sub-models, the plurality of sub-models including a first sub-model configured to execute on a user equipment and one or more second sub- models configured to execute on one or more network entities, including the entity, in a wireless communications network; coordinate, via transmission of control signaling by the entity to one or more of the user equipment or network entities in the wireless communications network, execution of one or more operations for the machine learning model using a set of sub- models from the plurality of sub-models, the one or more operations being based on the input prompt; generate a result responsive to the input prompt based on the one or more operations; and output the generated result” as recites in the claim.
The applicant argued that for the reasons set forth in the arguments, see page 9 paragraphs 1-3, with respect to claims 2, 5, 13 and 15, that they are allowable. Examiner respectfully disagree, per the above cited reasons, these claims are not allowable.
The applicant argued that for the reasons set forth in the arguments, see page 9 paragraphs 4-6, with respect to claims 3, 6-8, and 16, that they are allowable. Examiner respectfully disagree, per the above cited reasons, these claims are not allowable.
The applicant argued that for the reasons set forth in the arguments, see page 9 paragraphs 7 – page 10 paragraphs 1-2, with respect to claim 4, that claim 4 is allowable. Examiner respectfully disagree, per the above cited reasons, claim 4 is not allowable.
The applicant argued that for the reasons set forth in the arguments, see page 10 paragraphs 3-5, with respect to claims 9, 10 and 19, that they are allowable. Examiner respectfully disagree, per the above cited reasons, these claims are not allowable.
The applicant argued that for the reasons set forth in the arguments, see page 11 paragraphs 1-3, with respect to claim 18, that claim 18 is allowable. Examiner respectfully disagree, per the above cited reasons, claim 18 is not allowable.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1, 11, 12 and 17 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Shoki (XP091642173, SHOKI OHTA ET AL: "Lambda-Split: A Privacy-Preserving Split Computing Framework for Cloud-Powered Generative Al") 23 October 2023, IDS submitted 4/7/2025).
As per claim 1, Shoki disclose An entity for wireless communications (see figure 1 (c), figure 2(b) the local device: page 2, column 2, lines 8-9), comprising:, comprising:
at least one memory having executable instructions stored thereon ( figure 2(b), cache database); and
one or more processors (see page 4, column 2, line 22, one or more CPUs) configured to execute the executable instructions to cause the entity to:
receive (see page 3, column 2, lines 16-18, receiving), at the entity, an input prompt (see page 3, column 1, lines 23-24, inputting at the entity) for processing using a machine learning model (see figure 1, the DNN constituting generative AI mode / a machine learning model; page 1, column 1, lines 39-40) including a plurality of sub-models(see figures 1 (c) and 2 (b): the generative model is divided into three sub-models: the head sub-model, the body sub-model and the tail sub-model), the plurality of sub-models including a first sub-model configured to execute on a user equipment and one or more second sub-models configured to execute on one or more network entities, including the entity (see page 3, column 1, lines 24-28), in a wireless communications (page 1, column 2, lines 5-9; page 8, column 2, lines 4-6);
coordinate (see page 4, column 2, lines 27-30, coordinating between the entity and network/server, setting up a control channel), via transmission of control signaling by the entity to one or more of the user equipment or network entities in the wireless communications network (page 5, column 1, lines 2-6, see also Table 1, the different types are transmitted/coordinated via control signals, see also Fig.2, page 3 column 1, last paragraph and column 2 paragraphs 1-2, a preparation phase of establishing/coordinating control signaling split), execution of one or more operations for the machine learning model using a set of sub-models from the plurality of sub-models, the one or more operations being based on the input prompt (see page 3, column 1, lines 24-30, using modern generative AI models than encompass intricate mechanisms such as branching, autoregression, and loops);
generate a result responsive to the input prompt based on the one or more operations (see page 3, column 1, lines 28-30, strategies for reducing communication traffic in alignment with the architecture of each respective model); and
output the generated result (see figure 2(b) output text, output result/text).
As per claim 11, Shoki disclose the entity of claim 1.
Shoki further disclose wherein the generated result comprises parameters of an instance of the machine learning model to be deployed across one or more devices in at least one of the wireless communications network or another wireless communication network (see figure 1(c), 2(b), a split framework for generative machine learning model).
As per claim 12, claim 12 is rejected the same way as claim 1. Shoki also disclose A processor-implemented method by an entity in a wireless communications network (see figure 1 (c), figure 2(b) the local device: page 2, column 2, lines 8-9, page 4, column 2, line 22, one or more CPUs).
As per claim 17, Shoki disclose the entity of claim 1.
Shoki further disclose receiving a set of speculatively decoded tokens generated by the first sub-model, wherein coordinating the execution of the one or more operations comprises coordinating verification of the set of speculatively decoded tokens using the set of sub-models, wherein the set of second sub-models comprises one or more generative artificial intelligence models (see page 4, column 1, lines 2-17, where L sub n is the length of the input token sequence).
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 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.
Claims 2, 5, 13 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Shoki (XP091642173, SHOKI OHTA ET AL: "Lambda-Split: A Privacy-Preserving Split Computing Framework for Cloud-Powered Generative Al") 23 October 2023, IDS submitted 4/7/2025), and further in view of LEROUX (XP036289391, LEROUX SAM ET AL: "The cascading neural network: building the Internet of Smart Things") 16 February 2017, IDS submitted 4/7/2025).
As per claim 2, Shoki disclose the entity of claim 1.
Shoki however does not explicitly disclose wherein to coordinate the execution of the one or more operations, the one or more processors are configured to cause the entity to: generate an inference based on one of the second sub-models, the one of the second sub-models comprising a model executing on the entity; determine that the generated inference meets a threshold expected accuracy level; and transmit, to the one or more network entities, one or more control signals instructing sub-models configured to execute on the one or more network entities to terminate operations for the received input prompt.
Leroux however disclose wherein to coordinate an execution of the one or more operations, the one or more processors are configured to cause the entity to: generate an inference based on one of the second sub-models, the one of the second sub-models comprising a model executing on the entity; determine that the generated inference meets a threshold expected accuracy level; and transmit, to the one or more network entities, one or more control signals instructing sub-models configured to execute on the one or more network entities to terminate operations for the received input prompt (see page 795, line 1- page 797, line 20, the cascade network decides whether to accept or to reject a classification based on the threshold value. This value is not hard-coded into the network but can be passed as an argument at runtime, the threshold could depend on other measurements such as network latency or the cost associated with the network connection (WiFi vs. mobile connections)).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to provide the functionality of wherein to coordinate an execution of the one or more operations, the one or more processors are configured to cause the entity to: generate an inference based on one of the second sub-models, the one of the second sub-models comprising a model executing on the entity; determine that the generated inference meets a threshold expected accuracy level; and transmit, to the one or more network entities, one or more control signals instructing sub-models configured to execute on the one or more network entities to terminate operations for the received input prompt, as taught by Leroux, in the system of Shoki, so as to enable a new architecture called a cascading network that is capable of distributing a deep neural network between a local device and the cloud while keeping the required communication network traffic to a minimum, where the network begins processing on the constrained device, and only relies on the remote part when the local part does not provide an accurate enough result and the cascading network allows for an early-stopping mechanism during the recall phase of the network, see Leroux, paragraphs 21-22.
As per claim 5, Shoki disclose the entity of claim 1.
Shoki however does not explicitly disclose wherein the plurality of sub-models comprise versions of the machine learning model having differing sizes, and wherein sub-models with smaller sizes are configured for deployment on network entities having fewer available computational resources than network entities for which sub-models with larger sizes are configured for deployment.
Leroux however disclose wherein the plurality of sub-models comprise versions of the machine learning model having differing sizes, and wherein sub-models with smaller sizes are configured for deployment on network entities having fewer available computational resources than network entities for which sub-models with larger sizes are configured for deployment (see page 796, line 16- page 797, line 4, sub-models with smaller sizes are configured for deployment on network entities having fewer available computational resources than network entities for which sub-models with larger sizes are configured for deployment).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to provide the functionality of wherein the plurality of sub-models comprise versions of the machine learning model having differing sizes, and wherein sub-models with smaller sizes are configured for deployment on network entities having fewer available computational resources than network entities for which sub-models with larger sizes are configured for deployment, as taught by Leroux, in the system of Shoki, so as to enable a new architecture called a cascading network that is capable of distributing a deep neural network between a local device and the cloud while keeping the required communication network traffic to a minimum, where the network begins processing on the constrained device, and only relies on the remote part when the local part does not provide an accurate enough result and the cascading network allows for an early-stopping mechanism during the recall phase of the network, see Leroux, paragraphs 21-22.
As per claim 13, claim 13 is rejected the same way as claim 2.
As per claim 15, claim 15 is rejected the same way as claim 5.
Claims 3, 6-8 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Shoki (XP091642173, SHOKI OHTA ET AL: "Lambda-Split: A Privacy-Preserving Split Computing Framework for Cloud-Powered Generative Al") 23 October 2023, IDS submitted 4/7/2025), and further in view of Wang et al (US Pub. No.:2024/0365137).
As per claim 3, Shoki disclose the entity of claim 1.
Shoki however does not explicitly disclose wherein: to output the generated result, the one or more processors are configured to cause the entity to transmit, via downlink control information (DCI) signaling, the generated result from the entity for receipt by the user equipment, or to coordinate the execution of the one or more operations, the one or more processors are configured to cause the entity to transmit, from the entity to the user equipment via DCI signaling, instructions to execute an inference operation using the first sub-model.
Wang however disclose wherein: to output the generated result, the one or more processors are configured to cause the entity to transmit, via downlink control information (DCI) signaling, the generated result from the entity for receipt by the user equipment, or to coordinate the execution of the one or more operations, the one or more processors are configured to cause the entity to transmit, from the entity to the user equipment via DCI signaling, instructions to execute an inference operation using the first sub-model (see Fig.5, para. 0063, a base station (e.g., the base station 120) indicates to the UE 110 a modulation ML configuration selected by the BS neural network manager, such as by indicating a BS-side modulation ML configuration through a field in downlink control information (DCI) transmitted in a physical downlink control channel (PDCCH) message, the DCI includes a first field that specifies a channel coding scheme and a second field that specifies the modulation ML configuration).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to provide the functionality of wherein: to output the generated result, the one or more processors are configured to cause the entity to transmit, via downlink control information (DCI) signaling, the generated result from the entity for receipt by the user equipment, or to coordinate the execution of the one or more operations, the one or more processors are configured to cause the entity to transmit, from the entity to the user equipment via DCI signaling, instructions to execute an inference operation using the first sub-model, as taught by Wang, in the system of Shoki, so as to improve the performance (e.g., data throughput, reliability) of wireless communications , see Wang, paragraphs 2-6.
As per claim 6, Shoki disclose the entity of claim 1.
Shoki however does not explicitly disclose wherein: the one or more network entities comprise one or more of a radio unit (RU), a distributed unit (DU), or a centralized unit (CU) in a distributed radio access network; and the control signaling comprises at least one of signaling transmitted on an E1 interface between different CUs in the distributed radio access network or signaling transmitted on an F1 interface between the one or more of the RU, the DU, or the CU in the distributed radio access network.
Wang however disclose wherein: the one or more network entities comprise one or more of a radio unit (RU), a distributed unit (DU), or a centralized unit (CU) in a distributed radio access network; and the control signaling comprises at least one of signaling transmitted on an E1 interface between different CUs in the distributed radio access network or signaling transmitted on an F1 interface between the one or more of the RU, the DU, or the CU in the distributed radio access network (see Fig.2, para. 0035-0039, the device diagram for the base station 120, shown in FIG. 2, includes a single network node (e.g., a gNode B). The functionality of the base station 120 is distributed across multiple network nodes or devices, the nomenclature for this distributed base station functionality varies and includes terms such as Central Unit (CU), Distributed Unit (DU), Baseband Unit (BBU), Remote Radio Head (RRH), Radio Unit (RU), and/or Remote Radio Unit (RRU).).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to provide the functionality of wherein: the one or more network entities comprise one or more of a radio unit (RU), a distributed unit (DU), or a centralized unit (CU) in a distributed radio access network; and the control signaling comprises at least one of signaling transmitted on an E1 interface between different CUs in the distributed radio access network or signaling transmitted on an F1 interface between the one or more of the RU, the DU, or the CU in the distributed radio access network, as taught by Wang, in the system of Shoki, so as to improve the performance (e.g., data throughput, reliability) of wireless communications , see Wang, paragraphs 2-6.
As per claim 7, Shoki disclose the entity of claim 1.
Shoki however does not explicitly disclose wherein the entity comprises a radio unit (RU) in a distributed radio access network.
Wang however disclose wherein the entity comprises a radio unit (RU) in a distributed radio access network (see Fig.2, para. 0035-0039, the device diagram for the base station 120, shown in FIG. 2, the nomenclature for this distributed base station functionality includes a Radio Unit (RU), and/or Remote Radio Unit (RRU)).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to provide the functionality of wherein the entity comprises a radio unit (RU) in a distributed radio access network, as taught by Wang, in the system of Shoki, so as to improve the performance (e.g., data throughput, reliability) of wireless communications , see Wang, paragraphs 2-6.
As per claim 8, Shoki disclose the entity of claim 1.
Shoki however does not explicitly disclose wherein the entity comprises an access point in a radio access network.
Wang however disclose wherein the entity comprises an access point in a radio access network (see Fig.2, para. 0035-0039, the device diagram for the base station 120, shown in FIG. 2, includes a single network node (e.g., a gNode B / an access point)).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to provide the functionality of wherein the entity comprises an access point in a radio access network, as taught by Wang, in the system of Shoki, so as to improve the performance (e.g., data throughput, reliability) of wireless communications , see Wang, paragraphs 2-6.
As per claim 16, claim 16 is rejected the same way as claim 6.
Claims 4 are rejected under 35 U.S.C. 103 as being unpatentable over Shoki (XP091642173, SHOKI OHTA ET AL: "Lambda-Split: A Privacy-Preserving Split Computing Framework for Cloud-Powered Generative Al") 23 October 2023, IDS submitted 4/7/2025), and further in view of Gharib (GHARIB ANASTASSIA ET AL: "Distributed Spectrum Sensing for loT Networks: Architecture, Challenges, and Learning", 6 April 2021).
As per claim 4, Shoki disclose the entity of claim 1.
Shoki however does not explicitly disclose wherein: to output the generated result, the one or more processors are configured to cause the entity to transmit the generated result from the entity to the user equipment via a dedicated control channel (DCCH), or to coordinate the execution of the one or more operations, the one or more processors are configured to cause the entity to transmit, from the entity to the user equipment via signaling carried on the DCCH, instructions to execute an inference operation using the first sub-model.
Gharib however disclose wherein: to output the generated result, the one or more processors are configured to cause the entity to transmit the generated result from the entity to the user equipment via a dedicated control channel (DCCH), or to coordinate the execution of the one or more operations, the one or more processors are configured to cause the entity to transmit, from the entity to the user equipment via signaling carried on the DCCH, instructions to execute an inference operation using the first sub-model (page 70, column 1, line 18 - column 2, line 19, devices use a dedicated control channel to exchange/ to coordinate the execution of the one or more operations, their information with the learning model).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to provide the functionality of wherein: to output the generated result, the one or more processors are configured to cause the entity to transmit the generated result from the entity to the user equipment via a dedicated control channel (DCCH), or to coordinate the execution of the one or more operations, the one or more processors are configured to cause the entity to transmit, from the entity to the user equipment via signaling carried on the DCCH, instructions to execute an inference operation using the first sub-model, as taught by Gharib, in the system of Shoki, so as to utilize learning-based spectrum sensing by taking each sensor’s decision and computes the aggregated decision through incremental learning, see Gharib, paragraphs 2-6.
Claims 9-10 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Shoki (XP091642173, SHOKI OHTA ET AL: "Lambda-Split: A Privacy-Preserving Split Computing Framework for Cloud-Powered Generative Al") 23 October 2023, IDS submitted 4/7/2025), and further in view of Emna ((EMNA BACCOUR ET AL: "Pervasive Al for loT Applications: Resource-efficient Distributed Artificial Intelligence") 4 May 2021).
As per claim 9, Shoki disclose the entity of claim 1.
Shoki however does not explicitly disclose wherein to coordinate the execution of the one or more operations, the one or more processors are configured to cause the entity to select a set of network entities from the one or more network entities to execute the one or more operations based on an availability of computing resources at each of the one or more network entities for executing background operations.
Emna however disclose wherein to coordinate the execution of the one or more operations, the one or more processors are configured to cause the entity to select a set of network entities from the one or more network entities to execute the one or more operations based on an availability of computing resources at each of the one or more network entities for executing background operations (see page 14, column 2, line 32 - page 18, column 1, line 21, the input data is split and fed to different parallel segments. Then, their outputs are merged again, the computation order of different tasks (e.g., layers) and the outputs of some segments serve as the inputs of others (as shown in Fig. 13 (b)).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to provide the functionality of wherein to coordinate the execution of the one or more operations, the one or more processors are configured to cause the entity to select a set of network entities from the one or more network entities to execute the one or more operations based on an availability of computing resources at each of the one or more network entities for executing background operations, as taught by Emna, in the system of Shoki, so as to divide the trained model
into segments and subsequently, each segment is assigned to a participant. Each participant shares
the output to the next one until generating the final prediction, see Emna, page 3 column 1, line 43 – 56, column2 line -10.
As per claim 10, Shoki disclose the entity of claim 1.
Shoki however does not explicitly disclose wherein to coordinate the execution of the one or more operations, the one or more processors are configured to cause the entity to select a set of network entities from the one or more network entities to execute the one or more operations based on proportional fair scheduling across the one or more network entities.
Emna however disclose wherein to coordinate the execution of the one or more operations, the one or more processors are configured to cause the entity to select a set of network entities from the one or more network entities to execute the one or more operations based on proportional fair scheduling across the one or more network entities (see page 14, column 2, line 32 - page 18, column 1, line 21, executing the one or more operations based on proportional fair scheduling across the one or more network entities).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to provide the functionality of wherein to coordinate the execution of the one or more operations, the one or more processors are configured to cause the entity to select a set of network entities from the one or more network entities to execute the one or more operations based on proportional fair scheduling across the one or more network entities, as taught by Emna, in the system of Shoki, so as to divide the trained model into segments and subsequently, each segment is assigned to a participant. Each participant shares the output to the next one until generating the final prediction, see Emna, page 3 column 1, lines 43 – 56, column 2 lines 1 -10.
As per claim 19, Shoki disclose method of claim 12.
Shoki however does not explicitly disclose wherein coordinating the execution of the one or more operations comprises selecting a set of network entities from the one or more network entities to execute the one or more operations based on one or more of an availability of computing resources at each of the one or more network entities for executing background operations or proportional fair scheduling across the one or more network entities.
Emna however disclose wherein coordinating the execution of the one or more operations comprises selecting a set of network entities from the one or more network entities to execute the one or more operations based on one or more of an availability of computing resources at each of the one or more network entities for executing background operations (see page 14, column 2, line 32 - page 18, column 1, line 21, the input data is split and fed to different parallel segments. Then, their outputs are merged again, the computation order of different tasks (e.g., layers) and the outputs of some segments serve as the inputs of others (as shown in Fig. 13 (b)) or proportional fair scheduling across the one or more network entities(see page 14, column 2, line 32 - page 18, column 1, line 21, executing the one or more operations based on proportional fair scheduling across the one or more network entities).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to provide the functionality of wherein coordinating the execution of the one or more operations comprises selecting a set of network entities from the one or more network entities to execute the one or more operations based on one or more of an availability of computing resources at each of the one or more network entities for executing background operations or proportional fair scheduling across the one or more network entities, as taught by Emna, in the system of Shoki, so as to divide the trained model into segments and subsequently, each segment is assigned to a participant. Each participant shares the output to the next one until generating the final prediction, see Emna, page 3 column 1, lines 43 – 56, column 2 lines 1 -10.
Claims 14 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Shoki (XP091642173, SHOKI OHTA ET AL: "Lambda-Split: A Privacy-Preserving Split Computing Framework for Cloud-Powered Generative Al") 23 October 2023, IDS submitted 4/7/2025), in view of Wang et al (US Pub. No.:2024/0365137) and further in view of Gharib (GHARIB ANASTASSIA ET AL: "Distributed Spectrum Sensing for loT Networks: Architecture, Challenges, and Learning", 6 April 2021).
As per claim 14, Shoki disclose the method of claim 12.
Shoki however does not explicitly disclose wherein outputting the generated result comprises transmitting, via one or more of downlink control information (DCI) signaling or signaling carried on a dedicated control channel (DCCH), the generated result from the entity for receipt by the user equipment.
Wang however disclose wherein outputting the generated result comprises transmitting, via one or more of downlink control information (DCI) signaling (see Fig.5, para. 0063, a base station (e.g., the base station 120) indicates to the UE 110 a modulation ML configuration selected by the BS neural network manager, such as by indicating a BS-side modulation ML configuration through a field in downlink control information (DCI) transmitted in a physical downlink control channel (PDCCH) message, the DCI includes a first field that specifies a channel coding scheme and a second field that specifies the modulation ML configuration).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to provide the functionality of wherein outputting the generated result comprises transmitting, via one or more of downlink control information (DCI) signaling, as taught by Wang, in the system of Shoki, so as to improve the performance (e.g., data throughput, reliability) of wireless communications , see Wang, paragraphs 2-6.
The combination of Shoki and Wang however does not explicitly disclose signaling carried on a dedicated control channel (DCCH), the generated result from the entity for receipt by the user equipment.
Gharib however disclose signaling carried on a dedicated control channel (DCCH), the generated result from the entity for receipt by the user equipment (page 70, column 1, line 18 - column 2, line 19, devices use a dedicated control channel to exchange/ to coordinate the execution of the one or more operations, their information with the learning model).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to provide the functionality of signaling carried on a dedicated control channel (DCCH), the generated result from the entity for receipt by the user equipment, as taught by Gharib, in the system of Shoki and Wang, so as to utilize learning-based spectrum sensing by taking each sensor’s decision and computes the aggregated decision through incremental learning, see Gharib, paragraphs 2-6.
As per claim 20, Shoki disclose the method of claim 12.
Shoki however does not explicitly disclose wherein coordinating the execution of the one or more operations comprises transmitting, from the entity to the user equipment via one or more of downlink control information (DCI) signaling or signaling carried on a dedicated control channel (DCCH), instructions to execute an inference operation using the first sub-model.
Wang however disclose wherein coordinating the execution of the one or more operations comprises transmitting, from the entity to the user equipment via one or more of downlink control information (DCI) signaling (see Fig.5, para. 0063, a base station (e.g., the base station 120) indicates to the UE 110 a modulation ML configuration selected by the BS neural network manager, such as by indicating a BS-side modulation ML configuration through a field in downlink control information (DCI) transmitted in a physical downlink control channel (PDCCH) message, the DCI includes a first field that specifies a channel coding scheme and a second field that specifies the modulation ML configuration).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to provide the functionality of wherein coordinating the execution of the one or more operations comprises transmitting, from the entity to the user equipment via one or more of downlink control information (DCI) signaling, as taught by Wang, in the system of Shoki, so as to improve the performance (e.g., data throughput, reliability) of wireless communications , see Wang, paragraphs 2-6.
The combination of Shoki and Wang however does not explicitly disclose signaling carried on a dedicated control channel (DCCH), instructions to execute an inference operation using the first sub-model.
Gharib however disclose signaling carried on a dedicated control channel (DCCH), instructions to execute an inference operation using the first sub-model (page 70, column 1, line 18 - column 2, line 19, devices use a dedicated control channel to exchange/ to coordinate the execution of the one or more operations, their information with the learning model).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to provide the functionality of signaling carried on a dedicated control channel (DCCH), instructions to execute an inference operation using the first sub-model, as taught by Gharib, in the system of Shoki and Wang, so as to utilize learning-based spectrum sensing by taking each sensor’s decision and computes the aggregated decision through incremental learning, see Gharib, paragraphs 2-6.
Claims 18 are rejected under 35 U.S.C. 103 as being unpatentable over Shoki (XP091642173, SHOKI OHTA ET AL: "Lambda-Split: A Privacy-Preserving Split Computing Framework for Cloud-Powered Generative Al") 23 October 2023, IDS submitted 4/7/2025), and further in view of Liu et al (US Pub. No.:2021/0142164).
As per claim 18, Shoki disclose the method of claim 12.
Shoki further disclose the student model is refined using the outputs of the teacher models
paragraph [0064]) and the first sub-model is smaller in terms of size than the second sub-models
(paragraph [0064]).
Shoki however does not explicitly disclose wherein the first sub-model configured to execute on the user equipment comprises a student model, and wherein the one or more second sub-models configured to execute on the one or more network entities comprise teacher models whose outputs are usable by the student model to refine the student model.
Liu however disclose wherein the first sub-model configured to execute on the user equipment comprises a student model, and wherein the one or more second sub-models configured to execute on the one or more network entities comprise teacher models whose outputs are usable by the student model to refine the student model (see Fig. 1, see para. 0028, computing device 100 implements an architecture or framework that employs knowledge distillation for a language model under a multi-task learning setting, teacher module 130 is pre-trained and/or used for natural language processing (NLP), the teacher module 130 implements a neural network model that is relatively large in size and parameters used, the student module 140, also be used for NLP—implements a neural network model that is a smaller compared to the teacher model. The knowledge learned by teacher module 130 is transferred (knowledge distillation) to the smaller student module 140, under the multi-task learning setting or architecture, see also para. 0070).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to provide the functionality of wherein the first sub-model configured to execute on the user equipment comprises a student model, and wherein the one or more second sub-models configured to execute on the one or more network entities comprise teacher models whose outputs are usable by the student model to refine the student model, as taught by Liu, in the system of Shoki, so as to enable knowledge distillation that is employed to transfer knowledge from the teacher model to the student model by the student model updating its shared layers and task layers, according to the teacher logits of the teacher mode, see Liu, paragraphs 17-19.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Zhang et al (US Pub. No.:2025/0214606) – see para. 0112, “Example 1 includes a computer-implemented method for facilitating generative AI to pre-train and fine-tune models for multiple AV trajectories generation, where the method comprises: training, by a processing device, a teacher generative artificial intelligence (AI) model on a first set of training data; providing a student generative AI model with at least one distillation of the teacher generative AI model, the at least one distillation comprising transformer weights, embeddings, or predictions labels of the teacher generative AI model; training the student generative AI model that is initialized with the at least one distillation of the teacher generative AI model, wherein the student generative AI model is trained using a second set of training data that is smaller than the first set of training data; and deploying the student generative AI model to a resource-constrained environment”.
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
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/LAKERAM JANGBAHADUR/
Primary Examiner, Art Unit 2469
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