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 .
DETAILED ACTION
This Office Action is in response to the Applicant's communication filed on 06/18/2024. In virtue of this communication, claims 1 – 20 are currently pending in the instant application.
Claim Rejections - 35 USC § 103
2. 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.
3. Claims 1 – 4, 7 – 14, and 17 – 20 are rejected under 35 U.S.C. 103 as being unpatentable over Pezeshki et al. (hereinafter “Pezeshki”) (Pub # US 2024/0057021 A1) in view of Hindy et al. (hereinafter “Hindy”) (WO 2024/075101 A1).
Regarding claims 1 and 11, Pezeshki discloses a method performed by a terminal (see UE 104 in FIG. 2, 470 in FIG. 4 comprising: a transceiver (478 in FIG. 4); at least one processor (484 in FIG. 4); and at least one memory (486 in FIG. 4) storing instructions, which executed by the at least one processor (see [0074] – [0076]) in a wireless communication system (see FIG. 1, FIG. 2), the method comprising:
determining a plurality of physical characteristics associated with the terminal for at least one of an indoor environment and an outdoor environment (see [0035], [0109], [0111], [0115], [0118], [0127] for the UE uses physical, timing, or other characteristics of the environment such as an indoor or an urban environment, wherein data for a particular environment, site or geographic location is used to train the AI/ML model );
configuring a measurement process for at least one wireless communication channel based on the plurality of determined physical characteristics (see [0109], [0111], [0112]. [0116] for a site-specific dataset which can provide data to allow the UE measures the channel characteristics between the UE and the base station, i.e., a gNB);
generating a channel state information (CSI) feedback based on the configured measurement process and a characteristic of the at least one wireless communication channel (see [0035], [0090], [0109] - [0111] for the UE using AI/ML to estimate at least one characteristic associated with wireless communications including channel state predictions (e.g., for channel state information (CSI) or channel state feedback (CSF) by using channel measurements); and
applying the generated CSI feedback with potential adjustments of one or more wireless communication parameters for utilizing an artificial intelligence (AI) module of the terminal (see [0110], [0111], [0114], [0115], [0140] for initiate a retraining (e.g., fine-tuning or online adaptation) process for the AI/ML model so that the UE can adapt in circumstances (i.e., the UE move to a new geographical location, based on a time of day or other circumstances (where the same location that the UE is in has different physical characteristics that cause the model to drop in its performance), see [0117] for CSI/CSF based use case, the site-specific data can include channel conditions as input data and correctly-predicted beams as labeled output data for training purposes).
Pezeshki does not disclose that applying the generated CSI feedback to perform model inference.
In an analogous art, Hindy discloses that applying the generated CSI feedback to perform model inference (see Hindy, [0074] for the AI/ML model retrain when the environment changes, see [0018] – [0020] for providing CSI feedback for training an AI/ML model, see [0092] for AI-based CSI framework helps infer the characteristics of the channel distribution based on the training dataset, such that CSI feedback can utilize distribution-aware data compression schemes).
Therefore, it would have been obvious to one of ordinary skill in the art before effective filing date the invention was made, to modify the invention of Pezeshki, and have applying the generated CSI feedback to perform model inference, thereby reduce the overall CSI feedback overhead such that provides effect on the AI/ML model inference accuracy, as discussed by Hindy (see Hindy, [0077]).
Regarding claims 2 and 12, Pezeshki in view of Hindy disclose determining a channel quality indicator (CQI) based on the generated CSI feedback and the configured measurement process; and applying the potential adjustments of the one or more wireless communication parameters of the terminal to enhance the performance of the terminal, based on the determined CQI and the plurality of determined physical characteristics (see Pezeshki, [0058], [0059], and see Hindy, [0055], [0067]).
Regarding claims 3 and 13, Pezeshki in view of Hindy disclose wherein the one or more wireless communication parameters comprise an optimal transmission power (see Pezeshki, [0101], [0112]), an optimal modulation and coding scheme (MCS), an optimal coding rate, scheduling of data packets, a transport block size (TBS), and a resource block (RB).
Regarding claims 4 and 14, Pezeshki in view of Hindy disclose wherein the AI module is trained to generate the CSI feedback based on at least one of historical CSI image information, predicted CSI image information, live frequency data, the plurality of determined physical characteristics (see Pezeshki, [0035], [0109], [0111], [0115], [0118]), and the configured measurement process.
Regarding claims 7 and 17, Pezeshki in view of Hindy disclose wherein the plurality of physical characteristics comprise at least one of a terminal distribution, a carrier frequency, a speed of the terminal, a location of the terminal, an orientation of the terminal, a movement of the terminal, and a channel quality indicator (CQI) (see Pezeshki, [0035], [0109], [0111], [0115], [0118] for the UE moving into a new geographic area where data associated with the geographic area would be helpful to adapt or fine-tune an ML model implemented on the UE).
Regarding claims 8 and 18, Pezeshki in view of Hindy disclose wherein the plurality of physical characteristics is determined by a sensor module of the terminal comprises at least one of an accelerometer sensor, a gyro sensor, a magnetometer sensor, a global positioning system (GPS) sensor (see Pezeshki, [0147]), a temperature and humidity sensor, and a weather monitoring sensor.
Regarding claims 9 and 19, Pezeshki in view of Hindy disclose wherein configuring the measurement process comprises: receiving a request from a network device to perform one or more measurements associated with the at least one wireless communication channel, wherein the one or more measurements comprise at least one of a radio resource management (RRM) measurements, minimization of drive tests (MDT) measurements, a speed of the terminal, a position of the terminal, a channel state information reference signal (CSI RS), a CSI measurement, and a signal-to-interference-plus-noise ratio (SINR); and transmitting a report associated with the one or more performed measurements to the network device (see Pezeshki, [0102] for the UE report the best CSI-RS measurement, see [0106] for network entity (e.g., gNB) configure the UE to perform certain measurements and report results of the measurements).
Regarding claims 10 and 20, Pezeshki in view of Hindy disclose wherein configuring the measurement process comprises: receiving radio resource control (RRC) configuration information from the network device; and configuring a CSI-reporting comprises a CSI-RS resource mapping, a CSI informational measurement resource, a CSI semi-persistent on physical uplink shared channel (PUSCH) trigger state list, a CSI aperiodic trigger state list, a CSI resource configuration, and a CSI report configuration (see Hindy, FIG. 2, FIG. 3, FIG. 4, [0057] – [0067], [0093] – [0094] for an aperiodic trigger state defining a list of CSI report settings, an information element pertaining to CSI reporting, RRC configuration for wireless resources, and the CSI training dataset report is transmitted from a network node (e.g., a network entity such as a gNB) to the UE).
Allowable Subject Matter
Claims 5 and 15 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 if the AI module is trained to generate the CSI feedback based on predicted CSI image information, which is selected in claims 4 and 14. However, claims 4 and 14 were selected the AI module is trained to generate the CSI feedback based on the plurality of determined physical characteristics, thus the claims 5 and 15 will be rejected with the same rational as in rejected claims 4 and 14 since predicted CSI image information was not selected in claims 4 and 14.
Claims 6 and 16 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
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure are:
CN 115189740 (Zhao, Gu-liang) discloses a channel state information feedback system and method, by respectively deploying the CSI-RS feedback reasoning network intelligent body at the network side and the terminal side, and based on the network side and the CSI-RS feedback reasoning network intelligent terminal side of the terminal side to realize the specific feedback inference network model for federal learning, obtaining the feedback inference network model with better performance, so as to improve the accuracy of channel state information feedback.
WO 2024/069752 (Echigo et al.) discloses a terminal having: a transmission unit that transmits a channel state information (CSI) report; and a control unit that determines, if at least one channel quality indicator (CQI) is to be included in the CSI report, at least one of the number of CQIs, the bit width of the CQI, and an index of the CQI, on the basis of a certain condition such that it is possible to appropriately execute CSI reporting pertaining to the impact of movement.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MONG-THUY THI TRAN whose telephone number is (571)270-3199. The examiner can normally be reached Monday-Friday: 9AM - 6PM (IFP).
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, ANTHONY ADDY can be reached at (571)272-7795. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MONG-THUY T TRAN/Primary Examiner, Art Unit 2645
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