DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . This action is in response to the communication filed on 07/22/2026. Claims 1-20 are pending in this application.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 07/22/2026 has been entered.
Response to Arguments
Applicant’s arguments with respect to claims 1-20 have been considered but are moot based on the new grounds of rejection necessitated by Applicant’s amendments. Specifically, the arguments present that Bedekar fails to provide for the amended language, where the rejection below now relies on Xiao to teach this subject matter.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 11-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
Claims 11 and 17 recite the limitation “the target parameter” in line 24 and line 26 respectively. There is insufficient antecedent basis for this limitation in the claims. For examination purpose, “the target parameter” will read as “a target parameter.”
The dependent claims of the above rejected claims are rejected due to their dependencies.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20210385682 A1 (hereinafter Bedekar), in view of US 20240162959 A1 (hereinafter Xiao).
For Claim 1, Bedekar teaches an information interaction method, comprising:
sending, by a first communication device (Bedekar exemplifies RAN node 212 in FIG. 3), first processing resource information to a second communication device (Bedekar teaches the RAN node sending RAN capabilities information to the controller node, as exemplified in step 312 of FIG. 3; Examiner notes that the limitation “a second communication device” is not referred to in the remaining parts of Claim 1 or in the dependent claims of Claim 1, and is not differentiated from the later recited limitation “at least one target communication device,” the limitation “a second communication device” is therefore mapped to the Controller 210 exemplified in FIG. 3; ¶ 0073 “… At 312, the RAN node 212 sends to controller 210, its RAN capabilities, which may include neural network support information, e.g., to indicate the RAN node's capabilities or support for neural networks, for example. Thus, the RAN capabilities at 312 may include, for example, neural network support information, e.g., which may include neural network capability information and hardware information for the RAN node. Thus, for example, at 312, the RAN capabilities may include or indicate, by way of example, a list of supported neural network (NN) types, a list of functions (e.g., RAN functions) that can be supported or performed by the RAN node 212 with a neural network, and/or a list of hardware capabilities (e.g., hardware features) of the RAN node …”); and
performing, by the first communication device, information interaction with at least one target communication device (Bedekar exemplifies Controller node 210 in FIG. 3) according to the first processing resource information (Bedekar teaches the controller node exchanging information with the RAN node, determining and transmitting the types and configuration of neural network to the RAN node based on the RAN capabilities information, as exemplified in steps 314-326 of FIG. 3; ¶ 0076 “… at 316, the RAN node 212 may send controller 210 hardware availability information that indicates an availability of the hardware (e.g., processors or processor cores, memory, ...) of the RAN node to be used for neural network processing …”; ¶ 0078 “… At 320, the controller 210 sends to RAN node 212, neural network configuration information that indicates the configuration of the neural network for the RAN node 212 (e.g., including the neural network type and/or settings or parameters of the neural network) …”; also see the disclosure corresponding to the steps in FIG. 3),
wherein the first processing resource information is used for indicating at least one of the following:
available resource information of the first communication device for running a target algorithm, the target algorithm comprising at least one of a neural network algorithm, an artificial intelligence algorithm, and a machine learning algorithm (Bedekar, FIG. 3; ¶ 0073 “… At 312, the RAN node 212 sends to controller 210, its RAN capabilities, which may include neural network support information, e.g., to indicate the RAN node's capabilities or support for neural networks, for example. Thus, the RAN capabilities at 312 may include, for example, neural network support information, e.g., which may include neural network capability information and hardware information for the RAN node. Thus, for example, at 312, the RAN capabilities may include or indicate, by way of example, a list of supported neural network (NN) types, a list of functions (e.g., RAN functions) that can be supported or performed by the RAN node 212 with a neural network, and/or a list of hardware capabilities (e.g., hardware features) of the RAN node …”); and
a target algorithm set supported by the first communication device (Bedekar teaches a list of supported NN types in ¶ 0073, as exemplified in step 312 of FIG. 3),
wherein the performing, by the first communication device, information interaction with at least one target communication device according to the first processing resource information further comprises:
sending, by the first communication device, first interaction information to the at least one target communication device according to configuration information of the target algorithm, wherein the first interaction information comprises second processing resource information (Bedekar teaches the RAN node sending the updated available resource information (e.g. hardware resources) to the controller node according to the currently applied neural network configuration; FIG. 2, FIG. 3; ¶ 0072 “… Also for example, subsequently, the RAN node 212 may continue to provide to Controller 210, the measurement information as requested (e.g., in a format requested, specific types of requested measurement information and/or with specific processing or pre-processing before sending the measurement information), e.g., which may include data elements or information describing updated conditions or metrics or measurements of (or with respect to) a cell and/or UEs within a cell(s) at the RAN node 212, or an indication of currently available resources (e.g., hardware resources) of the RAN node (such as an amount of processor or memory resources available for neural network processing), for example …”; ¶ 0079 “… Also, at 322, the RAN node 212 may send controller 210 updated hardware availability information that indicates an updated availability of the hardware (e.g., processors or processor cores, memory, ...) of the RAN node to be used for neural network processing …”) and target algorithm recommendation information (Bedekar teaches the RAN node 212 sending controller 210 updated hardware availability information in ¶ 0079, which is implicitly information about whether the RAN node can support the target algorithm),
the second processing resource information being used for indicating resource information of the first communication device for running the target algorithm (Bedekar teaches the RAN node sending the updated available resource information (e.g. hardware resources) to the controller node according to the currently applied neural network configuration; FIG. 2, FIG. 3; ¶ 0072, ¶ 0079).
Bedekar does not explicitly teach, but Xiao teaches wherein the target algorithm recommendation information comprises at least one of the following:
recommendation information of the first communication device on a target parameter of the target algorithm (Xiao teaches the terminal device feeding back the suggested set of AI parameters; FIG. 7; ¶ 0102 “… At operation 2, the terminal device receives the CSI RS sent by the base station, and tests the coding and decoding of N sets of AI parameters for the CSI measured by the CSI RS, and the terminal device feeds back the information of the suggested set of AI parameters to the base station …”; ¶ 0127 “… In an embodiment, the set of neural network parameters in the apparatus includes the values of at least one of the following neural network parameters: compression ratio, activation function, number of network layers, network layer mapping, network layer weight, network layer offset, or network layer weight normalization coefficient …”);
revision information of the first communication device on the target parameter of the target algorithm (Xiao teaches the terminal device feeding back the mismatched set of neural network parameters and also the suggested set of neural network parameter to fix the problem; FIG. 7; ¶ 0108 “… The terminal device processes CSI according to the suggested set of neural network parameters by the base station. But with the change of the wireless channel environment, the currently employed set of neural network parameters may mismatch with the up-to-date wireless channel environment. Therefore, the terminal device can inform the base station of the encountering problems or even the suggested set of neural network parameters in the CSI feedback information through a specific bit sequence (or the value of the bit set in a specific position) …”).
Xiao and Bedekar are analogous art because they are both related to applying neural network algorithm in the wireless network system.
Before the effective filing date of the claimed invention it would have been obvious to one of ordinary skill in the art to use the terminal device suggesting sets of neural network parameters techniques of Xiao with the system of Bedekar to facilitate the AI technique into the wireless communication and improve the accurate feedback of the channel state information (Xiao ¶ 0003).
For Claim 2, Bedekar-Xiao teaches the method according to claim 1, wherein the performing, by the first communication device, information interaction with at least one target communication device according to the first processing resource information comprises:
receiving, by the first communication device, the configuration information of the target algorithm from the at least one target communication device (Bedekar teaches the RAN node receiving the NN configuration information from the controller node, as exemplified in steps 318 and 320 of FIG. 3; ¶ 0077 “… At 318, controller 210 determines a configuration of a neural network for the RAN node 212 (e.g., including selecting a neural network type and determining one or more parameters or settings of the neural network), based on the measurement information or data received from the RAN node 212. Thus, for example, at 318, the controller 210 may make an initial determination, for at least one RAN function, of a type of neural network to use, and a configuration of the neural network (e.g., inputs, output(s), input layers, hidden layers, number of neurons, activation functions, weights, and/or other neural network settings or parameters …”; ¶ 0078 “… At 320, the controller 210 sends to RAN node 212, neural network configuration information that indicates the configuration of the neural network for the RAN node 212 (e.g., including the neural network type and/or settings or parameters of the neural network) …”), wherein the configuration information comprises at least one of the following:
at least one expected time period for running the target algorithm;
an input type of the target algorithm (Bedekar teaches the configuration of the neural network including the inputs information in ¶ 0077, as exemplified in step 318 of FIG. 3);
preprocessing information corresponding to input information of the target algorithm;
different target parameters corresponding to neural network models of a same target algorithm type (Bedekar teaches various neural network settings or parameters including weights in ¶ 0077, as exemplified in step 318 of FIG. 3); and
a neural network model library.
For Claim 3, Bedekar-Xiao teaches the method according to claim 2, further comprising:
obtaining update indication information from the at least one target communication device, wherein the update indication information is used for indicating to update the configuration information (Bedekar, FIG. 3; ¶ 0080 “… At 324, controller 210 may determine an updated (or adjusted) neural network configuration (e.g., which may include an updated neural network type and/or one or more adjusted or updated neural network settings or parameters), e.g., based on the updated or changed measurement information, data or hardware availability information …”; ¶ 0081 “… At 326, the controller 210 sends to RAN node 212, updated or adjusted neural network configuration information that indicates the updated or adjusted configuration of the neural network for the RAN node 212 (e.g., including the updated or adjusted neural network type and/or the updated or adjusted settings or parameters of the neural network for the RAN node 212 …”); and
updating the configuration information of the target algorithm according to the update indication information (Bedekar, FIG. 3; ¶ 0081 “… At 326, the controller 210 sends to RAN node 212, updated or adjusted neural network configuration information that indicates the updated or adjusted configuration of the neural network for the RAN node 212 (e.g., including the updated or adjusted neural network type and/or the updated or adjusted settings or parameters of the neural network for the RAN node 212 …”).
For Claim 4, Bedekar-Xiao teaches the method according to claim 1, wherein the second processing resource information comprises at least one of the following:
a target algorithm supported or not supported by the first communication device;
whether the first communication device supports the target algorithm according to a predefined time period;
a time period required for running the target algorithm by the first communication device (Bedekar, FIG. 3, FIG. 4; ¶ 0107 “… RAN node may provide to the Controller over the API (e.g., one or more of): 1) A list of supported types of neural networks whose execution is supported by the RAN node … 2) A list of RAN functions for which it can execute a neural network to make decisions … 3) A list of HW capabilities descriptors describing the HW capabilities of the RAN node for executing neural networks, … available time cycles for executing time-critical tasks based on NN …”);
an expected time period corresponding to running the target algorithm by the first communication device; and
a target parameter of the target algorithm supported by the first communication device;
wherein the target algorithm recommendation information further comprises:
information about whether the first communication device supports the target algorithm (Bedekar teaches the RAN node 212 sending controller 210 updated hardware availability information in ¶ 0079, which is implicitly information about whether the RAN node can support the target algorithm).
For Claim 5, Bedekar-Xiao teaches the method according to claim 4, wherein the sending first interaction information comprises: sending at least one of the second processing resource information and the target algorithm recommendation information in a case that a preconfigured trigger condition is met (Bedekar teaches the RAN node sending updated hardware availability information based on the instructions from the controller node; FIG. 3; ¶ 0079 “… At 322, the RAN node 212 may send to controller 210 updated measurement information, e.g., as requested by the instructions from the controller 210 … Also, at 322, the RAN node 212 may send controller 210 updated hardware availability information that indicates an updated availability of the hardware (e.g., processors or processor cores, memory, ...) of the RAN node to be used for neural network processing … ”);
wherein the trigger condition comprises at least one of the following:
a quantity of available resources of the first communication device is less than a preset quantity of resources;
available resources of the first communication device are incapable of supporting running of an indicated target algorithm;
the first communication device cannot perform processing in a manner in the first processing resource information or the second interaction information;
the available resources of the first communication device change or a range of changes in the available resources exceeds a threshold (Bedekar, FIG. 3; ¶ 0080 “… hardware resources at the RAN node 212 available for neural network processing may be significantly less than before, e.g., thus causing the controller 210 to change or adjust settings or parameters (e.g., to use fewer neurons or layers) of a neural network at the RAN node 212, or possibly to cancel the use of such neural network at the RAN node. …”; ¶ 0101 “… 2. RAN-to-Controller API: to enable RAN to provide to the controller: A. capabilities of the RAN, such as HW capabilities or the types of neural network (NN) supported or the types of functions for which NN can be used; B. data related to the NN or the operation of the relevant functions, such as metrics or measurements, as requested by the Controller according to the mode (periodic/event-driven, or streaming/batch-mode etc.) …”); and
the first communication device needs to simultaneously run a plurality of target algorithms.
For Claim 6, Bedekar-Xiao teaches the method according to claim 1, wherein the first processing resource information comprises at least one of the following:
an activation function type supported by the first communication device (Bedekar, ¶ 0107 “… RAN node may provide to the Controller over the API (e.g., one or more of): 1) A list of supported types of neural networks whose execution is supported by the RAN node … This may further include parameters for each type of neural networks, such as: … type of activation functions supported …”);
an operation type supported by the first communication device (Bedekar, FIG. 3; ¶ 0073 “… at 312, the RAN capabilities may include or indicate, by way of example, a list of supported neural network (NN) types, a list of functions (e.g., RAN functions) that can be supported or performed by the RAN node 212 with a neural network, and/or a list of hardware capabilities (e.g., hardware features) of the RAN node …”);
a conversion relationship between available resources used between different operations supported by the first communication device; whether the first communication device supports different specific operations or hyperoperations within a same time unit; a combination relationship between the different specific operations supported by the first communication device; a quantity of operation times supported by the first communication device within a first target time period, the first target time period comprising at least one time unit; a relationship between the quantity of operation times supported within the first target time period and a quantity of operation times supported within a second target time period, the second target time period comprising at least one time unit, and the time unit comprised in the second target time period being different from the time unit comprised in the first target time period; a time period required for completing a specific operation of a specific target by the first communication device; information about the hyperoperation supported by the first communication device; information about a neural network model supported by the first communication device; a model size; a quantity of quantization bits of a target algorithm model; a quantity of layers of the model; a maximum quantity of neurons at each layer of the model; a running time period of each neural network algorithm; a running time period of a plurality of neural network algorithm combinations; recommendation information of the first communication device on the target parameter of the target algorithm; and support information of the first communication device for a pre-agreed target algorithm; indication information of a running priority of the target algorithm; a time period from configuration to taking effect of the neural network model; a time period required for switching between at least two neural network models; and whether a corresponding operation is runnable during switching between the at least two neural network models.
For Claim 7, Bedekar-Xiao teaches the method according to claim 1, wherein the sending, by the first communication device, first processing resource information comprises: sending, by the first communication device, the first processing resource information in a case that a preconfigured trigger condition is met (Bedekar teaches the RAN node sending updated hardware availability information based on the instructions from the controller node; FIG. 3; ¶ 0079 “… At 322, the RAN node 212 may send to controller 210 updated measurement information, e.g., as requested by the instructions from the controller 210 … Also, at 322, the RAN node 212 may send controller 210 updated hardware availability information that indicates an updated availability of the hardware (e.g., processors or processor cores, memory, ...) of the RAN node to be used for neural network processing … ”);
wherein the trigger condition comprises at least one of the following:
a quantity of available resources of the first communication device is less than a preset quantity of resources;
available resources of the first communication device are incapable of supporting running of an indicated target algorithm;
the first communication device cannot perform processing in a manner in the first processing resource information or the second interaction information;
the available resources of the first communication device change or a range of changes in the available resources exceeds a threshold (Bedekar, FIG. 3; ¶ 0080 “… hardware resources at the RAN node 212 available for neural network processing may be significantly less than before, e.g., thus causing the controller 210 to change or adjust settings or parameters (e.g., to use fewer neurons or layers) of a neural network at the RAN node 212, or possibly to cancel the use of such neural network at the RAN node. …”; ¶ 0101 “… 2. RAN-to-Controller API: to enable RAN to provide to the controller: A. capabilities of the RAN, such as HW capabilities or the types of neural network (NN) supported or the types of functions for which NN can be used; B. data related to the NN or the operation of the relevant functions, such as metrics or measurements, as requested by the Controller according to the mode (periodic/event-driven, or streaming/batch-mode etc.) …”); and
the first communication device needs to simultaneously run a plurality of target algorithms.
For Claim 8, Bedekar-Xiao teaches the method according to claim 1, further comprising:
sending, by the first communication device, at least one of second interaction information and third processing resource information in a case that a preconfigured trigger condition is met (Bedekar teaches the RAN node sending updated hardware availability information based on the instructions from the controller node, the information sent at steps 316 and/or 322 may be considered as “second interaction information” or “third processing resource information”; FIG. 3; ¶ 0079 “… At 322, the RAN node 212 may send to controller 210 updated measurement information, e.g., as requested by the instructions from the controller 210 … Also, at 322, the RAN node 212 may send controller 210 updated hardware availability information that indicates an updated availability of the hardware (e.g., processors or processor cores, memory, ...) of the RAN node to be used for neural network processing … ”),
wherein the second interaction information is used for indicating a sharing capability of a plurality of target algorithms for an NPU (Bedekar, FIG. 3; ¶ 0076 “… Also, at 316, the RAN node 212 may send controller 210 hardware availability information that indicates an availability of the hardware ( e.g., processors or processor cores, memory, ... ) of the RAN node to be used for neural network processing …”), and
the third processing resource information is used for indicating an available computing resource (Bedekar, FIG. 3; ¶ 0079 “… Also, at 322, the RAN node 212 may send controller 210 updated hardware availability information that indicates an updated availability of the hardware (e.g., processors or processor cores, memory, ...) of the RAN node to be used for neural network processing … ”);
wherein the trigger condition comprises at least one of the following:
a quantity of available resources of the first communication device is less than a preset quantity of resources;
available resources of the first communication device are incapable of supporting running of an indicated target algorithm;
the first communication device cannot perform processing in a manner in the first processing resource information or the second interaction information;
the available resources of the first communication device change or a range of changes in the available resources exceeds a threshold (Bedekar, FIG. 3; ¶ 0080 “… hardware resources at the RAN node 212 available for neural network processing may be significantly less than before, e.g., thus causing the controller 210 to change or adjust settings or parameters (e.g., to use fewer neurons or layers) of a neural network at the RAN node 212, or possibly to cancel the use of such neural network at the RAN node. …”; ¶ 0101 “… 2. RAN-to-Controller API: to enable RAN to provide to the controller: A. capabilities of the RAN, such as HW capabilities or the types of neural network (NN) supported or the types of functions for which NN can be used; B. data related to the NN or the operation of the relevant functions, such as metrics or measurements, as requested by the Controller according to the mode (periodic/event-driven, or streaming/batch-mode etc.) …”); and
the first communication device needs to simultaneously run a plurality of target algorithms.
For Claim 9, Bedekar-Xiao teaches the method according to claim 1, wherein the performing, by the first communication device, information interaction with at least one target communication device according to the first processing resource information comprises:
obtaining first indication information from the at least one target communication device; and determining, according to the first indication information, a target algorithm used by the first communication device (Bedekar, FIG. 3; ¶ 0078 “… At 320, the controller 210 sends to RAN node 212, neural network configuration information that indicates the configuration of the neural network for the RAN node 212 (e.g., including the neural network type and/or settings or parameters of the neural network) …”),
wherein the first indication information comprises at least one of the following:
indication information of a running priority of the target algorithm;
a time period from configuration to taking effect of the neural network model (Bedekar, FIG. 3, FIG. 4; ¶ 0107 “… RAN node may provide to the Controller over the API (e.g., one or more of): 1) A list of supported types of neural networks whose execution is supported by the RAN node … 2) A list of RAN functions for which it can execute a neural network to make decisions … 3) A list of HW capabilities descriptors describing the HW capabilities of the RAN node for executing neural networks, … available time cycles for executing time-critical tasks based on NN …”);
a time period required for switching between at least two neural network models; and
whether a corresponding operation is runnable during switching between the at least two neural network models.
For Claim 10, Bedekar-Xiao teaches the method according to claim 1, wherein the performing, by the first communication device, information interaction with at least one target communication device according to the first processing resource information comprises: obtaining second indication information from the at least one target communication device, wherein the second indication information is used for indicating a target algorithm used by the first communication device (Bedekar, FIG. 3; ¶ 0078 “… At 320, the controller 210 sends to RAN node 212, neural network configuration information that indicates the configuration of the neural network for the RAN node 212 (e.g., including the neural network type and/or settings or parameters of the neural network) …”; see also ¶ 0081).
For Claim 11, Bedekar teaches an information interaction method, comprising:
obtaining, by a second communication device (Bedekar exemplifies Controller node 210 in FIG. 3), first processing resource information sent by a first communication device (Bedekar teaches the controller node obtaining RAN capabilities information from the RAN node 212, as exemplified in step 312 of FIG. 3; ¶ 0073 “… At 312, the RAN node 212 sends to controller 210, its RAN capabilities, which may include neural network support information, e.g., to indicate the RAN node's capabilities or support for neural networks, for example. Thus, the RAN capabilities at 312 may include, for example, neural network support information, e.g., which may include neural network capability information and hardware information for the RAN node. Thus, for example, at 312, the RAN capabilities may include or indicate, by way of example, a list of supported neural network (NN) types, a list of functions (e.g., RAN functions) that can be supported or performed by the RAN node 212 with a neural network, and/or a list of hardware capabilities (e.g., hardware features) of the RAN node …”); and
performing, by the second communication device, interaction with a target communication device (Bedekar exemplifies RAN node 212 in FIG. 3; Examiner notes that “a first communication device” and “a target communication device” are both mapped to the RAN node 212 based on the claim structure) according to the first processing resource information, the target communication device comprising at least one of the first communication device and a third communication device (Bedekar teaches the controller node exchanging information with the RAN node, determining and transmitting the types and configuration of neural network to the RAN node based on the RAN capabilities information, as exemplified in steps 314-326 of FIG. 3; ¶ 0076 “… at 316, the RAN node 212 may send controller 210 hardware availability information that indicates an availability of the hardware (e.g., processors or processor cores, memory, ...) of the RAN node to be used for neural network processing …”; ¶ 0078 “… At 320, the controller 210 sends to RAN node 212, neural network configuration information that indicates the configuration of the neural network for the RAN node 212 (e.g., including the neural network type and/or settings or parameters of the neural network) …”; also see the disclosure corresponding to the steps in FIG. 3),
wherein the first processing resource information is used for indicating at least one of the following:
available resource information of the first communication device for running a target algorithm, the target algorithm comprising at least one of a neural network algorithm, an artificial intelligence algorithm, and a machine learning algorithm (Bedekar, FIG. 3; ¶ 0073 “… At 312, the RAN node 212 sends to controller 210, its RAN capabilities, which may include neural network support information, e.g., to indicate the RAN node's capabilities or support for neural networks, for example. Thus, the RAN capabilities at 312 may include, for example, neural network support information, e.g., which may include neural network capability information and hardware information for the RAN node. Thus, for example, at 312, the RAN capabilities may include or indicate, by way of example, a list of supported neural network (NN) types, a list of functions (e.g., RAN functions) that can be supported or performed by the RAN node 212 with a neural network, and/or a list of hardware capabilities (e.g., hardware features) of the RAN node …”); and
a target algorithm set supported by the first communication device (Bedekar teaches a list of supported NN types in ¶ 0073, as exemplified in step 312 of FIG. 3),
wherein the performing, by the second communication device, interaction with a target communication device according to the first processing resource information further comprises:
obtaining, by the second communication device, first interaction information sent by the target communication device, wherein the first interaction information comprises second processing resource information (Bedekar teaches the RAN node sending the updated available resource information (e.g. hardware resources) to the controller node according to the currently applied neural network configuration; FIG. 2, FIG. 3; ¶ 0072 “… Also for example, subsequently, the RAN node 212 may continue to provide to Controller 210, the measurement information as requested (e.g., in a format requested, specific types of requested measurement information and/or with specific processing or pre-processing before sending the measurement information), e.g., which may include data elements or information describing updated conditions or metrics or measurements of (or with respect to) a cell and/or UEs within a cell(s) at the RAN node 212, or an indication of currently available resources (e.g., hardware resources) of the RAN node (such as an amount of processor or memory resources available for neural network processing), for example …”; ¶ 0079 “… Also, at 322, the RAN node 212 may send controller 210 updated hardware availability information that indicates an updated availability of the hardware (e.g., processors or processor cores, memory, ...) of the RAN node to be used for neural network processing …”) and target algorithm recommendation information (Bedekar teaches the RAN node 212 sending controller 210 updated hardware availability information in ¶ 0079, which is implicitly information about whether the RAN node can support the target algorithm),
the second processing resource information being used for indicating resource information of the first communication device for running the target algorithm (Bedekar teaches the RAN node sending the updated available resource information (e.g. hardware resources) to the controller node according to the currently applied neural network configuration; FIG. 2, FIG. 3; ¶ 0072, ¶ 0079).
Bedekar does not explicitly teach, but Xiao teaches wherein the target algorithm recommendation information comprises at least one of the following:
recommendation information of the first communication device on a target parameter of the target algorithm (Xiao teaches the terminal device feeding back the suggested set of AI parameters; FIG. 7; ¶ 0102 “… At operation 2, the terminal device receives the CSI RS sent by the base station, and tests the coding and decoding of N sets of AI parameters for the CSI measured by the CSI RS, and the terminal device feeds back the information of the suggested set of AI parameters to the base station …”; ¶ 0127 “… In an embodiment, the set of neural network parameters in the apparatus includes the values of at least one of the following neural network parameters: compression ratio, activation function, number of network layers, network layer mapping, network layer weight, network layer offset, or network layer weight normalization coefficient …”);
revision information of the first communication device on the target parameter of the target algorithm (Xiao teaches the terminal device feeding back the mismatched set of neural network parameters and also the suggested set of neural network parameter to fix the problem; FIG. 7; ¶ 0108 “… The terminal device processes CSI according to the suggested set of neural network parameters by the base station. But with the change of the wireless channel environment, the currently employed set of neural network parameters may mismatch with the up-to-date wireless channel environment. Therefore, the terminal device can inform the base station of the encountering problems or even the suggested set of neural network parameters in the CSI feedback information through a specific bit sequence (or the value of the bit set in a specific position) …”).
Xiao and Bedekar are analogous art because they are both related to applying neural network algorithm in the wireless network system.
Before the effective filing date of the claimed invention it would have been obvious to one of ordinary skill in the art to use the terminal device suggesting sets of neural network parameters techniques of Xiao with the system of Bedekar to facilitate the AI technique into the wireless communication and improve the accurate feedback of the channel state information (Xiao ¶ 0003).
For Claim 12, Bedekar-Xiao teaches the method according to claim 11, wherein the performing, by the second communication device, interaction with a target communication device according to the first processing resource information comprises:
sending, by the second communication device, configuration information of the target algorithm (Bedekar teaches the controller node sending the NN configuration information to the RAN node, as exemplified in steps 318 and 320 of FIG. 3; ¶ 0077 “… At 318, controller 210 determines a configuration of a neural network for the RAN node 212 (e.g., including selecting a neural network type and determining one or more parameters or settings of the neural network), based on the measurement information or data received from the RAN node 212. Thus, for example, at 318, the controller 210 may make an initial determination, for at least one RAN function, of a type of neural network to use, and a configuration of the neural network (e.g., inputs, output(s), input layers, hidden layers, number of neurons, activation functions, weights, and/or other neural network settings or parameters …”; ¶ 0078 “… At 320, the controller 210 sends to RAN node 212, neural network configuration information that indicates the configuration of the neural network for the RAN node 212 (e.g., including the neural network type and/or settings or parameters of the neural network) …”), wherein the configuration information comprises at least one of the following:
at least one expected time period for running the target algorithm;
an input type of the target algorithm (Bedekar teaches the configuration of the neural network including the inputs information in ¶ 0077, as exemplified in step 318 of FIG. 3);
preprocessing information corresponding to input information of the target algorithm;
different target parameters corresponding to neural network models of a same target algorithm type (Bedekar teaches various neural network settings or parameters including weights in ¶ 0077, as exemplified in step 318 of FIG. 3); and
a neural network model library.
For Claim 13, Bedekar-Xiao teaches the method according to claim 11, wherein the second processing resource information comprises at least one of the following:
a target algorithm supported or not supported by the first communication device;
whether the first communication device supports the target algorithm according to a predefined time period;
a time period required for running the target algorithm by the first communication device (Bedekar, FIG. 3, FIG. 4; ¶ 0107 “… RAN node may provide to the Controller over the API (e.g., one or more of): 1) A list of supported types of neural networks whose execution is supported by the RAN node … 2) A list of RAN functions for which it can execute a neural network to make decisions … 3) A list of HW capabilities descriptors describing the HW capabilities of the RAN node for executing neural networks, … available time cycles for executing time-critical tasks based on NN …”);
an expected time period corresponding to running the target algorithm by the first communication device; and
a target parameter of the target algorithm supported by the first communication device;
wherein the target algorithm recommendation information further comprises:
information about whether the first communication device supports the target algorithm (Bedekar teaches the RAN node 212 sending controller 210 updated hardware availability information in ¶ 0079, which is implicitly information about whether the RAN node can support the target algorithm).
For Claim 14, Bedekar-Xiao teaches the method according to claim 11, wherein the performing, by the second communication device, interaction with a target communication device according to the first processing resource information comprises:
sending first indication information (Bedekar, FIG. 3; ¶ 0078 “… At 320, the controller 210 sends to RAN node 212, neural network configuration information that indicates the configuration of the neural network for the RAN node 212 (e.g., including the neural network type and/or settings or parameters of the neural network) …”), wherein the first indication information comprises at least one of the following:
indication information of a running priority of the target algorithm;
a time period from configuration to taking effect of the neural network model (Bedekar, FIG. 3, FIG. 4; ¶ 0107 “… RAN node may provide to the Controller over the API (e.g., one or more of): 1) A list of supported types of neural networks whose execution is supported by the RAN node … 2) A list of RAN functions for which it can execute a neural network to make decisions … 3) A list of HW capabilities descriptors describing the HW capabilities of the RAN node for executing neural networks, … available time cycles for executing time-critical tasks based on NN …”);
a time period required for switching between at least two neural network models; and
whether a corresponding operation is runnable during switching between the at least two neural network models.
For Claim 15, Bedekar-Xiao teaches the method according to claim 11, further comprising: sending second indication information, wherein the second indication information is used for indicating a target algorithm used by the first communication device (Bedekar, FIG. 3; ¶ 0078 “… At 320, the controller 210 sends to RAN node 212, neural network configuration information that indicates the configuration of the neural network for the RAN node 212 (e.g., including the neural network type and/or settings or parameters of the neural network) …”; see also ¶ 0081).
For Claim 16, Bedekar-Xiao teaches the method according to claim 12, further comprising: sending update indication information, wherein the update indication information is used for indicating to update the configuration information (Bedekar, FIG. 3; ¶ 0080 “… At 324, controller 210 may determine an updated (or adjusted) neural network configuration (e.g., which may include an updated neural network type and/or one or more adjusted or updated neural network settings or parameters), e.g., based on the updated or changed measurement information, data or hardware availability information …”; ¶ 0081 “… At 326, the controller 210 sends to RAN node 212, updated or adjusted neural network configuration information that indicates the updated or adjusted configuration of the neural network for the RAN node 212 (e.g., including the updated or adjusted neural network type and/or the updated or adjusted settings or parameters of the neural network for the RAN node 212 …”).
For Claim 17, the claim is substantially similar to claim 1 and therefore is rejected for the same reasoning set forth above. Additionally, Bedekar teaches a communication device, comprising a processor and a memory, the memory storing a program or instructions executable on the processor, the program or instructions, when executed by the processor, implementing (Bedekar, FIG. 7; ¶ 0168 “… The wireless station also includes a processor or control unit/entity (controller) 1004 to execute instructions or software and control transmission and receptions of signals, and a memory 1006 to store data and/or instructions …” ).
For Claim 18, the claim is substantially similar to claim 2 and therefore is rejected for the same reasoning set forth above.
For Claim 19, Bedekar-Xiao teaches the communication device according to claim 17, wherein the second processing resource information comprises at least one of the following:
a target algorithm supported or not supported by the first communication device;
whether the first communication device supports the target algorithm according to a predefined time period;
a time period required for running the target algorithm by the first communication device (Bedekar, FIG. 3, FIG. 4; ¶ 0107 “… RAN node may provide to the Controller over the API (e.g., one or more of): 1) A list of supported types of neural networks whose execution is supported by the RAN node … 2) A list of RAN functions for which it can execute a neural network to make decisions … 3) A list of HW capabilities descriptors describing the HW capabilities of the RAN node for executing neural networks, … available time cycles for executing time-critical tasks based on NN …”);
an expected time period corresponding to running the target algorithm by the first communication device; and
a target parameter of the target algorithm supported by the first communication device;
wherein the target algorithm recommendation information further comprises:
information about whether the first communication device supports the target algorithm (Bedekar teaches the RAN node 212 sending controller 210 updated hardware availability information in ¶ 0079, which is implicitly information about whether the RAN node can support the target algorithm).
For Claim 20, the claim is substantially similar to claim 11 and therefore is rejected for the same reasoning set forth above. Additionally, Bedekar teaches a communication device, comprising a processor and a memory, the memory storing a program or instructions executable on the processor, the program or instructions, when executed by the processor, implementing the steps of the information interaction method according to claim 11 (Bedekar, FIG. 7; ¶ 0168 “… The wireless station also includes a processor or control unit/entity (controller) 1004 to execute instructions or software and control transmission and receptions of signals, and a memory 1006 to store data and/or instructions …” ).
Citation of Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure is listed below, thank you:
i. US 20200367051 A1 (hereinafter Wang) teaches a terminal and a base station in a wireless communication system. The terminal includes: a processing unit for obtaining terminal network capability information about at least one of a computing capability, a storage capability, and a support capability for neural network of the terminal; and a transmitting unit for transmitting the terminal network capability information to a base station (Abstract).
ii. US 20220116764 A1 (hereinafter Pezeshki) teaches that A method of wireless communication by a user equipment (UE) receives a machine learning model from a base station. The UE reports, to the base station, a machine learning processing capability. The UE also transmits, to the base station, gradient updates or weight updates to the machine learning model. A base station transmits a machine learning model to a number of UEs. The base station receives, from each of the number of UEs, a machine learning processing capability report. The base station groups a number of UEs in accordance with the machine learning processing capability reports, to receive gradient updates to the machine learning model (Abstract).
Conclusion
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/Z.D./Examiner, Art Unit 2444
/SCOTT B CHRISTENSEN/Primary Examiner, Art Unit 2444
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