DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This action is in response to the communication filed on 09/02/2025.
Response to Arguments
Applicant's arguments with respect to claim(s) 1 have been considered but are moot in view of the new ground(s) of rejection.
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 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-2, 4-8, 12, 15-16, 18-22, 26 and 29-30 are rejected under 35 U.S.C. 103 as being unpatentable over Chavva et al. (U.S. Pub. 20210351885) in view of O'Shea et al. (WIPO. Pub. KR20220009392).
Regarding claim 1 Chavva disclose, an apparatus for wireless communication at a user equipment (UE), comprising: memory read as “memory 604”; and at least one processor read as “processor 602” coupled to the memory and configured to:
receive a channel state information (CSI) configuration that includes one or more parameters for a machine learning (MVL)” or neural network (NN) model, the CSI configuration associated with one or more reference signals to be measured para. 91, “receiving a Radio Resource Configuration (RRC) message, which comprises of feedback configuration for CSI-Reference Signals (CSI-RS). The feedback configuration for CSI-RS, received from the gNB”,
measure the one or more reference signals based on the CSI configuration para. 100, “The feedback parameters are computed using measurements performed using CSI-RS”, a CSI being based on the one or more parameters for the MVL or NN model received in the CSI configuration and a measurement of the one or more reference signals para. 162, “The joint neural network model allows joint optimization of the feedback parameters included in the CSI report. The input to the joint neural network model comprises of past CSI data”;
and report the CSI to a network entity based on output of the neural network para. 33, “generating, by the neural network (602c), a CSI report, by compiling at least one of the computed values of the CSI feedback parameters and the predicted values the CSI feedback parameters; and transmitting, by the UE (601), the CSI report to the gNB (607)”.
Chavva does not specifically disclose, channel state information (CSI) configuration that includes one or more parameters for a machine learning (MVL) or neural network (NN) model. However O'Shea teach, “In some cases, a system variable (e.g., a received signal strength indicator (RSSI) or metrics of CSI stability) or other parameter or notification of a machine-learning network approach”, see para. 29.
Chavva and O'Shea are analogous because they pertain to the field of wireless communication and, more specifically, to transmission parameters.
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of O'Shea in the system of Chavva so the system can integrate the use of the machine-learning networks to estimate and process the metrics with more sophisticated and accurate algorithms. The motivation for doing so would have been to improve the digital processing communication performance and optimization.
Regarding claim 2 Chavva disclose, wherein the one or more parameters received in the CSI configuration includes at least one of:
a first sequence of layers of the ML or NN model,
an input parameter for at least one layer of the ML or NN model para. 169, “The input layer can be, therefore, fed with past CSI data, HARQ information, current MCS, and Doppler spread”,
an output parameter for at least one layer of the ML or NN model,
a layer type for at least one layer of the ML or NN model, or
a second sequence of sub-layers of at least one layer of the ML or NN model. The claim list features in the alternative. While the claim lists a number of optional limitations only one limitation from the list is required and needs to be met by the prior art. The Examiner has chosen the second of the alternatives.
Regarding claim 4 Chavva disclose, wherein the one or more parameters received in the CSI configuration includes an indication of at least one type of the ML or NN model, the at least one type that corresponds to a defined sequence of layers para. 120, “The CodebookConfig can provide an indication to the UE 601 whether the CSI feedback configuration is Type-1 or Type-2”.
Regarding claim 5 Chavva disclose, wherein the indication indicates a plurality of ML or NN model types, the at least one processor further configured to:
select a type from the plurality of ML or NN model types para. 120, “For both Type-1 and Type-2 CSI reporting in NR, the gNB 607 can specify CSI reporting configuration in the CodebookConfig”; and
report the type selected by the UE to a second network entity, the second network entity being a same network entity as the network entity or a different network entity than the network entity para. 124, “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”.
Regarding claim 6 Chavva disclose, wherein the indication indicates a plurality of ML or NN model types, the at least one processor further configured to:
apply a concatenation of layers based on the plurality of ML or NN model types indicated by the network entity para. 47, “In an exemplary embodiment, wherein the neural network (602c) model is one of: a hierarchical model, a cascaded model, a joint neural network model, and a Deep Neural network (DNN) model comprising of Fully Connected layers and convolutional layers”.
Regarding claim 7 Chavva disclose, wherein the one or more parameters includes at least one of:
a periodicity of reporting of a weight of at least one layer of the ML or NN model, or
a channel resource identifier (ID) that indicates a resource for reporting the channel state information. The claim list features in the alternative. While the claim lists a number of optional limitations only one limitation from the list is required and needs to be met by the prior art. The Examiner has chosen the first of the alternatives.
Regarding claim 8 Chavva disclose, wherein the one or more parameters received in the CSI configuration indicates to the UE to report at least one of:
the output of the ML or NN model, or a weight of at least one layer of the ML or NN model para. 201, “The training data further includes the parameters stored in the database 602c, previous predicted values of PMI optimal MCS evaluated by the UE 601. At step 1903, the method includes updating the weights of the neural network 602c based on the training data”. The claim list features in the alternative. While the claim lists a number of optional limitations only one limitation from the list is required and needs to be met by the prior art. The Examiner has chosen the second of the alternatives.
Regarding claim 12 Chavva disclose, wherein the one or more parameters received in the CSI configuration includes a number of subbands for reporting the CSI, and wherein the UE reports an individual vector for each subband or differentially reports vectors for each subband para. 123, “The neural network 602c layers can extract a plurality of feature vectors and refine the plurality of feature vectors to compute the feedback parameters, the neural network 602c can compute at least one feedback parameter, if the current slot received by the UE 601 includes the CSI-RS”.
Claim 15 recites an apparatus corresponding to the device of claim 1 and thus is rejected under the same reason set forth in the rejection of claim 1.
Regarding claim 16 the limitations of claim 16 are rejected in the same manner as analyzed above with respect to claim 2.
Regarding claims 18-22 the limitations of claims 18-22, respectively, are rejected in the same manner as analyzed above with respect to claims 4-8, respectively.
Regarding claim 26 the limitations of claim 26 are rejected in the same manner as analyzed above with respect to claim 12.
Claim 29 recites a method corresponding to the apparatus of claim 1 and thus is rejected under the same reason set forth in the rejection of claim 1.
Claim 30 recites a computer–program product corresponding to the apparatus of claim 1 and thus is rejected under the same reason set forth in the rejection of claim 1.
Claim(s) 3 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Chavva et al. (U.S. Pub. 20210351885) in view of O'Shea et al. (WIPO. Pub. KR20220009392), further in view of Qin et al. (U.S. Pub. 20210233196).
Regarding claim 3 Chavva and O'Shea does not specifically disclose, wherein the first sequence of layers is a first ordered sequence of layers of the ML or NN model. However, Qin teach, “the first sequence of hidden layers comprise layers A.sub.x; the input layer is coupled to the layer A.sub.1; the layer A.sub.N's output is coupled to the output layer; the layer A.sub.x's output is coupled to the layer A.sub.x+1 as an input” see para. 9.
Qin further teach, wherein the second sequence of sub-layers is a second ordered sequence of sub-layers of the at least one layer of the neural network para. 9, “the second sequence of hidden layers comprise layers B.sub.x, X being 1, 2, . . . N”.
Chavva, O'Shea and Qin are analogous because they pertain to the field of wireless communication and, more specifically, to transmission parameters.
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Qin in the system of Chavva and O'Shea so the system can determine the order of the layer to be able to establish levels of priority. The motivation for doing so would have been to ensure the relationship and association between the layers to maintain a logical mapping sequency.
Regarding claim 17 the limitations of claim 17 are rejected in the same manner as analyzed above with respect to claim 3.
Claim(s) 9-11, and 23-25 are rejected under 35 U.S.C. 103 as being unpatentable over Chavva et al. (U.S. Pub. 20210351885) in view of O'Shea et al. (WIPO. Pub. KR20220009392), further in view of Zeng et al. (WIPO. Pub. CN111381499).
Regarding claim 9 Chavva and O'Shea does not specifically disclose, wherein the one or more parameters received in the CSI configuration indicates for the UE to provide an interference channel measurement based on the neural network and the measurement of the one or more reference signals. However, Zeng teach, “using the reference signal received power (Reference Signal Received Power, RSRP) defined in the cellular network standard, reference signal receiving quality (Reference SignalReceived Quality, RSRQ), reference signal signal-to-interference and noise ratio (RS SINR) measurement data to obtain the related data of the radio frequency map… using the monitoring learning algorithm for one or more network training, updating the parameter in the radio frequency map depth neural network”, see para. 65.
Chavva, O'Shea and Zeng are analogous because they pertain to the field of wireless communication and, more specifically, to transmission parameters.
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Zeng in the system of Chavva and O'Shea to be able to configurate the system according to the services being provided under the current conditions of the medium. The motivation for doing so would have been to maintain the highest levels of quality possible.
Regarding claim 10 Chavva and O'Shea does not specifically disclose, wherein the UE applies a same ML or NN model for the interference channel measurement as for a channel measurement However, Zeng teach, the cellular network terminal randomly selects several measuring data from the database E, the standard gradient descent method updates the neural network parameter ξ of the three-dimensional space radio frequency map”, see para. 61.
Chavva, O'Shea and Zeng are analogous because they pertain to the field of wireless communication and, more specifically, to transmission parameters.
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Zeng in the system of Chavva and O'Shea to be able to configurate the system according to the services being provided under the current conditions of the medium. The motivation for doing so would have been to maintain the highest levels of quality possible.
Regarding claim 11 Chavva disclose, wherein the UE applies a different ML or NN model for the interference channel measurement than a channel measurement, and wherein a first ML or NN model for the interference channel measurement is based, at least in part, on a second ML or NN model for the channel measurement para. 174, “The first neural network can predict the CRI based on the contents in the measurement database. The second neural network can predict the RI based on the contents in the measurement database and the predicted value of CRI”.
Regarding claims 23-25 the limitations of claims 23-25, respectively, are rejected in the same manner as analyzed above with respect to claims 9-11, respectively.
Claim(s) 13 and 27 are rejected under 35 U.S.C. 103 as being unpatentable over Chavva et al. (U.S. Pub. 20210351885) in view of O'Shea et al. (WIPO. Pub. KR20220009392), further in view of Haghighat et al. (US. Pub. 20200275416).
Regarding claim 13 Chavva and O'Shea does not specifically disclose, wherein the one or more parameters received in the CSI configuration includes a precoder resource group (PRG) to be applied for scheduling the UE. However, Haghighat teach, “The PRG parameter types may include any of a scheduled bandwidth, RBG size, a subband size for CSI reporting”, see para. 221.
Chavva, O'Shea and Haghighat are analogous because they pertain to the field of wireless communication and, more specifically, to transmission parameters.
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Haghighat in the system of Chavva and O'Shea to apply the group configuration according to the available resources. The motivation for doing so would have been to improve the managing of the limited bandwidth.
Regarding claim 27 the limitations of claim 27 are rejected in the same manner as analyzed above with respect to claim 13.
Claim(s) 14, and 28 are rejected under 35 U.S.C. 103 as being unpatentable over Chavva et al. (U.S. Pub. 20210351885) in view of O'Shea et al. (WIPO. Pub. KR20220009392), further in view of Yoo et al. (US. Pub. 20210273707).
Regarding claim 14 Chavva and O'Shea does not specifically disclose, wherein the one or more parameters received in the CSI configuration includes a beta (J3) parameter that is based on a sub-type of the ML or NN model, the J parameter indicative of available physical uplink shared channel (PUSCH) or physical sidelink shared channel (PSSCH) resources for reporting the CSI. However, Yoo teach, “CSI encoder 410 may provide encoded CSI as a payload on a physical uplink shared channel (PUSCH) or a physical uplink control channel (PUCCH). Decoder parameters 425 may include decoder weights obtained from machine learning, such as from the training of the neural network model associated with a CSI encoder and a CSI decoder”, see para. 67.
Chavva, O'Shea and Yoo are analogous because they pertain to the field of wireless communication and, more specifically, to transmission parameters.
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Yoo in the system of Chavva and O'Shea to be able to accurately identify and allocate the appropriated resources for transmission/reception of the terminal. The motivation for doing so would have been to improve the usage of the available resources.
Regarding claim 28 the limitations of claim 28 are rejected in the same manner as analyzed above with respect to claim 14.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure: Au et al. (U.S. Pub. 20190020530) which disclose(s) methods, devices, servers, apparatus, and systems for wireless internet of things applications.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RAUL RIVAS whose telephone number is (571)270–5590. The examiner can normally be reached on Monday – Friday, from 8:30am to 5:00pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Sujoy K. Kundu, can be reached on (571) 272 - 8586. The fax phone number for the organization where this application or proceeding is assigned is 571–273–8300.
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/RR/
Examiner, Art Unit 2471
/SUJOY K KUNDU/ Supervisory Patent Examiner, Art Unit 2471