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.
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) 1-2,4-8,10,13,15-24, and 39 are rejected under 35 U.S.C. 103 as being unpatentable over Li et al US 2022 0038349 in view of Yi et al. US 20220224484 .
As per claims 1,24 and 39, Li et al. teach a communication method and terminal device comprising (see fig. 3, para 0055 -0072 and claims 1-15 of the specification). Li et al teach the configuration information comprises at least one set of reference signal (RS) resources; determining a set of training reference signals for management of an artificial intelligence/machine learning (AI/ML) model based on the at least one set of RS resources (see fig. 3, para 0055 -0072 and claims 1-15 of the specification) ; and managing the AI/ML model based on the set of training reference signals, Li et al. teaches all the limitation except wherein receiving at a terminal device, configuration information from a network device. However, Yi et al. in similar field of endeavor teach receiving, at a terminal device, configuration information from a network device, (see Yi et al reference fig. 21-23, para 0187-0203 and claims 1-15 of the specification).
It would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to employ Yi et al.’s teachings within the system of Li et al. to obtain the claimed invention, because it will allow improve communication having AI/ML model to provide determining of reference signal.
As per claim 2, Li et al. teach the method of claim 1, wherein receiving the configuration information comprises: receiving a downlink bandwidth part configuration from the network device; and wherein the downlink bandwidth part configuration comprises a training configuration indicating at least one set of RS resources, or wherein the downlink bandwidth part configuration comprises a radio link monitoring configuration indicating the at least one set of RS resources (see fig. 3, para 0003, 0017, 0055 -0072 and claims 1-15 of the specification).
As per claim 4,7,8, 10, and 17 Li et al. does not expressly teach wherein receiving the configuration information comprises: receiving a configuration of channel state information (CSI) report from the network device; and wherein the configuration of CSI report indicates the at least one set of RS resources, and wherein the configuration of CSI report indicates a report quantity which indicates that the CSI reported is not required to be reported (Li et al. ref. para 0072, where reporting overhead is reduced), wherein determining the set of training reference signals comprises: determining the at least one set of RS resources associated with a CSI report; and determining the set of training reference signals based on the at least one set of RS resource and wherein determining the at least one set of RS resources associated with a CSI report comprises: determining the at least one set of RS resources associated with the CSI report, in accordance with a determination that the configuration information comprises an enable parameter which indicates the terminal device to determine the set of training reference signals, or a determination that the configuration information does not comprise the at least one set of RS resources and, wherein the CSI report comprises beam information or is configured not to report, if the AI/ML model corresponds to beam management; wherein the configuration information comprises a configuration of a CSI report, and wherein the method further comprises: determining the AI/ML model based on a report quantity of the CSI report
However, Yi et al. teach the use of such limitations (see Yi et al reference fig. 21-23, para 0187-0203 and claims 1-15 of the specification).
It would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to employ Yi et al.’s teachings within the system of Li et al. to obtain the claimed invention, because it will allow improve communication having AI/ML model to provide determining of CSI of associated with reference signal.
As per claim 5, Li et al. teach the method of claim 1, further comprising: reporting a first capability to the network device, wherein the first capability indicates at least one of: a first time delay of processing AI/ML related data, or a second time delay of updating the AI/ML model (see fig. 3, para 0055 -0072 and claims 1-15 of the specification).
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As per claim 6, Li et al. teach the method of claim 1, further comprising: determining a set of reference signals for a set of candidate beams based on the set of training reference signals, in accordance with a determination that at least one of the followings is fulfilled: the terminal device is not configured with the set of reference signals for the set of candidate beams, the terminal device is configured with the set of training reference signals, or the set of training reference signals is applied for the AI/ML model corresponding to beam management (see fig. 3, para 0055 -0072 and claims 1-15 of the specification).
As per claim 13, Li et al. teach the method of claim 1, wherein if the AI/ML model corresponds to beam management, the at least one set of RS resources fulfills at least one of conditions comprising: a sixth condition where the number of reference signal resources in the at least one set of RS resources is not smaller than a maximum number of beam that the AI/ML model applied for beam prediction, a seventh condition where the number of RS resources equals to a maximum number of beams that the terminal device supports, or an eighth condition where the number of reference signal resources equals to a maximum number of reference signal resources that the terminal device supports (see fig. 3, para 0003, 0017, 0055 -0072 and claims 1-15 of the specification).
As per claim 15, Li et al. teach the method of claim 1, further comprising: determining the AI/ML model based on model information associated with the set of training reference signals(see fig. 3, para 0003, 0017, 0055 -0072 and claims 1-15 of the specification).
As per claim 16, Li et al. teach the method of claim 1, further comprising: determining model information associated with the set of training reference signals based on usage information associated with the set of training reference signals; and determining the AI/ML model based on the model information (see fig. 3, para 0003, 0017, 0055 -0072 and claims 1-15 of the specification).
As per claim 18, Li et al. teach the method of claim 1, wherein the configuration information comprises a data set size or a step size, wherein the data set size indicates a size of data set, and wherein the step size indicates a frequency of updating the AI/ML model; and wherein the method further comprises: obtaining at least one of: a corresponding size of data set or a corresponding frequency of updating model of the AI/ML model based on the configuration information (see fig. 3, para 0003, 0017, 0060-0065 and claims 1-15 of the specification).
As per claim 19, Li et al. teach the method of claim 18, further comprising: reporting, to the network device, at least one of: a second capability or a third capability, wherein the second capability indicates a minimum size of the data set required by the terminal device to perform training and the third capability indicates a maximum size of the data set required by the terminal device to perform AI/ML model training; and wherein the data set size or the step size is determined based on at least one of the second capability or the third capability(see fig. 3, para 0003, 0017, 0060-0065 and claims 1-15 of the specification) .
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As per claim 20, Li et al. teach the method of claim 18, further comprising: reporting, to the network device, a fourth capability, wherein the fourth capability indicates a minimum delay required by the terminal device to perform AI/ML model training; and wherein the data set size or the step size is determined based on the fourth capability(see fig. 3, para 0003, 0017, 0060-0065 and claims 1-15 of the specification) .
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As per claim 21, Li et al. teach the method of claim 1, wherein the configuration information comprises second information which indicates a type of the set of training reference signals; and wherein the method further comprises: determining a type of management of the AI/ML model based on the second information, wherein the type of management of the AI/ML model comprises one of: updating the AI/ML model, monitoring the AI/ML model, testing the AI/ML model, or training the AI/ML model(see fig. 3, para 0003, 0017, 0060-0065 and claims 1-15 of the specification) .
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As per claim 22, Li et al. teach the method of claim 1, further comprising: transmitting, to the network device, a scheduling request to indicate that the management of the AI/ML model is completed; receiving, from the network device, downlink control information indicating a scheduled resource; and transmitting, to the network device, a media access control control element (MAC CE) which inform the managed AI/ML model, wherein the managed AI/ML model comprise at least one of: an updated AI/ML model, a trained AI/ML model or a deteriorated AI/ML model (see fig. 3, para 0003, 0017, 0060-0065 and claims 1-15 of the specification) .
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As per claim 23, Li et al. teach the method of claim 1, wherein the terminal device is configured with a predetermined duration, and wherein the method further comprises: applying the updated AI/ML model after the predetermined duration; or stopping applying the deteriorated AI/ML model after the predetermined duration(see fig. 3, para 0003, 0017, 0060-0065 and claims 1-15 of the specification) .
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Conclusion
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/AYAZ R SHEIKH/Supervisory Patent Examiner, Art Unit 2476
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