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 .
Response to Arguments
Applicant’s arguments with respect to claim(s) 1-20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
However, upon further review of the Applicant’s arguments with respect to the rejection under 35 U.S.C. § 101, the Examiner respectfully disagrees with the Applicant’s arguments because of the following reasons:
First, with regards to at least independent claim 1, under the broadest reasonable interpretation, the terms of the claim are presumed to have their meaning consistent with the specification as it would be interpreted by one of ordinary skill in the art. See MPEP 2111.
In this case, the claim recites an apparatus configured to:
Step (a) obtain one or more reference signals specific to a communication channel;
[given the limitation its plain meaning, i.e. receiving or collecting information and/or data].
Thus, the claim does not put any limits on how the “reference signals” are obtained, e.g. a computer programmer observing and/or writing down information or data (i.e. obtain) in order to perform evaluations (such as calculations), therefore step (a) is an abstract idea recited as being performed by a computer at a high level of generality.
Step (b) input at least one characteristic associated with the one or more reference signals into a machine learning model that is configured to predict one or more channel parameters associated with multipath propagation characteristics of the communication channel;
Step (c) output, from the machine learning model, one or more predicted channel parameters including a delay spread value and a projected energy tracking loop (PETL) value for the communication channel;
[given the limitations of steps (b) and (c) its’ plain meaning, i.e. inputting information and/or data into a machine learning model (ML) and outputting predicted information or result].
Thus, steps (b) and (c) does not provide any details about how the ML operates or how the result i.e. “predicted channel parameters” are derived. Therefore, the at least steps encompasses mental observations or evaluations, e.g. a computer programmer’s mental observations and/or calculations using pen and paper.
Step (d) and perform channel estimation for the communication channel based on the one or more predicted channel parameters delay spread value and the PETL value.
[given the limitation its plain meaning, i.e. evaluating and/or calculating channel estimation based on the predicted channel parameters].
Thus, the claim does not limit how the “channel estimation (evaluation) is performed. Therefore, without any additional details that explain the “perform” step, step (d) encompasses at least mental observation and/or evaluation wherein channel estimation is calculated (evaluated) using pen and paper and/or by observation or opinion.
Now analyzing independent claim 1, under step 2A, Prong One, as discussed above, the broadest reasonable interpretation of at least steps (a)-(d) is that those steps fall within the mental process groupings of abstract ideas because they cover concepts performed in the human mind, including observation, evaluation, judgment, and opinion. See MPEP 2106.04(a)(2), subsection III.
Therefore, contrary to the Applicant’s arguments (see remarks pg. 9-10), the Examiner respectfully disagrees with the Applicant’s arguments based on the discussions above.
Secondly, in response to analysis under step 2A, Prong Two, the Applicant argued and/or stated that: “In particular, the Specification explains that the disclosed ML model is used to predict channel parameters for wireless channel estimation, and that "[r]ather than using heuristics and thresholds, the ML model directly optimizes channel estimation accuracy by mapping channel feature inputs to delay spread and PETL parameters minimizing channel estimation error."”
In response, the Examiner respectfully disagrees with the Applicant’s arguments because the analysis under step 2A, Prong Two evaluates whether the claim as a whole integrates the recited judicial exception into a practical application of the exception or whether the claim is “directed to” the judicial exception. This evaluation is performed by (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (2) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. See MPEP 2106.04(d).
In this case, the claim recites the additional elements of “input…into a machine learning model” and/or “output, from the machine learning model” steps (b) and (c).
The limitations of steps (b) and (c), as shown above, provide nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). The limitations of “input…into a machine learning model” and/or “output, from the machine learning model” simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer. The “machine learning model” is used to generally apply the abstract idea without placing any limits on how the machine learning model functions. Rather, these limitations only recite the outcome i.e. “predicted channel parameters” and do not include any details about how the “predicted channel parameters” are accomplished. See MPEP 2106.05(f). This type of limitation merely confines the use of the abstract idea to a particular technological environment (e.g. neural networks) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h).
Furthermore, contrary to the Applicant’s arguments that the specification e.g. pp. [0033], “the ML model directly optimizes channel estimation accuracy by mapping channel feature inputs to delay spread and PETL parameters minimizing channel estimation error” reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field.
The Examiner kindly reminds the Applicant that the consideration of whether the claim as a whole includes an improvement to a computer or to a technological field requires an evaluation of the specification and the claim to ensure that a technical explanation of the asserted improvement is present in the specification, and that the claim reflects the asserted improvement. See MPEP 2106.04(d)(1). (Emphasis Added).
In this case, the Examiner respectfully disagrees that the at least claimed limitations of steps (a)-(d) reflects improvements to the technology because the limitations merely discussed “obtaining…reference signals”, “input characteristics associated with the…reference signals into a machine learning model”, “output, from the machine learning model…a delay spread value and a projected energy tracking loop (PETL)”, and “perform channel estimation…based on the delay spread value and the PETL”.
Nothing in the claim reflects these technical improvements discussed in the disclosure by reciting how the “machine learning model” aid in deriving “the delay spread value and the PETL” and/or how channel estimation error is minimized using the derived information “the delay spread value and the PETL”.
Thirdly, in response to analysis under step 2b, the Examiner kindly directs the Applicant to similar discussions above because the claim only recited “a machine learning model” as the additional element. Therefore, the limitations of steps (b) and (c) are at best mere instructions to “apply” the abstract idea, which cannot provide an inventive concept. See MPEP 2106.05(f).
Furthermore, the claimed limitations of steps (a)-(d) when considered in combination, these additional elements represent mere instructions to implement an abstract idea or other exception on a computer and insignificant extra-solution activity, which do not provide an inventive concept.
With respect to independent claim 20, although slightly different limitations, the Applicant did not provide any response for the 101 rejection on record. Thus, the Applicant is kindly directed to similar response as discussed in claim 1 above.
Therefore, the previous rejection is maintained.
Claim Rejections - 35 USC § 101
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.
Claims 1-20, are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claim 1 recites an apparatus configured for wireless communications, comprising: one or more memories; and one or more processors coupled to the one or more memories. Thus, the claim is to a machine, which is one of the statutory categories of invention. (Step 1: YES).
Step 2A, Prong One: Claim 1 is then analyzed to determine whether it is directed to any judicial exception. The claim recites step (a) obtain one or more reference signals specific to a communication channel; step (b) input at least one characteristic associated with the one or more reference signals into a machine learning model that is configured to predict one or more channel parameters associated with multipath propagation characteristics of the communication channel; step (c) output, from the machine learning model, one or more predicted channel parameters including a delay spread value and a projected energy tracking loop (PETL) value for the communication channel; and step (d) perform channel estimation for the communication channel based on the delay spread value and the PETL value.
Steps (b), (c), and (d), recited in the claim is no more than an abstract idea i.e., mental process that may be practically performed in the human mind using observation, evaluation, judgment, opinion and/or with a pen and paper. The inputting, predicting, and outputting information data fall within the mental process of collecting data set, performing evaluating of the data set, and outputting or identifying result based on the data set. See MPEP 2106.04(a)(2), subsection III. Thus, steps (b), (c), and (d) fall within the mental process grouping of abstract ideas. (Step 2A, Prong One: Yes).
Step 2A, Prong Two: Claim 1 is then analyzed if it requires an additional elements or a combination of additional elements in the claim to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception., such that the claim is more than a drafting effort designed to monopolize the exception – i.e., limitation that are indicative of integration into a practical application: improving to the functioning of a computer or to any other technology or technical field. The current claim recited step (a) obtain one or more reference signals specific to a communication channel; step (b) input at least one characteristic associated with the one or more reference signals into a machine learning model that is configured to predict one or more channel parameters associated with multipath propagation characteristics of the communication channel; step (c) output, from the machine learning model, one or more predicted channel parameters including a delay spread value and a projected energy tracking loop (PETL) value for the communication channel; and step (d) perform channel estimation for the communication channel based on the delay spread value and the PETL value.
The step (a) to “obtain one or more reference signals” is mere data gathering at a high level of generality, and thus is insignificant extra-solution activity. See MPEP 2106.05(g). Steps (b) and (c) recite a “machine learning model”, to input data and output result data without any details about how the outcomes are accomplished (i.e. how the “predicted channel parameters” was derived). The “machine learning model” is used to generally apply the abstract idea on a generic computer, and provide nothing more than mere instructions to implement an abstract idea on a generic computer (i.e. “apply it” (or an equivalent)). See MPEP 2106.05(f). Furthermore, Steps (b) and (c) merely indicates a field of use or technological environment in which the judicial exception is performed and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h). These additional elements do not integrate the recited judicial exception into a practical application (Step 2A: Prong Two: No), and the claim is directed to the judicial exception. (Step 2A: YES).
Step 2B: Claim 1 as a whole is analyzed to determine if there are additional limitation recited in the claim such that the claim amount to significantly more than an abstract idea. In the current scenario, the additional elements (e.g. a machine learning model in steps (b) and (c)) represent mere instructions to implement an abstract idea or other exception on a computer at a high level of generality which is well-understood, routine, conventional activity, and insignificant extra-solution activity, which do not provide an inventive concept. Therefore, the claim does not amount to significantly more than the abstract idea itself (Step 2B: No).
Accordingly, the claim is not patent eligible.
In regards to independent claim 19, the claim recited a method for performing the same and/or similar limitations of the apparatus claim 1. Thus, claim 19 is rejected for the same or similar reasons as discussed in claim 1 above.
In regards to independent claim 20, the claim recited an apparatus for performing the same and/or similar limitations of the apparatus claim 1, except for the limitations to “report the delay spread value and the PETL value to a network entity”.
As discussed in Step 2A, Prong Two: the step of “report” or outputting information data to a network entity is mere data gathering and outputting (i.e. receiving and/or transmitting) at a high level of generality, and thus is insignificant extra-solution activity. See MPEP 2106.05(g). Furthermore, the “network entity” is recited at a high level of generality and amounts to no more than mere instructions to “apply” the exception using a generic computer. Therefore, receiving or transmitting data over a network are well understood, routine, conventional activity. See MPEP 2106.05(d), subsection II. Thus, claim 20 is rejected for the same or similar reasons as discussed in claim 1 above.
In regards to dependent claims, the dependent claims do not add any positive limitation or step that recite within the scope of the claim and does not carry patentable weight they are also rejected for the same reasons as independent claims.
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) 1-4, 8, 11, 16, and 18-20, is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. (US Publication No. 20210326726) in view of Muralidhar et al. (US Publication No. 20240297765).
As to claims 1 and 19, Wang teaches an apparatus configured for wireless communications, comprising: one or more memories; and one or more processors coupled to the one or more memories and configured to cause the apparatus to (fig. 2, fig. 3): obtain one or more reference signals specific to a communication channel (fig. 2, fig. 5, fig. 8, and pp0103, one or more reference signals (e.g., SSB, CSI) configured for the UE 520, pp0053, a BS 105 may transmit cell specific reference signals (CRSs) and/or channel state information-reference signals (CSI-RSs) to enable a UE 115 to estimate a DL channel); input at least one characteristic associated with the one or more reference signals into a machine learning model that is configured to predict one or more channel parameters associated with multipath propagation characteristics of the communication channel (fig. 2, fig. 5, fig. 8, #810, apply a machine learning to a set of the received signal measurements, and pp0032, the ML algorithms are tasked to predict what is the delay spread of a channel); output, from the machine learning model, one or more predicted channel parameters including a delay spread value (fig. 2, fig. 5, fig. 8, #820, determining an output of the ML, pp0032, the ML algorithms are tasked to predict what is the delay spread of a channel. For example, the machine learning-based network may be implemented by a delay spread prediction network to predict the delay spread on a channel); and perform channel estimation for the communication channel based on the delay spread value (fig. 2, fig., 5, fig. 8, #830, communicate a report based on the ML output, pp0032, the ML algorithms are tasked to predict what is the delay spread of a channel. For example, the machine learning-based network may be implemented by a delay spread prediction network to predict the delay spread on a channel, and pp0005, UE may perform channel sounding to better understand these channel properties by measuring and/or estimating various parameters of the channel). However, Wang fails to explicitly teach the concept to output a projected energy tracking loop (PETL) value and perform channel estimation based on the PETL value.
In an analogous field of endeavor, Muralidhar teaches the concept to output a projected energy tracking loop (PETL) value (estimated energy feedback report) and perform channel estimation based on the PETL value (fig. 1, fig. 5, pp0077, one or more energy-feedback reports on one or more estimated fractions of energy as captured by the UE for one or more configured port muting patterns. The at least one UE node (104) can estimate the one or more fractions of energy as captured in the downlink CSI-RS channel estimate for the one or more configured port muting patterns, and pp0041, a neural processing unit (NPU) (e.g., an artificial intelligence (AI) chip)). Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Wang with the teachings of Muralidhar to achieve the goal of efficiently and reliably improving the spectral efficiency and the overall network performances in a communication system (Muralidhar, pp0006).
As to claim 2, Wang in view of Muralidhar teaches the limitations of the independent claims as discussed above. Wang further teaches wherein the one or more predicted channel parameters include at least one of an estimated delay spread associated with a) the communication channel (fig. 2, fig. 5, fig. 8, #810, apply a machine learning to a set of the received signal measurements, and pp0032, the ML algorithms are tasked to predict what is the delay spread of a channel) or b) a channel impulse response centering parameter.
As to claim 3, Wang in view of Muralidhar teaches the limitations of the independent claims as discussed above. Wang further teaches wherein the at least one characteristic associated with the one or more reference signals includes at least one of signal energy of the one or more reference signals (fig. 2, fig. 8, pp0039, the set of received signal measurements as input data, wherein the set of received signal measurements comprises historical measurements of a plurality of transmission beams associated with the BS and historical signal strength measurements of downlink specific reference signals carried in the plurality of transmission beams) or noise energy of the one or more reference signals.
As to claim 4, Wang in view of Muralidhar teaches the limitations of the independent claims as discussed above. Wang further teaches wherein the at least one characteristics comprises one or more of: a characteristic associated with a modulation and coding scheme, a characteristic of a demodulation reference signal (fig. 2, fig. 8, pp0088, demodulated and decoded data (e.g., reference signal, synchronization signal, SSBs), and pp0103), a channel rank, a number of transmit ports at a node used for transmitting channel state information reference signals, a sounding reference signal precoding matrix, or a characteristic associated with a measure of Doppler.
As to claim 8, Wang in view of Muralidhar teaches the limitations of the independent claims as discussed above. Wang further teaches wherein the one or more processors are configured to cause the apparatus to: extract features related to at least one of delay spread (fig. 2, fig. 5, fig. 8, #810, apply a machine learning to a set of the received signal measurements, and pp0032, the ML algorithms are tasked to predict what is the delay spread of a channel), Doppler spread, spatial correlation, or interference characteristics from the one or more reference signals; select a subset of features utilizing information gain for predicting the one or more channel parameters based on a correlation analysis (fig. 2, fig. 5, fig. 8, #810, apply a machine learning to a set of the received signal measurements, and pp0032, the ML algorithms are tasked to predict what is the delay spread of a channel, and pp0103, based on a comparison between at least one signal measurement in the set of received signal measurements and a signal measurement obtained by the UE 520); and input the selected subset of features into the machine learning model (fig. 2, fig. 5, fig. 8, #810, apply a machine learning to a set of the received signal measurements, and pp0032, the ML algorithms are tasked to predict what is the delay spread of a channel).
As to claim 15, Wang in view of Muralidhar teaches the limitations of the independent claims as discussed above. Wang further teaches wherein the one or more processors are configured to cause the apparatus to provide feedback to a network entity based on the one or more predicted channel parameters (fig. 2, fig. 5, fig. 8, #830, communicate a report based on the ML output, and pp0107, the report may include at least one of RSRP or CQI. In some aspects, the report is multiplexed between at least one of RSRP or CQI or other UL control information (UCI) when configured PUCCH resources of the RSRP, or CQI, or the other UCI overlap, and pp0005).
As to claim 16, Wang in view of Muralidhar teaches the limitations of the independent claims as discussed above. Wang further teaches wherein the one or more processors are configured to cause the apparatus to receive an indication, from the network entity, to adapt at least one transmission parameter based on the feedback (fig. 2, fig. 5, fig. 8, #830, communicate a report based on the ML output, and pp0107, the report may include at least one of RSRP or CQI. In some aspects, the report is multiplexed between at least one of RSRP or CQI or other UL control information (UCI) when configured PUCCH resources of the RSRP, or CQI, or the other UCI overlap, and pp0005).
As to claim 18, Wang in view of Muralidhar teaches the limitations of the independent claims as discussed above. Wang further teaches wherein the one or more processors are configured to cause the apparatus to predict the one or more reference signals over multiple time instances (fig. 2, fig. 5, fig. 8, and pp0039, the transceiver may be further configured to communicate, with the BS over a plurality of periodic intervals during a second time period greater than the first time period, the sampled data with the plurality of periodical signal measurements).
As to claim 20, Wang teaches an apparatus configured for wireless communications, comprising: one or more memories; and one or more processors coupled to the one or more memories and configured to cause the apparatus to (fig. 2, fig. 3): obtain one or more reference signals specific to a communication channel (fig. 2, fig. 5, fig. 8, and pp0103, one or more reference signals (e.g., SSB, CSI) configured for the UE 520, pp0053, a BS 105 may transmit cell specific reference signals (CRSs) and/or channel state information-reference signals (CSI-RSs) to enable a UE 115 to estimate a DL channel); input at least one characteristic associated with the one or more reference signals into a machine learning model that is configured to predict one or more channel parameters associated with multipath propagation characteristics of the communication channel (fig. 2, fig. 5, fig. 8, #810, apply a machine learning to a set of the received signal measurements, and pp0032, the ML algorithms are tasked to predict what is the delay spread of a channel); output, from the machine learning model, one or more predicted channel parameters including a delay spread value for the communication channel (fig. 2, fig. 5, fig. 8, #820, determining an output of the ML, pp0032, the ML algorithms are tasked to predict what is the delay spread of a channel. For example, the machine learning-based network may be implemented by a delay spread prediction network to predict the delay spread on a channel); and report the delay spread value to a network entity (fig. 2, fig. 5, fig. 8, #830, communicate a report based on the ML output, pp0032, the ML algorithms are tasked to predict what is the delay spread of a channel. For example, the machine learning-based network may be implemented by a delay spread prediction network to predict the delay spread on a channel and pp0005). However, Wang fails to explicitly teach the concept to output a projected energy tracking loop (PETL) value and report the PETL value to a network entity.
In an analogous field of endeavor, Muralidhar teaches the concept to output a projected energy tracking loop (PETL) value (estimated energy feedback report) and report the PETL value to a network entity (fig. 1, fig. 5, pp0077, one or more energy-feedback reports on one or more estimated fractions of energy as captured by the UE for one or more configured port muting patterns. The at least one UE node (104) can estimate the one or more fractions of energy as captured in the downlink CSI-RS channel estimate for the one or more configured port muting patterns, and pp0041, a neural processing unit (NPU) (e.g., an artificial intelligence (AI) chip)). Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Wang with the teachings of Muralidhar to achieve the goal of efficiently and reliably improving the spectral efficiency and the overall network performances in a communication system (Muralidhar, pp0006).
Claim(s) 5, 6, 13, 14, and 17, is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. (US Publication No. 20210326726) in view of Muralidhar et al. (US Publication No. 20240297765) and further in view of Chou et al. (US Publication No. 20230291454).
As to claim 5, Wang in view of Muralidhar teaches the limitations of the independent claims as discussed above. However, fails to explicitly teach wherein the one or more predicted channel parameters includes an index to a basis matrix and a rotation to apply to the basis matrix.
In an analogous field of endeavor, Chou teaches the concept in performing channel state information measurement (fig. 11, abs). Chou further teaches the concept wherein the one or more predicted channel parameters includes an index to a basis matrix and a rotation to apply to the basis matrix (fig. 11, fig. 25, pp0164, and pp0172, basis index and position). Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Wang and Muralidhar with the teachings of Chou to achieve the goal of efficiently and reliably providing Channel state information (CSI) to increase the transmission capacity of the spectrum in a communication system (Chou, pp0003).
As to claim 6, Wang in view of Muralidhar teaches the limitations of the independent claim as discussed above. However, fails to explicitly teach wherein the one or more processors are configured to cause the apparatus to select a basis matrix from a plurality of basis matrices based on the one or more predicted channel parameters, wherein the selected basis matrix provides one or more basis vectors that approximate a response of the communication channel.
In an analogous field of endeavor, Chou teaches the concept in performing channel state information measurement (fig. 11, abs). Chou further teaches the concept wherein the one or more processors are configured to cause the apparatus to select a basis matrix from a plurality of basis matrices based on the one or more predicted channel parameters (fig. 11, fig. 24, fig. 25, pp0072, pp0161, basis selection), wherein the selected basis matrix provides one or more basis vectors that approximate a response of the communication channel (fig. 11, fig. 24, fig. 25, pp0072, pp0161, basis selection, and basis vectors can be selected for the p-th TRP). Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Wang and Muralidhar with the teachings of Chou to achieve the goal of efficiently and reliably providing Channel state information (CSI) to increase the transmission capacity of the spectrum in a communication system (Chou, pp0003).
As to claim 13, Wang in view of Muralidhar teaches the limitations of the independent claim as discussed above. Wang further teaches wherein the one or more processors are configured to cause the apparatus to transmit information indicative of the one or more predicted channel parameters to a network entity (fig. 2, fig., 5, fig. 8, #830, communicate a report based on the ML output, and pp0107, the report may include at least one of RSRP or CQI. In some aspects, the report is multiplexed between at least one of RSRP or CQI or other UL control information (UCI) when configured PUCCH resources of the RSRP, or CQI, or the other UCI overlap, and pp0005). However, fails to explicitly teach wherein the information indicative of the one or more predicted channel parameters is utilized by the network entity to configure one or more transmission parameters comprising at least one of a number of orthogonal frequency division multiplexing symbols, a precoder matrix indicator, a rank indicator, or a modulation and coding scheme.
In an analogous field of endeavor, Chou teaches the concept in performing channel state information measurement (fig. 11, abs). Chou further teaches the concept wherein the information indicative of the one or more predicted channel parameters is utilized by the network entity to configure one or more transmission parameters comprising at least one of a number of orthogonal frequency division multiplexing symbols, a precoder matrix indicator (fig. 11, PMI), a rank indicator, or a modulation and coding scheme. Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Wang and Muralidhar with the teachings of Chou to achieve the goal of efficiently and reliably providing Channel state information (CSI) to increase the transmission capacity of the spectrum in a communication system (Chou, pp0003).
As to claim 14, Wang in view of Muralidhar teaches the limitations of the independent claim as discussed above. Wang further teaches wherein the one or more processors are configured to cause the apparatus to: determine a delay spread feedback report based on the one or more predicted channel parameters (fig. 2, fig., 5, fig. 8, #830, communicate a report based on the ML output, and pp0107, the report may include at least one of RSRP or CQI. In some aspects, the report is multiplexed between at least one of RSRP or CQI or other UL control information (UCI) when configured PUCCH resources of the RSRP, or CQI, or the other UCI overlap, and pp0032, delay spread output); and transmit the delay spread feedback report to a network entity to adapt one or more downlink transmission parameters (fig. 2, fig., 5, fig. 8, #830, communicate a report based on the ML output, and pp0107, the report may include at least one of RSRP or CQI. In some aspects, the report is multiplexed between at least one of RSRP or CQI or other UL control information (UCI) when configured PUCCH resources of the RSRP, or CQI, or the other UCI overlap, and pp0032, delay spread output). However, fails to explicitly teach transmission parameter comprising at least one of a precoder matrix indicator, a rank indicator, or a modulation and coding scheme.
In an analogous field of endeavor, Chou teaches the concept in performing channel state information measurement (fig. 11, abs). Chou further teaches the concept of transmission parameter comprising at least one of a precoder matrix indicator, a rank indicator, or a modulation and coding scheme (fig. 11, PMI). Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Wang and Muralidhar with the teachings of Chou to achieve the goal of efficiently and reliably providing Channel state information (CSI) to increase the transmission capacity of the spectrum in a communication system (Chou, pp0003).
As to claim 17, Wang in view of Muralidhar teaches the limitations of the independent claim as discussed above. However, fails to explicitly teach wherein to adapt the at least one transmission parameter comprises to adapt at least one of a precoding matrix, a precoding matrix granularity, a demodulation reference signal pattern, a number of transmission ports, or a transmission beam direction.
In an analogous field of endeavor, Chou teaches the concept in performing channel state information measurement (fig. 11, abs). Chou further teaches the concept wherein to adapt the at least one transmission parameter comprises to adapt at least one of a precoding matrix (fig. 11, PMI), a precoding matrix granularity, a demodulation reference signal pattern, a number of transmission ports, or a transmission beam direction (fig. 11, PMI). Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Wang and Muralidhar with the teachings of Chou to achieve the goal of efficiently and reliably providing Channel state information (CSI) to increase the transmission capacity of the spectrum in a communication system (Chou, pp0003).
Allowable Subject Matter
Claims 7 and 9-12, 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.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 OMONIYI OBAYANJU whose telephone number is (571)270-5885. The examiner can normally be reached M-Thur 10:30-7pm.
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/OMONIYI OBAYANJU/ Primary Examiner, Art Unit 2645
