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 .
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 08/23/2024 and 06/23/2026 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
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)(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.
(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.
Claims 1-11, 13-14, 18-19, 41 and 43 are rejected under 35 U.S.C. 102(a)(1) and 35 U.S.C. 102(a)(2) as being clearly anticipated by Rahman et al. (US-20210297134-A1, published: 2021-09-23) hereinafter Rahman.
For examination purposes, claims 41 and 43 referring to an apparatus and claims 1-11, and 13-19 referring to a method are henceforth grouped together for claims mirroring the same limitations or which disclose analogous art to the invention as claimed.
Regarding claims 1 and 41, Rahman discloses a method and device for transmitting Channel State Indicator (CSI) feedback information, performed by a User Equipment (UE), and comprising (Rahman. Fig. 3, par. 55; UE 116 according to embodiments of the present disclosure): determining CSI feedback corresponding to each basic unit according to the basic unit of a CSI processing granularity, wherein the basic unit is smaller than a CSI measurement resource indicated by a network side (Rahman, fig. 1, par. 43; the UEs 111-116 include circuitry, programing, or a combination thereof, for receiving channel state information (CSI) reporting configuration information including a CSI reporting band and a frequency granularity of CSI reporting, where the CSI reporting band is within a bandwidth part (BWP) comprising M physical resource blocks (PRBs), and the frequency granularity of CSI reporting is based on whether M<N, where N is a threshold); and sending the CSI feedback information comprising the CSI feedback to a base station (Rahman, fig. 1, par. 43; the CSI report includes at least one of a precoding matrix indicator (PMI) and a channel quality indicator (CQI); and transmitting the CSI report over an uplink (UL) channel) see also fig. 14.
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Regarding claim 2, Rahman discloses the method according to claim 1, wherein the CSI measurement resource is one BandWidth Part (BWP) (Rahman, par. 43; where the CSI reporting band is within a bandwidth part (BWP)).
Regarding claim 3, Rahman discloses the method according to claim 1, wherein the basic unit covers N frequency domain resources corresponding to a Channel State Indicator-Reference Signal (CSI-RS) (Rahman, par. 43; CSI reporting is based on whether M<N, where N is a threshold) in frequency domain (Rahman, par. 69; block 470 then performs an FFT algorithm to produce N parallel frequency-domain signals), wherein N is a positive integer and N is less than M (Rahman, par. 68; N variable may be any integer number); and wherein M is a total number of frequency domain resources corresponding to the CSI-RS indicated by the network side (Rahman, par. 43; CSI reporting band is within a bandwidth part (BWP) comprising M physical resource blocks (PRBs)).
Regarding claim 4, Rahman discloses the method according to claim 3, wherein time domain positions of the N frequency domain resources are the same; or the time domain positions of the N frequency domain resources are not all the same (Rahman, par 334; The UE is further configured with a CSI reporting band and a frequency granularity of CSI reporting, where the CSI reporting band is within a bandwidth part (BWP) comprising M physical resource blocks (PRBs), and the frequency granularity of CSI reporting is based on whether M<N, where N is a threshold. In one example, N=24. In one example, the frequency granularity of CSI reporting is one of wideband and subband, whose details are according to some embodiments or examples (or their straightforward extensions to D-MIMO setup) of this disclosure. For example, when M<N, then the frequency granularity can be fixed to wideband only, and when M>=N, the frequency granularity can be wideband or subband (configurable)) see also par. 355, and table 14.
Regarding claim 5, Rahman discloses the method according to claim 1, wherein the determining the CSI feedback corresponding to each basic unit according to the basic unit of the CSI processing granularity comprises: determining the basic unit of the CSI processing granularity (Rahman, par. 96; the frequency resolution (reporting granularity) and span (reporting bandwidth) of CSI reporting can be defined in terms of frequency “subbands” and “CSI reporting band” (CRB)) see also par. 334 and fig. 14 step 1404; and using a machine learning model corresponding to the basic unit to determine the CSI feedback corresponding to the basic unit (Rahman, par. 66; it is noted that the FFT blocks and the IFFT blocks described in this disclosure document may be implemented as configurable software algorithms, where the value of Size N may be modified according to the implementation).
Regarding claim 7, Rahman discloses the method according to claim 1, wherein the sending the CSI feedback information comprising the CSI feedback to the base station comprises: sending the CSI feedback information comprising the CSI feedback corresponding to a plurality of basic units respectively (Rahman, par. 70; each one of user equipment 111-116 may implement a transmit path corresponding to the architecture for transmitting in the uplink to gNBs 101-103 and may implement a receive path corresponding to the architecture for receiving in the downlink from gNBs 101-103) see also pars. 43 and 60.
Regarding claim 8, Rahman discloses the method according to claim 1, wherein the basic unit is determined based on at least one of the CSI measurement resource or CSI processing capability of the UE (Rahman, fig. 14, par. 377; the UE, based on the CSI reporting configuration information, identifies the frequency granularity of CSI reporting) see also step. 1406.
Regarding claim 9, Rahman discloses the method according to claim 8, wherein in response to a bandwidth of the CSI measurement resource in frequency domain being greater than a bandwidth threshold, a bandwidth of the basic unit in frequency domain is a first bandwidth; or in response to the bandwidth of the CSI measurement resource in frequency domain being less than or equal to the bandwidth threshold, the bandwidth of the basic unit in frequency domain is a second bandwidth, wherein the first bandwidth is greater than the second bandwidth. (Rahman, par 334; The UE is further configured with a CSI reporting band and a frequency granularity of CSI reporting, where the CSI reporting band is within a bandwidth part (BWP) comprising M physical resource blocks (PRBs), and the frequency granularity of CSI reporting is based on whether M<N, where N is a threshold. In one example, N=24. In one example, the frequency granularity of CSI reporting is one of wideband and subband, whose details are according to some embodiments or examples (or their straightforward extensions to D-MIMO setup) of this disclosure. For example, when M<N, then the frequency granularity can be fixed to wideband only, and when M>=N, the frequency granularity can be wideband or subband (configurable)) see also pars. 172, 355, and table 14.
Regarding claim 10, Rahman discloses the method according to claim 1, wherein the CSI measurement resource comprises I basic units, wherein I is a positive integer greater than or equal to 2 (Rahman, par. 122; for a given i, the number of basis vectors is M.sub.i and the corresponding basis vectors are {b.sub.i,m}. Note that M.sub.i is the number of coefficients c.sub.l,i,m reported by the UE for a given i, where M.sub.i≤M (where {M.sub.i} or ΣM.sub.i is either fixed, configured by the gNB or reported by the UE)).
Regarding claims 11 and 43, Rahman discloses a method for transmitting Channel State Indicator (CSI) feedback information, performed by a base station, and comprising (Rahman, fig. 1, par, 43; gNBs 101-103 includes circuitry, programing, or a combination thereof, for generating channel state information (CSI) reporting configuration): receiving CSI feedback information for a plurality of basic units sent by a User Equipment UE, wherein the CSI feedback information comprises CSI feedback corresponding to each basic unit; wherein the CSI feedback is determined by the UE according to the basic unit corresponding to a CSI processing granularity; and wherein the basic unit is smaller than a CSI measurement resource indicated by a network side (Rahman, fig. 1, par. 43; the UEs 111-116 include circuitry, programing, or a combination thereof, for receiving channel state information (CSI) reporting configuration information including a CSI reporting band and a frequency granularity of CSI reporting, where the CSI reporting band is within a bandwidth part (BWP) comprising M physical resource blocks (PRBs), and the frequency granularity of CSI reporting is based on whether M<N, where N is a threshold) see also fig. 14.
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Regarding claim 13, Rahman discloses the method according to claim 11, wherein the basic unit covers N frequency domain resources corresponding to a Channel State Indicator-Reference Signal (CSI-RS) in frequency domain, wherein N is a positive integer and N is less than M; and wherein M is a total number of frequency domain resources corresponding to the CSI-RS indicated by the network side (Rahman, par 334; The UE is further configured with a CSI reporting band and a frequency granularity of CSI reporting, where the CSI reporting band is within a bandwidth part (BWP) comprising M physical resource blocks (PRBs), and the frequency granularity of CSI reporting is based on whether M<N, where N is a threshold. In one example, N=24. In one example, the frequency granularity of CSI reporting is one of wideband and subband, whose details are according to some embodiments or examples (or their straightforward extensions to D-MIMO setup) of this disclosure. For example, when M<N, then the frequency granularity can be fixed to wideband only, and when M>=N, the frequency granularity can be wideband or subband (configurable)) see also pars. 43, and 60.
Regarding claim 14, Rahman discloses the method according to claim 13, wherein time domain positions of the N frequency domain resources are the same; or the time domain positions of the N frequency domain resources are not all the same (Rahman, par 334; The UE is further configured with a CSI reporting band and a frequency granularity of CSI reporting, where the CSI reporting band is within a bandwidth part (BWP) comprising M physical resource blocks (PRBs), and the frequency granularity of CSI reporting is based on whether M<N, where N is a threshold. In one example, N=24. In one example, the frequency granularity of CSI reporting is one of wideband and subband, whose details are according to some embodiments or examples (or their straightforward extensions to D-MIMO setup) of this disclosure. For example, when M<N, then the frequency granularity can be fixed to wideband only, and when M>=N, the frequency granularity can be wideband or subband (configurable)) see also par. 355, and table 14.
Regarding claim 17, Rahman discloses the method according to claim 11, wherein the receiving the CSI feedback information comprising the CSI feedback corresponding to each basic unit and sent by the User Equipment UE comprises: receiving the CSI feedback information comprising the CSI feedback corresponding to the plurality of basic units respectively (Rahman, par. 70; each one of user equipment 111-116 may implement a transmit path corresponding to the architecture for transmitting in the uplink to gNBs 101-103 and may implement a receive path corresponding to the architecture for receiving in the downlink from gNBs 101-103) see also pars. 43 and 60.
Regarding claim 18, Rahman discloses the method according to claim 11, wherein the basic unit is determined based on at least one of the CSI measurement resource or CSI processing capability of the UE (Rahman, fig. 14, par. 377; the UE, based on the CSI reporting configuration information, identifies the frequency granularity of CSI reporting) see also step. 1406.
Regarding claim 19, Rahman discloses the method according to claim 11, wherein in response to a bandwidth of the CSI measurement resource in frequency domain being greater than a bandwidth threshold, a bandwidth of the basic unit in frequency domain is a first bandwidth; or in response to the bandwidth of the CSI measurement resource in frequency domain being less than or equal to the bandwidth threshold, the bandwidth of the basic unit in frequency domain is a second bandwidth, wherein the first bandwidth is greater than the second bandwidth (Rahman, par 334; The UE is further configured with a CSI reporting band and a frequency granularity of CSI reporting, where the CSI reporting band is within a bandwidth part (BWP) comprising M physical resource blocks (PRBs), and the frequency granularity of CSI reporting is based on whether M<N, where N is a threshold. In one example, N=24. In one example, the frequency granularity of CSI reporting is one of wideband and subband, whose details are according to some embodiments or examples (or their straightforward extensions to D-MIMO setup) of this disclosure. For example, when M<N, then the frequency granularity can be fixed to wideband only, and when M>=N, the frequency granularity can be wideband or subband (configurable)) see also pars. 172, 355, and table 14.
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 6, and 15-16 are rejected under 35 U.S.C. 103 as being unpatentable over Rahman et al. (US-20210297134-A1, published: 2021-09-23) hereinafter Rahman in view of Zeng et al. (US-20210273706-A1, published 2021-09-02) hereinafter Zeng.
Regarding claim 6, Rahman discloses the method according to claim 5, wherein the machine learning model corresponding to the basic unit is obtained by training with at least one of full channel information or feature vector corresponding to the basic unit (Rahman, par. 66; it is noted that the FFT blocks and the IFFT blocks described in this disclosure document may be implemented as configurable software algorithms, where the value of Size N may be modified according to the implementation).
Rahman does not explicitly disclose the algorithm is trained using one of the full channel information or feature vector processing. However Zeng discloses a system for facilitating feedback of robust CSI, using encoders and decoders in a network node to implement channel compression based on neural network training (Zeng, par. 59; CSI encoders and decoders used by network nodes may implement channel compression/reconstruction based upon NN training of collected channels).
Therefore, a person of ordinary skill in the art before the effective filing date seeking to enhance processing of large CSI payloads would be motivated to implement Rahman teachings CSI reporting with Zeng’s teaching for CSI compression using neural networks to enhance system performance by dynamically reducing payload size based on channel conditions.
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Regarding claim 15, Rahman discloses the method according to claim 11, further comprising: using a machine learning model corresponding to the basic unit to decompress the CSI corresponding to the basic unit (Rahman, par. 66; it is noted that the FFT blocks and the IFFT blocks described in this disclosure document may be implemented as configurable software algorithms, where the value of Size N may be modified according to the implementation).
Rahman does not explicitly disclose the algorithm used to decompress the CSI. However Zeng discloses a system for facilitating feedback of robust CSI, using encoders and decoders in a network node to implement channel compression based on neural network training (Zeng, par. 59; CSI encoders and decoders used by network nodes may implement channel compression/reconstruction based upon NN training of collected channels).
Therefore, a person of ordinary skill in the art before the effective filing date seeking to enhance processing of large CSI payloads would be motivated to implement Rahman teachings CSI reporting with Zeng’s teaching for CSI compression using neural networks to enhance system performance by dynamically reducing payload size based on channel conditions.
Regarding claim 16, The combination of Rahman and Zeng, further teach the method according to claim 15, wherein the machine learning model corresponding to the basic unit is obtained by training with at least one of full channel information (Rahman, par. 66; it is noted that the FFT blocks and the IFFT blocks described in this disclosure document may be implemented as configurable software algorithms, where the value of Size N may be modified according to the implementation) or feature vector corresponding to the basic unit (Zeng, par. 59; CSI encoders and decoders used by network nodes may implement channel compression/reconstruction based upon NN training of collected channels).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
QI (US-20210234662-A1), Channel State Feedback Method And Apparatus In Communication System, 2021.
Muruganathan (US-10833747-B2), Mechanisms For Reduced Density CSI-RS, 2020.
Vitthaladevuni (US-20230246693-A1), Configurations For Channel State Feedback, 2023.
Nemeth (US-20240106579-A1), Soft HARQ Feedback Reporting Density, Enabling Mechanism, Processing Timeline And Codebook Construction In Mobile Communications, 2024.
Hajri (US-20230361842-A1), Improving Precoding, 2023.
Babaei (US-11153060-B2), Selection Of Grant And CSI, 2021.
Gao (US-10447368-B2), Multi-resolution CSI Feedback, 2017.
Peng (WO-2021160149-A1), Feedback Method Of Channel State Information (CSI) Report, Involves Generating Channel State Information Report According To Channel State Information Report Configuration And CSI Measurement Resource Group, And Reporting CSI Report, 2021.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MARIO R CAMPERO MIRAMONTES whose telephone number is (571)272-5792. The examiner can normally be reached Monday -Thursday 0600 - 1600.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Yuwen (Kevin) Pan can be reached at (571) 272-7855. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MARIO R CAMPERO MIRAMONTES/Examiner, Art Unit 2649 /YUWEN PAN/Supervisory Patent Examiner, Art Unit 2649
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