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 .
Information Disclosure Statement
2. The information disclosure statements (IDSs) submitted on January 30, 2025, were filed before the mailing of a first Office action on the merits. The submissions are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Claim Objections
3. Claims 7 and 17 are objected to because of the following informalities:
Claim 7 (line 1) recites “the neural network” and it should be - - a neural network - -, as “the neural network” lacks antecedent basis.
Claim 17 (line 5) recites “the user plane traffic quality-related report” and it should be - - a user plane traffic quality-related report - -, as “the user plane traffic quality-related report” lacks antecedent basis.
Claim Rejections - 35 USC § 112(b)
4. The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
5. Claim 7 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
Claim 7 (line 1) recites “the apparatus of claim 3, when dependent from claim 5.” It is unclear whether claim 7 is dependent from claim 3 or claim 5, as claim 3 precedes claim 5. For purposes of examination, the examiner’s interpretation of “the apparatus of claim 3, when dependent from claim 5” is “the apparatus of claim 5.” Whether the intent is for “the apparatus of claim 3, when dependent from claim 5” to be “the apparatus of claim 5,” or not, correction is required for claim 7 to be definite.
Claim Rejections - 35 USC § 103
6. 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.
7. Claims 1-2, 19, and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Horiuchi ‘542 (US 2009/0239542, “Horiuchi ‘542”), in view of Kakishima ‘480 (US 2017/0149480, “Kakishima ‘480”).
Regarding claim 1, Horiuchi ‘542 discloses an apparatus for user plane traffic quality analysis in a wireless communication network (FIGS. 1 and 6, para 2, 4-5, 7, 25-26, and 44-47; a relay station comprises a channel quality acquiring section that is connected to the radio receiving section and a modulation and coding scheme section (MCS) parameter setting section; the relay station measures signal quality for the signal received from the base station, where the signal carries audio data, estimates the signal quality for the signal received at the base station, and sets the MCS parameter according to the signal quality, in the MCS parameter setting section; thus, the relay station comprises an apparatus for data traffic quality analysis, where the apparatus is the channel quality acquiring section with a connection to a user plane probe that is the radio receiving section), the apparatus comprising:
an interface configured to be coupled to a user plane probe (FIGS. 1 and 6, para 2, 4-5, 7, 25-26, and 44-47; the relay station comprises the channel quality acquiring section that is connected to the radio receiving section and the MCS parameter setting section; the relay station measures signal quality for the signal received from the base station, where the signal carries audio data, estimates the signal quality for the signal received at the base station, and sets the MCS parameter according to the signal quality, in the MCS parameter setting section; thus, the relay station comprises an apparatus for data traffic quality analysis, where the apparatus is an estimation unit that is the channel quality acquiring section with a connection to the user plane probe that is the radio receiving section; therefore, the apparatus for data traffic quality analysis comprises an estimation unit that is the channel quality acquiring section that estimates signal quality for the signal received at the base station, and an interface that is the connection between the estimation unit that is the channel quality acquiring section and the user plane probe that is the radio receiving section),
arranged on a bidirectional user plane traffic flow path of user plane traffic flowing through the wireless communication network between a first terminal and a second terminal of the wireless communication network (FIGS. 1 and 6, para 2, 4-5, 7, 21-26, and 44-47; the relay station relays data signals for uplink and downlink communication between the base station and the mobile station; the relay station comprises a radio receiving section that receives data signals on the uplink and data signals on the downlink; the relay station measures signal quality for the received signals; thus, the radio receiving section of the relay station is a probe arranged on a bidirectional traffic flow path of traffic flowing through the mobile communication network between the base station and the mobile station; the base station reads on a first terminal; the mobile station reads on a second terminal); and
an estimation unit coupled to the interface (FIGS. 1 and 6, para 2, 4-5, 7, 26, and 44-47; the relay station comprises the channel quality acquiring section that is connected to the radio receiving section and the MCS parameter setting section; the relay station measures signal quality for the signal received from the base station, where the signal carries audio data, estimates the signal quality for the signal received at the base station, and sets the MCS parameter according to the signal quality, in the MCS parameter setting section; thus, the relay station comprises an apparatus for data traffic quality analysis, where the apparatus is an estimation unit that is the channel quality acquiring section with a connection to the user plane probe that is the radio receiving section; therefore, the apparatus for data traffic quality analysis comprises an estimation unit that is the channel quality acquiring section that estimates signal quality for the signal received at the base station, and an interface that is the connection between the estimation unit that is the channel quality acquiring section and the user plane probe that is the radio receiving section; the channel quality acquiring section of the relay station reads on an estimation unit),
wherein the estimation unit is configured to estimate, based on a probing, by the user plane probe, of the user plane traffic flowing in a first direction in a first segment of the user plane traffic flow path from the first terminal to the user plane probe, a user plane traffic quality of the user plane traffic flowing in a second direction in the first segment from the user plane probe to the first terminal (FIGS. 1 and 6, para 2, 4-5, 7, 26, and 44-47; the relay station comprises the channel quality acquiring section that is connected to the radio receiving section and the MCS parameter setting section; the relay station measures signal quality for the signal received from the base station, where the signal carries audio data, estimates the signal quality for the signal received at the base station based on the measured signal quality for the signal received from the base station, and sets the MCS parameter according to the signal quality, in the MCS parameter setting section; thus, the estimation unit that is the channel quality acquiring section estimates signal quality in the uplink direction between the user plane probe that is the radio receiving section and the base station, based on the signal received by the user plane probe in the downlink direction between the base station and the user plane probe; the downlink direction reads on a first direction, the uplink direction reads on a second direction, the base station reads on the first terminal, the channel quality acquiring section of the relay station reads on an estimation unit, the channel quality acquiring section of the relay station reads on an estimation unit, and the first segment is between the user plane probe and the base station),
wherein the first direction is opposite to the second direction (FIGS. 1 and 6, para 2, 4-5, 7, 26, and 44-47; the downlink direction is opposite to the uplink direction).
However, Horiuchi ‘542 does not specifically disclose wherein the user plane traffic is not testable via the user plane probe in the second direction.
Kakishima ‘480 teaches wherein the user plane traffic is not testable via the user plane probe in the second direction (para 11, 58, and 154; when the direction of the data signal beam directed to a user equipment (UE) is different from the direction of the cell-specific reference signal (CRS) beam, the UE cannot measure quality of data reception).
Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to combine Horiuchi ‘542’s apparatus for user plane traffic quality analysis, to include Kakishima ‘480’s UE that cannot measure quality of data reception. The motivation for doing so would have been to address the problem of UE not being able to estimate a direction of a beam suitable for the user equipment or link adaptive control, such as adaptive modulation coding (Kakishima ‘480, para 18).
Regarding claim 2, Horiuchi ‘542 in combination with Kakishima ‘480 discloses all the limitations with respect to claim 1, as outlined above.
Further, Horiuchi ‘542 teaches wherein the user plane traffic flowing in the first direction in the first segment and the user plane traffic flowing in the second direction in the first segment are based on the user plane traffic flowing from the first terminal to the second terminal and from the second terminal to the first terminal, respectively (FIGS. 1 and 6, para 2, 4-5, 7, 21-26 and 44-47; the relay station relays data signals for uplink and downlink communication between the base station and the mobile station; thus, the traffic flowing in the downlink direction between the base station and the relay station and the traffic flowing in the uplink direction between the relay station and the base station are based on the traffic flowing in the downlink direction from the base station to the mobile station and the traffic flowing in the uplink direction from the mobile station to the base station, respectively; the downlink direction reads on a first direction, the uplink direction reads on a second direction, the base station reads on the first terminal, and the mobile station reads on the second terminal).
Regarding claim 19, Horiuchi ‘542 in combination with Kakishima ‘480 discloses all the limitations with respect to claim 1, as outlined above.
Further, Horiuchi ‘542 teaches wherein the user plane traffic comprises audio traffic (FIGS. 1 and 6, para 2, 4, 21-26, and 44-47; the relay station relays data signals for uplink and downlink communication between the base station and the mobile station, where the data includes audio data, still pictures, and moving pictures).
Regarding claim 21, Horiuchi ‘542 discloses a method for user plane traffic quality analysis in a wireless communication network (FIGS. 1 and 6, para 2, 4-5, 7, 25-26, and 44-47; a relay station comprises a channel quality acquiring section that is connected to the radio receiving section and a modulation and coding scheme section (MCS) parameter setting section; the relay station measures signal quality for the signal received from the base station, where the signal carries audio data, estimates the signal quality for the signal received at the base station, and sets the MCS parameter according to the signal quality, in the MCS parameter setting section; thus, the relay station comprises an apparatus for data traffic quality analysis, where the apparatus is the channel quality acquiring section with a connection to a user plane probe that is the radio receiving section), the method comprising:
probing a first segment of a user plane traffic flow path of user plane traffic flowing in a first direction through the wireless communication network between a first node and a second node of the wireless communication network (FIGS. 1 and 6, para 2, 4-5, 7, 26, and 44-47; the relay station relays data signals for uplink and downlink communication between the base station and the mobile station; the relay station comprises the channel quality acquiring section that is connected to the radio receiving section and the MCS parameter setting section; the relay station measures signal quality for the signal received from the base station, where the signal carries audio data, estimates the signal quality for the signal received at the base station based on the measured signal quality for the signal received from the base station, and sets the MCS parameter according to the signal quality, in the MCS parameter setting section; thus, the radio receiving section of the relay station is a probe arranged on a bidirectional traffic flow path of traffic flowing through the mobile communication network between the base station and the mobile station; further, the channel quality acquiring section of the relay station probes the data traffic on the downlink direction between the relay station and the base station; the downlink direction reads on a first direction, the relay station reads on a first node, the base station reads on a second node, and the first segment is between the first node and the second node); and
estimating, based on the probing, a user plane traffic quality of the user plane traffic flowing in the first segment in a second direction which is opposite to the first direction (FIGS. 1 and 6, para 2, 4-5, 7, 26, and 44-47; the relay station comprises the channel quality acquiring section that is connected to the radio receiving section and the MCS parameter setting section; the relay station measures signal quality for the signal received from the base station, where the signal carries audio data, estimates the signal quality for the signal received at the base station based on the measured signal quality for the signal received from the base station, and sets the MCS parameter according to the signal quality, in the MCS parameter setting section; thus, the relay station estimates, based on the measurement in the downlink direction, quality of the data traffic flowing in the uplink direction, where the uplink direction is opposite to the downlink direction).
However, Horiuchi ‘542 does not specifically disclose wherein the user plane traffic is not testable via the user plane probe in the second direction.
Kakishima ‘480 teaches wherein the first segment is not testable in the second direction via a said probing (para 11, 58, and 154; when the direction of the data signal beam directed to a user equipment (UE) is different from the direction of the cell-specific reference signal (CRS) beam, the UE cannot measure quality of data reception).
Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to combine Horiuchi ‘542’s apparatus for user plane traffic quality analysis, to include Kakishima ‘480’s UE that cannot measure quality of data reception. The motivation for doing so would have been to address the problem of UE not being able to estimate a direction of a beam suitable for the user equipment or link adaptive control, such as adaptive modulation coding (Kakishima ‘480, para 18).
8. Claims 3-5 are rejected under 35 U.S.C. 103 as being unpatentable over Horiuchi ‘542 in view of Kakishima ‘480, and further in view of Cai ‘302 (US 2008/0267302, “Cai ‘302”).
Regarding claim 3, Horiuchi ‘542 in combination with Kakishima ‘480 discloses all the limitations with respect to claim 1, as outlined above.
Further, Horiuchi ‘542 teaches wherein the estimation unit is configured to estimate an end-to-end user plane traffic quality of the user plane traffic flowing from the second terminal to the first terminal based on (i) the estimated user plane traffic quality of the user plane traffic flowing in the second direction in the first segment from the user plane probe to the first terminal (FIGS. 1 and 6, para 2, 4-5, 7, 21-26, and 44-47; the relay station relays data signals for uplink and downlink communication between the base station and the mobile station, where the data includes audio data; the relay station comprises the channel quality acquiring section that is connected to the radio receiving section and the MCS parameter setting section; the relay station measures signal quality for the signal received from the base station, where the signal carries audio data, estimates the signal quality for the signal received at the base station based on the measured signal quality for the signal received from the base station, and sets the MCS parameter according to the signal quality, in the MCS parameter setting section; thus, the relay station estimates signal quality for the signal received at the base station from the relay station, where the signal carries audio data; therefore, the relay station estimates quality of traffic flowing from the mobile station to the base station, based on the estimated quality of traffic flowing in the downlink direction from the relay station to the base station; the uplink direction reads on a second direction, the base station reads on the first terminal, and the mobile station reads on the second terminal).
Although Horiuchi ‘542 in combination with Kakishima ‘480 discloses wherein the estimation unit is configured to estimate an end-to-end user plane traffic quality of the user plane traffic flowing from the second terminal to the first terminal based on (i) the estimated user plane traffic quality of the user plane traffic flowing in the second direction in the first segment from the user plane probe to the first terminal, Horiuchi ‘542 in combination with Kakishima ‘480 does not specifically disclose wherein the estimation unit is configured to estimate an end-to-end user plane traffic quality of the user plane traffic flowing from the second terminal to the first terminal based on (ii) a probing, by the user plane probe, of the user plane traffic flowing in a second segment of the user plane traffic flow path from the second terminal to the user plane probe.
Cai ‘302 teaches wherein the estimation unit is configured to estimate an end-to-end user plane traffic quality of the user plane traffic flowing from the second terminal to the first terminal based on (ii) a probing, by the user plane probe, of the user plane traffic flowing in a second segment of the user plane traffic flow path from the second terminal to the user plane probe (FIG. 1, para 2-3, 13-14, 19-20, and 31; eNB receives from a UE a channel quality indication (CQI) report as feedback to IP traffic the UE receives from the eNB; thus, the estimation unit that is the UE estimates IP traffic quality for the IP traffic flowing from the eNB to the UE, based on the probing of the received IP traffic by the probe that is the UE; the UE reads on the estimation unit, the first terminal, and the user plane probe; the eNB reads on the second terminal).
Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to add features to the combined apparatus of Horiuchi ‘542 and Kakishima ‘480, to include Cai ‘302’s UE that estimates IP traffic quality for the IP traffic flowing from the eNB to the UE. The motivation for doing so would have been to address the bursty nature of data traffic, where a UE could be idle for a significant portion of time during which CQI feedback is ongoing (Cai ‘302, para 4).
Regarding claim 4, Horiuchi ‘542 in combination with Kakishima ‘480 and Cai ‘302 discloses all the limitations with respect to claim 3, as outlined above.
Further, Horiuchi ‘542 teaches wherein the estimation unit is further configured to estimate an end-to-end user plane traffic quality of the user plane traffic flowing from the first terminal to the second terminal based on (i) an estimated user plane traffic quality of the user plane traffic flowing in the second segment from the user plane probe to the second terminal (FIGS. 1 and 6, para 2, 4-5, 7, 21-26, and 44-47; the relay station relays data signals for uplink and downlink communication between the base station and the mobile station, where the data includes audio data; the relay station comprises the channel quality acquiring section that is connected to the radio receiving section and the MCS parameter setting section; the relay station measures signal quality for the signal received from the base station, where the signal carries audio data, estimates the signal quality for the signal received at the base station based on the measured signal quality for the signal received from the base station, and sets the MCS parameter according to the signal quality, in the MCS parameter setting section; thus, the relay station estimates signal quality for the signal received at the base station from the relay station, where the signal carries audio data; therefore, quality of the traffic signal flowing from the base station to the mobile terminal is estimated based on the estimated quality of the traffic signal flowing from the relay station to the UE; the base station reads on a first terminal; the mobile station reads on a second terminal, and the second segment is between the user plane probe and the mobile station).
Furthermore, Cai ‘302 teaches wherein the estimation unit is further configured to estimate an end-to-end user plane traffic quality of the user plane traffic flowing from the first terminal to the second terminal based on (ii) a probing, by the user plane probe, of the user plane traffic flowing in the first segment of the user plane traffic flow path from the first terminal to the user plane probe (FIG. 1, para 2-3, 13-14, 19-20, and 31; eNB receives from a UE a CQI report as feedback to IP traffic the UE receives from the eNB; thus, UE estimates IP traffic quality for the IP traffic flowing from the eNB to the UE, based on the probing of the received IP traffic by the probe; the UE reads on the estimation unit, the first terminal, and the user plane probe; the eNB reads on the second terminal).
Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to add features to the combined apparatus of Horiuchi ‘542, Kakishima ‘480, and Cai ‘302, to further include Cai ‘302’s UE that estimates IP traffic quality for the IP traffic flowing from the eNB to the UE. The motivation for doing so would have been to address the bursty nature of data traffic, where a UE could be idle for a significant portion of time during which CQI feedback is ongoing (Cai ‘302, para 4).
Regarding claim 5, Horiuchi ‘542 in combination with Kakishima ‘480 discloses all the limitations with respect to claim 1, as outlined above.
However, Horiuchi ‘542 in combination with Kakishima ‘480 does not specifically disclose wherein the apparatus is configured to receive, from the user plane probe, a user plane traffic quality-related report indicative of the user plane traffic quality of the user plane traffic, and wherein the estimation unit is configured to perform the estimation of the user plane traffic quality of the user plane traffic based on the user plane traffic quality-related report.
Cai ‘302 teaches wherein the apparatus is configured to receive, from the user plane probe, a user plane traffic quality-related report indicative of the user plane traffic quality of the user plane traffic (FIG. 1, para 2-3, 13-14, 19-20, and 31; eNB receives from a UE a CQI report as feedback to IP traffic), and
wherein the estimation unit is configured to perform the estimation of the user plane traffic quality of the user plane traffic based on the user plane traffic quality-related report (FIG. 1, para 2-3, 13-14, 19-20, and 31; eNB receives from the UE the CQI report as feedback to IP traffic; eNB chooses a MCS based on the received CQI information in the CQI report; thus, eNB determines CQI based on the received CQI report).
Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to add features to the combined apparatus of Horiuchi ‘542 and Kakishima ‘480, to include Cai ‘302’s eNB that determines CQI based on the received CQI report. The motivation for doing so would have been to address the bursty nature of data traffic, where a UE could be idle for a significant portion of time during which CQI feedback is ongoing (Cai ‘302, para 4).
9. Claims 6, 8, and 15-17 are rejected under 35 U.S.C. 103 as being unpatentable over Horiuchi ‘542 in view of Kakishima ‘480, and further in view of Laiho ‘399 (US 2007/0004399, “Laiho ‘399”).
Regarding claim 6, Horiuchi ‘542 in combination with Kakishima ‘480 discloses all the limitations with respect to claim 1, as outlined above.
However, Horiuchi ‘542 in combination with Kakishima ‘480 does not specifically disclose wherein the estimation unit comprises a neural network, and wherein the estimation of the user plane traffic quality of the user plane traffic is based on an output from the neural network.
Laiho ‘399 teaches wherein the estimation unit comprises a neural network, and wherein the estimation of the user plane traffic quality of the user plane traffic is based on an output from the neural network (FIGS. 1-2 and 3a-b, para 9, 47-52, 57-58, 62-65; quality of data service in a communication network is assessed by collecting data from a number of information sources such as user groups, and processing the collected data in a neural network; the monitoring of the information sources is performed by the neural network; the collected quality of service (QoS) data is used for the neural network to predict other QoS information, to provide an augmented end-to-end performance picture for each data communication session, and to classify cells of a cellular network in terms of the performance; thus, the neural network output is the augmented end-to-end performance that includes estimated QoS information).
Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to add features to the combined apparatus of Horiuchi ‘542 and Kakishima ‘480, to include Laiho ‘399’s collected QoS data that is used for the neural network to predict other QoS information. The motivation for doing so would have been to provide a satisfactory technique for assessing QoS taking into account multiple applications and services provided by multiple virtual operators (Laiho ‘399, para 7).
Regarding claim 8, Horiuchi ‘542 in combination with Kakishima ‘480 and Laiho ‘399 discloses all the limitations with respect to claim 6, as outlined above.
Further, Laiho ‘399 teaches wherein the neural network is further configured to be trainable based on one or both of
(i) one or more user plane traffic quality-related reports relating to one or more dedicated terminals (FIGS. 1-2 and 3a-b, para 9, 47-52, 57-58, and 62-65; quality of data service in a communication network is assessed by collecting data from a number of information sources such as user groups, and processing the collected data in a neural network; the monitoring of the information sources is performed by the neural network; the collected QoS data is used for the neural network to predict other QoS information and an end-to-end performance picture for each data communication session, and to classify cells of a cellular network in terms of the performance; the neural network is trained using the collected data; thus, the neural network is trained using the collected QoS data; examiner notes the use of alternative language; for rejection purposes, only one of the alternative limitations must be disclosed by prior art) and
(ii) one or more cell quality parameters receivable by the neural network from the wireless communication network.
Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to add features to the combined apparatus of Horiuchi ‘542, Kakishima ‘480, and Laiho ‘399, to further include Laiho ‘399’s neural network that is trained using collected QoS data. The motivation for doing so would have been to provide a satisfactory technique for assessing QoS taking into account multiple applications and services provided by multiple virtual operators (Laiho ‘399, para 7).
Regarding claim 15, Horiuchi ‘542 in combination with Kakishima ‘480 and Laiho ‘399 discloses all the limitations with respect to claim 6, as outlined above.
Further, Laiho ‘399 teaches wherein the neural network is configured to be trained by user plane traffic-related metrics reported to the apparatus by a predetermined number of terminals extended with cell-related information provided by the wireless communication network (FIGS. 1-2 and 3a-b, para 9, 20-23, 47-52, 57-58, and 62-65; quality of data service in a communication network is assessed by collecting data from a number of information sources such as mobile terminals, including cell-related user-entered parameters, and processing the collected data in a neural network; the monitoring of the information sources is performed by the neural network; the collected QoS data is used for the neural network to predict other QoS information and an end-to-end performance picture for each data communication session, and to classify cells of a cellular network in terms of the performance; thus, the neural network is trained using user traffic-related metrics from a number of mobile terminals, extended with cell-related information).
Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to add features to the combined apparatus of Horiuchi ‘542, Kakishima ‘480, and Laiho ‘399, to further include Laiho ‘399’s neural network that is trained using user traffic-related metrics from a number of mobile terminals, extended with cell-related information. The motivation for doing so would have been to provide a satisfactory technique for assessing QoS taking into account multiple applications and services provided by multiple virtual operators (Laiho ‘399, para 7).
Regarding claim 16, Horiuchi ‘542 in combination with Kakishima ‘480 and Laiho ‘399 discloses all the limitations with respect to claim 15, as outlined above.
Further, Horiuchi ‘542 teaches wherein the cell-related information comprises one or more of
a relative number of active users in the cell,
downlink radio metrics (FIGS. 1 and 6, para 5, 26, 44-47, and 104; the relay station comprises a channel quality acquiring section that is connected to the radio receiving section and an MCS parameter setting section; the relay station measures signal quality for the signal received from the base station, where the signal carries audio data, estimates the signal quality for the signal received at the base station, and sets the MCS parameter according to the signal quality, in the MCS parameter setting section; channel quality is measured using receive power for the signal received from the base station; thus, channel quality that is related to the base station comprises downlink radio metrics that include received power; examiner notes the use of alternative language; for rejection purposes, only one of the alternative limitations must be disclosed by prior art),
reference signal received power, RSRP, reference signal received quality, RSRQ, and
hybrid automatic repeat request, HARQ.
Regarding claim 17, Horiuchi ‘542 in combination with Kakishima ‘480 discloses all the limitations with respect to claim 1, as outlined above.
However, Horiuchi ‘542 in combination with Kakishima ‘480 does not specifically disclose further comprising a machine learning unit coupled to the estimation unit, wherein the machine learning unit is configured to receive a user plane traffic quality-related report from the user plane probe via the interface and analyze the user plane traffic quality-related report, wherein the estimation, by the estimation unit, of the user plane traffic quality of the user plane traffic is based on the user plane traffic quality-related report analyzed by the machine learning unit.
Laiho ‘399 teaches further comprising a machine learning unit coupled to the estimation unit, wherein the machine learning unit is configured to receive a user plane traffic quality-related report from the user plane probe via the interface and analyze the user plane traffic quality-related report (FIGS. 1-2 and 3a-b, para 9, 20-23, 47-52, 57-58, 62-65, and 97; quality of data service in a communication network is assessed by collecting data from a number of information sources such as mobile terminals, including cell-related user-entered parameters, and processing the collected data in a neural network; the monitoring of the information sources is performed by the neural network; the collected QoS data is used for the neural network to predict other QoS information and an end-to-end performance picture for each data communication session, and to classify cells of a cellular network in terms of the performance; the neural network is trained to learn, using the collected data; the learning is achieved by using a neural network self-organizing map (SOM); thus, the neural network is a SOM machine learning unit that is the estimation unit for estimating QoS information based on the collected QoS data),
wherein the estimation, by the estimation unit, of the user plane traffic quality of the user plane traffic is based on the user plane traffic quality-related report analyzed by the machine learning unit (FIGS. 1-2 and 3a-b, para 9, 20-23, 47-52, 57-58, 62-65, and 97; quality of data service in a communication network is assessed by collecting data from a number of information sources such as mobile terminals, including cell-related user-entered parameters, and processing the collected data in a neural network; the monitoring of the information sources is performed by the neural network; the collected QoS data is used for the neural network to predict other QoS information and an end-to-end performance picture for each data communication session, and to classify cells of a cellular network in terms of the performance; the neural network is trained to learn, using the collected data; the learning is achieved by using a neural network SOM; thus, the neural network is a SOM machine learning unit that is the estimation unit for estimating QoS information based on the collected QoS data analyzed by the SOM machine learning unit).
Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to add features to the combined apparatus of Horiuchi ‘542 and Kakishima ‘480, to include Laiho ‘399’s neural network that is a SOM machine learning unit that is the estimation unit for estimating QoS information based on the collected QoS data analyzed by the SOM machine learning unit. The motivation for doing so would have been to provide a satisfactory technique for assessing QoS taking into account multiple applications and services provided by multiple virtual operators (Laiho ‘399, para 7).
10. Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Horiuchi ‘542 in view of Kakishima ‘480, further in view of Cai ‘302, and further in view of Laiho ‘399.
Regarding claim 7, Horiuchi ‘542 in combination with Kakishima ‘480 and Cai ‘302 discloses all the limitations with respect to claim 5, as outlined above.
However, Horiuchi ‘542 in combination with Kakishima ‘480 and Cai ‘302 does not specifically disclose wherein the neural network is configured to receive the user plane traffic quality-related report from the user plane probe via the interface, and to output, based on the received user plane traffic quality-related report, the output for the estimation of the user plane traffic quality of the user plane traffic.
Laiho ‘399 teaches wherein the neural network is configured to receive the user plane traffic quality-related report from the user plane probe via the interface, and to output, based on the received user plane traffic quality-related report, the output for the estimation of the user plane traffic quality of the user plane traffic (FIGS. 1-2 and 3a-b, para 9, 20-23, 47-52, 57-58, 62-65, and 97; quality of data service in a communication network is assessed by collecting data from a number of information sources such as mobile terminals, including cell-related user-entered parameters, and processing the collected data in a neural network; the monitoring of the information sources is performed by the neural network; the collected QoS data is used for the neural network to predict other QoS information and an end-to-end performance picture for each data communication session, and to classify cells of a cellular network in terms of the performance; the neural network is trained to learn, using the collected data; the learning is achieved by using a neural network self-organizing map (SOM); thus, the neural network is the estimation unit for estimating QoS information based on the collected QoS data).
Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to add features to the combined apparatus of Horiuchi ‘542, Kakishima ‘480, and Cai ‘302, to include Laiho ‘399’s neural network that is the estimation unit for estimating QoS information based on the collected QoS data analyzed by the SOM machine learning unit. The motivation for doing so would have been to provide a satisfactory technique for assessing QoS taking into account multiple applications and services provided by multiple virtual operators (Laiho ‘399, para 7).
11. Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Horiuchi ‘542 in view of Kakishima ‘480, further in view of Laiho ‘399, and further in view of Kalderen ‘606 (US 2019/0166606, “Kalderen ‘606”).
Regarding claim 9, Horiuchi ‘542 in combination with Kakishima ‘480 and Laiho ‘399 discloses all the limitations with respect to claim 8, as outlined above.
However, Horiuchi ‘542 in combination with Kakishima ‘480 and Laiho ‘399 does not specifically disclose the neural network is trainable by continuously updating weights of recurrent neural network connections based on data received from the one or more dedicated terminals.
Kalderen ‘606 teaches the neural network is trainable by continuously updating weights of recurrent neural network connections based on data received from the one or more dedicated terminals (FIGS. 4 and 9, para 14, 24, 46, 56, and 63; input features are key performance indicators (KPIs) that are received from multiple devices; a training algorithm of a recurrent neural network is used to learn to predict behavior of most influential features, by updating weights in the recurrent neural network, based on 30+ hours of past values for the input features).
Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to add features to the combined apparatus of Horiuchi ‘542, Kakishima ‘480, and Laiho ‘399, to include Kalderen ‘606’s updating weights in the recurrent neural network. The motivation for doing so would have been to address underutilization of available channel capacity (Kalderen ‘606, para 13).
12. Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over Horiuchi ‘542 in view of Kakishima ‘480, and further in view of Yan ‘851 (US 2015/0009851, “Yan ‘851”).
Regarding claim 18, Horiuchi ‘542 in combination with Kakishima ‘480 discloses all the limitations with respect to claim 1, as outlined above.
However, Horiuchi ‘542 in combination with Kakishima ‘480 does not specifically disclose wherein the estimation unit is configured to perform the estimation of the user plane traffic quality of the user plane traffic in real-time.
Yan ‘851 teaches wherein the estimation unit is configured to perform the estimation of the user plane traffic quality of the user plane traffic in real-time (para 40-41; base station acquires from a UE channel quality information in real time, where the channel quality information is based on the downlink data signal).
Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to add features to the combined apparatus of Horiuchi ‘542 and Kakishima ‘480, to include Yan ‘851’s base station that acquires from a UE channel quality information in real time, where the channel quality information is based on the downlink data signal. The motivation for doing so would have been to address a great decrease of system performance due to a linear precoding solution providing a gain that is too small to meet a transmission requirement of the system (Yan ‘851, para 6).
13. Claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over Horiuchi ‘542 in view of Kakishima ‘480, and further in view of He ‘988 (US 2019/0086988, “He ‘988”).
Regarding claim 20, Horiuchi ‘542 in combination with Kakishima ‘480 discloses all the limitations with respect to claim 1, as outlined above.
However, Horiuchi ‘542 in combination with Kakishima ‘480 does not specifically disclose wherein probing the user plane traffic flowing in the first direction in the first segment by the user plane probe comprises deriving one or more of RTP-based jitter, packet loss metrics.
He ‘988 teaches wherein probing the user plane traffic flowing in the first direction in the first segment by the user plane probe comprises deriving one or more of
RTP-based jitter,
packet loss metrics (FIGS. 5 and 6A, para 75-76, 79, and 81; machine learning system interfaces with a UE device associated with a user that gave permission to provide user data to the machine learning system to perform machine learning calculations; the machine learning system stores UE device status information in a device status database (DB); the DB includes a network connection field that stores information identifying a measure of connection quality for the UE device, including a packet loss rate value; examiner notes the use of alternative language; for rejection purposes, only one of the alternative limitations must be disclosed by prior art) and
burst ratio.
Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to add features to the combined apparatus of Horiuchi ‘542 and Kakishima ‘480, to include He ‘988’s DB that includes a network connection field that stores information identifying a measure of connection quality for the UE device, including a packet loss rate value. The motivation for doing so would have been to address a problem of machine learning processes requiring complex computations that drain resources of a wireless communication device (He ‘988, para 1).
Allowable Subject Matter
14. Claims 10-14 are objected to as being dependent upon rejected base claims, but would be allowable if rewritten in independent form including all of the limitations of the base claims and any intervening claims.
Conclusion
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/NEVENA ZECEVIC SANDHU/Examiner, Art Unit 2474
/Michael Thier/Supervisory Patent Examiner, Art Unit 2474
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