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 .
This action is response to the application filed on 07/11/2024. Claims 1-20 are pending and herein considered.
Priority
Receipt is acknowledged of papers submitted under 35 U.S.C. 119(a)-(d), which papers have been placed of record in the file.
Drawings
The drawings were received on 07/11/2024. These drawings are reviewed and accepted by the Examiner.
Specification
The lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification.
Information Disclosure Statement
The information disclosure statement (IDS), submitted on 02/20/2025 are uncompleted information from NPL (Other Art) citation. Accordingly, the information disclosure statement is NOT being considered by the examiner. Please resubmitted the IDS.
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 of this title, 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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 1-2, 12-13 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over anticipated IEEE- Y. Tian, X. Wu, J. Liu, J. Yang and D. Liu, "Beam Sweeping Assisted Compressive Channel Estimation for Wideband mmWave Massive MIMO Systems with Hybrid Beamforming," 2022 IEEE/CIC International Conference on Communications in China (ICCC), Sanshui, Foshan, China, 2022, pp. 1026-1031; hereinafter “IEEE-Sanshui” in view of Bayesteh et al. (U.S 2021/0076367).
For claim 1:
IEEE-Sanshui discloses a method comprising:
performing, by a user equipment (UE), beam sweeping to generate one or more measurement matrices (see IEEE-Sanshui, at least pages 1026-1028; 5G NR protocol provides a periodic beam sweeping mechanism without additional overhead and UE sorts all AoD/AoA pairs in descending according to RSRP measured in beam sweeping)
obtaining one or more sensing matrices based on the one or more measurement matrices by compressing the one or more measurement matrices with matrices training (see IEEE-Sanshui, at least pages 1026-1028, section III.B; measurement of a received signal power to obtain a set of measurement for beam directions which is identical to “a measurement matrix”),
wherein a sensing matrix is a network sensing matrix or a UE sensing matrix (see IEEE-Sanshui, at least pages 1028, section III.B, equation 16 and 17; generation of a sensing matrix having a reduced dimension from the measurement obtained by beam sweeping which related to both sides (UE and the network side); and
IEEE-Sanshui further discloses the channel estimates are obtained using pilots transmitted in the sensing beam directions (see IEEE-Sanshui, at least page 1029).
IEEE-Sanshui does not explicitly disclose performing weighted beam measurements using the one or more sensing matrices to generate one or more weighted beam measurement matrices.
Bayesteh, from the same or similar fields of endeavor, discloses what IEEE-Sanshui fails: weighted beam sweeping can be performed. In weighted beam sweeping and the total number of beam sweeping time slots is equal to w, after w(t) time slots the sensing signals are transmitted over the AoD (see Bayesteh, at least paragraph [0157]; [0161] and [0123]; Sensing signal configurations can be target-specific and/or sensing node-specific. Target-specific means that the sensing signal is configured for a particular target where Target-specific parameters could be obtained by a sensing node through measurement, training).
Therefore, it would have been obvious statement before the effective filing date of the claimed invention to have a system comprises a method as taught by Bayesteh. The motivation for doing this is to provide a system networks can provide possible benefits of implementing target-specific and/or sensing node-specific sensing signal configurations include the flexibility to adjust the configuration of a sensing signal based on a desired sensing quality, and/or to reduce interference between sensing signals from different sensing nodes.
For claims 2 and 13:
In addition to rejection in claims 2 and 13, IEEE-Sanshui- Bayesteh further discloses wherein the one or more measurement matrices are reference signal received power (RSRP) matrices, and wherein the UE sweeps and measures all beams to generate the RSRP matrices (see IEEE-Sanshui, at least section III, pages 1028; the BS measures the received signal power(RSRP), and UE sorts all AoD/AoA pairs in descending according to RSRP measured in beam sweeping).
For claim 16:
In addition to rejection in claim 16, IEEE-Sanshui- Bayesteh further discloses wherein the matrices training is performed by the wireless network, and wherein the compress module further receives the UE sensing matrix from the wireless network (see Bayesteh, at least paragraph [0145]; [0176]; figure 4; a sensing signal is selected from a set of physical resources associated with the wireless communication network). The motivation for doing this is to provide a system networks can provide possible benefits of implementing target-specific and/or sensing node-specific sensing signal configurations include the flexibility to adjust the configuration of a sensing signal based on a desired sensing quality, and/or to reduce interference between sensing signals from different sensing nodes.
For claim 12:
For claim 12, claim 12 is directed to a user equipment (UE), which has similar scope as claim 1. Therefore, claim 12 remains un-patentable for the same reasons.
Claims 3-6, 8, 14 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over anticipated IEEE- Y. Tian, X. Wu, J. Liu, J. Yang and D. Liu, "Beam Sweeping Assisted Compressive Channel Estimation for Wideband mmWave Massive MIMO Systems with Hybrid Beamforming," 2022 IEEE/CIC International Conference on Communications in China (ICCC), Sanshui, Foshan, China, 2022, pp. 1026-1031; hereinafter “IEEE-Sanshui” in view of Bayesteh et al. (U.S 2021/0076367) further in view of Pourahmadi et al. (U.S 2026/0046642; hereinafter “Pourahmadi” cited with provisional 63/396,596).
For citation purposes, hereinafter, the Office Action refers to the cited by Pourahmadi et al. with provisional 63/396,596, which qualifies as prior art date.
For claim 3 and 14:
In addition to rejection in claims 3 and 14, IEEE-Sanshui- Bayesteh does not explicitly disclose wherein the matrices training is performed by the UE using an artificial intelligence (AI) model.
Pourahmadi, from the same or similar fields of endeavor, discloses what IEEE- IEEE-Sanshui- Bayesteh fails: a Techniques of classifying and possibly sharing the training data set between the UE and the network, as well as selecting the AI/ML model and sharing the AI/ML model parameters and selections between the UE and the network where the AI/ML model is trained at the UE (see Pourahmadi, at least paragraph [0180]-[0181]).
Therefore, it would have been obvious statement before the effective filing date of the claimed invention to have a system comprises a method as taught by Pourahmadi. The motivation for doing this is to provide a system networks in order to support such a framework, a training data set may be partitioned into multiple training data sets, where each training data set is used to train a distinct AI/ML model.
For claims 4 and 15:
In addition to rejection in claims 4 and 15, IEEE-Sanshui- Bayesteh- Pourahmadi further discloses indicating to a wireless network the network sensing matrix (see Bayesteh, at least paragraph [0145]; [0176]; figure 4; a sensing signal is selected from a set of physical resources associated with the wireless communication network). The motivation for doing this is to provide a system networks can provide possible benefits of implementing target-specific and/or sensing node-specific sensing signal configurations include the flexibility to adjust the configuration of a sensing signal based on a desired sensing quality, and/or to reduce interference between sensing signals from different sensing nodes.
For claim 5:
In addition to rejection in claim 5, IEEE-Sanshui- Bayesteh further disclose wherein the matrices training is performed by a wireless network using an artificial intelligence (AI) model (see Bayesteh, at least paragraph [0145]; [0176]; figure 4; a sensing signal is selected from a set of physical resources associated with the wireless communication network).
Pourahmadi, from the same or similar fields of endeavor, discloses what IEEE- IEEE-Sanshui- Bayesteh fails: a Techniques of classifying and possibly sharing the training data set between the UE and the network, as well as selecting the AI/ML model and sharing the AI/ML model parameters and selections between the UE and the network where the AI/ML model is trained at the UE (see Pourahmadi, at least paragraph [0180]-[0181]).
Therefore, it would have been obvious statement before the effective filing date of the claimed invention to have a system comprises a method as taught by Pourahmadi. The motivation for doing this is to provide a system networks in order to support such a framework, a training data set may be partitioned into multiple training data sets, where each training data set is used to train a distinct AI/ML model.
For claim 6:
In addition to rejection in claim 6, IEEE-Sanshui- Bayesteh- Pourahmadi further disclose receiving the UE sensing matrix from the wireless network (see Bayesteh, at least paragraph [0145]; [0176]; figure 4; a sensing signal is selected from a set of physical resources associated with the wireless communication network). The motivation for doing this is to provide a system networks can provide possible benefits of implementing target-specific and/or sensing node-specific sensing signal configurations include the flexibility to adjust the configuration of a sensing signal based on a desired sensing quality, and/or to reduce interference between sensing signals from different sensing nodes.
For claim 8:
In addition to rejection in claim 8, IEEE-Sanshui- Bayesteh- Pranav further discloses wherein the reconstruction is performed using an image reconstruction greedy algorithm or using a trained AI model (see Pranav, at least paragraph [0152]; Reconstruction using UL partial reciprocity: When using AI/ML based CSI feedback mechanism, base station reconstructs the channel matrix (CSI) from the extracted feature (AI-CPI) it received from the UE). The motivation for doing this is to provide a system networks in order to help improve the reconstruction of the high-resolution channel.
Claims 7 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over anticipated IEEE- Y. Tian, X. Wu, J. Liu, J. Yang and D. Liu, "Beam Sweeping Assisted Compressive Channel Estimation for Wideband mmWave Massive MIMO Systems with Hybrid Beamforming," 2022 IEEE/CIC International Conference on Communications in China (ICCC), Sanshui, Foshan, China, 2022, pp. 1026-1031; hereinafter “IEEE-Sanshui” in view of Pranav et al. (WO2022220642-A1).
For claims 7 and 17:
In addition to rejection in claims 7 and 17, IEEE-Sanshui- Bayesteh does not explicitly disclose performing measurement matrix reconstruction based on the one or more weighted beam measurement matrices and the one or more sensing matrices.
Pranav, from the same or similar fields of endeavor, discloses what IEEE- IEEE-Sanshui- Bayesteh fails: when using AI/ML based CSI feedback mechanism, base station reconstructs the channel matrix (CSI) from the extracted feature (AI-CPI) it received from the UE (see Pranav, at least paragraph [0152]).
Therefore, it would have been obvious statement before the effective filing date of the claimed invention to have a system comprises a method as taught by Pranav. The motivation for doing this is to provide a system networks in order to help improve the reconstruction of the high-resolution channel.
Allowable Subject Matter
Claims 9-11 and 18-20 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 (e.g: claims 1+ 9+ 10+ 11 and claims 12+18 or 12+ 19+20) and any intervening claims, set forth in this Office action.
Conclusion
The prior arts made or record and not relied upon are considered pertinent to applicant's disclosures. Aiammouri et al. (U.S 2024/0430819), discloses analog beam can be configured to sweep across a wider range of angles by varying the phase shifter bank across symbols or subframes or slots.
Fonteneau et al. (U.S 2024/0380473), discloses estimating, for at least one sub-carrier, a propagation channel by means of data collected during a sweeping of the beams, determining a broadband covariance matrix of a multicarrier propagation channel from the estimated propagation channel.
Chen et al. (U.S 2023/0283350), disclose the network device determines the second channel matrix based on the channel elements fed back by the terminal device, an error between an actual beam direction and an ideal beam direction can be reduced, beam sweeping precision can be improved.
Wang et al. (U.S 2023/0147737), discloses obtaining system load of a cell at a current moment; configuring a quantity of sweeping beams in a sweeping periodicity based on the system load at the current moment; and configuring a quantity of shutdown symbols based on the quantity of sweeping beams
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LAN-HUONG TRUONG whose telephone number is (571)270-5829. The examiner can normally be reached Monday-Friday 8am-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, Ricky Ngo can be reached at 571-272-3139. 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.
/Lan-Huong Truong/
Primary Examiner, Art Unit: 2464
07/24/2026
Prosecution Insights