Prosecution Insights
Last updated: April 19, 2026
Application No. 18/348,748

METHOD FOR FEEDBACK MODE DETERMINATION AND DEVICE

Non-Final OA §102§103
Filed
Jul 07, 2023
Examiner
GEORGE, AYANAH S
Art Unit
2467
Tech Center
2400 — Computer Networks
Assignee
Guangdong OPPO Mobile Telecommunications Corp., Ltd.
OA Round
2 (Non-Final)
86%
Grant Probability
Favorable
2-3
OA Rounds
2y 5m
To Grant
93%
With Interview

Examiner Intelligence

Grants 86% — above average
86%
Career Allow Rate
431 granted / 498 resolved
+28.5% vs TC avg
Moderate +6% lift
Without
With
+6.1%
Interview Lift
resolved cases with interview
Typical timeline
2y 5m
Avg Prosecution
31 currently pending
Career history
529
Total Applications
across all art units

Statute-Specific Performance

§101
4.1%
-35.9% vs TC avg
§103
55.3%
+15.3% vs TC avg
§102
23.8%
-16.2% vs TC avg
§112
11.1%
-28.9% vs TC avg
Black line = Tech Center average estimate • Based on career data from 498 resolved cases

Office Action

§102 §103
DETAILED ACTION This action is a response to a arguments filed on 11/10/25 in which claims 1-20 are pending. 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 Rejections - 35 USC § 102 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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention. Claim(s) 1-11 and 13 -17 is/are rejected under 35 U.S.C. 102(a) as being anticipated by Vitthaladevuni et al. (Pub. No.: 2023/0188302), herein Vitthaladevuni. As to claim 1, Vitthaladevuni teaches a terminal device, comprising: a processor and a transceiver connected with the processor, wherein the processor is configured to (Vitthaladevuni Fig. 5): determine first information (Vitthaladevuni [0061] In wireless communications system 100, a UE 115 may be configured to perform channel measurements on downlink signals received from a base station 105 and report the channel measurements to the base station 105. The UE 115 may report the channel measurements as CSI feedback); and determine a feedback mode for channel-state information (CSI) of a wireless channel based on first information, the first information being used for indicating channel characteristics of the wireless channel (Vitthaladevuni [0013] the UE may train a set of neural network pairs, the base station may dynamically indicate a level of accuracy to the UE (first information), and the UE may select one of the set of neural network pairs (determine feedback mode) corresponding to the level of accuracy to use to encode the channel state feedback) wherein the transceiver is configured to send the first information to the network device (Vitthaladevuni [0061] In wireless communications system 100, a UE 115 may be configured to perform channel measurements on downlink signals received from a base station 105 and report the channel measurements to the base station 105. The UE 115 may report the channel measurements as CSI feedback); As to claim 2, Vitthaladevuni teaches the terminal device of claim 1, wherein the processor configured to determine the feedback mode for the CSI of the wireless channel based on the first information is configured to: configure the feedback mode based on the first information (Vitthaladevuni [0013] the UE may train a set of neural network pairs, the base station may dynamically indicate a level of accuracy to the UE (first information), and the UE may select one of the set of neural network pairs (determine feedback mode) corresponding to the level of accuracy to use to encode the channel state feedback) or update the feedback mode based on the first information. As to claim 3, Vitthaladevuni teaches the terminal device of claim 1, wherein the feedback mode comprises feeding back the CSI through a feedback model, and the feedback model comprises a neural network model used for feeding back the CSI (Vitthaladevuni [0013] the UE may train a set of neural network pairs, the base station may dynamically indicate a level of accuracy to the UE (first information), and the UE may select one of the set of neural network pairs (determine feedback mode) corresponding to the level of accuracy to use to encode the channel state feedback) As to claim 4, Vitthaladevuni teaches the terminal device of claim 3, wherein the feedback model comprises an encoding model; wherein the processor is further configured to invoke the encoding model to encode the CSI to obtain first channel-information after determining the feedback mode for the CSI of the wireless channel based on the first information (Vitthaladevuni Fig. 2 encoder [0023] Some examples of the method, apparatuses, and non-transitory computer-readable medium described herein may further include operations, features, means, or instructions for transmitting an indication of a subset of the set of neural network pairs for the UE to train. In some examples of the method, apparatuses, and non-transitory computer-readable medium described herein, transmitting the indication of the level of accuracy for reporting channel state feedback may include operations, features, means, or instructions for transmitting indications of different levels of accuracy for reporting channel state feedback for different subbands, spatial layers, channel taps, or in response to failing to decode different numbers of downlink transmissions including same data. In some examples of the method, apparatuses, and non-transitory computer-readable medium described herein, the indicated level of accuracy may include operations, features, means, or instructions for transmitting an indication of a second level of accuracy for reporting channel state feedback to be used to schedule a second downlink transmission, the first level of accuracy being different from the second level of accuracy.; and wherein the transceiver is configured to send the first channel-information to a second device. As to claim 5, Vitthaladevuni teaches the terminal device of claim 1, wherein the transceiver is configured to receive the first information from the network device (Vitthaladevuni [0013] the UE may train a set of neural network pairs, the base station may dynamically indicate a level of accuracy to the UE (first information), and the UE may select one of the set of neural network pairs (determine feedback mode) corresponding to the level of accuracy to use to encode the channel state feedback) As to claim 6, Vitthaladevuni teaches the terminal device of claim 5, wherein the first information is carried in at least one of: a broadcast message, a system information block (SIB), a radio resource control (RRC) message, RRC reconfiguration signaling, downlink control information (DCI), a media access control (MAC) control element (CE), a physical downlink control channel (PDCCH) order, or data information (Vitthaladevuni [0074] The base station 105-a may transmit the indication of the level of accuracy 305 via a higher layer message (e.g., RRC signaling) or dynamic signaling (e.g., in a MAC control element (MAC-CE)).) As to claim 8, first terminal device of claim 1, wherein the processor configured to determine the first information is configured to determine the first information based on a measurement result obtained by measuring a first reference signal. (Vitthaladevuni [0077] The level of accuracy may refer to a difference between raw CSI entered (or the actual channel condition measured) as input to an encoder at the UE 115-b and the CSI produced by a decoder at the base station 105-b. For example, a high level of accuracy may indicate a small or no difference between the raw CSI entered (or the actual channel condition measured) as input to the encoder and the CSI produced by the decoder, and a low level of accuracy may indicate a large difference between the raw CSI entered (or the actual channel condition measured) as input to the encoder and the CSI produced by the decoder. In other words, the higher the level of accuracy, the lower the amount of compression, and vice versa. [0081] At 425, the base station 105-b may transmit downlink data or reference signals (e.g., CSI-RSs) to the UE 115-b, and the UE 115-b may perform channel measurements to generate CSI feedback based on the downlink data or the reference signals received from the base station 105-b) As to claim 9, Vitthaladevuni teaches the terminal device of claim 8, wherein the first reference information comprises at least one of: a synchronization signal block (SSB), a CSI reference signal (CSI-RS), or a de-modulation reference signal (DMRS) (Vitthaladevuni [0081] At 425, the base station 105-b may transmit downlink data or reference signals (e.g., CSI-RSs) to the UE 115-b, and the UE 115-b may perform channel measurements to generate CSI feedback based on the downlink data or the reference signals received from the base station 105-b) As to claim 10, Vitthaladevuni teaches the terminal device of claim 7, wherein the processor configured to determine the first information is configured to determine the first information based on environmental information (Vitthaladevuni [0037] The indicated level of accuracy may depend on how the base station intends to use the CSI. For example, the base station may indicate different levels of accuracy for CSI associated with different subbands, channel taps, spatial streams, feedback instances, etc. (e.g., such that latency-sensitive and reliability-sensitive transmissions are scheduled based on highly accurate CSI feedback). Accordingly, when a higher level of accuracy is appropriate, the UE may report CSI with the higher level of accuracy. Otherwise, the UE may report CSI with a lower level of accuracy) As to claim 11, Vitthaladevuni teaches the terminal device of claim 10, wherein the environmental information comprises at least one of: location information, region information, pre-configuration information for the first device, or pre-configuration information for the second device (Vitthaladevuni [0037] As such, the UE may report the CSI at the level of accuracy indicated by the base station. The indicated level of accuracy may depend on how the base station intends to use the CSI. (preconfiguration)) As to claim 13, Vitthaladevuni teaches a method for feedback mode determination, applied to a network device and comprising: Receiving first information from a terminal device (Vitthaladevuni [0061] In wireless communications system 100, a UE 115 may be configured to perform channel measurements on downlink signals received from a base station 105 and report the channel measurements to the base station 105. The UE 115 may report the channel measurements as CSI feedback); determining a feedback mode for channel-state information (CSI) of a wireless channel based on first information, the first information being used for indicating channel characteristics of the wireless channel (Vitthaladevuni [0013] the UE may train a set of neural network pairs, the base station may dynamically indicate a level of accuracy to the UE (first information), and the UE may select one of the set of neural network pairs (determine feedback mode) corresponding to the level of accuracy to use to encode the channel state feedback) As to claim 14, Vitthaladevuni teaches the method of claim 13, wherein the feedback mode comprises feeding back the CSI through a feedback model, and the feedback model comprises a neural network model used for feeding back the CSI (Vitthaladevuni [0013] the UE may train a set of neural network pairs, the base station may dynamically indicate a level of accuracy to the UE (first information), and the UE may select one of the set of neural network pairs (determine feedback mode) corresponding to the level of accuracy to use to encode the channel state feedback); and wherein the feedback model comprises a decoding model, and after determining the feedback mode for the CSI of the wireless channel based on the first information, the method further comprises: invoking the decoding model to decode first channel-information to obtain the CSI, wherein the first channel-information is obtained at the terminal device by encoding the CSI (Vitthaladevuni Fig. 2) As to claim 15, Vitthaladevuni teaches the method of claim 13, further comprising: determining the first information (Vitthaladevuni [0061] In wireless communications system 100, a UE 115 may be configured to perform channel measurements on downlink signals received from a base station 105 and report the channel measurements to the base station 105. The UE 115 may report the channel measurements as CSI feedback); and sending the first information to the terminal device (Vitthaladevuni [0013] the UE may train a set of neural network pairs, the base station may dynamically indicate a level of accuracy to the UE (first information), and the UE may select one of the set of neural network pairs (determine feedback mode) corresponding to the level of accuracy to use to encode the channel state feedback) As to claim 16, Vitthaladevuni teaches the method of claim 15, wherein determining the first information comprises: receiving, from the terminal device, feedback information for channel quality of the wireless channel; and determining the first information based on the feedback information (Vitthaladevuni (Vitthaladevuni [0061] In wireless communications system 100, a UE 115 may be configured to perform channel measurements on downlink signals received from a base station 105 and report the channel measurements to the base station 105. The UE 115 may report the channel measurements as CSI feedback); [0013] the UE may train a set of neural network pairs, the base station may dynamically indicate a level of accuracy to the UE (first information), and the UE may select one of the set of neural network pairs (determine feedback mode) corresponding to the level of accuracy to use to encode the channel state feedback) As to claim 17, Vitthaladevuni teaches the second device, comprising: a processor and a transceiver connected with the processor (Vitthaladevuni Fig. 8); wherein the processor is configured to: determine a feedback mode for channel-state information of a wireless channel based on the first information, the first information being used for indicating channel characteristics of the wireless channel. (Vitthaladevun (Vitthaladevuni [0061] In wireless communications system 100, a UE 115 may be configured to perform channel measurements on downlink signals received from a base station 105 and report the channel measurements to the base station 105. The UE 115 may report the channel measurements as CSI feedback); [0013] the UE may train a set of neural network pairs, the base station may dynamically indicate a level of accuracy to the UE (first information), and the UE may select one of the set of neural network pairs (determine feedback mode) corresponding to the level of accuracy to use to encode the channel state feedback) 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) 12 and 18-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Vitthaladevuni, Chen et al. (Pub. No.: 2018/028768 A1), herein Chen. As to claim 12, Vitthaladevuni teaches the terminal device of claim 1, Vitthaladevuni does not each wherein the channel characteristics comprise at least one of: delay spread characteristics, azimuth angle of departure (AOD) spread characteristics, azimuth angle-of-arrival (AOA) spread characteristics, zenith AOD spread characteristics, zenith AOA spread characteristics, shadow fading characteristics, multipath-cluster number characteristics, sub-path number characteristics in a multipath cluster, intra-cluster delay spread characteristics, intra-cluster azimuthal AOD spread characteristics, intra-cluster azimuthal AOA spread characteristics, intra-cluster zenith AOD spread characteristics, intra-cluster zenith AOA spread characteristics, or intra-cluster shadow fading characteristics. However Chen does teach wherein the channel characteristics comprise at least one of: delay spread characteristics, azimuth angle of departure (AOD) spread characteristics, azimuth angle-of-arrival (AOA) spread characteristics, zenith AOD spread characteristics, zenith AOA spread characteristics, shadow fading characteristics, multipath-cluster number characteristics, sub-path number characteristics in a multipath cluster, intra-cluster delay spread characteristics, intra-cluster azimuthal AOD spread characteristics, intra-cluster azimuthal AOA spread characteristics, intra-cluster zenith AOD spread characteristics, intra-cluster zenith AOA spread characteristics, or intra-cluster shadow fading characteristics (Chen [0074] The large-scale characteristic of the channel in step S201 may refer to delay spread) It would have been obvious before the effective filing date of the claimed invention to combine the teachings of Vitthaladevuni and Chen, because Chen teaches us [0074] By the steps, the terminal can perform matching to obtain quasi-co-location information of current data according to the acquired large-scale characteristic of the channel. By virtue of the solution, the problem that a large-scale characteristic of a channel acquired by a terminal is inaccurate due to indefinite indication in quasi-co-location information notification signaling is solved, and an effect of improving channel estimation performance of the terminal is achieved. As to claim 18, the combination of Vitthaladevuni and Chen teach the network device of claim 17, wherein the channel characteristics comprise delay spread characteristics (Chen [0074] The large-scale characteristic of the channel in step S201 may refer to delay spread) and the first information comprises delay spread information and/or category indication information; and wherein the category indication information is used for indicating a category to which the delay spread information corresponds in a first association relationship, and the first association relationship comprises an association relationship between the delay spread information and the category indication information ((Vitthaladevuni [0073] Because the level of accuracy 305 or the loss function may be configured by the base station 105-a, the accuracy of the CSI feedback 310 reported by the UE 115-a may be aligned with the intentions of the base station 105-a. For example, the base station 105-a may configure the UE 115-a to report more accurate (e.g., highly accurate) CSI feedback 310 for latency-sensitive or reliability-sensitive communications, and the base station 105-a may configure the UE 115-a to report less accurate CSI feedback 310 for other communications) It would have been obvious before the effective filing date of the claimed invention to combine the teachings of Vitthaladevuni and Chen for the same reasons stated in claim 12. As to claim 19, the combination of Vitthaladevuni and Chen teach the network device of claim 18, wherein the first association relationship is predefined in a communication protocol ((Vitthaladevuni [0073] Because the level of accuracy 305 or the loss function may be configured by the base station 105-a, the accuracy of the CSI feedback 310 reported by the UE 115-a may be aligned with the intentions of the base station 105-a. For example, the base station 105-a may configure the UE 115-a to report more accurate (e.g., highly accurate) CSI feedback 310 for latency-sensitive or reliability-sensitive communications, and the base station 105-a may configure the UE 115-a to report less accurate CSI feedback 310 for other communications (communication protocol)) As to claim 20, the combination of Vitthaladevuni and Chen teach the network device of claim 18, wherein the transceiver is configured to send a second message to a terminal device, wherein the first association relationship is carried in the second message, wherein the second message comprises at least one of: a broadcast message, a system information block (SIB), an RRC message, RRC reconfiguration signaling, downlink control information (DCI), a media access control (MAC) control element (CE), a physical downlink control channel (PDCCH) order, or data information (Vitthaladevuni [0074] The base station 105-a may transmit the indication of the level of accuracy 305 via a higher layer message (e.g., RRC signaling) or dynamic signaling (e.g., in a MAC control element (MAC-CE)).) Response to Arguments Applicant’s arguments, see filed 11/10/25 with respect to the rejection(s) of claim(s) 1 under 102 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Vitthaladevuni (2023/0188302). Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to AYANAH S GEORGE whose telephone number is (571)272-8880. The examiner can normally be reached 7:00 AM - 5:00 PM. 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, Hassan Phillips can be reached at 572-272-3940. 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. AYANAH S. GEORGE Primary Examiner Art Unit 2467 /AYANAH S GEORGE/Primary Examiner, Art Unit 2467
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Prosecution Timeline

Jul 07, 2023
Application Filed
Aug 07, 2025
Non-Final Rejection — §102, §103
Nov 10, 2025
Response Filed
Mar 16, 2026
Non-Final Rejection — §102, §103 (current)

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Prosecution Projections

2-3
Expected OA Rounds
86%
Grant Probability
93%
With Interview (+6.1%)
2y 5m
Median Time to Grant
Moderate
PTA Risk
Based on 498 resolved cases by this examiner. Grant probability derived from career allow rate.

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