Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
Response to Applicant’s Remarks
1a. Applicant’s arguments and remarks, filed on 12/29/2025 (hereinafter Remarks), are acknowledged, and have been fully considered.
Claims 8 and 12 are cancelled.
After further analysis, the claim amendments do not overcome the cited prior art. The Examiner update the office action based on Applicant’s amendment accordingly. The office action is made final.
Claim Rejections - 35 USC § 103
1. 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 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.
2a. Claims 1-7, 9-11 and 13-20 are rejected under 35 U.S.C. 103 as being unpatentable over Manolakos (US 20230328559 A1) in view of Yoo (US 20240129008 A1).
2b. Summary of the Cited Prior Art
Manolakos discloses a method for reporting neural network based channel state information (CSI).
Yoo discloses a method for neural network based channel state information feedback.
2c. Claim Analysis
Regarding Claim 1, Manolakos discloses:
A method for information transmission (Figs 4, 11 and 16-17), applicable to a second node (Fig 11, Network Entity 1104) in a mobile communication system (Fig 11) and comprising:
transmitting information of a first model (Fig 4, CSI Sequence Encoder 406 or CSI Sequence Decoder, 414) to a first node (Fig 11, UE 1102) in the mobile communication system (see: [0175] At 1106a, the network entity 1104 may transmit CSI having one or more parameters 1106b for a neural network);
wherein the second node (Fig 11, Network Entity 1104) is provided with a second model (Fig 4, CSI Sequence Encoder 406 or CSI Sequence Decoder, 414), and the information of the first model (Fig 4, CSI Sequence Encoder 406 or CSI Sequence Decoder, 414) is used by the first node (Fig 11, UE 1102) to set up in the first node the first model matched with the second model (Examiner’s Note: CSI encoder and CSI decoder match technically);
wherein the first model is a decoding model (Fig 4, CSI Sequence Decoder, 414), and the second model is an encoding model (Fig 4, CSI Sequence Encoder, 406) matched with the decoding model (Examiner’s Note: CSI encoder and CSI decoder match technically);
or the first model is an encoding model (Fig 4, CSI Sequence Encoder, 406), and the second model is a decoding model (Fig 4, CSI Sequence Decoder, 414) matched with the encoding model (Examiner’s Note: CSI encoder and CSI decoder match technically).
Manolakos does not elaborate about encoder and decoder associate with neural network.
However, Yoo discloses:
wherein the first model is a decoding model (Fig 5, CSI Decoder 520), and the second model (Fig 5, CSIO encoder 520) is an encoding model matched with the decoding model (Examiner’s Note: CSI neural network encoder and CSI neural network decoder match accordingly).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to integrate Manolakos’ method for reporting neural network based channel state information (CSI) with Yoo’s method for neural network based channel state information feedback with the motivation being to improving spectral efficiency, lowering costs (Yoo, [0005]).
Regarding Claim 2, Manolakos discloses:
wherein transmitting the information of the first model to the first node in the mobile communication system comprises [(Fig 11, Steps 1106a-1116)]:
transmitting a type of the first model to the first node in the mobile communication system
[(Manolakos discloses transmitting a first model including CSI and neural network parameters:
[0175] At 1106a, the network entity 1104 may transmit CSI having one or more parameters 1106b for a neural network. The one or more parameters 1106b may include (1) a sequence/ordered sequence of layers or sub-layers of the neural network; (2) an input/output parameter for the neural network or for layer(s) or sub-layers of the neural network; (3) layer weights; (4) an indication of a layer type (e.g., residual network block, convolutional layer, fully connected layer, etc.); (5) a periodicity of reporting (e.g., CSI or layer weights); (6) a channel resource identifier (ID) for channels, such as PUCCH, PUSCH, PSCCH, or PSSCH; (7) an indication for the UE 1102 to provide an interference channel measurement via the neural network; (8) a number of subbands for reporting CSI; (9) a precoder resource group (PRG) to be applied for scheduling the UE 1102; and/or (10) a beta (13) parameter indicative of available PUCCH or PUSCH resources for reporting the CSI.
Fig 11, Steps 1106a-1116; Figs 12-15; see also Figs 4A and 9-10)].
Regarding Claim 3, Manolakos discloses:
wherein transmitting the information of the first model to the first node in the mobile communication system comprises [(Fig 11, Steps 1106a-1116)]:
transmitting a model parameter of the first model to the first node in the mobile communication system
[(Manolakos discloses transmitting a first model including CSI and neural network parameters:
[0175] At 1106a, the network entity 1104 may transmit CSI having one or more parameters 1106b for a neural network. The one or more parameters 1106b may include (1) a sequence/ordered sequence of layers or sub-layers of the neural network; (2) an input/output parameter for the neural network or for layer(s) or sub-layers of the neural network; (3) layer weights; (4) an indication of a layer type (e.g., residual network block, convolutional layer, fully connected layer, etc.); (5) a periodicity of reporting (e.g., CSI or layer weights); (6) a channel resource identifier (ID) for channels, such as PUCCH, PUSCH, PSCCH, or PSSCH; (7) an indication for the UE 1102 to provide an interference channel measurement via the neural network; (8) a number of subbands for reporting CSI; (9) a precoder resource group (PRG) to be applied for scheduling the UE 1102; and/or (10) a beta (13) parameter indicative of available PUCCH or PUSCH resources for reporting the CSI.
Fig 11, Steps 1106a-1116; Figs 12-15; see also Figs 4A and 9-10)].
Regarding Claim 4, Manolakos discloses:
wherein the model parameter comprises at least one of: a structure of a neural network; a network coefficient of the neural network; an algorithm of the neural network; and interface information of the neural network
[(Manolakos discloses transmitting a first model including CSI and neural network parameters:
[0175] At 1106a, the network entity 1104 may transmit CSI having one or more parameters 1106b for a neural network. The one or more parameters 1106b may include (1) a sequence/ordered sequence of layers or sub-layers of the neural network; (2) an input/output parameter for the neural network or for layer(s) or sub-layers of the neural network; (3) layer weights; (4) an indication of a layer type (e.g., residual network block, convolutional layer, fully connected layer, etc.); (5) a periodicity of reporting (e.g., CSI or layer weights); (6) a channel resource identifier (ID) for channels, such as PUCCH, PUSCH, PSCCH, or PSSCH; (7) an indication for the UE 1102 to provide an interference channel measurement via the neural network; (8) a number of subbands for reporting CSI; (9) a precoder resource group (PRG) to be applied for scheduling the UE 1102; and/or (10) a beta (13) parameter indicative of available PUCCH or PUSCH resources for reporting the CSI.
Fig 11, Steps 1106a-1116; Figs 12-15; see also Figs 4A and 9-10)].
Regarding Claim 5, Manolakos discloses:
wherein transmitting the information of the first model to the first node in the mobile communication system comprises [(Fig 11, Steps 1106a-1116)]:
transmitting the first model and interface information of the first model to the first node in the mobile communication system
[(Manolakos discloses transmitting a first model including CSI and neural network parameters:
[0175] At 1106a, the network entity 1104 may transmit CSI having one or more parameters 1106b for a neural network. The one or more parameters 1106b may include (1) a sequence/ordered sequence of layers or sub-layers of the neural network; (2) an input/output parameter for the neural network or for layer(s) or sub-layers of the neural network; (3) layer weights; (4) an indication of a layer type (e.g., residual network block, convolutional layer, fully connected layer, etc.); (5) a periodicity of reporting (e.g., CSI or layer weights); (6) a channel resource identifier (ID) for channels, such as PUCCH, PUSCH, PSCCH, or PSSCH; (7) an indication for the UE 1102 to provide an interference channel measurement via the neural network; (8) a number of subbands for reporting CSI; (9) a precoder resource group (PRG) to be applied for scheduling the UE 1102; and/or (10) a beta (13) parameter indicative of available PUCCH or PUSCH resources for reporting the CSI.
Fig 11, Steps 1106a-1116; Figs 12-15; see also Figs 4A and 9-10)].
Regarding Claim 6, Manolakos discloses:
wherein the interface information comprises at least one of: information of an input interface of the first model; and information of an output interface of the first model
[(Manolakos discloses transmitting a first model including CSI and neural network parameters:
[0175] At 1106a, the network entity 1104 may transmit CSI having one or more parameters 1106b for a neural network. The one or more parameters 1106b may include (1) a sequence/ordered sequence of layers or sub-layers of the neural network; (2) an input/output parameter for the neural network or for layer(s) or sub-layers of the neural network; (3) layer weights; (4) an indication of a layer type (e.g., residual network block, convolutional layer, fully connected layer, etc.); (5) a periodicity of reporting (e.g., CSI or layer weights); (6) a channel resource identifier (ID) for channels, such as PUCCH, PUSCH, PSCCH, or PSSCH; (7) an indication for the UE 1102 to provide an interference channel measurement via the neural network; (8) a number of subbands for reporting CSI; (9) a precoder resource group (PRG) to be applied for scheduling the UE 1102; and/or (10) a beta (13) parameter indicative of available PUCCH or PUSCH resources for reporting the CSI.
Fig 11, Steps 1106a-1116; Figs 12-15; see also Figs 4A and 9-10)].
Regarding Claim 7, Manolakos discloses:
wherein the encoding model and the decoding model matched with the encoding model comprise at least one group of [(Fig 4A, CSI Sequence Encoder 406, CSI Sequence Decoder 414)]:
a channel state information (CSI) encoding model and a CSI decoding model; a channel encoding model and a channel decoding model; a source encoding model and a source decoding model; and a modulation model and a demodulation model
[(Manolakos discloses a first model including CSI and neural network parameters for encoding and decoding:
[0074] FIG. 4A illustrates an example architecture of components of an encoding device 400 and a decoding device 425 that use previously stored CSI, in accordance with aspects of the present disclosure. In some examples, the encoding device 400 may be a UE (e.g., 104 or 350), and the decoding device 425 may be a base station (e.g., 102, 180, 310), a transmission reception point (TRP) (e.g., TRP 103), another UE (e.g., UE 104), etc. The encoding device 400 and the decoding device 425 may save and use previously stored CSI and may encode and decode a change in the CSI from a previous instance. This may provide for less CSI feedback overhead and may improve performance. The encoding device 400 may also be able to encode more accurate CSI, and neural networks may be trained with the more accurate CSI. The example architecture of the encoding device 400 and the decoding device 425 may be used for the determination, e.g., computation, of CSI and provision of feedback from the encoding device 400 to the decoding device 425 including processing based on a neural network or machine learning.
Fig 4A, CSI Sequence Encoder 406, CSI Sequence Decoder 414, see also Fig 5-8 and 9-15)].
Regarding Claim 8, Manolakos discloses:
wherein the second node is provided with a second model, wherein the second model is the other of the encoding model and the decoding model matched with the encoding model
[(Manolakos discloses a first model including CSI and neural network parameters for encoding and decoding:
[0074] FIG. 4A illustrates an example architecture of components of an encoding device 400 and a decoding device 425 that use previously stored CSI, in accordance with aspects of the present disclosure. In some examples, the encoding device 400 may be a UE (e.g., 104 or 350), and the decoding device 425 may be a base station (e.g., 102, 180, 310), a transmission reception point (TRP) (e.g., TRP 103), another UE (e.g., UE 104), etc. The encoding device 400 and the decoding device 425 may save and use previously stored CSI and may encode and decode a change in the CSI from a previous instance. This may provide for less CSI feedback overhead and may improve performance. The encoding device 400 may also be able to encode more accurate CSI, and neural networks may be trained with the more accurate CSI. The example architecture of the encoding device 400 and the decoding device 425 may be used for the determination, e.g., computation, of CSI and provision of feedback from the encoding device 400 to the decoding device 425 including processing based on a neural network or machine learning.
Fig 4A, CSI Sequence Encoder 406, CSI Sequence Decoder 414, see also Fig 5-8 and 9-15)].
Regarding Claim 9, Manolakos discloses:
wherein the first model is a CSI decoding model, the second model is a CSI encoding model, and the method further comprises: generating first channel information by encoding to-be-fed back information with the CSI encoding model; and transmitting the first channel information to the first node in the mobile communication system
[(see:
[0177] At 1112, the UE 1102 may apply a concatenation of layers of the neural network (e.g., the neural network selected at 1108). For instance, the UE 1102 may combine layers of the neural network into a group of continuous memory. At 1113a, the network entity 1104 may transmit one or more reference signals and, at 1113b, the UE 1102 may measure the reference signal(s) based on the CSI configuration received at 1106a. At 1114, the UE 1102 may determine CSI based on the one or more parameters 1106b for the neural network received, at 1106a. The UE 1102 may report, at 1116, the CSI to the network entity 1104 based on output of the neural network.
Fig 11, Steps 1106a-1116; Figs 12-15; see also Figs 4A and 9-10)].
Regarding Claim 10, Manolakos discloses:
wherein the first model is a CSI encoding model, the second model is a CSI decoding model, and the method further comprises [(Fig 4A, CSI Sequence Encoder 406, CSI Sequence Decoder 414)]:
receiving first channel information transmitted by the first node in the mobile communication system, wherein the first channel information is generated by encoding to-be-fed back channel information with the CSI encoding model; and
[(see:
[0177] At 1112, the UE 1102 may apply a concatenation of layers of the neural network (e.g., the neural network selected at 1108). For instance, the UE 1102 may combine layers of the neural network into a group of continuous memory. At 1113a, the network entity 1104 may transmit one or more reference signals and, at 1113b, the UE 1102 may measure the reference signal(s) based on the CSI configuration received at 1106a. At 1114, the UE 1102 may determine CSI based on the one or more parameters 1106b for the neural network received, at 1106a. The UE 1102 may report, at 1116, the CSI to the network entity 1104 based on output of the neural network.
Fig 11, Steps 1106a-1116; Figs 12-15; see also Figs 4A and 9-10)];
generating feedback channel information by decoding the first channel information with the CSI decoding model
[(Manolakos discloses a first model including CSI and neural network parameters for encoding and decoding:
[0074] FIG. 4A illustrates an example architecture of components of an encoding device 400 and a decoding device 425 that use previously stored CSI, in accordance with aspects of the present disclosure. In some examples, the encoding device 400 may be a UE (e.g., 104 or 350), and the decoding device 425 may be a base station (e.g., 102, 180, 310), a transmission reception point (TRP) (e.g., TRP 103), another UE (e.g., UE 104), etc. The encoding device 400 and the decoding device 425 may save and use previously stored CSI and may encode and decode a change in the CSI from a previous instance. This may provide for less CSI feedback overhead and may improve performance. The encoding device 400 may also be able to encode more accurate CSI, and neural networks may be trained with the more accurate CSI. The example architecture of the encoding device 400 and the decoding device 425 may be used for the determination, e.g., computation, of CSI and provision of feedback from the encoding device 400 to the decoding device 425 including processing based on a neural network or machine learning.
Fig 4A, CSI Sequence Encoder 406, CSI Sequence Decoder 414, see also Fig 5-8 and 9-15)].
Regarding Claims 11-16, the claims disclose similar features as of Claims 1-3, 5, 7 and 10, and are rejected accordingly. Further, Claims 11-16 disclose the same operations of Claims 1-10, but are performed by a transmitter.
Regarding Claims 17-20, the claims disclose similar features as of Claims 1-3 and 5, and are rejected accordingly.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any 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 mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Jung-Jen Liu whose telephone number is 571-270-7643. The examiner can normally be reached on Monday to Friday, 9:00 AM to 5:00 PM.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kwang B. Yao can be reached on 571-272-3182. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JUNG LIU/Primary Examiner, Art Unit 2473