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 .
Response to Arguments
Applicant's arguments filed 4/1/26 have been fully considered but they are not persuasive.
The applicant argues “Yoo's cited portions (reproduced above) concern neural-
network training, prediction accuracy, and encoder/decoder parameter updates. Those passages of Yoo do not teach or suggest classifying CSI compression quality based on a level of predicted performance loss associated with reconstruction of the one or more radio channels of the compressed CSI, as expressly required by the claims. ” See page 7 of applicant’s remarks. The examiner respectfully disagrees.
Applicant's arguments fail to comply with 37 CFR 1.111(b) because they amount to a general allegation that the claims define a patentable invention without specifically pointing out how the language of the claims patentably distinguishes them from the references.
Paragraph [0063] of Yoo discloses a transmitting device, such as a UE, may use encoder weights from a trained neural network model, to encode CSI into a more compact representation of CSI that is accurate. As a result of using encoder weights (in other words, classifying CSI compression quality) of a trained neural network model for a CSI encoder and using decoder weights of the trained neural network model for a CSI decoder, the encoded CSI that the UE transmits may be smaller (more compressed) and/or more accurate than without using machine learning (in other words, where there is a change/reconstruction of the one radio channel of the compressed CSI as shown in figures 4 & 5). Paragraph [0076] of Yoo discloses a training model based on predictions. The updated training model continues until the threshold level of accuracy for predictions are generated by the model. The device may obtain encoder weights and decoder weights based at least in part on the predictions (in other words, classifying CSI compression quality resulting in a change/reconstruction of the one radio channel of the compressed CSI as shown in figures 4 & 5). In summary, a channel state information (CSI) instance for a channel, determining a neural network model including a CSI encoder and a CSI decoder, and training the neural network model based at least in part on encoding the CSI instance into encoded CSI, decoding the encoded CSI into decoded CSI, and comparing the CSI instance and the decoded CSI. The comparing may be part of, for example, computing and minimizing a loss function between the CSI instance and the decoded CSI. See paragraph [0005] of Yoo. Therefore, it can be said that Yoo discloses classifying CSI compression quality based on a level of predicted performance loss associated with reconstruction of the one or more radio channels of the compressed CSI as required in the claims.
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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-4, 7-8 and 13-15 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Yoo et al. (Yoo), U.S. Publication No. 2021/0273707.
Regarding Claim 1, Yoo discloses a method for classifying channel state
information (CSI) compression quality, the method being performed by a user equipment (UE) (i.e., UE; see figure 4) in a wireless communication network (shown in figure 4), wherein the method comprises:
obtaining CSI associated with one or more radio channels (i.e., a device, such as UE 120, may include means for obtaining a CSI instance for a channel; see paragraph [0050]);
compressing the CSI (see figure 5) into an encoded format representing a compressed CSI (i.e., UE 120 may include means for encoding a first CSI instance for a channel estimate of a channel to a base station into first encoded CSI; see paragraph [0051]); and
classifying a CSI compression quality related to reconstruction of the one or more radio channels of the compressed CSI (shown in figure 4) using a classifier (in other words, determined encoder weights θ and decoder weights ϕ; see paragraph [0077]) predicting a resulting performance loss (in other words, predictions not meeting the threshold level of accuracy as described in paragraph [0076]) associated with the reconstruction of the one or more radio channels, wherein the classification of the CSI compression quality is based on a level of predicted performance loss (in other words, the device may provide training data to the neural network model and receive predictions based at least in part on providing the training data to the neural network model. Based at least in part on the predictions, the device may update the neural network model and provide the estimates to the updated neural network model. The device may repeat this process until a threshold level of accuracy for predictions are generated by the neural network model. The device may obtain encoder weights and decoder weights based at least in part on the predictions; see paragraphs [0076]-[0078]).
Regarding Claim 2, Yoo discloses further comprising: transmitting one or more
of the CSI compression quality classification and the encoded format representing the compressed CSI to a network node in the wireless communication network (see figure 4).
Regarding Claim 3, Yoo discloses wherein the transmitting comprises:
determining whether the CSI compression quality classification triggers a secondary CSI report; and transmitting one or more of the CSI compression quality classification and the secondary CSI report when it is determined that CSI compression quality classification triggers a secondary CSI report (see paragraphs [0076] and [0077]).
Regarding Claim 4, Yoo discloses wherein the secondary CSI report is
configured by the network node (see paragraphs [0076] and [0078]).
Regarding Claim 7, Yoo discloses wherein the CSI compression quality is
classified using a neural network based classifier (see paragraph [0092] and figure 8).
Regarding Claim 8, Yoo discloses wherein the neural network based classifier is
configured by a network node (see paragraph [0092] and figure 8).
Regarding Claim 13, Yoo discloses a method for classifying channel state
information (CSI) compression quality, the method being performed by a network node (i.e., gNB; see figure 4) in a wireless communication network (shown in figure 4), wherein the method comprises:
receiving an encoded format representing a compressed CSI from a user equipment (UE) (i.e., the gNB may receive the encoded CSI, and CSI decoder 420 may decode the encoded CSI into decoded CSI using decoder parameters 425; see paragraph [0067]); and
classifying a CSI compression quality related to reconstruction of one or more radio channels of the compressed CSI using a classifier predicting a resulting performance loss associated with the reconstruction of the one or more radio channels, wherein the classification of the CSI compression quality is based on a level of predicted performance loss (i.e., additionally, or alternatively, a CSI receiving device (e.g., the gNB) may perform the training, obtain the encoder weights and the decoder weights, provide the encoder weights to the encoder, and use the decoder weights.; see paragraph [0078]). Also, see rejection for claim 1 above.
Regarding Claim 14, Yoo discloses wherein the CSI compression quality is
classified using a neural network based classifier (see paragraph [0092] and figure 8).
Regarding Claim 15, Yoo discloses further comprising: deciding on how to use
a CSI feedback for scheduling the UE using the classified CSI compression quality (see paragraphs [0077]-[0078]).
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) 5-6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yoo in view of Beluri et al. (Beluri), U.S. Publication No. 2025/0038816.
Regarding Claim 5, Yoo discloses the method as described above. Yoo fails to
disclose wherein a type of the secondary CSI report is configured by the network node. Beluri discloses wherein a type of the secondary CSI report is configured by the network node (see paragraph [0155]). It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to consider Beluri’s invention with Yoo’s invention to reduce the overhead and improve the CSI quality as discussed throughout Beluri.
Regarding Claim 6, Yoo discloses the method as described above. Yoo fails to
disclose wherein the secondary CSI report is an NR CSI type I report. Beluri discloses wherein the secondary CSI report is an NR CSI type I report (see paragraph [0155]). It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to consider Beluri’s invention with Yoo’s invention to reduce the overhead and improve the CSI quality as discussed throughout Beluri.
Claim(s) 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yoo in view of Vitthaladevuni et al. (Vitthaladevuni), U.S. Publication No. 2024/0275557.
Regarding Claim 16, Yoo discloses the method as described above. Yoo fails
to disclose wherein the deciding comprises: deciding whether to schedule a multi-user MIMO or a single-user MIMO transmission for the UE using the classified CSI compression quality. Vitthaladevuni discloses wherein the deciding comprises: deciding whether to schedule a multi-user MIMO (see paragraph [0047]) or a single-user MIMO transmission for the UE using the classified CSI compression quality. It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to consider Vitthaladevuni’s invention with Yoo’s invention to improve signal reliability and efficiency (see paragraph [0047] of Vitthaladevuni).
Allowable Subject Matter
Claims 9-12 and 17-20 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
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHANTELL LAKETA HEIBER whose telephone number is (571)272-0886. The examiner can normally be reached on M-F from 9am to 5pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Anthony Addy, can be reached at telephone number (571)272-0886. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/SHANTELL L HEIBER/Primary Examiner, Art Unit 2645
May 21, 2026
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