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 .
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 .
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.
Claim(s) 1, 5-7, 9, 13-15, 17 and 20-23 is/are rejected under 35 U.S.C. 103 as being unpatentable over Tarver (U.S. PG-PUB NO. 2023/0046481) in view of Bellamkonda (U.S. PG-PUB NO. 2022/0210789).
-Regarding claim 1, Tarver discloses an apparatus comprising: a wireless receiver configured to receive a respective plurality of receive signals from a respective receiving antenna of a plurality of receiving antennas (RX subarrays 904, FIG. 9A, 9B, paragraph 90); an interference mitigation circuit coupled to the respective receiving antenna (subtracts the self-interference estimate from each receiver to produce a residual signal, paragraph 106), the interference mitigation circuit configured to receive an interference mitigation mode signal (processor 960 learns the GMP coefficients via the following optimization for each transmit signal, paragraph 111) wherein, in response to the interference mitigation mode signal, the interference mitigation circuit is configured to mitigate the interference while receiving the plurality of receive signals (the processor 960 learns FIR filters of length K, hi,s;j,r[k], that minimize the squared error between all N*S PA estimated output signals and the jth receive signal, paragraph 113).
Tarver is silent to teaching that the interference mitigation circuit including a neural network and wherein the interference mitigation mode signal indicating two or more interference types of a plurality of interference types and to mitigate the interference of the plurality of interference types; and wherein the interference mitigation circuit is configured to mitigate the two or more interference types based on adjusting weights applied by the neural network for adjusted signals. However, the claimed limitation is well known in the art as evidenced by Bellamkonda.
In the same field of endeavor, Bellamkonda teaches the interference mitigation circuit including a neural network (paragraph 19) and wherein the interference mitigation mode signal indicating two or more interference types of a plurality of interference types and to mitigate the interference of the plurality of interference types (interference models 203, FIG. 3, paragraph 51), and wherein the interference mitigation circuit is configured to mitigate the two or more interference types based on adjusting weights applied by the neural network for adjusted signals (paragraphs 19, 85, 92).
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to combine the teaching of Tarver with the teaching of Bellamkonda in order to reducing or eliminating the need for manual intervention in order to determine or implement such actions.
-Regarding claim 5, the combination further discloses the interference mitigation mode signal indicates a full mode of operation, wherein in the full mode of operation, the interference mitigation circuit is configured to adjust the weights to a common set of values for all of the two or more interference types (Bellamkonda, set of sector models 201, such as example sector models 201-1, 201-2, and 201-M, paragraph 33).
-Regarding claim 6, the combination further disclose the interference mitigation mode signal indicates an individual mode of operation, wherein in the individual mode of operation, the interference mitigation circuit is configured to adjust the weights to a different set of values for each of the two or more interference types (Bellamkonda, interference models 203-1, 203-2, and 203-L, paragraph 33).
-Regarding claim 7, the combination further discloses he interference mitigation mode signal indicates a partial mode of operation, wherein in the partial mode of operation, the interference mitigation circuit is configured to adjust the weights to a first set of values for at least two of the two or more interference types, and adjust the weights to a second set of values for others of the two or more interference types (Bellamkonda, a set of actions/parameters 205, such as example actions/parameters 205-1, 205-2, and 205-N, paragraph 33).
-Regarding claim 9, Tarver discloses a non-transitory computer readable medium comprising instructions (paragraph 11) that, when executed, cause a wireless communication device to: receive a respective receive signal from a respective receiving antenna of a plurality of receiving antennas (RX subarrays 904, FIG. 9A, 9B, paragraph 90); and in response to the interference mitigation mode signal, mitigate interference while receiving the receive signal (the processor 960 learns FIR filters of length K, hi,s;j,r[k], that minimize the squared error between all N*S PA estimated output signals and the jth receive signal, paragraph 113).
Tarver is silent to teaching that receive an interference mitigation mode signal indicating two or more interference types of a plurality of interference types to mitigate, wherein the two or more interference types of the plurality interference types are mitigated by adjusting weights by the neural network for adjusted signals. However, the claimed limitation is well known in the art as evidenced by Bellamkonda.
In the same field of endeavor, Bellamkonda teaches receive an interference mitigation mode signal indicating two or more interference types of a plurality of interference types to mitigate (interference models 203, FIG. 3, paragraph 51), wherein the two or more interference types of the plurality interference types are mitigated by adjusting weights by the neural network for adjusted signals (paragraphs 19, 85, 92).
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to combine the teaching of Tarver with the teaching of Bellamkonda in order to reducing or eliminating the need for manual intervention in order to determine or implement such actions.
-Regarding claim 13, the combination further discloses cause the wireless communication device to, in response to the interference mitigation mode signal indicating a full mode of operation, adjusting the weights to a common set of values for all of the two or more interference types (Bellamkonda, set of sector models 201, such as example sector models 201-1, 201-2, and 201-M, paragraph 33).
-Regarding claim 14, the combination further discloses the executable instructions are further configured to cause the wireless communication device to, in response to the interference mitigation mode signal indicating an individual mode of operation, adjusting the weights to different sets of values for each of the two or more interference types (Bellamkonda, interference models 203-1, 203-2, and 203-L, paragraph 33).
-Regarding claim 15, the combination further discloses the executable instructions are further configured to cause the wireless communication device to, in response to the interference mitigation mode signal indicates a partial mode of operation, adjusting the weights to a first set of values for at least two of the two or more interference types, and adjusting the weights to a second set of values for others of the two or more interference types (Bellamkonda, a set of actions/parameters 205, such as example actions/parameters 205-1, 205-2, and 205-N, paragraph 33).
-Regarding claim 17, Tarver discloses a method, comprising: receiving a respective plurality of receive signals from a respective receiving antenna of a plurality of receiving antennas (RX subarrays 904, FIG. 9A, 9B, paragraph 90); and in response to the interference mitigation mode signal, mitigating interference of the plurality of interference while receiving the plurality of receive signals (the processor 960 learns FIR filters of length K, hi,s;j,r[k], that minimize the squared error between all N*S PA estimated output signals and the jth receive signal, paragraph 113).
Tarver is silent to teaching that receiving an interference mitigation mode signal indicating two or more interference types of a plurality of interference types to mitigate; and mitigating the two or more interference types by adjusting weights by a neural network for adjusted signals. However, the claimed limitation is well known in the art as evidenced by Bellamkonda.
In the same field of endeavor, Bellamkonda teaches receiving an interference mitigation mode signal indicating two or more interference types of a plurality of interference types to mitigate (interference models 203, FIG. 3, paragraph 51); and mitigating the two or more interference types by adjusting weights by a neural network for adjusted signals (Bellamkonda, paragraph 19, 85, 92).
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to combine the teaching of Tarver with the teaching of Bellamkonda in order to reducing or eliminating the need for manual intervention in order to determine or implement such actions.
-Regarding claim 21, the combination further discloses in response to the interference mitigation mode signal indicating a full mode of operation, adjusting the weights to a common set of values for all of the two or more interference types (Bellamkonda, set of sector models 201, such as example sector models 201-1, 201-2, and 201-M, paragraph 33).
-Regarding claim 22, the combination further discloses in response to the interference mitigation mode signal indicating an individual mode of operation, adjusting the weights to a different set of values for each of the two or more interference types (Bellamkonda, interference models 203-1, 203-2, and 203-L, paragraph 33).
-Regarding claim 23, the combination further discloses in response to the interference mitigation mode signal indicating a partial mode of operation, adjusting the weights to a first set of values for at least two of the two or more interference types, and adjusting the weights to a second set of values for others of the two or more interference types (Bellamkonda, a set of actions/parameters 205, such as example actions/parameters 205-1, 205-2, and 205-N, paragraph 33).
Claim(s) 3, 4, 8, 11, 12, 16 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Tarver (U.S. PG-PUB NO. 2023/0046481) in view of Bellamkonda (U.S. PG-PUB NO. 2022/0210789) and further in view of Luo (U.S. PG-PUB NO. 2021/0320678).
-Regarding claim 3, the combination is silent to teaching that a first layer of multiplication/accumulation units (MAC units) of a plurality of layers of MAC units configured to mix the plurality of transmit signals as input data and delayed versions of respective outputs of the first layer of MAC units using a plurality of weights to generate first intermediate processing results; and additional layers of MAC units of the plurality of layers of MAC units, each additional layer of MAC units configured to mix the first intermediate processing results and delayed versions of respective outputs of the respective additional layer of MAC units using additional weights of the plurality of weights to generate second intermediate processing results. However, the claimed limitation is well known in the art as evidenced by Luo.
In the same field of endeavor, Luo teaches a first layer of multiplication/accumulation units (MAC units) of a plurality of layers of MAC units configured to mix the plurality of transmit signals as input data (MAC units 562a-c, FIG. 5D, paragraph 112) and delayed versions of respective outputs of the first layer of MAC units using a plurality of weights to generate first intermediate processing results (delay units 563a-c, FIG. 5D, paragraph 112); and additional layers of MAC units of the plurality of layers of MAC units, each additional layer of MAC units configured to mix the first intermediate processing results (MAC units 566a-c, FIG. 5D, paragraph 112) and delayed versions of respective outputs of the respective additional layer of MAC units using additional weights of the plurality of weights to generate second intermediate processing results (delay units 567a-c, FIG. 5D, paragraph 112).
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to combine the teaching of the combination with the teaching of Luo in order to increase capacity of their respective communication network.
-Regarding claim 4, the combination further discloses the interference mitigation circuit is configured to provide the adjusted signals as output data, the output data based partly on the second intermediate processing results, and wherein each wireless receiver is configured to receive a corresponding adjusted signal of the plurality of adjusted signals (Luo, MAC units 566a-c, FIG. 5D, paragraph 112).
-Regarding claim 8, the combination further discloses a number of the plurality of layers of MAC units corresponds to a number of transmitting antennas of the plurality of transmitting antennas (Luo, input data to be transmitted from a plurality of antennas coupled to an electronic device 110 in which the recurrent neural network 512 is implemented, paragraph 95).
-Regarding claim 11, the combination further discloses the instructions are further executable to cause the wireless communication device to: mix, by a first layer of multiplication/accumulation units (MAC units) of a plurality of layers of MAC units of the neural network, the plurality of transmit signals as input data (Luo, MAC units 562a-c, FIG. 5D, paragraph 112) and delayed versions of respective outputs of the first layer of MAC units using a plurality of weights to generate first intermediate processing results (Luo, delay units 563a-c, FIG. 5D, paragraph 112); and mix, by each additional layer of additional layers of MAC units of the plurality of layers of MAC units, the first intermediate processing results (Luo, MAC units 566a-c, FIG. 5D, paragraph 112) and delayed versions of respective outputs of the respective additional layer of MAC units using additional weights of the plurality of weights to generate second intermediate processing results (Luo, delay units 567a-c, FIG. 5D, paragraph 112).
-Regarding claim 12, the combination further discloses provide the adjusted signals as output data, the output data based partly on the second intermediate processing results, and receive a corresponding adjusted signal of the adjusted signals (Luo, MAC units 566a-c, FIG. 5D, paragraph 112).
-Regarding claim 16, the combination further discloses a number of the plurality of layers of MAC units corresponds to a number of transmitting antennas of a plurality of transmitting antennas including the respective transmitting antenna (Luo, input data to be transmitted from a plurality of antennas coupled to an electronic device 110 in which the recurrent neural network 512 is implemented, paragraph 95).
-Regarding claim 19, the combination further discloses mixing, by a first layer of multiplication/accumulation units (MAC units) of a plurality of layers of MAC units of the neural network, the plurality of transmit signals as input data (Luo, MAC units 562a-c, FIG. 5D, paragraph 112) and delayed versions of respective outputs of the first layer of MAC units using a plurality of weights to generate first intermediate processing results (Luo, delay units 563a-c, FIG. 5D, paragraph 112); and mixing, by each additional layer of additional layers of MAC units of the plurality of layers of MAC units, the first intermediate processing results (Luo, MAC units 566a-c, FIG. 5D, paragraph 112) and delayed versions of respective outputs of the respective additional layer of MAC units using additional weights of the plurality of weights to generate second intermediate processing results (Luo, delay units 567a-c, FIG. 5D, paragraph 112).
-Regarding claim 20, the combination further discloses providing the adjusted signals as output data, the output data based partly on the second intermediate processing results, and receiving a corresponding adjusted signal of the plurality of adjusted signals (Luo, MAC units 566a-c, FIG. 5D, paragraph 112).
Response to Arguments
Applicant's arguments filed 5/4/2026 have been fully considered but they are not persuasive. Applicant argues that Bellamkonda does not teach mitigating the two or more interference types based on adjusting weights applied by the neural network for adjusted signals because Bellamkonda is silent about adjusting weights.
The examiner respectfully disagrees. In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986). In this case, Tarver expressly teaches mitigating interference based on adjusting weights applied for adjusted signals: the processor 960 is configured to "set or revise one or more parameters of the equalizer 952," including setting "one or more coefficients of an equalizer function" (paragraph 100); learned FIR filter coefficients hi,s;j,r[k] (i.e., weights) are applied to compute the residual on each receive signal (paragraph 0113; see also paragraph 0105, expressly reciting "weights"); and those weights are "updated as necessary" during normal receive operation, updated via least-squares or LMS adaptation, and "retrained" for new configurations (paragraphs 116, 131, 133-134), the adjusted weights being applied to produce the adjusted residual signals from which the interference estimate has been subtracted (paragraphs 106, 132). Bellamkonda is relied upon for implementing this adaptive processing with a neural network. Bellamkonda expressly discloses that its interference models and model-action associations are "generated and/or refined" using "neural networks," deep learning, and reinforced machine learning (paragraphs 19, 35). Bellamkonda further discloses that its learned affinity scores are "modified (e.g., strengthened or weakened)" based on monitoring of the post-remediation signal environment (paragraph 92), (i.e., feedback-driven adjustment of the model's learned values based on the adjusted signals). Accordingly, in the combined apparatus the weights adjusted per Tarver are applied by the neural network of Bellamkonda, and the combination teaches all claimed limitations as recited in claim 1, 9 and 17.
Conclusion
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PING Y HSIEH whose telephone number is (571)270-3011. The examiner can normally be reached Monday-Friday, 9am-4pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jennifer Mehmood can be reached at (571) 272-2976. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/PING Y HSIEH/ Primary Examiner, Art Unit 2664
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