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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on February 23, 2026, has been entered under the RCE filed on March 3, 2026.
Remarks
This Office Action is in response to applicant’s after-final amendment filed on February 23, 2026, and RCE filed on March 3, 2026, under which claims 1-31 are pending and under consideration.
Response to Arguments
Applicant’s arguments have been fully considered but are not persuasive in distinguishing over the claims over the cited references. The prior art rejections have been updated to account for the amended claim language, but the claims remain rejected over the previously applied references.
In regards to amended claim 1, applicant argues:
Although the abstract of Tan states "a selection component that selects a subsets of units of the neural network, and a freeze-out component that freezes the selected subset of units of the neural network so that weights of output connections from the frozen subset of units will not be updated for a training run," FIG. 1 of Tan shows that the "selection component 106" is part of the "machine learning component 102" for which the selection is made.
In contrast, in claim 1, the UE "receive[s], in a first wireless transmission, a radio resource control (RRC) configuration from a wireless network entity that indicates a layer identifier (ID) information for one or more neural network training parameters of a neural network for wireless communication by the UE, wherein the neural network includes a plurality of layers; [and] receive[s], in a second wireless transmission, control signaling for the one or more neural network training parameters based on the layer ID information from the RRC configuration, wherein the one or more neural network training parameters indicate whether at least a subset of layers are to be frozen during training of the neural network," (emphasis added).
Tan does not include "wireless transmission" of the selection from the "selection
component," and further does not provide an "RRC configuration" of "layer ID information" on which "control signaling" in a "second wireless transmission" is based.
(Applicant’s response, pages 11-12).
This argument is not persuasive in distinguishing over the cited references as applied in the updated rejections of this action.
While it is true that Tan does not teach the communications concepts noted above, these communications concepts are already taught in Ma, and the general techniques of Tan are applicable to the context of a base station and UE taught in Ma. Furthermore, while it is true that Tan does not teach a system with the UE and a network entity communicating with each other, this system as recited in the claim merely specifies a context in which certain machine learning concepts (such as freezing layers) are performed, and the presence of this context does not change the functionality of the machine learning concepts for which Tan is relied upon. In other words, while Tan merely teaches a machine learning component, its techniques are applicable regardless of whether this component is one system or split across multiple systems.
In the updated rejection, the Examiner is relying on a combination of Tan and Ma to teach the limitations at issue, rather than Tan alone. Therefore, since the rejection is based on a combination of the references, whether Tan or Ma alone teaches the limitations quoted above is not dispositive. For further details please see the rejections below.
Next, in regards to amended claim 2, applicant argues:
As an example, the cited references further fail to disclose or suggest that "the at least the subset of layers to be frozen during training of the neural network is based on one or more of a channel characteristic, an environment condition, a condition at the UE, or a hierarchical training to train different layers of the neural network in a configured order," in combination with the other aspects of dependent claim 2.
In contrast to the basis for the "subset of layers to be frozen during training" in dependent claim 2, Tan is cited as stating that "Freeze-out provides an improved regularization technique" and "Regularization refers to techniques to solve the overfitting problem." See Tan at paragraphs [0028] and [0030].
Tan does not address a "subset of layers to be frozen during training of the neural
network" that is "based on one or more of a channel characteristic, an environment condition, a condition at the UE, or a hierarchical training to train different layers of the neural network in a configured order," as in claim 2 (emphasis added).
(Applicant’s response, pages 11-12).
This argument is not persuasive in distinguishing over the cited reference as applied in the updated rejections of this action. In detail, the amended part of claim 2 broadly recites that the subset is “based on” a list of alternatives, e.g., “a condition at the UE.” Here, Ma already teaches a “condition at the UE,” which is a term that is recited at high degree of generality so as to not require any specific condition. Furthermore, the term “based on” as used here does not require any specific relationship or methodology in selecting the subset of layers. Therefore, for the reasons stated in the rejections below, the limitations at issue are taught by the combination of Ma and Tan.
Finally, in regards to amended claim 3, applicant argues:
For example, the cited references further fail to disclose or suggest that "the control signaling in the second wireless transmission includes: a first indication of at least one layer of the plurality of layers to be trained, wherein remaining layers are to be frozen during the training of the neural network, a second indication of at least one layer to be frozen, wherein the remaining layers are to be trained during the training of the neural network, or both the first indication of the at least one layer to be trained and the second indication of the at least one layer to be frozen during the training of the neural network," as in claim 31 (emphasis added).
The Office Action cites "subset of units of the neural network" in the abstract of Tan, and in the rejection of claim 1 cites "a freeze-out component that freezes the selected subset of units of the neural network." See Tan at abstract.
However, Tan does not disclose or suggest "control signaling in the second wireless transmission includes" the cited "second indication of at least one layer to be frozen," as in claim 31.
In contrast to the wireless transmission in claim 31, Tan shows the "selection component 106" and the "freeze-out component 108" both as components in a machine learning component 102 in which the "freeze-out" is applied. See FIG. 1 of Tan (copy shown below).
(Applicant’s response, page 13).
This argument is not persuasive in distinguishing over the cited references for reasons similar to those discussed for the arguments on claim 1. In summary, applicant is arguing that Tan does not disclose the communications system concepts, e.g., “wireless transmission.” However, the communications system concepts are already disclosed by Ma, and the combination of Ma and Tan render obvious the claims. As noted above, while it is true that Tan does not teach a system with the UE and a network entity communicating with each other, this system as recited in the claim merely specifies a context in which certain machine learning concepts (such as freezing layers) are performed, and the presence of this context does not change the functionality of the machine learning concepts for which Tan is relied upon. For the reasons stated in the rejection below, the combination of Ma and Tan render obvious the claimed limitations at issue.
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 (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
1. Claims 1-2, 4-8, 10, 17-23, and 28-31 are rejected under 35 U.S.C. 103 as being unpatentable over Ma et al. (US 2021/0160149 A1) (“Ma”) in view of Tan et al. (US 2021/0232909 A1) (“Tan”).
As to claim 1, Ma teaches an apparatus for wireless communication at a user equipment (UE) comprising: [[0003]: “a user equipment (UE) (also commonly referred to as a mobile station, a subscriber, a user, a terminal, a phone, and the like).” [0034]: “The EDs 110 are configured to operate, communicate, or both, in the wireless system 100. For example, the EDs 110 may be configured to transmit, receive, or both via wireless or wired communication channels. Each ED 110 represents any suitable end user device for wireless operation and may include such devices (or may be referred to) as a user equipment/device (UE).”]
memory; [[0182]: “a computer readable storage medium operatively coupled to the processor, the computer readable storage medium storing programming for execution by the processor.” [0043]: “The memory 208 stores instructions and data used, generated, or collected by the ED 110.”] and
at least one processor coupled to the memory, the at least one processor configured to: [[0182]: “a computer readable storage medium operatively coupled to the processor…” [0040]: “As shown in FIG. 2, the ED 110 includes at least one processing unit 200. The processing unit 200 implements various processing operations of the ED 110.”]
receive, in a first wireless transmission, a radio resource control (RRC) configuration from a wireless network entity that indicates […] information for one or more neural network training parameters of a neural network for wireless communication by the UE, [[0136]: “the BS sends a training request to the UE at 1112 to trigger a training phase 1150. … In some embodiments, the training request may be set to the UE via RRC signaling...the training request may include initial training setting(s)/parameter(s), such as initial NN weights.” See also [0137]-[0139]: “the BS may also send AI/ML related information to the UE to facilitate joint training such as: Information indicating which AI/ML module is to be trained if there…”; [0140]: “the AI/ML related information may include an instruction for the UE to download initial AI/ML algorithm(s) and/or setting(s)/parameter(s).” That is, the “training setting(s)/parameter(s)” in the training request and the additional AI/ML related information correspond to a “configuration” and are also “for one or more neural network training parameters” since they include those training settings and parameters. See also [0142]: “the BS notifies the UE which AI/ML module(s)/component(s) is/are to be trained by including information in the training request that identifies one or more AI/ML modules/components.” The “AI/ML” in this reference is a “neural network” as described in [0129] (“AI/ML components, such as a neural network, is trained”), [0145], and [0146]. The neural network is “for wireless communication” as described in [0073]: “The methods and devices disclosed herein provide a mechanism of AI/ML enabled/assisted air interface personalized optimization that supports different levels of per-UE/device based optimization.”] wherein the neural network includes a plurality of layers; [[0152]: “where an AI/ML component is implemented with a deep neural network (DNN)… standardization may include a standard definition of the type(s) of neural network to be used, and certain parameters of the neural network (e.g., number of layers, number of neurons in each layer, etc.).” Note that a deep neural network, by definition, is a neural network with a plurality of layers.]
receive, in a second wireless transmission, control signaling for the one or more neural network training parameters based on the […] information from the RRC configuration, [[0122]: “At 1016 the BS starts the training phase 1050 by sending a training signal that includes a training sequence or training data to the UE. In some embodiments, the BS may send a training sequence/training data to the UE after a certain predefined time gap following transmission of the training request act 1012.” The training signal is “for” the training parameters because it is for the use of those training parameters, and the training signal (and transmission thereof) is “based on” the earlier information because the signal was based on the UE’s request (step 1014) received in response to the earlier training request.] […] and
train the neural network based on the RRC configuration and the control signaling received from the wireless network entity. [[0141]: “after the UE has received the training request and initial training information from the network, the UE may send a response to the training request to the BS, as indicated at 1114 in FIG. 13. This response may confirm that the UE has entered a training mode.” [0142]: “As noted above, in some embodiments the BS notifies the UE which AI/ML module(s)/component(s) is/are to be trained by including information in the training request that identifies one or more AI/ML modules/components… By doing so, the BS informs the UE which AI/ML modules(s)/component(s) is/are to be trained…” [0145]: “training of an AI/ML module that includes one or more AI/ML components takes place jointly in the network and at the UE, as indicated at 1119 in FIG. 13.” [0146]: “In other embodiments, the UE and/or the BS may be able to update the training setup and parameters autonomously based on their own training process.” That is, the training may be at the UE or at both the UE and in the network, either of which reads on the instant claim limitation.]
Ma does not explicitly teach:
(1) The limitation that the information is “layer identifier (ID) information.”
(2) The limitation of wherein the one or more neural network training parameters “indicate whether at least a subset of layers are to be frozen during training of the neural network.”
Tan teaches “layer identifier (ID) information” and training parameters that “indicate whether at least a subset of layers are to be frozen during training of the neural network.” [Abstract: “The computer executable components can include: an assessment component that identifies units of a neural network, a selection component that selects a subset of units of the neural network, and a freeze-out component that freezes the selected subset of units of the neural network so that weights of output connections from the frozen subset of units will not be updated for a training run.” The selected subset of units can be on the basis entire layers, as disclosed in [0033]: “the selection component 106 can select a subset of units of the neural network comprising one or more entire layers of units”; [0035]: “In another example, the freeze-out component 108 freezes one or more layers of the neural network selected by the selection component 106 so that weights of output connections from the one or more frozen layers will not be updated for a training run.” In regards to the limitation of “layer identifier (ID) information,” [0045] teaches: “Block 902 represents a first act that includes identifying units of a neural network (e.g., via the assessment component 104). At 904, a subset of units of the neural network are selected (e.g., via the selection component 106). At 906, the selected subset of units of the neural network are frozen so that weights of output connections from the frozen subset of units will not be updated for a training run (e.g., via the freeze-out component 108).” Therefore, the selection of the subset of units is “layer identifier (ID) information” (noting that “ID” here is interpreted to be an abbreviation of “identifier” and not a separate limitation), since it identifies the units for the freeze-out component. Although this reference does not expressly use the term “layer identifier,” it is understood that layer identifier information is indicated in the technique of this reference because the identification and selection of the subset that is then used by the freeze-out component implies an indication of “layer identifier information” in order for the subset to be identified, selected, and used by different components of the system. The instant claim does not explicitly require a specific format for a layer identifier, but instead only recites layer identifier information in the abstract. Therefore, “layer identifier information” is considered to be disclosed at least implicitly. Additionally, or as another basis for a teaching or suggestion of layer identifier information, [0040] states: “FIG. 5A illustrates four layers of a neural network 502, 504, 506 and 508. In this example, the entire layer 506 and all units therein are randomly frozen for a training run.” That is, a particular layer is identified for being frozen, which also teaches that there is layer identifier information in order for that layer (506) to be identified. Therefore, noting that the claim does not require the layer ID information to be in any specific form or format, the reference numerals in FIG. 5A can be considered teachings of identifiers for particular layers.]
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teachings of Ma with the teachings of Tan by implementing the layer freezing technique of Tan, particularly by implementing the one or more neural network training parameters to “indicate whether at least a subset of layers are to be frozen during training of the neural network” and the information indicated by the RRC configuration to be or include “layer identifier (ID) information,” so as to arrive at the limitations of the instant claim. The motivation for doing so would have been to mitigate overfitting and improve the neural network’s ability to generalize, as suggested by Tan (see [0028]: “Regularization refers to techniques to solve the overfitting problem by making slight modifications to the learning algorithm, enabling the neural network model to more accurately generalize to new situations or data sets”; [0030]: “Freeze-out provides an improved regularization technique as it eliminates the need to update the weights of output connections.”).
As to claim 2, the combination of Ma and Tan teaches the apparatus of claim 1, further comprising a transceiver coupled to the at least one processor, [Ma, [0041]: “The ED 110 also includes at least one transceiver 202.” See FIG. 2 which shows that the transceiver 202 is coupled to the processor (processing unit 200).]
wherein the wireless network entity includes a base station, a transmission reception point (TRP), a core network component, a server or another UE, [Ma, [0035]: “In FIG. 1, the RANs 120 include base stations (BSs) 170 a-170 b (generically referred to as BS 170), respectively.” The instant limitation is an alternative expression, and the alternative of “base station” is taught as quoted above.] wherein the at least the subset of layers to be frozen during training of the neural network is based on one or more of a channel characteristic, an environment condition, a condition at the UE, or a hierarchical training to train different layers of the neural network in a configured order [Ma, [0113] teaches that “The information exchange procedure begins with UE sending information indicating an AI/ML capability of the UE to the BS at 1010. The information indicating an AI/ML capability of the UE may indicate whether or not the UE supports AI/ML for optimization of an air interface.” That is, the capability of the UE corresponds to the alternative of “a condition at the UE.” Since this step occurs before the other transmissions, the other transmissions, including the training information and signal, are “based on” the condition at the UE. The Examiner notes that the instant claim merely recites “based on,” which only requires, for example, a relationship in which the existence of the subset of layers was in response to the condition of the UE, and does not require any specific relationships or algorithm for selecting the subset of layers.]
As to claim 4, the combination of Ma and Tan teaches the apparatus of claim 1, wherein the control signaling of the one or more neural network training parameters is received in at least one of: a medium access control (MAC) control element (CE) (MAC-CE), downlink control information (DCI), sidelink control information (SCI), or a sidelink message. [Ma, [0124]: “Dynamic control channel: When the number of bits required to send the training sequence/training data is less than a certain threshold, a dynamic control channel may be used to send the training sequence/training data. In some embodiment, several levels of bit lengths may be defined. The different bit lengths may correspond to different DCI formats or different DCI payloads. The same DCI can be used for carrying training sequences/data for different AI/ML modules. In some embodiments, a DCI field may contain information indicating an AI/ML module the training sequence/training data is to be used to train.” Note that “training sequences/data” refers to the transmission in step 1016 as described in [0122] (“At 1016 the BS starts the training phase 1050 by sending a training signal that includes a training sequence or training data to the UE.”). Therefore, the alternative of using DCI is disclosed in Ma.]
As to claim 5, the combination of Ma and Tan teaches the apparatus of claim 4, wherein, to receive the RRC configuration and the control signaling, the at least one processor is configured to:
receive multiple sets of neural network training parameters in higher-layer signaling; [Ma, [0136]: “In some embodiments, the training request may be set to the UE via RRC signaling.” Here, the RRC signaling is a type of higher-layer signaling in the absence of further limitations as to a more specific definition of this term. The Examiner interprets the instant limitation as being met if RRC is used for any part of the multiple sets of neural network training parameters.] and
receive an indication of one of the multiple sets of neural network training parameters in at least one of the MAC-CE, the DCI, or a combination thereof. [Ma, [0136]: “In some embodiments, the training request may be sent to the UE through DCI (dynamic signaling) on a downlink control channel or on a data channel. For example, in some embodiments the training request may be sent to the UE with UE specific or UE common DCI. For example, UE common DCI may be used to send a training request to all UEs or a group of UEs. In some embodiments, the training request may be set to the UE via RRC signaling. In some embodiments, the training request may include initial training setting(s)/parameter(s), such as initial NN weights.” Ma, [0210]: “Example Embodiment 131. …transmitting the AI/ML training request through downlink control information (DCI) on a downlink control channel or RRC signaling or the combination of the DCI and RRC signaling. …Example Embodiment 135…wherein the dynamic control channel includes a dynamic control information (DCI) field containing information indicating an AI/ML module that is to be trained.” That is Ma, [0210] teaches that DCI is used to indicate the training parameter of “an AI/ML module that is to be trained” and further teaches that the combination of DCI and RRC may be used.]
As to claim 6, the combination of Ma and Tan teaches the apparatus of claim 1, wherein the one or more neural network training parameters includes at least one of: a channel state information reporting identifier, a channel state reference signal identifier, a component carrier identifier, a bandwidth part (BWP) identifier, a neural network identifier, a first indication of at least one layer to be trained, a second indication of the at least one layer to be frozen, a group of multiple layers to be trained, a subset of layers to be trained, or a combination thereof. [Noting that the instant claim recites an alternative expression, the alternative of “a neural network identifier” is taught Ma, [0138]: “Information indicating which AI/ML module is to be trained if there is more than one AI/ML module that is trainable”; Ma, [0142]: “As noted above, in some embodiments the BS notifies the UE which AI/ML module(s)/component(s) is/are to be trained by including information in the training request that identifies one or more AI/ML modules/components or by sending such information to the UE in a separate communication.” Note that “AI/ML module” refers to a neural network. See Ma, [0152]: “For example, a table may be used to list the standard-defined neural network types and parameters to be used for specific applications. In the context of the wireless system 100 of FIG. 1, standardized definitions may be stored in the memory of the BS 170, to enable the BS 170 to select the appropriate DNN architecture and parameters to be trained for a particular wireless communication scenario.” For this reason, the alternatives of “a first indication of at least one layer to be trained” is also disclosed, the architecture of the neural networks includes at least a layer.]
As to claim 7, the combination of Ma and Tan teaches the apparatus of claim 1, wherein the at least one processor is further configured to:
receive a training command in the second wireless transmission, [Ma, [0122]: “At 1016 the BS starts the training phase 1050 by sending a training signal that includes a training sequence or training data to the UE. In some embodiments, the BS may send a training sequence/training data to the UE after a certain predefined time gap following transmission of the training request at 1012.” Since this training signal “starts the training phase,” it is a training command.] wherein the UE applies the one or more neural network training parameters to train the neural network at the UE in response to receiving the training command. [Ma, [0138]: “Information indicating which AI/ML module is to be trained if there is more than one AI/ML module that is trainable”; Ma, [0142]: “As noted above, in some embodiments the BS notifies the UE which AI/ML module(s)/component(s) is/are to be trained by including information in the training request that identifies one or more AI/ML modules/components or by sending such information to the UE in a separate communication.” Then, the training signal described in [0122] initiates the training phase, which applies this information that was sent in the training request.]
As to claim 8, the combination of Ma and Tan teaches the apparatus of claim 7, wherein the training command is a group common command, and the group common command is received over a group common downlink control information (DCI). [Ma, [0121]: “For example, in some embodiments the training request may be sent to the UE as UE specific or UE common DCI. For example, UE common DCI may be used to send a training request to all UEs or a group of UEs.” Here, “UE common DCI” refers to a “group common DCI” in the sense of common to the group, since the context is a group of UE.]
As to claim 10, the combination of Ma and Tan teaches the apparatus of claim 7, wherein the at least one processor is further configured to:
apply the one or more neural network training parameters to train one or more neural networks identified in the training command. [Ma, [0138]: “Information indicating which AI/ML module is to be trained if there is more than one AI/ML module that is trainable”; Ma, [0142]: “As noted above, in some embodiments the BS notifies the UE which AI/ML module(s)/component(s) is/are to be trained by including information in the training request that identifies one or more AI/ML modules/components or by sending such information to the UE in a separate communication.” Note that “AI/ML module” refers to a neural network. See Ma, [0152]: “For example, a table may be used to list the standard-defined neural network types and parameters to be used for specific applications. In the context of the wireless system 100 of FIG. 1, standardized definitions may be stored in the memory of the BS 170, to enable the BS 170 to select the appropriate DNN architecture and parameters to be trained for a particular wireless communication scenario.” That is, the base station selects (identifies) a specific type of DNN to be trained.]
As to claims 17-19, these claims are directed to a method comprising the same or substantially the same operations as those of claims 1, 5, and 7. Therefore, the rejections made to claims 1, 5, and 7 are applied to claims 17-19, respectively.
As to claim 20, Ma teaches an apparatus for wireless communication, comprising:
memory; [[0044]: “As shown in FIG. 3, the base station 170 includes … at least one memory 258.” [0045]: “the memory 258 could store software instructions or modules configured to implement some or all of the functionality and/or embodiments described herein and that are executed by the processing unit(s) 250.”] and
at least one processor coupled to the memory, the at least one processor configured to: [[0044]: “The processing unit 250 can also be configured to implement some or all of the functionality and/or embodiments described in more detail herein.” [0045]: “the memory 258 could store software instructions or modules…that are executed by the processing unit(s) 250.”]
transmit, in a first wireless transmission to the UE, a radio resource control (RRC) configuration that indicates […] information for one or more neural network training parameters of a neural network for wireless communication by the UE, [[0136]: “the BS sends a training request to the UE at 1112 to trigger a training phase 1150. … In some embodiments, the training request may be set to the UE via RRC signaling...the training request may include initial training setting(s)/parameter(s), such as initial NN weights.” See also [0137]-[0139]: “the BS may also send AI/ML related information to the UE to facilitate joint training such as: Information indicating which AI/ML module is to be trained if there…”; [0140]: “the AI/ML related information may include an instruction for the UE to download initial AI/ML algorithm(s) and/or setting(s)/parameter(s).” That is, the “training setting(s)/parameter(s)” in the training request and the additional AI/ML related information correspond to a “configuration” and are also “for one or more neural network training parameters” since they include those training settings and parameters. See also [0142]: “the BS notifies the UE which AI/ML module(s)/component(s) is/are to be trained by including information in the training request that identifies one or more AI/ML modules/components.” The “AI/ML” in this reference is a “neural network” as described in [0129] (“AI/ML components, such as a neural network, is trained”), [0145], and [0146]. The neural network is “for wireless communication” as described in [0073]: “The methods and devices disclosed herein provide a mechanism of AI/ML enabled/assisted air interface personalized optimization that supports different levels of per-UE/device based optimization.”] wherein the neural network includes a plurality of layers; [[0152]: “where an AI/ML component is implemented with a deep neural network (DNN)… standardization may include a standard definition of the type(s) of neural network to be used, and certain parameters of the neural network (e.g., number of layers, number of neurons in each layer, etc.).” Note that a deep neural network, by definition, is a neural network with a plurality of layers.] and
transmit, in a second wireless transmission, control signaling for the one or more parameters based on the […] information from the RRC configuration, […] [[0122]: “At 1016 the BS starts the training phase 1050 by sending a training signal that includes a training sequence or training data to the UE. In some embodiments, the BS may send a training sequence/training data to the UE after a certain predefined time gap following transmission of the training request act 1012.” The training signal is “for” the training parameters because it is for the use of those training parameters, and the training signal (and transmission thereof) is “based on” the earlier information because the signal was based on the UE’s request (step 1014) received in response to the earlier training request.]
Ma does not explicitly teach:
(1) The limitation that the information is “layer identifier (ID) information.”
(2) the limitation that the one or more neural network training parameters “indicate to the UE whether at least a subset of layers are to be frozen during training of the neural network.”
Tan teaches “layer identifier (ID) information” and training parameters that “indicate to the UE whether at least a subset of layers are to be frozen during training of the neural network.” [Abstract: “The computer executable components can include: an assessment component that identifies units of a neural network, a selection component that selects a subset of units of the neural network, and a freeze-out component that freezes the selected subset of units of the neural network so that weights of output connections from the frozen subset of units will not be updated for a training run.” The selected subset of units can be on the basis entire layers, as disclosed in [0033]: “the selection component 106 can select a subset of units of the neural network comprising one or more entire layers of units”; [0035]: “In another example, the freeze-out component 108 freezes one or more layers of the neural network selected by the selection component 106 so that weights of output connections from the one or more frozen layers will not be updated for a training run.” In regards to the limitation of “layer identifier (ID) information,” [0045] teaches: “Block 902 represents a first act that includes identifying units of a neural network (e.g., via the assessment component 104). At 904, a subset of units of the neural network are selected (e.g., via the selection component 106). At 906, the selected subset of units of the neural network are frozen so that weights of output connections from the frozen subset of units will not be updated for a training run (e.g., via the freeze-out component 108).” Therefore, the selection of the subset of units is “layer identifier (ID) information” (noting that “ID” here is interpreted to be an abbreviation of “identifier” and not a separate limitation), since it identifies the units for the freeze-out component. Although this reference does not expressly use the term “layer identifier,” it is understood that layer identifier information is indicated in the technique of this reference because the identification and selection of the subset that is then used by the freeze-out component implies an indication of “layer identifier information” in order for the subset to be identified, selected, and used by different components of the system. The instant claim does not explicitly require a specific format for a layer identifier, but instead only recites layer identifier information in the abstract. Therefore, “layer identifier information” is considered to be disclosed at least implicitly. Additionally, or as another basis for a teaching or suggestion of layer identifier information, [0040] states: “FIG. 5A illustrates four layers of a neural network 502, 504, 506 and 508. In this example, the entire layer 506 and all units therein are randomly frozen for a training run.” That is, a particular layer is identified for being frozen, which also teaches that there is layer identifier information in order for that layer (506) to be identified. Therefore, noting that the claim does not require the layer ID information to be in any specific form or format, the reference numerals in FIG. 5A can be considered teachings of identifiers for particular layers.]
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teachings of Ma with the teachings of Tan by implementing the layer freezing technique of Tan, particularly by implementing the one or more neural network training parameters “indicate to the UE whether at least a subset of layers are to be frozen during training of the neural network” and to implement the information indicated by the RRC configuration so as to be or include “layer identifier (ID) information.” The motivation for doing so would have been to mitigate overfitting and improve the neural network’s ability to generalize, as suggested by Tan (see [0028]: “Regularization refers to techniques to solve the overfitting problem by making slight modifications to the learning algorithm, enabling the neural network model to more accurately generalize to new situations or data sets”; [0030]: “Freeze-out provides an improved regularization technique as it eliminates the need to update the weights of output connections.”).
As to claim 21, the combination of Ma and Tan teaches the apparatus of claim 20, wherein the apparatus includes a network entity for a wireless communication system or another UE, [The base station described above is a network entity for wireless communication. See also Ma, [0035]: “In FIG. 1, the RANs 120 include base stations (BSs) 170 a-170 b (generically referred to as BS 170), respectively. Each BS 170 is configured to wirelessly interface with one or more of the EDs 110 to enable access to any other BS 170, the core network 130, the PSTN 140, the internet 150, and/or the other networks 160.”] wherein the at least the subset of layers to be frozen during training of the neural network is based on one or more of a channel characteristic, an environment condition, a condition at the UE, or a hierarchical training to train different layers of the neural network in a configured order [Ma, [0113] teaches that “The information exchange procedure begins with UE sending information indicating an AI/ML capability of the UE to the BS at 1010. The information indicating an AI/ML capability of the UE may indicate whether or not the UE supports AI/ML for optimization of an air interface.” That is, the capability of the UE corresponds to the alternative of “a condition at the UE.” Since this step occurs before the other transmissions, the other transmissions, including the training information and signal, are “based on” the condition at the UE. The Examiner notes that the instant claim merely recites “based on,” which only requires, for example, a relationship in which the existence of the subset of layers was in response to the condition of the UE, and does not require any specific relationships or algorithm for selecting the subset of layers.]
As to claim 22, the combination of Ma and Tan teaches the apparatus of claim 21, wherein, to transmit the one or more neural network training parameters for the neural network, the at least one processor is configured to:
transmit multiple sets of parameters for neural network training in a higher-layer signaling; [Ma, [0136]: “In some embodiments, the training request may be set to the UE via RRC signaling.” Here, the RRC signaling is a type of the limitation of “higher-layer signaling” in the absence of further limitations as to a more specific definition of this term. The Examiner interprets the instant limitation as being met if RRC is used for any part of the multiple sets of neural network training parameters.] and
transmit an indication of one of the multiple sets of parameters in at least one of a MAC-CE, DCI, or a combination thereof. [Ma, [0136]: “In some embodiments, the training request may be sent to the UE through DCI (dynamic signaling) on a downlink control channel or on a data channel. For example, in some embodiments the training request may be sent to the UE with UE specific or UE common DCI. For example, UE common DCI may be used to send a training request to all UEs or a group of UEs. In some embodiments, the training request may be set to the UE via RRC signaling. In some embodiments, the training request may include initial training setting(s)/parameter(s), such as initial NN weights.” Ma, [0210]: “Example Embodiment 131. …transmitting the AI/ML training request through downlink control information (DCI) on a downlink control channel or RRC signaling or the combination of the DCI and RRC signaling. …Example Embodiment 135…wherein the dynamic control channel includes a dynamic control information (DCI) field containing information indicating an AI/ML module that is to be trained.” That is Ma, [0210] teaches that DCI is used to indicate the training parameter of “an AI/ML module that is to be trained” and further teaches that the combination of DCI and RRC may be used.]
As to claim 23, the combination of Ma and Tan teaches the apparatus of claim 20, wherein the at least one processor and the memory are further configured to:
transmit a training command in the second wireless transmission [0122]: “At 1016 the BS starts the training phase 1050 by sending a training signal that includes a training sequence or training data to the UE. In some embodiments, the BS may send a training sequence/training data to the UE after a certain predefined time gap following transmission of the training request at 1012.” Since this training signal “starts the training phase,” it is a training command.] to indicate to the UE to apply the one or more neural network training parameters to train the neural network at the UE. [Ma, [0138]: “Information indicating which AI/ML module is to be trained if there is more than one AI/ML module that is trainable”; Ma, [0142]: “As noted above, in some embodiments the BS notifies the UE which AI/ML module(s)/component(s) is/are to be trained by including information in the training request that identifies one or more AI/ML modules/components or by sending such information to the UE in a separate communication.” Then, the training signal described in [0122] initiates the training phase, which applies this information that was sent in the training request.]
As to claims 28-30, these claims are directed to a method comprising the same or substantially the same operations as those of claims 20 and 22-23. Therefore, the rejections made to claims 20 and 22-23 are applied to claims 28-30, respectively.
Additionally, the preamble recitation of “at a base station” is taught by the parts of Ma cited in the rejection of claim 20 which discuss a base station (BS) (e.g., [0044]: “As shown in FIG. 3, the base station 170”).
As to claim 31, the combination of Ma and Tan teaches the apparatus of claim 1, as set forth above.
Tan further teaches wherein the configuration includes:
a first indication of at least one layer of the plurality of layers to be trained, wherein remaining layers are to be frozen during the training of the neural network,
a second indication of the at least one layer to be frozen, wherein the remaining layers are to be trained during the training of the neural network, or
both the first indication of the at least one layer to be trained and the second indication of the at least one layer to be frozen during the training of the neural network. [Noting that the instant claim recites an “or”-delimited alternative expression, the second alternative of “a second indication of the at least one layer to be frozen, wherein the remaining layers are to be trained during the training of the neural network” is taught by Tan, since the “subset of units of the neural network” to be frozen an indication of those elements to be frozen. See also FIG. 7, where the “specified node set FN which can be frozen out” is an indication that is an input to the algorithm. Additionally, as shown in the middle of the algorithm in FIG. 7, the layers not in the subset (as stated in the line “if node i does not belong to FN”) correspond to remaining layers to be trained.]
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teachings of Man and Tan to have further arrived at the limitations of the instant dependent claim. The motivation for doing so is the same as the motivation already given for the teachings of Tan in the rejection of the parent independent claim, since the teachings of Tan discussed above for the instant dependent claim are part of the layer freezing technique of Tan.
2. Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Ma in view of Tan, and further in view of Timo et al. (US 2022/0149904 A1) (“Timo”).
As to claim 3, the combination of Ma and Tan teaches the apparatus of claim 1, but does not explicitly teach the further limitations of the instant dependent claim.
Timo teaches “wherein the neural network is trained to perform at least one of: wireless channel compression at the UE, wireless channel measurement at the UE, wireless interference measurement at the UE, UE positioning, or wireless waveform determination at the UE.” [Abstract: “A terminal device (502) receives the parameters, forms the compression function, compresses downlink channel estimates using the compression function, and transmits the compressed downlink channel estimates.” That is, the alternative of “wireless channel compression at the UE” is disclosed. Additionally, the downlink channel estimates also corresponds to the alternative of “wireless channel measurement at the UE” and “wireless interference measurement at the UE.” See [0042]: “CSI Feedback: Raw Channel Measurements”; [0298]: “The input channel estimates X=[X1, X2, . . . , XN TX ]T can represent the estimated channels across a given frequency interval based on the measured CSI-RS”; [0213]: “The UE uses the CS compressor to compress its raw CSI (for example, channel and/or interference plus noise covariance matrix estimates)”]
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teachings of the references combined thus far with the teachings of Timo by implementing the neural network to be trained to perform wireless channel compression at the UE, wireless channel measurement at the UE, and wireless interference measurement at the UE, as taught in Timo. The motivation would have been to compress downlink channel estimates into a more compact or bitrate-efficient format or representation, as suggested by Timo (see [0127]: “compress the downlink channel estimates into a more compact or bitrate-efficient format or representation.”), particularly since Timo teaches that such may be performed using a neural network at the UE (see Timo, [0110]: “a compression function and a decompression function provided in the form of a neural network”).
3. Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Ma in view of Tan, and further in view of Zhang et al. (US 2019/0147344 A1) (“Zhang”).
As to claim 9, the combination of Ma and Tan teaches the apparatus of claim 7, wherein the control signaling indicates for the UE to apply the configuration to train […] of multiple neural networks at the UE in response to receiving the training command. [Ma, [0122]: “At 1016 the BS starts the training phase 1050 by sending a training signal that includes a training sequence or training data to the UE. In some embodiments, the BS may send a training sequence/training data to the UE after a certain predefined time gap following transmission of the training request at 1012.” Since this training signal “starts the training phase,” it is an indication to train the neural network based on the settings and parameters previously transmitted.]
Ma as modified thus far does not explicitly teach the limitation that “each layer” of the multiple neural networks is trained. However, this is a conventional feature of neural network training.
Zhang teaches training “each layer” of a neural network [[0005]: “a neural network has two different computing phases, namely a training phase and an inference phase. The training phase is used to adjust the weights of each layer to make the neural network fit a specific function.”]
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teachings of the references combined thus far with the teachings of Zheng by training each layer of the multiple neural networks. The motivation for doing so would have been to implement a standard method for training a neural network to fit a specific function, as suggested by Zhang.
4. Claims 11-12 and 24-25 are rejected under 35 U.S.C. 103 as being unpatentable over Ma in view of Tan, and further in view of Wang et al. (US 2021/0182658 A1) (“Wang”).
As to claim 11, the combination of Ma and Tan teaches the apparatus of claim 7, wherein the memory and the at least one processor are configured to receive training command […] and to train the neural network […]. [As shown in FIG. 13, the UE and BS communicate wirelessly. See Ma, [0037]: The BSs 170 communicate with one or more of the EDs 110 over one or more air interfaces 190 a using wireless communication links (e.g. radio frequency (RF), microwave, infrared (IR), etc.). This includes the training request (which is sent via RRC channel, a downlink control channel or on a data channel as described see [0136]), and also the training signal on which the training is performed (as described in Ma, [0142]: “At 1116 the BS starts the training phase 1150 by sending a training signal that includes a training sequence or training data to the UE… Non-limiting examples of channels that may be used by the BS to send training sequences or training data to UE include those discussed above with reference to FIG. 12, namely a dynamic control channel, a data channel and/or RRC channel.”). The AI/ML module is trained on a channel for communication between the UE and the base station, in addition to being trained using a channel for such communication. See Ma, [0074]: “an AI/ ML module 502,552 that is trainable in order to provide a tailored personalized air interface between the base station 170 and UE 110.”]
Ma as modified thus far does not explicitly teach the limitations that the training command is received “in a first frequency range or a first frequency band” and that the neural network is trained “on a second frequency range or a second frequency band.”
Wang teaches, “in a first frequency range or a first frequency band” and “on a second frequency range or a second frequency band” [[0175]: “the base station 120 (and/or the core network server 302 by way of the base station 120) communicates the configuration of the DNN to the UE 110 using a first component carrier, where the DNN configuration corresponds to forming a DNN for processing a second component carrier of the carrier aggregation.” That is, the first component carrier is used for communication of the configuration, which is analogous to the training command of the instant claim, while the second component carrier is for the function of the DNN, which is analogous for a function for which the neural network of the instant claim is being configured for on. See [0025]: “training a DNN on transmitter and/or receiver processing chain operations.” Note that the two component carriers have different frequency bands. See [0156]: “the first component carrier resides in a licensed band and the second component carrier resides in an unlicensed band”; [0144]: “Image 1020 depicts a non-contiguous, inter-band configuration, where at least one component carrier used in the carrier-aggregation-communications resides in a different frequency band. For example, in various implementations, band 1 of image 1020 corresponds to licensed bands allocated to the base station 120 and band 2 of image 1020 corresponds to unlicensed bands used by the base station 120, such as frequency bands accessed through Licensed-Assisted Access (LAA).”]
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teachings of the references combined thus far with the teachings of Wang by implementing the use of the different component carriers as taught in Wang for communication and DNN functionality, so as to arrive at the limitations of receiving the training command “in a first frequency range or a first frequency band” and having the neural network be trained “on a second frequency range or a second frequency band.” The motivation would have been to enable the neural network to process information exchanged with a user equipment (UE) over a wireless communication system using carrier aggregation that includes at least a first component carrier and a second component carrier (see Wang, [0026]: “at least one deep neural network (DNN) configuration for processing information exchanged with a user equipment (UE) over a wireless communication system using carrier aggregation that includes at least a first component carrier and a second component carrier.”).
As to claim 12, the combination of Ma and Tan teaches the apparatus of claim 7, wherein the memory and the at least one processor are configured to receive the training command […] and to train the neural network […]. [As shown in FIG. 13, the UE and BS communicate wirelessly. See Ma, [0037]: The BSs 170 communicate with one or more of the EDs 110 over one or more air interfaces 190 a using wireless communication links (e.g. radio frequency (RF), microwave, infrared (IR), etc.). This includes the training request (which is sent via RRC channel, a downlink control channel or on a data channel as described see Ma, [0136]), and also the training signal on which the training is performed (as described in [0142]: “At 1116 the BS starts the training phase 1150 by sending a training signal that includes a training sequence or training data to the UE… Non-limiting examples of channels that may be used by the BS to send training sequences or training data to UE include those discussed above with reference to FIG. 12, namely a dynamic control channel, a data channel and/or RRC channel.”). The AI/ML module is trained on a channel for communication between the UE and the base station, in addition to being trained using a channel for such communication. See Ma, [0074]: “an AI/ ML module 502,552 that is trainable in order to provide a tailored personalized air interface between the base station 170 and UE 110.”]
Ma as modified thus far does not explicitly teach the limitations that the training command is received “in a first component carrier” and that the neural network is trained “on a second component carrier.”
Wang teaches, “in a first component carrier” and “on a second component carrier” [[0175]: “the base station 120 (and/or the core network server 302 by way of the base station 120) communicates the configuration of the DNN to the UE 110 using a first component carrier, where the DNN configuration corresponds to forming a DNN for processing a second component carrier of the carrier aggregation.” That is, the first component carrier is used for communication of the configuration, which is analogous to the training command of the instant claim, while the second component carrier is for the function of the DNN, which is analogous for a function for which the neural network of the instant claim is being configured for on.]
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teachings of the references combined thus far with the teachings of Wang by implementing the use of the different component carriers as taught in Wang for communication and DNN functionality, so as to arrive at the limitations of receiving the training command “in a first component carrier” and having the neural network be trained “on a second component carrier.” The motivation would have been to enable the neural network to process information exchanged with a user equipment (UE) over a wireless communication system using carrier aggregation that includes at least a first component carrier and a second component carrier (see Wang, [0026]: “at least one deep neural network (DNN) configuration for processing information exchanged with a user equipment (UE) over a wireless communication system using carrier aggregation that includes at least a first component carrier and a second component carrier.”).
As to claim 24, the combination of Ma and Tan teaches the apparatus of claim 23, wherein the training command is transmitted […] for the UE to train the neural network […]. [As shown in FIG. 13, the UE and BS communicate wirelessly. See [0037]: The BSs 170 communicate with one or more of the EDs 110 over one or more air interfaces 190 a using wireless communication links (e.g. radio frequency (RF), microwave, infrared (IR), etc.). This includes the training request (which is sent via RRC channel, a downlink control channel or on a data channel as described see [0136]), and also the training signal on which the training is performed (as described in [0142]: “At 1116 the BS starts the training phase 1150 by sending a training signal that includes a training sequence or training data to the UE… Non-limiting examples of channels that may be used by the BS to send training sequences or training data to UE include those discussed above with reference to FIG. 12, namely a dynamic control channel, a data channel and/or RRC channel.”). The AI/ML module is trained on a channel for communication between the UE and the base station, in addition to being trained using a channel for such communication. See [0074]: “an AI/ ML module 502,552 that is trainable in order to provide a tailored personalized air interface between the base station 170 and UE 110.” The training may be at the UE or at both the UE and in the network (see [0142], [0145]-[0146]), either of which reads on the instant claim limitation.]
Ma as modified thus far does not explicitly teach the limitations that the training command is transmitted “in a first frequency range or a first frequency band” and that the neural network is trained “on a second frequency range or a second frequency band.”
Wang teaches, “in a first frequency range or a first frequency band” and “on a second frequency range or a second frequency band” [[0175]: “the base station 120 (and/or the core network server 302 by way of the base station 120) communicates the configuration of the DNN to the UE 110 using a first component carrier, where the DNN configuration corresponds to forming a DNN for processing a second component carrier of the carrier aggregation.” That is, the first component carrier is used for communication of the configuration, which is analogous to the training command of the instant claim, while the second component carrier is for the function of the DNN, which is analogous for a function for which the neural network of the instant claim is being configured for on. See [0025]: “training a DNN on transmitter and/or receiver processing chain operations.” Note that the two component carriers have different frequency bands. See [0156]: “the first component carrier resides in a licensed band and the second component carrier resides in an unlicensed band”; [0144]: “Image 1020 depicts a non-contiguous, inter-band configuration, where at least one component carrier used in the carrier-aggregation-communications resides in a different frequency band. For example, in various implementations, band 1 of image 1020 corresponds to licensed bands allocated to the base station 120 and band 2 of image 1020 corresponds to unlicensed bands used by the base station 120, such as frequency bands accessed through Licensed-Assisted Access (LAA).”]
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teachings of the references combined thus far with the teachings of Wang by implementing the use of the different component carriers as taught in Wang for communication and DNN functionality, so as to arrive at the limitations of transmitting the training command “in a first frequency range or a first frequency band” for the neural network to be trained “on a second frequency range or a second frequency band.” The motivation would have been to enable the neural network to process information exchanged with a user equipment (UE) over a wireless communication system using carrier aggregation that includes at least a first component carrier and a second component carrier (see Wang, [0026]: “at least one deep neural network (DNN) configuration for processing information exchanged with a user equipment (UE) over a wireless communication system using carrier aggregation that includes at least a first component carrier and a second component carrier.”).
As to claim 25, the combination of Ma and Tan teaches the apparatus of claim 23, wherein the training command is transmitted […] for the UE to train the neural network […]. [As shown in FIG. 13, the UE and BS communicate wirelessly. See Ma, [0037]: The BSs 170 communicate with one or more of the EDs 110 over one or more air interfaces 190 a using wireless communication links (e.g. radio frequency (RF), microwave, infrared (IR), etc.). This includes the training request (which is sent via RRC channel, a downlink control channel or on a data channel as described see Ma, [0136]), and also the training signal on which the training is performed (as described in Ma, [0142]: “At 1116 the BS starts the training phase 1150 by sending a training signal that includes a training sequence or training data to the UE… Non-limiting examples of channels that may be used by the BS to send training sequences or training data to UE include those discussed above with reference to FIG. 12, namely a dynamic control channel, a data channel and/or RRC channel.”). The AI/ML module is trained on a channel for communication between the UE and the base station, in addition to being trained using a channel for such communication. See Ma, [0074]: “an AI/ ML module 502,552 that is trainable in order to provide a tailored personalized air interface between the base station 170 and UE 110.” The training may be at the UE or at both the UE and in the network (see Ma, [0142], [0145]-[0146]), either of which reads on the instant claim limitation.]
Ma as modified thus far does not explicitly teach the limitations that the training command is received “in a first component carrier” and that the neural network is trained “on a second component carrier.”
Wang teaches, “in a first component carrier” and “on a second component carrier” [[0175]: “the base station 120 (and/or the core network server 302 by way of the base station 120) communicates the configuration of the DNN to the UE 110 using a first component carrier, where the DNN configuration corresponds to forming a DNN for processing a second component carrier of the carrier aggregation.” That is, the first component carrier is used for communication of the configuration, which is analogous to the training command of the instant claim, while the second component carrier is for the function of the DNN, which is analogous for a function for which the neural network of the instant claim is being configured for on.]
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teachings the references combined thus far with the teachings of Wang by implementing the use of the different component carriers as taught in Wang for communication and DNN functionality, so as to arrive at the limitations of receiving the training command “in a first component carrier” for the neural network to be trained “on a second component carrier.” The motivation would have been to enable the neural network to process information exchanged with a user equipment (UE) over a wireless communication system using carrier aggregation that includes at least a first component carrier and a second component carrier (see Wang, [0026]: “at least one deep neural network (DNN) configuration for processing information exchanged with a user equipment (UE) over a wireless communication system using carrier aggregation that includes at least a first component carrier and a second component carrier.”).
5. Claims 13-16 and 26-27 are rejected under 35 U.S.C. 103 as being unpatentable over Ma in view of Tan, and further in view of Kobayashi et al. (US 2017/0061329 A1) (“Kobayashi”).
As to claim 13, the combination of Ma and Tan teaches the apparatus of claim 1, but does not teach the further limitation of the instant dependent claim.
Kobayashi teaches “wherein the RRC configuration indicates a period of time associated with the one or more neural network training parameters for training the neural network.” [[0230]: “the user may wish to stop execution of a learning step that takes much time by setting a time limit.” [0157]: “(S27) The learning control unit 135 determines whether the time that has elapsed since the start of the machine learning has exceeded the time limit specified by the time limit input unit 131. If the elapsed time has exceeded the time limit, the operation proceeds to step S28.” [0107]: “the learning time is limited and the machine learning is stopped before its completion.” [0127]: “The time limit input unit 131 acquires information about the time limit of machine learning and notifies the learning control unit 135 of the time limit. The information about the time limit may be inputted by a user via the input device 112. The information about the time limit may be read from a setting file held in the RAM 102 or the HDD 103.” Note that the feature of “the neural network” is already disclosed in Ma, and Kobayashi’s techniques are applicable to machine learning models generically.]
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teachings of the references combined thus far with the teachings of Kobayashi by modifying the configuration to include a settable time limit (and thus period of time) associated with the training, so as to arrive at the limitations of the instant claim. The motivation would have been to enable control of the learning time such that execution of learning is stopped if the training is taking too much time, as suggested by Kobayashi (see [0230] quoted above).
As to claim 14, the combination of Ma, Tan, and Kobayashi teaches the apparatus of claim 13, as set forth above.
Kobayashi further teaches “wherein the RRC configuration indicates an action for the UE to perform when the period of time expires.” [[0157]: “(S27) The learning control unit 135 determines whether the time that has elapsed since the start of the machine learning has exceeded the time limit specified by the time limit input unit 131. If the elapsed time has exceeded the time limit, the operation proceeds to step S28.” [0107]: “the learning time is limited and the machine learning is stopped before its completion.”]
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teachings of the references combined thus far so as to further arrive at the limitations of the instant dependent claim. Since the parts of Kobayashi cited above for this claim are the same as those cited in the rejection of the parent claim, the motivation for doing so is the same as the motivation given for Kobayashi in the rejection of the parent claim.
As to claim 15, the combination of Ma, Tan, and Kobayashi teaches the apparatus of claim 13, as set forth above.
Kobayashi further teaches “wherein, when the period of time expires, the at least one processor is further configured to perform at least one of: cease training the neural network based on the one or more neural network training parameters, freeze layers of the neural network, or resume the training of one or more layers of the neural network.” [The first alternative of “cease training the neural network” is disclosed. See [0157]: “(S27) The learning control unit 135 determines whether the time that has elapsed since the start of the machine learning has exceeded the time limit specified by the time limit input unit 131. If the elapsed time has exceeded the time limit, the operation proceeds to step S28.” [0107]: “the learning time is limited and the machine learning is stopped before its completion.”]
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teachings of the references combined thus far so as to further arrive at the limitations of the instant dependent claim. Since the parts of Kobayashi cited above for this claim are the same as those cited in the rejection of the parent claim 13, the motivation for doing so is the same as the motivation given for Kobayashi in the rejection of the parent claim 13.
As to claim 16, the combination of Ma, Tan, and Kobayashi teaches the apparatus of claim 13, as set forth above.
Kobayashi further teaches “wherein the period of time is a periodic time, semi-persistent time, or aperiodic time for training the neural network, and wherein the memory and the at least one processor are further configured to periodically or aperiodically train the neural network based on the period of time.” [[0157]: “(S27) The learning control unit 135 determines whether the time that has elapsed since the start of the machine learning has exceeded the time limit specified by the time limit input unit 131. If the elapsed time has exceeded the time limit, the operation proceeds to step S28.” [0107]: “the learning time is limited and the machine learning is stopped before its completion.” That is, the time limit is aperiodic since it does not repeat.]
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teachings of the references combined thus far so as to further arrive at the limitations of the instant dependent claim. Since the parts of Kobayashi cited above for this claim are the same as those cited in the rejection of the parent claim 13, the motivation for doing so is the same as the motivation given for Kobayashi in the rejection of the parent claim 13.
As to claim 26, the combination of Ma and Tan teaches the apparatus of claim 20, but does not teach the further limitation of the instant dependent claim.
Kobayashi teaches “wherein the RRC configuration indicates a period of time associated with the one or more neural network training parameters for training the neural network.” [[0230]: “the user may wish to stop execution of a learning step that takes much time by setting a time limit.” [0157]: “(S27) The learning control unit 135 determines whether the time that has elapsed since the start of the machine learning has exceeded the time limit specified by the time limit input unit 131. If the elapsed time has exceeded the time limit, the operation proceeds to step S28.” [0107]: “the learning time is limited and the machine learning is stopped before its completion.” [0127]: “The time limit input unit 131 acquires information about the time limit of machine learning and notifies the learning control unit 135 of the time limit. The information about the time limit may be inputted by a user via the input device 112. The information about the time limit may be read from a setting file held in the RAM 102 or the HDD 103.” Note that the feature of “the neural network” is already disclosed in Ma, and Kobayashi’s techniques are applicable to machine learning models generically.]
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teachings of the references combined thus far with the teachings of Kobayashi by modifying the configuration to include a settable time limit (and thus period of time) associated with the training, so as to arrive at the limitations of the instant claim. The motivation would have been to enable control of the learning time such that execution of learning is stopped if the training is taking too much time, as suggested by Kobayashi (see [0230] quoted above).
As to claim 27, the combination of Ma, Tan, and Kobayashi teaches the apparatus of claim 26, as set forth above.
Kobayashi further teaches “wherein the period of time is a periodic time, semi-persistent time, or aperiodic time for the UE to train the neural network.” [[0157]: “(S27) The learning control unit 135 determines whether the time that has elapsed since the start of the machine learning has exceeded the time limit specified by the time limit input unit 131. If the elapsed time has exceeded the time limit, the operation proceeds to step S28.” [0107]: “the learning time is limited and the machine learning is stopped before its completion.” That is, the time limit is aperiodic since it does not repeat.]
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teachings of the references combined thus far so as to further arrive at the limitations of the instant dependent claim. Since the parts of Kobayashi cited above for this claim are the same as those cited in the rejection of the parent claim, the motivation for doing so is the same as the motivation given for Kobayashi in the rejection of the parent claim.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. The following references depict the state of the art.
Timo et al. (US 2022/0149904 A1) teaches the use of RRC and DCI for transmitting instruction signals to a UE.
Wei et al. (US 2020/0218985 A1) teaches the use of identifiers (IDs) for specific layers, as shown in FIG. 2B.
Xiao et al., “Fast Deep Learning Training through Intelligently Freezing Layers,” 2019 International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), Atlanta, GA, USA, 2019, pp. 1225-1232 teaches layer freezing techniques in which layers are identified by layer numbers, evidencing that layer identifiers are well known and conventional.
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/Y.D.H./Examiner, Art Unit 2124
/MIRANDA M HUANG/Supervisory Patent Examiner, Art Unit 2124