Response to Amendment
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 .
The response/remark dated 2/2/26 has ben received and made of record.
Claims 2-10 and 12-19 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.
Applicants’ argument alleging the cited art fails to teach claims 1,11 and 20 element have been fully considered but are persuasive for the following reason.
For example, applicant alleges O`Shea fails to disclose claims 1,11 and 20 elements such as:
acquiring a to-be-processed signal; taking the to-be-processed signal as an input and a label of signal processing model, performing an online training fine-tuning process for the signal processing model, and obtaining an optimized signal processing model; and inputting the to-be-processed signal into the optimized signal processing model, and obtaining a target signal output by the optimized signal processing model.
In response examiner respectfully disagrees and asserts O`Shea discloses mechanism for processing communications signals using a machine-learning network including acquiring a to-be-processed signal [see par..0047, 0091, where the method 300 includes generating data signal (302), such that a pilot and data information is already generated and a resulting signal is received), taking the to-be-processed signal as an input (110) and a label of a signal processing model [see par..0047, 0093: which shows input data 110 is an OFDM or CP-OFDM signal (e.g., in the 3GPP 5G-NR uplink (UL) or downlink (DL) PHY), as shown graphically as an input plot 111 that is a time-frequency spectrum grid of pilot and data subcarriers and time slots within an OFDM signal block. performing an online training fine-tuning process for the signal processing model ) and obtaining an optimized signal processing model [see par. 0047, 0096, updating the machine learning network based on the error term]; and inputting the to-be-processed signal into the optimized signal processing model, and obtaining a target signal output by the optimized signal processing model [see par. 0046, 0094, obtaining a prediction related to the input modified information from the machine learning network). The A propagated signal is an artificially generated signal, e.g., a machine-generated electrical, optical, or electromagnetic signal that is generated to encode information for transmission to suitable receiver apparatus(optimized for output)(see par. 0115)
Also, ss illustrated fig. 1, described here [0046] FIG. 1 is a diagram showing an example of a system 100 for processing digital communications using a machine-learning network. The system 100 includes input data 110 fed into a machine-learning network 120 that produces output data 130. The machine-learning network 120 can perform tasks such as channel estimation, interpolation, and equalization, replacing separate components for performing these tasks that are used in deployed present day systems, for example, components for channel estimation 121, interpolation 122 and equalization 123 that would be used in a present day processing stage group 124. As discussed above, other tasks performed in transmitting or processing a signal can also be a part of the machine-learning network 120. The example system 100, and the tasks in which the machine-learning network 120 performs, is not meant to limit the scope of the present disclosure. Implementations of systems with additional tasks replaced by a machine-learning network are discussed later in this disclosure, for example in reference to FIG. 5 and corresponding description.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
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.
Claims 1, 11 and 20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by O`Shea et al. U.S. Patent Application Publication No. 2020/0343985[hereinafter O`Shea].
As per claim 1, 11 and 20 O’Shea discloses a signal processing method, performed by a communication device [see par. 0090: 'fig. 3 is a flow diagram illustrating an example of a method 300 for training a machine-learning network for processing digital communications’, Fig. 3], characterized by comprising:
acquiring a to-be-processed signal [see oar.0091, where rh method 300 includes generating one or more of pilot and data information for a data signal (302), such that a pilot and data information is already generated and a resulting signal is received);
taking the to-be-processed signal as an input and a label of a signal processing model [see par..0093: 'The method 300 includes inputting modified information into a machine learning network (306)', par. 0047, 0095, 0115 ‘comparing the prediction obtained from the machine learning network to a set of ground truths’,
performing an online training fine-tuning process for the signal processing model [see par. 0110: 'Updates can be obtained using online learning to update the network weights over one or more communications channels'], and
obtaining an optimized signal processing model [see par. 0047, 0096: 'The method 300 includes updating the machine learning network based on the error term]; and
inputting the to-be-processed signal into the optimized signal processing model, and obtaining a target signal output by the optimized signal processing model [see par. 0094, 0115) 'The method 300 includes obtaining a prediction related to the input modified information from the machine learning network’.
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ABDULLAHI ELMI SALAD whose telephone number is (571)272-4009. The examiner can normally be reached 9:30AM-6: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.
/ABDULLAHI E SALAD/Primary Examiner, Art Unit 2466
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