DETAILED ACTION
This Office action is in response to the preliminary amendment filed 16 December 2024. Claims 2-21 are pending in this application.
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 .
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 6-9 and 16-19 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
For Claims 6 and 16, it is unclear what is meant by “unknown channel state effects in the information frame”. The claim goes on to recite “the one or more unknown channel state effects comprise at least one of randomization, spreading, permutation of time-slots, permutation of frequency slots, or permutation of modulation parameters”. Accordingly the channel state effects do not appear to be unknown. Also, it is not clear what is being corrected. If the corrections are being made to errors in the information frame caused by the channel state effects, then the claim should make this clear.
For Claims 9 and 19, the antecedent basis of “the one or more fields in the OFDM data symbols” is not clear.
For Claims 9 and 19, it is not clear whether “the machine learning channel model that includes one or more machine learning models” has antecedent basis in the claim.
Remaining claims are rejected as depending from a rejected claim.
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, 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 2-5, 9, 11-15, 19, and 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ye et al. (US 2021/0091979) in view of Pare, Jr. et al. (US 2003/0231709).
For Claims 2, 12, and 21, Ye teaches a computer-implemented method, a system comprising: one or more computers and one or more storage devices storing instructions that are operable, when executed by the one or more computers, to cause the one or more computers to perform operations (see paragraphs 78-82), and a non-transitory computer-readable medium storing software comprising instructions executable by one or more computers which, upon such execution, cause the one or more computers to perform operations (see paragraphs 78-82); the method comprising:
obtaining, at a first network node, analog communications signals from a second network node over a wireless radio frequency communication channel (see paragraphs 27, 42, 44, 55; paragraph 25: analog fronthaul);
extracting, by the first network node, one or more fields from the digital information (see paragraphs 44, 55, 60),
based at least on the one or more channel effects, managing, by the first network node, a machine learning channel model that is configured to reproduce channel conditions of the wireless radio frequency communication channel at one or more locations corresponding to the first network node (see paragraphs 30-32, 45, and 61).
Though Ye does teach digital signal processing (see paragraph 79) and A/D conversion is a well known part of the receive chain, Ye as applied above is not explicit as to, but Pare teaches converting, by the first network node, the obtained analog communications signals to digital information (see paragraphs 61, 25);
the one or more fields comprising parameter values corresponding to one or more characteristics of the wireless radio frequency communication channel (see paragraph 61: preamble);
determining, by the first network node, one or more channel effects of the wireless radio frequency communication channel using the parameter values of the one or more extracted fields (see paragraphs 61-63).
Thus it would have been obvious to one of ordinary skill in the art at the time the application was filed to use the signal structure as in Pare when training the model of Ye. One of ordinary skill would have been able to use a known signal component for a known purpose with the reasonably predictable result of appropriately training the channel model.
For Claims 3 and 13, Ye further teaches the computer-implemented method, wherein the wireless radio frequency communication channel comprises at least one of a WiFi channel, a Bluetooth channel, 4G cellular communication channel, a 5G cellular communication channel, or a 6G cellular communication channel (see paragraph 25).
For Claims 4 and 14, Ye as applied above is not explicit as to, but Pare teaches the computer-implemented method, wherein converting the obtained analog communication signals to the digital information comprises converting a radio frequency analog signal to a digitally converted signal (see paragraph 25).
Thus it would have been obvious to one of ordinary skill in the art at the time the application was filed to use the A/D conversion as in Pare when implementing the receiving as in Ye. A/D conversion is a well known part of a wireless receive chain and one of ordinary skill would have been able to implementing such with only routine skill in the art.
For Claims 5 and 15, Ye as applied above is not explicit as to, but Pare teaches the computer-implemented method, wherein the one or more fields from the digital information comprise at least one of a preamble frame, one or more reference signals, a sounding frame, a baseline encoding frame, or a learned encoding frame (see paragraph 61: preamble).
Thus it would have been obvious to one of ordinary skill in the art at the time the application was filed to use the signal structure as in Pare when training the model of Ye. One of ordinary skill would have been able to use a known signal component for a known purpose with the reasonably predictable result of appropriately training the channel model.
For Claims 9 and 19, Ye further teaches the computer-implemented method, wherein obtaining the analog communications signals from the second network node over the wireless radio frequency communication channel comprises obtaining one or more orthogonal frequency division multiplexing (OFDM) data symbols over the wireless radio frequency communication channel from the second network node (see paragraph 42);
extracting, by the first network node, the one or more fields in the OFDM data symbols (see paragraph 42); and
training, by the first network node and using the one or more extracted fields from the one or more OFDM data symbols, the machine learning channel model that includes one or more machine learning models (see paragraphs 30-32, 45, 61).
For Claim 11, Ye further teaches the computer-implemented method, wherein the machine learning channel model comprises at least one of a neural network, a convolutional neural network, a recurrent neural network, and a reservoir-computing model (see paragraph 47).
Claims not rejected over prior art
Claims 6-8 and 16-18 are not rejected over prior art. However, because of the issues raised under 35 USC 112(b), these claims are not indicated as allowable.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Sjodin et al. (US 2019/0394657) teaches training a machine learning model using channel conditions and detecting disturbances on the channel. Barman et al. (US 7224714) teaches a spread spectrum training sequence. Finkelstein (US 2019/0020524) teaches a system for training a machine learning model and using the trained and updated machine learning model to modify transmission parameters such as modulation and symbol rate.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CASSANDRA L DECKER whose telephone number is (571)270-3946. The examiner can normally be reached 7:30 am - 4:00 pm.
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, Faruk Hamza can be reached at 571-272-7969. 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.
/CASSANDRA L DECKER/Examiner, Art Unit 2466 5/4/2026
/FARUK HAMZA/Supervisory Patent Examiner, Art Unit 2466
Prosecution Insights