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 .
This office action is in response to remarks filed on 09/02/2025.
Claims 1, 3, 6, 8, and 11 are pending and presented for examination. Claims 1, 3, 6, 8, and 11 are amended. Claims 2, 4-5, 7, and 9-10 are cancelled.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on10/22/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Response to Amendments
Objections to informalities in claims 2 and 7 are withdrawn.
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 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 non-obviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 6, and 11 are rejected under 35 U.S.C. 103 as being unpatentable over O’Shea et al (US20200343985A1) (hereinafter "O’Shea") in view of Wang et al (US20210049451A1) (hereinafter "Wang '451"), Park et al (US20190312623A1) (hereinafter "Park"), and Yan et al (US20150215012A1) (hereinafter "Yan").
Regarding claim 1, O’Shea discloses a method, by a terminal ([0135] user equipment), of controlling operation of a deep neural network ([0135] machine-learning network) in a wireless communication system, the method comprising ([0135] The machine-learning network approach can also be deployed within user equipment(s) (UE), for instance where ML can be used within the baseband processing and modem on mobile units where it may also reduce power consumption and complexity, improve signal fidelity under harsh conditions, and better enable processing of many antenna elements):
pre-processing the downlink signal based on a result of the operation of the deep neural network ([0058] The machine-learning network 120 processes the input data 110, to produce the output data 130. Where the input data 110, in the example of FIG. 1, is a collection of unequalized resource elements or subcarriers, the output data 130 is a collection of equalized resource elements or subcarriers, which is shown as the output plot 131. In some cases, for the equalized resource elements, the complex value of each grid element in the output data 130 closely resembles the values transmitted prior to transmission over the channel, having removed random phase and amplitude changes, or the addition of other interference or channel effects on these elements, which can be present in the unequalized grid. The output data 130 represents an estimation of a received signal depicted as the input data 111. As discussed later in this specification, the equalized collection of resource elements or subcarriers can be used in further processes involved in transmission or reception of communications signals. [0121] Because detailed CSI information is used within the pre-coding of downlink multiple input, multiple output (MIMO) precoding weights, a machine-learning network used for receiving and processing of a received signal, as shown in item 522 on the right of item 520, can also be used to produce pre-coding weight values for single or multi-user MIMO schemes simply through a learning process);
O’Shea fails to disclose a method, comprising: receiving, from a base station of the wireless communication system, a downlink signal.
However, Wang '451 discloses a method, comprising: receiving, from a base station of the wireless communication system, a downlink signal; and ([0149] the user equipment (e.g., UE 110) receives downlink data channel communications from a base station (e.g., base station 121), downlink control channel communications from the base station, etc. At 1120, the user equipment processes the communications using the deep neural network to extract information transmitted in the communications).
O’Shea and Wang '451 are considered to be analogous to the claimed invention because both are in the same endeavor of processing communications signals using artificial intelligence/machine-learning/neural networks.
Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have a motivation to combine the teachings of O’Shea and Wang '451 to create a method, comprising: receiving, from a base station of the wireless communication system, a downlink signal.
The motivation to combine both references would come from the need to allow communications between the base station and terminal.
O’Shea fails to disclose a method, wherein the pre-processing projects the downlink signal into a transmit antenna number dimension using a singular value decomposition (SVD) to apply at least one reference signal to the downlink signal while maintaining statistical features related to noise of the downlink signal.
However, Park discloses a method, wherein the pre-processing projects the downlink signal into a transmit antenna number dimension using a singular value decomposition (SVD) to apply at least one reference signal to the downlink signal while maintaining statistical features related to noise of the downlink signal ([0009] a method for transmitting channel state information (CSI) of terminal in a wireless communication system may include: receiving a CSI-reference signal (RS); generating a first matrix for a channel based on the CSI-RS; generating a second matrix having a lower dimension than the first matrix by calculating the first matrix and an orthogonal beam matrix having a lower dimension than the first matrix; and transmitting to a base station information on the second matrix and/or the orthogonal beam matrix as the CSI, in which the orthogonal beam matrix may be a matrix including a plurality of orthogonal beams orthogonal to each other as elements. [0010] Furthermore, step of the generating of the second matrix may be step of obtaining the second matrix by projecting the first matrix to the orthogonal beam matrix. [0314] The explicit CSI reporting scheme is a scheme that reports information maximally approximate to a measurement value without the process of interpreting the channel measured by the receiver. Various schemes such as quantization or singular value decomposition (SVD) operations of an MIMO channel expressed in a matrix form may be used to reduce the signaling overhead used in the CSI reporting. For example, the explicit CSI (feedback) information may include the following information. [0315] Channel coefficient quantization & quantization index feedback; [0316] MIMO matrix or vector quantization & quantization index feedback; [0317] Channel covariance matrix feedback; [0318] Eigen matrix feedback or eigen vector and eigen value of channel matrix; and/or [0319] Channel matrix.)
O’Shea and Park are considered to be analogous to the claimed invention because both are in the same endeavor of analyzing the communication channel between multiple antennas.
Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have a motivation to combine the teachings of O’Shea and Park to create a method, wherein the pre-processing projects the downlink signal into a transmit antenna number dimension using a singular value decomposition (SVD) to apply at least one reference signal to the downlink signal while maintaining statistical features related to noise of the downlink signal.
The motivation to combine both references would come from the need enable a device to acquire better received signal quality via processing and maximize data transmission.
O’Shea fails to disclose a method, wherein the pre-processing inputs information for channel coefficient to the downlink signal as an input factor, wherein the information for the channel coefficient is based on a transformed channel matrix that is dependent on a number of transmit antennas and independent of a number of receive antennas
However, Yan discloses a method, wherein the pre-processing inputs information for channel coefficient to the downlink signal as an input factor, wherein the information for the channel coefficient is based on a transformed channel matrix that is dependent on a number of transmit antennas and independent of a number of receive antennas ([0075] The training data is obtained by performing a phase transformation to a right singular vector of a channel coefficient matrix. [0077] Performing singular value decomposition to the channel coefficient matrix, and selecting first right singular vectors corresponding to the number of streams of a target codebook, where the number of the selected right singular vectors equals to the number of streams. vni = [ vni (1), vni (2), … vni (NT) ] is used to represent the ith right singular vector of the nth channel coefficient matrix, where vni, (j) represents the jth component of vni, NT represents the number of transmitting antennas, and i ranges from 1 to the number of streams).
O’Shea and Yan are considered to be analogous to the claimed invention because both are in the same endeavor of determining parameters for a channel model with multiple antennas.
Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have a motivation to combine the teachings of O’Shea and Yan to create a method, wherein the pre-processing inputs information for channel coefficient to the downlink signal as an input factor, wherein the information for the channel coefficient is based on a transformed channel matrix that is dependent on a number of transmit antennas and independent of a number of receive antennas.
The motivation to combine both references would come from the need to reduce the dimension of the channel information in order to reduce the feedback signaling overhead.
Regarding claim 6, O’Shea discloses a terminal for controlling operation of a deep neural network in a wireless communication system, the terminal comprising:
a processor ([0143] The mobile computing device 750 includes a processor 752 ) configured to perform pre-processing the downlink signal based on a result of the operation of the deep neural network ([0135] The machine-learning network approach can also be deployed within user equipment(s) (UE), for instance where ML can be used within the baseband processing and modem on mobile units where it may also reduce power consumption and complexity, improve signal fidelity under harsh conditions, and better enable processing of many antenna elements [0058] The machine-learning network 120 processes the input data 110, to produce the output data 130. Where the input data 110, in the example of FIG. 1, is a collection of unequalized resource elements or subcarriers, the output data 130 is a collection of equalized resource elements or subcarriers, which is shown as the output plot 131. In some cases, for the equalized resource elements, the complex value of each grid element in the output data 130 closely resembles the values transmitted prior to transmission over the channel, having removed random phase and amplitude changes, or the addition of other interference or channel effects on these elements, which can be present in the unequalized grid. The output data 130 represents an estimation of a received signal depicted as the input data 111. As discussed later in this specification, the equalized collection of resource elements or subcarriers can be used in further processes involved in transmission or reception of communications signals. [0121] Because detailed CSI information is used within the pre-coding of downlink multiple input, multiple output (MIMO) precoding weights, a machine-learning network used for receiving and processing of a received signal, as shown in item 522 on the right of item 520, can also be used to produce pre-coding weight values for single or multi-user MIMO schemes simply through a learning process);
O’Shea fails to disclose a terminal comprising: a communication unit configured to receive from a base station of the wireless communication system a downlink signal.
However, Wang '451 discloses a terminal comprising: a communication unit (Fig 2 Radio Frequency Front End 204) configured to receive from a base station of the wireless communication system a downlink signal; and ([0149] the user equipment (e.g., UE 110) receives downlink data channel communications from a base station (e.g., base station 121), downlink control channel communications from the base station, etc. At 1120, the user equipment processes the communications using the deep neural network to extract information transmitted in the communications).
O’Shea and Wang '451 are considered to be analogous to the claimed invention because both are in the same endeavor of processing communications signals using artificial intelligence/machine-learning/neural networks.
Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have a motivation to combine the teachings of O’Shea and Wang '451 to create a terminal comprising: a communication unit configured to receive from a base station of the wireless communication system a downlink signal.
The motivation to combine both references would come from the need to allow communications between the base station and terminal.
O’Shea fails to disclose a terminal, wherein the pre-processing projects the downlink signal into a transmit antenna number dimension using a singular value decomposition (SVD) to apply at least one reference signal to the downlink signal while maintaining statistical features related to noise of the downlink signal.
However, Park discloses a terminal, wherein the pre-processing projects the downlink signal into a transmit antenna number dimension using a singular value decomposition (SVD) to apply at least one reference signal to the downlink signal, while maintaining statistical features related to noise of the downlink signal ([0009] a method for transmitting channel state information (CSI) of terminal in a wireless communication system may include: receiving a CSI-reference signal (RS); generating a first matrix for a channel based on the CSI-RS; generating a second matrix having a lower dimension than the first matrix by calculating the first matrix and an orthogonal beam matrix having a lower dimension than the first matrix; and transmitting to a base station information on the second matrix and/or the orthogonal beam matrix as the CSI, in which the orthogonal beam matrix may be a matrix including a plurality of orthogonal beams orthogonal to each other as elements. [0010] Furthermore, step of the generating of the second matrix may be step of obtaining the second matrix by projecting the first matrix to the orthogonal beam matrix. [0314] The explicit CSI reporting scheme is a scheme that reports information maximally approximate to a measurement value without the process of interpreting the channel measured by the receiver. Various schemes such as quantization or singular value decomposition (SVD) operations of an MIMO channel expressed in a matrix form may be used to reduce the signaling overhead used in the CSI reporting. For example, the explicit CSI (feedback) information may include the following information. [0315] Channel coefficient quantization & quantization index feedback; [0316] MIMO matrix or vector quantization & quantization index feedback; [0317] Channel covariance matrix feedback; [0318] Eigen matrix feedback or eigen vector and eigen value of channel matrix; and/or [0319] Channel matrix.)
O’Shea and Park are considered to be analogous to the claimed invention because both are in the same endeavor of analyzing the communication channel between multiple antennas.
Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have a motivation to combine the teachings of O’Shea and Park to create a terminal, wherein the pre-processing projects the downlink signal into a transmit antenna number dimension using a singular value decomposition (SVD) to apply at least one reference signal to the downlink signal while maintaining statistical features related to noise of the downlink signal.
The motivation to combine both references would come from the need to enable a device to acquire better received signal quality via processing and maximize data transmission.
O’Shea fails to disclose a terminal, wherein the pre-processing inputs information for channel coefficient to the downlink signal as an input factor, wherein the information for the channel coefficient is based on a transformed channel matrix that is dependent on a number of transmit antennas and independent of a number of receive antennas
However, Yan discloses a terminal, wherein the pre-processing inputs information for channel coefficient to the downlink signal as an input factor, wherein the information for the channel coefficient is based on a transformed channel matrix that is dependent on a number of transmit antennas and independent of a number of receive antennas ([0075] The training data is obtained by performing a phase transformation to a right singular vector of a channel coefficient matrix. [0077] Performing singular value decomposition to the channel coefficient matrix, and selecting first right singular vectors corresponding to the number of streams of a target codebook, where the number of the selected right singular vectors equals to the number of streams. vni = [ vni (1), vni (2), … vni (NT) ] is used to represent the ith right singular vector of the nth channel coefficient matrix, where vni, (j) represents the jth component of vni, NT represents the number of transmitting antennas, and i ranges from 1 to the number of streams).
O’Shea and Yan are considered to be analogous to the claimed invention because both are in the same endeavor of determining parameters for a channel model with multiple antennas.
Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have a motivation to combine the teachings of O’Shea and Yan to create a terminal, wherein the pre-processing inputs information for channel coefficient to the downlink signal as an input factor, wherein the information for the channel coefficient is based on a transformed channel matrix that is dependent on a number of transmit antennas and independent of a number of receive antennas.
The motivation to combine both references would come from the need to reduce the dimension of the channel information in order to reduce the feedback signaling overhead.
Regarding claim 11, O’Shea discloses a terminal comprising:
one or more transceivers ([0143] transceiver 768);
one or more processors ([0139] In other implementations, multiple processors and/or multiple buses may be used, as appropriate, along with multiple memories and types of memory); and
one or more memories coupled to the one or more processors and storing first control information and instructions ([0139] In other implementations, multiple processors and/or multiple buses may be used, as appropriate, along with multiple memories and types of memory [0144] The processor 752 can execute instructions within the mobile computing device 750, including instructions stored in the memory 764),
wherein the instructions, when executed by the one or more processors, cause the one or more processors to support operations for intelligent beam prediction, the operations comprising ([0035] a system for processing digital communications as described in this specification uses a machine-learning network to perform one or more tasks for transmitting or receiving, or both, RF signals ... Some implementations include additional tasks performed using a machine-learning network, such as spatial combining, beam-forming (BF)):
pre-processing the downlink signal based on a result of an operation of a deep neural network ([0058] The machine-learning network 120 processes the input data 110, to produce the output data 130. Where the input data 110, in the example of FIG. 1, is a collection of unequalized resource elements or subcarriers, the output data 130 is a collection of equalized resource elements or subcarriers, which is shown as the output plot 131. In some cases, for the equalized resource elements, the complex value of each grid element in the output data 130 closely resembles the values transmitted prior to transmission over the channel, having removed random phase and amplitude changes, or the addition of other interference or channel effects on these elements, which can be present in the unequalized grid. The output data 130 represents an estimation of a received signal depicted as the input data 111. As discussed later in this specification, the equalized collection of resource elements or subcarriers can be used in further processes involved in transmission or reception of communications signals. [0121] Because detailed CSI information is used within the pre-coding of downlink multiple input, multiple output (MIMO) precoding weights, a machine-learning network used for receiving and processing of a received signal, as shown in item 522 on the right of item 520, can also be used to produce pre-coding weight values for single or multi-user MIMO schemes simply through a learning process);
O’Shea fails to disclose a terminal, the operations comprising: receiving, from a base station of a wireless communication system, a downlink signal.
However, Wang '451 discloses a terminal, the operations comprising: receiving, from a base station of a wireless communication system, a downlink signal; and ([0149] the user equipment (e.g., UE 110) receives downlink data channel communications from a base station (e.g., base station 121), downlink control channel communications from the base station, etc. At 1120, the user equipment processes the communications using the deep neural network to extract information transmitted in the communications).
O’Shea and Wang '451 are considered to be analogous to the claimed invention because both are in the same endeavor of processing communications signals using artificial intelligence/machine-learning/neural networks.
Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have a motivation to combine the teachings of O’Shea and Wang '451 to create a terminal, the operations comprising: receiving, from a base station of a wireless communication system, a downlink signal.
The motivation to combine both references would come from the need to allow communications between the base station and terminal.
O’Shea fails to disclose a terminal, wherein the pre-processing projects the downlink signal into a transmit antenna number dimension using a singular value decomposition (SVD) to apply at least one reference signal to the downlink signal while maintaining statistical features related to noise of the downlink signal.
However, Park discloses a terminal, wherein the pre-processing projects the downlink signal into a transmit antenna number dimension using a singular value decomposition (SVD) to apply at least one reference signal to the downlink signal, while maintaining statistical features related to noise of the downlink signal ([0009] a method for transmitting channel state information (CSI) of terminal in a wireless communication system may include: receiving a CSI-reference signal (RS); generating a first matrix for a channel based on the CSI-RS; generating a second matrix having a lower dimension than the first matrix by calculating the first matrix and an orthogonal beam matrix having a lower dimension than the first matrix; and transmitting to a base station information on the second matrix and/or the orthogonal beam matrix as the CSI, in which the orthogonal beam matrix may be a matrix including a plurality of orthogonal beams orthogonal to each other as elements. [0010] Furthermore, step of the generating of the second matrix may be step of obtaining the second matrix by projecting the first matrix to the orthogonal beam matrix. [0314] The explicit CSI reporting scheme is a scheme that reports information maximally approximate to a measurement value without the process of interpreting the channel measured by the receiver. Various schemes such as quantization or singular value decomposition (SVD) operations of an MIMO channel expressed in a matrix form may be used to reduce the signaling overhead used in the CSI reporting. For example, the explicit CSI (feedback) information may include the following information. [0315] Channel coefficient quantization & quantization index feedback; [0316] MIMO matrix or vector quantization & quantization index feedback; [0317] Channel covariance matrix feedback; [0318] Eigen matrix feedback or eigen vector and eigen value of channel matrix; and/or [0319] Channel matrix.)
O’Shea and Park are considered to be analogous to the claimed invention because both are in the same endeavor of analyzing the communication channel between multiple antennas.
Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have a motivation to combine the teachings of O’Shea and Park to create a terminal, wherein the pre-processing projects the downlink signal into a transmit antenna number dimension using a singular value decomposition (SVD) to apply at least one reference signal to the downlink signal while maintaining statistical features related to noise of the downlink signal.
The motivation to combine both references would come from the need to enable a device to acquire better received signal quality via processing and maximize data transmission.
O’Shea fails to disclose a terminal, wherein the pre-processing inputs information for channel coefficient to the downlink signal as an input factor, wherein the information for the channel coefficient is based on a transformed channel matrix that is dependent on a number of transmit antennas and independent of a number of receive antennas
However, Yan discloses a terminal, wherein the pre-processing inputs information for channel coefficient to the downlink signal as an input factor, wherein the information for the channel coefficient is based on a transformed channel matrix that is dependent on a number of transmit antennas and independent of a number of receive antennas ([0075] The training data is obtained by performing a phase transformation to a right singular vector of a channel coefficient matrix. [0077] Performing singular value decomposition to the channel coefficient matrix, and selecting first right singular vectors corresponding to the number of streams of a target codebook, where the number of the selected right singular vectors equals to the number of streams. vni = [ vni (1), vni (2), … vni (NT) ] is used to represent the ith right singular vector of the nth channel coefficient matrix, where vni, (j) represents the jth component of vni, NT represents the number of transmitting antennas, and i ranges from 1 to the number of streams).
O’Shea and Yan are considered to be analogous to the claimed invention because both are in the same endeavor of determining parameters for a channel model with multiple antennas.
Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have a motivation to combine the teachings of O’Shea and Yan to create a terminal, wherein the pre-processing inputs information for channel coefficient to the downlink signal as an input factor, wherein the information for the channel coefficient is based on a transformed channel matrix that is dependent on a number of transmit antennas and independent of a number of receive antennas.
The motivation to combine both references would come from the need to reduce the dimension of the channel information in order to reduce the feedback signaling overhead.
Claims 3 and 8 are rejected under 35 U.S.C. 103 as being unpatentable over O’Shea in view of Wang '451, Park, and Yan, as applied to claim 1, 6, or 11 above, and in further view of Wang et al (US20120002741A1) (hereinafter "Wang '741").
Regarding claim 3, O’Shea, modified by Wang '451, Park, and Yan, fails to disclose the method further comprising: detecting, from the base station, MIMO data based on a result of the pre-processing.
However, Wang '741 discloses the method further comprising: detecting, from the base station, MIMO data based on a result of the pre-processing ([0007] a method of controlling, in collaborative MIMO of a wireless communication network, content synchronization of downlink service data in the collaborative MIMO: performing, by a serving base station in the collaborative MIMO, data link layer processing for service data to be transmitted to the mobile station).
O’Shea, modified by Wang '451, Park, and Yan, and Wang '741 are considered to be analogous to the claimed invention because both are in the same endeavor of MIMO optimization and artificial intelligence/machine-learning/neural networks.
Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have a motivation to combine the teachings of O’Shea, modified by Wang '451, Park, and Yan, with Wang '741 to create the method further comprising: detecting, from the base station, MIMO data based on a result of the pre-processing.
The motivation to combine both references would come from the need to utilize adaptive iterative learning processes in order increase MIMO capabilities.
Regarding claim 8, O’Shea, modified by Wang '451, Park, and Yan, fails to disclose the terminal, wherein the processor detects, from the base station, MIMO data based on a result of the pre-processing.
However, Wang '741 discloses the terminal, wherein the processor detects, from the base station, MIMO data based on a result of the pre-processing ([0007] a method of controlling, in collaborative MIMO of a wireless communication network, content synchronization of downlink service data in the collaborative MIMO: performing, by a serving base station in the collaborative MIMO, data link layer processing for service data to be transmitted to the mobile station).
O’Shea, modified by Wang '451, Park, and Yan, and Wang '741 are considered to be analogous to the claimed invention because both are in the same endeavor of MIMO optimization and artificial intelligence/machine-learning/neural networks.
Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have a motivation to combine the teachings of O’Shea, modified by Wang '451, Park, and Yan, with Wang '741 to create the terminal, wherein the processor detects, from the base station, MIMO data based on a result of the pre-processing.
The motivation to combine both references would come from the need to utilize adaptive iterative learning processes in order increase MIMO capabilities.
Response to Arguments
Applicant’s arguments with respect to claims 1, 6, and 11 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 specifically challenged in the argument.
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 D Little whose telephone number is (571)272-5748. The examiner can normally be reached M-Th 8-6 ET.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Nishant Divecha can be reached at 571-270-3125. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/D LITTLE/Examiner, Art Unit 2419
/Nishant Divecha/Supervisory Patent Examiner, Art Unit 2419