DETAILED ACTION
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 .
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 12/17/2024 and 12/27/2024 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
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.
Claim(s) 31-34, 38, 42 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ryden et al. (WO 2022/013104 A1; cited in Applicant’s IDS submitted on 12/17/2024; “Ryden”) in view of Jin et al. (US 2011/0126037 A1; cited in Applicant’s IDS submitted on 12/17/2024; “Jin”).
Regarding claim 31, Ryden teaches an apparatus, comprising:
at least one processor; and at least one memory including computer program code [Ryden p. 27, ll. 27-36, Fig. 22: wireless device having processor 2202 and memory 2204 storing computer program 2250]; the at least one memory and the computer program code configured to cause the apparatus at least to:
perform at least one machine learning process to produce an output; determine an action to perform based on the output produced by the performing of the at least one machine learning process; transmit, to a network node serving the apparatus, a request to perform the action [Ryden p. 10, ll. 8-14: RAN node may receive, from the wireless device, information based on an output of the ML model executed by the wireless device (i.e. UE transmits output of ML model to network node), wherein the received information may for example comprise an output of the ML model 230a, a derivative of an output of the ML model 230b and/or information relating to a RAN operation performed by the wireless device and configured on the basis of an output of the ML model, wherein the information relating to a RAN operation may comprise configuration information for the RAN operation, a request to perform a RAN operation, a result of a RAN operation, etc. (here, an operation that is indicated by the UE and related to an output of the ML model is analogous to a determined action to perform based on the ML model)].
However, Ryden does not explicitly disclose receive, from the network node, a message indicating whether the apparatus may perform the action; in response to the message indicating the apparatus may perform the action, perform the action; and in response to the message indicating the apparatus may not perform the action, not perform the action.
However, in a similar field of endeavor, Jin teaches receive, from the network node, a message indicating whether the apparatus may perform the action; in response to the message indicating the apparatus may perform the action, perform the action; and in response to the message indicating the apparatus may not perform the action, not perform the action [Jin ¶ 0103: the mobile terminal requests that the partial parameter of class 1 is replaced by the parameter of class 2 (i.e. indicates an action), wherein the second base station may transmit a response message permitting the parameter change request to the corresponding mobile terminal (i.e. terminal receives a message indicating whether the action is permitted; here, a message permitting a parameter change would implicitly result in the terminal performing or not performing the parameter change)].
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to combine the method of determining network operations according to output of a machine learning model, and informing the network of the determined operation as taught by Ryden, with the method of requesting permission to perform an operation of changing power saving mode of a terminal, and receiving permission for performing the operation from a base station as taught by Jin. The motivation to do so would be to minimize power consumption by controlling a plurality of power saving mode classes to not be overlappedly activated [Jin ¶ 0008].
Regarding claim 32, Ryden in view of Jin teaches the apparatus as in claim 31, wherein the at least one memory and the computer program code are further configured to cause the apparatus at least to:
transmit a request for at least one machine learning process signaling configuration to be used during performance of the at least one machine learning-based process [Ryden p. 12, ll. 4-14, Fig. 4: the wireless device may first, in step 402, send to a RAN node of the communication network information about a capability of the wireless device to execute an ML model]; and
receive the at least one machine learning [Ryden p. 12, ll. 16-19, Fig. 4: the wireless device receive, from the RAN node, configuration information for an ML model to be executed by the wireless device].
Regarding claim 33, Ryden in view of Jin teaches the apparatus as in claim 32, wherein the at least one memory and the computer program code are further configured to cause the apparatus at least to:
receive, from the network node in response to the request for at least one machine learning process signaling configuration to be used during performance of the at least one machine learning process, the at least one machine learning process signaling configuration [Ryden p. 12, ll. 16-19, Fig. 4: the wireless device receive, from the RAN node, configuration information for an ML model to be executed by the wireless device], indicating that the apparatus may perform the at least one machine learning process [Ryden p. 12, ll. 21-35: configuration information may include: a representation of the ML model or an update to the ML model 210a, information about inputs to the ML model 210b, information about outputs from the model, 210c, hyperparameters for training of the model at the wireless device 210d, a reporting criterion for reporting of information relating to an output of the ML model executed by the wireless device 21 Oe, information relating to configuration, on the basis of an output of the ML model, of a RAN operation performed by the wireless device 21 Of, a model update criterion for updating of the ML model 21 Oh, and/or a validity specification for the ML model (here, the above configuration information would indicate that a wireless device may use the configured model)].
Regarding claim 34, Ryden in view of Jin teaches the apparatus as in claim 31, wherein the at least one memory and the computer program code are further configured to cause the apparatus at least to: transmit, to the network node, a message indicating an estimate of an outcome of performing the action [Ryden p. 10, ll. 8-14: RAN node may receive, from the wireless device, information based on an output of the ML model executed by the wireless device (i.e. UE transmits output of ML model to network node); p. 21, ll. 11-15: model may alternatively return information elements representing the probability (i.e. estimate) of certain events (i.e. actions)].
Regarding claim 38, Ryden in view of Jin teaches the apparatus as in claim 31, wherein the at least one memory and the computer program code are further configured to cause the apparatus at least to: measuring the output of the at least one machine learning process to produce a process measurement [Ryden p. , ll. : a predicted value from an ML model may be used as an input to a Handover or load balancing decision, in place of a measured value, or may replace a measurement value in a report for provision to the RAN node].
Regarding claim 42, Ryden in view of Jin teaches the apparatus as in claim 31, wherein the at least one machine learning process produces a plurality of actions, and wherein the request to perform the action includes respective identifiers of the plurality of actions [Ryden p. 10, ll. 8-14: RAN node may receive, from the wireless device, information based on an output of the ML model executed by the wireless device (i.e. UE transmits output of ML model to network node), wherein the received information may for example comprise an output of the ML model 230a, a derivative of an output of the ML model 230b and/or information relating to a RAN operation performed by the wireless device and configured on the basis of an output of the ML model, wherein the information relating to a RAN operation may comprise configuration information for the RAN operation, a request to perform a RAN operation, a result of a RAN operation, etc. (here, an operation that is indicated by the UE and related to an output of the ML model is analogous to a determined action to perform based on the ML model); p. 5, ln. 31-p. 6, l. 3: Any one of more of these example operations or operation types may be configured on the basis of an output of an ML model (i.e. a plurality of actions may be related to the output of the ML model)].
Claim(s) 35-37 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ryden in view of Jin in view of Zhang et al. (US 2025/0168081 A1; “Zhang”).
Regarding claim 35, Ryden in view of Jin teaches the apparatus as in claim 31, however, does not explicitly disclose wherein each of the at least one machine learning process has a respective machine learning process identifier, wherein the message indicating whether the apparatus may perform the action includes a reference to the respective machine learning process identifier producing the action.
However, in a similar field of endeavor, Zhang teaches wherein each of the at least one machine learning process has a respective machine learning process identifier [Zhang ¶ 0136: machine learning model information may include, for example, a model identifier, a location of the machine learning model, a version of the machine learning model, a valid time for performing analytics according to the machine learning mode; ¶ 0127: indication of a set of models with associated identifiers],
wherein the message indicating whether the apparatus may perform the action includes a reference to the respective machine learning process identifier producing the action [Zhang ¶ 0136: AMF entity 405 may transmit control signaling 420 to the UE 115 to configure the UE 115 with the machine learning model including machine learning model configuration information, e.g., a machine learning model training request (i.e. indication to perform an action), and model identifier].
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to combine the method of determining network operations according to output of a machine learning model, and informing the network of the determined operation as taught by Ryden, with the method of indicating, to a UE, an action relating to an identified ML model as taught by Zhang. The motivation to do so would be facilitate distributed machine learning model management thereby improving network resource utilization [Zhang ¶ 0052].
Regarding claim 36, Ryden in view of Jin in view of Zhang teaches the apparatus as in claim 35, however, does not explicitly disclose wherein the respective machine learning process identifier includes a version number of the at least one machine learning process.
However, Zhang teaches wherein the respective machine learning process identifier includes a version number of the at least one machine learning process [Zhang ¶ 0136: machine learning model information may include, for example, a model identifier, a location of the machine learning model, a version of the machine learning model].
The motivation to combine these references is illustrated in the rejection of claim 35 above.
Regarding claim 37, Ryden in view of Jin in view of Zhang teaches the apparatus as in claim 35, wherein the respective machine learning process identifier includes a range of outputs of the at least one machine learning process [Ryden p. 12, ll. 21-35: configuration information may include: a representation of the ML model or an update to the ML model 210a, information about outputs from the model].
Claim(s) 43, 45-50 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ryden in view of Tullberg et al. (US 2022/0078637 A1; “Tullberg”).
Regarding claim 43, Ryden teaches an apparatus, comprising:
at least one processor; and at least one memory including computer program code [Ryden p. 26, ll. 31-36, Fig. 20: RAN node 2000 having processor 2002 and memory 2004 storing computer program 2050]; the at least one memory and the computer program code configured to cause the apparatus at least to:
receive, from a user equipment served by the apparatus in a wireless network, a request for an action, determined by the user equipment based on an output of at least one machine learning process, to be performed by the user equipment [Ryden p. 10, ll. 8-14: RAN node may receive, from the wireless device, information based on an output of the ML model executed by the wireless device (i.e. UE transmits output of ML model to network node), wherein the received information may for example comprise an output of the ML model 230a, a derivative of an output of the ML model 230b and/or information relating to a RAN operation performed by the wireless device and configured on the basis of an output of the ML model, wherein the information relating to a RAN operation may comprise configuration information for the RAN operation, a request to perform a RAN operation, a result of a RAN operation, etc. (here, an operation that is indicated by the UE and related to an output of the ML model is analogous to a determined action to perform based on the ML model)].
However, Ryden does not explicitly disclose generate a respective prediction of an outcome of a performance of the action by the user equipment on the wireless network; and transmit, to the user equipment, a message indicating whether the user equipment may perform the action based on the respective prediction.
However, in a similar field of endeavor, Tullberg teaches generate a respective prediction of an outcome of a performance of the action by the user equipment on the wireless network; and transmit, to the user equipment, a message indicating whether the user equipment may perform the action based on the respective prediction [Tullberg ¶¶ 0088-0089: network node 110, 130, 143 receives, from the wireless device 120, information relating to at least one prediction of an operation of the wireless device 120 and to at least one result of the operation as output from a ML model; ¶ 0159-0160: the wireless device 120 receives, from the network node 110, 130, 143, information relating to updated one or more parameters of the second instance of the machine learning model when a model difference between a prediction of the operation obtained by the second instance of the machine learning model comprising the updated one or more parameters and the prediction of the operation obtained by means of the first instance of the machine learning model is indicative of a need of updating the first instance of the machine learning model (here, the network node indicates to the wireless device to update a ML model based on a comparison, e.g.., determining a model difference with respect to a threshold, of the first models predicted output and the second model predicted output)].
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to combine the method of determining network operations according to output of a machine learning model, and informing the network of the determined operation as taught by Ryden, with the method of permitting a wireless device to perform a ML update action based on predicted outputs from a first and second ML model as taught by Tullberg. The motivation to do so would be to improve resource usage through implementation of distributed machine learning [Tullberg ¶¶ 0010-0011].
Regarding claim 45, Ryden in view of Tullberg teaches the apparatus as in claim 43, however, Ryden does not explicitly disclose wherein a generation of the respective prediction of the outcome of the performance of the action is based on at least one of quality of service requirements, traffic conditions, user equipment radio capabilities, or requests from other user equipments in the wireless network.
However, Ryden teaches wherein a generation of the respective prediction of the outcome of the performance of the action is based on at least one of quality of service requirements, traffic conditions, user equipment radio capabilities, or requests from other user equipments in the wireless network [Tullberg ¶ 0111: message relating to the updating or a ML model/parameters is based on a load of the communication link (i.e. traffic condition)].
The motivation to combine these references is illustrated in the rejection of claim 43 above.
Regarding claim 46, Ryden in view of Tullberg teaches the apparatus as in claim 45, wherein the at least one memory and the computer program code are further configured to cause the apparatus at least to: receive, from the user equipment, a message indicating an estimate of an outcome of performing the action [Ryden p. 10, ll. 8-14: RAN node may receive, from the wireless device, information based on an output of the ML model executed by the wireless device (i.e. UE transmits output of ML model to network node); p. 21, ll. 11-15: model may alternatively return information elements representing the probability (i.e. estimate) of certain events (i.e. actions)].
However, Ryden does not explicitly disclose wherein the message indicates the user equipment may perform the action [Tullberg ¶ 0159-0160: the wireless device 120 receives, from the network node 110, 130, 143, information relating to updated one or more parameters of the second instance of the machine learning model when a model difference between a prediction of the operation obtained by the second instance of the machine learning model comprising the updated one or more parameters and the prediction of the operation obtained by means of the first instance of the machine learning model is indicative of a need of updating the first instance of the machine learning model].
The motivation to combine these references is illustrated in the rejection of claim 43 above.
Regarding claim 47, Ryden in view of Tullberg teaches the apparatus as in claim 46, however, Ryden does not explicitly disclose wherein the at least one memory and the computer program code are further configured to cause the apparatus at least to: store the estimate of the outcome of taking the at least one machine learning process; and evaluate an impact of the estimate of the outcome of taking the at least one machine learning process.
However, Tullberg teaches wherein the at least one memory and the computer program code are further configured to cause the apparatus at least to: store the estimate of the outcome of taking the at least one machine learning process [Tullberg ¶¶ 0141-0142: network node 110, 130, 143 comprises a memory 306 configured to store the data]; and
evaluate an impact of the estimate of the outcome of taking the at least one machine learning process [Tullberg ¶ 0114: performance is evaluated to determine whether action/deferral of action should be indicated].
The motivation to combine these references is illustrated in the rejection of claim 43 above.
Regarding claim 48, Ryden teaches an apparatus, comprising:
at least one processor; and at least one memory including computer program code [Ryden p. 27, ll. 27-36, Fig. 22: wireless device having processor 2202 and memory 2204 storing computer program 2250]; the at least one memory and the computer program code configured to cause the apparatus at least to:
perform at least one machine learning process to produce an output; determine an action to perform based on the output produced by the performing of the at least one machine learning process [Ryden p. 13, ll. 30-34: wireless device executes the ML model in accordance with the received configuration information at step 420, wherein the wireless device may then configure one or more RAN operations on the basis of than output of the ML model in step 422];
perform the action to produce a performance of the action [Ryden p. 14, ll. 1-2: in step 430, the wireless device performs a RAN operation configured on the basis of an output of the executed ML model];
transmit, to a network node serving the apparatus, a first message including an indication of an estimated local outcome of the performance of the action [Ryden p. 14, ll. 1-11: the wireless device may send, to the RAN node, information relating to a RAN operation performed by the wireless device and configured on the basis of an output of the ML model 440c, e.g., a result of a RAN operation (i.e. an indication of a local outcome of the operation)]; and
receive, from the network node, a second message indicating an effect of the performance of the action on the wireless network globally [Tullberg ¶ 0159-0160: the wireless device 120 receives, from the network node 110, 130, 143, information relating to updated one or more parameters of the second instance of the machine learning model when a model difference between a prediction of the operation obtained by the second instance of the machine learning model comprising the updated one or more parameters and the prediction of the operation (i.e. indication of global effect) obtained by means of the first instance of the machine learning model].
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to combine the method of determining network operations according to output of a machine learning model, and informing the network of the determined operation as taught by Ryden, with the method of permitting a wireless device to perform a ML update action based on predicted outputs from a first and second ML model and indicating the prediction of the operation as taught by Tullberg. The motivation to do so would be to improve resource usage through implementation of distributed machine learning [Tullberg ¶¶ 0010-0011].
Regarding claim 49, Ryden in view of Tullberg teaches the apparatus as in claim 48, however, Ryden does not explicitly disclose wherein the second message indicates that the apparatus should not continue performing the action, and wherein the at least one memory and the computer program code is further configured to cause the apparatus at least to: cease performing the action.
However, Tullberg teaches wherein the second message indicates that the apparatus should not continue performing the action, and wherein the at least one memory and the computer program code is further configured to cause the apparatus at least to: cease performing the action [Tullberg ¶ 0112: the network node 110, 130, 143 transmits, to the wireless device 120, an indication of a deferral of updating the first instance of the machine learning model, when the model difference is indicative of a deferral of updating the first instance of the machine learning model].
The motivation to combine these references is illustrated in the rejection of claim 48 above.
Regarding claim 50, Ryden in view of Tullberg teaches the apparatus as in claim 49, however, Ryden does not explicitly disclose wherein the second message further indicates that the apparatus is to transmit, to the network node, a request to perform the action prior to a subsequent performance of the action.
However, Tullberg teaches wherein the second message further indicates that the apparatus is to transmit, to the network node, a request to perform the action prior to a subsequent performance of the action [Tullberg ¶ 0112: the network node 110, 130, 143 transmits, to the wireless device 120, an indication of a deferral of updating the first instance of the machine learning model, when the model difference is indicative of a deferral of updating the first instance of the machine learning model (here, a deferral to updating the model implies that the model may be updated at a later time, therefore, the deferral message is analogous to a request to perform the action prior to a subsequent performance of the action)].
The motivation to combine these references is illustrated in the rejection of claim 48 above.
Claim(s) 44 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ryden in view of Tullberg in view of Zhang.
Regarding claim 44, Ryden in view of Tullberg teaches the apparatus as in claim 43, however, does not explicitly disclose, wherein the request includes a respective identifier for the at least one machine learning process, and wherein the message includes a respective identifier for the respective prediction of the outcome of the performance of the action.
However, in a similar field of endeavor, Zhang teaches wherein the request includes a respective identifier for the at least one machine learning process [Zhang ¶ 0136: machine learning model information may include, for example, a model identifier, a location of the machine learning model, a version of the machine learning model, a valid time for performing analytics according to the machine learning mode; ¶ 0127: indication of a set of models with associated identifiers], and wherein the message includes a respective identifier for the respective prediction of the outcome of the performance of the action [Zhang ¶ 0136: AMF entity 405 may transmit control signaling 420 to the UE 115 to configure the UE 115 with the machine learning model including machine learning model configuration information, e.g., a machine learning model training request (i.e. indication to perform an action), and model identifier].
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to combine the method of determining network operations according to output of a machine learning model, and informing the network of the determined operation as taught by Ryden, with the method of indicating, to a UE, an action relating to an identified ML model as taught by Zhang. The motivation to do so would be facilitate distributed machine learning model management thereby improving network resource utilization [Zhang ¶ 0052].
Allowable Subject Matter
Claims 39-41 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
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/BRIAN P COX/Primary Examiner, Art Unit 2474
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