DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 102
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 following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-18, 25, 48 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Beluri et al. US 20250038816.
Regarding claim 1, A method of communication, comprising: determining, at a terminal device (wireless transmit/receive unit, WTRU, Figure 1B, a WTRU configured to perform Al/ML-based CSI compression for CSI feedback reporting, para. 0092), channel state information based on a measurement on a set of reference signals from a network device (the WTRU can select one or more pre-processing type(s) based on whether a data processing model at the WTRU supports the pre-processing type(s) and can receive one or more configured reference signal(s) and perform CSI measurements, para. 0251), for example using the configured reference signals); determining a compression method based on a configuration from a network device, the configuration comprising at least one of the following: a time-domain parameter, a frequency-domain parameter, or a spatial-domain parameter (the WTRU may determine to use the spatial-domain, frequency-domain, and/or time-domain pre-processing of the CSI, the WTRU may be configured by the gNB, Figure 1A, element 114, with a combination or order of dimensions in the frequency, time, and/or spatial domain on which the WTRU may perform pre-processing, para. 0200, 0242); compressing the channel state information based on the compression method; and transmitting, to the network device, the compressed channel state information (the WTRU may pre-process the measured CSI using the selected pre-processing type and the WTRU generates compressed CSI by compressing the pre-processed CSI using the configured data processing model compatible with the pre-processing type and then report the compressed CSI to the gNB, para. 0251).
Regarding claim 2, The method of claim 1, wherein the channel state information comprises at least information of a precoding matrix (CSI may include channel quality index (CQI); rank indicator (RI); precoding matrix index (PMI), para. 0099).
Regarding claim 3, The method of claim 1, wherein the compression method is an artificial intelligence or machine learning based compression comprising at least an encoding part of an autoencoder or a transformer (the WTRU may receive configuration information that indicates a reference signal and a data processing model such as AI NN encoder model for channel state information compression, para. 0004, Figure 6, for artificial intelligence (AI)/ML capable WTRUs configured for AI/ML-based CSI compression, the WTRU complexity can utilize a AI NN encoder model or AI NN encoder and the WTRU may select to pre-process the CSI measurement(s) submitted to the AI NN encoder input, para. 0088).
Regarding claim 4, The method of claim 1, wherein determining the compression method comprises: determining a set of compression parameter values based on at least one of the following: a mapping between a value of the time-domain parameter and at least one compression parameter value in the set of compression parameter values, or a mapping among at least two compression parameter values in the set of compression parameter values (the WTRU may be configured with parameters associated with one or more AI NN encoders and the parameters may include model training information, the WTRU may be configured with frequency domain training data, angle-delay domain training data, time domain training data, and/or spatial domain training data, para. 0164, the WTRU supports Al/ML NN encoder-based CSI compression and CSI compression configuration may include the size of the NN encoder model and/or the compression ratio, the WTRU performs channel measurements for explicit CSI reporting, para. 0280).
Regarding claim 5, The method of claim 1, wherein the time-domain parameter comprises at least one of the following: a periodicity of the set of reference signals, a periodicity of the transmission of the compressed channel state information, a time interval between two transmission occasions of the set of reference signals, a time interval between two transmissions of the compressed channel state information, a time-domain density, or a restriction for the measurement (for the time domain, the WTRU may perform measurements of the radio link interfaces and report the measurements to determine the periodicity to send reference signals for the WTRU to do channel estimation, changes in reporting frequency and/or periodicity/offset may be triggered by detection of errors in CSI compression and the AI NN encoder can be trained, para. 0275).
Regarding claim 6, The method of claim 1, wherein determining the compression method comprises: determining a set of compression methods based on the time-domain parameter; and determining the compression method from the set of compression methods (pre-processing types may include one or more of the following: frequency domain; time domain; spatial domain; or linear transformation of the channel matrix H, for example, to convert to the angular-delay domain, para. 0136, a pre-processing type may be applicable to one or more of the supported AI NN encoder models and an AI NN encoder model may use multiple pre-processing types, para. 0283).
Regarding claim 7. The method of claim 1, wherein determining the compression method comprises: in accordance with a determination that a value of the time-domain parameter is below a first threshold value, determining a first predetermined compression method as the compression method; in accordance with a determination that a value of the time-domain parameter is below a second threshold value, determining that no compression is applied; or in accordance with a determination that a value of the time-domain parameter is above a third threshold value, determining a second predetermined compression method as the compression method (a parameter may include a threshold value which is compared to a measurement performed by the WTRU , the parameter can include a dimension and/or a value to be used by the WTRU in the application of the pre-processing type and include an applicable or associated AI NN encoder, such as an AI NN encoder associated with a pre-processing type, para. 0150, the parameter may be associated with pre-processing types using time domain pre-processing, the WTRU can compare the measured coherence time value to the coherence time threshold, a measured coherence time value below the coherence time threshold may indicate a fast fading channel and a measured coherence time value above the coherence time threshold may indicate a slow fading channel, selection of a pre-processing type may be based, at least in part, on the comparison of the coherence time value to the coherence time threshold, para. 0151).
Regarding claim 8, The method of claim 1, wherein determining the compression method comprises: determining a set of compression methods based on the time-domain parameter (a parameter may be associated with pre-processing types using time domain pre-processing, the WTRU can compare the measured coherence time value to the coherence time threshold, a measured coherence time value below the coherence time threshold may indicate a fast fading channel and a measured coherence time value above the coherence time threshold may indicate a slow fading channel, selection of a pre-processing type may be based, at least in part, on the comparison of the coherence time value to the coherence time threshold, para. 0151).
Regarding claim 9, The method of claim 8, wherein compressing the channel state information comprises: applying a first compression method in the set of compression methods for a first time- domain behavior; and applying a second compression method in the set of compression methods for a second time-domain behavior (a parameter may be associated with pre-processing types using time domain pre-processing, the WTRU can compare the measured coherence time value to the coherence time threshold, a measured coherence time value below the coherence time threshold may indicate a fast fading channel and a measured coherence time value above the coherence time threshold may indicate a slow fading channel, selection of a pre-processing type may be based, at least in part, on the comparison of the coherence time value to the coherence time threshold, para. 0151).
Regarding claim 10, The method of claim 1, wherein determining the compression method comprises: determining a set of compression parameter values based on at least one of the following: a mapping between a value of the frequency-domain parameter and at least one compression parameter value in the set of compression parameter values, a mapping among at least two compression parameter values in the set of compression parameter values, or a mapping among at least two frequency-domain parameter values in a set of frequency- domain parameter values (the WTRU may be configured with parameters associated with one or more AI NN encoders and the parameters may include model training information, the WTRU may be configured with frequency domain training data, angle-delay domain training data, time domain training data, and/or spatial domain training data, para. 0164, the WTRU supports Al/ML NN encoder-based CSI compression and CSI compression configuration may include the size of the NN encoder model and/or the compression ratio, the WTRU performs channel measurements for explicit CSI reporting, para. 0280).
Regarding claim 11, The method of claim 1, wherein the frequency-domain parameter comprises at least one of the following: a size of a sub-band, the number of sub-bands, a bandwidth for the measurement, the number of physical resource blocks, or a frequency-domain density (for frequency domain pre-processing, the WTRU may select to average CSI-RS over every n number of RBs, with the value of n depending on channel conditions related to channel coherence bandwidth; previous channel measurements, frequency correlation coefficients, para. 0274, the WTRU may perform measurements for frequency domain pre-processing to determine the channel coherence bandwidth and may determine the number of CSI such as channel frequency response samples that may be averaged or down-sampled) in a frequency domain, para. 0093).
Regarding claim 12, The method of claim 1, wherein determining the compression method comprises: determining a set of compression methods based on the frequency-domain parameter; and determining the compression method from the set of compression methods (an AI NN encoder model may use multiple pre-processing types, the CSI report configuration may include the maximum number of CSI bits to be reported by the WTRU for the selected configuration and include the minimum compression quality expected by the gNB, the WTRU may perform channel measurements for explicit CSI reporting, such as the channel response matrix for all Tx antenna ports and Rx antennas, at different granularities in frequency domain, the WTRU may select the frequency-domain pre-processing if the measured channel coherence BW is large and above a threshold, para. 0283).
Regarding claim 13, The method of claim 1, wherein determining the compression method comprises: in accordance with a determination that a value of the frequency-domain parameter is below a fourth threshold value, determining a third predetermined compression method as the compression method; in accordance with a determination that a value of the frequency-domain parameter is below a fifth threshold value, determining that no compression is applied; or in accordance with a determination that a value of the frequency-domain parameter is above a sixth threshold value, determining a fourth predetermined compression method as the compression method (an AI NN encoder model may use multiple pre-processing types, the CSI report configuration may include the maximum number of CSI bits to be reported by the WTRU for the selected configuration and include the minimum compression quality expected by the gNB, the WTRU may perform channel measurements for explicit CSI reporting, such as the channel response matrix for all Tx antenna ports and Rx antennas, at different granularities in frequency domain, the WTRU may select the frequency-domain pre-processing if the measured channel coherence BW is large and above a threshold, para. 0283).
Regarding claim 14, The method of claim 1, wherein determining the compression method comprises: determining a set of compression methods based on the frequency-domain parameter (a parameter may include a threshold value which is compared to a measurement performed by the WTRU , the parameter can include a dimension and/or a value to be used by the WTRU in the application of the pre-processing type and include an applicable or associated AI NN encoder, such as an AI NN encoder associated with a pre-processing type, para. 0150, a parameter may be associated with pre-processing types using frequency domain pre-processing, a WTRU may compare a measured coherence BW to the coherence BW threshold, the WTRU may determine whether a channel is flat or frequency-selective, a measured coherence BW value greater than the coherence BW threshold may indicate a flat fading channel, a measured coherence BW value lower than the coherence BW threshold may indicate a frequency-selective channel, para. 0151, para. 0171-0172).
Regarding claim 15, The method of claim 14, wherein compressing the channel state information comprises: applying a third compression method in the set of compression methods for a first value of the frequency-domain parameter; and applying a fourth compression method in the set of compression methods for a second value of the frequency-domain parameter (a parameter may be associated with pre-processing types using frequency domain pre-processing, a WTRU may compare a measured coherence BW to the coherence BW threshold, the WTRU may determine whether a channel is flat or frequency-selective, a measured coherence BW value greater than the coherence BW threshold may indicate a flat fading channel, a measured coherence BW value lower than the coherence BW threshold may indicate a frequency-selective channel, para. 0151, para. 0171-0172).
Regarding claim 16, The method of claim 1, wherein determining the compression method comprises: determining a set of compression parameter values based on at least one of the following: a mapping between a value of the spatial-domain parameter and at least one compression parameter value in the set of compression parameter values, or a mapping among at least two compression parameter values in the set of compression parameter values (a set of parameters may include a number of layers, an input dimension, an output dimension, a number of iterations, an input type, an output type, a parameter can also be a spatial domain correlation threshold, the parameter may be associated with pre-processing types using spatial domain pre-processing, the WTRU may determine the spatial domain correlation and compare the measured spatial domain correlation to the spatial domain correlation threshold and determine the level of spatial domain correlation based on the comparison, selection of a pre-processing type may be based, at least in part, on the level of spatial domain correlation, para. 0152).
Regarding claim 17, The method of claim 1, wherein the spatial-domain parameter comprises at least one of the following: the number of antenna ports, the number of panels, an oversampling factor, the number of beams for linear combination, the number of phase quantization bits, the number of beams, a codebook type, an antenna structure, or polarizations (a set of spatial domain parameters may include a number of layers, an input dimension, an output dimension, a number of iterations, an input type, an output type, para. 0152, number of input values in one or more dimensions such as number of transmit antennas, number of receive antennas, number of sub-bands/subcarrier spacings, para. 0256).
Regarding claim 18, The method of claim 1, wherein determining the compression method comprises: determining a set of compression methods based on the spatial-domain parameter; and determining the compression method from the set of compression methods (the WTRU may determine the spatial domain correlation and compare the measured spatial domain correlation to the spatial domain correlation threshold and determine the level of spatial domain correlation based on the comparison, selection of a pre-processing type may be based, at least in part, on the level of spatial domain correlation, para. 0152).
Regarding claim 25, A method of communication, comprising: transmitting, at a network device and to a terminal device, a configuration comprising at least one of the following: a time-domain parameter, a frequency-domain parameter, or a spatial-domain parameter (the WTRU may determine to use the spatial-domain, frequency-domain, and/or time-domain pre-processing of the CSI, the WTRU may be configured by the gNB, Figure 1A, element 114, with a combination or order of dimensions in the frequency, time, and/or spatial domain on which the WTRU may perform pre-processing, para. 0200, 0242); receiving compressed channel state information from the terminal device (the WTRU may pre-process the measured CSI using the selected pre-processing type and the WTRU generates compressed CSI by compressing the pre-processed CSI using the configured data processing model compatible with the pre-processing type and then report the compressed CSI to the gNB, para. 0251); determining a compression method applied for the compressed channel state information based on the configuration (the WTRU may send an indication of the selected pre-processing type and/or the selected pre-processing parameters to the gNB, para. 0251); and recovering channel state information based on the compressed channel state information and the compression method (the gNB may perform an inverse operation using an AI NN decoder to reconstruct the original CSI from the received compressed CSI, the AI NN encoder and decoder of an autoencoder may be jointly trained, para. 0111).
Claim 48 is rejected under the same rationale.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure, some of which are described below:
Zhang et al. US 20260172095 discloses performing CSI feedback based on AI/ML module which includes an AI/ML-based CSI generation part and an AI/ML-based CSI reconstruction part, the AI/ML-based CSI generation part includes an AI/ML model, the AI/ML model may include an AI/ML encoder and a quantizer, and a preprocessing module, a AI/ML-based CSI reconstruction part includes an AI/ML reconstruction model, the AI/ML reconstruction model includes a dequantizer and an AI/ML decoder.
Hindy et al. US 20260095222 discloses supports a UE performing efficient channel state information (CSI) measurement and/or reporting for multiple spatial domain adaptations of a network by facilitating signaling of antenna ports associated with the multiple spatial domain adaptation patterns employed by the network, which enables the UE to generate CSI reports that include information for the antenna ports.
Kim et al. US 20240313919 discloses performing CSI report by a UE by receiving, from a base station, configuration information on the channel state information report; receiving, from the base station, at least one channel state information-reference signal (CSI-RS) based on the configuration information; and transmitting, to the base station, CSI calculated based on the at least one CSI-RS and priority-related information for omission of the corresponding CSI.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MELANIE JAGANNATHAN whose telephone number is (571)272-3163. The examiner can normally be reached M-F 9-5.
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/MELANIE JAGANNATHAN/Primary Examiner, Art Unit 2468
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