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 .
Status of Claims
Claims 1-20 are pending in the instant application.
Information Disclosure Statement
The information disclosure statement(s) (IDS) submitted on 11/22/2024 and 06/23/2025 were filed before the mailing of a First Office Action on the Merits. The information disclosure statements are being considered by the examiner.
Specification
Applicant is reminded of the proper language and format for an abstract of the disclosure.
The abstract should be in narrative form and generally limited to a single paragraph on a separate sheet within the range of 50 to 150 words. It is important that the abstract not exceed 150 words in length since the space provided for the abstract on the computer tape used by the printer is limited. The form and legal phraseology often used in patent claims, such as "means" and "said," should be avoided. The abstract should describe the disclosure sufficiently to assist readers in deciding whether there is a need for consulting the full patent text for details.
The language should be clear and concise and should not repeat information given in the title. It should avoid using phrases which can be implied, such as, "The disclosure concerns," "The disclosure defined by this invention," "The disclosure describes," etc.
The abstract of the disclosure is objected to because of undue length (i.e., 45 words). Correction is required. See MPEP § 608.01(b).
Claim Objections
Claims 6 and 16 are objected to because of the following informalities: The phrase “based on based on” contains a typographical/grammatical error (duplication). Appropriate correction is required.
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, 6, 11 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Nystrom et al. [Nystrom] (US 2020/0162218 A1) in view of FARMANBAR at al. [Farmanbar] (US 2021/0067294 A1).
Regarding claim 11, Nystrom discloses an apparatus comprising: a memory storing instructions; and at least one processor, wherein execution of the instructions by the at least one processor causes the apparatus to perform operations (Nystrom: ¶ 0052, A base station 20 communicates with a mobile terminal 30 via an uplink 24 and a downlink 22 connection) including: obtaining a first configuration defining a plurality of first network resources for transmissions to a first apparatus using a channel (Nystrom: ¶ 0052, The downlink control unit 25 is responsible for allocating resources for communication on the downlink 22 between the base station 20 and the mobile terminal 30 and comprises in tum a pilot manager 26 and a radio condition processor 28. ¶ 0060, The resource sub-space is reported anyway, and is typically defined by frequency and time "coordinates" and the number of frequency DF and time DT slots that are comprised in the sub-space. ¶ 0030, Data is transmitted in quantities limited in time and frequency. These data quantities correspond to the small squares 104 in the diagram. [Examiner’s Note: The allocated sub-space definition (coordinates plus DF x DT slots) is a “first configuration”. It defines the plurality of first network resources (data quantities 104) for transmission to mobile terminal 30 on downlink 22.]); and transmitting the one or more reference signals to the first apparatus using the one or more second network resources (Nystrom: ¶ 0030, Selected ones 102 of these data quantities contain pilot data and are illustrated in the diagram with hatching. ¶ 0037, The transmitter manages the sending of pilots according to this configurations and the receiver is capable of performing channel estimation based on the at least two pilot configurations. [Examiner’s Note: The base station transmits pilots to the user on the selected pilot units 102, which are the second network resources.]).
Nystrom does not explicitly disclose obtaining an assignment of one or more reference signals to one or more second network resources from the plurality of first network resources, wherein the assignment is based on first channel estimates for one or more second apparatus having a same location and a same first configuration as the first apparatus.
However, Farmanbar teaches obtaining an assignment of one or more reference signals to one or more second network resources from the plurality of first network resources, wherein the assignment is based on first channel estimates for one or more second apparatus having a same location and a same first configuration as the first apparatus (Farmanbar: ¶ 0080, a channel can be considered in the context of a four-dimensional grid of transmit antenna port index, receive antenna port index, subcarrier index, and OFDM symbol index. ¶ 0081, During training, a BS transmits (possibly dense) CSI-RS signaling to a UE so that the UE can estimate the channel at CSI-RS positions (antenna port index, subcarrier index in this example) and feed back CSI to the BS. This process continues for different channel realizations, through UE movement for example, until sufficient UE channel estimates are collected by the BS. A non-dense (sparse) signaling pattern, which may be defined in RRC configuration or other configuration signaling for example, includes a set of points with a pilot or other reference signal (AP) and a set of points without a pilot or other reference signal (Anp). ¶ 0056, each of UE1 and UE2 also provides an indication of current VE location corresponding to the CSI feedback, or the BS otherwise determines the VE locations. ¶ 0052, the same sparse CSI-RS configuration is used for all VEs at or near the location(s) from which CSI feedback was previously collected for determining or obtaining the sparse CSI-RS configuration. ¶ 0061, Channel characteristics may vary depending on UE location, and therefore the particular sparse reference signal pattern that is used at 322 may be UE location-dependent. [Examiner’s Note: The sparse pattern assigns reference signals to a subset Ap of the dense grid positions. It is determined from channel estimates collected on that same grid from UE1 and UE2 (the second apparatus) at a given location, and is applied to UEs “at or near” that location.]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Nystrom by further including, obtaining an assignment of one or more reference signals to one or more second network resources from the plurality of first network resources, wherein the assignment is based on first channel estimates for one or more second apparatus having a same location and a same first configuration as the first apparatus, as taught by Farmanbar, since doing so would have achieved the desirable result of reducing pilot overhead through location-specific channel matching, which maximizes spectral efficiency and increases data throughput by exploiting predictable, repeatable radio propagation characteristics shared by identical device configurations at those specific coordinates.
Regarding claim 16, Nystrom discloses a first apparatus comprising: a memory storing instructions; and at least one processor, wherein execution of the instructions by the at least one processor causes the first apparatus to perform operations (Nystrom: ¶ 0052, A base station 20 communicates with a mobile terminal 30 via an uplink 24 and a downlink 22 connection) including: obtaining a first configuration defining a plurality of first network resources for transmissions to the first apparatus using a channel (Nystrom: ¶ 0059, When there are several possible pilot patterns to use in a system, the receiver has to be informed about which one is actually used. ¶ 0060, The resource sub-space is reported anyway, and is typically defined by frequency and time "coordinates" and the number of frequency DF and time DT slots that are comprised in the sub-space. [Examiner’s Note: Nystrom teaches the UE obtains the reported sub-space definition (the first configuration), which defines its downlink resources.])); and measuring the one or more reference signals received on the one or more second network resources (Nystrom: ¶ 0053, When the pilot manager has decided what pilot pattern to apply, the user equipment 30 has to be informed about the selection, in order to be able to perform the right channel estimation upon reception of the data. [Examiner’s Note: Nystrom teaches channel estimation on the pilots received at the assigned positions is “measuring” the reference signals.]).
Nystrom does not explicitly disclose obtaining an assignment of one or more reference signals to one or more second network resources selected from the plurality of first network resources, wherein the assignment is based on based on first channel estimates for one or more second apparatus having a same location and a same first configuration as the first apparatus.
However, Farmanbar teaches obtaining an assignment of one or more reference signals to one or more second network resources selected from the plurality of first network resources, wherein the assignment is based on based on first channel estimates for one or more second apparatus having a same location and a same first configuration as the first apparatus (Farmanbar: ¶ 0080, a channel can be considered in the context of a four-dimensional grid of transmit antenna port index, receive antenna port index, subcarrier index, and OFDM symbol index. ¶ 0081, During training, a BS transmits (possibly dense) CSI-RS signaling to a UE so that the UE can estimate the channel at CSI-RS positions (antenna port index, subcarrier index in this example) and feed back CSI to the BS. This process continues for different channel realizations, through UE movement for example, until sufficient UE channel estimates are collected by the BS. A non-dense (sparse) signaling pattern, which may be defined in RRC configuration or other configuration signaling for example, includes a set of points with a pilot or other reference signal (AP) and a set of points without a pilot or other reference signal (Anp). ¶ 0056, each of UE1 and UE2 also provides an indication of current VE location corresponding to the CSI feedback, or the BS otherwise determines the VE locations. ¶ 0052, the same sparse CSI-RS configuration is used for all VEs at or near the location(s) from which CSI feedback was previously collected for determining or obtaining the sparse CSI-RS configuration. ¶ 0061, Channel characteristics may vary depending on UE location, and therefore the particular sparse reference signal pattern that is used at 322 may be UE location-dependent. [Examiner’s Note: The sparse pattern assigns reference signals to a subset Ap of the dense grid positions. It is determined from channel estimates collected on that same grid from UE1 and UE2 (the second apparatus) at a given location, and is applied to UEs “at or near” that location.]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Nystrom by further including, obtaining an assignment of one or more reference signals to one or more second network resources selected from the plurality of first network resources, wherein the assignment is based on based on first channel estimates for one or more second apparatus having a same location and a same first configuration as the first apparatus, as taught by Farmanbar, since doing so would have achieved the desirable result of reducing pilot overhead through location-specific channel matching, which maximizes spectral efficiency and increases data throughput by exploiting predictable, repeatable radio propagation characteristics shared by identical device configurations at those specific coordinates.
Claims 2-4, 7-9, 12-14 and 17-19 are rejected under 35 U.S.C. 103 as being unpatentable over Nystrom in view of Farmanbar, and in further view of Liang (US 2020/0143236 A1).
Regarding claim 12, Nystrom in view of Farmanbar teaches the apparatus of claim 11.
Nystrom in view of Farmanbar do not explicitly disclose wherein the obtaining the assignment comprises: obtaining one or more eigenvectors from a singular value decomposition of the first channel estimates; and determining the assignment based on the one or more eigenvectors.
However, Liang teaches wherein the obtaining the assignment comprises: obtaining one or more eigenvectors from a singular value decomposition of the first channel estimates (Liang: ¶ 0046, For deep learning applications with 1-D input, we can construct a matrix Ψ based on its multiple input from training set. ¶ 0047, Calculate the SVD [ ] of Ψ as Ψ = U [ Σ 0 ; 0 0 ]
V
T
and save V. ¶ 0054, The main theoretical basis for LMSVD is that the k leading eigenvectors of ΨΨT maximize the following Rayleigh-Ritz function under orthogonality constraint. [Examiner’s Note: Liang teaches SVD of Ψ yields the eigenvectors (V) of the first channel estimates]); and determining the assignment based on the one or more eigenvectors (Liang: ¶ 0006, For large scale data set, we propose to use SVD-QR for the data subset selection, where SVD is used to sort the singular values and corresponding singular vectors, and the size of the data subset could be determined based on singular values; and QR is used to select which data samples should be selected as input for deep learning. ¶ 0051, The row positions of 1’s in the r̂ columns will enable us to find the r̂ most significant columns in Ψ. [Examiner’s Note: Liang teaches selecting which column positions of Ψ to keep based on the singular vectors.]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Nystrom and Farmanbar by further including, obtaining one or more eigenvectors from a singular value decomposition of the first channel estimates; and determining the assignment based on the one or more eigenvectors, as taught by Liang, since doing so would have achieved the desirable result of optimizing pilot placement via deterministic, low-cost singular value decomposition to maximize channel state information retention while minimizing training overhead.
Regarding claim 13, modified Nystrom teaches the apparatus of claim 12.
Nystrom does not explicitly disclose wherein the determining the assignment based on the one or more eigenvectors comprises: performing at least a partial pivoted Quadratic Right (QR) decomposition of the one or more eigenvectors to assign the one or more reference signals to the one or more second network resources.
However, Liang teaches wherein the determining the assignment based on the one or more eigenvectors comprises: performing at least a partial pivoted Quadratic Right (QR) decomposition of the one or more eigenvectors to assign the one or more reference signals to the one or more second network resources (Liang: ¶ 0050, Using QR decomposition with column pivoting, determine
π
such that
Q
T
v
11
T
,
v
21
T
π
=
R
11
,
R
12
where Q is a unitary matrix. ¶ 0051, The permutation matrix
π
corresponds to the ordered most-significant singular values based on the position of 1’s in each column, and we can choose the first r̂ columns. The row positions of 1’s in the r̂ columns will enable us to find the r̂ most significant columns in Ψ. [Examiner’s Note: Liang performs QR decomposition with column pivoting on the retained singular vectors. The pivot order identifies the selected column positions, which in the combination are the second network resources assigned the reference signals.]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Nystrom and Farmanbar by further including, performing at least a partial pivoted Quadratic Right (QR) decomposition of the one or more eigenvectors to assign the one or more reference signals to the one or more second network resources, as taught by Liang, since doing so would have achieved the desirable result of optimizing pilot placement via deterministic, low-cost singular value decomposition to maximize channel state information retention while minimizing training overhead.
Regarding claim 14, modified Nystrom teaches the apparatus of claim 12.
Nystrom does not explicitly disclose wherein the obtaining the first configuration comprises: obtaining a plurality of eigenvectors, wherein, for each of a plurality of configurations, one or more respective eigenvectors from a singular value decomposition of respective second channel estimates for one or more third apparatus having the same location; selecting the one or more eigenvectors from the plurality of eigenvectors based on at least one of: a rank of the one or more eigenvectors or a sparsity of the one or more eigenvectors; and identifying, from the plurality of configurations, the first configuration associated with the one or more eigenvectors.
However, Farmanbar teaches respective second channel estimates for one or more third apparatus having the same location (Farmanbar: ¶ 0059, Depending on such factors as UE locations and/or whether the training phase 310 is to develop UE-specific sparse reference signal pattern, data samples collected from UE1 and UE2 may be used at 319 in determining multiple sparse reference signal patterns, or one sparse reference signal pattern. In general, a sparse reference signal pattern may be determined based on channel estimation data samples collected from one or more UEs. ¶ 0056, For example, the BS may associate UE location information with a sparse reference signal-related signaling configuration that is determined based on CSI feedback received from a UE at one UE location, and then use the same sparse reference signal-related signaling configuration for other UEs at or near that same UE location. [Examiner’s Note: The “channel estimation data samples” used for each of the “multiple sparse reference signal patterns” are respective second channel estimates. They come from UE1 and UE2 (the third apparatus), which share a location (“at or near that same UE location”)]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Nystrom by further including, respective second channel estimates for one or more third apparatus having the same location, as taught by Farmanbar, since doing so would have achieved the desirable result of improving the accuracy of channel estimation and mitigating interference for co-located devices by leveraging shared spatial characteristics.
Nystrom in view of Farmanbar do not explicitly disclose wherein the obtaining the first configuration comprises: obtaining a plurality of eigenvectors, wherein, for each of a plurality of configurations, one or more respective eigenvectors from a singular value decomposition; selecting the one or more eigenvectors from the plurality of eigenvectors based on at least one of: a rank of the one or more eigenvectors or a sparsity of the one or more eigenvectors; and identifying, from the plurality of configurations, the first configuration associated with the one or more eigenvectors.
However, Liang teaches obtaining a plurality of eigenvectors, wherein, for each of a plurality of configurations, one or more respective eigenvectors from a singular value decomposition of respective second channel estimates for one or more third apparatus having the same location (Liang: ¶ 0054, The main theoretical basis for LMSVD is that the k leading eigenvectors of ΨΨT maximize the following Rayleigh-Ritz function under orthogonality constraint… Based on LMSYD, we are able to obtain Uk,
Σ
k Vk. [Examiner’s Note: Liang obtains the k leading eigenvectors (Uk,Vk), which is a plurality of eigenvectors.]); selecting the one or more eigenvectors from the plurality of eigenvectors based on at least one of: a rank of the one or more eigenvectors or a sparsity of the one or more eigenvectors (Liang: ¶ 0048, Based on the diagonal values of Σ, σ₁, σ₂, . . . , σᵣ (r=rank(Ψ)) and desired percentage of kept singular values λ, to determine r̂, (r̂≤r). ¶ 0049, Partition
v
=
v
11
v
12
v
21
v
22
. [Examiner’s Note: Liang keeps the r̂ leading singular vectors, with r̂ bounded by and derived from the rank r. This is selection of the eigenvectors based on rank; Only one element is required to meet the claimed limitation. Emphasis Added.]); and identifying, from the plurality of configurations, the first configuration associated with the one or more eigenvectors (Liang: ¶ 0006, For large scale data set, we propose to use SVD-QR for the data subset selection, where SVD is used to sort the singular values and corresponding singular vectors, and the size of the data subset could be determined based on singular values; and QR is used to select which data samples should be selected as input for deep learning. ¶ 0032, The SVD is able to find the maximum singular values, and QR helps to identify which columns are corresponding to these singular values. [Examiner’s Note: Liang teaches identifying the item that corresponds to the selected singular values and their “corresponding singular vectors”. An item identified this way is associated with the one or more eigenvectors.]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Nystrom and Farmanbar by further including, wherein the obtaining the first configuration comprises: obtaining a plurality of eigenvectors, wherein, for each of a plurality of configurations, one or more respective eigenvectors from a singular value decomposition of [respective second channel estimates for one or more third apparatus having the same location]; selecting the one or more eigenvectors from the plurality of eigenvectors based on at least one of: a rank of the one or more eigenvectors or a sparsity of the one or more eigenvectors; and identifying, from the plurality of configurations, the first configuration associated with the one or more eigenvectors, as taught by Liang, since doing so would have achieved the desirable result of optimizing pilot placement via deterministic, low-cost singular value decomposition to maximize channel state information retention while minimizing training overhead.
Regarding claim 17, the rejection applied to claim 12 is adopted herein because the limitations of claim 17 are substantially the same as those of claim 12.
Regarding claim 18, the rejection applied to claim 13 is adopted herein because the limitations of claim 18 are substantially the same as those of claim 13.
Regarding claim 19, modified Nystrom teaches the first apparatus of claim 18.
Nystrom further discloses the operations further comprising: receiving an indication of a number of the one or more reference signals to be received (Nystrom: ¶ 0061, In FIG. 13, a pattern having two neighbour pilots distributed in pairs over the resource space. In order to describe this pattern, only one extra vector is needed, the relative vector between the two pilots in each pair. [Examiner’s Note: ]).
Nystrom does not explicitly disclose selecting the one or more second network resources from the plurality of first network resources based on the at least partial pivoted QR decomposition and the number of the one or more reference signals.
However, Liang teaches selecting the one or more second network resources from the plurality of first network resources based on the at least partial pivoted QR decomposition and the number of the one or more reference signals (Liang: ¶ 0051, we can choose the first r̂ columns. [Examiner’s Note: Liang teaches the UE selects the first r̂ pivot positions. This selection is based on both the pivoted QR and the number.]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Nystrom by further including, selecting the one or more second network resources from the plurality of first network resources based on the at least partial pivoted QR decomposition and the number of the one or more reference signals, as taught by Liang, since doing so would have achieved the desirable result of optimizing the selection of network resources while reducing computational complexity.
Claims 5, 15, 10 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Nystrom in view of Farmanbar and Liang, and in further view of Zhou et al. [Zhou] (US 2011/0038445 A1).
Regarding claim 15, modified Nystrom teaches the apparatus of claim 12.
Nystrom does not explicitly disclose the operations further comprising: receiving, from the first apparatus, one or more coefficients determined based on measurements of the one or more reference signals; and determining an estimate of the channel based on the one or more coefficients and the one or more eigenvectors.
However, Farmanbar teaches the operations further comprising: receiving, from the first apparatus, one or more coefficients determined based on measurements of the one or more reference signals (Farmanbar: ¶ 0086, The UE estimates channels corresponding to the AP grid points with CSI-RS from the received signaling, and feeds back then channel estimates corresponding to CSI-RS antenna ports to the BS. [Examiner’s Note: Farmanbar teaches the fed-back channel estimates at the Ap grid points with CSI-RS from the received signaling, and feeds back the channel estimates corresponding to CSI-RS antenna ports to the BS]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Nystrom by further including, the operations further comprising: receiving, from the first apparatus, one or more coefficients determined based on measurements of the one or more reference signals, as taught by Farmanbar, since doing so would have achieved the desirable result of providing the base station with accurate channel state information (CSI) to optimize downlink scheduling and beamforming based on the actual channel conditions experienced by the user equipment.
Modified Nystrom does not explicitly disclose determining an estimate of the channel based on the one or more coefficients and the one or more eigenvectors.
However, Zhou teaches determining an estimate of the channel based on the one or more coefficients and the one or more eigenvectors (Zhou: ¶ 0035, According to the present disclosure, it is desired to derive the eigen-coefficients in
λ
D
using the received pilot vector z to synthesize an estimate
h
^
of the channel vector h. ¶ 0038, From the estimated eigen-coefficient vector
λ
D
, an estimate
h
^
of the channel vector h may then be synthesized from UD as follows:
h
^
=
UD
λ
D
. (Eq.10). [Examiner’s Note: Zhou teaches determining the full channel estimate from pilot-derived coefficients and the dominant eigenvectors.]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of modified Nystrom by further including, determining an estimate of the channel based on the one or more coefficients and the one or more eigenvectors, as taught by Zhou, since doing so would have achieved the desirable result of optimizing channel reconstruction fidelity through a known, predictable combination of an efficient matrix-approximation framework with explicit eigenvector-based parameter estimation.
Regarding claim 20, modified Nystrom teaches the first apparatus of claim 17. Nystrom further teaches the operations further comprising: determining a sampled channel estimate based on measurement of the one or more reference signals, the sampled channel estimate corresponding to an estimate of the channel at the one or more second network resources (Nystrom: ¶ 0031, The time between two consecutive pilot messages in time dimension is 11 time slots, and even if information from neighbouring frequencies are used for channel estimation in the meantime, at least 4 time slots will pass between two consecutive updates. [Examiner’s Note: The channel estimate the UE updates at each pilot occurrences is a sampled estimate of the channel at the pilot resources]).
Nystrom does not explicitly disclose determining, based on the sampled channel estimate and the one or more eigenvectors, one or more coefficients; and sending the one or more coefficients to the second apparatus.
However, Farmanbar teaches sending the one or more coefficients to the second apparatus (Farmanbar: ¶ 0086, feeds back the channel estimates corresponding to CSI-RS antenna ports to the BS. [Examiner’s Note: Farmanbar teaches the fed-back channel estimates at the Ap grid points with CSI-RS from the received signaling, and feeds back the channel estimates corresponding to CSI-RS antenna ports to the BS]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Nystrom by further including, sending the one or more coefficients to the second apparatus, as taught by Farmanbar, since doing so would have achieved the desirable result of providing the base station with accurate channel state information (CSI) to optimize downlink scheduling and beamforming based on the actual channel conditions experienced by the user equipment.
Modified Nystrom does not explicitly disclose determining, based on the sampled channel estimate and the one or more eigenvectors, one or more coefficients.
However, Zhou teaches determining, based on the sampled channel estimate and the one or more eigenvectors, one or more coefficients (Zhou: ¶ 0035, According to the present disclosure, it is desired to derive the eigen-coefficients in
λ
D
using the received pilot vector z to synthesize an estimate
h
^
of the channel vector h. ¶ 0038, From the estimated eigen-coefficient vector
λ
D
, an estimate
h
^
of the channel vector h may then be synthesized from UD as follows:
h
^
=
UD
λ
D
. (Eq.10). [Examiner’s Note: Zhou teaches determining the full channel estimate from pilot-derived coefficients and the dominant eigenvectors.]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of modified Nystrom by further including, determining, based on the sampled channel estimate and the one or more eigenvectors, one or more coefficients, as taught by Zhou, since doing so would have achieved the desirable result of optimizing channel reconstruction fidelity through a known, predictable combination of an efficient matrix-approximation framework with explicit eigenvector-based parameter estimation.
Features of claims 1-10 correspond to features of claims 11-20 and are therefore rejected using the same rationale(s) and same prior art(s) applied to claims 11-20, above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
FECHTEL et al. (US 2020/0052931 A1); FECHTEL et al. describes a method to estimate wireless communication channel behavior with fewer resources by transforming pilot symbols and channel statistics, like Doppler shift, delay spread, and power profiles, into a reduced-dimensional mathematical subspace derived from the channel's autocorrelation structure. Because traditional channel estimation is a highly demanding receiver task due to dynamic variations in frequency, time, noise, and interference, conventional methods can overwhelm memory and processing limits on simpler devices. To solve this, the disclosed system builds a correlation model from statistical data, selects basis vectors (such as eigenvectors) to define a subspace mapping, and computes filter coefficients directly within this reduced space. Operating entirely within this subspace eliminates the need for expensive back-transformations to the full observation space, allowing the system to cut memory use, reuse the same subspace across multiple blocks, and filter out less useful pilot data. The resulting estimated channel coefficients are then passed on for equalization or other receiver processing tasks, offering an efficient approach implemented across telecommunication devices, methods, and computer programs.. See Figs. 3, 6a-6c, ¶¶ [0003-06, 08, 27-29, 54-55, 70, 81, 86].
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MAJDI ALSOMIRI whose telephone number is (571) 270-0427. The examiner can normally be reached 7AM-5PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ayman Abaza can be reached at (571) 270-0422. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/M.A./Examiner, Art Unit 2465
/John Pezzlo/
Primary Examiner, AU 2465B
30 September 2026
Prosecution Insights