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
Priority
1. Receipt is acknowledged of papers submitted under 35 U.S.C. 119(a)-(d), which papers have been placed of record in the file.
Information Disclosure Statement
2. The information disclosure statement (IDS) submitted on 07/29/2024 has been considered by Examiner and made of record in the application file.
Claim Rejections - 35 USC §103
3. 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.
4. Claims 1-5 and 11-12 are rejected under 35 U.S.C. 103 as being unpatentable over Kela et al. (U.S PAT. 11,129,192, hereinafter “Kela”) in view of Aldossari et al. (U.S PAT. 11,128,391, hereinafter “Aldossari”).
Consider claim 1, Kela teaches a method for estimating signal to interference and noise ratio (SINR) in a wireless network environment, the method comprising: collecting (report) a plurality of wireless connection datasets about the wireless network environment (col. 13, line 39 through col. 14, line 3), wherein each of the plurality of wireless connection datasets includes at least one measured environment parameter and a measured SINR (col. 9, lines 3-16); and training an SINR optimization machine learning algorithm to determine an optimized estimated SINR based at least in part on the estimated SINR (fig. 6, col. 10, lines 46-64).
Kela does not explicitly show that generating a regression model based at least in part on the plurality of wireless connection datasets, wherein the regression model is configured to determine an estimated SINR based on the at least one measured environment parameter.
In the same field of endeavor, Aldossari teaches generating a regression model based at least in part on the plurality of wireless connection datasets (col. 6, lines 23-50 i.e., Regression is one of the main methods used in machine learning where regression models learn the mechanism based on a dataset from prior measurements or simulations), wherein the regression model is configured to determine an estimated SINR based on the at least one measured environment parameter (col. 7, lines 21-35).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to use, generating a regression model based at least in part on the plurality of wireless connection datasets, wherein the regression model is configured to determine an estimated SINR based on the at least one measured environment parameter, as taught by Aldossari, in order for wireless channel modeling utilizing machine learning (ML) to revolutionize system design for 5G and beyond.
Consider claim 2, Kela further teaches wherein collecting the plurality of wireless connection datasets further comprises: receiving the plurality of wireless connection datasets from one or more wireless devices, wherein the at least one measured environment parameter of each of the plurality of wireless connection datasets includes at least one of: a channel quality indication (CQI) value, a weather condition value, and an environment complexity value (col. 13, lines 38-58).
Consider claim 3, Aldossari further teaches wherein the environment complexity value quantifies at least one of: a number of obstacles and an average height of obstacles which are obstructive to wireless transmissions (col. 5, lines 17-32).
Consider claim 4, Kela further teaches wherein the environment complexity value is determined by each of the one or more wireless devices using an environment complexity identification machine learning model (col. 12, lie 64 through col. 13, line 6).
Consider claim 5, Kela further teaches wherein the one or more wireless devices includes at least: a vehicle equipped with a vehicle communication system (col. 14, lines 12-31).
Consider claim 11, the subject-matter of independent claim 11 relates to a system for estimating signal to interference and noise ratio (SINR) in a wireless network environment with features fully corresponding to the characteristics of claim 1. Therefore, the same argumentation presented in relation to claim 1 is, mutatis mutandis, of application to claim 11.
Consider claim 12, the previous rejections of claim 2 apply mutatis mutandis to corresponding claim 12.
Allowable Subject Matter
5. Claims 6-10 and 13-17 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.
Consider claim 6, the prior arts made of record, alone or in combination, fail to clearly teach or fairly suggest wherein training the SINR optimization machine learning algorithm further comprises: determining a plurality of tested SINR deviations between the estimated SINR determined by the regression model and the measured SINR in each of the plurality of wireless connection datasets; and training the SINR optimization machine learning algorithm based at least in part on the plurality of tested SINR deviations, wherein the SINR optimization machine learning algorithm is trained to receive the at least one measured environment parameter and the estimated SINR as an input and provide the optimized estimated SINR as an output, in combination with other limitations, as specified in the claims 1-2, and further limitations of their respective dependent claims 7-10.
Consider claim 13, the prior arts made of record, alone or in combination, fail to clearly teach or fairly suggest wherein to train the SINR optimization machine learning algorithm, the one or more central computers are further programmed to: determine a plurality of tested SINR deviations between the estimated SINR determined by the regression model and the measured SINR in each of the plurality of wireless connection datasets; and train the SINR optimization machine learning algorithm based at least in part on the plurality of tested SINR deviations, wherein the SINR optimization machine learning algorithm is trained to receive the at least one measured environment parameter and the estimated SINR as an input and provide the optimized estimated SINR as an output, in combination with other limitations, as specified in the claims 11-12, and further limitations of their respective dependent claims 14-17.
Reasons for Allowance
6. Claims 18-20 are allowed over the prior art record.
7. The following is an examiner’s statement of reasons for allowance:
Kela teaches a client device is configured to receive an indication of a first network SINR for a first data transmission from a network node, and to derive the first network SINR based on the indication of a first network SINR. The client device is further configured to estimate a SINR adjustment for a second data transmission from the network node, and to compute a SINR difference value between the first network SINR and the SINR adjustment. Furthermore, the client device is configured to transmit at least one first control message having an indication of the SINR adjustment to the network node if the SINR difference value is larger than a threshold value.
Aldossari teaches a system and method for applying supervised learning to model a second wireless channel environment based upon data collected for a first wireless channel environment. In various embodiments, regression techniques are used to overcome known channel modeling issues. Using the data of one particular communication environment, it is possible to predict a path loss model of a different communication environment. As such, the required number of measurements and the complexity of the model prediction is greatly reduced.
Consider claims 18-20, the prior arts made of record, alone or in combination, fail to clearly teach or fairly suggest determining a plurality of tested SINR deviations between the estimated SINR determined by the regression model and the measured SINR in each of the plurality of wireless connection datasets; and training an SINR optimization machine learning algorithm based at least in part on the plurality of tested SINR deviations, wherein the SINR optimization machine learning algorithm is trained to receive the at least one measured environment parameter and the estimated SINR as an input and provide an optimized estimated SINR as an output, in combination with other limitations, as specified in the independent claim 18, and further limitations of their respective dependent claims 19-20.
Conclusion
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Any inquiry concerning this communication or earlier communications from the examiner should be directed to Tuan H. Nguyen whose telephone number is (571) 272-8329. The examiner can normally be reached on 8:00Am - 5:00Pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Pan Yuwen can be reached on (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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/TUAN H NGUYEN/Primary Examiner, Art Unit 2649