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 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.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Ryden et al. (WO-2022013104-A1), hereinafter “Ryden”.
Claims 1, 10, and 18:
Regarding claim 1, Ryden teaches, a model configuration method ([abstract], “The method comprises determining, on the basis of information about an operating environment of the wireless device, configuration information for a Machine Learning (ML) model to be executed by the wireless device (110)”).
Ryden teaches, acquiring, by a first device (RAN node of the communication network; Fig.20), obtaining information about operating environment of wireless device, as disclosed in Fig.2a, step 200, “Obtain information about an operating environment of wireless device(s) (internal environment/external environment/communication network environment)”.
Though Ryden does not expressly teach model demand information or device state information, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention, to modify the disclosure by Ryden regarding operating environment to device state and come up with the claimed invention for broader representation of the device in the present context of machine learning (ML) model configuration. operating environment is one of the factors that define device state.
Ryden teaches, determining, by the first device, at least one first model corresponding to the model demand information or the device state information (Fig.2a, step 210, “Determine, on the basis of information about an operating environment of the wireless device(s), configuration information for an ML model to be executed by the wireless device(s)”); and
sending, by the first device, a first message to the second device, wherein the first message comprises related information of the at least one first model (Fig.2a, step 220, “Send, to the wireless device(s), the determined configuration information”; Fig.4a, step 410, “Receive, from the RAN node, configuration information for an ML model to be executed by the wireless device”), or the first message is used for notifying the second device that no model is applicable to the model demand information or the device state information.
Claims 10 and 18 are change in category with respect to claim 1. Existence of memory and processor are implied.
Claims 2, 11, and 19:
Regarding claim 2, Ryden teaches the model configuration method according to claim 1 (discussed above), wherein the model demand information comprises at least one of the following:
at least one of a model use, a model function, a model input/output physical quantity, or a reference point corresponding to a model input/output required by the second device;
a model input/output dimension required by the second device;
a model input/output format required by the second device;
model performance required by the second device;
a model execution time required by the second device;
an expected model use time of the second device;
a model delivery mode required by the second device;
a computing capability description of a model required by the second device (Fig.2, block 202a, “Obtain (request and receive) information about capability of the wireless device to execute an ML model”; capability of execution of ML model implies computing capability.”);
a model type required by the second device;
an execution framework of a model required by the second device;
a model size required by the second device;
a storage demand of the second device;
a trimming data quantity of a model required by the second device; or
a quantity of times of trimming update of a model required by the second device.
Claims 11 and 19 are change in category with respect to claim 2.
Claims 3, 12, and 20:
Regarding claim 3, Ryden teaches the model configuration method according to claim 1 (discussed above), wherein the device state information comprises at least one of the following:
a working mode of the second device;
an environment that the second device is in;
computing capability information of the second device (Fig.2, block 202a, “Obtain (request and receive) information about capability of the wireless device to execute an ML model”; capability of execution of ML model implies computing capability.); or
mobility information of the second device.
Claims 12 and 20 are change in category with respect to claim 3.
Claims 4 and 13:
Regarding claim 4, Ryden teaches the model configuration method according to claim 1 (discussed above), wherein the acquiring, by a first device, model demand information or device state information comprises at least one of the following:
receiving, by the first device, the model demand information or the device state information sent by the second device; or
acquiring, by the first device, the model demand information or the device state information through detection or perception of the first device (Fig.2 step 202, Pg.6, ll. 29-34, “The information obtained at step 202 may be obtained from one or more different sources. For example, some information may be available locally at the RAN node, while other information may be obtained from a current or previous serving node of the wireless device, or from a core network or other management node. In further examples, information about the operational environment of the wireless device may be obtained in step 202 from the wireless device itself.”).
Claim 13 is change in category with respect to claim 4.
Claims 5 and 14:
Regarding claim 5, Ryden teaches the model configuration method according to claim 1 (discussed above), wherein the sending, by the first device, a first message to the second device comprises:
sending, by the first device, the first message to the second device when channel quality between the second device and the first device meets a first condition (implied by disclosure in Pg.21, lines 11-15, “The type of information returned by a model in each information element as output may depend on the specific networking operation that the model is associated with. For example, the model may provide one or more information elements associated to estimates of signal strength or signal quality, such as RSRP, RSRQ, SI NR, SINR, spectral efficiency, for a given cell, carrier frequency, etc.”).
Claim 14 is change in category with respect to claim 5.
Claims 6 and 15:
Regarding claim 6, Ryden teaches the model configuration method according to claim 1 (discussed above), wherein after the sending, by the first device, a first message to the second device, the method further comprises:
receiving, by the first device, a model deployment or update result reported by the second device (Fig.4c, step 440; Fig.5, step 502; Fig.8, step 803); and
recording, by the first device, the model deployment or update result.
Claim 15 is change in category with respect to claim 6.
Claims 7 and 16:
Regarding claim 7, Ryden teaches the model configuration method according to claim 6 (discussed above), wherein the model deployment or update result comprises at least one of the following:
whether the second device has performed a model deployment or update;
related information of a second model, wherein the second model is a model used by the second device to perform a deployment or an update, and the second model belongs to the at least one first model; and
the model verification result (Fig. 4a, step 411, “Determine a compatibility of the ML model configured in accordance with the received configuration information with an operating environment of the wireless device”; Fig.4c, step 413, “Send a message to the RAN node rejecting the received configuration information”).
Claim 16 is change in category with respect to claim 7.
Claims 8 and 17:
Regarding claim 8, Ryden teaches the model configuration method according to claim 1 (discussed above), wherein after the sending, by the first device, a first message to the second device, the method further comprises:
receiving, by the first device, a model verification result reported by the second device (discussed above in claim 7; second device either rejects the model or alternate model proposal (steps 415 and 416 in Fig. 4b)); and
delivering, by the first device, model deployment or update decision information, wherein the model deployment or update decision information is determined by the first device based on the model verification result (Fig.2b, steps 250, “Generate configuration information for an updated ML model to be executed by the wireless device”, and 260 (“Send, to the wireless device, the generated configuration information”); Fig.4a, step 411, “Determine a compatibility of the ML model configured in accordance with the received configuration information with an operating environment of the wireless device”).
Claim 17 is change in category with respect to claim 8.
Regarding claim 9, Ryden teaches the model configuration method according to claim 1 (discussed above), wherein the second device comprises a terminal or a network-side device, the first device comprises a terminal or a network-side device (implied by disclosure in Pg.7, ll. 2-5, “obtaining information at step 202 comprises obtaining (for example requesting and receiving) information about a capacity of the wireless device to execute an ML model in step 202a. The information may be requested and received from the wireless device itself,”).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US-20240129759-A1 teaches machine learning (ML) models to support wireless communications;
US-20230164817-A1 teaches reporting of AI capabilities between network nodes, such as between a user equipment (UE) and a base station; and
US 2023/0100253 A1 teaches method of wireless communication by a base station includes receiving a user equipment (UE) radio capability and a UE machine learning capability. The method also includes determining a neural network function (NNF) based on the UE radio capability
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/INTEKHAAB A SIDDIQUEE/Primary Examiner, Art Unit 2462
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