DETAILED ACTION
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 .
This communication is response to claims filed on 02/22/24.
Claims 1-20 are presented for examination.
Information Disclosure Statement’s
4. The information disclosure statement(s) submitted on 02/22/24, 02/04/25 & 10/21/25 have being considered by the examiner and made of record in the application file.
Priority
5. Acknowledgment is made of applicant's claim for foreign priority under 35 U.S.C. 119(a)-(d). Receipt is acknowledged of papers submitted under 35 U.S.C. 119(a)-(d), which papers have been placed of record in the file.
Drawing
6. The drawings filed on 02/22/24 are accepted by the examiner.
Claims Objections
7. Claims 11-15 are objected to because of minor informalities:
8. Claim 11, in part, recites, “…memory…in line 2. Since it is recited for the first time in the claim, for clarity it is suggested to change “memory” to “a memory”.
9. Claims 12-15 are also objected since they are depend upon objected independent claim 11 set forth above.
Claim Rejections - 35 USC § 103
10. 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 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.
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 of this title, 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
11. Claims 1, 3, 11-12 & 16-17 are rejected under 35 U.S.C. 103 as being unpatentable over Moon et al. (hereinafter referred as Moon) Korean Patent Application No. KR 102154481 B1 (as disclosed in the IDS), in view of Huangfu et al. (hereinafter referred as Huangfu) US Patent Application Publication No. 2021/0194733 A1.
Regarding claim 1: Moon discloses a channel estimation method, the channel estimation method (See Page 4; a beamforming apparatus is a channel estimator for estimating a millimeter wave channel) comprising:
inputting a received pilot signal of a time slot to a long short-term memory network (See Pages 4-5 & Claim 1; using an uplink training pilot sequence commonly received by a plurality of base stations to determine a channel vector of a current time slot using the long short term memory (LSTM));
extracting a time-varying channel feature embedding vector by estimating a change state of a channel by using the received pilot signal of the time slot as an input in the long short-term memory network (See Page 7 & Claim 8; tracking an estimated channel vector of a subsequent time slot by using an LSTM technique for an estimated channel vector of the current time slot derived through a deep learning algorithm); and
estimating a channel for the received pilot signal of the time slot, using the parameter of the channel model (See Page 4; estimating a millimeter wave channel for each base station by learning a mapping function between a received signal and a channel based on the constructed broadband geometric channel model).
Moon does not explicitly teach estimating a parameter of a channel model by using the time-varying channel feature embedding vector as an input in a fully connected network.
However, Huangfu from the same field of endeavor discloses estimating a parameter of a channel model by using the time-varying channel feature embedding vector as an input in a fully connected network (See Para. 0047 & 0065; Determine a prediction value of the channel coefficient in a second time period based on the first channel coefficient sequence and a preset vocabulary of channel changes. The channel prediction model is determined with reference to the foregoing channel change difference matrix and the loss function).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to include estimating a parameter of a channel model by using the time-varying channel feature embedding vector as an input in a fully connected network as taught by Huangfu in the system of Moon to provide a channel prediction to improve channel prediction accuracy (See Para. 0004; lines 1-2).
Regarding claim 3: The combination of Moon and Huangfu disclose a channel estimation method.
Furthermore, Moon disclose a channel estimation method, wherein the received pilot signal of the time slot is converted into a real number and input to the long short-term memory network (See Page 8 & Claim 2; the uplink training pilot sequence of an omnidirectional antenna patten is received to a terminal UE in the LSTM network).
Regarding claim 11: Moon discloses a channel estimation device (See Page 4; a beamforming apparatus is a channel estimator for estimating a millimeter wave channel), the channel estimation device comprising:
when a received pilot signal of a time slot is input (See Pages 4-5 & Claim 1; using an uplink training pilot sequence commonly received by a plurality of base stations to determine a channel vector of a current time slot using the long short term memory (LSTM)),
extract, by a long short-term memory network, a time-varying channel feature embedding vector by estimating a change state of a channel by using the received pilot signal of the time slot as an input (See Page 7 & Claim 8; tracking an estimated channel vector of a subsequent time slot by using an LSTM technique for an estimated channel vector of the current time slot derived through a deep learning algorithm), and
estimate, by a channel reproduction unit, a channel for the received pilot signal of the time slot, using the parameter of the channel model (See Page 4; estimating a millimeter wave channel for each base station by learning a mapping function between a received signal and a channel based on the constructed broadband geometric channel model).
Moon does not explicitly disclose memory storing one or more computer programs; and
one or more processors and estimate, by a fully connected network, a parameter of a channel model by using the time-varying channel feature embedding vector as an input.
However, Huangfu from the same field of endeavor discloses a channel estimation device (See FIG. 8 & Para. 0090; a terminal (i.e., a channel prediction device) comprising: memory (See FIG. 8 & Para. 0090; a channel prediction device includes at least one memory) storing one or more computer programs;
one or more processors (See FIG. 8 & Para. 0090; a channel prediction device includes processor) communicatively coupled to the memory, wherein the one or more computer programs include computer-executable instructions that, when executed by the one or more processors, cause the channel estimation device to:
estimate, by a fully connected network, a parameter of a channel model by using the time-varying channel feature embedding vector as an input (See Para. 0047 & 0065; Determine a prediction value of the channel coefficient in a second time period based on the first channel coefficient sequence and a preset vocabulary of channel changes. The channel prediction model is determined with reference to the foregoing channel change difference matrix and the loss function).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to include memory storing one or more computer programs; and one or more processors and estimate, by a fully connected network, a parameter of a channel model by using the time-varying channel feature embedding vector as an input as taught by Huangfu in the system of Moon to provide a channel prediction to improve channel prediction accuracy (See Para. 0004; lines 1-2).
Regarding claim 12: The combination of Moon and Huangfu disclose a channel estimation device.
Furthermore, Moon disclose a channel estimation device, wherein the received pilot signal of the time slot is converted into a real number and input to the long short-term memory network (See Page 8 & Claim 2; the uplink training pilot sequence of an omnidirectional antenna patten is received to a terminal UE in the LSTM network).
Regarding claim 16: Moon discloses one or more non-transitory computer-readable storage media storing one or more computer programs including computer-executable instructions that, when executed by one or more processors of a channel estimation device, cause the channel estimation device to perform operations, the operations comprising:
inputting a received pilot signal of a time slot to a long short-term memory network (See Pages 4-5 & Claim 1; using an uplink training pilot sequence commonly received by a plurality of base stations to determine a channel vector of a current time slot using the long short term memory (LSTM));
extracting a time-varying channel feature embedding vector by estimating a change state of a channel by using the received pilot signal of the time slot as an input in the long short-term memory network (See Page 7 & Claim 8; tracking an estimated channel vector of a subsequent time slot by using an LSTM technique for an estimated channel vector of the current time slot derived through a deep learning algorithm); and
estimating a channel for the received pilot signal of the time slot, using the parameter of the channel model (See Page 4; estimating a millimeter wave channel for each base station by learning a mapping function between a received signal and a channel based on the constructed broadband geometric channel model).
Moon does not explicitly disclose one or more non-transitory computer-readable storage media storing one or more computer programs including computer-executable instructions that, when executed by one or more processors of a channel estimation device, cause the channel estimation device to perform operations:
estimating a parameter of a channel model by using the time-varying channel feature embedding vector as an input in a fully connected network.
However, Huangfu from the same field of endeavor discloses one or more non-transitory computer-readable storage media (See FIG. 8 & Para. 0090; a channel prediction device includes at least one memory) storing one or more computer programs including computer-executable instructions that, when executed by one or more processors (See FIG. 8 & Para. 0090; a channel prediction device includes processor) of a channel estimation device (See FIG. 8 & Para. 0090; a terminal (i.e., a channel prediction device), cause the channel estimation device to perform operations:
estimating a parameter of a channel model by using the time-varying channel feature embedding vector as an input in a fully connected network (See Para. 0047 & 0065; Determine a prediction value of the channel coefficient in a second time period based on the first channel coefficient sequence and a preset vocabulary of channel changes. The channel prediction model is determined with reference to the foregoing channel change difference matrix and the loss function).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to include one or more non-transitory computer-readable storage media storing one or more computer programs including computer-executable instructions that, when executed by one or more processors of a channel estimation device, cause the channel estimation device to perform operations: estimating a parameter of a channel model by using the time-varying channel feature embedding vector as an input in a fully connected network as taught by Huangfu in the system of Moon to provide a channel prediction to improve channel prediction accuracy (See Para. 0004; lines 1-2).
Regarding claim 17: The combination of Moon and Huangfu disclose a one or more non-transitory computer-readable storage media.
Furthermore, Moon disclose a one or more non-transitory computer-readable storage media, wherein the received pilot signal of the time slot is converted into a real number and input to the long short-term memory network (See Page 8 & Claim 2; the uplink training pilot sequence of an omnidirectional antenna patten is received to a terminal UE in the LSTM network).
12. Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over Moon (as disclosed in the IDS), in view of Huangfu, further in view of Kim et al. (hereinafter referred as Kim) NPL Document, “Deep Neural network-Based Joint Active User Detection and Channel Estimation for mMTC” July 27, 2020 (as disclosed in the IDS).
Regarding claim 2: The combination of Moon and Huangfu disclose a channel estimation method.
Furthermore, Moon disclose a channel estimation method, wherein the parameter of the channel model comprises, an arrival angle, a path delay (See; Page 3; a channel vector of each cluster on the basis of a time delay, an angle of arrive and pulse shaping function), but fails to explicitly discloses a departure angle and a path gain.
However, Kim from the same field of endeavor discloses a departure angle and a path gain (See Page 5; angle of departure and path gain).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to include a departure angle and a path gain as taught by Kim in the combined system of Huangfu and Moon, would have yield predictable results of interoperability and compatibility between the telecommunication equipment vendors and service providers and resulted in the improve system (KSR Int’l Co. v. Teleflex Inc., 127 S.Ct. 1727, 1742, 82 USPQ2d 1385, 1396 (2007)).
Allowable Subject Matter
13. Claims 4-10, 13-15 & 18-20 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.
Conclusion
14. The prior art of record and not relied upon is considered pertinent to applicant’s disclosure.
A. Saber et al. 2023/0412230 A1 (Title: System and method and apparatus for AI and ML based reporting…) (See Abstract, Para. 0012 & 0037-0038).
B. Jiang et al. 2024/0275642 A1 (Title: System and method for AI-assisted wireless channel) (See abstract, Para. 0006 & 00813-0016).
C. Choi et al. 2024/0080227 A1 (Title: Device for estimating channel in wireless communication system) (See FIG. 1, Para. 0046, 0050 & 0160).
15. Any inquiry concerning this communication or earlier communications from the examiner should be directed to MEWALE A AMBAYE whose telephone number is (571)270-1076. The examiner can normally be reached on M.F 6a.m.-2p.m..
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ian Moore can be reached on (571)272-3085. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MEWALE A AMBAYE/Primary Examiner, Art Unit 2469