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
Status of the Application
This is in response to the Response to Election filed on 08/11/2026. Claims 1 – 13 and 26-31 are pending and presented for examination; claims 14-25 and 32-36 are withdrawn from examination for being not elected claims.
Response to Election of Species
In the response to election filed 08/11/2026, the applicant elected Species A (claim 1-13) with travers. The applicant argued and showed how at least Species C (claims 26-31) have the same concept and will cause any burden in searching both claims 1 and 26. The applicant did not provide detailed argument regarding species B and Species D. Also, the examiner believes here that the claimed inventions B and D are distinct in that they require different search strategies and implicate different fields of prior art. Thus, the withdraw the restriction for species C and maintain the restriction requirements for Species B and D. Thus, claims 1-13 and 29-31 are pending and presented for examination. Claims 14-28 and 32-36 are withdrawn form examination.
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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1 – 13 and 26 - 31 are rejected under 35 USC 103 as being unpatentable over Bai et al (US Pub. No. 2020/0259545 A1) in view of Yerramalli et al (US Pub. No. 2021/0409086 A1).
Regarding claim 1, Bai discloses “A method for wireless communication at a user equipment (UE),”: (see Bai figure 2 and ¶ 0018, ¶ 0023; headset 22 is connected via a wired IP network) comprising “transmitting a plurality of reference signals indicating information associated with a first set of channel measurement resources, a second set of channel measurement resources, the UE transmitting one or more measurements that include channel quality parameters (e.g., RSRP, SNR, CQI) and side information (including receive beam change history, mobility, Doppler, time stamps) to the base station; ¶ 0113; discloses that the UE measure an RSRP, an RSRQ, an SNR, or an SINR, or the like, and report the channel measurements to the base station. The UE may additionally or alternatively transmit one or more uplink reference signals to the base station; ¶ 0135; ); . Moreover, Bai discloses the limitation of “receiving, based at least in part on transmitting the plurality of reference signals, signaling indicating a machine learning model for obtaining a channel characteristic prediction associated with the first set of channel measurement resources, the machine learning model based at least in part on the receive beam at the UE” (see Bai ¶ 0113; may select and use a learning algorithm (also referred to herein as a prediction algorithm) from a set of learning algorithms to determine a future value of a channel quality parameter of a communication link, ¶ 0135; the prediction algorithm 405 may output one or more predicted values 420 (e.g., a future value), such as a future value of a channel quality parameter);; “inputting, to the machine learning model, an input to obtain the channel characteristic prediction” (see Bai ¶ 0111- 0112, 0138-0139; the UE and/or BS use a prediction algorithm (e.g., neural network, Kalman filter) to forecast future channel quality based on the reported measurements and side information (including receive beam info). The base station may signal the use of a prediction model to the UE ); and “receiving signaling based at least in part on obtaining the channel characteristic prediction associated with the first set of channel measurement resources” (see Bai ¶ 0141; the base station determine, based in part on the determined future value of the channel quality parameter, resources for the UE to use to communicate with the base station 105-b on the wireless link).
Bai does not appear to explicitly disclose “transmitting a plurality of reference signals indicating information associated with a first set of channel measurement resources, a second set of channel measurement resources, and a direction of reception for communications” and “the information corresponding to a receive beam at the UE corresponding to the direction of reception for the communications”.
However, Yerramalli discloses “transmitting a plurality of reference signals indicating information associated with a first set of channel measurement resources, a second set of channel measurement resources, and a direction of reception for communications” and “the information corresponding to a receive beam at the UE corresponding to the direction of reception for the communications” (See Yerramalli ¶ 00000 ; my wording .. ) ; and “pairing the endpoint with the first relay arrangement if the endpoint is authenticated with respect to the first relay arrangement”( See Yerramalli ¶ 0081). It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, having the teachings of Bai and Yerramalli before him or her, to modify the invention of Bai to perform include in a reference signals information including beam direction. The suggestion for doing so would have been optimization of channel state information (CSI) reporting (¶ 0001).
Regarding claim 2, claim 1 is incorporated as stated above. In addition, the combination of Bai and Yerramalli further discloses “determining the receive beam for the UE corresponding to the direction of reception for the communications based at least in part on one or more channel measurement resource identifiers corresponding to the second set of channel measurement resources and one or more first channel characteristics associated with the second set of channel measurement resources, wherein the information comprises the one or more first channel characteristics”; (see Bai ¶ 0117, ¶ 0118 and ¶ Bai discloses that side information includes “an indication of past receive beam changes by the UE”, and may include the current receive beam index or history; Yerramalli ¶ 0081 Channel measurement resources may include time-frequency resources, along with a beam direction, within which a particular reference signal can be transmitted ); and transmitting the one or more channel measurement resource identifiers in a same signal as a subset of the plurality of reference signals associated with the second set of channel measurement resources, wherein the receiving the signaling is based at least in part on transmitting the one or more channel measurement resource identifiers.“(See Bai ¶s 0117, 0137, 0138).
Regarding claim 3, claim 2 is incorporated as stated above. In addition, the combination of Bai and Yerramalli further discloses “inputting, to the machine learning model based at least in part on transmitting the plurality of reference signals, the one or more channel measurement resource identifiers, one or more second channel characteristics associated with the first set of channel measurement resources, or both”; (see Bai ¶ 0111- 0112, 0138-0139; the UE and/or BS use a prediction algorithm (e.g., neural network, Kalman filter) to forecast future channel quality based on the reported measurements and side information (including receive beam info). The base station may signal the use of a prediction model to the UE); “obtaining the channel characteristic prediction associated with the first set of channel measurement resources based at least in part on inputting the one or more channel measurement resource identifiers, the one or more second channel characteristics associated with the first set of channel measurement resources, or both”( see Bai ¶ 0141; the base station determine, based in part on the determined future value of the channel quality parameter, resources for the UE to use to communicate with the base station 105-b on the wireless link).
Regarding claim 4, claim 2 is incorporated as stated above. In addition, the combination of Bai and Yerramalli further discloses “transmitting, in the same signal as the subset of the plurality of reference signals, an indication of a reference signal receive power associated with the first set of channel measurement resources. (See Bai ¶ 0028, the channel quality parameter of the wireless link includes a reference signal received power (RSRP), or a signal to noise ratio (SNR); Yerramalli ¹¶ 0078, The UE may measure the reference signal received power (RSRP) or signal-to-interference-plus-noise ratio (SINR) on each of the beams and transmit a beam measurement report).
Regarding claim 5, claim 2 is incorporated as stated above. In addition, the combination of Bai and Yerramalli further discloses “wherein the one or more first channel characteristics comprise one or more reference signal receive power values for the subset of the plurality of reference signals”; (See Bai ¶ 0028, the channel quality parameter of the wireless link includes a reference signal received power (RSRP), or a signal to noise ratio (SNR); Yerramalli ¹¶ 0078, The UE may measure the reference signal received power (RSRP) or signal-to-interference-plus-noise ratio (SINR) on each of the beams and transmit a beam measurement report).
Regarding claim 6, claim 2 is incorporated as stated above. In addition, the combination of Bai and Yerramalli further discloses “determining a spatial filter corresponding to the receive beam based at least in part on one or more sounding reference signal resource identifiers corresponding to the second set of channel measurement resources, wherein the one or more channel measurement resource identifiers comprise the one or more sounding reference signal resource identifiers”; (see Bai ¶ 0082, Yerramalli, ¶ 0073 and ¶ 0074).
Regarding claim 7, claim 1 is incorporated as stated above. In addition, the combination of Bai and Yerramalli further discloses “receiving control signaling indicating to the UE to transmit, with the plurality of reference signals, a plurality of channel characteristics associated with the first set of channel measurement resources” (see Bai ¶ 0139 transmit control information, via signaling (e.g., RRC signaling, UCI signaling), that may include an indication of the value of the channel quality parameter to the base station); “and transmitting the plurality of channel characteristics in a same signal as the plurality of reference signals, the plurality of channel characteristics comprising one or more of a reference signal receive power, a signal interference-to-noise ratio, a rank indicator, a channel quality indicator, or a precoding matrix indicator”( See Bai ¶ 0028, the channel quality parameter of the wireless link includes a reference signal received power (RSRP), or a signal to noise ratio (SNR); Yerramalli ¹¶ 0078, The UE may measure the reference signal received power (RSRP) or signal-to-interference-plus-noise ratio (SINR) on each of the beams and transmit a beam measurement report)
Regarding claim 8, claim 1 is incorporated as stated above. In addition, the combination of Bai and Yerramalli further discloses “transmitting a message indicating a capability of the UE to support reporting the information associated with the first set of channel measurement resources, the second set of channel measurement resources, or the direction of reception for the communications, wherein transmitting the plurality of reference signals indicating the information is based at least in part on the capability.”( See Bai ¶ 0106 ; Yerramalli ¹¶ 0014, 0119, 0127).)
Regarding claim 9, claim 1 is incorporated as stated above. In addition, the combination of Bai and Yerramalli further discloses “transmitting the plurality of reference signals according to a periodicity; and receiving, according to the periodicity, additional signaling indicating an update to the machine learning model based at least in part on transmitting the plurality of reference signals.”( See Bai ¶ 0028, the channel quality parameter of the wireless link includes a reference signal received power (RSRP), or a signal to noise ratio (SNR); Yerramalli ¹¶ 0078, The UE may measure the reference signal received power (RSRP) or signal-to-interference-plus-noise ratio (SINR) on each of the beams and transmit a beam measurement report)
Regarding claim 10, claim 1 is incorporated as stated above. In addition, the combination of Bai and Yerramalli further discloses “transmitting a first channel state information report of a first priority; and transmitting a second channel state information report of a second priority, the second channel state information report comprising the information, wherein the first priority is greater than the second priority”( See Bai ¶ 0087; Yerramalli ¹¶ 0086,)
Regarding claim 11, claim 1 is incorporated as stated above. In addition, the combination of Bai and Yerramalli further discloses “wherein a first angular spread of a first reference signal of the plurality of reference signals associated with the second set of channel measurement resources is smaller than a second angular spread of a second reference signal of the plurality of reference signals associated with the first set of channel measurement resources.”( See Bai ¶ 0028, the channel quality parameter of the wireless link includes a reference signal received power (RSRP), or a signal to noise ratio (SNR); Yerramalli ¹¶ 0078, The UE may measure the reference signal received power (RSRP) or signal-to-interference-plus-noise ratio (SINR) on each of the beams and transmit a beam measurement report)
Regarding claim 12, claim 1 is incorporated as stated above. In addition, the combination of Bai and Yerramalli further discloses “wherein the plurality of reference signals comprise a channel state information-reference signal, a synchronization signal block, or both”( See Bai ¶ 0113; Yerramalli ¹¶ 0081)
Regarding claim 13, claim 1 is incorporated as stated above. In addition, the combination of Bai and Yerramalli further discloses “wherein the channel characteristic prediction associated with the first set of channel measurement resources is for a time domain channel characteristic of the plurality of reference signals, a spatial domain channel characteristic of the plurality of reference signals, or both”( See Bai ¶ 0100; Yerramalli ¹¶ 0059, ¶ 0073)
Regarding claim 26, Bai discloses “A method for wireless communication at a user equipment (UE)”: (see Bai figure 2) comprising “transmitting information associated with one or more channel measurement resources the UE transmitting one or more measurements that include channel quality parameters (e.g., RSRP, SNR, CQI) and side information (including receive beam change history, mobility, Doppler, time stamps) to the base station; ¶ 0113; discloses that the UE measure an RSRP, an RSRQ, an SNR, or an SINR, or the like, and report the channel measurements to the base station. The UE may additionally or alternatively transmit one or more uplink reference signals to the base station; ¶ 0135; ); . Moreover, Bai discloses the limitation of “receiving signaling indicating a machine learning model for obtaining a channel characteristic prediction associated with the one or more channel measurement resources based at least in part on transmitting the information” (see Bai ¶ 0113; may select and use a learning algorithm (also referred to herein as a prediction algorithm) from a set of learning algorithms to determine a future value of a channel quality parameter of a communication link, ¶ 0135; the prediction algorithm 405 may output one or more predicted values 420 (e.g., a future value), such as a future value of a channel quality parameter);; “inputting, to the machine learning model, an input to obtain the channel characteristic prediction associated with the one or more channel measurement resources” (see Bai ¶ 0111- 0112, 0138-0139; the UE and/or BS use a prediction algorithm (e.g., neural network, Kalman filter) to forecast future channel quality based on the reported measurements and side information (including receive beam info). The base station may signal the use of a prediction model to the UE ); and “receiving signaling based at least in part on obtaining the channel characteristic prediction associated with the one or more channel measurement resources” (see Bai ¶ 0141; the base station determine, based in part on the determined future value of the channel quality parameter, resources for the UE to use to communicate with the base station 105-b on the wireless link).
Bia does not appear to explicitly disclose “transmitting information associated with one or more channel measurement resources and a direction of reception for communications”.
However, Yerramalli discloses “transmitting information associated with one or more channel measurement resources and a direction of reception for communications” (See Yerramalli ¶ 00000 ; my wording .. ) ; and “pairing the endpoint with the first relay arrangement if the endpoint is authenticated with respect to the first relay arrangement”( See Yerramalli ¶ 0081). It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, having the teachings of Bai and Yerramalli before him or her, to modify the invention of Bai to perform include in a reference signals information including beam direction. The suggestion for doing so would have been optimization of channel state information (CSI) reporting (¶ 0001).
Regarding claim 27, claim 26 is incorporated as stated above. In addition, the combination of Bai and Yerramalli further discloses “inputting, to the machine learning model, one or more channel characteristics associated with the one or more channel measurement resources, the transmitted information, or both”; (see Bai ¶ 0111- 0112, 0138-0139; the UE and/or BS use a prediction algorithm (e.g., neural network, Kalman filter) to forecast future channel quality based on the reported measurements and side information (including receive beam info). The base station may signal the use of a prediction model to the UE); “obtaining the channel characteristic prediction associated with the one or more channel measurement resources, the transmitted information, or both based at least in part on the inputting”( see Bai ¶ 0141; the base station determine, based in part on the determined future value of the channel quality parameter, resources for the UE to use to communicate with the base station 105-b on the wireless link).
Regarding claim 28, claim 26 is incorporated as stated above. In addition, the combination of Bai and Yerramalli further discloses “transmitting a message indicating a capability of the UE to support transmitting the information, wherein transmitting the information is based at least in part on the capability.”( See Bai ¶ 0106 ; Yerramalli ¹¶ 0014, 0119, 0127).
Regarding claim 29, claim 26 is incorporated as stated above. In addition, the combination of Bai and Yerramalli further discloses “transmitting the information according to a periodicity; and receiving, according to the periodicity, additional signaling indicating an update to the machine learning model based at least in part on transmitting the information.”( See Bai ¶ 0028, the channel quality parameter of the wireless link includes a reference signal received power (RSRP), or a signal to noise ratio (SNR); Yerramalli ¹¶ 0078, The UE may measure the reference signal received power (RSRP) or signal-to-interference-plus-noise ratio (SINR) on each of the beams and transmit a beam measurement report).
Regarding claim 30, claim 26 is incorporated as stated above. In addition, the combination of Bai and Yerramalli further discloses “transmitting a first channel state information report of a first priority; and transmitting a second channel state information report of a second priority, the second channel state information report comprising the information, wherein the first priority is greater than the second priority ( See Bai ¶ 0087; Yerramalli ¹¶ 0086,)
Regarding claim 31, claim 26 is incorporated as stated above. In addition, the combination of Bai and Yerramalli further discloses “wherein the information comprises a phase coefficient associated with a radio-frequency chain, an amplitude coefficient associated with the radio-frequency chain, a phase coefficient associated with a phase shifter, an amplitude associated with the phase shifter, an antenna panel identifier associated with the direction of reception, an orientation of an antenna panel associated with the direction of reception, a target angle of arrival for the communications, or a zenith of arrival for the communications” ( See Bai ¶ 0082; 0106; Yerramalli ¹¶ 0073, 0104)
Conclusion
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/KHALED M KASSIM/supervisory patent examiner, Art Unit 2475
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