DETAILED ACTION
The following is a non-final office action in response to applicant’s remarks submitted on 08/26/2026 for response of the office action mailed on 05/27/2026. Claims 12, 14 and 16-17 were cancelled previously. Claim 2 is cancelled currently. Claims 18-19 are added currently. Therefore, claims 1, 3-11, 13, 15 and 18-19 are pending and addressed below.
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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 08/26/2026 has been entered.
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.
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The factual inquiries 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.
Claims 1, 4-6, 8, 10-11, 13, 15 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Chavva et al. (2021/0351885, corresponding to WO 2020213964 as submitted in IDS) Chavva hereinafter, in view of Madadi et al. (2022/0338189), Madadi hereinafter.
Re. Claims 1 and 13, Chavva teaches a method (Fig.8 & ¶0149/¶0151/¶0153) and an apparatus (Fig. 23 / Fig. 2/Fig. 4, UE) comprising: at least one transceiver (Fig. 23, 2320); and at least one processor (Fig. 23, 2310) coupled with the at least one transceiver (Fig. 23, 2320), wherein the at least one processor (Fig. 23, 2310) is configured to: receive , from a base station (Fig. 22 / Fig. 2/Fig. 4, gNB/Base Station), configuration information related to a channel state information (CSI) report (Fig. 8 & ¶0149 - At step 801, the method includes receiving a feedback configuration, by the UE 601, from the gNB 607. The feedback configuration is relevant to reception of CSI-RS and/or SSB. The feedback configuration can be used by the UE 601 to send the CSI as a feedback report. The UE 601 can receive the feedback configuration in a RRC message. The RRC message includes CSI-MeasConfig, CSI-ResourceConfig, CSI-ReportConfig, and CodebookConfig. Fig. 8 & ¶0150 - The CSI-MeasConfig IE can indicate whether the UE 601 needs to perform at least one of interference measurement and channel measurement. The CSI-ResourceConfig IE can include information pertaining to allocation of time/frequency resources for CSI-RS reception such as time slots in which the UE 601 can expect to receive the CSI-RS, frequency of the CSI-RS, and ports through which the CSI-RS can be received. The CSI-ReportConfig IE can include time slots in which the UE 601 can send the CSI report, feedback parameters to be included in the CSI report, and so on. The CodebookConfig indicates to the UE 601 as to whether the CSI feedback configuration, provided to the UE 601, is pertaining to type-1 CSI or type-2 CSI.); receive, from the base station (Fig. 22 / Fig. 2/Fig. 4, gNB/Base Station), information for a payload configuration related to the CSI report (Fig. 6 & ¶0036 - wherein the probable values of the CSI feedback parameters at the future time instance are predicted based on at least one of: …. a delay in scheduling a Physical Downlink Scheduling Channel (PDSCH) by the gNB (607) after receiving the CSI report from the UE (601); a block error rate pertaining to reception of the PDSCH. Fig. 6 & ¶0056 - wherein the probable values of the CSI feedback parameters at the future time instance are predicted based on at least one of: ….a delay in scheduling a Physical Downlink Scheduling Channel (PDSCH) by the gNB (607) after receiving the CSI report from the UE (601); a block error rate pertaining to reception of the PDSCH; Fig. 4 & ¶0112 - the UE receives CSI-RS, followed by PDSCH through a first beam. The UE utilizes the CSI-RS, which is received periodically from the gNB, to generate a CSI-RS report. Based on the CSI report, the gNB can choose appropriate MCS to transmit the subsequent PDSCH); and report CSI based on the configuration information and the information for the payload configuration (Fig. 6 & ¶0036 - wherein the probable values of the CSI feedback parameters at the future time instance are predicted based on at least one of: …. a delay in scheduling a Physical Downlink Scheduling Channel (PDSCH) by the gNB (607) after receiving the CSI report from the UE (601); a block error rate pertaining to reception of the PDSCH). Fig. 6 & ¶0056 - wherein the probable values of the CSI feedback parameters at the future time instance are predicted based on at least one of: ….a delay in scheduling a Physical Downlink Scheduling Channel (PDSCH) by the gNB (607) after receiving the CSI report from the UE (601); a block error rate pertaining to reception of the PDSCH; a CSI reporting periodicity; and a code rate for scheduling the PDSCH by the gNB (607)),
Yet, Chavva does not expressly teach wherein the information for the payload configuration includes an indicator for a payload size, determined by the base station based on information predicted through an artificial intelligence model, for at least one of a physical uplink control channel (PUCCH) or a physical uplink shared channel (PUSCH) carrying the CSI, wherein the information predicted through the artificial intelligence model includes information for a channel state at a timing of data scheduling after reporting the CSI, and wherein the CSI is calculated, by the UE, based on the payload size indicated by the indicator and the channel state at the timing of data scheduling.
However, in the analogous art, Madadi explicitly discloses wherein the information for the payload configuration includes an indicator for a payload size, determined by the base station based on information predicted through an artificial intelligence model, for at least one of a physical uplink control channel (PUCCH) or a physical uplink shared channel (PUSCH) carrying the CSI (Fig. 4-15 & ¶0006 - receiving CSI reporting configurations that include indications that enable or disable at least one of: ML-assisted CSI prediction and artificial intelligence channel feature information (AI-CFI) reporting. ML model training is performed or trained ML model parameters are received, and CSI reference signals corresponding to at least one of the CSI reporting configurations are received. If ML-assisted CSI prediction is enabled, the CSI reporting configurations further include: a timing offset for future CSI prediction, and ML configurations including indication of an ML model used for the ML-assisted CSI prediction. If AI-CFI reporting is enabled, the CSI reporting configurations further include: a configuration for a report of the AI-CFI, and ML configurations including indication of an ML model used for the ML assisted-CSI feedback determination. Fig. 4-15 & ¶0113 - At operation 601, a UE's AI/ML capability to support the AI/ML assisted CSI feedback is reported to the BS. Such capabilities include the support of AI/ML model training and/or inference … The report of the capability information can be via PUCCH and/or PUSCH. A new UCI type, a new PUCCH format and/or a new MAC CE can be defined for the capability information report. Fig. 4-15 & ¶0114 - At operation 602, the UE receives CSI related configuration(s) from the BS, including the enabling or disabling of the AI-based CSI feedback mechanism. When AI-based CSI feedback is enabled, the UE also receives the AI/ML related configuration information, such as the AI/ML model used, and the trained model parameters of the model. Fig. 4-15 & ¶0115 - The UE receives the higher layer CSI reporting configuration to the UE using RRC messages, such as the CSI reporting parameters (PMI, RI, CQI, L1, CRI, AI-CFI <Artificial intelligence channel feature information, see ¶0006>), CSI configuration type (periodic, semi-persistent PUCCH, semi-persistent PUSCH, aperiodic), report frequency configuration (frequency granularity, i.e., wideband/subband), codebook configuration (Type-1/Type-2 codebook parameters), AI-CFI configuration (size of the AI-CFI), etc. Part of or all of the CSI report configuration information is set in the RRC IE CSI-ReportConfig. … all CSI report configuration information associated with AI methods are set in new IE CSI-AIReportConfig that specifies the reporting configuration for CSI feedback using AI methods. Fig. 4-15 & ¶0116 - At operation 603, the UE receives the CSI reference signal(s) from the BS, after which the UE sends the CSI reports. … Semi-persistent CSI reporting on PUSCH is triggered by DCI and semi-persistent CSI reporting on PUCCH is activated/deactivated by MAC CE. Fig. 4-15 & ¶0117 - At operation 604, the UE performs the inference, i.e., extraction of compressed feature(s) from the CSI channel based on the received CSI reporting configuration information, trigger received and the triggering configuration. For example, the UE follows the configured ML model and model parameters, and CSI reporting parameters sent from the BS, to perform the inference operation. The UE sends the CSI report with the CSI reporting parameter AI-CFI, which is the outcome of the inference of the AI/ML model to the BS.), wherein the information predicted through the artificial intelligence model includes information for a channel state at a timing of data scheduling after reporting the CSI (Fig. 4-15 & ¶0006 - receiving CSI reporting configurations that include indications that enable or disable at least one of: ML-assisted CSI prediction and artificial intelligence channel feature information (AI-CFI) reporting. ML model training is performed or trained ML model parameters are received, and CSI reference signals corresponding to at least one of the CSI reporting configurations are received. If ML-assisted CSI prediction is enabled, the CSI reporting configurations further include: a timing offset for future CSI prediction, and ML configurations including indication of an ML model used for the ML-assisted CSI prediction. If AI-CFI reporting is enabled, the CSI reporting configurations further include: a configuration for a report of the AI-CFI, and ML configurations including indication of an ML model used for the ML assisted-CSI feedback determination. Fig. 4-15 & ¶0113 - At operation 601, a UE's AI/ML capability to support the AI/ML assisted CSI feedback is reported to the BS. Such capabilities include the support of AI/ML model training and/or inference … The report of the capability information can be via PUCCH and/or PUSCH. A new UCI type, a new PUCCH format and/or a new MAC CE can be defined for the capability information report. Fig. 4-15 & ¶0114 - At operation 602, the UE receives CSI related configuration(s) from the BS, including the enabling or disabling of the AI-based CSI feedback mechanism. When AI-based CSI feedback is enabled, the UE also receives the AI/ML related configuration information, such as the AI/ML model used, and the trained model parameters of the model. Fig. 4-15 & ¶0115 - The UE receives the higher layer CSI reporting configuration to the UE using RRC messages, such as the CSI reporting parameters (PMI, RI, CQI, L1, CRI, AI-CFI <Artificial intelligence channel feature information, see ¶0006>), CSI configuration type (periodic, semi-persistent PUCCH, semi-persistent PUSCH, aperiodic), report frequency configuration (frequency granularity, i.e., wideband/subband), codebook configuration (Type-1/Type-2 codebook parameters), AI-CFI configuration (size of the AI-CFI), etc. Part of or all of the CSI report configuration information is set in the RRC IE CSI-ReportConfig. … all CSI report configuration information associated with AI methods are set in new IE CSI-AIReportConfig that specifies the reporting configuration for CSI feedback using AI methods. Fig. 4-15 & ¶0116 - At operation 603, the UE receives the CSI reference signal(s) from the BS, after which the UE sends the CSI reports. … Semi-persistent CSI reporting on PUSCH is triggered by DCI and semi-persistent CSI reporting on PUCCH is activated/deactivated by MAC CE. Fig. 4-15 & ¶0117 - At operation 604, the UE performs the inference, i.e., extraction of compressed feature(s) from the CSI channel based on the received CSI reporting configuration information, trigger received and the triggering configuration. For example, the UE follows the configured ML model and model parameters, and CSI reporting parameters sent from the BS, to perform the inference operation. The UE sends the CSI report with the CSI reporting parameter AI-CFI, which is the outcome of the inference of the AI/ML model to the BS. Fig. 4-15 & ¶0119 - the local information at the UE such as the UE's velocity, location, and trajectory information are employed to predict channel conditions for the UE at a future time, when the UE is more likely to be scheduled by the BS.), and wherein the CSI is calculated, by the UE, based on the payload size indicated by the indicator and the channel state at the timing of data scheduling (Fig. 4-15 & ¶0115 - The UE receives the higher layer CSI reporting configuration to the UE using RRC messages, such as the CSI reporting parameters (PMI, RI, CQI, L1, CRI, AI-CFI <Artificial intelligence channel feature information, see ¶0006>), CSI configuration type (periodic, semi-persistent PUCCH, semi-persistent PUSCH, aperiodic), report frequency configuration (frequency granularity, i.e., wideband/subband), codebook configuration (Type-1/Type-2 codebook parameters), AI-CFI configuration (size of the AI-CFI), etc. Part of or all of the CSI report configuration information is set in the RRC IE CSI-ReportConfig. … all CSI report configuration information associated with AI methods are set in new IE CSI-AIReportConfig that specifies the reporting configuration for CSI feedback using AI methods. Fig. 4-15 & ¶0118 - signaling method to support predicted feedback of CSI at a future time, predicted by an AI/ML model at the UE, is supported. In current NR, the CSI feedback reported to the BS is based on the CSI estimation by the UE at the current time. Fig. 4-15 & ¶0119 - the local information at the UE such as the UE's velocity, location, and trajectory information are employed to predict channel conditions for the UE at a future time, when the UE is more likely to be scheduled by the BS. Fig. 4-15 & ¶0132 - an additional CSI reporting parameter labeled AI-Channel Feature information (AI-CFI), that is the output of the AI/ML model deployed at UE. … the BS can enable only AI/ML based CSI feedback reporting by setting AI/ML based CSI feedback reporting as the sole CSI reporting parameter. … the BS can enable AI/ML based CSI feedback along with original feedback mechanism by setting the reporting parameters to include both PMI, RI and AI-CFI. … … This reporting parameter is the compressed extracted feature from the estimated CSI at the UE, which can be used by BS to reconstruct the CSI.).
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filling date of the claimed invention to combine Chavva’s invention of a system and a method for generating a CSI (Channel State Information) report comprising of parameters estimated and predicted using Machine Learning (ML) in a 5th Generation (5G)-New Radio (NR) communication system to include Madadi’s invention of a system and a method for method and apparatus for support of machine learning or artificial intelligence techniques for CSI < channel state information > feedback in FDD < Frequency Division Duplex > & MIMO < multiple-input multiple-output > technology in a wireless communication system, because it provides an efficient mechanism in supporting high-resolution CSI reporting for UEs (User Equipment) with reduced feedback overhead by further compressing CSI overhead while maintaining large MU-MIMO performance gain by implementing AI/ML technique in enabling the reduction of overhead for the high-resolution CSI feedback in the FDD MU-MIMO technology in the wireless communication system. (¶0065-¶0071, Madadi)
Re. Claim 4, Chavva and Madadi teach claim 1.
Chavva further teaches wherein: based on the information for the payload configuration, a codebook parameter for the CSI is determined by using an artificial intelligence model. (Fig. 1-21 & ¶0081 - methods and systems for reporting Channel State Information (CSI), to a Next Generation Node B (gNB), comprising of at least one parameter, wherein the at least one parameter is computed and/or predicted using at least one Machine Learning (ML) based learning model. The embodiments include computing at least one transmission parameter using the at least one ML based model. The parameter(s) can be considered as feedback parameters, when the parameters are included in the CSI report that is sent to the gNB. The embodiments include computing the feedback parameter(s) based on measurement data comprising channel metrics and baseband metrics, determined using CSI-Reference Signal (CSI-RS) and/or Synchronization Signal Block (SSB)); and a relationship between the measurement data and measurement data obtained from the sensors of the UE. The gNB can utilize the feedback parameters for scheduling transmission of Physical Downlink Scheduling Channel (PDSCH). Fig. 1-21 & ¶0117 - The UE 601, through the communication interface 603, can receive a Radio Resource Configuration (RRC) message. The gNB 607 can include a feedback configuration for CSI-RS in the RRC message. The feedback configuration comprises Information Elements (IEs) such as CSI-MeasConfig, CSI-ResourceConfig, CodebookConfig, and CSI-ReportConfig. Fig. 1-21 & ¶0120 - The CodebookConfig can provide an indication to the UE 601 whether the CSI feedback configuration is Type-1 or Type-2. For both Type-1 and Type-2 CSI reporting in NR, the gNB 607 can specify CSI reporting configuration in the CodebookConfig. For Type-1 CSI reporting, the UE 601 can send gNB 607 antenna port configuration, and a set of values of the feedback parameters (such as PMI and RI), which can be considered as valid, to the gNB 607. Fig. 1-21 & ¶0124 - The neural network 602c can predict the probable values of the feedback parameters at a future time instance as configured by the gNB 607 in the CSI-ReportConfig IE. For example, consider that the CSI-ReportConfig IE indicates that the UE 601 needs to report the values of PMI and RI to the gNB 607 in a CSI report. The CodebookConfig can specify a set of values for each of the PMI and RI, which are considered as valid by the gNB 607 for reporting. The neural network 602c of the UE 601 can compute and/or predict a plurality of values of PMI and a plurality of values of RI for type-1 or type-2 CSI reporting. Fig. 1-21 & ¶0154 - feedback parameters using a ML based learning model. In an embodiment, the ML based learning model can be a neural network 602c. The embodiments include generating feature vectors using at least one of channel metrics, basement metrics, RX beam pattern information, and sensor measurements. The feature vectors can be provided to the neural network 602c, for computing the feedback parameters. Fig. 1-21 & ¶0236 - methods and systems for reporting CSI to a gNB, by a UE, wherein the CSI report can include parameters that are computed and predicted using ML based learning models).
Re. Claim 5, Chavva and Madadi teach claim 1.
Chavva also teaches further comprising: receiving information for a rank value at any one of a timing of reporting the CSI or the timing of data scheduling, wherein the CSI is calculated based on the rank value. (Fig. 9A-B/Fig. 10/Fig.12 & ¶0103 - In 5th Generation (5G) New Radio (NR) communication systems, a User Equipment (UE) is configured to compute Channel State Information (CSI) parameters such as Rank Indicator (RI), Precoding Matrix Indicator (PMI), Channel Quality Indicator (CQI), CSI-Reference Signals (CSI-RS) Indicator (CRI), and so on, for at least one beam, as per CSI-RS or Synchronization Signal Block (SSB) configuration. Thereafter, the computed parameters can be sent to a Next Generation Node B (gNB), as part of a CSI report or CSI feedback. The CSI report, sent to the gNB, is used for scheduling data transmissions with a delay (known as feedback delay). Fig. 9A-B/Fig. 10/Fig.12 & ¶0104 - At step 104, the UE can process data included in the reference signal (CSI-RS) for estimating feedback parameters, i.e., CSI parameters. The CSI parameters can be estimated based on channel coefficients, which in turn can be determined based on the CSI-RS data. For example, the UE can compute CSI parameters such as PMI, RI, CQI and LI. Fig. 9A-B/Fig. 10/Fig.12 & ¶0107 - The CSI enable trigger informs the UE that the gNB is going to send the CSI-RS. The UE can compute the feedback parameters (such as RI, PMI, CQI, CRI, and so on), to be included in the CSI report, using the content in the CSI-RS. The UE can utilize the estimated channel coefficients and the measurements to compute the feedback parameters. Fig. 9A-B/Fig. 10/Fig.12 & ¶0120 - For Type-1 CSI reporting, the UE 601 can send gNB 607 antenna port configuration, and a set of values of the feedback parameters (such as PMI and RI), which can be considered as valid, to the gNB 607. The gNB 607 can choose at least one of the reported values from the range, provided to the gNB 607 by the UE 601).
Re. Claim 6, Chavva and Madadi teach claim 1.
Chavva further teaches further comprising: based on that the configuration information includes information on multiple report objects related to the CSI report (Fig. 9A-B/Fig. 10/Fig.12 & ¶0120 - The CodebookConfig can provide an indication to the UE 601 whether the CSI feedback configuration is Type-1 or Type-2. For both Type-1 and Type-2 CSI reporting in NR, the gNB 607 can specify CSI reporting configuration in the CodebookConfig. For Type-1 CSI reporting, the UE 601 can send gNB 607 antenna port configuration, and a set of values of the feedback parameters (such as PMI and RI), which can be considered as valid. Fig. 8 & ¶0149 - The UE 601 can receive the feedback configuration in a RRC message. The RRC message includes CSI-MeasConfig, CSI-ResourceConfig, CSI-ReportConfig, and CodebookConfig. Fig. 8 & ¶0150 - The CSI-MeasConfig IE can indicate whether the UE 601 needs to perform at least one of interference measurement and channel measurement. The CSI-ResourceConfig IE can include information pertaining to allocation of time/frequency resources for CSI-RS reception such as time slots in which the UE 601 can expect to receive the CSI-RS, frequency of the CSI-RS, and ports through which the CSI-RS can be received. The CSI-ReportConfig IE can include time slots in which the UE 601 can send the CSI report, feedback parameters to be included in the CSI report, and so on. The CodebookConfig indicates to the UE 601 as to whether the CSI feedback configuration, provided to the UE 601, is pertaining to type-1 CSI or type-2 CSI), receiving information indicating at least one report object among the multiple reporting objects (Fig. 8 & ¶0150 - The CSI-ResourceConfig IE can include information pertaining to allocation of time/frequency resources for CSI-RS reception such as time slots in which the UE 601 can expect to receive the CSI-RS, frequency of the CSI-RS, and ports through which the CSI-RS can be received. The CSI-ReportConfig IE can include time slots in which the UE 601 can send the CSI report, feedback parameters to be included in the CSI report, and so on. Fig. 8 & ¶0151 - At step 802, the method includes receiving CSI-RS and/or SSB, by the UE 601, if a current slot includes the CSI-RS and/or SSB. The UE 601 determines whether a current slot includes the CSI-RS, and which future slot is likely to include the CSI-RS; based on the information included in the CSI-ResourceConfig.), and wherein the CSI is calculated based on the at least one report object. (Fig. 8 & ¶0153 - At step 803, the method includes computing, by the UE 601, feedback parameters based on the information included in the CSI-RS and/or SSB. The embodiments compute the feedback parameters periodically, wherein the periodicity is indicated in the CSI-ResourceConfig and CSI-ReportConfig IEs. Fig. 8 & ¶0159 - At step 805, the method includes generating, by the UE 601, at least one CSI report comprising the computed feedback parameters and the predicted values of the feedback parameters. The embodiments include generating the CSI report at the reporting time slot. In an embodiment, a single CSI report is generated, wherein the CSI report includes the predicted values of the feedback parameters at a single future time instance. In an embodiment, a plurality of CSI reports is generated, wherein the plurality of CSI reports include the predicted values of the feedback parameters at multiple future time instances).
Re. Claim 8, Chavva and Park teach claim 1.
Chavva also discloses further comprising: based on that the configuration information includes information indicating at least one frequency domain resource for the CSI report (Fig. 8 & ¶0150 - The CSI-MeasConfig IE can indicate whether the UE 601 needs to perform at least one of interference measurement and channel measurement. The CSI-ResourceConfig IE can include information pertaining to allocation of time/frequency resources for CSI-RS reception such as time slots in which the UE 601 can expect to receive the CSI-RS, frequency of the CSI-RS, and ports through which the CSI-RS can be received.), receiving information indicating at least one specific frequency domain resource among the at least one frequency domain resource (Fig. 8 & ¶0150 - The CSI-MeasConfig IE can indicate whether the UE 601 needs to perform at least one of interference measurement and channel measurement. The CSI-ResourceConfig IE can include information pertaining to allocation of time/frequency resources for CSI-RS reception such as time slots in which the UE 601 can expect to receive the CSI-RS, frequency of the CSI-RS, and ports through which the CSI-RS can be received. Fig. 8 & ¶0151 - At step 802, the method includes receiving CSI-RS and/or SSB, by the UE 601, if a current slot includes the CSI-RS and/or SSB. The UE 601 determines whether a current slot includes the CSI-RS, and which future slot is likely to include the CSI-RS; based on the information included in the CSI-ResourceConfig),
Yet, Chavva does not expressly teach wherein the at least one specific frequency domain resource is associated with frequency domain resource allocation according to the data scheduling, and the CSI is calculated based on the at least one specific frequency domain resource.
However, in the analogous art, Madadi explicitly discloses wherein the at least one specific frequency domain resource is associated with frequency domain resource allocation according to the data scheduling, and the CSI is calculated based on the at least one specific frequency domain resource. (Fig. 4-15 & ¶0108 - FIG. 5 is an example illustrating generation of AI-CFI from estimated CSI at a UE according to embodiments of the present disclosure. In one example 500, the high resolution estimated channel information 501 (i.e., the spatial-frequency domain channel at each subcarrier level) can be an input to an encoder 502 at the UE, such as a convolutional neural network (CNN), that extracts various feature maps and a fully connected layer to generate AI-CFI 503 of certain fixed length, as illustrated in FIG. 5. Fig. 4-15 & ¶0109 - Conventional CSI feedback mechanism such as Type-2 in Release 15, the CSI compression was only in spatial domain (SD) where spatial Discrete Fourier transform (DFT) basis set is generated at UE based on the antenna configuration and oversampling factors. After that, L orthogonal DFT beams are selected which are common to both antenna polarizations and across sub-bands. Dominant eigenvectors per subband are represented as linear combination (LC) of these selected L beams. The overhead associated with the feedback includes bits to communicate the L basis selection, amplitude and phase values, which for one particular configuration of L=4 with 32 transmit antennas and 4 receive antennas will be 351 bits. Using AI methods, the same dominant eigenvectors per subband information can be sent by using much less overhead (˜100 bits) and with better resolution, by exploiting the correlation using CNN architecture as in FIG. 5. Fig. 4-15 & ¶0115 - The UE receives the higher layer CSI reporting configuration to the UE using RRC messages, such as the CSI reporting parameters (PMI, RI, CQI, L1, CRI, AI-CFI), CSI configuration type (periodic, semi-persistent PUCCH, semi-persistent PUSCH, aperiodic), report frequency configuration (frequency granularity, i.e., wideband/subband), codebook configuration (Type-1/Type-2 codebook parameters), AI-CFI configuration (size of the AI-CFI), etc. Part of or all of the CSI report configuration information is set in the RRC IE CSI-ReportConfig. Fig. 4-15 & ¶0119 - the local information at the UE such as the UE's velocity, location, and trajectory information are employed to predict channel conditions for the UE at a future time, when the UE is more likely to be scheduled by the BS).
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filling date of the claimed invention to combine Chavva’s invention of a system and a method for generating a CSI (Channel State Information) report comprising of parameters estimated and predicted using Machine Learning (ML) in a 5th Generation (5G)-New Radio (NR) communication system to include Madadi’s invention of a system and a method for method and apparatus for support of machine learning or artificial intelligence techniques for CSI < channel state information > feedback in FDD < Frequency Division Duplex > & MIMO < multiple-input multiple-output > technology in a wireless communication system, because it provides an efficient mechanism in supporting high-resolution CSI reporting for UEs (User Equipment) with reduced feedback overhead by further compressing CSI overhead while maintaining large MU-MIMO performance gain by implementing AI/ML technique in enabling the reduction of overhead for the high-resolution CSI feedback in the FDD MU-MIMO technology in the wireless communication system. (¶0065-¶0071, Madadi)
Re. Claim 10, Chavva and Madadi teach claim 1.
Chavva further teaches wherein: the CSI is calculated further based on information for a transmission scheme at the timing of data scheduling (Fig. 1-21 & ¶0056 - wherein the probable values of the CSI feedback parameters at the future time instance are predicted based on at least one of: ….a delay in scheduling a Physical Downlink Scheduling Channel (PDSCH) by the gNB (607) after receiving the CSI report from the UE (601); Fig. 1-21 & ¶0097 - determining an optimal CSI-RS resource allocation and an optimal periodicity for reporting CSI. The embodiments include reporting the optimal CSI-RS resource allocation and the optimal periodicity to the gNB for optimizing the throughput and CSI feedback overhead. The gNB can send updated CSI-RS resources and updated feedback configurations for CSI-RS. The embodiments include determining the optimal CSI-RS resource allocation and the optimal periodicity periodically based on variation in the channel metrics and the baseband metrics. Fig. 1-21 & ¶0108 - When the gNB needs to send data to the UE, the gNB requests the UE to send measurements, including the CSI report. At time instance t1, the UE can send a CSI feedback report to the gNB. The UE can evaluate the ideal Modulation and Coding Scheme (MCS), and report the ideal MCS to the gNB at t1. The gNB can utilize the CSI report for scheduling downlink data transmission. …. Based on the CSI report received from the UE, the gNB can schedule the transmission of Physical Downlink Control Channel (PDSCH) and choose the appropriate MCS to encode the PDSCH.), and the information for the transmission scheme includes at least one of a precoding scheme or resource allocation information(Fig. 1-21 & ¶0049 - wherein one of the CSI feedback parameters is a Precoding Matrix Indicator (PMI), wherein the neural network (602c) determines a most probable PMI value amongst a predefined number of probable PMI values, wherein the predefined number of PMI values are selected amongst a plurality of predicted PMI values. Fig. 1-21 & ¶0091 - methods and systems for reporting Channel State Information (CSI), to a Next Generation Node B (gNB), comprising of parameters, wherein the parameters are computed and/or predicted using Neural Network (NN) based learning models. For example, the parameters can be Precoding Matrix Indicator (PMI), Channel Quality Indicator (CQI), CSI-Reference Signals (CSI-RS) Indicator (CRI), Rank Indicator (RI). Fig. 1-21 & ¶0097 - determining an optimal CSI-RS resource allocation and an optimal periodicity for reporting CSI. The embodiments include reporting the optimal CSI-RS resource allocation and the optimal periodicity to the gNB for optimizing the throughput and CSI feedback overhead. The gNB can send updated CSI-RS resources and updated feedback configurations for CSI-RS. The embodiments include determining the optimal CSI-RS resource allocation and the optimal periodicity periodically based on variation in the channel metrics and the baseband metrics. Fig. 1-21 & ¶0141 - the neural network 602c can determine an optimal CSI-RS resource allocation and an optimal periodicity of sending CSI reports. The UE 601 can send the optimal CSI-RS resource allocation and the optimal periodicity for sending CSI reports, to the gNB 607, for optimizing the throughput and CSI feedback overhead).
Re. Claim 11, Chavva and Madadi teach claim 1.
Chavva further teaches wherein: the configuration information is received through higher layer signaling (Fig. 8 & ¶0149 - At step 801, the method includes receiving a feedback configuration, by the UE 601, from the gNB 607. The feedback configuration is relevant to reception of CSI-RS and/or SSB. The feedback configuration can be used by the UE 601 to send the CSI as a feedback report. The UE 601 can receive the feedback configuration in a RRC message. The RRC message includes CSI-MeasConfig, CSI-ResourceConfig, CSI-ReportConfig, and CodebookConfig.),
Yet, Chavva does not expressly teach the information for the payload configuration is received through dynamic signaling.
However, in the analogous art, Madadi explicitly discloses the information for the payload configuration is received through dynamic signaling (Fig. 4-15 & ¶0113 - At operation 601, a UE's AI/ML capability to support the AI/ML assisted CSI feedback is reported to the BS. Such capabilities include the support of AI/ML model training and/or inference … The report of the capability information can be via PUCCH and/or PUSCH. A new UCI type, a new PUCCH format and/or a new MAC CE can be defined for the capability information report. Fig. 4-15 & ¶0116 – At operation 603, the UE receives the CSI reference signal(s) from the BS, after which the UE sends the CSI reports. While the periodic CSI reporting is based on the parameters configured in the RRC messages, the aperiodic CSI reporting is triggered by the DCI or DCI+MAC CE. Semi-persistent CSI reporting on PUSCH is triggered by DCI and semi-persistent CSI reporting on PUCCH is activated/deactivated by MAC CE. The DCI trigger is used to dynamically move from one CSI reporting configuration to another, such as moving from Type-2 codebook based CSI feedback mechanism to AI/ML based CSI feedback mechanism using the reporting parameter CFI.)
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filling date of the claimed invention to combine Chavva’s invention of a system and a method for generating a CSI (Channel State Information) report comprising of parameters estimated and predicted using Machine Learning (ML) in a 5th Generation (5G)-New Radio (NR) communication system to include Madadi’s invention of a system and a method for method and apparatus for support of machine learning or artificial intelligence techniques for CSI < channel state information > feedback in FDD < Frequency Division Duplex > & MIMO <multiple-input multiple-output> technology in a wireless communication system, because it provides an efficient mechanism in supporting high-resolution CSI reporting for UEs (User Equipment) with reduced feedback overhead by further compressing CSI overhead while maintaining large MU-MIMO performance gain by implementing AI/ML technique in enabling the reduction of overhead for the high-resolution CSI feedback in the FDD MU-MIMO technology in the wireless communication system. (¶0065-¶0071, Madadi)
Re. Claim 15, Chavva teaches an apparatus (Fig. 22 / Fig. 2/Fig. 4, gNB/Base Station) comprising: at least one transceiver (Fig. 22, 2220); and at least one processor (Fig. 22, 2210) coupled with the at least one transceiver (Fig. 22, 2220), wherein the at least one processor (Fig. 22, 2210) is configured to: transmit to a user equipment (UE) (Fig. 23 / Fig. 2/Fig. 4, UE), configuration information related to a channel state information (CSI) report (Fig. 8 & ¶0149 - At step 801, the method includes receiving a feedback configuration, by the UE 601, from the gNB 607. The feedback configuration is relevant to reception of CSI-RS and/or SSB. The feedback configuration can be used by the UE 601 to send the CSI as a feedback report. The UE 601 can receive the feedback configuration in a RRC message. The RRC message includes CSI-MeasConfig, CSI-ResourceConfig, CSI-ReportConfig, and CodebookConfig. Fig. 8 & ¶0150 - The CSI-MeasConfig IE can indicate whether the UE 601 needs to perform at least one of interference measurement and channel measurement. The CSI-ResourceConfig IE can include information pertaining to allocation of time/frequency resources for CSI-RS reception such as time slots in which the UE 601 can expect to receive the CSI-RS, frequency of the CSI-RS, and ports through which the CSI-RS can be received. The CSI-ReportConfig IE can include time slots in which the UE 601 can send the CSI report, feedback parameters to be included in the CSI report, and so on. The CodebookConfig indicates to the UE 601 as to whether the CSI feedback configuration, provided to the UE 601, is pertaining to type-1 CSI or type-2 CSI.); transmit to a user equipment (UE) (Fig. 23 / Fig. 2/Fig. 4, UE), information for a payload configuration related to the CSI report (Fig. 6 & ¶0036 - wherein the probable values of the CSI feedback parameters at the future time instance are predicted based on at least one of: …. a delay in scheduling a Physical Downlink Scheduling Channel (PDSCH) by the gNB (607) after receiving the CSI report from the UE (601); a block error rate pertaining to reception of the PDSCH. Fig. 6 & ¶0056 - wherein the probable values of the CSI feedback parameters at the future time instance are predicted based on at least one of: ….a delay in scheduling a Physical Downlink Scheduling Channel (PDSCH) by the gNB (607) after receiving the CSI report from the UE (601); a block error rate pertaining to reception of the PDSCH; Fig. 4 & ¶0112 - the UE receives CSI-RS, followed by PDSCH through a first beam. The UE utilizes the CSI-RS, which is received periodically from the gNB, to generate a CSI-RS report. Based on the CSI report, the gNB can choose appropriate MCS to transmit the subsequent PDSCH); receive, from the UE, the CSI which is reported based on the configuration information and the information for the payload configuration (Fig. 6 & ¶0036 - wherein the probable values of the CSI feedback parameters at the future time instance are predicted based on at least one of: …. a delay in scheduling a Physical Downlink Scheduling Channel (PDSCH) by the gNB (607) after receiving the CSI report from the UE (601); a block error rate pertaining to reception of the PDSCH). Fig. 6 & ¶0056 - wherein the probable values of the CSI feedback parameters at the future time instance are predicted based on at least one of: ….a delay in scheduling a Physical Downlink Scheduling Channel (PDSCH) by the gNB (607) after receiving the CSI report from the UE (601); a block error rate pertaining to reception of the PDSCH; a CSI reporting periodicity; and a code rate for scheduling the PDSCH by the gNB (607)),
Yet, Chavva does not expressly teach determine, based on information predicted through an artificial intelligence model, a payload size for at least one of a physical uplink control channel (PUCCH) or a physical uplink shared channel (PUSCH) carrying CSI, wherein the information for the payload configuration includes an indicator for the payload size ; wherein the information predicted through the artificial intelligence model includes information for a channel state at a timing of data scheduling after reporting the CSI, wherein the CSI received from the UE is calculated, by the UE, based on the payload size indicated by the indicator and the channel state at the timing of data scheduling.
However, in the analogous art, Madadi explicitly discloses determine, based on information predicted through an artificial intelligence model, a payload size for at least one of a physical uplink control channel (PUCCH) or a physical uplink shared channel (PUSCH) carrying CSI, wherein the information for the payload configuration includes an indicator for the payload size (Fig. 4-15 & ¶0006 - receiving CSI reporting configurations that include indications that enable or disable at least one of: ML-assisted CSI prediction and artificial intelligence channel feature information (AI-CFI) reporting. ML model training is performed or trained ML model parameters are received, and CSI reference signals corresponding to at least one of the CSI reporting configurations are received. If ML-assisted CSI prediction is enabled, the CSI reporting configurations further include: a timing offset for future CSI prediction, and ML configurations including indication of an ML model used for the ML-assisted CSI prediction. If AI-CFI reporting is enabled, the CSI reporting configurations further include: a configuration for a report of the AI-CFI, and ML configurations including indication of an ML model used for the ML assisted-CSI feedback determination. Fig. 4-15 & ¶0113 - At operation 601, a UE's AI/ML capability to support the AI/ML assisted CSI feedback is reported to the BS. Such capabilities include the support of AI/ML model training and/or inference … The report of the capability information can be via PUCCH and/or PUSCH. A new UCI type, a new PUCCH format and/or a new MAC CE can be defined for the capability information report. Fig. 4-15 & ¶0114 - At operation 602, the UE receives CSI related configuration(s) from the BS, including the enabling or disabling of the AI-based CSI feedback mechanism. When AI-based CSI feedback is enabled, the UE also receives the AI/ML related configuration information, such as the AI/ML model used, and the trained model parameters of the model. Fig. 4-15 & ¶0115 - The UE receives the higher layer CSI reporting configuration to the UE using RRC messages, such as the CSI reporting parameters (PMI, RI, CQI, L1, CRI, AI-CFI <Artificial intelligence channel feature information, see ¶0006>), CSI configuration type (periodic, semi-persistent PUCCH, semi-persistent PUSCH, aperiodic), report frequency configuration (frequency granularity, i.e., wideband/subband), codebook configuration (Type-1/Type-2 codebook parameters), AI-CFI configuration (size of the AI-CFI), etc. Part of or all of the CSI report configuration information is set in the RRC IE CSI-ReportConfig. … all CSI report configuration information associated with AI methods are set in new IE CSI-AIReportConfig that specifies the reporting configuration for CSI feedback using AI methods. Fig. 4-15 & ¶0116 - At operation 603, the UE receives the CSI reference signal(s) from the BS, after which the UE sends the CSI reports. … Semi-persistent CSI reporting on PUSCH is triggered by DCI and semi-persistent CSI reporting on PUCCH is activated/deactivated by MAC CE. Fig. 4-15 & ¶0117 - At operation 604, the UE performs the inference, i.e., extraction of compressed feature(s) from the CSI channel based on the received CSI reporting configuration information, trigger received and the triggering configuration. For example, the UE follows the configured ML model and model parameters, and CSI reporting parameters sent from the BS, to perform the inference operation. The UE sends the CSI report with the CSI reporting parameter AI-CFI, which is the outcome of the inference of the AI/ML model to the BS); wherein the information predicted through the artificial intelligence model includes information for a channel state at a timing of data scheduling after reporting the CSI (Fig. 4-15 & ¶0006 - receiving CSI reporting configurations that include indications that enable or disable at least one of: ML-assisted CSI prediction and artificial intelligence channel feature information (AI-CFI) reporting. ML model training is performed or trained ML model parameters are received, and CSI reference signals corresponding to at least one of the CSI reporting configurations are received. If ML-assisted CSI prediction is enabled, the CSI reporting configurations further include: a timing offset for future CSI prediction, and ML configurations including indication of an ML model used for the ML-assisted CSI prediction. If AI-CFI reporting is enabled, the CSI reporting configurations further include: a configuration for a report of the AI-CFI, and ML configurations including indication of an ML model used for the ML assisted-CSI feedback determination. Fig. 4-15 & ¶0113 - At operation 601, a UE's AI/ML capability to support the AI/ML assisted CSI feedback is reported to the BS. Such capabilities include the support of AI/ML model training and/or inference … The report of the capability information can be via PUCCH and/or PUSCH. A new UCI type, a new PUCCH format and/or a new MAC CE can be defined for the capability information report. Fig. 4-15 & ¶0114 - At operation 602, the UE receives CSI related configuration(s) from the BS, including the enabling or disabling of the AI-based CSI feedback mechanism. When AI-based CSI feedback is enabled, the UE also receives the AI/ML related configuration information, such as the AI/ML model used, and the trained model parameters of the model. Fig. 4-15 & ¶0115 - The UE receives the higher layer CSI reporting configuration to the UE using RRC messages, such as the CSI reporting parameters (PMI, RI, CQI, L1, CRI, AI-CFI <Artificial intelligence channel feature information, see ¶0006>), CSI configuration type (periodic, semi-persistent PUCCH, semi-persistent PUSCH, aperiodic), report frequency configuration (frequency granularity, i.e., wideband/subband), codebook configuration (Type-1/Type-2 codebook parameters), AI-CFI configuration (size of the AI-CFI), etc. Part of or all of the CSI report configuration information is set in the RRC IE CSI-ReportConfig. … all CSI report configuration information associated with AI methods are set in new IE CSI-AIReportConfig that specifies the reporting configuration for CSI feedback using AI methods. Fig. 4-15 & ¶0116 - At operation 603, the UE receives the CSI reference signal(s) from the BS, after which the UE sends the CSI reports. … Semi-persistent CSI reporting on PUSCH is triggered by DCI and semi-persistent CSI reporting on PUCCH is activated/deactivated by MAC CE. Fig. 4-15 & ¶0117 - At operation 604, the UE performs the inference, i.e., extraction of compressed feature(s) from the CSI channel based on the received CSI reporting configuration information, trigger received and the triggering configuration. For example, the UE follows the configured ML model and model parameters, and CSI reporting parameters sent from the BS, to perform the inference operation. The UE sends the CSI report with the CSI reporting parameter AI-CFI, which is the outcome of the inference of the AI/ML model to the BS. Fig. 4-15 & ¶0119 - the local information at the UE such as the UE's velocity, location, and trajectory information are employed to predict channel conditions for the UE at a future time, when the UE is more likely to be scheduled by the BS), wherein the CSI received from the UE is calculated, by the UE, based on the payload size indicated by the indicator and the channel state at the timing of data scheduling (Fig. 4-15 & ¶0115 - The UE receives the higher layer CSI reporting configuration to the UE using RRC messages, such as the CSI reporting parameters (PMI, RI, CQI, L1, CRI, AI-CFI <Artificial intelligence channel feature information, see ¶0006>), CSI configuration type (periodic, semi-persistent PUCCH, semi-persistent PUSCH, aperiodic), report frequency configuration (frequency granularity, i.e., wideband/subband), codebook configuration (Type-1/Type-2 codebook parameters), AI-CFI configuration (size of the AI-CFI), etc. Part of or all of the CSI report configuration information is set in the RRC IE CSI-ReportConfig. … all CSI report configuration information associated with AI methods are set in new IE CSI-AIReportConfig that specifies the reporting configuration for CSI feedback using AI methods. Fig. 4-15 & ¶0118 - signaling method to support predicted feedback of CSI at a future time, predicted by an AI/ML model at the UE, is supported. In current NR, the CSI feedback reported to the BS is based on the CSI estimation by the UE at the current time. Fig. 4-15 & ¶0119 - the local information at the UE such as the UE's velocity, location, and trajectory information are employed to predict channel conditions for the UE at a future time, when the UE is more likely to be scheduled by the BS. Fig. 4-15 & ¶0132 - an additional CSI reporting parameter labeled AI-Channel Feature information (AI-CFI), that is the output of the AI/ML model deployed at UE. … the BS can enable only AI/ML based CSI feedback reporting by setting AI/ML based CSI feedback reporting as the sole CSI reporting parameter. … the BS can enable AI/ML based CSI feedback along with original feedback mechanism by setting the reporting parameters to include both PMI, RI and AI-CFI. … … This reporting parameter is the compressed extracted feature from the estimated CSI at the UE, which can be used by BS to reconstruct the CSI).
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filling date of the claimed invention to combine Chavva’s invention of a system and a method for generating a CSI (Channel State Information) report comprising of parameters estimated and predicted using Machine Learning (ML) in a 5th Generation (5G)-New Radio (NR) communication system to include Madadi’s invention of a system and a method for method and apparatus for support of machine learning or artificial intelligence techniques for CSI <channel state information> feedback in FDD <Frequency Division Duplex> & MIMO <multiple-input multiple-output> technology in a wireless communication system, because it provides an efficient mechanism in supporting high-resolution CSI reporting for UEs (User Equipment) with reduced feedback overhead by further compressing CSI overhead while maintaining large MU-MIMO performance gain by implementing AI/ML technique in enabling the reduction of overhead for the high-resolution CSI feedback in the FDD MU-MIMO technology in the wireless communication system. (¶0065-¶0071, Madadi)
Re. Claim 18, Chavva and Madadi teach claim 1.
Yet, Chavva does not expressly teach wherein: the information for the channel state at the timing of data scheduling is predicted through the artificial intelligence model by the base station.
However, in the analogous art, Madadi explicitly discloses teach wherein: the information for the channel state at the timing of data scheduling is predicted through the artificial intelligence model by the base station. (Fig. 4-15 & ¶0006 - receiving CSI reporting configurations that include indications that enable or disable at least one of: ML-assisted CSI prediction and artificial intelligence channel feature information (AI-CFI) reporting. ML model training is performed or trained ML model parameters are received, and CSI reference signals corresponding to at least one of the CSI reporting configurations are received. If ML-assisted CSI prediction is enabled, the CSI reporting configurations further include: a timing offset for future CSI prediction, and ML configurations including indication of an ML model used for the ML-assisted CSI prediction. If AI-CFI reporting is enabled, the CSI reporting configurations further include: a configuration for a report of the AI-CFI, and ML configurations including indication of an ML model used for the ML assisted-CSI feedback determination. Fig. 4-15 & ¶0113 - At operation 601, a UE's AI/ML capability to support the AI/ML assisted CSI feedback is reported to the BS. Such capabilities include the support of AI/ML model training and/or inference … The report of the capability information can be via PUCCH and/or PUSCH. A new UCI type, a new PUCCH format and/or a new MAC CE can be defined for the capability information report. Fig. 4-15 & ¶0114 - At operation 602, the UE receives CSI related configuration(s) from the BS, including the enabling or disabling of the AI-based CSI feedback mechanism. When AI-based CSI feedback is enabled, the UE also receives the AI/ML related configuration information, such as the AI/ML model used, and the trained model parameters of the model. Fig. 4-15 & ¶0115 - The UE receives the higher layer CSI reporting configuration to the UE using RRC messages, such as the CSI reporting parameters (PMI, RI, CQI, L1, CRI, AI-CFI <Artificial intelligence channel feature information, see ¶0006>), CSI configuration type (periodic, semi-persistent PUCCH, semi-persistent PUSCH, aperiodic), report frequency configuration (frequency granularity, i.e., wideband/subband), codebook configuration (Type-1/Type-2 codebook parameters), AI-CFI configuration (size of the AI-CFI), etc. Part of or all of the CSI report configuration information is set in the RRC IE CSI-ReportConfig. … all CSI report configuration information associated with AI methods are set in new IE CSI-AIReportConfig that specifies the reporting configuration for CSI feedback using AI methods. Fig. 4-15 & ¶0116 - At operation 603, the UE receives the CSI reference signal(s) from the BS, after which the UE sends the CSI reports. … Semi-persistent CSI reporting on PUSCH is triggered by DCI and semi-persistent CSI reporting on PUCCH is activated/deactivated by MAC CE. Fig. 4-15 & ¶0117 - At operation 604, the UE performs the inference, i.e., extraction of compressed feature(s) from the CSI channel based on the received CSI reporting configuration information, trigger received and the triggering configuration. For example, the UE follows the configured ML model and model parameters, and CSI reporting parameters sent from the BS, to perform the inference operation. The UE sends the CSI report with the CSI reporting parameter AI-CFI, which is the outcome of the inference of the AI/ML model to the BS. Fig. 4-15 & ¶0119 - the local information at the UE such as the UE's velocity, location, and trajectory information are employed to predict channel conditions for the UE at a future time, when the UE is more likely to be scheduled by the BS).
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filling date of the claimed invention to combine Chavva’s invention of a system and a method for generating a CSI (Channel State Information) report comprising of parameters estimated and predicted using Machine Learning (ML) in a 5th Generation (5G)-New Radio (NR) communication system to include Madadi’s invention of a system and a method for method and apparatus for support of machine learning or artificial intelligence techniques for CSI <channel state information> feedback in FDD <Frequency Division Duplex> & MIMO <multiple-input multiple-output> technology in a wireless communication system, because it provides an efficient mechanism in supporting high-resolution CSI reporting for UEs (User Equipment) with reduced feedback overhead by further compressing CSI overhead while maintaining large MU-MIMO performance gain by implementing AI/ML technique in enabling the reduction of overhead for the high-resolution CSI feedback in the FDD MU-MIMO technology in the wireless communication system. (¶0065-¶0071, Madadi)
Claims 3 and 7 are rejected under 35 U.S.C. 103 as being unpatentable over Chavva, in view of Madad, further in view of Zeineddine et al. (2023/0171623), Zeineddine hereinafter.
Re. Claim 3, Chavva and Madad teach claim 1.
Yet, Chavva and Madad do not expressly teach wherein: the information for the payload configuration is configured or indicated based on a format indicator for the at least one of the PUCCH or the PUSCH.
However, in the analogous art, Zeineddine explicitly discloses wherein: the information for the payload configuration is configured or indicated based on a format indicator for the at least one of the PUCCH or the PUSCH. (Fig. 1-11 & ¶0091 - Table 3 (See snapshots below) shows uplink channels used for CSI reporting as a function of the CSI codebook type. Fig. 1-11 & ¶0092 - CSI reporting, PUSCH-based reports may be divided into two CSI parts: CSI part 1 and CSI part 2. The reason for this may be that a size of CSI payload varies significantly, and, therefore, a worst-case UCI payload size design may result in large overhead. Fig. 1-11 & ¶0093 - CSI part 1 has a fixed payload size (e.g., may be decoded by a gNB without prior information) and may contain the following: 1) RI (if reported), CRI (if reported) and CQI for the first codeword; and/or 2) a number of non-zero wideband amplitude coefficients per layer for Type II CSI feedback on PUSCH. Fig. 1-11 & ¶0094 - CSI part 2 has a variable payload size that may be derived from CSI parameters in CSI part 1, and may contain PMI and the CQI for the second codeword if RI>4)
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Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filling date of the claimed invention to combine Chavva’s invention of a system and a method for generating a CSI (Channel State Information) report comprising of parameters estimated and predicted using Machine Learning (ML) in a 5th Generation (5G)-New Radio (NR) communication system and Madadi’s invention of a system and a method for method and apparatus for support of machine learning or artificial intelligence techniques for CSI <channel state information> feedback in FDD <Frequency Division Duplex> & MIMO <multiple-input multiple-output> technology in a wireless communication system to include Zeineddine’s invention of a system and a method for channel state information reporting in a wireless communication system, because it provides an efficient mechanism for reporting channel state information (CSI) in a multi-TRP and/or multi-panel networks in the wireless communication system. (¶0002-¶0003, Zeineddine)
Re. Claim 7, Chavva and Madad teach claim 6.
Yet, Chavva and Madad do not expressly teach wherein: the CSI is calculated based on a differential value for remaining report objects excluding the at least one report object among the multiple report objects.
However, in the analogous art, Zeineddine explicitly discloses wherein: the CSI is calculated based on a differential value for remaining report objects excluding the at least one report object among the multiple report objects. (Fig. 1-11 & ¶0120 - WB CQI value q′t (e.g., 4 bits) may be reported in CSI report 2(t−1)+1, indicating CQI for TRP t transmission with rank v′t. In certain embodiments, differential WB CQI value q″t (e.g., 2 bits) may be reported in CSI report 2(t−1)+2, indicating CQI index offset value for TRP t single transmission with full rank vt, where the offset value is with respect to q′t. In some embodiments, differential WB CQI value q′t,t* (e.g., 2 bits) may be reported in CSI report 2(t−1)+1, CSI report 2(t*−1)+1, or both, indicating a CQI index offset under joint transmission from both TRPs t, t* with rank v′t, v′t*, respectively. Fig. 1-11 & ¶0121 - sub-band (“SB”) CQI values for each CQI sub-band index w may be reported in a similar manner (e.g., reporting sub-band differential CQI values p″t(w) with respect to a function f 2(q′t, q″t)) for full-rank transmission vt of TRP t to be reported in CSI report 2(t−1)+2. … a sub-band differential CQI values pJTt,t*(w) may be defined with respect to qJTt,t* to be reported in CSI report 2(t−1)+1, CSI report 2(t*−1)+1, or both.).
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filling date of the claimed invention to combine Chavva’s invention of a system and a method for generating a CSI (Channel State Information) report comprising of parameters estimated and predicted using Machine Learning (ML) in a 5th Generation (5G)-New Radio (NR) communication system and Madadi’s invention of a system and a method for method and apparatus for support of machine learning or artificial intelligence techniques for CSI <channel state information> feedback in FDD <Frequency Division Duplex> & MIMO <multiple-input multiple-output> technology in a wireless communication system to include Zeineddine’s invention of a system and a method for channel state information reporting in a wireless communication system, because it provides an efficient mechanism for reporting channel state information (CSI) in a multi-TRP and/or multi-panel networks in the wireless communication system. (¶0002-¶0003, Zeineddine)
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Chavva, in view of Madadi, further in view of Tang et al. (2020/0314698), Tang hereinafter.
Re. Claim 9, Chavva and Madadi teach claim 1.
Chavva further teaches further comprising: based on that the configuration information includes at least one parameter related to a reference resource for the CSI report (Fig. 1-21 & ¶0104 - At step 101, a CSI feedback configuration is initialized. The gNB can send a feedback configuration for CSI-RS to the UE. The feedback configuration, received by the UE, includes CSI-MeasConfig, CSI-ResourceConfig, and CSI-ReportConfig. The feedback configuration informs the UE about the feedback parameters that are to be included in the CSI report, periodicity of transmission of the CSI report, time/frequency resource allocation for CSI-RS, and port information for receiving the CSI-RS. …At step 102, the UE can check whether a currently received symbol/slot includes the CSI-RS or SSB. If a received symbol/slot includes CSI-RS/SSB, then, at step 103, the UE can receive reference signal data corresponding to at least one configured Transmitter (TX) beam.),
Yet, Chavva and Madadi do not expressly teach receiving information for an adjustment value for the at least one parameter.
However, in the analogous art, Tang explicitly discloses receiving information for an adjustment value for the at least one parameter. (Fig. 3 & ¶0162 - At S330, the network device # A may perform an adjustment process for the code rate used by the terminal device # A based on the quality of the link # A reported by the terminal device # A. Fig. 4 & ¶0182 - at S430, the network device # B may determine a target code rate to which the code rate currently used by the terminal device # A needs to be adjusted based on the quality of the link # A. Fig. 4 & ¶0186 - performing, by the first network device, an adjustment process of a code rate currently used by the first terminal device according to a relationship between the target code rate and the first code rate comprises: if the first code rate is greater than the target code rate, the first network device adjusts the code rate currently used by the first terminal device to the target code rate. ).
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filling date of the claimed invention to combine Chavva’s invention of a system and a method for generating a CSI (Channel State Information) report comprising of parameters estimated and predicted using Machine Learning (ML) in a 5th Generation (5G)-New Radio (NR) communication system and Madadi’s invention of a system and a method for method and apparatus for support of machine learning or artificial intelligence techniques for CSI <channel state information> feedback in FDD <Frequency Division Duplex> & MIMO <multiple-input multiple-output> technology in a wireless communication system to include Tang’s invention of a system and a method for sending encoding data in a wireless communication system, because it provides an efficient mechanism for ensuring reliability and accuracy of transmission by detecting link quality of a communication link between a terminal device and a network device and adjust a code rate used by the terminal device in a process of data encoding according to the link quality of the communication link in the wireless communication system. (¶0002-¶0005, Tang)
Claim 19 is rejected under 35 U.S.C. 103 as being unpatentable over Chavva, in view of Madadi, further in view of KWAK et al. (2023/0254872), KWAK hereinafter.
Re. Claim 19, Chavva and Madadi teach claim 8.
Yet, Chavva and Madadi do not expressly teach wherein: a physical downlink shared channel (PDSCH) for the data scheduling is scheduled within the at least one specific frequency domain resource.
However, in the analogous art, KWAK explicitly discloses wherein: a physical downlink shared channel (PDSCH) for the data scheduling is scheduled within the at least one specific frequency domain resource. (Fig. 1-33 & ¶0010 - a method performed by user equipment (UE) in a communication system includes receiving, from a base station, control information on a frequency region configured by system information, wherein the control information defines a transmission time interval for a physical downlink shared channel (PDSCH) among a plurality of transmission time intervals, identifying a transport block size associated with a number of symbols, and receiving, from the base station, the PDSCH associated with the transport block size in the number of symbols from a start symbol based on the resource allocation information, wherein the resource allocation information indicates the start symbol of the transmission time interval, and the transmission time interval is defined based on the number of symbols from the start symbol. Fig. 1-33 & ¶0282 - Downlink data may be transmitted on a physical downlink shared channel (PDSCH), which is a physical downlink data channel. The PDSCH may be transmitted after the control channel transmission interval, and scheduling information, such as a specific mapping position in the frequency domain and a modulation scheme, is determined on the basis of the DCI transmitted through the PDCCH. Also, see claims 1-3, e.g., claim 3, “wherein a frequency region for the PDSCH in the transmission time interval is defined within the frequency region configured by the system information, in a frequency domain.”).
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filling date of the claimed invention to combine Chavva’s invention of a system and a method for generating a CSI (Channel State Information) report comprising of parameters estimated and predicted using Machine Learning (ML) in a 5th Generation (5G)-New Radio (NR) communication system and Madadi’s invention of a system and a method for method and apparatus for support of machine learning or artificial intelligence techniques for CSI <channel state information> feedback in FDD <Frequency Division Duplex> & MIMO <multiple-input multiple-output> technology in a wireless communication system to include KWAK’s invention of a system and a method for performing semi-persistent channel state reporting (semi-persistent CSI reporting) in a wireless communication system, because it provides an efficient mechanism for supporting transmitting two or more pieces of data for the same UE or respective pieces of data for different UEs in one transmission interval (minimum scheduling unit) operating in the wireless communication system. (¶0002-¶0010, KWAK)
Response to Arguments
Applicant’s arguments filed on 08/26/2026 with respect to claims 1, 13 and 15 have been considered but they are not persuasive.
Regarding arguments at pages 7-10 as submitted on 08/26/2026 for independent claim 1, applicant asserts that either Chavva or Park (2019/0109626 [Wingdings font/0xF3] Old reference) fails to teach,” wherein the information for the payload configuration includes an indicator for a payload size, determined by the base station based on information predicted through an artificial intelligence model, for at least one of a physical uplink control channel (PUCCH) or a physical uplink shared channel (PUSCH) carrying the CSI, wherein the information predicted through the artificial intelligence model includes information for a channel state at a timing of data scheduling after reporting the CSI, and wherein the CSI is calculated, by the UE, based on the payload size indicated by the indicator and the channel state at the timing of data scheduling," as recited in the amended independent claim 1 as submitted on 08/26/2026;
Examiner agrees, however, in the analogous art, Madadi (2022/0338189 [Wingdings font/0xF3] New reference) discloses those limitations as mapped in §103 rejection.
Park (2019/0109626 [Wingdings font/0xF3] Old reference), is NOT used in the instant office action, hence, moot.
There are NO specific allegations for any other references, hence, moot.
Similar arguments are applicable for the independent claims 13 and 15.
For reasons as explained supra, it is maintained that independent claims 1, 13 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Chavva, in view of Madadi (2022/0338189 [Wingdings font/0xF3] New reference).
As all other dependent claims depend either directly or indirectly from the independent claim 1, similar rationale also applies to all respective dependent claims.
Conclusion
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/MOHAMMED S CHOWDHURY/Primary Examiner, Art Unit 2467
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