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 .
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.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim 20 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because claim 20 is directed towards a computer-readable medium which encompasses transitory media such as a signal in the broadest reasonable interpretation, accordingly, the claim is rejected for encompassing non-statutory subject matter. Amending the claim to recite a “non transitory computer-readable medium” will effectively overcome this grounds of rejection.
Claim Rejections - 35 USC § 102
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, 5-7, 16 and 20 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Rydén (US 2023/0262448 A1).
Regarding claim 1, Rydén appears to disclose a method of wireless communication of a base station, comprising:
receiving a sounding reference signal (SRS) from a user equipment (UE) ([0084] disclosing SRS measurements in the uplink);
estimating an uplink (UL) channel state information (CSI) based on the received SRS ([0084] disclosing signal quality measurements on SRS in the uplink);
monitoring the estimated UL CSI to track changes ([0084] disclosing detected by a change of signal quality); and
determining whether to update or switch an artificial intelligence (AI) / machine learning (ML) model used for downlink (DL) CSI compression based on the monitoring of the estimated UL CSI ([0080]-[0082] disclosing selection of a machine learning model for compression of CSI measurements of SSB and/or CSI-RS reported to the network; [0084] disclosing selection of the machine learning model selection based on a change in the measured signal quality of the SRS in the uplink and that measurements of SSB and/or CSI-RS are downlink measuremnts).
Regarding claim 5, Rydén appears to disclose the method of claim 1, wherein monitoring the estimated UL CSI comprises:
calculating one or more metrics based on the estimated UL CSI ([0084]); and
comparing the calculated one or more metrics to one or more previous metrics of a previously estimated UL CSI or one or more reference metrics ([0084]).
Regarding claim 6, Rydén appears to disclose the method of claim 5, wherein the one or more metrics include at least one of:
a historical entropy of the estimated UL CSI;
a power spectral entropy of the estimated UL CSI;
or a distance between the estimated UL CSI and one or more reference UL CSIs ([0084]).
Regarding claim 7, Rydén appears the method of claim 5, wherein determining whether to update or switch the AI/ML model comprises:
determining that the AI/ML model is to be updated or switched if a difference between the calculated one or more metrics and the one or more previous metrics or reference metrics exceeds a predefined threshold ([0084]).
Regarding claim 16, the claim is directed towards an apparatus for wireless communication, the apparatus being a base station, comprising: a memory; and at least one processor coupled to the memory and configured to perform the method of claim 1. Rydén discloses such implementations (Fig. 9, [0151]), accordingly, claim 16 is rejected on the grounds presented above for claim 1.
Regarding claim 20, the claim is directed towards a computer-readable medium storing computer executable code for wireless communication of a base station, comprising code to to perform the method of claim 1. Rydén discloses such implementations (Fig. 9, [0151]), accordingly, claim 20 is rejected on the grounds presented above for claim 1.
Claim Rejections - 35 USC § 103
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) 2-4 and 17-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Rydén (US 2023/0262448 A1) in view of Ambati et al. (US 2018/0293501 A1).
Regarding claim 2, Rydén does not disclose the following; however, Ambati suggests the method of claim 1, wherein monitoring the estimated UL CSI comprises:
calculating one or more statistics of the estimated UL CSI ([0030], [0101] disclosing a statistical volatility value of a feature); and
comparing the calculated one or more statistics to one or more previous statistics of a previously estimated UL CSI ([0030]-[0101] disclosing comparing the statistical volatility of a feature ot a statistical baseline of the feature).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the techniques of Rydén with the teaching in Ambati because this can lead tyo improved accuracy of trained machine learning models ([0101]).
Regarding claim 3, Rydén does not disclose the following; however, Ambati suggests the method of claim 2, wherein the one or more statistics include at least one of:
a mean of elements in the estimated UL CSI ([0101] disclosing the average of the feature); and
a variance of elements in the estimated UL CSI ([0101] disclosing the standard deviation, variance is just the inverse of a standard deviation and it is a matter of obvious engineering design choice to use one over the other).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the techniques of Rydén with the teaching in Ambati because this can lead to improved accuracy of trained machine learning models ([0101]).
Regarding claim 4, Rydén does not disclose the following; however, Ambati suggests the method of claim 2, wherein determining whether to update or switch the AI/ML model comprises:
determining that the AI/ML model is to be updated or switched if a difference between the calculated one or more statistics and the one or more previous statistics exceeds a predefined threshold ([0030], [0101] disclosing if the statistical volatility value varies from the statistical baseline value by more than a threshold (e.g., (one standard deviation)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the techniques of Rydén with the teaching in Ambati because this can lead to improved accuracy of trained machine learning models ([0101]).
Regarding claims 17-19, the claims are directed towards the apparatus that performs the method of claims 2-4; accordingly, claims 17-19 are rejected on the grounds presented above for claims 2-4.
Claim(s) 8-10 and 13-14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Rydén (US 2023/0262448 A1) in view of Song et al. (US 2023/0216597 A1).
Regarding claim 8, Rydén does not disclose the following; however, Song suggests the method of claim 1, wherein monitoring the estimated UL CSI comprises:
inputting the estimated UL CSI into a hypothetical autoencoder to generate a reconstructed UL CSI (Fig. 1, module 110, [0041]-[0043] disclosing a data processing model is determined using an uplink dataset (for example, an uplink reference signal), module 110 performs data processing on CSI; Fig. 2, [0044] disclosing the processed UL-CSI is recovered); and
calculating a key performance indicator (KPI) based on the estimated UL CSI and the reconstructed UL CSI ([0044] disclosing comparing the processed CSI and the recovered CSI to determine how to update the model, the result of the comparison seen as a KPI).
It would have been obvious to one of ordinary skill in the art to apply the techniques of Song to the invention of Rydén because this allows the data processing model can be trained more accurately ([0078]).
Regarding claim 9, Rydén does not disclose the following; however, Song suggests the method of claim 8, wherein the hypothetical autoencoder comprises a hypothetical encoder and a hypothetical decoder (Fig. 1, 110, [0042], Fig. 2, [0044]).
It would have been obvious to one of ordinary skill in the art to apply the techniques of Song to the invention of Rydén because this allows the data processing model can be trained more accurately ([0078]).
Regarding claim 10, Rydén does not disclose the following; however, Song suggests the method of claim 8, wherein the hypothetical autoencoder comprises a hypothetical encoder and an actual decoder used in CSI compression (Fig. 1, 110, [0042], Fig. 2, [0044]).
It would have been obvious to one of ordinary skill in the art to apply the techniques of Song to the invention of Rydén because this allows the data processing model can be trained more accurately ([0078]).
Regarding claim 13, Rydén does not disclose the following; however, Song suggests the method of claim 1, further comprising:
triggering a DL CSI-based monitoring if the AI/ML model is to be updated or switched based on the monitoring of the estimated UL CSI ([0078]).
It would have been obvious to one of ordinary skill in the art to apply the techniques of Song to the invention of Rydén because this allows the data processing model can be trained more accurately ([0078]).
Regarding claim 14, Rydén appears to disclose the method of claim 13, wherein the DL CSI-based monitoring comprises at least one of:
a key performance indicator (KPI)-based monitoring with model transfer ([0084]-[0089]);
a KPI-based monitoring with target CSI transfer ([0081]-[0084]); or
a proxy encoder-based monitoring.
Claim(s) 11-12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Rydén (US 2023/0262448 A1) in view of Song et al. (US 2023/0216597 A1) further in view of Echigo et al. (WO 2024/004218 A1) citations correspond to the paragraphs above the cited paragraph numbers in the attached machine translation.
Regarding claim 11, Rydén does not disclose the following; however, Echigo suggests the method of claim 8, wherein the KPI comprises at least one of: a normalized mean squared error (NMSE) between the estimated UL CSI and the reconstructed UL CSI; a generalized cosine similarity (GCS) between the estimated UL CSI and the reconstructed UL CSI; or a squared generalized cosine similarity (SGCS) between the estimated UL CSI and the reconstructed UL CSI ([0008], [0101]-[0102]).
It would have been obvious to one of ordinary skill in the art to apply the techniques of Echigo to the invention of Rydén to monitor the performance of the model because this can achieve suitable overhead reduction high-precision channel estimation and highly efficient resource utilization ([0009]-[0010]).
Regarding claim 12, Rydén does not disclose the following; however, Echigo suggests the method of claim 8, wherein determining whether to update or switch the AI/ML model comprises:
determining that the AI/ML model is to be updated or switched if the calculated KPI falls below a predefined threshold ([0008], [0127]-[0128]).
It would have been obvious to one of ordinary skill in the art to apply the techniques of Echigo to the invention of Rydén to monitor the performance of the model because this can achieve suitable overhead reduction high-precision channel estimation and highly efficient resource utilization ([0009]-[0010]).
Claim(s) 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Rydén (US 2023/0262448 A1) in view of Echigo et al. (WO 2024/004218 A1) citations correspond to the paragraphs above the cited paragraph numbers in the attached machine translation.
Regarding claim 15, Rydén does not disclose the following; however, Echigo suggests the method of claim 1, further comprising:
sending a request for additional information to the UE based on the monitoring of the estimated UL CSI (Fig. 4, [0088]-[0092]).
It would have been obvious to one of ordinary skill in the art to apply the techniques of Echigo to the invention of Rydén to monitor the performance of the model because this can achieve suitable overhead reduction high-precision channel estimation and highly efficient resource utilization ([0009]-[0010]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Elshafie et al. (US 2023/0198717 A1) discloses adaptation of DL CSI reports based on monitored acknowledgements and negative acknowledgements. Wu et al. (US 2025/0023610 A1) discloses performing a linear combination of CSI on a spatial, frequency, and time domain basis to compress CSI.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Joseph A Bednash whose telephone number is (571)270-7500. The examiner can normally be reached 7 AM - 4:30 PM M-F.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Huy Vu can be reached at (571)272-3155. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JOSEPH A BEDNASH/ Primary Examiner, Art Unit 2461
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