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 Objections
Claim 1 objected to because of the following informalities:
Line 10 reads “the first terminal device is configured to transmits encoded channel state information to the second terminal device”. It appears as if transmits should read transmit. Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 9 and 12 rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because the claimed terminals, absent any hardware such as a processor, transceiver, and non-transitory computer readable medium which stores instructions, can be implemented as a software solution and are paramount to software per se.
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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1,4-5 and 9-12 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Yoo et al. (US 20210273707 A1) hereafter Yoo.
Regarding Claim 1:
Yoo discloses:
A wireless communication system comprising: a first terminal device; and a second terminal device to communicate with the first terminal device by Side Link (SL) communication; and a base station to communicate with the second terminal device by the SL communication,( [0034] FIG. 1 is a diagram illustrating a wireless network 100 in which aspects of the present disclosure may be practiced. The wireless network 100 may be an LTE network or some other wireless network, such as a 5G or NR network. The wireless network 100 may include a number of BSs 110 (shown as BS 110a, BS 110b, BS 110c, and BS 110d) and other network entities. ABS is an entity that communicates with user equipment (UEs) and may also be referred to as a base station, a NR BS, a Node B, a gNB, a 5G node B (NB), an access point, a transmit receive point (TRP), and/or the like. Each BS may provide communication coverage for a particular geographic area. In 3GPP, the term “cell” can refer to a coverage area of a BS and/or a BS subsystem serving this coverage area, depending on the context in which the term is used. [¶0037] Wireless network 100 may also include relay stations. A relay station is an entity that can receive a transmission of data from an upstream station (e.g., a BS or a UE) and send a transmission of the data to a downstream station (e.g., a UE or a BS). A relay station may also be a UE that can relay transmissions for other UEs. In the example shown in FIG. 1, a relay station 110d may communicate with macro BS 110a and a UE 120d in order to facilitate communication between BS 110a and UE 120d. A relay station may also be referred to as a relay BS, a relay base station, a relay, and/or the like. [¶0043] In some aspects, two or more UEs 120 (e.g., shown as UE 120a and UE 120e) may communicate directly using one or more sidelink channels (e.g., without using a base station 110 as an intermediary to communicate with one another). For example, the UEs 120 may communicate using peer-to-peer (P2P) communications, device-to-device (D2D) communications, a vehicle-to-everything (V2X) protocol (e.g., which may include a vehicle-to-vehicle (V2V) protocol, a vehicle-to-infrastructure (V2I) protocol, and/or the like), a mesh network, and/or the like. In this case, the UE 120 may perform scheduling operations, resource selection operations, and/or other operations described elsewhere herein as being performed by the base station 110.) wherein the first terminal device is configured to transmit channel state information for learning, the channel state information being information for reporting a channel state of the SL communication,([¶0063] In some aspects, a transmitting device, such as a UE, may use encoder weights from a trained neural network model, to encode CSI into a more compact representation of CSI that is accurate. As a result of using encoder weights of a trained neural network model for a CSI encoder and using decoder weights of the trained neural network model for a CSI decoder, the encoded CSI that the UE transmits may be smaller (more compressed) and/or more accurate than without using machine learning.) the base station is configured to learn an encoding model and a decoding model by using the channel state information for learning,([¶0052] In some aspects, a device, such as base station 110, may include means for obtaining a CSI instance for a channel, means for determining a neural network model including a CSI encoder and a CSI decoder, means for training the neural network model based at least in part on encoding the CSI instance into encoded CSI, decoding the encoded CSI into decoded CSI, and comparing the CSI instance and the decoded CSI, means for obtaining one or more encoder weights and one or more decoder weights based at least in part on training the neural network model, and/or the like. In some aspects, such means may include one or more components of base station 110 described in connection with FIG. 2, such as antenna 234, DEMOD 232, MIMO detector 236, receive processor 238, controller/processor 240, transmit processor 220, TX MIMO processor 230, MOD 232, antenna 234, and/or the like.) the first terminal device is configured to transmits encoded channel state information to the second terminal device, the encoded channel state information being the channel state information encoded by using the encoding model,( [¶0099] As shown by reference number 935, UE 920 may transmit the first encoded CSI to BS 910. As shown by reference number 940, BS 910 may decode the first encoded CSI into first decoded CSI based at least in part on decoder weights. BS 910 may have received the decoder weights, or determined the decoder weights from training a neural network model associated with a CSI encoder and a CSI decoder. [¶0053] In some aspects, base station 110 may include means for receiving first encoded CSI from a UE, the first encoded CSI being a first CSI instance for a channel to the UE that is encoded by the UE, based at least in part on one or more encoder weights that correspond to a neural network model associated with a CSI encoder and a CSI decoder, means for decoding the first encoded CSI into first decoded CSI based at least in part on one or more decoder weights that correspond to the neural network model, and/or the like. In some aspects, such means may include one or more components of base station 110 described in connection with FIG. 2, such as antenna 234, DEMOD 232, MIMO detector 236, receive processor 238, controller/processor 240, transmit processor 220, TX MIMO processor 230, MOD 232, antenna 234, and/or the like. [¶0043] In some aspects, two or more UEs 120 (e.g., shown as UE 120a and UE 120e) may communicate directly using one or more sidelink channels (e.g., without using a base station 110 as an intermediary to communicate with one another). For example, the UEs 120 may communicate using peer-to-peer (P2P) communications, device-to-device (D2D) communications, a vehicle-to-everything (V2X) protocol (e.g., which may include a vehicle-to-vehicle (V2V) protocol, a vehicle-to-infrastructure (V2I) protocol, and/or the like), a mesh network, and/or the like. In this case, the UE 120 may perform scheduling operations, resource selection operations, and/or other operations described elsewhere herein as being performed by the base station 110.) and the second terminal device is configured to decode the encoded channel state information by using the decoding model.([¶0066] [0066] As shown by FIG. 4, CSI encoder 410 may receive and encode one or more downlink (DL) channel estimates. CSI encoder 410 may encode the one or more DL channel estimates and any interference information using encoder parameters 415. Encoder parameters 415 may include encoder weights obtained from machine learning, such as from a training of a neural network model associated with a CSI encoder and a CSI decoder. The training may have been performed by another device and such encoder weights may have been provided to CSI encoder 410, or CSI encoder 410 may be configured based at least in part on specified encoder weights. A neural network model may be characterized by a structure that indicates how neural network layers are composed in the neural network model. CSI encoder 410 may also be configured based at least in part on one or more encoder structures of the neural network model.)
Regarding Claim 4:
Yoo discloses:
Yoo discloses the limitations of parent claims:
wherein the second terminal device is configured to receive the channel state information for learning transmitted by the first terminal device and transfers the channel state information for learning to the base station, and the base station is configured to receive the channel state information for learning transferred by the second terminal device. ([¶0053] In some aspects, base station 110 may include means for receiving first encoded CSI from a UE, the first encoded CSI being a first CSI instance for a channel to the UE that is encoded by the UE, based at least in part on one or more encoder weights that correspond to a neural network model associated with a CSI encoder and a CSI decoder, means for decoding the first encoded CSI into first decoded CSI based at least in part on one or more decoder weights that correspond to the neural network model, and/or the like. In some aspects, such means may include one or more components of base station 110 described in connection with FIG. 2, such as antenna 234, DEMOD 232, MIMO detector 236, receive processor 238, controller/processor 240, transmit processor 220, TX MIMO processor 230, MOD 232, antenna 234, and/or the like. [¶0043] In some aspects, two or more UEs 120 (e.g., shown as UE 120a and UE 120e) may communicate directly using one or more sidelink channels (e.g., without using a base station 110 as an intermediary to communicate with one another). For example, the UEs 120 may communicate using peer-to-peer (P2P) communications, device-to-device (D2D) communications, a vehicle-to-everything (V2X) protocol (e.g., which may include a vehicle-to-vehicle (V2V) protocol, a vehicle-to-infrastructure (V2I) protocol, and/or the like), a mesh network, and/or the like. In this case, the UE 120 may perform scheduling operations, resource selection operations, and/or other operations described elsewhere herein as being performed by the base station 110.)
Regarding Claim 5:
Yoo discloses:
Yoo discloses the limitations of parent claims:
wherein the base station is configured to receive the channel state information for learning transmitted by the first terminal device. ([¶0053] In some aspects, base station 110 may include means for receiving first encoded CSI from a UE, the first encoded CSI being a first CSI instance for a channel to the UE that is encoded by the UE, based at least in part on one or more encoder weights that correspond to a neural network model associated with a CSI encoder and a CSI decoder, means for decoding the first encoded CSI into first decoded CSI based at least in part on one or more decoder weights that correspond to the neural network model, and/or the like. In some aspects, such means may include one or more components of base station 110 described in connection with FIG. 2, such as antenna 234, DEMOD 232, MIMO detector 236, receive processor 238, controller/processor 240, transmit processor 220, TX MIMO processor 230, MOD 232, antenna 234, and/or the like. [¶0043] In some aspects, two or more UEs 120 (e.g., shown as UE 120a and UE 120e) may communicate directly using one or more sidelink channels (e.g., without using a base station 110 as an intermediary to communicate with one another). For example, the UEs 120 may communicate using peer-to-peer (P2P) communications, device-to-device (D2D) communications, a vehicle-to-everything (V2X) protocol (e.g., which may include a vehicle-to-vehicle (V2V) protocol, a vehicle-to-infrastructure (V2I) protocol, and/or the like), a mesh network, and/or the like. In this case, the UE 120 may perform scheduling operations, resource selection operations, and/or other operations described elsewhere herein as being performed by the base station 110.)
Regarding Claim 9:
Yoo discloses:
A first terminal device to communicate with a second terminal device by Side Link (SL) communication, ([¶0043] In some aspects, two or more UEs 120 (e.g., shown as UE 120a and UE 120e) may communicate directly using one or more sidelink channels) wherein the first terminal device is configured to transmit encoded channel state information to the second terminal device, the encoded channel state information being channel state information encoded by using an encoding model learned for the channel state information, the channel state information being information for reporting the channel state of the SL communication. ( [¶0099] As shown by reference number 935, UE 920 may transmit the first encoded CSI to BS 910. As shown by reference number 940, BS 910 may decode the first encoded CSI into first decoded CSI based at least in part on decoder weights. BS 910 may have received the decoder weights, or determined the decoder weights from training a neural network model associated with a CSI encoder and a CSI decoder. [¶0053] In some aspects, base station 110 may include means for receiving first encoded CSI from a UE, the first encoded CSI being a first CSI instance for a channel to the UE that is encoded by the UE, based at least in part on one or more encoder weights that correspond to a neural network model associated with a CSI encoder and a CSI decoder, means for decoding the first encoded CSI into first decoded CSI based at least in part on one or more decoder weights that correspond to the neural network model, and/or the like. In some aspects, such means may include one or more components of base station 110 described in connection with FIG. 2, such as antenna 234, DEMOD 232, MIMO detector 236, receive processor 238, controller/processor 240, transmit processor 220, TX MIMO processor 230, MOD 232, antenna 234, and/or the like. [¶0043] In some aspects, two or more UEs 120 (e.g., shown as UE 120a and UE 120e) may communicate directly using one or more sidelink channels (e.g., without using a base station 110 as an intermediary to communicate with one another). For example, the UEs 120 may communicate using peer-to-peer (P2P) communications, device-to-device (D2D) communications, a vehicle-to-everything (V2X) protocol (e.g., which may include a vehicle-to-vehicle (V2V) protocol, a vehicle-to-infrastructure (V2I) protocol, and/or the like), a mesh network, and/or the like. In this case, the UE 120 may perform scheduling operations, resource selection operations, and/or other operations described elsewhere herein as being performed by the base station 110.)
Regarding Claim 10:
Yoo discloses:
Yoo discloses the limitations of parent claims:
wherein the first terminal device is configured to transmit the channel state information for learning to a base station via the second terminal device, and the encoding model is learned using the channel state information for learning and transmitted to the first terminal by the base station. ([¶0008] obtain a CSI instance for a channel, determine a neural network model including a CSI encoder and a CSI decoder, and train the neural network model based at least in part on encoding the CSI instance into encoded CSI, decoding the encoded CSI into decoded CSI, and comparing the CSI instance and the decoded CSI. The memory and the one or more processors may be configured to obtain one or more encoder weights and one or more decoder weights based at least in part on training the neural network model.)
Regarding Claim 11:
Yoo discloses:
A second terminal device to communicate with a first terminal device by Side Link (SL) communication, ([¶0043] In some aspects, two or more UEs 120 (e.g., shown as UE 120a and UE 120e) may communicate directly using one or more sidelink channels) wherein the second terminal device is configured to receive encoded channel state information from the first terminal device and decode the encoded channel state information by using a decoding model learned for channel state information, the channel state information being information for reporting the channel state of the SL communication. ( [¶0099] As shown by reference number 935, UE 920 may transmit the first encoded CSI to BS 910. As shown by reference number 940, BS 910 may decode the first encoded CSI into first decoded CSI based at least in part on decoder weights. BS 910 may have received the decoder weights, or determined the decoder weights from training a neural network model associated with a CSI encoder and a CSI decoder. [¶0053] In some aspects, base station 110 may include means for receiving first encoded CSI from a UE, the first encoded CSI being a first CSI instance for a channel to the UE that is encoded by the UE, based at least in part on one or more encoder weights that correspond to a neural network model associated with a CSI encoder and a CSI decoder, means for decoding the first encoded CSI into first decoded CSI based at least in part on one or more decoder weights that correspond to the neural network model, and/or the like. In some aspects, such means may include one or more components of base station 110 described in connection with FIG. 2, such as antenna 234, DEMOD 232, MIMO detector 236, receive processor 238, controller/processor 240, transmit processor 220, TX MIMO processor 230, MOD 232, antenna 234, and/or the like. [¶0043] In some aspects, two or more UEs 120 (e.g., shown as UE 120a and UE 120e) may communicate directly using one or more sidelink channels (e.g., without using a base station 110 as an intermediary to communicate with one another). For example, the UEs 120 may communicate using peer-to-peer (P2P) communications, device-to-device (D2D) communications, a vehicle-to-everything (V2X) protocol (e.g., which may include a vehicle-to-vehicle (V2V) protocol, a vehicle-to-infrastructure (V2I) protocol, and/or the like), a mesh network, and/or the like. In this case, the UE 120 may perform scheduling operations, resource selection operations, and/or other operations described elsewhere herein as being performed by the base station 110.)
Regarding Claim 12:
Yoo discloses:
Yoo discloses the limitations of parent claims:
wherein the second terminal device is configured to communicate with a base station, the second terminal device is configured to transmit the channel state information for learning from the first terminal device to the base station, and the decoding mode is learned using the channel state information for learning and transmitted to the second terminal by the base station. ([¶0010] In some aspects, a base station that receives communications on a channel from a UE may include memory and one or more processors operatively coupled to the memory. The memory and the one or more processors may be configured to receive first encoded CSI from the UE. The first encoded CSI may be a first CSI instance for the channel that is encoded by the UE, based at least in part on one or more encoder weights that correspond to a neural network model associated with a CSI encoder and a CSI decoder. The memory and the one or more processors may be configured to decode the first encoded CSI into first decoded CSI based at least in part on one or more decoder weights that correspond to the neural network model.)
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HUGH MARK ASHLEY whose telephone number is (571)272-0199. The examiner can normally be reached M-F 8-430.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Asad Nawaz can be reached at (571) 272-3988. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/HUGH MARK ASHLEY/Examiner, Art Unit 2463
/ASAD M NAWAZ/Supervisory Patent Examiner, Art Unit 2463
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