DETAILED ACTION
The amendments and remarks filed 11/17/2025 were received.
PRIOR ART
The following references are prior art:
1. US 2012/0069927 Al (“Oyman”) is prior art under 35 U.S.C. 102(a)(1) since it published on Mar. 22, 2012 before Nov. 13, 2020 the effective filing date of the claimed invention.
2. US 2020/0304259 A1 (“Iha”) is prior art under 35 U.S.C. 102(a)(1) since it published on Sep. 24, 2020 before Nov. 13, 2020 the effective filing date of the claimed invention.
3. US 2020/0229206 A1 (“Badic”) is prior art under 35 U.S.C. 102(a)(1) since it published on Jul. 16, 2020 before Nov. 13, 2020 the effective filing date of the claimed invention.
4. (05/09/2023 IDS) US 2018/0367192 A1 (“D1,” cited in the International Search Report) is prior art under 35 U.S.C. 102(a)(1) since it published on Dec. 20, 2018 before Nov. 13, 2020 the effective filing date of the claimed invention.
CLAIM REJECTIONS — 35 U.S.C. 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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:
35 U.S.C. 102 Conditions for patentability; novelty.
(a) NOVELTY; PRIOR ART.—A person shall be entitled to a patent unless—
(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention;
CLAIMS 1, 2, 4, 5, 9, 12, 13, 19, 21, 22, AND 30
Claims 1, 2, 4, 5, 9, 12, 13, 19, 21, 22, and 30 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Oyman for the reasons given below.
Claim 1
With respect to claim 1, Oyman disclosed:
A computer implemented method in a transmitter for transmitting information carried by a signal over a channel to a receiver, wherein signal processing by the transmitter and/or by the receiver is degraded by one or more hardware impairments (Oyman [Abstract] Link adaptation parameters for encoding of a source are selected to minimize distortion between the source and a reconstructed source induced by transmission of the source over a multiple input multiple output (MIMO) channel. [0013] Some implementations herein provide a distortion-aware MIMO-MCS (Multiple Input Multiple Output Modulation-and-Coding Schemes) and packet size selections toward a communication system that minimizes end-to-end distortion of transmissions. [0015] FIG. 2 illustrates an example of a system 200 for minimizing end-to-end distortion using distortion-aware MIMO link adaptation according to some implementations. To this end, the system 200 includes a transmitter 202 configured to communicate wirelessly with a receiver 204 over a MIMO channel 206… Transmitter 202 also includes a transmitter circuit or device 212, such as a radio front end or other wireless transmission mechanism for transmitting signals over the MIMO channel 206. Similarly, receiver 204 may include a receiver circuit or device 214, such as a radio front end or other wireless receiving mechanism for receiving the signals from transmitter 202. [0021] FIG. 3 illustrates a flow diagram of an exemplary process 300 corresponding to the implementation of FIG. 1, 2, or 4. [0050] In the architecture of FIG. 4, a transmitter 402 is able to communicate with a receiver 404 via a MIMO channel 406. The Examiner finds that Oyman disclosed a computer implemented method in a transmitter (i.e., transmitter 102, 202, 402) for transmitting information (i.e., the “source to be transmitted”) carried by a signal over a channel (i.e., signals transmitted over MIMO channel 106, 206, 406) to a receiver (i.e., receiver 104, 204, 404), wherein signal processing by the transmitter and/or by the receiver is degraded by one or more hardware impairments (i.e., end-to-end distortion (an impairment) between the transmitter radio front end and the receiver radio front end (which are hardware))),
the method comprising: receiving feedback data indicating contextual information of the hardware impairments (Oyman [Abstract] CQI feedback mechanisms may ensure that these distortion-aware MIMO link adaptation techniques are applicable for downlink channels. [0014] the receiver 104 may be distortion-aware… and provide feedback to the transmitter 102 for enabling the transmitter 102 to be distortion aware. For example the receiver 104 may determine link adaptation parameters to minimize end-to-end distortion and provide these parameters as feedback to the transmitter 102, which then uses the provided parameters. [0025] At block 312, optionally, the receiver can provide feedback to the transmitter to provide the transmitter with the distortion-minimizing parameters. [0050] FIG. 4 illustrates a block diagram of an example of a distortion-aware MIMO link adaptation architecture 400 and associated CQI feedback mechanism according to some implementations herein. The Examiner finds that Oyman disclosed receiving feedback data (i.e., feedback of parameters to minimize end-to-end distortion) indicating contextual information of the hardware impairments (i.e., the parameters indicate contextual information (i.e., end-to-end being the context) of the hardware impairments (i.e., end-to-end distortion including the transmitter and receiver front end hardware)),
selecting a signal format for use in generating the signal based on a mapping between the contextual information of the hardware impairments and a pre-determined set of signal formats (Oyman [0013] Some implementations herein provide a distortion-aware MIMO-MCS (Multiple Input Multiple Output Modulation-and-Coding Schemes) and packet size selections toward a communication system that minimizes end-to-end distortion of transmissions. [0017] Memories 218, 224 may also include data structures, such as stored SNR vectors, lookup tables, MIMO MCS schemes, precoding matrices, packet sizes, and the like. [0023] At block 306, channel encoding is carried out by the transmitter to mitigate the errors in the bit stream that will be induced by the channel, while incorporating distortion-minimizing parameters during the encoding. [0042] the MIMO link adaptation technique to minimize an expected value of end-to-end distortion chooses the MIMO MCS, packet size, and precoding matrix Q for each transmitted layer using the following distortion-based criterion: [see equation in PG Pub] where Dave (MIMO13 MCS, P13 SIZE,Q) represents the average end-to-end distortion for a given MIMO MCS, packet size, and precoding matrix Q. In other words, the selection of MIMO MCS, packet size, precoding matrix Q and MIMO space-time modulation mode (e.g., MIMO diversity or MIMO SM) for the multimedia transmission is decided according to implementations herein so that the resulting end-to-end distortion Dave (MIMO_MCS,P SIZE, Q) is minimized. The Examiner finds that Oyman disclosed selecting a signal format (i.e., MIMO MCS, packet size, precoding matrix) for use in generating the signal based on a mapping (i.e., the equation in [0036]) between the contextual information of the hardware impairments and a pre-determined set of signal formats (i.e., stored (pre-determined) lookup tables, MIMO MCS schemes, precoding matrices, packet sizes)),
generating the signal based on the contextual information and on the selected signal format, and transmitting the signal to the receiver (Oyman [0023] At block 304, source coding is carried out by the transmitter to convert the continuous source into a finite stream of bits. At block 306, channel encoding is carried out by the transmitter to mitigate the errors in the bit stream that will be induced by the channel, while incorporating distortion- minimizing parameters during the encoding. [0024] At block 308, the encoded source is transmitted to the receiver over the MIM O channel.).
Claim 2
With respect to claim 2, Oyman disclosed:
The method according to claim 1 (see rejection above),
wherein selecting the signal format comprises selecting a modulation and coding scheme (MCS) from a pre-determined set of MCSs for use in generating the signal, based on a mapping between the contextual information of the hardware impairments and the set of MCSs (Oyman [0013] Some implementations herein provide a distortion-aware MIMO-MCS (Multiple Input Multiple Output Modulation-and-Coding Schemes) and packet size selections toward a communication system that minimizes end-to-end distortion of transmissions. [0017] Memories 218, 224 may also include data structures, such as stored SNR vectors, lookup tables, MIMO MCS schemes, precoding matrices, packet sizes, and the like. [0023] At block 306, channel encoding is carried out by the transmitter to mitigate the errors in the bit stream that will be induced by the channel, while incorporating distortion-minimizing parameters during the encoding. [0042] the MIMO link adaptation technique to minimize an expected value of end-to-end distortion chooses the MIMO MCS, packet size, and precoding matrix Q for each transmitted layer using the following distortion-based criterion: [see equation in PG Pub] where Dave (MIMO13 MCS, P13 SIZE,Q) represents the average end-to-end distortion for a given MIMO MCS, packet size, and precoding matrix Q. In other words, the selection of MIMO MCS, packet size, precoding matrix Q and MIMO space-time modulation mode (e.g., MIMO diversity or MIMO SM) for the multimedia transmission is decided according to implementations herein so that the resulting end-to-end distortion Dave (MIMO_MCS,P SIZE, Q) is minimized. [0046] Selection of the MIMO modulation and coding scheme (MIMO-MCS) herein includes (a) selection of the modulation order, (b) selection of the forward error correction (FEC) type and coding rate, and (c) determination of which space-time modulation techniques will be used.).
Claim 4
With respect to claim 4, Oyman disclosed:
The method according to claim 1 (see rejection above),
wherein selecting the signal format comprises selecting an antenna beamforming configuration from a pre-determined set of antenna beamforming configurations for use in transmitting the signal, based on a mapping between the contextual information of the hardware impairments and the set of antenna beamforming configurations (Oyman [0017] Memories 218, 224 may also include data structures, such as stored SNR vectors, lookup tables, MIMO MCS schemes, precoding matrices, packet sizes, and the like. [0023] At block 306, channel encoding is carried out by the transmitter to mitigate the errors in the bit stream that will be induced by the channel, while incorporating distortion-minimizing parameters during the encoding. [0042] the MIMO link adaptation technique to minimize an expected value of end-to-end distortion chooses the MIMO MCS, packet size, and precoding matrix Q for each transmitted layer using the following distortion-based criterion: [see equation in PG Pub] where Dave (MIMO13 MCS, P13 SIZE,Q) represents the average end-to-end distortion for a given MIMO MCS, packet size, and precoding matrix Q. In other words, the selection of MIMO MCS, packet size, precoding matrix Q and MIMO space-time modulation mode (e.g., MIMO diversity or MIMO SM) for the multimedia transmission is decided according to implementations herein so that the resulting end-to-end distortion Dave (MIMO_MCS,P SIZE, Q) is minimized. [0046] Selection of the MIMO modulation and coding scheme (MIMO-MCS) herein includes … (c) determination of which space-time modulation techniques will be used. Options for space-time modulation include spatial multiplexing (SM), space-time coding (STC), orthogonal space-time block coding (OSTBC), beamforming, etc.).
Claim 5
With respect to claim 5, Oyman disclosed:
The method according to claim 1 (see rejection above),
further comprising selecting a hardware configuration at the transmitter in dependence of the feedback data (Oyman [0013] some implementations provide channel quality indicator feedback mechanisms for supporting … rate/power adaptation … antenna selection … techniques subject to one or more end-to-end distortion minimization criteria. [0033] Furthermore, the selection of the precoding matrix Q, includes (a) beamforming to convert a MIMO channel into an equivalent single-input single-output (SISO) channel, (b) precoded spatial multiplexing, (c) precoded OSTBC, (d) transmit power allocation and covariance optimization, and (e) transmit antenna selection techniques where M out of M, transmit antennas are selected for transmission.).
Claim 9
With respect to claim 9, Oyman disclosed:
The method according to claim 1 (see rejection above),
wherein the mapping between the contextual information of the hardware impairments and the pre-determined set of signal formats is represented by a look-up table (LUT) and/or by a pre-determined function (Oyman [0017] Memories 218, 224 may also include data structures, such as stored SNR vectors, lookup tables, MIMO MCS schemes, precoding matrices, packet sizes, and the like. [0042] the MIMO link adaptation technique to minimize an expected value of end-to-end distortion chooses the MIMO MCS, packet size, and precoding matrix Q for each transmitted layer using the following distortion-based criterion: [see equation in PG Pub] where Dave (MIMO13 MCS, P13 SIZE,Q) represents the average end-to-end distortion for a given MIMO MCS, packet size, and precoding matrix Q. In other words, the selection of MIMO MCS, packet size, precoding matrix Q and MIMO space-time modulation mode (e.g., MIMO diversity or MIMO SM) for the multimedia transmission is decided according to implementations herein so that the resulting end-to-end distortion Dave (MIMO_MCS,P SIZE, Q) is minimized.).
Claim 12
With respect to claim 12, Oyman disclosed:
The method according to claim 1 (see rejection above),
comprising selecting a plurality of signal formats in sequence and monitoring received feedback data indicating hardware impairment contextual information corresponding to the signal formats (Oyman [0023] At block 304, source coding is carried out by the transmitter to convert the continuous source into a finite stream of bits. At block 306, channel encoding is carried out by the transmitter to mitigate the errors in the bit stream that will be induced by the channel, while incorporating distortion-minimizing parameters during the encoding. [0024] At block 308, the encoded source is transmitted to the receiver over the MIM O channel. Along with the encoded source, the rate-distortion characteristics of the source may optionally be transmitted over the MIMO channel, so that this information may be used by the receiver toward distortion-aware link adaptation. [0025] At block 310, the receiver receives the transmission from the transmitter and decodes the transmission to reconstruct the source. At block 312, optionally, the receiver can provide feedback to the transmitter to provide the transmitter with the distortion-minimizing parameters. When the transmitter receives the feedback, the newly received distortion minimizing parameters can be applied to the source and channel encoding. [FIG. 3] illustrates that 312 returns to 306).
Claim 13
With respect to claim 13, Oyman disclosed:
The method according to claim 1 (see rejection above),
wherein the feedback data comprises feedback validity information indicating a time window and/or a frequency range and/or a beamforming antenna configuration where the feedback data is assumed valid (Oyman [0054] In order to optimally perform radio resource management and link adaptation in downlink, the BS needs to learn the link qualities to each MS, i.e., toward executing functions such as scheduling and MCS selection. To this end, CQ I feedback mechanisms are designed, so that each MS can periodically report its channel state information to the BS. [0055] For the closed-loop distortion-aware MIMO link adaptation architecture 400 illustrated in FIG. 4, in order to enable the CQI feedback mechanism in the distortion-aware MIMO link adaptation system architecture, the space-time decoder 440 at the receiving end 404 also includes a distortion-aware feedback design block 446 that periodically provides feedback to transmitter 402 for enabling the distortion awareness of the distortion-aware channel coding block 410).
Claim 19
Claim 19 recites limitations similar to claim 1 (see rejection above) except that claim 19 additionally recites “A network node, comprising: processing circuitry; a network interface coupled to the processing circuitry; and a memory coupled to the processing circuitry, wherein the memory comprises machine readable computer program instructions that, when executed by the processing circuitry, causes the network node to” perform operations similar to the method of claim 1.
With respect to claim 19, Oyman disclosed:
A network node, comprising: processing circuitry; a network interface coupled to the processing circuitry; and a memory coupled to the processing circuitry, wherein the memory comprises machine readable computer program instructions that, executed by the processing circuitry (Oyman [0015] Transmitter 202 also includes a transmitter circuit or device 212, such as a radio front end or other wireless transmission mechanism for transmitting signals over the MIMO channel 206… In addition, transmitter 202 may include one or more processors 216 coupled to a memory 218 or other processor-readable storage media. For example, memory 218 may contain a distortion awareness component 220 able to be executed by the one or more processors 216 to cause transmitter 202 to carry out the functions described above for minimizing end-to-end distortion.).
Claim 21
Claim 21 recites limitations similar to claim 1 (see rejection above) except it from the receiver perspective and it additionally recites “configuring a contextual model in the receiver, wherein the contextual model is arranged to generate contextual information of the hardware impairments based on samples of the received signal, receiving the signal, generating contextual information by the contextual model applied to samples of the received signal.”
With respect to claim 21, Oyman disclosed:
configuring a contextual model in the receiver, wherein the contextual model is arranged to generate contextual information of the hardware impairments based on samples of the received signal, receiving the signal, generating contextual information by the contextual model applied to samples of the received signal (Oyman [0051] channel-encoded data 424, which is sent to receiver 404 over MIMO channel 406. [0055] For the closed-loop distortion-aware MIMO link adaptation architecture 400 illustrated in FIG. 4, in order to enable the CQI feedback mechanism in the distortion-aware MIMO link adaptation system architecture, the space-time decoder 440 at the receiving end 404 also includes a distortion-aware feedback design block 446 that periodically provides feedback to transmitter 402 for enabling the distortion awareness of the distortion-aware channel coding block 410, after the distortion-minimizing MIMO link adaptation parameters (i.e., SINR information, statistical or instantaneous channel state information, MIMO MCS, packet size, and precoding matrix Q) have been determined at the receiver 404 based on receiver's knowledge of the average or long-term received SINR and instantaneous or statistical knowledge of the short-term SINR over the MIMO channel. For example, the distortion-aware feedback block 446 at the receiver 404 may determine from the space-time decoding block 440 link adaptation information 448 (i.e., the estimated). MIMO channel parameters, and the MIMO MCS, packet size, and precoding matrix Q parameters). The distortion-aware feedback block 446 uses the link adaptation information 448 along with rate distortion information 416 (Op. A) and/or rate distortion information 450 (Op. B) to determine distortion-minimizing link adaptation parameters 452, e.g., a MIMO MCS scheme, packet size and/or precoding matrix Q. The Examiner finds that Oyman disclosed configuring a contextual model (i.e., the distortion-aware MIMO link adaptation system architecture) in the receiver (i.e., receiver 404), wherein the contextual model is arranged to generate contextual information of the hardware impairments (i.e., the feedback of parameters to minimize end-to-end distortion indicate contextual information (i.e., end-to-end being the context) of the hardware impairments (end-to-end distortion/impairment including the transmitter and receiver front end hardware)) based on samples of the received signal (i.e., the signal received by receiver 404).).
Claim 22
With respect to claim 22, Oyman disclosed:
The method according to claim 21 (see rejection above), comprising receiving the contextual model from the transmitter (Oyman [0051] channel-encoded data 424, which is sent to receiver 404 over MIMO channel 406 (along with rate-distortion information 416 in the case of Op. A). [0056] Alternatively, or in addition, transmitter 402 may send rate-distortion information 416 on the source along with channel-encoded data 424 to receiver 404 over the MIMO channel 406 (Op. A), so that distortion-aware feedback block 446 at receiver 404 may utilize this information in determining distortion-minimizing MIMO link adaptation parameters 452. The rate distortion information 416 and/or 450 are taken into consideration by distortion-aware feedback block 446 when determining the distortion minimizing link adaptation parameters 452, e.g., MIMO MCS, packet size, and/or pre-coding matrix, which are then passed to the transmitter 402 through a feedback channel. For example, transmitter 402 may be incorporated into a first device that also includes a receiver (not shown), while receiver 404 may be in the incorporated into a second device that also includes a transmitter (not shown), thus enabling the receiver 404 to provide feedback wirelessly to the transmitter 402 such as over MIMO channel 406, or other wireless channel, link, or the like.).
Claim 30
Claim 30 recites limitations similar to claim 19 (see rejection above) and is rejected by the same reasoning.
CLAIM REJECTIONS — 35 U.S.C. 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:
35 U.S.C. 103 Conditions for patentability; non-obvious subject matter.
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 nonobviousness.
CLAIM 11
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Oyman.
Claim 11
With respect to claim 11, Oyman taught:
The method according to claim 1 (see rejection above),
comprising transmitting a request from the transmitter to the receiver for a context reporting capability of the receiver (Oyman [0087] While the distortion-aware link adaptation concepts may be beneficial toward enhanced multimedia communications, their full benefits may not be realizable without enabling the property of "distortion-awareness" at the client devices and network infrastructure components. In current wireless networks, however, the network infrastructure components, including the base-stations and radio network controllers, typically are not expected to possess distortion-aware processing capabilities.).
Oyman taught “a context reporting capability of the receiver.” Oyman suggested, although it did not explicitly teach, “transmitting a request from the transmitter to the receiver” for the capability as discussed below.
The Examiner finds that it 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 to “transmitting a request from the transmitter to the receiver” for the capability as claimed because Oyman [0087] indicates that such capabilities are beneficial but the capability is not necessarily expected to be possessed. Accordingly, a person of ordinary skill in the art would be motivated to configure the transmitter of Oyman to request the capabilities of the receiver such that Oyman’s distortion-aware link adaptation techniques can be implemented, thereby enhancing communications, if the receiver is capable. The Examiner finds that there would be a reasonable expectation of success in implementing a capability request since Oyman already acknowledges said capability.
CLAIM 3
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Oyman in view of Iha.
Claim 3
With respect to claim 3, Oyman taught:
The method according to claim 1, including selecting the signal format based on a mapping between the contextual information of the hardware impairments and a pre-determined set of signal formats (see rejection above).
Oyman did not explicitly disclose that selecting the signal format based on the mapping involved “selecting a phase tracking reference signal (PTRS) allocation from a pre-determined set of PTRS allocations for use in generating the signal.”
With respect to claim 3, Iha taught:
selecting a phase tracking reference signal (PTRS) allocation from a pre-determined set of PTRS allocations for use in generating the signal (Iha [Abstract] One method may include forming, by a network node, a phase tracking reference signal (PT-RS) sample sequence using outer-most constellation points corresponding to scheduled modulation order of data channel, and scrambling the phase tracking reference signal (PT-RS) sample sequence with a user equipment-specific sequence. [0032] At carrier frequencies above 6 GHz, oscillator induced phase noise (PN) ( e.g., due to implementation imperfections) becomes gradually more significant with increasing frequency and may result in severe degradation of detection performance, especially in case of higher order modulation schemes, unless properly addressed. Phase variations in time may also be caused by other phenomena such as frequency drifts due to Doppler shift or due to insufficient frequency synchronization. Depending on the receiver's (e.g., UE's) operation point, which is characterized by factors including, e.g., carrier frequency, subcarrier spacing, scheduled bandwidth and/or modulation and coding scheme (MCS), or UE velocity, the phase variation due to any of the above-mentioned effects may require compensation to guarantee successful data transmission. [0041] According to one embodiment, a gNB may UE specifically configure the PT-RS burst periodicity and burst length based on, for example, i) residual frequency offset (FO) (due to insufficient frequency synchronization and/or Doppler) estimated by the gNB, ii) channel quality indication (CQI) reported by the UE, and/or iii) information on time-correlation properties of UE's local oscillator (LO) phase noise (PN) (obtained, e.g., in the form UE device category during the RRC hand-shake). Based on this information and the ability to perform advanced interpolation methods (e.g., Wiener estimation compared to linear), the gNB can optimize the PT-RS time pattern (periodicity and length of the PT-RS bursts) and optimize the L parameter.) [0050] Depending on a UE's operation point (e.g., scheduled MCS and BW, numerology, carrier frequency, Doppler/residual FO), either PT-RS pattern scheme A (e.g., as depicted in FIG. 4) or pattern scheme B (e.g., as depicted in FIG. 5) may be adopted, according to certain embodiments.).
The Examiner finds that it 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 to implement Iha’s PT-RS selection technique with Oyman’s CQI feedback mechanism for distortion-aware link adaptation with the motivation being to reduce end-to-end distortion. Oyman’s technique was designed for older 3G and 4G 3GPP standard networks but as the market has moved onto 5G New Radio operating in millimeter bands (see Iha [0031]), there is a more pronounced end-to-end distortion (as described in Oyman) since “at carrier frequencies above 6 GHz, oscillator induced phase noise (PN) (e.g., due to implementation imperfections) becomes gradually more significant with increasing frequency and may result in severe degradation of detection performance… depending on the receiver’s (e.g., UE’s)… MCS” (see Iha [0032]). The Examiner finds that there would be a reasonable expectation of success in doing so since both Oyman and Iha are directed to 3GPP wireless communication standards (see Oyman [0022] and Iha [0004]), reporting of CQI, and distortion reduction.
Claim 7
With respect to claim 7, Oyman taught:
The method according to claim 5 including selecting a hardware configuration at the transmitter in dependence of the feedback data (see rejection above).
Oyman did not explicitly teach “wherein the hardware configuration comprises an oscillator circuit power consumption level associated with the transmitter.”
With respect to claim 7, Iha taught:
wherein the hardware configuration comprises an oscillator circuit power consumption level associated with the transmitter (Iha [0008] With IoT and machine-to-machine (M2M) communication becoming more widespread, there will be a growing need for networks that meet the needs of lower power, low data rate, and long battery life. In 5G or NR, the node B or eNB may be referred to as a next generation node B (gNB). [0032] At carrier frequencies above 6 GHz, oscillator induced phase noise (PN) (e.g., due to implementation imperfections) becomes gradually more significant with increasing frequency and may result in severe degradation of detection performance, especially in case of higher order modulation schemes, unless properly addressed. [0041] According to one embodiment, a gNB may UE-specifically configure the PT-RS burst periodicity and burst length based on, for example, i) residual frequency offset (FO) (due to insufficient frequency synchronization and/or Doppler) estimated by the gNB, ii) channel quality indication (CQI) reported by the UE, and/or iii) information on time-correlation properties of UE's local oscillator (LO) phase noise (PN). The Examiner notes that oscillator phase noise inherently corresponds to oscillator circuit power consumption level1).
The Examiner finds that it 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 to implement Iha’s PT-RS selection technique based on CQI and oscillator phase noise with Oyman’s CQI feedback mechanism for distortion-aware link adaptation with the motivation being to reduce end-to-end distortion. Oyman’s technique was designed for older 3G and 4G 3GPP standard networks but as the market has moved onto 5G New Radio operating in millimeter bands (see Iha [0031]), there is a more pronounced end-to-end distortion (as described in Oyman) since “at carrier frequencies above 6 GHz, oscillator induced phase noise (PN) (e.g., due to implementation imperfections) becomes gradually more significant with increasing frequency and may result in severe degradation of detection performance… depending on the receiver’s (e.g., UE’s)… MCS” (see Iha [0032]). The Examiner finds that there would be a reasonable expectation of success in doing so since both Oyman and Iha are directed to 3GPP wireless communication standards (see Oyman [0022] and Iha [0004]), reporting of CQI, and distortion reduction.
Claim 8
With respect to claim 8, Oyman taught:
The method according to claim 5 (see rejection above).
Oyman did not explicitly teach “wherein the hardware configuration comprises an optimized signalling constellation.”
With respect to claim 8, Iha taught:
wherein the hardware configuration comprises an optimized signalling constellation (Iha [0060] The sensitivity of the data channel detection performance to PN and/or FO impairments increases with scheduled modulation and coding scheme (MCS). Furthermore, obtaining high quality PN and/or FO estimates at low SNRs with low RS overhead impose a challenge. [0061] Consequently, one embodiment may be configured to utilize the outer-most constellation points corresponding to the scheduled modulation order of the Physical Uplink Shared Data Channel (PUSCH) for the PT-RS sample sequence. FIG. 10 illustrates an example of the outer-most constellation points usage for PT-RS sequence (assuming UE is scheduled with 16-QAM), according to an embodiment. More specifically, FIG. 10 illustrates the example case where a UE is assumed to transmit PUSCH with 16-QAM. By doing so, the PT-RS transmit power can be maximized without impact on PAPR/CM of the PT-RS carrying DFT-s-OFDM symbols. As a result, received SNR of PT-RS samples used for PN and/or residual FO estimation can be improved, and enhanced estimation accuracy versus PT-RS overhead trade-off can be obtained.).
The Examiner finds that it 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 to implement Iha’s constellation point selection technique with Oyman’s CQI feedback mechanism for distortion-aware link adaptation with the motivation being to improve estimation of phase noise/distortion (see Iha [0061]). The Examiner finds that there would be a reasonable expectation of success in doing so since both Oyman and Iha are directed to 3GPP wireless communication standards (see Oyman [0022] and Iha [0004]), reporting of CQI, and distortion reduction.
CLAIM 6
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Oyman in view of Badic.
Claim 6
With respect to claim 6, Oyman taught:
The method according to claim 5, which includes selecting a hardware configuration at the transmitter in dependence of the feedback data (see rejection above).
Oyman did not explicitly teach “wherein the hardware configuration comprises a back-off level associated with a power amplifier (PA) of the transmitter.”
With respect to claim 6, Badic taught:
wherein the hardware configuration comprises a back-off level associated with a power amplifier (PA) of the transmitter (Badic [1222] some modulation schemes may be less power efficient than other modulation schemes… higher-order modulation schemes (e.g., 64-QAM) may be less power efficient than lower-order modulation schemes (e.g., QPSK). Such differences in power efficiency can arise from the extra power that is expended by the power amplifier (PA) when transmitting modulation symbols from more complex symbol constellations… Higher-order QAM schemes may use a higher power backoff from the point of maximum power added efficiency (PAE) of the power amplifier. Power amplifier can be very linear at low efficiency power amplifier is very linear, which can therefore allow for higher-order modulation schemes. By contrast, at high efficiency the power amplifiers can be very nonlinear in terms of amplitude to phase distortion (AM-PM) and gain expansion. This power amplifier nonlinearity can be measured and characteristic by the error vector magnitude (EVM) of the modulation constellation diagram, which characterizes the error between the ideal modulation symbol (on the modulation constellation diagram) and the actual transmitted symbol.)
The Examiner finds that it 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 to implement Badic’s modulation-based power amplifier backoff configuration with Oyman’s CQI feedback mechanism for distortion-aware link adaptation with the motivation being to reduce end-to-end distortion since power amplifier configuration affects distortion (see Badic [1222]). The Examiner finds that there would be a reasonable expectation of success in doing so since both Oyman and Badic are directed to 3GPP wireless communication standards (see Oyman [0022] and Badic [0245]).
CLAIMS 10 AND 14-16
Claims 10 and 14-16 are rejected under 35 U.S.C. 103 as being unpatentable over Oyman in view of D1.
Claim 10
With respect to claim 10, Oyman taught:
The method according to claim 1, including a mapping between the contextual information of the hardware impairments and a pre-determined set of signal formats (see rejection above).
Oyman did not explicitly teach that the mapping “is represented by a reinforcement learning, RL, structure.”
With respect to claim 10, D1 taught:
a mapping between the contextual information of the hardware impairments and a pre-determined set of signal formats is represented by a reinforcement learning (RL) structure (D1 [0006] The method where updating the at least one machine-learning network includes at least one of: (i) updating at least one encoding network weight or network connectivity in one or more layers of an encoder machine-learning network at the transmitter, (ii) updating at least one decoding network weight or network connectivity in one or more layers of a decoder machine-learning network at the receiver, or (iii) updating at least one network weight or network connectivity in one or more layers of a CSI embedding machine-learning network that is configured to generate the CSL The method where the transmitter includes an encoder machine-learning network and the receiver includes a decoder machine-learning network that are jointly trained as an auto-encoder to learn communication over a MIMO communication channel, and wherein the auto-encoder includes at least one channel-modeling layer representing effects of the MIMO channel model or other impairments on transmitted waveforms. The method where the at least one channel-modeling layer represents at least one of (i) additive Gaussian thermal noise in the MIMO communication channel, (ii) delay spread caused by time-varying effects of the MIMO communication channel, (iii) phase noise or other distortions caused by transmission and reception over the MIMO communication channel or hardware, or (iv) offsets in phase, frequency, rate, or timing caused by transmission and reception over the MIMO communication channel. [0033] In some implementations, further learning and adaptation of the machine-learning network(s) may be implemented during deployment in real-world systems, for example based on feedback information. The Examiner notes that machine learning based on feedback information, as described in D1, is reinforcement learning2).
The Examiner finds that it 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 to implement D1’s machine learning techniques in Oyman’s CQI feedback mechanism for distortion-aware link adaptation since the machine learning techniques described in D1 provide improve the algorithms and encoding for specific impairments or other statistical features, improving performance (see D1 [0033]) and offer improvements in MIMO communications having complex sets of effects which are hard to model, especially when considering effects introduced by hardware, amplifiers, interferers or other effects (see D1 [0034]) which would be advantageous in Oyman’s method since it is directed to determining and mitigating end-to-end distortion.
Claim 14
With respect to claim 14, Oyman taught:
The method according to claim 1 (see rejection above).
Oyman did not explicitly teach “comprising extracting the contextual information from the feedback data based on a neural network decoder structure.”
With respect to claim 14, D1 taught:
comprising extracting the contextual information from the feedback data based on a neural network decoder structure (D1 [0006] The method where updating the at least one machine-learning network includes at least one of: (i) updating at least one encoding network weight or network connectivity in one or more layers of an encoder machine-learning network at the transmitter, (ii) updating at least one decoding network weight or network connectivity in one or more layers of a decoder machine-learning network at the receiver, or (iii) updating at least one network weight or network connectivity in one or more layers of a CSI embedding machine-learning network that is configured to generate the CSL The method where the transmitter includes an encoder machine-learning network and the receiver includes a decoder machine-learning network that are jointly trained as an auto-encoder to learn communication over a MIMO communication channel, and wherein the auto-encoder includes at least one channel-modeling layer representing effects of the MIMO channel model or other impairments on transmitted waveforms. The method where the at least one channel-modeling layer represents at least one of (i) additive Gaussian thermal noise in the MIMO communication channel, (ii) delay spread caused by time-varying effects of the MIMO communication channel, (iii) phase noise or other distortions caused by transmission and reception over the MIMO communication channel or hardware, or (iv) offsets in phase, frequency, rate, or timing caused by transmission and reception over the MIMO communication channel… The method where the at least one machine-learning network includes at least one of a deep dense neural network (DNN), a convolutional neural network (CNN), or a recurrent neural network (RNN) including parametric multiplications, additions, and non-linearities.).
The Examiner finds that it 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 to implement D1’s machine learning techniques in Oyman’s CQI feedback mechanism for distortion-aware link adaptation since the machine learning techniques described in D1 provide improve the algorithms and encoding for specific impairments or other statistical features, improving performance (see D1 [0033]) and offer improvements in MIMO communications having complex sets of effects which are hard to model, especially when considering effects introduced by hardware, amplifiers, interferers or other effects (see D1 [0034]) which would be advantageous in Oyman’s method since it is directed to determining and mitigating end-to-end distortion.
Claim 15
With respect to claim 15, Oyman taught:
The method according to claim 14 (see rejection above).
Oyman did not explicitly teach “comprising sending a neural network encoder corresponding to the neural network decoder to the receiver, or sending a parameter which defines the neural network encoder, for encoding the contextual information into the feedback data at the receiver.”
With respect to claim 15, D1 taught:
comprising sending a neural network encoder corresponding to the neural network decoder to the receiver, or sending a parameter which defines the neural network encoder, for encoding the contextual information into the feedback data at the receiver (D1 [0006] The method where updating the at least one machine-learning network includes at least one of: (i) updating at least one encoding network weight or network connectivity in one or more layers of an encoder machine-learning network at the transmitter, (ii) updating at least one decoding network weight or network connectivity in one or more layers of a decoder machine-learning network at the receiver, or (iii) updating at least one network weight or network connectivity in one or more layers of a CSI embedding machine-learning network that is configured to generate the CSL The method where the transmitter includes an encoder machine-learning network and the receiver includes a decoder machine-learning network that are jointly trained as an auto-encoder to learn communication over a MIMO communication channel, and wherein the auto-encoder includes at least one channel-modeling layer representing effects of the MIMO channel model or other impairments on transmitted waveforms. The method where the at least one channel-modeling layer represents at least one of (i) additive Gaussian thermal noise in the MIMO communication channel, (ii) delay spread caused by time-varying effects of the MIMO communication channel, (iii) phase noise or other distortions caused by transmission and reception over the MIMO communication channel or hardware, or (iv) offsets in phase, frequency, rate, or timing caused by transmission and reception over the MIMO communication channel… The method where the at least one machine-learning network includes at least one of a deep dense neural network (DNN), a convolutional neural network (CNN), or a recurrent neural network (RNN) including parametric multiplications, additions, and non-linearities. [0071] In the example of FIG. 2, the encoder network 202 and decoder network 204 are implemented using a neural network structure 200 that is configured as an autoencoder. In the scenario of an autoencoder structure, the encoder and decoder are jointly trained to learn best representations of information for communication over the MIMO channel 206. In general, however, the network structure 200 may be configured as separate networks in the encoder network 202 and decoder network 204, which may be jointly or iteratively trained. During training, the encoder network 202 and/or decoder network 204 may be updated by a network update process 216.).
The Examiner finds that it 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 to implement D1’s machine learning techniques in Oyman’s CQI feedback mechanism for distortion-aware link adaptation since the machine learning techniques described in D1 provide improve the algorithms and encoding for specific impairments or other statistical features, improving performance (see D1 [0033]) and offer improvements in MIMO communications having complex sets of effects which are hard to model, especially when considering effects introduced by hardware, amplifiers, interferers or other effects (see D1 [0034]) which would be advantageous in Oyman’s method since it is directed to determining and mitigating end-to-end distortion.
Claim 16
With respect to claim 16, Oyman taught:
The method according to claim 14 (see rejection above).
Oyman did not explicitly teach “comprising receiving a neural network decoder from the receiver corresponding to a neural network encoder used at the receiver for encoding the contextual information into the feedback data.”
With respect to claim 16, D1 taught:
comprising receiving a neural network decoder from the receiver corresponding to a neural network encoder used at the receiver for encoding the contextual information into the feedback data (D1 [0006] The method where updating the at least one machine-learning network includes at least one of: (i) updating at least one encoding network weight or network connectivity in one or more layers of an encoder machine-learning network at the transmitter, (ii) updating at least one decoding network weight or network connectivity in one or more layers of a decoder machine-learning network at the receiver, or (iii) updating at least one network weight or network connectivity in one or more layers of a CSI embedding machine-learning network that is configured to generate the CSL The method where the transmitter includes an encoder machine-learning network and the receiver includes a decoder machine-learning network that are jointly trained as an auto-encoder to learn communication over a MIMO communication channel, and wherein the auto-encoder includes at least one channel-modeling layer representing effects of the MIMO channel model or other impairments on transmitted waveforms. The method where the at least one channel-modeling layer represents at least one of (i) additive Gaussian thermal noise in the MIMO communication channel, (ii) delay spread caused by time-varying effects of the MIMO communication channel, (iii) phase noise or other distortions caused by transmission and reception over the MIMO communication channel or hardware, or (iv) offsets in phase, frequency, rate, or timing caused by transmission and reception over the MIMO communication channel… The method where the at least one machine-learning network includes at least one of a deep dense neural network (DNN), a convolutional neural network (CNN), or a recurrent neural network (RNN) including parametric multiplications, additions, and non-linearities. [0071] In the example of FIG. 2, the encoder network 202 and decoder network 204 are implemented using a neural network structure 200 that is configured as an autoencoder. In the scenario of an autoencoder structure, the encoder and decoder are jointly trained to learn best representations of information for communication over the MIMO channel 206. In general, however, the network structure 200 may be configured as separate networks in the encoder network 202 and decoder network 204, which may be jointly or iteratively trained. During training, the encoder network 202 and/or decoder network 204 may be updated by a network update process 216.).
The Examiner finds that it 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 to implement D1’s machine learning techniques in Oyman’s CQI feedback mechanism for distortion-aware link adaptation since the machine learning techniques described in D1 provide improve the algorithms and encoding for specific impairments or other statistical features, improving performance (see D1 [0033]) and offer improvements in MIMO communications having complex sets of effects which are hard to model, especially when considering effects introduced by hardware, amplifiers, interferers or other effects (see D1 [0034]) which would be advantageous in Oyman’s method since it is directed to determining and mitigating end-to-end distortion.
RESPONSE TO ARGUMENTS
Applicant’s arguments, see Remarks p. 7-8, filed 11/17/2025, with respect to the Claim Rejections under 35 U.S.C. 112(b) have been fully considered and are persuasive in view of the claim amendments. These rejections have been withdrawn.
Applicant’s arguments filed 11/25/2027 with respect to the Claim Rejections under 35 U.S.C. 102 and 103 have been fully considered but they are not persuasive. On pages 8-9 Applicant copies a portion of the rejection including the Examiner’s explicit claim interpretation and mapping of the Oyman reference thereto. On pages 9-10, Applicant explains their own understanding of Oyman and then concludes that “Oyman does not teach the feature "generating the signal based on a mapping between the contextual information of the hardware impairments and a pre-determined set of signal formats" without ever specifically explaining why the Examiner’s interpretation or mapping would be unreasonable. The Examiner disagrees because as stated above and in the previous rejection: the Examiner finds that Oyman disclosed selecting a signal format (i.e., MIMO MCS, packet size, precoding matrix) for use in generating the signal based on a mapping (i.e., the equation in [0036]) between the contextual information of the hardware impairments and a pre-determined set of signal formats (i.e., stored (pre-determined) lookup tables, MIMO MCS schemes, precoding matrices, packet sizes)). Applicant’s arguments are not persuasive because Applicant merely presents their own opinion without specifically refuting the rejection. As stated in 37 CRF 1.111(b), the reply by the applicant or patent owner must be reduced to a writing which distinctly and specifically points out the supposed errors in the examiner's action and must reply to every ground of objection and rejection in the prior Office action. The reply must present arguments pointing out the specific distinctions believed to render the claims, including any newly presented claims, patentable over any applied references.
Applicants arguments on page 10 with respect to claim 19 are derivative of their arguments regarding claim 1. The arguments for claim 19 are unpersuasive for the same reason.
Regarding claim 21, on page 10-12 Applicant copied a portion of the rejection, explained their own understanding of Oyman, including FIG. 4, and then concluded that “Oyman does not teach that a contextual model is configured in a receiver, the contextual model is to generate contextual information of hardware impairments based on samples of received signal.” The crux of Applicants argument appears to be that “Oyman discloses that a space-time decoder at the receiving end includes a distortion-aware feedback design block that periodically provides feedback to a transmitter for enabling the distortion awareness of the distortion-aware channel coding block. This is done after the distortion-minimizing MIMO link adaptation parameters have been determined at the receiver based on receiver's knowledge of the average or long-term received SINR and instantaneous or statistical knowledge of the short-term SINR over the MIMO channel” as stated on page 12 of their Remarks. However, it is unclear how the specifically applies to the twenty-eight words of claim limitations the Applicant states are not disclosed by Oyman. In fact, to the Examiner’s understanding Applicant is actually reiterating why and how Oyman’s disclosure reads on the claim limitations. Specifically, the Examiner finds that Oyman disclosed configuring a contextual model (i.e., the distortion-aware MIMO link adaptation system architecture) in the receiver (i.e., receiver 404), wherein the contextual model is arranged to generate contextual information of the hardware impairments (i.e., the feedback of parameters to minimize end-to-end distortion indicate contextual information (i.e., end-to-end being the context) of the hardware impairments (end-to-end distortion/impairment including the transmitter and receiver front end hardware)) based on samples of the received signal (i.e., the signal received by receiver 404).
Applicants arguments regarding the dependent claims are derivate and present no new arguments to respond to.
CONCLUSION
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 extension fee 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 date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Christopher Davis whose telephone number is 703-756-1832. The examiner can normally be reached Mon-Fri from 11AM to 7PM ET. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ayaz Sheikh, can be reached at telephone number 571-272-3795. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/C.R.D./
Examiner, Art Unit 2476
/AYAZ R SHEIKH/Supervisory Patent Examiner, Art Unit 2476
1 https://en.wikipedia.org/w/index.php?title=Oscillator_phase_noise&oldid=886957844 (see form PTO-892 filed herewith)
2 https://en.wikipedia.org/wiki/Reinforcement_learning