DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 01/12/2026 has been entered.
Response to Arguments
Applicant’s arguments with respect to claims 1, 3-9, 11-16 and 18-21 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 3, 6, 9, 11, 14, 16, 18 and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Winslow et al. (US 20210136601, hereinafter “Winslow”) and further in view of Annavajjala (US 20150312008, hereinafter “Annav”).
Regarding claim 1, Winslow discloses,
A method of adjusting a user equipment (UE) ( FIG. 2 depicts an example system 200 for performing adaptive antenna tuning in a wireless electronic device) configuration based on physical factors at the UE (The different modes of usage or “use cases” of such devices poses another challenge because different use cases affect the performance of the wireless data transmission system(s) differently. For example, holding a wireless electronic device, such as a smartphone or tablet computer, in a left hand versus a right hand, or with both hands, may change the wireless data transmission performance because the different hand placements affect different antennas differently, [0022]-[0023]), the method comprising:
receiving, by a neural network circuit of the UE, one or more inputs related to a physical interaction with the UE (FIG. 4 depicts an example of a use case determination model 400 architecture. Use case determination model 400 may be an example of the use case determination model 214 in FIG. 2 or 304 in FIG. 3. Use case determination model 400 includes two classifiers 404 and 406, which may be referred to as first level or first stage and second level or second stage classifiers, [0068]-[0076]), the one or more inputs comprising:
a first input value associated with a reflection coefficient of the UE; a second input value associated with a frequency band for transmitting or receiving a signal with the UE and a correlation coefficient correlating two frequency bands for transmitting or receiving the signal with the UE (Adaptive antenna tuning component 216 may receive a determined use case for the wireless electronic device from use case determination model 214 and retrieve associated use case settings from use case setting database 218 in order to determine one or more antenna tuning parameters, such as aperture and/or impedance tuning parameters. Adaptive antenna tuning component 216 is further configured to provide the tuning parameters to impedance tuner 232 and aperture tuner 234 in radio frequency front end 230 in order to improve the performance of antenna 250, Adaptive antenna tuning component 216 may receive a determined use case for the wireless electronic device from use case determination model 214 and retrieve associated use case settings from use case setting database 218 in order to determine one or more antenna tuning parameters, such as aperture and/or impedance tuning parameters. Adaptive antenna tuning component 216 is further configured to provide the tuning parameters to impedance tuner 232 and aperture tuner 234 in radio frequency front end 230 in order to improve the performance of antenna 250, [0047]-[0053]); and a third input value associated with a carrier frequency for transmitting or receiving the signal with the UE (wireless transceiver 220 may include a feedback receiver (FBRx), which is a circuit that compares a measurement of a transmitted signal at different points along the transmission chain. In such aspects, a voltage standing wave ratio (VSWR) may be determined, which provides a measurement of complex impedance of the transmit signal. Then, an aspect of modem 210, such as adaptive antenna tuning 216, may take the complex impedance and translates it to impedance at the antenna. As above, this antenna impedance may be used to determine how the antenna is affected by different device use cases. This process is similar to using a network analyzer to measure impedance and/or return loss of the antenna, [0045]-[0062]);
outputting, by the neural network circuit, one or more output values associated with detecting a use case of the physical interaction or associated with adjusting at least one of an antenna impedance and an antenna aperture of the UE (When use case determination model 214 determines a new use case at step 506, i.e., one different than the current use case of the wireless electronic device, it then forwards the determined use case to an adaptive antenna tuner at step 508, such as adaptive antenna tuning component 216 in FIG. 2 and Fig. 5 and [0081]-[0087]); and
adjusting at least one of an impedance tuner circuit and an aperture tuner circuit of the UE based on the one or more output values (Adaptive antenna tuning component 216 then requests use case-specific antenna tuning settings based on the determined use case from a use case setting database (e.g., a look-up table or relational database) at step 510. The use case setting database 218 returns the use case-specific antenna settings at step 512. The use case-specific antenna settings may comprise, for example, impedance tuner settings and/or aperture tuner settings, such as for impedance tuner 232 and aperture tuner 234 of FIG. 2 and Fig. 5 and [0081]-[0087]).
However, Winslow does not disclose, a second input value comprising a correlation coefficient representing a strength of a relationship between two frequency for transmitting or receiving signal with the UE.
In the same field of endeavor, Annav discloses, a second input value comprising a correlation coefficient representing a strength of a relationship between two frequency for transmitting or receiving signal with the UE ( A channel quality for an out-of-band channel can be predicted based on the received data and a cross-correlation between an in-band channel and one or more out-of-band channels… the cross-correlation can be pre-computed using a predetermined channel model. The cross-correlation can be computed based on prior knowledge of a type of channel model associated with the out-of-band channel. The out-of-band channel can be within a usable transmission bandwidth, [0005]-[0006]).
Therefore, it would have obvious to one of ordinary skill in art before the effective filing date of the claimed invention to modify Winslow by specifically providing a second input value comprising a correlation coefficient representing a strength of a relationship between two frequency for transmitting or receiving signal with the UE, as taught by Annay for the purpose of reducing overhead significantly, and thereby improving the uplink spectral efficiency [0066].
Regarding claim 3, the combination of Winslow and Annav discloses everything claimed as applied above (see claim 1), further Winslow discloses,
wherein the neural network circuit comprises a regression neural network configured to output a reflection coefficient corresponding to a target tuner code, based on one or more input values comprising a bypass reflection coefficient and a tuner code (Based on the inputs 402, first level classifier 404 may output one of a plurality of determinations 406. For example, first level classifier 404 may determine that a wireless electronic device should keep its current use case, which means that the aperture tuner settings and impedance tuner settings may be left in their current states and the second level classifier may be bypassed, [0069]-[0074]).
Regarding claim 6, the combination of Winslow and Annav discloses everything claimed as applied above (see claim 1), further Winslow discloses,
wherein the outputting of the one or more output values comprises outputting a tuner code based on a voltage standing wave ratio (VSWR) (measurement component 212 may receive measurement data from wireless transceiver 220. For example, in some aspects, wireless transceiver 220 may include a feedback receiver (FBRx), which is a circuit that compares a measurement of a transmitted signal at different points along the transmission chain. In such aspects, a voltage standing wave ratio (VSWR) may be determined, which provides a measurement of complex impedance of the transmit signal, [0046]-[0051]).
Regarding claim 9, Winslow discloses,
A user equipment (UE) ( FIG. 2 depicts an example system 200 for performing adaptive antenna tuning in a wireless electronic device) for adjusting a configuration of the UE based on physical factors at the UE (The different modes of usage or “use cases” of such devices poses another challenge because different use cases affect the performance of the wireless data transmission system(s) differently. For example, holding a wireless electronic device, such as a smartphone or tablet computer, in a left hand versus a right hand, or with both hands, may change the wireless data transmission performance because the different hand placements affect different antennas differently, [0022]-[0023]), the UE comprising:
an antenna having an antenna impedance and an antenna aperture (see, elements 230 and 25);
a tuner circuit configured to adjust at least one of the antenna impedance and the antenna aperture (Adaptive antenna tuning component 216 may receive a determined use case for the wireless electronic device from use case determination model 214 and retrieve associated use case settings from use case setting database 218 in order to determine one or more antenna tuning parameters, such as aperture and/or impedance tuning parameters. Adaptive antenna tuning component 216 is further configured to provide the tuning parameters to impedance tuner 232 and aperture tuner 234 in radio frequency front end 230 in order to improve the performance of antenna 250, [0048]-[0050]); and a neural network circuit (see, Fig. 2; 210 and Fig. 3) configured to:
receive one or more inputs related to a physical interaction with the UE (FIG. 4 depicts an example of a use case determination model 400 architecture. Use case determination model 400 may be an example of the use case determination model 214 in FIG. 2 or 304 in FIG. 3. Use case determination model 400 includes two classifiers 404 and 406, which may be referred to as first level or first stage and second level or second stage classifiers, [0068]-[0076]), the one or more inputs comprising at least one of:
a first input value associated with a reflection coefficient of the UE; a second input value associated with a frequency band for transmitting or receiving a signal with the UE and comprising a correlation coefficient correlating two frequency bands for transmitting or receiving the signal with the UE (Adaptive antenna tuning component 216 may receive a determined use case for the wireless electronic device from use case determination model 214 and retrieve associated use case settings from use case setting database 218 in order to determine one or more antenna tuning parameters, such as aperture and/or impedance tuning parameters. Adaptive antenna tuning component 216 is further configured to provide the tuning parameters to impedance tuner 232 and aperture tuner 234 in radio frequency front end 230 in order to improve the performance of antenna 250, Adaptive antenna tuning component 216 may receive a determined use case for the wireless electronic device from use case determination model 214 and retrieve associated use case settings from use case setting database 218 in order to determine one or more antenna tuning parameters, such as aperture and/or impedance tuning parameters. Adaptive antenna tuning component 216 is further configured to provide the tuning parameters to impedance tuner 232 and aperture tuner 234 in radio frequency front end 230 in order to improve the performance of antenna 250, [0047]-[0053]); and a third input value associated with a carrier frequency for transmitting or receiving the signal with the UE (wireless transceiver 220 may include a feedback receiver (FBRx), which is a circuit that compares a measurement of a transmitted signal at different points along the transmission chain. In such aspects, a voltage standing wave ratio (VSWR) may be determined, which provides a measurement of complex impedance of the transmit signal. Then, an aspect of modem 210, such as adaptive antenna tuning 216, may take the complex impedance and translates it to impedance at the antenna. As above, this antenna impedance may be used to determine how the antenna is affected by different device use cases. This process is similar to using a network analyzer to measure impedance and/or return loss of the antenna, [0045]-[0062]);
output one or more output values associated with detecting a use case of the physical interaction or associated with adjusting at least one of the antenna impedance and the antenna aperture (When use case determination model 214 determines a new use case at step 506, i.e., one different than the current use case of the wireless electronic device, it then forwards the determined use case to an adaptive antenna tuner at step 508, such as adaptive antenna tuning component 216 in FIG. 2 and Fig. 5 and [0081]-[0087]); and
transmit the one or more output values to the tuner circuit (Adaptive antenna tuning component 216 then requests use case-specific antenna tuning settings based on the determined use case from a use case setting database (e.g., a look-up table or relational database) at step 510. The use case setting database 218 returns the use case-specific antenna settings at step 512. The use case-specific antenna settings may comprise, for example, impedance tuner settings and/or aperture tuner settings, such as for impedance tuner 232 and aperture tuner 234 of FIG. 2 and Fig. 5 and [0081]-[0087]).
However, Winslow does not disclose, a second input value comprising a correlation coefficient representing a strength of a relationship between two frequency for transmitting or receiving signal with the UE.
In the same field of endeavor, Annav discloses, a second input value comprising a correlation coefficient representing a strength of a relationship between two frequency for transmitting or receiving signal with the UE ( A channel quality for an out-of-band channel can be predicted based on the received data and a cross-correlation between an in-band channel and one or more out-of-band channels… the cross-correlation can be pre-computed using a predetermined channel model. The cross-correlation can be computed based on prior knowledge of a type of channel model associated with the out-of-band channel. The out-of-band channel can be within a usable transmission bandwidth, [0005]-[0006]).
Therefore, it would have obvious to one of ordinary skill in art before the effective filing date of the claimed invention to modify Winslow by specifically providing a second input value comprising a correlation coefficient representing a strength of a relationship between two frequency for transmitting or receiving signal with the UE, as taught by Annay for the purpose of reducing overhead significantly, and thereby improving the uplink spectral efficiency [0066].
Regarding claim 11, the combination of Winslow and Annav discloses everything claimed as applied above (see claim 9), further Winslow discloses,
wherein the neural network circuit comprises a regression neural network configured to output a reflection coefficient corresponding to a target tuner code, based on one or more input values comprising a bypass reflection coefficient and a tuner code (Based on the inputs 402, first level classifier 404 may output one of a plurality of determinations 406. For example, first level classifier 404 may determine that a wireless electronic device should keep its current use case, which means that the aperture tuner settings and impedance tuner settings may be left in their current states and the second level classifier may be bypassed, [0069]-[0074]).
Regarding claim 14, the combination of Winslow and Annav discloses everything claimed as applied above (see claim 9), further Winslow discloses,
wherein the outputting of the one or more output values comprises outputting a tuner code based on a voltage standing wave ratio (VSWR) or based on a relative transducer gain (RTG) (measurement component 212 may receive measurement data from wireless transceiver 220. For example, in some aspects, wireless transceiver 220 may include a feedback receiver (FBRx), which is a circuit that compares a measurement of a transmitted signal at different points along the transmission chain. In such aspects, a voltage standing wave ratio (VSWR) may be determined, which provides a measurement of complex impedance of the transmit signal, [0046]-[0051]).
Regarding claim 16, Winslow discloses,
A system ( FIG. 2 depicts an example system 200 for performing adaptive antenna tuning in a wireless electronic device) for adjusting a configuration of a user equipment based on physical factors at the UE (The different modes of usage or “use cases” of such devices poses another challenge because different use cases affect the performance of the wireless data transmission system(s) differently. For example, holding a wireless electronic device, such as a smartphone or tablet computer, in a left hand versus a right hand, or with both hands, may change the wireless data transmission performance because the different hand placements affect different antennas differently, [0022]-[0023]), the system comprising:
the UE configured to be communicably coupled with a network node, the UE comprising an antenna (see, elements 230 and 25);
a tuner circuit configured to adjust at least one of an antenna impedance and an antenna aperture (Adaptive antenna tuning component 216 may receive a determined use case for the wireless electronic device from use case determination model 214 and retrieve associated use case settings from use case setting database 218 in order to determine one or more antenna tuning parameters, such as aperture and/or impedance tuning parameters. Adaptive antenna tuning component 216 is further configured to provide the tuning parameters to impedance tuner 232 and aperture tuner 234 in radio frequency front end 230 in order to improve the performance of antenna 250, [0048]-[0050]); and a neural network circuit (see, Fig. 2; 210 and Fig. 3), the neural network circuit being configured to:
receive one or more inputs related to a physical interaction with the UE (FIG. 4 depicts an example of a use case determination model 400 architecture. Use case determination model 400 may be an example of the use case determination model 214 in FIG. 2 or 304 in FIG. 3. Use case determination model 400 includes two classifiers 404 and 406, which may be referred to as first level or first stage and second level or second stage classifiers, [0068]-[0076]), the one or more inputs comprising at least one of:
a first input value associated with a reflection coefficient of the UE; a second input value associated with a frequency band for transmitting or receiving a signal with the UE and comprising a correlation coefficient correlating two frequency bands for transmitting or receiving the signal with the UE (Adaptive antenna tuning component 216 may receive a determined use case for the wireless electronic device from use case determination model 214 and retrieve associated use case settings from use case setting database 218 in order to determine one or more antenna tuning parameters, such as aperture and/or impedance tuning parameters. Adaptive antenna tuning component 216 is further configured to provide the tuning parameters to impedance tuner 232 and aperture tuner 234 in radio frequency front end 230 in order to improve the performance of antenna 250, Adaptive antenna tuning component 216 may receive a determined use case for the wireless electronic device from use case determination model 214 and retrieve associated use case settings from use case setting database 218 in order to determine one or more antenna tuning parameters, such as aperture and/or impedance tuning parameters. Adaptive antenna tuning component 216 is further configured to provide the tuning parameters to impedance tuner 232 and aperture tuner 234 in radio frequency front end 230 in order to improve the performance of antenna 250, [0047]-[0053]); and a third input value associated with a carrier frequency for transmitting or receiving the signal with the UE (wireless transceiver 220 may include a feedback receiver (FBRx), which is a circuit that compares a measurement of a transmitted signal at different points along the transmission chain. In such aspects, a voltage standing wave ratio (VSWR) may be determined, which provides a measurement of complex impedance of the transmit signal. Then, an aspect of modem 210, such as adaptive antenna tuning 216, may take the complex impedance and translates it to impedance at the antenna. As above, this antenna impedance may be used to determine how the antenna is affected by different device use cases. This process is similar to using a network analyzer to measure impedance and/or return loss of the antenna, [0045]-[0062]);
output one or more output values associated with detecting a use case of the physical interaction or associated with adjusting at least one of the antenna impedance and the antenna aperture (When use case determination model 214 determines a new use case at step 506, i.e., one different than the current use case of the wireless electronic device, it then forwards the determined use case to an adaptive antenna tuner at step 508, such as adaptive antenna tuning component 216 in FIG. 2 and Fig. 5 and [0081]-[0087]); and
transmit the one or more output values to the tuner circuit to adjust at least one of the antenna impedance and the antenna aperture, wherein the UE is configured to transmit a signal to the network node, by way of the antenna, based on at least one of an adjusted antenna impedance and an adjusted antenna aperture (Adaptive antenna tuning component 216 then requests use case-specific antenna tuning settings based on the determined use case from a use case setting database (e.g., a look-up table or relational database) at step 510. The use case setting database 218 returns the use case-specific antenna settings at step 512. The use case-specific antenna settings may comprise, for example, impedance tuner settings and/or aperture tuner settings, such as for impedance tuner 232 and aperture tuner 234 of FIG. 2 and Fig. 5 and [0081]-[0087]).
However, Winslow does not disclose, a second input value comprising a correlation coefficient representing a strength of a relationship between two frequency for transmitting or receiving signal with the UE.
In the same field of endeavor, Annav discloses, a second input value comprising a correlation coefficient representing a strength of a relationship between two frequency for transmitting or receiving signal with the UE ( A channel quality for an out-of-band channel can be predicted based on the received data and a cross-correlation between an in-band channel and one or more out-of-band channels… the cross-correlation can be pre-computed using a predetermined channel model. The cross-correlation can be computed based on prior knowledge of a type of channel model associated with the out-of-band channel. The out-of-band channel can be within a usable transmission bandwidth, [0005]-[0006]).
Therefore, it would have obvious to one of ordinary skill in art before the effective filing date of the claimed invention to modify Winslow by specifically providing a second input value comprising a correlation coefficient representing a strength of a relationship between two frequency for transmitting or receiving signal with the UE, as taught by Annay for the purpose of reducing overhead significantly, and thereby improving the uplink spectral efficiency [0066].
Regarding claim 18, the combination of Winslow and Annav discloses everything claimed as applied above (see claim 16), further Winslow discloses,
wherein the neural network circuit comprises a regression neural network configured to output a reflection coefficient corresponding to a target tuner code, based on one or more input values comprising a bypass reflection coefficient and a tuner code (Based on the inputs 402, first level classifier 404 may output one of a plurality of determinations 406. For example, first level classifier 404 may determine that a wireless electronic device should keep its current use case, which means that the aperture tuner settings and impedance tuner settings may be left in their current states and the second level classifier may be bypassed, [0069]-[0074]).
Regarding claim 21, the combination of Winslow and Annav discloses everything claimed as applied above (see claim 1), further Winslow discloses,
Wherein the parameter comprises a correlation coefficient (the cross-correlation can be pre-computed using a predetermined channel model. The cross-correlation can be computed based on prior knowledge of a type of channel model associated with the out-of-band channel. The out-of-band channel can be within a usable transmission bandwidth, [0005]-[0006]))
Claims 4, 5, 12, 13 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Winslow, in view of Annav and further in view of Calzolari et al. (US 20230061864, hereinafter “Calzo”).
Regarding claim 4, the combination of Winslow and Annav discloses everything claimed as applied above (see claim 1), however the combination of Winslow and Annav discloses everything claimed as applied above (see claim 3), however Winslow does not explicitly disclose, wherein the regression neural network is configured to serve as a model for a transfer function associated with an antenna model, a tuner model, and a radio frequency printed circuit board (RF PCB) model of the UE.
In the same field of endeavor, Calzo discloses, wherein the regression neural network is configured to serve as a model for a transfer function associated with an antenna model, a tuner model, and a radio frequency printed circuit board (RF PCB) model of the UE (agent 304 may implement its policy by way of a machine learning model (e.g., a “policy model” or “wireless data transmission system configuration model”), such as a neural network model, which takes one or more inputs 302 (e.g., operating characteristics of a wireless communication system in a device) and outputs a policy decision (e.g., a target wireless data transmission system configuration), such as an impedance tuner setting 303 (“IT Setting” in FIG. 3) and/or an aperture tuner setting 305 (“AT Setting” in FIG. 3), [0053]-[0060]).
Therefore, it would have been obvious to one of ordinary skill in art before the effective filing date of the claimed invention to modify the combination of Winslow and Annav by specifically providing wherein the regression neural network is configured to serve as a model for a transfer function associated with an antenna model, a tuner model, and a radio frequency printed circuit board (RF PCB) model of the UE, as taught by Calzo for the purpose of providing a technique for dynamically adapting antenna tuning to improve wireless device performance [0003].
Regarding claim 5, the combination of Winslow, Annav and Calzo discloses everything claimed as applied above (see claim 4), further Calzo discloses, wherein the regression neural network is configured to serve as a model for only the tuner model of the UE (agent 304 may implement its policy by way of a machine learning model (e.g., a “policy model” or “wireless data transmission system configuration model”), such as a neural network model, which takes one or more inputs 302 (e.g., operating characteristics of a wireless communication system in a device) and outputs a policy decision (e.g., a target wireless data transmission system configuration), such as an impedance tuner setting 303 (“IT Setting” in FIG. 3) and/or an aperture tuner setting 305 (“AT Setting” in FIG. 3), [0053]-[0060]).
Regarding claim 12, the combination of Winslow and Annav discloses everything claimed as applied above (see claim 3), however the combination of Winslow and Annav does not explicitly disclose, wherein the regression neural network is configured to serve as a model for a transfer function associated with an antenna model, a tuner model, and a radio frequency printed circuit board (RF PCB) model of the UE.
In the same field of endeavor, Calzo discloses, wherein the regression neural network is configured to serve as a model for a transfer function associated with an antenna model, a tuner model, and a radio frequency printed circuit board (RF PCB) model of the UE (agent 304 may implement its policy by way of a machine learning model (e.g., a “policy model” or “wireless data transmission system configuration model”), such as a neural network model, which takes one or more inputs 302 (e.g., operating characteristics of a wireless communication system in a device) and outputs a policy decision (e.g., a target wireless data transmission system configuration), such as an impedance tuner setting 303 (“IT Setting” in FIG. 3) and/or an aperture tuner setting 305 (“AT Setting” in FIG. 3), [0053]-[0060]).
Therefore, it would have been obvious to one of ordinary skill in art before the effective filing date of the claimed invention to modify the combination of Winslow and Annav by specifically providing wherein the regression neural network is configured to serve as a model for a transfer function associated with an antenna model, a tuner model, and a radio frequency printed circuit board (RF PCB) model of the UE, as taught by Calzo for the purpose of providing a technique for dynamically adapting antenna tuning to improve wireless device performance [0003].
Regarding claim 13, the combination of Winslow, Annav and Calzo discloses everything claimed as applied above (see claim 12), further Calzo discloses, wherein the regression neural network is configured to serve as a model for only the tuner model of the UE (agent 304 may implement its policy by way of a machine learning model (e.g., a “policy model” or “wireless data transmission system configuration model”), such as a neural network model, which takes one or more inputs 302 (e.g., operating characteristics of a wireless communication system in a device) and outputs a policy decision (e.g., a target wireless data transmission system configuration), such as an impedance tuner setting 303 (“IT Setting” in FIG. 3) and/or an aperture tuner setting 305 (“AT Setting” in FIG. 3), [0053]-[0060]).
Regarding claim 19, the combination of Winslow and Annav discloses everything claimed as applied above (see claim 18), however the combination of Winslow and Annav does not explicitly disclose, wherein the regression neural network is configured to serve as a model for a transfer function associated with an antenna model, a tuner model, and a radio frequency printed circuit board (RF PCB) model of the UE.
In the same field of endeavor, Calzo discloses, wherein the regression neural network is configured to serve as a model for a transfer function associated with an antenna model, a tuner model, and a radio frequency printed circuit board (RF PCB) model of the UE (agent 304 may implement its policy by way of a machine learning model (e.g., a “policy model” or “wireless data transmission system configuration model”), such as a neural network model, which takes one or more inputs 302 (e.g., operating characteristics of a wireless communication system in a device) and outputs a policy decision (e.g., a target wireless data transmission system configuration), such as an impedance tuner setting 303 (“IT Setting” in FIG. 3) and/or an aperture tuner setting 305 (“AT Setting” in FIG. 3), [0053]-[0060]).
Therefore, it would have been obvious to one of ordinary skill in art before the effective filing date of the claimed invention to modify the combination of Winslow and Annav by specifically providing wherein the regression neural network is configured to serve as a model for a transfer function associated with an antenna model, a tuner model, and a radio frequency printed circuit board (RF PCB) model of the UE, as taught by Calzo for the purpose of providing a technique for dynamically adapting antenna tuning to improve wireless device performance [0003].
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Winslow, in view of Annav and further in view of Solomko et al. (US 20200088773, hereinafter “Solomko”).
Regarding claim 7, the combination of Winslow and Annav discloses everything claimed as applied above (see claim 1), however the combination of Winslow and Annav does not explicitly disclose, wherein the outputting of the one or more output values comprises outputting a tuner code based on a relative transducer gain (RTG).
In the same field of endeavor, Solomko discloses, wherein the outputting of the one or more output values comprises outputting a tuner code based on a relative transducer gain (RTG) (the look-up table 114 further includes a column of tuner states, e.g., as shown in Table 1. Each tuner state yields a maximum relative transducer gain for the reflection coefficient Γ.sub.L′ of the load port 110 of the impedance tuning network 102 associated with the tuner state. The controller 108 may set a tuner state of the RF system 400 based on the tuner state stored in the lookup table 114 and associated with the reflection coefficient Γ.sub.L′ or scalar value |Γ.sub.L′| of the load port 110 identified from the lookup table 114 by the controller 108, [0053]).
Therefore, it would have been obvious to one of ordinary skill in art before the effective filing date of the claimed invention to modify the combination of Winslow and Annav by specifically providing wherein the outputting of the one or more output values comprises outputting a tuner code based on a relative transducer gain (RTG), as taught by Solomko for the purpose of providing an improved RF impedance measurement and tuning system [0003].
Claims 8, 15 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Winslow, in view of Annav and further in view of Barbu et al. (US 20240397468, hereinafter “Barbu”).
Regarding claim 8, the combination of Winslow and Annav discloses everything claimed as applied above (see claim 1), however the combination of Winslow and Annav does not explicitly disclose, wherein the outputting of the one or more output values comprises determining a use case based on a cascade system comprising a regression neural network having an output coupled to an input of a classification neural network.
In a similar field of endeavor, Barbu discloses, wherein the outputting of the one or more output values comprises determining a use case based on a cascade system comprising a regression neural network having an output coupled to an input of a classification neural network (As the joint architecture comprises three neural networks implemented in a cascade fashion, the forward propagation phase is performed in a joint way such that the training input data is fed into the compression neural network and the estimated values to be compared to the expected values associated with the training input values are obtained as the output of the distance correction neural network, [0151]-[0153]).
Therefore, it would have been obvious to one of ordinary skill in art before the effective filing date of the claimed invention to modify the combination of Winslow and Annav by specifically providing wherein the outputting of the one or more output values comprises determining a use case based on a cascade system comprising a regression neural network having an output coupled to an input of a classification neural network, as taught by Barbu for the purpose of providing enhanced positioning techniques for sending positioning information while meeting positioning accuracy and latency requirements [0008].
Regarding claim 15, the combination of Winslow and Annav discloses everything claimed as applied above (see claim 9), however the combination of Winslow and Annav does not explicitly disclose, wherein the neural network circuit is configured to output the one or more output values based on determining a use case based on a cascade system comprising a regression neural network having an output coupled to an input of a classification neural network.
In a similar field of endeavor, Barbu discloses, wherein the neural network circuit is configured to output the one or more output values based on determining a use case based on a cascade system comprising a regression neural network having an output coupled to an input of a classification neural network (As the joint architecture comprises three neural networks implemented in a cascade fashion, the forward propagation phase is performed in a joint way such that the training input data is fed into the compression neural network and the estimated values to be compared to the expected values associated with the training input values are obtained as the output of the distance correction neural network, [0151]-[0153]).
Therefore, it would have been obvious to one of ordinary skill in art before the effective filing date of the claimed invention to modify the combination of Winslow and Annav by specifically providing wherein the neural network circuit is configured to output the one or more output values based on determining a use case based on a cascade system comprising a regression neural network having an output coupled to an input of a classification neural network, as taught by Barbu for the purpose of providing enhanced positioning techniques for sending positioning information while meeting positioning accuracy and latency requirements [0008].
Regarding claim 20, the combination of Winslow and Annav discloses everything claimed as applied above (see claim 16), however the combination of Winslow and Annav does not explicitly disclose, wherein the outputting of the one or more output values comprises determining a use case based on a cascade system comprising a regression neural network having an output coupled to an input of a classification neural network.
In a similar field of endeavor, Barbu discloses, wherein the outputting of the one or more output values comprises determining a use case based on a cascade system comprising a regression neural network having an output coupled to an input of a classification neural network (As the joint architecture comprises three neural networks implemented in a cascade fashion, the forward propagation phase is performed in a joint way such that the training input data is fed into the compression neural network and the estimated values to be compared to the expected values associated with the training input values are obtained as the output of the distance correction neural network, [0151]-[0153]).
Therefore, it would have been obvious to one of ordinary skill in art before the effective filing date of the claimed invention to modify Winslow by specifically providing wherein the outputting of the one or more output values comprises determining a use case based on a cascade system comprising a regression neural network having an output coupled to an input of a classification neural network, as taught by Barbu for the purpose of providing enhanced positioning techniques for sending positioning information while meeting positioning accuracy and latency requirements [0008].
Prior Art of the Record:
The prior art made of record not relied upon and considered pertinent to
Applicant’s disclosure:
US 20240097352: A modular, radio frequency (“RF”) system includes one or more directional antennas and is configured with both hardware and software components to enable the RF system to monitor (e.g., detect or track signals or objects) and/or interact with (e.g., track signals or objects, or transmit signals) objects in particular directions. The RF system includes one or more machine learning models to determine, based on received signals, one or more signals to transmit.
US 20230110141: The present disclosure generally relates to transceiving data streams via an antenna in an information handling system. The present disclosure more specifically relates to tuning and correcting the operation of an antenna based on, in an open loop fashion, band aggregation and loading using radio and system telemetry of an information handling system.
Conclusion
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/GOLAM SOROWAR/Primary Examiner, Art Unit 2641