CTNF 18/722,430 CTNF 88784 DETAILED ACTION This office action is a response to an application filed on 06/20/2024, in which claims 1-12 and 15 are pending and ready for examination. Claims 12-13 and 16 have been cancelled. Priority Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged. Claim Rejections - 35 USC § 103 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 Claim 1 is rejected under 35 U.S.C 103 (a) as being unpatentable over LIAN et al. (hereinafter, “LIAN”; CN109729596) in view of Han et al. (hereinafter, “Han”; 20140003452) and in further view of non-patent literature Deep Transfer Learning-Based Downlink Channel Prediction for FDD Massive MIMO System by Yuwen et al. (hereinafter, “Yuwen”, publication date August/24/2020).(For citation purpose, examiner has used English translation of CN110574302. The publication date of CN 110574302 is January/29/2021; therefore, it qualifies as a prior art under 35 U.S.C 103 (a)) . In reference to claim 1, LIAN teaches a method for performed by operating a terminal in a wireless communication system, the method comprising: generating at least one transport block ( page 7, step S43, paragraph 2, resource block (RBs) are equated to transport block, page 7, step S43, paragraph transmitting by the wireless device is read as generating by the terminal ); encoding the at least one transport block to generate a codeword ( page 7, step S43, paragraph 2,encoding converted bit by a terminal explicitly teaches this limitation ); converting the codeword into a bit sequence that is scrambled ( page 7, step S43, paragraph 2, converted data in bits form and scrambling encoded bits explicitly teaches this limitation ); modulating the bit sequence to generate modulated symbols ( page 7, step S43, paragraph 2, encoding the bits explicitly teaches this limitation ); LIAN does not teach explicitly about generating a signal by performing at least one of an inverse fast Fourier transform (IFFT), a cyclic prefix (CP) insertion, a digital-to-analog conversion, and a frequency uplink converter, transmitting the signal to a base station, the signal including a requesting, for reference signal configuration information related to a reference signal (RS), receiving configuration information related to the RS from the base station, learning a first parameter related to a meta-learning learning model based on the configuration information related to the RS, and wherein configuration information related to the RS includes pattern information an identifier and a weight value of at least one of a demodulation-reference signal (DM-RS), a phase-tracking reference signal (PTRS), a channel status information-reference signal (CSI-RS), a positioning reference signal (PRS), a synchronization signal block (SSB), a sounding reference signal (SRS), and a meta-learning RS. Han in view of LIAN teaches generating a signal by performing at least one of an inverse fast Fourier transform (IFFT), a cyclic prefix (CP) insertion, a digital-to-analog conversion, and a frequency uplink converter ( paragraph 166,transmitting uplink control information is read as generating a signal for performing, using a cyclic prefix (CP) for a HARQACK/NACK is read as generating a signal by performing a CP ); transmitting the signal to a base station, the signal including a requesting, for reference signal configuration information related to a reference signal (RS) ( paragraph 166, transmitting uplink control information is also read as transmitting a request signal to a base station, CSI is equated to a reference signal ); It would have been obvious within the scope of a person of ordinary skill in the art before the effective filing date of the claimed invention to modify LIAN for generating a signal by performing at least one of an inverse fast Fourier transform (IFFT), a cyclic prefix (CP) insertion, a digital-to-analog conversion, and a frequency uplink converter, and transmitting the signal to a base station, the signal including a requesting, for reference signal configuration information related to a reference signal (RS) as taught by Han because it would allow stop frequent dropping of reference signal and losing downlink throughput due to collision between a CSI and a HARQ information. LIAN and Han do not teach explicitly about receiving configuration information related to the RS from the base station, learning a first parameter related to a meta-learning learning model based on the configuration information related to the RS, receiving a RS from the base station or transmitting a RS to the base station based on the first parameters and wherein configuration information related to the RS includes pattern information an identifier and a weight value of at least one of a demodulation-reference signal (DM-RS), a phase-tracking reference signal (PTRS), a channel status information-reference signal (CSI-RS), a positioning reference signal (PRS), a synchronization signal block (SSB), a sounding reference signal (SRS), and a meta-learning RS. Yuwen in view of LIAN and Han teaches receiving configuration information related to the RS from the base station ( page 1789, section “Direct Transfer Algorithm”, direct transfer algorithm (ADAM) is equated to a configuration, CSI is equated to configuration information RS information, predicting a downlink CSI for user by the network is interpreted as receiving a configuration information related to the RS from the base station ); learning a first parameter related to a meta-learning learning model based on the configuration information related to the RS ( page 7490, section V, “META LEARNING ALGORITHM”, training support data set and training support query dataset are equated to a first parameter related to a meta-learning learning model based on the configuration information related to the RS, collecting multiple sample pairs is interpreted as learning ); and receiving a RS from the base station or transmitting a RS to the base station based on the first parameters ( page 1789, section “Direct Transfer Algorithm”, direct transfer algorithm (ADAM) is equated to a configuration, CSI is also equated to a RS information from a base station, predicting a downlink CSI for user by a network is interpreted as receiving a RS from the base station or transmitting a RS to the base station based on the first parameters ); wherein configuration information related to the RS includes pattern information ( page 1789, section “Direct Transfer Algorithm”, direct transfer algorithm (ADAM) is equated to a configuration information related to the RS includes pattern information ), an identifier and a weight value of at least one of a demodulation-reference signal (DM-RS), a phase-tracking reference signal (PTRS), a channel status information-reference signal (CSI-RS), a positioning reference signal (PRS), a synchronization signal block (SSB), a sounding reference signal (SRS), and a meta-learning RS. It would have been obvious within the scope of a person of ordinary skill in the art before the effective filing date of the claimed invention to modify LIAN and Han for receiving configuration information related to the RS from the base station, learning a first parameter related to a meta-learning learning model based on the configuration information related to the RS, receiving a RS from the base station or transmitting a RS to the base station based on the first parameters and wherein configuration information related to the RS includes pattern information an identifier and a weight value of at least one of a demodulation-reference signal (DM-RS), a phase-tracking reference signal (PTRS), a channel status information-reference signal (CSI-RS), a positioning reference signal (PRS), a synchronization signal block (SSB), a sounding reference signal (SRS), and a meta-learning RS as taught by Yuwen because it would allow Artificial Intelligence based Meta learning procedure for downlink CSI prediction for MIMO communication . 07-21 AIA Claim s 7 and 15 are rejected under 35 U.S.C 103 (a) as being unpatentable over Han et al. (hereinafter, “Han”; 20140003452) and in further view of non-patent literature Deep Transfer Learning-Based Downlink Channel Prediction for FDD Massive MIMO System by Yuwen et al. (hereinafter, “Yuwen”, publication date August/24/2020).(For citation purpose, examiner has used English translation of CN110574302. The publication date of CN 110574302 is January/29/2021; therefore, it qualifies as a prior art under 35 U.S.C 103 (a)) . In response to claim 7, Han teaches a terminal in a wireless communication system, the terminal comprising: a transceiver; and a processor coupled with the transceiver ( paragraph 123 teaches this limitation ), configured to: transmit a request for reference signal (RS) group-related configuration information to a base station ( paragraph 166, transmit a request for reference signal (RS) group-related configuration information to a base station as transmitting, CSI is equated to a reference signal ), Han does not teach explicitly about receive configuration information related to the RS from the base station, learn a first parameter related to a meta-learning learning model based on the configuration information related to the RS, receive a RS from the base station or transmit a RS to the base station based on the first parameter, wherein the configuration information related to the RS includes pattern information an identifier and a weight value of at least one of a demodulation-reference signal (DM-RS), a phase-tracking reference signal (PTRS), a channel status information-reference signal (CSI-RS), a positioning reference signal (PRS), a synchronization signal block (SSB), a sounding reference signal (SRS), and a meta-learning RS. Yuwen in view of Han teaches receive configuration information related to the RS from the base station ( page 1789, section “Direct Transfer Algorithm”, direct transfer algorithm (ADAM) is equated to a configuration, CSI is also equated to a RS information from a base station, predicting a downlink CSI for user by a network is interpreted as receiving configuration information related to the RS from the base station ), learn a first parameter related to a meta-learning learning model based on the configuration information related to the RS ( page 7490, section V, “META LEARNING ALGORITHM”, training support data set and training support query dataset are equated to a first parameter related to a meta-learning learning model based on the configuration information related to the RS, collecting multiple sample pairs is interpreted as learning ), and receive a RS from the base station or transmit a RS to the base station based on the first parameter ( page 1789, section “Direct Transfer Algorithm”, direct transfer algorithm (ADAM) is equated to a configuration, CSI is also equated to a RS information from a base station, predicting a downlink CSI for user by a network is interpreted as receiving a RS from the base station or transmitting a RS to the base station based on the first parameters ), and wherein the configuration information related to the RS includes pattern information ( page 1789, section “Direct Transfer Algorithm”, direct transfer algorithm (ADAM) is equated to a configuration information related to the RS includes pattern information ), an identifier and a weight value of at least one of a demodulation-reference signal (DM-RS), a phase-tracking reference signal (PTRS), a channel status information-reference signal (CSI-RS), a positioning reference signal (PRS), a synchronization signal block (SSB), a sounding reference signal (SRS), and a meta-learning RS. It would have been obvious within the scope of a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Han for receiving configuration information related to the RS from the base station, learning a first parameter related to a meta-learning learning model based on the configuration information related to the RS, receiving a RS from the base station or transmitting a RS to the base station based on the first parameters and wherein configuration information related to the RS includes pattern information an identifier and a weight value of at least one of a demodulation-reference signal (DM-RS), a phase-tracking reference signal (PTRS), a channel status information-reference signal (CSI-RS), a positioning reference signal (PRS), a synchronization signal block (SSB), a sounding reference signal (SRS), and a meta-learning RS as taught by Yuwen because it would allow Artificial Intelligence based Meta learning procedure for downlink CSI prediction for MIMO communication. In response to claim 15, Han teaches a method performed by a base station in a wireless communication system, the method comprising ( paragraph 130, a network interface is equated to a base station ): receiving a request for reference signal (RS) group-related configuration information from a terminal ( paragraph 166, transmit a request for reference signal (RS) group-related configuration information to a base station is read as receiving, CSI is equated to a reference signal ); Han does not teach explicitly about transmitting the configuration information related to the RS to the terminal; and receiving a RS from the terminal based on a first parameter related to a meta-learning learning model or transmitting a RS to the terminal, wherein configuration information related to the RS, the first parameter related to the meta-learning learning model is learned, and wherein the configuration information related to the RS includes pattern information, an identifier and a weight value of at least one of a demodulation-reference signal (DM-RS), a phase-tracking reference signal (PTRS), a channel status information-reference signal (CSI-RS), a positioning reference signal (PRS), a synchronization signal block (SSB), a sounding reference signal (SRS), and a meta-learning RS. Yuwen in view of Han teaches transmitting the configuration information related to the RS to the terminal ( page 1789, section “Direct Transfer Algorithm”, direct transfer algorithm (ADAM) is equated to a configuration, CSI is also equated to a RS, predicting a downlink CSI for user by a network is interpreted as transmitting the configuration information related to the RS to the terminal ); and receiving a RS from the terminal based on a first parameter related to a meta-learning learning model or transmitting a RS to the terminal ( page 7490, section V, “META LEARNING ALGORITHM”, training support data set and training support query dataset are equated to a first parameter related to a meta-learning learning model based on the configuration information related to the RS, collecting multiple sample pairs is interpreted as using a meta learning procedure, page 1789, section “Direct Transfer Algorithm”, direct transfer algorithm (ADAM) is equated to a configuration, CSI is also equated to a RS information from a base station, predicting a downlink CSI for user by a network is interpreted as transmitting a RS to the terminal), wherein configuration information related to the RS, the first parameter related to the meta-learning learning model is learned ( page 1789, section “Direct Transfer Algorithm”, direct transfer algorithm (ADAM) is equated to a configuration information related to the RS, page 7490, section V, “META LEARNING ALGORITHM”, training support data set and training support query dataset are equated to a first parameter related to a meta-learning learning model based on the configuration information related to the RS, collecting multiple sample pairs is interpreted as learning ), and wherein the configuration information related to the RS includes pattern information ( page 1789, section “Direct Transfer Algorithm”, direct transfer algorithm (ADAM) is equated to a configuration information related to the RS includes pattern information ), an identifier and a weight value of at least one of a demodulation-reference signal (DM-RS), a phase-tracking reference signal (PTRS), a channel status information-reference signal (CSI-RS), a positioning reference signal (PRS), a synchronization signal block (SSB), a sounding reference signal (SRS), and a meta-learning RS. It would have been obvious within the scope of a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Han for receiving configuration information related to the RS from the base station, learning a first parameter related to a meta-learning learning model based on the configuration information related to the RS, receiving a RS from the base station or transmitting a RS to the base station based on the first parameters and wherein configuration information related to the RS includes pattern information an identifier and a weight value of at least one of a demodulation-reference signal (DM-RS), a phase-tracking reference signal (PTRS), a channel status information-reference signal (CSI-RS), a positioning reference signal (PRS), a synchronization signal block (SSB), a sounding reference signal (SRS), and a meta-learning RS as taught by Yuwen because it would allow Artificial Intelligence based Meta learning procedure for downlink CSI prediction for MIMO communication . Allowable Subject Matter 12-151-08 AIA 07-43 12-51-08 Claim s 2-6 and 8-12 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. As for claims 2 and 8, these claims are objected, because there is no prior art in the record that teaches claimed limitations “learning a second parameter related to a meta-learning learning model for the RS received from the base station or the RS transmitted to the base station based on the first parameter.” The closest prior art in the record non-patent literature Deep Transfer Learning-Based Downlink Channel Prediction for FDD Massive MIMO System by Yuwen et al. teaches in pages 7490-1791, section V, “META LEARNING ALGORITHM” about collecting and adopting parameters for downlink CSI prediction for a MIMO communication. Claims 3-6 and 9-12 are objected because these claims depend on claims 2 and 8. Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Wo2012097433……….page 21, line 26 to page 22,line 16. Any inquiry concerning this communication or earlier communications from the examiner should be directed to ABUSAYEED HAQUE whose telephone number is (571)270-7252. The examiner can normally be reached 9 am -7:30 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. 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If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /ABUSAYEED M HAQUE/Examiner, Art Unit 2466 /CHRISTOPHER M CRUTCHFIELD/Primary Examiner, Art Unit 2466 Application/Control Number: 18/722,430 Page 2 Art Unit: 2466 Application/Control Number: 18/722,430 Page 3 Art Unit: 2466 Application/Control Number: 18/722,430 Page 4 Art Unit: 2466 Application/Control Number: 18/722,430 Page 5 Art Unit: 2466 Application/Control Number: 18/722,430 Page 6 Art Unit: 2466 Application/Control Number: 18/722,430 Page 7 Art Unit: 2466 Application/Control Number: 18/722,430 Page 8 Art Unit: 2466 Application/Control Number: 18/722,430 Page 9 Art Unit: 2466 Application/Control Number: 18/722,430 Page 10 Art Unit: 2466 Application/Control Number: 18/722,430 Page 11 Art Unit: 2466 Application/Control Number: 18/722,430 Page 12 Art Unit: 2466 Application/Control Number: 18/722,430 Page 13 Art Unit: 2466 Application/Control Number: 18/722,430 Page 14 Art Unit: 2466
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