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 .
Claim Objections
Claim 56 is objected to because of the following informalities: claim 56 recites the limitation RNO which not defined in the claim . Appropriate correction is required.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1,33,35,38,57,59, and 61 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Tomala et al. (US Application 2022/0279341, hereinafter Tomala).
Regarding claims 1, 59,Tomala discloses a method(Figs. 2,6-8) for managing a wireless device (131-135,202) that is operable to connect to a communication network(150), wherein the communication network comprises a Radio Access Network (RAN)(204), and wherein the wireless device has available for execution a Machine Learning (ML) model that is operable to provide an output, on the basis of which a RAN operation performed by the wireless device may be configured(item 220, [0043], which recites UE 202 may save (e.g., store) the received ML configuration at the UE for execution a Machine Learning (ML) mode), the RAN node comprising processing circuitry(804,808,see [0101]) configured to cause the RAN node ,the method, performed by a RAN node of the communication network, comprising:
on fulfilment of a trigger condition([0035]-[0036], where the UECapabilityEnquiry message and UECapabilitylnformation message are considered as fulfilment of a trigger condition), causing an ML model Assurance Information (MAI) Request to be sent to the wireless device, the MAI Request comprising an indication of the ML model to which the MAI Request relates(item 610,[0037]-[0038],[0060]-[0061], where the ML configuration is considered as ML model Assurance Information (MAI) Request sent to the wireless device );
receiving, from the wireless device, an MAI Response, wherein the MAI Response comprises ML model characteristic information generated by the wireless device using the ML model(items 630, 720, [0044]-[0045], [0063]-[0064], [0087], which recites gNB 204 may receive ML data from the UE. In an example implementation, the ML data may be received by gNB 204 in response to the transmission of the ML configuration to the UE); and
configuring the RAN operation performed by the wireless device according to the received MAI Response([0047]-[0048],[0089], which recites the gNB may process the received ML data and perform one or more actions. In some implementation, gNB 204 may forward the received ML data to another entity, e.g., core network entity, for further processing).
Regarding claims 33,61, Tomala discloses a method (Figs. 2,6-8) for managing a wireless device (131-135,202) that is operable to connect to a communication network(150), wherein the communication network comprises a Radio Access Network (RAN) (204), and wherein the wireless device has available for execution a Machine Learning (ML) model that is operable to provide an output, on the basis of which a RAN operation performed by the wireless device may be configured (item 220, [0043], which recites UE 202 may save (e.g., store) the received ML configuration at the UE for execution a Machine Learning (ML) mode), the wireless device comprising processing circuitry (804,808, see [0101]) configured to cause the wireless device to configured to cause the RAN node , the method, performed by the wireless device, comprising:
receiving, from a RAN node of the communication network, an ML model Assurance Information (MAI) Request, the MAI Request comprising an indication of the ML model to which the MAI Request relates(item 610,[0037]-[0038],[0060]-[0061], where the ML configuration is considered as ML model Assurance Information (MAI) Request sent to the wireless device );
generating ML model characteristic information using the ML model indicated in the MAI Request(item 610,[0037]-[0038],[0060]-[0061], which recites gNB 204 may generate an ML configuration for the UE); and
transmitting, to the RAN node, an MAI Response, wherein the MAI Response comprises the generated ML model characteristic information(items 630, 720, [0044]-[0045], [0063]-[0064], [0087], which recites gNB 204 may receive ML data from the UE. In an example implementation, the ML data may be received by gNB 204 in response to the transmission of the ML configuration to the UE).
Regarding claim 35, Tomala discloses the method of claim 33, wherein generating the ML model characteristic using the ML model comprises generating a value using at least one of: the ML model([0047]-[0048]); one or more parameters of the ML model([0047]-[0048]).
Regarding claim 38, Tomala discloses the method of claim 33, wherein generating the ML model characteristic information using the ML model comprises: generating a function of the ML model or at least one ML model parameter([0047]-[0048]).
Regarding claim 57, Tomala discloses the method of claim 33, wherein the RAN operation performed by the wireless device comprises at least one of: beam measurement prediction; secondary carrier prediction; signal quality forecast; signal quality drop prediction; compression of radio measurements; power control in uplink, UL, transmission; timing advance in UL transmission; link adaptation in UL transmission; estimation of performance metrics; information compression for UL transmission; coverage estimation for secondary carrier; estimation of signal quality degradation; estimation of signal strength degradation; a mobility related operation; an energy saving operation; a positioning operation([0023]-[0024]).
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.
The factual inquiries 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.
Claim(s) 34,36-37,40-43,45-47,52, and 56 is/are rejected under 35 U.S.C. 103 as being unpatentable over Tomala in view of Kumar et al. (US Application 2022/0377844, hereinafter Kumar).
Regarding claim 34, Tomala discloses the method of claim 33 as addressed above, except further comprising:
receiving, from the RAN node, information for configuration of the RAN operation performed by the wireless device.
However, Kumar teaches receiving, from the RAN node, information for configuration of the RAN operation performed by the wireless device(Fig. 5, item 518, [0086], which recites the RAN/base station 504 may transmit, at 518, a model training configuration to a UE 502 based on receiving, at 516, the model training request ).
Therefore, it would have been obvious for one with ordinary skill in the art before the effective filling date of the claimed invention to combine the teaching of Kumar with the teaching of Tomala by using the above features such as receiving, from the RAN node, information for configuration of the RAN operation performed by the wireless device as taught by Kumar for the purpose of providing a machine learning (ML) model training procedure([0001]).
Regarding claim 36, Tomala discloses the method of claim 33 as addressed above, except further comprising:
obtaining the ML model by performing at least one of: receiving the ML model in a transmission; receiving an instruction to download the ML model from a repository using an authenticated connection, and downloading the ML model according to the instruction.
However, Kumar teaches obtaining the ML model by performing at least one of: receiving the ML model in a transmission(Fig. 5, item 518, [0086]-[0087]); receiving an instruction to download the ML model from a repository using an authenticated connection, and downloading the ML model according to the instruction (Fig. 5, item 520, [0086]-[0087]).
Therefore, it would have been obvious for one with ordinary skill in the art before the effective filling date of the claimed invention to combine the teaching of Kumar with the teaching of Tomala by using the above features such as obtaining the ML model by performing at least one of: receiving the ML model in a transmission; receiving an instruction to download the ML model from a repository using an authenticated connection, and downloading the ML model according to the instruction as taught by Kumar for the purpose of providing a machine learning (ML) model training procedure([0001]).
Regarding claim 37, Tomala discloses the method of claim 36 as addressed above, except wherein obtaining the ML model comprises obtaining a version of the ML model that comprises at least one difference from a version of the model obtained by another wireless device, wherein the difference is such that the characteristic information for the ML model will be different to characteristic information for the version of the ML model obtained by the other wireless device.
However, Kumar teaches wherein obtaining the ML model comprises obtaining a version of the ML model that comprises at least one difference from a version of the model obtained by another wireless device, wherein the difference is such that the characteristic information for the ML model will be different to characteristic information for the version of the ML model obtained by the other wireless device [0086]-[0087]).
Therefore, it would have been obvious for one with ordinary skill in the art before the effective filling date of the claimed invention to combine the teaching of Kumar with the teaching of Tomala by using the above features such as wherein obtaining the ML model comprises obtaining a version of the ML model that comprises at least one difference from a version of the model obtained by another wireless device, wherein the difference is such that the characteristic information for the ML model will be different to characteristic information for the version of the ML model obtained by the other wireless device as taught by Kumar for the purpose of providing a machine learning (ML) model training procedure([0001]).
Regarding claim 40, Tomala discloses the method of claim 33 as addressed above, except wherein generating the ML model characteristic information using the ML model comprises: providing a specific assurance input to the ML model; and generating a function of the ML model that corresponds to the specific assurance input provided by the wireless device to the ML model.
However, Kumar teaches wherein generating the ML model characteristic information using the ML model comprises: providing a specific assurance input to the ML model; and generating a function of the ML model that corresponds to the specific assurance input provided by the wireless device to the ML model [0086]-[0087]).
Therefore, it would have been obvious for one with ordinary skill in the art before the effective filling date of the claimed invention to combine the teaching of Kumar with the teaching of Tomala by using the above features such as wherein generating the ML model characteristic information using the ML model comprises: providing a specific assurance input to the ML model; and generating a function of the ML model that corresponds to the specific assurance input provided by the wireless device to the ML model as taught by Kumar for the purpose of providing a machine learning (ML) model training procedure([0001]).
Regarding claim 41, Tomala discloses the method of claim 40 as addressed above, except wherein the function comprises at least one of: an output of the ML model; or an input or output of an activation function of an intermediate element of the ML model.
However, Kumar teaches wherein the function comprises at least one of: an output of the ML model([0089]-[0091]); or an input or output of an activation function of an intermediate element of the ML model ([0089]-[0091]).
Therefore, it would have been obvious for one with ordinary skill in the art before the effective filling date of the claimed invention to combine the teaching of Kumar with the teaching of Tomala by using the above features such as wherein the function comprises at least one of: an output of the ML model; or an input or output of an activation function of an intermediate element of the ML model as taught by Kumar for the purpose of providing a machine learning (ML) model training procedure([0001]).
Regarding claim 42, Tomala discloses the method of claim 40 as addressed above, except further comprising obtaining the specific assurance input from the RAN node.
However, Kumar teaches further comprising obtaining the specific assurance input from the RAN node ([0089]-[0091]).
Therefore, it would have been obvious for one with ordinary skill in the art before the effective filling date of the claimed invention to combine the teaching of Kumar with the teaching of Tomala by using the above features such as further comprising obtaining the specific assurance input from the RAN node as taught by Kumar for the purpose of providing a machine learning (ML) model training procedure([0001]).
Regarding claim 43, Tomala discloses the method of claim 40 as addressed above, except obtaining the specific assurance input from the RAN node comprises obtaining an assurance input that is different to an assurance input provided to another wireless device.
However, Kumar teaches obtaining the specific assurance input from the RAN node comprises obtaining an assurance input that is different to an assurance input provided to another wireless device ([0089]-[0091]).
Therefore, it would have been obvious for one with ordinary skill in the art before the effective filling date of the claimed invention to combine the teaching of Kumar with the teaching of Tomala by using the above features such as obtaining the specific assurance input from the RAN node comprises obtaining an assurance input that is different to an assurance input provided to another wireless device as taught by Kumar for the purpose of providing a machine learning (ML) model training procedure([0001]).
Regarding claim 45, Tomala discloses the method of claim 33 as addressed above, except wherein generating the ML model characteristic information using the ML model comprises: generating a combination of an output of the ML model and an identifier of the version of the ML model used to generate the output.
However, Kumar teaches wherein generating the ML model characteristic information using the ML model comprises generating a combination of an output of the ML model and an identifier of the version of the ML model used to generate the output ([0090]).
Therefore, it would have been obvious for one with ordinary skill in the art before the effective filling date of the claimed invention to combine the teaching of Kumar with the teaching of Tomala by using the above features such as wherein generating the ML model characteristic information using the ML model comprises: generating a combination of an output of the ML model and an identifier of the version of the ML model used to generate the output as taught by Kumar for the purpose of providing a machine learning (ML) model training procedure([0001]).
Regarding claim 46, Tomala discloses the method of claim 45 as addressed above, except wherein the identifier of the version of the ML model comprises at least one of: an assigned alphanumeric identifier; a function of parameters of the version of the ML model.
However, Kumar teaches wherein the identifier of the version of the ML model comprises at least one of: an assigned alphanumeric identifier; a function of parameters of the version of the ML model ([0090]).
Therefore, it would have been obvious for one with ordinary skill in the art before the effective filling date of the claimed invention to combine the teaching of Kumar with the teaching of Tomala by using the above features such as wherein the identifier of the version of the ML model comprises at least one of: an assigned alphanumeric identifier; a function of parameters of the version of the ML model as taught by Kumar for the purpose of providing a machine learning (ML) model training procedure([0001]).
Regarding claim 47, Tomala discloses the method of claim 33 as addressed above, except wherein generating the ML model characteristic information using the ML model comprises: deriving a value from the ML model and an information item available to both the wireless device and the RAN node.
However, Kumar teaches wherein generating the ML model characteristic information using the ML model comprises: deriving a value from the ML model and an information item available to both the wireless device and the RAN node ([0080]).
Therefore, it would have been obvious for one with ordinary skill in the art before the effective filling date of the claimed invention to combine the teaching of Kumar with the teaching of Tomala by using the above features such as wherein generating the ML model characteristic information using the ML model comprises: deriving a value from the ML model and an information item available to both the wireless device and the RAN node as taught by Kumar for the purpose of providing a machine learning (ML) model training procedure([0001]).
Regarding claim 52, Tomala discloses the method of claim 33 as addressed above, except wherein generating the ML model characteristic information using the ML model comprises: calculating a function of a derivative of at least one of the weights of the ML model, wherein the derivative is calculated using a secret shared with the RAN node.
However, Kumar teaches wherein generating the ML model characteristic information using the ML model comprises: calculating a function of a derivative of at least one of the weights of the ML model, wherein the derivative is calculated using a secret shared with the RAN node ([0080]).
Therefore, it would have been obvious for one with ordinary skill in the art before the effective filling date of the claimed invention to combine the teaching of Kumar with the teaching of Tomala by using the above features such as wherein generating the ML model characteristic information using the ML model comprises: calculating a function of a derivative of at least one of the weights of the ML model, wherein the derivative is calculated using a secret shared with the RAN node as taught by Kumar for the purpose of providing a machine learning (ML) model training procedure([0001]).
Regarding claim 56, Tomala discloses the method of claim 34 as addressed above, except wherein receiving, from the RAN node, information for configuration of the RAN operation performed by the wireless device comprises receiving at least one of: an instruction to perform the RNO operation without using the ML model; an instruction to perform additional measurements; a correct current version of the ML model for wireless device; a warning from the RAN node.
However, Kumar teaches wherein receiving, from the RAN node, information for configuration of the RAN operation performed by the wireless device comprises receiving at least one of: an instruction to perform the RNO operation without using the ML model([0080]-[0090]); an instruction to perform additional measurements; a correct current version of the ML model for wireless device([0080]-[0090]).; a warning from the RAN node ([0080]-[0090]).
Therefore, it would have been obvious for one with ordinary skill in the art before the effective filling date of the claimed invention to combine the teaching of Kumar with the teaching of Tomala by using the above features such as wherein receiving, from the RAN node, information for configuration of the RAN operation performed by the wireless device comprises receiving at least one of: an instruction to perform the RNO operation without using the ML model; an instruction to perform additional measurements; a correct current version of the ML model for wireless device; a warning from the RAN node as taught by Kumar for the purpose of providing a machine learning (ML) model training procedure([0001]).
Allowable Subject Matter
Claim 44 is 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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/DADY CHERY/Primary Examiner, Art Unit 2418
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