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 .
Response to Amendment
This is in response to an amendment/response filed on July 29, 2026.
Claim 14 has been amended.
Claim 15 has been cancelled.
No Claims have been added.
Claims 1-14,16-21 are currently pending.
Response to Arguments
Applicant's arguments filed on July 29, 2026have been fully considered but they are not persuasive.
The applicant argues on pages 11-14 of the arguments/remarks that Lee fails to disclose: receiving, from the second communications node, an indication of an NN-based AE-decoder model out of the one or more trained NN-based AE-decoder models; selecting a trained NN-based AE-encoder model out of the one or more trained NN-based AE-encoder models to use for the NN-based AE-encoder based on the received indication; transmitting an indication of an NN-based AE-decoder model out of the one or more trained NN-based AE-decoder models; receiving the CSI from the first communications node based on output from a trained NN-based AE-encoder model selected out of the one or more trained NN-based AE-encoder models based on the transmitted indication.
The examiner respectfully disagrees. Lee discloses method of efficiently learning and updating a weight of an autoencoder neural network (NN) when an autoencoder, which is a type of deep neural network (DNN), is utilized in signal transmission or reception between a UE and a BS. The training method in the disclosure is referred to as “shadow training”. The autoencoder is a neural network (NN) which is trained in a manner in which an output 602 and an input 601 are identical. According to an embodiment, a signal to be transmitted may be input 601 to the autoencoder NN, and the input signal may be calculated via the trained autoencoder NN and may be output 602([0100]-[0101]). Based on the above disclosure Lee discloses communication between two nodes for transmission trained NN based AE-encoder and AE-decoder as claimed by the instant application. Therefore, Lee discloses receiving, from the second communications node, an indication of an NN-based AE-decoder model out of the one or more trained NN-based AE-decoder models.
Lee further discloses the UE and the BS may select an autoencoder NN having an appropriate structure among a plurality of autoencoder NNs defined/trained in advance based on the obtained information associated with the physical situation after the initial access([0112]) which is the same function as selecting a trained NN-based AE-encoder model out of the one or more trained NN-based AE-encoder models to use for the NN-based AE-encoder based on the received indication.
Lee further teaches a Tx NN 705 of the autoencoder NN, which is learned via deep learning, may be disposed in a UE 701, and an Rx NN 708 may be disposed in a BS 702. The UE may perform pre-processing 704 of an estimated downlink channel matrix H 703 so as to produce a new matrix {tilde over (H)}. The Tx NN 705 may convert the pre-processed output matrix {tilde over (H)} into a codeword vector. The codeword vector is converted into a signal in a form that is transmissible via a CSI transmitter 706, and may be fed back to the BS (CSI report). Feedback may be performed periodically or aperiodically via a PUCCH or a PUSCH. The BS may obtain {tilde over (H)} by decoding the codeword vector received via a CSI receiver 707 using the Rx NN 708([0106]). Which is the same function as transmitting an indication of an NN-based AE-decoder model out of the one or more trained NN-based AE-decoder models; receiving the CSI from the first communications node based on output from a trained NN-based AE-encoder model selected out of the one or more trained NN-based AE-encoder models based on the transmitted indication as claimed by the instant application.
Based on the above disclosures and others Lee discloses : receiving, from the second communications node, an indication of an NN-based AE-decoder model out of the one or more trained NN-based AE-decoder models; selecting a trained NN-based AE-encoder model out of the one or more trained NN-based AE-encoder models to use for the NN-based AE-encoder based on the received indication; transmitting an indication of an NN-based AE-decoder model out of the one or more trained NN-based AE-decoder models; receiving the CSI from the first communications node based on output from a trained NN-based AE-encoder model selected out of the one or more trained NN-based AE-encoder models based on the transmitted indication.
For at least the reasons provided above, the applicant arguments regarding independent claims are not persuasive. The applicant argues that independent claims are patentable for similar reasons and are also not persuasive. The applicant further argues that since dependent claims depend on the argued independent claim; they are patentable at least by virtue of their dependencies. Since the applicant's arguments regarding independent claims are not persuasive, the applicant's arguments regarding dependent claims are also not persuasive.
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) 1-4,6-14, and 16-21 is/are rejected under 35 U.S.C. 102a1 as being anticipated by Lee et al. (US Application 2021/0110261, hereinafter Lee).
Regarding claim 1,14,16, Lee discloses a method, a computer readable storage medium (Figs. 7-9), performed by a first communications node(701,801), for providing channel state information, CSI(706,806), in a wireless communications network(700), to a second communications node (702,802), the first communications node (701,801) having access to one or more trained Neural Network, NN(705,805),-based Auto Encoder, AE,-encoder models (705,805) for encoding the CSI and the second communications node having access to one or more trained NN (708,808) -based AE-decoder models(708,808) for decoding the CSI provided by the first communications node(701,801), the method (see [0106], [0111]) comprising:
receiving, from the second communications node, an indication of an NN-based AE-decoder model out of the one or more trained NN-based AE-decoder models(Abstract,[0111]-[0112], which recites the UE 801 and the BS 802 may obtain information associated with the physical situation of the other thereof after the initial access 901 and 907 );
selecting a trained NN-based AE-encoder model out of the one or more trained NN-based AE-encoder models to use for the NN-based AE-encoder based on the received indication of the NN-based AE-decoder model such that the selected trained NN-based AE-encoder model is compatible with the indicated NN-based AE-decoder model(Abstract,[0112]-[0119], which recites the UE and the BS may select an autoencoder NN having an appropriate structure among a plurality of autoencoder NNs defined/trained in advance based on the obtained information associated with the physical situation after the initial access. The UE 801 may dispose a Tx NN 805 of the selected autoencoder NN on a UE side in operation 903, and the BS 802 may dispose an Rx N 808 of the selected NN on a BS side in operation 909. In this instance, the BS may dispose, in the base station side, a plurality of Rx NNs different for each of the UEs existing in a cell. The UE 801 and the BS 802 may perform the autoencoder-based downlink channel feedback 700 of FIG. 7 using the Tx NN 805 and the Rx NN 808, respectively disposed therein. Autoencoder-based downlink channel feedback 700 has been described with reference to FIG. 7); and
transmitting the CSI to the second communications node based on output from the selected trained NN-based AE-encoder model([0112]-[0119], which recites the UE 801 may transmit a weight of an Rx NN (weight report) among weights of the new entire autoencoder NN (Tx NN and Rx NN), updated via shadow training 810, to the BS 802 in operation 905. According to an embodiment, the weight of the Rx NN may be transmitted to the BS via an uplink channel).
Regarding claims 11,18, Lee discloses a method, performed by a second communications node(1002,1202,1402,1602), for assisting a first communications node (1001,1201,1401,1601), in providing channel state information, CSI(1006,1204,1406,1604), to the second communications node (1002,1202,1402,1602), in a wireless communications network(1200,1400,1600), the first communications node having access to one or more trained Neural Network, NN(1005,1405),-based Auto Encoder, AE,-encoder models (1005,1405),- for encoding the CSI(1006,1204,1406,1604) and the second communications node having access to one or more trained NN(1007,1205,1407,1605)-based AE-decoder models (1007,1205,1407,1605) for decoding the CSI provided by the first communications node, the method (see [0122]-[0124]) comprising:
transmitting an indication of an NN-based AE-decoder model out of the one or more trained NN-based AE-decoder models (Abstract, [0122]-[0127], which recites the BS 1002 may transmit, to the UE 1001, the weight of a Tx NN (weight report) among weights of the new entire autoencoder NN (Tx NN and Rx NN), updated via shadow training 1010, in operation 1105he BS 1002 may prepare an entire autoencoder NN including both a Tx NN and an Rx NN for shadow training, in addition to the Rx NN 1008 disposed in the BS. According to an embodiment, the UE may estimate a downlink channel matrix H 1003 using a signal received from the BS, and may transmit the same to the BS via the autoencoder-based downlink channel feedback 700); and
receiving the CSI from the first communications node based on output from a trained NN-based AE-encoder model selected out of the one or more trained NN-based AE-encoder models based on the transmitted indication of the NN-based AE-decoder model(Abstract, [0122]-[0125], which recites the UE 1001 and the BS 1002 may obtain information associated with the physical situation of the other thereof after the initial access 1101 and 1107, and may select one element from the set of autoencoder NNs in operations 1102 and 1108. That is, the UE and the BS may select an autoencoder NN having an appropriate structure among a plurality of autoencoder NNs defined/trained in advance based on the obtained information associated with the physical situation after the initial access).
Regarding claims 2,17, Lee discloses the method according to claim 1, wherein the indicated NN-based AE-decoder model is known to the first communications node(Abstract,[0104],[0106], [0122]-[0125]).
Regarding claim 3, Lee discloses the method according to claim 1, wherein the indication of the NN-based AE-decoder model is received from the second communications node with a CSI reporting configuration(Abstract,[0104],[0106], [0122]-[0125]).
Regarding claims 4,12,19,20, Lee discloses the method according to claim 1, further comprising: transmitting an AE-decoder capability to the second communications node on joining the wireless communications network(Abstract,[0104],[0106], [0122]-[0125]); and receiving, from the second communications node, a decoder configuration comprising an indication to use an AE CSI reporting mode for which the first communications node is configured to use the trained NN-based AE-encoder model compatible with the indicated NN-based AE-decoder model(Abstract,[0104],[0106], [0122]-[0125]).
Regarding claim 6, Lee discloses the method according to claim 3, wherein the CSI configuration indicates whether the CSI report shall use periodic PUCCH or aperiodic PUSCH to convey the CSI report to the second communications node([0106]-[0107]).
Regarding claim 7, Lee discloses the method according to claim 4, wherein the AE CSI reporting mode is configured with aperiodic CSI reporting([0106]-[0107]).
Regarding claim 8, Lee discloses the method according to claim 1, wherein the indication of the NN-based CSI decoder model is determined by at least a CSI resource configuration used for channel measurement(Abstract,[0104],[0106], [0122]-[0125]).
Regarding claim 9, Lee discloses the method according to claim 8, wherein the CSI resource configuration comprises one or more of: a configuration of a number of CSI-RS ports of the second communications node, a CSI-RS port layout, and an indication of whether the CSI report shall be periodic or aperiodic([0063]).
Regarding claim 10, Lee discloses the method according to claim 1, wherein transmitting the CSI to the second node is performed over a radio-based air interface and using a standardized radio transmission protocol (Abstract, [0122]-[0127]).
Regarding claims 13,21, Lee discloses the method according to claim 11, wherein receiving the AE-decoder capability from the first communications node is performed using RRC signalling when the first communications node joins the wireless communications network([0071],[0078]).
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 nonprovisional extension fee (37 CFR 1.17(a)) 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 DADY CHERY whose telephone number is (571)270-1207. The examiner can normally be reached M to T, 8 am to 5pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Moo Jeong can be reached at 571-272-9617. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/DADY CHERY/ Primary Examiner, Art Unit 2418
/Moo Jeong/ Supervisory Patent Examiner, Art Unit 2418
Prosecution Insights