DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. 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 § 103
2. 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.
3. Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Sohn et al (US 2013/0223427A1) in view of LEE IN KYU et al (KR 20220151449 )(see IDS) ( citations are from translated English version).
With regards to claim 1, Sohn et al discloses in fig. 1, a mobile device (see STA1, STA2, STA3, STA4, [0035], all STAs may be composed of mobile STAs). comprising:
an antenna configured to communicate with an access point using wireless signals (see AP (10) communication with STA (30), STA 21, STA2-- -);
a transceiver coupled to the antenna (see fig. 9, transceiver 930); and
a processor (shown in fig, 9, 910) configured to:
receive a first announcement packet and a first sounding packet through the antenna and the transceiver, perform first channel estimation based on the first sounding packet, ([0079] Referring to FIG. 4, the AP 410 transmits an NDP Announcement (NDPA) frame to the STA1 421, the STA2 422, and the STA3 423 at step S410. The NDPA frame informs that channel sounding will be initiated and an NDP will be transmitted. The NDPA frame may be called a sounding announcement frame).
transmit full information about a result of the first channel estimation (see [0091], channel state information according to channel estimation performed by an STA is included in a feedback frame transmitted from the STA to an AP and then transmitted) using the transceiver and the antenna
receive a second announcement packet and a second sounding packet through the antenna and the transceiver (see fig. 1 and fig. 9 ,transceiver 930) perform second channel estimation based on the second sounding packet, and transmit partial information, selected from full information about a result of the second channel estimation ([0130], obtain channel estimation information and channel state information-- - ), using the transceiver and the antenna ([0080], the STA determines whether it is an STA participating in channel sounding by receiving the NDPA frame. Accordingly, the AP 410 includes an STA information field, including information about a target sounding STA, in the NDPA frame and then transmits the NDPA frame. The STA information field may be included for every target sounding STA),
wherein the second announcement packet includes selection information indicating the partial information, and (see claim 23, beamforming feedback matrix- - -)
Sohn et al discloses all of the subject matter discussed above, but is explicit about wherein the selection information is determined using a machine learning module included in the access point.
However, LEE IN KYU et al discloses page 5. Paragraph 11, method for giving feedback of a selected vector by a reception end in a wireless communication system. 3 is a diagram for explaining the structure of a fully connected deep neural network to which the present disclosure can be applied. In the present disclosure, a deep neural network (DNN) model may be applied as an example of a machine learning technique. However, the scope of the present disclosure is not limited to the DNN model, and the principles of the present disclosure may be equally applied to other similar machine learning techniques. Also see claim 1, A method of feeding back a selection vector by a receiving end in a wireless communication system, the method- - -.
Thus, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to combine Sohn et al’s channel sounding method with LEE IN KYU et al’s machine learning technique. For this combination, the motivation would have been to improve system gain and communication quality
With regards to claim 6, A mobile device comprising: an antenna configured to communicate with an access point using wireless signals; a transceiver coupled to the antenna; and a processor configured to: receive an announcement packet and a sounding packet through the antenna and the transceiver, perform first channel estimation based on the sounding packet, select partial information, from full information about a result of the first channel estimation, by executing a machine learning module based on a result of channel estimation, and transmit the partial information using the transceiver and the antenna ( claim 6 recites similar limitations as in claim 1 claim is rejected similarly as in claim 1 above).
With regards to claim 14, the combination of Sohn et al’ and LEE IN KYU et al’ discloses an electronic device comprising: an antenna configured to communicate with a mobile device using wireless signals; a modem (transceiver 930 shown in fig. 9) coupled to the antenna; and a processor (910 shown in fig. 9) configured to:
transmit a first announcement packet and a first sounding packet using the antenna and the modem, receive a first report corresponding to the first announcement packet and the first sounding packet, select, using a machine learning module, partial information, from full information about a result of first channel estimation, based on full information about a result of channel estimation included in the first report, and transmit, using the antenna and the modem, a second sounding packet and second announcement packet including selection information indicating the partial information.
(the rest of claim 14 recites similar limitations as in claim 1 claim is rejected similarly as in claim 1 above).
With regards to claim 2, the combination of Sohn et al’ and LEE IN KYU et al’ discloses the mobile device of claim 1, wherein the partial information includes information about an angle associated with beamforming by the access point (Sohn et al discloses in table 4, order of an angle of a beamforming feedback matrix for a relevant subcarrier)
.
With regards to claim 3, the combination of Sohn et al’ and LEE IN KYU et al’ discloses the mobile device of claim 1, wherein the partial information includes information about at least two angles associated with beamforming by the access point. (Sohn et al discloses in table 4, Beamforming feedback matrix (subcarrier index Ns), order of an angle of a beamforming feedback matrix for a relevant subcarrier)
With regards to claim 4, The mobile device of claim 1, wherein the partial information includes at least one of: single user information, multiple user information, signal-to-noise ratio information, or channel quality indicator information (see Table 3 and Table 4, SU-MIMO, MU-MIMO, SNR are disclosed).
With regards to claim 5, the combination of Sohn et al’ and LEE IN KYU et al’ discloses the mobile device of claim 1, wherein the processor is configured to, based on a result of channel estimation changing, transmit full information about the result of the channel estimation through the transceiver and the antenna (see fig. 9, [0129] - -- and obtain channel information and channel information by interpreting a field value include in the frame. The processor -- -). Also see claim 18, a device configured to perform channel sounding in a wireless local area network, the device comprising: a transceiver configured to transmit and receive signals; and a processor operatively connected to the transceiver and configured to: generate a 20 MHz null data packet announcement (NDPA) frame, duplicate the 20 MHz NDPA frame to generate a duplicate 20 MHz NDPA frame, transmit the 20 MHz NDPA frame and the duplicate 20 MHz NDPA frame followed by a null data packet (NDP) over a transmission bandwidth that includes two or more 20 MHz channels for initiating a channel sounding, and receive a report frame over a reception bandwidth (interpreted as variant information) from a receiver that has received the NDP and at least one of the 20 MHz NDPA frame and duplicate 20 MHz NDPA frame, wherein the reception bandwidth is not wider than the transmission bandwidth, and wherein each of the 20 MHz NDPA frame and the duplicate 20 MHz NDPA frame comprises first bandwidth information indicating the transmission bandwidth)
With regards to claim 7, the combination of Sohn et al’ and LEE IN KYU et al’ discloses the mobile device of claim 6, wherein the processor is configured to: during a preset time period prior to receiving the announcement packet, transmit the full information about the result of the channel estimation using the transceiver and the antenna( see claim 18, a device configured to perform channel sounding in a wireless local area network, the device comprising: a transceiver configured to transmit and receive signals; and a processor operatively connected to the transceiver and configured to: generate a 20 MHz null data packet announcement (NDPA) frame, duplicate the 20 MHz NDPA frame to generate a duplicate 20 MHz NDPA frame, transmit the 20 MHz NDPA frame and the duplicate 20 MHz NDPA frame followed by a null data packet (NDP) over a transmission bandwidth that includes two or more 20 MHz channels for initiating a channel sounding, and receive a report frame over a reception bandwidth (interpreted as variant information) from a receiver that has received the NDP and at least one of the 20 MHz NDPA frame and duplicate 20 MHz NDPA frame, wherein the reception bandwidth is not wider than the transmission bandwidth, and wherein each of the 20 MHz NDPA frame and the duplicate 20 MHz NDPA frame comprises first bandwidth information indicating the transmission bandwidth)
; and execute the machine learning module based on the full information about the result of the channel estimation collected during the preset time period (LEE IN KYU et al discloses page 5. Paragraph 11, method for giving feedback of a selected vector by a reception end in a wireless communication system. 3 is a diagram for explaining the structure of a fully connected deep neural network to which the present disclosure can be applied. In the present disclosure, a deep neural network (DNN) model may be applied as an example of a machine learning technique).
With regards to claim 8, the combination of Sohn et al’ and LEE IN KYU et al’ discloses the mobile device of claim 7, wherein the processor is configured to continuously execute the machine learning module based on the result of the channel estimation collected during the preset time period (see Sohn et al’ [0123] The NDPA frame includes information that the NDPA frame spans the 160 MHz bandwidth. It is assumed that when receiving the NDPA frame, the STA1821 has not normally received the NDPA frame for the entire 160 MHz bandwidth, but has normally received the NDPA frame for an 80 MHz bandwidth, including a primary subchannel, because interference has occurred in a specific band including a non-primary subchannel, also see Table 5).
With regards to claim 9, the combination of Sohn et al’ and LEE IN KYU et al’ discloses the mobile device of claim 6, wherein the processor is configured to execute the machine learning module based on approval information included in the announcement packet ( Sohn et al’ ,[0128] In addition, if the reason code indicates that a channel occupancy state (i.e., a CCA busy state) has been detected in a subchannel, such as 20, 40, 80, 160, or 80+80 MHz, or that an NDPA frame or an NDP frame has not been normally received, an STA may include information indicative of a relevant subchannel in a feedback frame and transmit the feedback frame.
With regards to claim 10, the combination of Sohn et al’ and LEE IN KYU et al’ discloses the mobile device of claim 9, wherein the processor is configured to, based on a second announcement packet not including the approval information, transmit full information about a result of second channel estimation using the transceiver and the antenna, without executing the machine learning module ( see claim 18, a device configured to perform channel sounding in a wireless local area network, the device comprising: a transceiver configured to transmit and receive signals; and a processor operatively connected to the transceiver and configured to: generate a 20 MHz null data packet announcement (NDPA) frame, duplicate the 20 MHz NDPA frame to generate a duplicate 20 MHz NDPA frame, transmit the 20 MHz NDPA frame and the duplicate 20 MHz NDPA frame followed by a null data packet (NDP) over a transmission bandwidth that includes two or more 20 MHz channels for initiating a channel sounding, and receive a report frame over a reception bandwidth (interpreted as variant information) from a receiver that has received the NDP and at least one of the 20 MHz NDPA frame and duplicate 20 MHz NDPA frame, wherein the reception bandwidth is not wider than the transmission bandwidth, and wherein each of the 20 MHz NDPA frame and the duplicate 20 MHz NDPA frame comprises first bandwidth information indicating the transmission bandwidth)
With regards to claim 11, the combination of Sohn et al’ and LEE IN KYU et al’ discloses the mobile device of claim 6, wherein the processor is configured to, based on an execution result of the machine learning module.( LEE IN KYU et al discloses page 5. Paragraph 11, method for giving feedback of a selected vector by a reception end in a wireless communication system. 3 is a diagram for explaining the structure of a fully connected deep neural network to which the present disclosure can be applied. In the present disclosure, a deep neural network (DNN) model may be applied as an example of a machine learning technique) indicating that full information is to be transmitted, select full information about a result of second channel estimation to be transmitted using the transceiver and the antenna. ( see claim 18, a device configured to perform channel sounding in a wireless local area network, the device comprising: a transceiver configured to transmit and receive signals; and a processor operatively connected to the transceiver and configured to: generate a 20 MHz null data packet announcement (NDPA) frame, duplicate the 20 MHz NDPA frame to generate a duplicate 20 MHz NDPA frame, transmit the 20 MHz NDPA frame and the duplicate 20 MHz NDPA frame followed by a null data packet (NDP) over a transmission bandwidth that includes two or more 20 MHz channels for initiating a channel sounding, and receive a report frame over a reception bandwidth (interpreted as variant information) from a receiver that has received the NDP and at least one of the 20 MHz NDPA frame and duplicate 20 MHz NDPA frame, wherein the reception bandwidth is not wider than the transmission bandwidth, and wherein each of the 20 MHz NDPA frame and the duplicate 20 MHz NDPA frame comprises first bandwidth information indicating the transmission bandwidth)
With regards to claim 12, the combination of Sohn et al’ and LEE IN KYU et al’ discloses the mobile device of claim 6, wherein the processor is configured to: classify a type of variant information, from full information about the result of the channel estimation ( ( see claim 18, a device configured to perform channel sounding in a wireless local area network, the device comprising: a transceiver configured to transmit and receive signals; and a processor operatively connected to the transceiver and configured to: generate a 20 MHz null data packet announcement (NDPA) frame, duplicate the 20 MHz NDPA frame to generate a duplicate 20 MHz NDPA frame, transmit the 20 MHz NDPA frame and the duplicate 20 MHz NDPA frame followed by a null data packet (NDP) over a transmission bandwidth that includes two or more 20 MHz channels for initiating a channel sounding, and receive a report frame over a reception bandwidth (interpreted as variant information) from a receiver that has received the NDP and at least one of the 20 MHz NDPA frame and duplicate 20 MHz NDPA frame, wherein the reception bandwidth is not wider than the transmission bandwidth, and wherein each of the 20 MHz NDPA frame and the duplicate 20 MHz NDPA frame comprises first bandwidth information indicating the transmission bandwidth), by executing the machine learning module, and select, as the partial information, the type of variant information.( LEE IN KYU et al discloses page 5. Paragraph 11, method for giving feedback of a selected vector by a reception end in a wireless communication system. 3 is a diagram for explaining the structure of a fully connected deep neural network to which the present disclosure can be applied. In the present disclosure, a deep neural network (DNN) model may be applied as an example of a machine learning technique).
With regards to claim 13, the combination of Sohn et al’ and LEE IN KYU et al’ discloses the mobile device of claim 6, wherein the processor is configured to: predict a type of information that is expected to be variant, from full information about the result of the channel estimation ( ( see claim 18, a device configured to perform channel sounding in a wireless local area network, the device comprising: a transceiver configured to transmit and receive signals; and a processor operatively connected to the transceiver and configured to: generate a 20 MHz null data packet announcement (NDPA) frame, duplicate the 20 MHz NDPA frame to generate a duplicate 20 MHz NDPA frame, transmit the 20 MHz NDPA frame and the duplicate 20 MHz NDPA frame followed by a null data packet (NDP) over a transmission bandwidth that includes two or more 20 MHz channels for initiating a channel sounding, and receive a report frame over a reception bandwidth (interpreted as variant information) from a receiver that has received the NDP and at least one of the 20 MHz NDPA frame and duplicate 20 MHz NDPA frame, wherein the reception bandwidth is not wider than the transmission bandwidth, and wherein each of the 20 MHz NDPA frame and the duplicate 20 MHz NDPA frame comprises first bandwidth information indicating the transmission bandwidth), by executing the machine learning module, and select, as the partial information, the type of information expected to be variant.( LEE IN KYU et al discloses page 5. Paragraph 11, method for giving feedback of a selected vector by a reception end in a wireless communication system. 3 is a diagram for explaining the structure of a fully connected deep neural network to which the present disclosure can be applied. In the present disclosure, a deep neural network (DNN) model may be applied as an example of a machine learning technique).
With regards to claim 15, the combination of Sohn et al’ and LEE IN KYU et al’ discloses the electronic device of claim 14, wherein the processor is configured to: collect the full information (channel occupancy state disclosed in Table 5) about the result of the channel estimation during a preset time period (see Sohn et al’ [0123] The NDPA frame includes information that the NDPA frame spans the 160 MHz bandwidth. It is assumed that when receiving the NDPA frame, the STA1821 has not normally received the NDPA frame for the entire 160 MHz bandwidth, but has normally received the NDPA frame for an 80 MHz bandwidth, including a primary subchannel, because interference has occurred in a specific band including a non-primary subchannel, also see Table 5) ; and execute the machine learning module based on the full information about the result of the channel estimation collected during the preset time period.( LEE IN KYU et al discloses page 5. Paragraph 11, method for giving feedback of a selected vector by a reception end in a wireless communication system. 3 is a diagram for explaining the structure of a fully connected deep neural network to which the present disclosure can be applied. In the present disclosure, a deep neural network (DNN) model may be applied as an example of a machine learning technique).
With regards to claim 16, the combination of Sohn et al’ and LEE IN KYU et al’ discloses the electronic device of claim 14, wherein the processor is configured to: classify a type of variant information (BW reception) from the full information about the result of the channel estimation, and select the type of variant information as the partial information ( see claim 18, a device configured to perform channel sounding in a wireless local area network, the device comprising: a transceiver configured to transmit and receive signals; and a processor operatively connected to the transceiver and configured to: generate a 20 MHz null data packet announcement (NDPA) frame, duplicate the 20 MHz NDPA frame to generate a duplicate 20 MHz NDPA frame, transmit the 20 MHz NDPA frame and the duplicate 20 MHz NDPA frame followed by a null data packet (NDP) over a transmission bandwidth that includes two or more 20 MHz channels for initiating a channel sounding, and receive a report frame over a reception bandwidth (interpreted as variant information) from a receiver that has received the NDP and at least one of the 20 MHz NDPA frame and duplicate 20 MHz NDPA frame, wherein the reception bandwidth is not wider than the transmission bandwidth, and wherein each of the 20 MHz NDPA frame and the duplicate 20 MHz NDPA frame comprises first bandwidth information indicating the transmission bandwidth).
With regards to claim 17, the combination of Sohn et al’ and LEE IN KYU et al’ discloses the electronic device of claim 14, wherein the processor is configured to: predict a type of information that is expected to be variant, from the full information about the result of the channel estimation( see claim 18, a device configured to perform channel sounding in a wireless local area network, the device comprising: a transceiver configured to transmit and receive signals; and a processor operatively connected to the transceiver and configured to: generate a 20 MHz null data packet announcement (NDPA) frame, duplicate the 20 MHz NDPA frame to generate a duplicate 20 MHz NDPA frame, transmit the 20 MHz NDPA frame and the duplicate 20 MHz NDPA frame followed by a null data packet (NDP) over a transmission bandwidth that includes two or more 20 MHz channels for initiating a channel sounding, and receive a report frame over a reception bandwidth (interpreted as variant information) from a receiver that has received the NDP and at least one of the 20 MHz NDPA frame and duplicate 20 MHz NDPA frame, wherein the reception bandwidth is not wider than the transmission bandwidth, and wherein each of the 20 MHz NDPA frame and the duplicate 20 MHz NDPA frame comprises first bandwidth information indicating the transmission bandwidth), by executing the machine learning module, and select, as the partial information, the type of information expected to be variant.( LEE IN KYU et al discloses page 5. Paragraph 11, method for giving feedback of a selected vector by a reception end in a wireless communication system. 3 is a diagram for explaining the structure of a fully connected deep neural network to which the present disclosure can be applied. In the present disclosure, a deep neural network (DNN) model may be applied as an example of a machine learning technique).
With regards to claim 18, the combination of Sohn et al’ and LEE IN KYU et al’ discloses the electronic device of claim 14, wherein the processor is configured to receive a second report including the partial information through the antenna and the modem after transmitting the second sounding packet and the second announcement packet(see , [0090], see feedback information discussed, Table 3 on page 7, fig. 9, [0129] - -- and obtain channel information and channel information by interpreting a field value include in the frame. The processor -- -). Also see claim 18.
With regards to claim 19, the combination of Sohn et al’ and LEE IN KYU et al’ discloses the electronic device of claim 18, wherein the processor is configured to execute the machine learning module based on the partial information. ( LEE IN KYU et al discloses method for giving feedback of a selected vector by a reception end in a wireless communication system. 3 is a diagram for explaining the structure of a fully connected deep neural network to which the present disclosure can be applied. In the present disclosure, a deep neural network (DNN) model may be applied as an example of a machine learning technique.
With regards to claim 20, the combination of Sohn et al’ and LEE IN KYU et al’ discloses the electronic device of claim 14, wherein the processor is configured to transmit the first announcement packet and a third sounding packet to the mobile device at least one of (i) periodically or (ii) based on a type of the partial information selected by the machine learning module changing (see LEE IN KYU et al’ discloses page 5. Paragraph 11, - - - the present disclosure, a deep neural network (DNN) model may be applied as an example of a machine learning technique.
Conclusion
4. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. MAHADEVAPPA et al (US 20120314594 A1) discloses improving multi-user downlink reception quality in WLANS
5. Any inquiry concerning this communication or earlier communications from the examiner should be directed to HELENE E TAYONG whose telephone number is (571)270-1675. The examiner can normally be reached 9am-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, Hannah S Wang can be reached at 571-272-9018. 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.
/HELENE E TAYONG/Primary Examiner, Art Unit 2631 April 2, 2026
Prosecution Insights