CTNF 18/880,319 CTNF 80685 DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Status of the claims Claims 1 – 15 were originally filed in the application. With the preliminary amendment filed on December 31, 2024, Applicant have: Amended claims 1 – 12. Cancelled claims 13 – 15. Added new claims 16 – 23. Claims 1 – 12, and 16 – 23 are pending in the application. Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 35 U.S.C. 101 reads as follows: Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title. Claim 12 rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because claim 12 is drawn to a computer program per se. Computer programs per se intrinsically require no tangible physical structure, thus do not constitute tangible physical articles or other forms of matter. Therefore, computer programs per se are not considered to be statutory subject matter. Thus a computer program itself is a non-statutory subject matter. The statutory subject matters are new and useful process (method), machine (apparatus), manufacture, composition of matter, and any useful improvement thereof. Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 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. 07-20-aia AIA 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. 07-21-aia AIA Claim (s) 1 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sellathurai et al (WO 2022/123259 A1) in view of Ye et al, "Deep Learning Based End-to-End Wireless Communication Systems Without Pilots," in IEEE Transactions on Cognitive Communications and Networking, vol. 7, no. 3, pp. 702-714, Sept. 2021, doi: 10.1109/TCCN.2021.3061464 Regarding claim 1, Sellathurai et al teach a radio transmitter device (see figure 10A and 10B), comprising: at least one processor (see figure 10, component 1110 and 1150 and “processing circuitry”); and at least one memory including computer program code (see figure 10A, component 1130 and 1140 “memory” and “instructions”); the at least one memory and the computer program code configured to, with the at least one processor, cause the radio transmitter device at least to perform: obtaining at least two parallel transmission bit streams (see figure 1, component 101 and figure 6, component 601, figure 7, component 710 “obtain a plurality of user input streams”); and modulating the obtained at least two parallel transmission bit streams for a transmission over a radio channel based on transmission bit stream-specific customized constellation shapes (see figure 1, component 110, figure 6, component 610, 614 and page 4 line 12 – page 5 line 19, and page 33, lines 27 – 33 “learned constellation diagram” and “NOMA”), the customized constellation shapes generated with an end-to-end machine learning (see figure 1 and 6 page 9, lines 17 – page 11, line 19 and “end-to-end learning”), ML, model representing the radio transmitter device (see figure 1, component 110), a radio receiver device (see figure 1, component 120, 130) and the radio channel (see figure 1, component 103, 104), and the end-to-end ML model being executable to learn a separate customized constellation shape for each of the at least two parallel transmission bit streams (see figure 1 and 6, page 5 -line 30 – page 8, line 19, 9, lines 17 – page 11, line 19 and page 32, line 18 – page 34, line 32). Sellathurai et al further disclose that the communication system does not use CSI information for encoding and decoding the data streams (see page 31, lines 19 – 21 “The encoding by the encoder deep learning circuitry 510 may be performed without knowledge or use of (fed-back) channel state information of the multi-user downlink communication network”). That is the communication does not require pilot signal for channel state information. Sellathurai et al does not disclose the communication system is pilotless MIMO. However, in analogous art, Ye et al teach a communication system that uses end-to-end machine learning to transmit the data for a pilot less MIMO communication (see section V. “the pilot-free end-to-end communication system can work effectively under the frequency-selective fading channels and flat-fading MIMO channels”). Therefore it would have been obvious to an ordinary skilled in the art at the time the invention was filed to use pilotless communication. The motivation or suggestion to do so is to increase the throughput. Regarding claim 2, which inherits the limitations of claim 1, Sellathurai et al in view of Ye et al further teach wherein the end-to-end ML model is further executable to learn at least one customized constellation shape of the customized constellation shapes via learning at least two transformations mapping from a predefined constellation shape to the respective customized constellation shape. (see Sellathurai et al, Figure 6: component 661; 610; 620 and Page 32, line 31 to Page.33, line 18 and Figure 6B: 610; 620; 630 and page.34, lines 6-12). Regarding claim 3, which inherits the limitations of claim 2, Sellathurai et al in view of Ye et al further teach wherein the end-to-end ML model is further executable to construct a final constellation shape of the respective customized constellation shape as a linear combination of the learned at least two transformations (see Sellathurai et al, Figure 6: component 661; 610 661 and Pg.33, lines 7-23 and Fig.6B: 661 and Pg.34 and lines 13-15 and Fig.7: 740 and Pg.35, lines 24-31). Regarding claim 4, which inherits the limitations of claim 1, Sellathurai et al in view of Ye et al further teach wherein the predefined constellation shape comprises a quadrature amplitude modulation, QAM, constellation shape (see Sellathurai et al, page 7, lines 1 – 5, page 10, lines 1 – 33, page 10, lines 1 – page 11, line 19, and page 34, lines 6 – 32). Regarding claim 5, which inherits the limitations of claim 1, Sellathurai et al in view of Ye et al further teach wherein the end-to-end ML model is further executable to learn at least one customized constellation shape of the customized constellation shapes via learning a single layer specific transformation mapping from a predefined constellation shape to the respective customized constellation shape as a single fully connected neural network. (see Sellathurai et al, Pge 6, line 31 – page 8 line 10, page 10, line 1 – page 11, line 19, and page 34, lines 6 – 32). Regarding claim 6, which inherits the limitations of claim 1, Sellathurai et al in view of Ye et al further teach wherein the end-to-end ML model is further executable to learn at least one customized constellation shape of the customized constellation shapes directly from a random initialization (see Sellathurai et al, page 2, lines 11 - 17). Regarding claim 7, which inherits the limitations of claim 1, Sellathurai et al in view of Ye et al further teach wherein the end-to-end ML model is further executable to refine at least one learned customized constellation shape via contextual information (see Sellathurai et al, page 12, lines 9 -31, page 13, lines 5 – 32, and page 22, lines 11 - 35). Regarding claim 8, which inherits the limitations of claim 1, Sellathurai et al in view of Ye et al further teach wherein the contextual information comprises at least one of an expected signal-to-noise ratio of a client device, a mobility level of a client device, a number of MIMO layers, a number of overlapping client devices, a model size of the radio receiver device, or one or more channel conditions (see Sellathurai et al, page 12, lines 9 -31, page 13, lines 5 – 32, and page 22, lines 11 - 35). Regarding claim 9, which inherits the limitations of claim 1, Sellathurai et al in view of Ye et al further teach wherein the at least one memory and the computer program code are further configured to, with the at least one processor, cause the radio transmitter device to perform training the end-to-end ML model by applying a loss comprising a constellation quality metric indicating maximum and minimum distances between two constellation points (see Sellathurai et al, page 7, line 11 – page 8, line 10, page 8, line 25 – page 9, line 11, page 22, lines 11 – 35, and page 24 – line 16 – page 26, line 35). Regarding claim 10, which inherits the limitations of claim 1, Sellathurai et al in view of Ye et al further teach wherein the loss further comprises a binary cross entropy. (see Sellathurai et al, page 25, lines 20 – page 26, line 35 “The reconstruction loss function may be a binary cross entropy function”). Regarding claim 11, the claimed method including the features corresponds to subject matter mentioned above in the rejection of claim 1 is applicable hereto. Regarding claim 12, the claimed computer program including the features corresponds to subject matter mentioned above in the rejection of claim 1 is applicable hereto. Regarding claim 16, which inherits the limitations of claim 11, the claimed method including the features corresponds to subject matter mentioned above in the rejection of claim 2 is applicable hereto. Regarding claim 17, which inherits the limitations of claim 16, the claimed method including the features corresponds to subject matter mentioned above in the rejection of claim 3 is applicable hereto. Regarding claim 18, which inherits the limitations of claim 16, the claimed method including the features corresponds to subject matter mentioned above in the rejection of claim 4 is applicable hereto. Regarding claim 19, which inherits the limitations of claim 11, the claimed method including the features corresponds to subject matter mentioned above in the rejection of claim 5 is applicable hereto. Regarding claim 20, which inherits the limitations of claim 11, the claimed method including the features corresponds to subject matter mentioned above in the rejection of claim 6 is applicable hereto. Regarding claim 21, which inherits the limitations of claim 11, the claimed method including the features corresponds to subject matter mentioned above in the rejection of claim 7 is applicable hereto. Regarding claim 22, which inherits the limitations of claim 21, the claimed method including the features corresponds to subject matter mentioned above in the rejection of claim 8 is applicable hereto. Regarding claim 23, which inherits the limitations of claim 11, the claimed method including the features corresponds to subject matter mentioned above in the rejection of claim 9 is applicable hereto. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to JAISON JOSEPH whose telephone number is (571)272-6041. The examiner can normally be reached M-F 8 - 4. 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, Sam K Ahn can be reached at 571 272 3044. 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. JAISON . JOSEPH Primary Examiner Art Unit 2633 /JAISON JOSEPH/ Primary Examiner, Art Unit 2633 Application/Control Number: 18/880,319 Page 2 Art Unit: 2633 Application/Control Number: 18/880,319 Page 3 Art Unit: 2633 Application/Control Number: 18/880,319 Page 4 Art Unit: 2633 Application/Control Number: 18/880,319 Page 5 Art Unit: 2633 Application/Control Number: 18/880,319 Page 6 Art Unit: 2633 Application/Control Number: 18/880,319 Page 7 Art Unit: 2633 Application/Control Number: 18/880,319 Page 8 Art Unit: 2633 Application/Control Number: 18/880,319 Page 9 Art Unit: 2633 Application/Control Number: 18/880,319 Page 10 Art Unit: 2633
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