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
Applicant’s amendment filed on 09/22/2025 has been entered. Independent Claims 1 and 13 have been amended. Dependent claims 2-6, and 14 have been amended. Claims 8 has been cancelled. Dependent Claim 5 is now an independent claim. No claims are new and have been entered. Claims 1-6 and 13-14 are still pending in this application.
Response to Arguments
Applicant’s arguments/amendments with respect to the Claim Objections have been considered and are persuasive. Therefore, the Objections are withdrawn.
Applicant’s arguments filed on 09/22/2025 on pages 7-8 of applicant’s remark regarding Claims 1, 5, and 13. The applicant argues that the combination of Yoo in view of Kyung does not teach the amended claim for a learning system for encoding and decoding based on the capability of the response system, as well as the type of UE, and LLR generation, as the independent claims have been amended to say that.
Applicant’s arguments with respect to claim(s) 1, 5, and 13 for the 35 USC § 103 rejection have been considered, but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specified challenged in the argument.
Claim Objections
Claim 1 is objected to because of the following informalities: pg. 1 line 16 states “by inputting the CSI, the information related to capability of the RX, -and the information”, the “-“ before “and” is superfluous. Appropriate correction is required.
Claim 5 is objected to because of the following informalities: pg. 3 line 25 states “generation log likelihood”, the applicant likely meant “generating” as a generational log likelihood ratio is not a term of art. Appropriate correction is required.
Claim 5 is objected to because of the following informalities: pg. 3 line 25 states “log likelihood ration (LLR)”, the applicant likely meant “ratio” as a log likelihood ration is not a term of art. Appropriate correction is required.
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, 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) 1-4, 5-6, 8, 13, 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yoo (Pub.: No.: US 20210273707 A1, hereafter “Yoo”) in view of Kyung (Pub. No.: US 20060218461 A1, hereafter “Kyung”).
Regarding Claim 1 and Claim 13
Yoo teaches a method and UE comprising
A method for operating user equipment (UE) (Yoo Fig. 1: 120d) in a wireless communication system (Yoo Fig. 1: 100), the method comprising: receiving (Yoo Fig. 8: 830), from a receiving end (Rx) (Yoo Fig. 2 252r), information related to capability of the Rx (Yoo Fig. 8: 810; Yoo teaches a UE in a wireless communication receiving information from another wireless machine); performing learning (Yoo Fig. 8: 835) for at least one of an encoding scheme (Yoo ¶0063: UE may use encoder weights from a trained neural network model to encode the CSI) or a decoding (Not given patentable weight due to non-selective option in the claim) scheme for data transmission based on information related to capability of the UE (Yoo Fig. 8: 830) and the information related to capability of the Rx (Yoo ¶0092: CSI encoder or decoder may include different BS configurations; Yoo teaches the UE using machine learning for an encoding system using information sent to the UE which includes different BS configurations), wherein a type of the UE is any one of a first type UE (Yoo Fig. 1: 120a), a second type UE (Yoo Fig. 1: 120b) and a third type UE (Yoo Fig. 1: 120c; Yoo teaches multiple types of UEs); wherein, based on the UE being the third type UE and the Rx being the first type UE, the UE obtains channel state information (CSI) (Yoo ¶0083: CSI instance) and information on a fixed decoding scheme (Yoo ¶0083: previously stored CSI encode) from the Rx (Yoo Fig. 6: DL channel estimates; Yoo teaches a UE using previously stored CSI instances to encode a transmission), wherein the UE performs learning for the encoding scheme of by inputting the CSI (Yoo Fig. 8: 835), the information related to capability of the Rx (Yoo Fig. 8: 830), -and the information on the fixed decoding scheme into a leaning model (Yoo ¶0093: UE may receive a trained model associated with the CSI decoder; Yoo teaches a UE receiving a CSI and performing machine learning as a part of the decoding mechanism), wherein the information related to capability of the Rx (Yoo Fig. 8: 810) comprises information on whether the Rx includes a neural network and information on whether the Rx has a learning capability (Yoo Fig. 8: 845), wherein the first type UE is without the neural network and without the learning capability (Yoo ¶0043: UE 120 may perform scheduling operations and/or other operations described as being performed by the Base Station, during D2D communications; Yoo teaches that the Base Station operations, i.e. the machine without the neural network and without learning capabilities, can be performed by a UE in D2D communication), wherein the second type UE includes a neural network (Yoo ¶0079: UE or Base Station) and is without a learning capability (Yoo ¶0080: the neural network model may refer to a structure plus weights, which may or may not have been trained; Yoo teaches that the UE could optionally contain the neural network, but not apply the learning model, i.e. function without the learning), and wherein the third type UE includes a neural network (Yoo Fig. 8: 820) and has the learning capability (Yoo Fig. 8: 835; Yoo teaches a UE with a neural network and learning capability).
Yoo does not explicitly teach
generating at least one codeword by encoding information bits based on the encoding scheme, generating modulation symbols based on the at least one codeword
However, Kyung teaches
generating (Kyung ¶0045: generates) at least one codeword (Kyung ¶0047: codeword) by encoding information bits (Kyung ¶0045: bits) based on the encoding scheme (Kyung Fig. 3: 311), generating modulation symbols (Kyung: Fig. 3: 315, Modulator) based on the at least one codeword (Kyung Fig. 3: 300; Kyung teaches generating a codeword by modulating bits in an encoding scheme);
It would have been obvious for one skilled in the art, before the effective filing date of the claimed invention, to modify Yoo by way of Kyung, to include an element that teaches generating a codeword by encoding bits in an encoding scheme, as taught by Kyung in Fig. 3 and ¶0045-¶0047, to increase data transmission for a large amount of data in a wireless network in a capacity parallel to that of wired network and improve high-speed high-capacity communication systems capable of processing and transmitting data.
Claim 13 Differs by the following limitation, which is also taught by the prior art, Yoo teaches
at least one transmitter (Yoo Fig. 2: 252a); at least one receiver (Yoo Fig. 2: 252r); at least one processor (Yoo Fig. 2: 280); and at least one memory that is coupled with the at least one processor in an operable manner and stores instructions which, when being executed, enable the at least one processor to perform a specific operation (Yoo Fig. 2: 282; Yoo teaches a UE with a transmitter, receiver, a processor, and memory coupled together),
Regarding Claim 2
Yoo by way of Kyung teaches a method and UE comprising the method and UE as explained above in Claim 1. Yoo further discloses
wherein, based on the UE being the third type UE, the UE has a neural network (Yoo ¶0101: UE 120) and performs learning for channel coding (Yoo ¶0101: BS performs operations associated with neural network based CSI feedback).
Regarding Claim 3
Yoo by way of Kyung teaches a method and UE comprising the method and UE as explained above in Claim 1. Yoo further discloses
wherein, based on the UE being the third type UE (Yoo Fig. 2: 120) and being the UE that transmits the signal (Yoo Fig. 2: 252a), the UE further receives, from the Rx, resource information of the Rx (Yoo Fig. 2: 258), wherein the UE performs learning of the encoding scheme (Yoo ¶0050: training the neural network) by further inputting the resource information (Yoo ¶0050: obtaining decoder weights based on training the neural network model), and wherein the UE transmits the signal based on the learned encoding scheme (Yoo ¶0067: CSI encoder can be used for PUCCH; Yoo teaches the UE being able to transmit and receive based on the training for the neural network of a CSI encoder, and using said encoder for PUCCH).
Regarding Claim 14
Yoo by way of Kyung teaches a method and UE comprising the method and UE as explained above in Claim 13. Yoo further discloses
wherein the user equipment communicates with at least one of a moving terminal (Not given patentable weight due to non-selective option in the claim), a network (Yoo ¶0034: NR network), or an autonomous vehicle apart from a vehicle including the UE (Not given patentable weight due to non-selective option in the claim; Yoo teaches the UE communicating with a NR network).
Claim(s) 5-6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yoo (Pub.: No.: US 20210273707 A1, hereafter “Yoo”) in view of Kyung (Pub. No.: US 20060218461 A1, hereafter “Kyung”), further in view of Moorti (Pub. No.: US 20070183541 A1, hereafter “Moorti”).
Regarding Claim 5
Yoo teaches a method and UE comprising
A method for operating user equipment (UE) (Yoo Fig. 1: 120d) in a wireless communication system (Yoo Fig. 1: 100), the method comprising: receiving (Yoo Fig. 8: 830), from a receiving end (Rx) (Yoo Fig. 2 252r), information related to capability of the Rx (Yoo Fig. 8: 810; Yoo teaches a UE in a wireless communication receiving information from another wireless machine); performing learning (Yoo Fig. 8: 835) for at least one of an encoding scheme (Yoo ¶0063: UE may use encoder weights from a trained neural network model to encode the CSI) or a decoding (Not given patentable weight due to non-selective option in the claim) scheme for data transmission based on information related to capability of the UE (Yoo Fig. 8: 830) and the information related to capability of the Rx (Yoo ¶0092: CSI encoder or decoder may include different BS configurations; Yoo teaches the UE using machine learning for an encoding system using information sent to the UE which includes different BS configurations), wherein a type of the UE is any one of a first type UE (Yoo Fig. 1: 120a), a second type UE (Yoo Fig. 1: 120b) and a third type UE (Yoo Fig. 1: 120c; Yoo teaches multiple types of UEs); receiving a signal including modulation symbols from the Tx (Yoo ¶0057: CSI feedback may include a Type-II CSI feedback including Rank, which may refer to the number of modulated symbols before precoding is applied);
wherein, based on the UE being the third type UE and the Rx being the first type UE, the UE obtains channel state information (CSI) (Yoo ¶0083: CSI instance) and information on a fixed decoding scheme (Yoo ¶0083: previously stored CSI encode) from the Rx (Yoo Fig. 6: DL channel estimates; Yoo teaches a UE using previously stored CSI instances to encode a transmission), wherein the UE performs learning for the encoding scheme of by inputting the CSI (Yoo Fig. 8: 835), the information related to capability of the Rx (Yoo Fig. 8: 830), -and the information on the fixed decoding scheme into a leaning model (Yoo ¶0093: UE may receive a trained model associated with the CSI decoder; Yoo teaches a UE receiving a CSI and performing machine learning as a part of the decoding mechanism), wherein the information related to capability of the Rx (Yoo Fig. 8: 810) comprises information on whether the Rx includes a neural network and information on whether the Rx has a learning capability (Yoo Fig. 8: 845), wherein the first type UE is without the neural network and without the learning capability (Yoo ¶0043: UE 120 may perform scheduling operations and/or other operations described as being performed by the Base Station, during D2D communications; Yoo teaches that the Base Station operations, i.e. the machine without the neural network and without learning capabilities, can be performed by a UE in D2D communication), wherein the second type UE includes a neural network (Yoo ¶0079: UE or Base Station) and is without a learning capability (Yoo ¶0080: the neural network model may refer to a structure plus weights, which may or may not have been trained; Yoo teaches that the UE could optionally contain the neural network, but not apply the learning model, i.e. function without the learning), and wherein the third type UE includes a neural network (Yoo Fig. 8: 820) and has the learning capability (Yoo Fig. 8: 835; Yoo teaches a UE with a neural network and learning capability).
Yoo does not explicitly teach
generating at least one codeword by encoding information bits based on the encoding scheme, generating modulation symbols based on the at least one codeword
However, Kyung teaches
generating (Kyung ¶0045: generates) at least one codeword (Kyung ¶0047: codeword) by encoding information bits (Kyung ¶0045: bits) based on the encoding scheme (Kyung Fig. 3: 311), generating modulation symbols (Kyung: Fig. 3: 315, Modulator) based on the at least one codeword (Kyung Fig. 3: 300; Kyung teaches generating a codeword by modulating bits in an encoding scheme);
It would have been obvious for one skilled in the art, before the effective filing date of the claimed invention, to modify Yoo by way of Kyung, to include an element that teaches generating a codeword by encoding bits in an encoding scheme, as taught by Kyung in Fig. 3 and ¶0045-¶0047, to increase data transmission for a large amount of data in a wireless network in a capacity parallel to that of wired network and improve high-speed high-capacity communication systems capable of processing and transmitting data.
Yoo in view of Kyung does not explicitly teach
generation log likelihood ration (LLR) based on the modulation symbols;
However, Moorti teaches
generation (Moorti ¶0114: generating) log likelihood ration (LLR) (Moorti ¶0114: LLR) based on the modulation symbols (Moorti ¶0114: modulation reference; Moorti teaches generating an LLR based off of a modulation reference for the modulation types);
It would have been obvious for one skilled in the art, before the effective filing date of the claimed invention, to modify Yoo in view of Kyung by way of Moorti, to include an element that teaches generating an LLR based off of a modulation reference for the modulation types, as taught by Moorti in ¶0114, to improve communication with various frame formats with different data structures to allow a more seamless transition from one system to another.
Regarding Claim 4 and Claim 6
Yoo by way of Kyung, and further in view of Moorti teaches a method and UE comprising the method and UE as explained above in Claim 3. Yoo further discloses
wherein, based on the UE being the third type of UE (Yoo Fig. 8: 820) and the Rx being the second type of UE (Yoo ¶0079: UE or Base Station), the UE obtains the CSI (Yoo Fig. 8: 830), wherein the UE performs learning for the encoding scheme (Yoo Fig. 8: 835) and the decoding scheme by inputting the CSI (Yoo Fig. 8: 840) and the information related to the capability of the Rx (Yoo Fig. 8: 830), and transmits information on the learned decoding scheme (Yoo ¶0051: CSI decoder) to the Rx (Yoo Fig. 2: 252r), and wherein the Rx performs decoding for the signal (Yoo Fig. 2: 258), which is transmitted from the UE based on the learned encoding scheme (Yoo ¶0051: CSI encoder and CSI decoder), based on information on the learned decoding scheme (Yoo ¶0051: encoded CSI is based on in part to the neural network model; Yoo teaches the UE transmitting and receiving through the learned CSI scheme).
Conclusion
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 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 JUSTIN MICHAEL WHITAKER whose telephone number is (703)756-4763. The examiner can normally be reached Monday - Thursday 7:30am - 4:00pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jeffrey Rutkowski can be reached on (571) 270-1215. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JUSTIN MICHAEL WHITAKER/Examiner, Art Unit 2415
/Sudesh M. Patidar/Primary Examiner, Art Unit 2415
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