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 .
Response to Arguments
Applicant's arguments filed 05/21/2026 have been fully considered but they are not persuasive because of the following reason:
Applicant argues that the combination of Winslow and Annavajjala does not teach or suggest “receiving, by a neural network circuit of the UE, one or more inputs related to a physical interaction with the UE.” Applicant also argues that the references do not teach the claimed “parameter indicating a strength of a relationship between two frequency bands for transmitting or receiving the signal with the UE” (see pages 9-11).
These arguments are not persuasive because the rejection is based on the combined teachings of Winslow and Annavajjala. Winslow is relied upon for the neural network/use case/physical interaction features. Annavajjala is relied upon for the correlation/correlation coefficient feature, which corresponds to the claimed parameter indicating a strength of a relationship between two frequency bands.
Winslow teaches determining use cases associated with how a wireless electronic device is being used. Winslow explains that different physical uses of the device, such as holding the device in a left hand, holding the device in a right hand, holding the device with both hands, placing the device on a surface, placing the device in a pocket, car, or plane, or using the device while plugged in, can affect wireless transmission performance. See Winslow, paragraph [0023]. These are physical interactions with the wireless electronic device.
Winslow further teaches using trained machine learning models, including neural network models, to determine use cases associated with how the device is being used based on measurement data. See Winslow, paragraph [0041]. Winslow also teaches that measurement component 212 may receive impedance and frequency measurements. See Winslow, paragraph [0046]. Winslow further teaches receiving measurement data from a wireless transceiver, including feedback receiver measurements used to determine VSWR and complex impedance, and explains that the antenna impedance may be used to determine how the antenna is affected by different device use cases. See Winslow, paragraph [0047].
Winslow also teaches that use case determination model 214 receives measurements through measurement component 212 and determines a use case for the wireless electronic device. See Winslow, paragraph [0048]. Winslow further teaches that adaptive antenna tuning component 216 receives the determined use case and determines antenna tuning parameters, such as aperture and/or impedance tuning parameters, for impedance tuner 232 and aperture tuner 234. See Winslow, paragraph [0049].
Winslow also teaches that the use case determination model may be a neural network model. See Winslow, paragraph [0059]. Winslow's example model 400 is described in paragraphs [0068]-[0076]. In particular, Winslow teaches that first level classifier 404 may be a trained machine learning model, such as a neural network model, that takes as inputs one or more of: a real impedance measurement, an imaginary- impedance measurement, a frequency, an aperture tuner state, and an impedance tuner state. Winslow also states that the model may take additional inputs that are not shown. See Winslow, paragraph [0069]. Winslow further teaches that the model may determine whether to keep the current use case, whether the device is in transition, or whether a new use case should be determined. See Winslow, paragraphs [0070]-[0074]. Winslow also teaches that different inputs or mixes of inputs may be used. See Winslow, paragraph [0075].
Thus, Winslow teaches a neural network model in the wireless electronic device that receives inputs related to physical interaction with the device and uses those inputs to determine a use case and support antenna tuning. Under the broadest reasonable interpretation, this teaches the claimed feature of receiving, by a neural network circuit of the UE, one or more inputs related to a physical interaction with the UE.
Applicant's argument that Winslow does not teach channel estimation or channel quality prediction (see page 10) does not overcome the rejection. Examiner respectfully submits that Winslow is not being relied upon for Annavajjala's channel quality prediction. Winslow is relied upon for the neural network/use case/physical interaction features. Annavajjala is relied upon for the known frequency relationship parameter, namely a cross-correlation/correlation coefficient between frequencies or channels.
Annavajjala teaches predicting channel quality for an out-of-band channel based on received data and a cross-correlation between an in-band channel and one or more out-of-band channels. See Annavajjala, paragraph [0005]. Annavajjala further teaches that the cross-correlation can be characterized by stored correlation coefficients, with each correlation coefficient corresponding to an average of two or more subcarrier channel estimates. See Annavajjala, paragraph [0006]. A correlation coefficient is a parameter that indicates the strength of a relationship between the channels or frequencies being compared.
Annavajjala further supports this teaching in paragraphs [0043]-[0045]. Annavajjala teaches computing the cross-correlation between the channel on a desired tone and a frequency-domain channel estimate, and explains that the cross-correlation can be based on received in-band pilot pulses and correlation coefficients characterizing out-of-band frequencies, which are different frequencies from the received pilot pulses. See Annavajjala, paragraph [0043]. Annavajjala also teaches that the correlation of frequency-domain channel estimates across two distinct tones is a function of the frequency separation between the tones. See Annavajjala, paragraph [0043]. Annavajjala further teaches that if the channel model is known, the correlation coefficients can be pre-computed, and gives examples of frequency-domain channel correlation between tones separated by one resource block. See Annavajjala, paragraph [0045].
Therefore, Annavajjala teaches a parameter, such as a correlation coefficient, that indicates a relationship between two different frequencies, channels, or frequency bands. This corresponds to the claimed parameter indicating a strength of a relationship between two frequency bands for transmitting or receiving the signal with the UE.
Applicant also argues that Annavajjala does not discuss neural networks or use case determination (see page 10). This argument is not persuasive because Annavajjala is not relied upon for the neural network or use case determination features. Those features are taught by Winslow. Annavajjala is relied upon for the correlation coefficient/frequency relationship feature. A prior art reference does not need to teach every claimed feature by itself when the rejection is based on a combination of references.
Applicant further argues that the Office action relies on hindsight (see page 10). This argument is not persuasive. The reason for the combination comes from the references themselves. Winslow already teaches a neural network model that receives frequency, impedance, and tuner-state inputs to determine use case and support antenna tuning. Winslow also expressly states that additional inputs may be used. See Winslow, paragraphs [0069] and [0075]. Annavajjala teaches a known frequency relationship parameter, namely a cross-correlation/correlation coefficient between in-band and out-of-band frequencies or channels. See Annavajjala, paragraphs [0005]-[0006] and [0043]-[0045].
One of ordinary skill in the art would have had reason to use Annavajjala's correlation coefficient as an additional input in Winslow's neural network model because Winslow's model already uses frequency-related and antenna-related measurements to determine use case and antenna tuning. Adding a known parameter that describes the relationship between two frequencies or channels would provide the model with more frequency relationship information for making the use case and tuning determination. This is consistent with Winslow's express teaching that additional inputs and different mixes of inputs may be used.
Applicant also argues that Annavajjala's paragraph [0066] relates to CQI feedback, channel estimation, and channel quality metrics (see page 10). The Examiner agrees that Annavajjala discusses channel quality. However, this does not make Annavajjala irrelevant. The rejection does not require Winslow's neural network to perform Annavajjala's full CQI process. Instead, the rejection uses Annavajjala for its teaching that a cross-correlation/correlation coefficient can represent a relationship between different frequencies or channels. That known parameter is used as an additional input to Winslow's model.
Applicant further argues that combining the references would result in a separate channel quality prediction circuit rather than an input to Winslow's use case determination model (see page 11). This argument is not persuasive. The proposed combination does not require bodily importing Annavajjala's entire channel quality system into Winslow. The combination uses Annavajjala's known correlation coefficient as an additional frequency-related input. Winslow expressly allows additional inputs and different mixes of inputs. See Winslow, paragraphs [0069] and [0075]. Thus, one of ordinary skill in the art would have understood that Annavajjala's correlation coefficient could be used as an additional input to Winslow's neural network model without requiring a separate channel quality prediction circuit.
Applicant also argues that measurements of signals are not the same as inputs related to a physical interaction with the UE (see page 11). This argument is not persuasive because the claim recites “one or more inputs related to a physical interaction with the UE” and Winslow already teaches such inputs. Winslow teaches that physical use cases, such as holding the device in different ways or placing the device in different environments, affect antenna and wireless transmission performance. See Winslow, paragraph [0023]. Winslow also teaches that impedance and frequency measurements are used by the neural network model to determine use case. See Winslow, paragraphs [0046]-[0048] and [0069]. Therefore, the received inputs in Winslow are related to physical interaction with the device.
The claimed second input additionally includes a frequency relationship parameter. Annavajjala teaches that feature. The claim does not require that every part of every input independently measure a hand position, grip, or other physical contact. Rather, the claimed inputs are used by the neural network circuit in connection with detecting the use case or adjusting the antenna impedance/aperture. Winslow teaches that overall process, and Annavajjala teaches the frequency relationship parameter used in the combination.
Applicant also argues that one of ordinary skill in the art would find Annavajjala irrelevant because Annavajjala focuses on channel quality in an OFDM system (see page 11). This argument is not persuasive. Both Winslow and Annavajjala are in the field of wireless communication and both deal with information related to transmitting or receiving signals at different frequencies. Winslow uses frequency and impedance information for use case determination and antenna tuning. Annavajjala teaches a known way to describe the relationship between different frequencies or channels using correlation coefficients. Because Winslow already uses frequency-related inputs and allows additional inputs, one of ordinary skill in the art would have had reason to include Annavajjala's correlation coefficient as another input to improve the information available to Winslow's neural network model.
Accordingly, the combination of Winslow and Annavajjala teaches or suggests the disputed limitations of claim 1. For the same reasons, the similar limitations recited in independent claims 9 and 16 are also taught or suggested by the combination.
Applicant's arguments regarding the dependent claims are also not persuasive. The dependent claims depend, directly or indirectly, from claims 1, 9, or 16. Since the rejection of independent claims 1, 9, and 16 is maintained, and since Applicant has not presented separate persuasive arguments for the dependent claims, the rejection of the dependent claims is also maintained.
Therefore, the rejection of claims 1, 3-9, 11-16, and 18-21 under 35 U.S.C. 103 over Winslow in view of Annavajjala, and the further cited references where applied, is maintained.
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].
Allowable Subject Matter
Claim 21 objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Regarding claim 21, The following is a statement of reasons for the indication of allowable subject matter: the closest prior art, Winslow and Annavajjala, whether taken alone or in combination does not teach the following novel feature:
“wherein the relationship between the two frequency bands for transmitting or receiving the signal with the UE is a linear relationship”, in combination with the other limitations in claim 1.
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
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/GOLAM SOROWAR/Primary Examiner, Art Unit 2641
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