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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 13 May, 2026 has been entered.
Examiner’s Note
For applicant’s benefit, portions of the cited reference(s) have been cited to aid in the review of the rejection(s). While every attempt has been made to be thorough and consistent within the rejection it is noted that the PRIOR ART MUST BE CONSIDERED IN ITS ENTIRETY, including disclosures that teach away from the claims. See MPEP 2141.02 VI.
“The use of patents as references is not limited to what the patentees describe as their own inventions or to the problems with which they are concerned. They are part of the literature of the art, relevant for all they contain.” In re Heck, 699 F.2d 1331, 1332-33, 216 USPQ 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 USPQ 275, 277 (CCPA 1968)). A reference may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art, including non-preferred embodiments. Merck & Co. v.Biocraft Laboratories, 874 F.2d 804, 10 USPQ2d 1843 (Fed. Cir.), cert. denied, 493 U.S. 975 (1989). See also Upsher-Smith Labs. v. Pamlab, LLC, 412 F.3d 1319, 1323, 75 USPQ2d 1213, 1215 (Fed. Cir. 2005) See MPEP 2123.
Response to Arguments
Applicant’s remarks filed 20 April, 2026 have been fully considered but are moot in view of a new ground of rejection.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 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.
Claim(s) 1-2, 6, 8, 15, and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Majjigi et al. (US 2019/0368884 A1 “MAJJIGI”), in view of Liu et al. (US 2016/0195620 A1 “LIU”), and Swaminathan et al. (US 2017/0078854 A1 “SWAMINATHAN”), and further in view of Abrahami et al. (US 2017/0039045 A1 “ABRAHAMI”).
Regarding claim 1, MAJJIGI discloses (Examiner’s note: What MAJJIGI does not disclose is ) a global navigation satellite system (GNSS) receiver comprising: one or more processors (one or more processors of a wearable computer [0041]) configured to implement:
a signal processing module configured to receive a satellite signal from a satellite, receiving global navigation satellite system (GNSS) data [0041])
a classification module configured to:
determine a reception environment of the satellite signal wearable computer 101 includes a number of sensors that provide sensor data 103 as shown in FIG. 1C, including but not limited to: accelerometers, gyroscopes, barometric pressure sensor, GNSS receiver (e.g. a GPS receiver), wireless transceiver, magnetometer and a heart rate (HR) sensor [0022]); (determining, by the one or more processors, a second state of the wearable computer based on a comparison of the GNSS data and the inertial sensor data (1103) [0043])
and a position calculation module configured to calculate position information indicating a position of the GNSS receiver based on the satellite signal and the environment information (outputting, by the one or more processors, an indoor/outdoor signal indicating that the wearable computer is indoors or outdoors [0046])
wherein the first plurality of features comprises a speed of the user, an acceleration of the user, and motion sensor(s) 1210 can include one or more accelerometers and/or gyros configured to determine change of speed and direction of movement of the wearable computer [0049]); (running [0039]),
In a same or similar field of endeavor, LIU teaches a radio frequency circuit structure that implements the process of converting GNSS multi-mode multi-frequency satellite signal into baseband signal [0045].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of MAJJIGI to include the teachings of LIU, because GNSS satellite signal in each frequency band is frequency converted and subsequently signal processed, which can reduce crosstalk between the various satellite signals, ensure signal quality and provide adequate SNR for the baseband processing circuit and be with a wider application areas, as recognized by LIU. In addition, both of the prior art references, MAJJIGI and LIU, teach features that are directed to analogous art and they are directed to the same field of endeavor, that is, GNSS signal processing.
MAJJIGI, as modified by LIU, discloses the invention as set forth above, but does not disclose a machine learning model, and a posture of the user performing an activity, and wherein the posture of the user performing the activity comprises a body alignment or a body orientation of the user while performing a type of physical activity.
In a same or similar field of endeavor, SWAMINATHAN teaches that mobile device 100 can also include or have access to one or more sensors 180, such as a Global Positioning System (GPS) sensor, an Estimote sensor, a location Beacon, an iBeacon sensor, or other suitable location sensor), an altimeter, a gyroscope, a magnetometer, an impact sensor, an accelerometer, an infra-red sensor, an ambient light sensor, a motion sensor, a gesture sensor, a temperature sensor or thermometer, or any other suitable sensor [0031]. Specifically, SWAMINATHAN further teaches that ground truth information, used for training, can represent known indoor/outdoor states of a device. For example, during training, a user can enter ground truth information as to whether the mobile device is located indoors or outdoors. Trained models can be developed at the mobile device 100 using data obtained from its sensors and externally obtained information using ground truth and/or ground truth proxies [0055]. Ground truth proxies can represent scenarios where it can be determined to a sufficient degree of accuracy that the mobile device is outdoors. For example, if the mobile device can detect several GPS satellites, it is most likely outdoors [0058].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of MAJJIGI to include the teachings of SWAMINATHAN, because doing so would improve accuracy and efficiency of the detection, as recognized by SWAMINATHAN. In addition, both of the prior art references, MAJJIGI and SWAMINATHAN, teach features that are directed to analogous art and they are directed to the same field of endeavor, that is, detection of indoor/outdoor state.
MAJJIGI, as modified by LIU and SWAMINATHAN, discloses the invention as set forth herein, but does not disclose a posture of the user performing an activity, and wherein the posture of the user performing the activity comprises a body alignment or a body orientation of the user while performing a type of physical activity.
In a same or similar field of endeavor, ABRAHAMI teaches a wearable element 60 implementing inertial sensors to detect head motions (e.g. nodding of the head) as well as general body position (e.g. vertical or horizontal position) [0135]. Specifically, ABRAHAMI further teaches that behaviors detectable through motion detection and integrated sensor input may include smoking (detecting a pattern of lifting hand to mouth), eating (possibly including speed of eating and chewing time), specific sport activities (e.g. walking, running, cycling), body posture (sitting, lying down, standing etc.) and body shaking (stress, nervousness) [0321].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of MAJJIGI to include the teachings of ABRAHAMI, because doing so would assist and support users in achieving specific goals by enabling the detection of certain behaviors, as recognized by ABRAHAMI. In addition, both of the prior art references, MAJJIGI and ABRAHAMI, teach features that are directed to analogous art and they are directed to the same field of endeavor, that is, detection of user state(s) and motion.
Regarding claim 2, MAJJIGI/ LIU/ SWAMINATHAN/ ABRAHAMI discloses the GNSS receiver of claim 1, wherein the second plurality of features comprises a number of visible satellites, a dilution of precision (DOP), a user predicted position error, an average signal strength, a signal strength variation, or a duration of signal tracking (if the mobile device can detect several GPS satellites, it is most likely outdoors [SWAMINATHAN 0058]). It is further noted that the limitation is in alternative form; therefore, only one alternative was given patentable weight.
Regarding claim 6, MAJJIGI/ LIU/ SWAMINATHAN/ ABRAHAMI discloses the GNSS receiver of claim 1, wherein the one or more processors are further configured to implement a learning module configured to: receive learning data, wherein the learning data comprises: i) first information corresponding to the satellite signal, which is a first satellite signal, received in real time from the satellite, which is a first satellite, or a second satellite signal measured in a second reception environment originating from a second satellite or ii) second information related to a second user's motion characteristics comprising speed and acceleration measured using an acceleration sensor, a geomagnetic sensor, or a camera, and train the machine learning model based on the first information or the second information (ground truth proxies can represent scenarios where it can be determined to a sufficient degree of accuracy that the mobile device is outdoors. For example, if the mobile device can detect several GPS satellites, it is most likely outdoors [SWAMINATHAN 0058], cited and incorporated in the rejection of claim 1). It is further noted that the limitation is in alternative form; therefore, only one alternative was given patentable weight.
Regarding claim 8, MAJJIGI/ LIU/ SWAMINATHAN/ ABRAHAMI discloses the GNSS receiver of claim 1, wherein the reception environment is one of an open area or an urban area (outputting, by the one or more processors, an indoor/outdoor signal indicating that the wearable computer is indoors or outdoors [MAJJIGI 0046], cited and incorporated in the rejection of claim 1). It is further noted that the limitation is in alternative form; therefore, only one alternative was given patentable weight.
Regarding claim 15, MAJJIGI discloses a mobile device comprising: a first processor (one or more processors of a wearable computer [0041]); a memory configured to store data processed by the first processor (memory storing instructions that when executed by one or more processors [0006]); and a global navigation satellite system (GNSS) receiver controlled by the first processor, and wherein the GNSS receiver comprises one or more second processors (one or more processors of a wearable computer [0041]) configured to implement:
a signal processing module configured to: receive a satellite signal from a satellite, receiving global navigation satellite system (GNSS) data [0041])
a classification module configured to: determine a reception environment of the satellite signal wearable computer 101 includes a number of sensors that provide sensor data 103 as shown in FIG. 1C, including but not limited to: accelerometers, gyroscopes, barometric pressure sensor, GNSS receiver (e.g. a GPS receiver), wireless transceiver, magnetometer and a heart rate (HR) sensor [0022]); (determining, by the one or more processors, a second state of the wearable computer based on a comparison of the GNSS data and the inertial sensor data (1103) [0043])
and a position calculation module configured to calculate position information indicating a position of the GNSS receiver based on the satellite signal and the environment information (outputting, by the one or more processors, an indoor/outdoor signal indicating that the wearable computer is indoors or outdoors [0046])
wherein the first plurality of features comprises a speed of the user, an acceleration of the user, and motion sensor(s) 1210 can include one or more accelerometers and/or gyros configured to determine change of speed and direction of movement of the wearable computer [0049]); (running [0039]),
In a same or similar field of endeavor, LIU teaches a radio frequency circuit structure that implements the process of converting GNSS multi-mode multi-frequency satellite signal into baseband signal [0045].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of MAJJIGI to include the teachings of LIU, because GNSS satellite signal in each frequency band is frequency converted and subsequently signal processed, which can reduce crosstalk between the various satellite signals, ensure signal quality and provide adequate SNR for the baseband processing circuit and be with a wider application areas, as recognized by LIU.
MAJJIGI, as modified by LIU, discloses the invention as set forth above, but does not disclose a machine learning model, and a posture of the user performing an activity, and wherein the posture of the user performing the activity comprises a body alignment or a body orientation of the user while performing a type of physical activity.
In a same or similar field of endeavor, SWAMINATHAN teaches that mobile device 100 can also include or have access to one or more sensors 180, such as a Global Positioning System (GPS) sensor, an Estimote sensor, a location Beacon, an iBeacon sensor, or other suitable location sensor), an altimeter, a gyroscope, a magnetometer, an impact sensor, an accelerometer, an infra-red sensor, an ambient light sensor, a motion sensor, a gesture sensor, a temperature sensor or thermometer, or any other suitable sensor [0031]. Specifically, SWAMINATHAN further teaches that ground truth information, used for training, can represent known indoor/outdoor states of a device. For example, during training, a user can enter ground truth information as to whether the mobile device is located indoors or outdoors. Trained models can be developed at the mobile device 100 using data obtained from its sensors and externally obtained information using ground truth and/or ground truth proxies [0055]. Ground truth proxies can represent scenarios where it can be determined to a sufficient degree of accuracy that the mobile device is outdoors. For example, if the mobile device can detect several GPS satellites, it is most likely outdoors [0058].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of MAJJIGI to include the teachings of SWAMINATHAN, because doing so would improve accuracy and efficiency of the detection, as recognized by SWAMINATHAN.
MAJJIGI, as modified by LIU and SWAMINATHAN, discloses the invention as set forth herein, but does not disclose a posture of the user performing an activity, and wherein the posture of the user performing the activity comprises a body alignment or a body orientation of the user while performing a type of physical activity.
In a same or similar field of endeavor, ABRAHAMI teaches a wearable element 60 implementing inertial sensors to detect head motions (e.g. nodding of the head) as well as general body position (e.g. vertical or horizontal position) [0135]. Specifically, ABRAHAMI further teaches that behaviors detectable through motion detection and integrated sensor input may include smoking (detecting a pattern of lifting hand to mouth), eating (possibly including speed of eating and chewing time), specific sport activities (e.g. walking, running, cycling), body posture (sitting, lying down, standing etc.) and body shaking (stress, nervousness) [0321].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of MAJJIGI to include the teachings of ABRAHAMI, because doing so would assist and support users in achieving specific goals by enabling the detection of certain behaviors, as recognized by ABRAHAMI.
Regarding claim 19, MAJJIGI/ LIU/ SWAMINATHAN/ ABRAHAMI discloses the mobile device of claim 15, wherein the one or more second processors are further configured to implement a learning module configured to: receive learning data, wherein the learning data comprises: i) first information corresponding to a second satellite signal measured in a second reception environment originating from a second satellite or the satellite signal received in real time from the satellite or second information related to a second user's motion characteristics comprising speed and acceleration measured using an acceleration sensor, a geomagnetic sensor, or a camera, and train the machine learning model based on the first information or the second information (ground truth proxies can represent scenarios where it can be determined to a sufficient degree of accuracy that the mobile device is outdoors. For example, if the mobile device can detect several GPS satellites, it is most likely outdoors [SWAMINATHAN 0058], cited and incorporated in the rejection of claim 15). It is further noted that the limitation is in alternative form; therefore, only one alternative was given patentable weight.
Claim(s) 3-5, 7, 10-14, 16-18, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over MAJJIGI, in view of LIU, and SWAMINATHAN, and ABRAHAMI, and further in view of Ali et al. (US 2017/0030716 A1 “ALI”).
Regarding claim 3, MAJJIGI/ LIU/ SWAMINATHAN/ ABRAHAMI discloses the GNSS receiver of claim 1,
In a same or similar field of endeavor, ALI teaches the image pre-processing techniques applied in 1102. Image pre-processing may be done prior to the Object and feature extraction process. Examples of suitable techniques that may be applied in 1102 include image normalization, de-noising, and filtering. Other pre-processing techniques such as color schemes conversion and dimension reduction may also be applied. The raw image sensor data may be obtained in 1200. Next, as indicated by the dashed lines, any one or combination of parallel operations may be performed on the image sensor data [0103 & FIG. 11]. Additionally, ALI discloses that in 1204, a reduced dimension version of the raw image sensor data may be generated. Dimensionality reduction is an effective approach to downsizing data and may result in a more compact representation of the original data. Dimensionality reduction techniques using linear transformations have been used in determining the intrinsic dimensionality of the manifold as well as extracting its principal directions. In one embodiment, a Principal Component Analysis (PCA) may be used which defines mutually-orthogonal linear combinations of the original attributes [0105 & FIG. 12].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of MAJJIGI to include the teachings of ALI, because doing so would enhance the readability of the captured images by the later stages of the routine and decrease the amount of information being processed, as recognized by ALI. In addition, both of the prior art references, MAJJIGI and ALI, teach features that are directed to analogous art and they are directed to the same field of endeavor, that is, machine learning technique.
Regarding claim 4, MAJJIGI/ LIU/ SWAMINATHAN/ ABRAHAMI/ ALI discloses the GNSS receiver of claim 3, wherein the preprocessing module preprocesses the first plurality of features and the second plurality of features using any one of an average subtraction, a normalization, and a moving average (examples of suitable techniques that may be applied in 1102 include image normalization, de-noising, and filtering [ALI 0103 & FIG. 11], cited and incorporated in the rejection of claim 3). It is further noted that the limitation is in alternative form; therefore, only one alternative was given patentable weight.
Regarding claim 5, MAJJIGI/ LIU/ SWAMINATHAN/ ABRAHAMI/ ALI discloses the GNSS receiver of claim 3, wherein the dimension reduction module is configured to select the third plurality of features used for the classification using a principal component analysis (PCA) with respect to the first plurality of features and the second plurality of features (in 1204, a reduced dimension version of the raw image sensor data may be generated. In one embodiment, a Principal Component Analysis (PCA) may be used which defines mutually-orthogonal linear combinations of the original attributes [ALI 0105 & FIG. 12], cited and incorporated in the rejection of claim 3).
Regarding claim 7, MAJJIGI/ LIU/ SWAMINATHAN/ ABRAHAMI discloses the GNSS receiver of claim 1,
In a same or similar field of endeavor, ALI teaches that classification may be a decision tree, support vector machine, artificial neural network, Bayesian network, or any other machine learning of pattern recognition model, or combination thereof [0085]. It is further noted that the limitation is in alternative form; therefore, only one alternative was given patentable weight.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of MAJJIGI to include the teachings of ALI, because doing so would enhance the detection and improve accuracy, as recognized by ALI.
Regarding claim 10, MAJJIGI discloses a method of determining a reception environment of a GNSS (global navigation satellite system) receiver, the method comprising:
receiving a satellite signal from a satellite, receiving global navigation satellite system (GNSS) data [0041])
extracting, from one or more sensors coupled to the GNSS receiver, a first plurality of features related to motion characteristics of a user wearing the GNSS receiver and extracting, from the input data, a second plurality of features related to the satellite (wearable computer 101 includes a number of sensors that provide sensor data 103 as shown in FIG. 1C, including but not limited to: accelerometers, gyroscopes, barometric pressure sensor, GNSS receiver (e.g. a GPS receiver), wireless transceiver, magnetometer and a heart rate (HR) sensor [0022])
outputting environment information indicating the reception environment (determining, by the one or more processors, a second state of the wearable computer based on a comparison of the GNSS data and the inertial sensor data (1103) [0043])
and calculating position information indicating a position of the GNSS receiver based on the satellite signal and the environment information (outputting, by the one or more processors, an indoor/outdoor signal indicating that the wearable computer is indoors or outdoors [0046])
wherein the first plurality of features comprises a speed of the user, an acceleration of the user, and motion sensor(s) 1210 can include one or more accelerometers and/or gyros configured to determine change of speed and direction of movement of the wearable computer [0049]); (running [0039]),
In a same or similar field of endeavor, LIU teaches a radio frequency circuit structure that implements the process of converting GNSS multi-mode multi-frequency satellite signal into baseband signal [0045].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of MAJJIGI to include the teachings of LIU, because GNSS satellite signal in each frequency band is frequency converted and subsequently signal processed, which can reduce crosstalk between the various satellite signals, ensure signal quality and provide adequate SNR for the baseband processing circuit and be with a wider application areas, as recognized by LIU.
MAJJIGI, as modified by LIU, discloses the invention as set forth above, but does not disclose preprocessing the first plurality of features and the second plurality of features for input to a machine learning model; selecting a third plurality of features used for classification of the machine learning model by reducing dimensions of the first plurality of features and the second plurality of features, and a posture of the user performing an activity, and wherein the posture of the user performing the activity comprises a body alignment or a body orientation of the user while performing a type of physical activity.
In a same or similar field of endeavor, SWAMINATHAN teaches that mobile device 100 can also include or have access to one or more sensors 180, such as a Global Positioning System (GPS) sensor, an Estimote sensor, a location Beacon, an iBeacon sensor, or other suitable location sensor), an altimeter, a gyroscope, a magnetometer, an impact sensor, an accelerometer, an infra-red sensor, an ambient light sensor, a motion sensor, a gesture sensor, a temperature sensor or thermometer, or any other suitable sensor [0031]. Specifically, SWAMINATHAN further teaches that ground truth information, used for training, can represent known indoor/outdoor states of a device. For example, during training, a user can enter ground truth information as to whether the mobile device is located indoors or outdoors. Trained models can be developed at the mobile device 100 using data obtained from its sensors and externally obtained information using ground truth and/or ground truth proxies [0055]. Ground truth proxies can represent scenarios where it can be determined to a sufficient degree of accuracy that the mobile device is outdoors. For example, if the mobile device can detect several GPS satellites, it is most likely outdoors [0058].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of MAJJIGI to include the teachings of SWAMINATHAN, because doing so would improve accuracy and efficiency of the detection, as recognized by SWAMINATHAN.
MAJJIGI, as modified by LIU and SWAMINATHAN, discloses the invention as set forth herein, but does not disclose preprocessing the first plurality of features and the second plurality of features for input to a machine learning model; selecting a third plurality of features used for classification of the machine learning model by reducing dimensions of the first plurality of features and the second plurality of features; and a posture of the user performing an activity, and wherein the posture of the user performing the activity comprises a body alignment or a body orientation of the user while performing a type of physical activity.
In a same or similar field of endeavor, ABRAHAMI teaches a wearable element 60 implementing inertial sensors to detect head motions (e.g. nodding of the head) as well as general body position (e.g. vertical or horizontal position) [0135]. Specifically, ABRAHAMI further teaches that behaviors detectable through motion detection and integrated sensor input may include smoking (detecting a pattern of lifting hand to mouth), eating (possibly including speed of eating and chewing time), specific sport activities (e.g. walking, running, cycling), body posture (sitting, lying down, standing etc.) and body shaking (stress, nervousness) [0321].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of MAJJIGI to include the teachings of ABRAHAMI, because doing so would assist and support users in achieving specific goals by enabling the detection of certain behaviors, as recognized by ABRAHAMI.
MAJJIGI, as modified by LIU, and SWAMINATHAN, and ABRAHAMI, discloses the invention as set forth above, but does not disclose preprocessing the first plurality of features and the second plurality of features for input to a machine learning model; selecting a third plurality of features used for classification of the machine learning model by reducing dimensions of the first plurality of features and the second plurality of features.
In a same or similar field of endeavor, ALI teaches the image pre-processing techniques applied in 1102. Image pre-processing may be done prior to the Object and feature extraction process. Examples of suitable techniques that may be applied in 1102 include image normalization, de-noising, and filtering. Other pre-processing techniques such as color schemes conversion and dimension reduction may also be applied. The raw image sensor data may be obtained in 1200. Next, as indicated by the dashed lines, any one or combination of parallel operations may be performed on the image sensor data [0103 & FIG. 11]. Additionally, ALI discloses that in 1204, a reduced dimension version of the raw image sensor data may be generated. Dimensionality reduction is an effective approach to downsizing data and may result in a more compact representation of the original data. Dimensionality reduction techniques using linear transformations have been used in determining the intrinsic dimensionality of the manifold as well as extracting its principal directions. In one embodiment, a Principal Component Analysis (PCA) may be used which defines mutually-orthogonal linear combinations of the original attributes [0105 & FIG. 12].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of MAJJIGI to include the teachings of ALI, because doing so would enhance the readability of the captured images by the later stages of the routine and decrease the amount of information being processed, as recognized by ALI.
Regarding claim 11, MAJJIGI/ LIU/ SWAMINATHAN/ ABRAHAMI/ ALI discloses the method of claim 10, wherein the second plurality of features related to the satellite comprises a number of visible satellites, a dilution of precision (DOP), a user predicted position error, an average signal strength, a signal strength variation, and a duration of signal tracking (if the mobile device can detect several GPS satellites, it is most likely outdoors [SWAMINATHAN 0058], cited and incorporated in the rejection of claim 10).
Regarding claim 12, MAJJIGI/ LIU/ SWAMINATHAN/ ABRAHAMI/ ALI discloses the method of claim 10, wherein the preprocessing of the first plurality of features and the second plurality of features comprises preprocessing the first plurality of features and the second plurality of features using any one of an average subtraction, a normalization, and a moving average (examples of suitable techniques that may be applied in 1102 include image normalization, de-noising, and filtering [ALI 0103 & FIG. 11], cited and incorporated in the rejection of claim 10). It is further noted that the limitation is in alternative form; therefore, only one alternative was given patentable weight.
Regarding claim 13, MAJJIGI/ LIU/ SWAMINATHAN/ ABRAHAMI/ ALI discloses the method of claim 10, wherein the selecting the third plurality of features comprises selecting using a principal component analysis (PCA) with respect to the first plurality of features and the second plurality of features (in 1204, a reduced dimension version of the raw image sensor data may be generated. In one embodiment, a Principal Component Analysis (PCA) may be used which defines mutually-orthogonal linear combinations of the original attributes [ALI 0105 & FIG. 12], cited and incorporated in the rejection of claim 10).
Regarding claim 14, MAJJIGI, as modified, discloses the method of claim 10,
In a same or similar field of endeavor, ALI teaches that classification may be a decision tree, support vector machine, artificial neural network, Bayesian network, or any other machine learning of pattern recognition model, or combination thereof [0085]. It is further noted that the limitation is in alternative form; therefore, only one alternative was given patentable weight.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of MAJJIGI to include the teachings of ALI, because doing so would enhance the detection and improve accuracy, as recognized by ALI.
Regarding claim 16, MAJJIGI/ LIU/ SWAMINATHAN/ ABRAHAMI discloses the mobile device of claim 15,
In a same or similar field of endeavor, ALI teaches the image pre-processing techniques applied in 1102. Image pre-processing may be done prior to the Object and feature extraction process. Examples of suitable techniques that may be applied in 1102 include image normalization, de-noising, and filtering. Other pre-processing techniques such as color schemes conversion and dimension reduction may also be applied. The raw image sensor data may be obtained in 1200. Next, as indicated by the dashed lines, any one or combination of parallel operations may be performed on the image sensor data [0103 & FIG. 11]. Additionally, ALI discloses that in 1204, a reduced dimension version of the raw image sensor data may be generated. Dimensionality reduction is an effective approach to downsizing data and may result in a more compact representation of the original data. Dimensionality reduction techniques using linear transformations have been used in determining the intrinsic dimensionality of the manifold as well as extracting its principal directions. In one embodiment, a Principal Component Analysis (PCA) may be used which defines mutually-orthogonal linear combinations of the original attributes [0105 & FIG. 12].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of MAJJIGI to include the teachings of ALI, because doing so would enhance the readability of the captured images by the later stages of the routine and decrease the amount of information being processed, as recognized by ALI.
Regarding claim 17, MAJJIGI/ LIU/ SWAMINATHAN/ ABRAHAMI/ ALI discloses the mobile device of claim 16, wherein the preprocessing module is configured to preprocess the first plurality of features and the second plurality of features using any one of an average subtraction, a normalization, or a moving average (examples of suitable techniques that may be applied in 1102 include image normalization, de-noising, and filtering [ALI 0103 & FIG. 11], cited and incorporated in the rejection of claim 16). It is further noted that the limitation is in alternative form; therefore, only one alternative was given patentable weight.
Regarding claim 18, MAJJIGI/ LIU/ SWAMINATHAN/ ABRAHAMI/ ALI discloses the mobile device of claim 16, wherein the dimension reduction module is configured to select the third plurality of features used for the classification using a principal component analysis (PCA) with respect to the first plurality of features and the second plurality of features (in 1204, a reduced dimension version of the raw image sensor data may be generated. In one embodiment, a Principal Component Analysis (PCA) may be used which defines mutually-orthogonal linear combinations of the original attributes [ALI 0105 & FIG. 12], cited and incorporated in the rejection of claim 16).
Regarding claim 20, MAJJIGI/ LIU/ SWAMINATHAN/ ABRAHAMI discloses the mobile device of claim 15,
In a same or similar field of endeavor, ALI teaches that classification may be a decision tree, support vector machine, artificial neural network, Bayesian network, or any other machine learning of pattern recognition model, or combination thereof [0085]. It is further noted that the limitation is in alternative form; therefore, only one alternative was given patentable weight.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of MAJJIGI to include the teachings of ALI, because doing so would enhance the detection and improve accuracy, as recognized by ALI.
Claim(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over MAJJIGI, in view of LIU, and SWAMINATHAN, and ABRAHAMI, and further in view of Werner et al. (US 2020/0049837 A1 “WERNER”).
Regarding claim 9, MAJJIGI/ LIU/ SWAMINATHAN/ ABRAHAMI discloses the GNSS receiver of claim 1,
In a same or similar field of endeavor, WERNER teaches that pseudorange measurements are range measurements plus a time offset corresponding to the difference between the real GNSS time and the GNSS time as estimated by a GNSS receiver [0018]. Furthermore, WERNER teaches that the inputs to the machine learning model may be these pseudorange errors and range rate errors [0021].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of MAJJIGI to include the teachings of WERNER, because doing so would improve system detection accuracy, as recognized by WERNER. In addition, both of the prior art references, MAJJIGI and WERNER, teach features that are directed to analogous art and they are directed to the same field of endeavor, that is, GNSS signal processing.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Lukaszewski et al. (US 2023/0400826 A1) is considered pertinent art for the disclosure of systems and methods for deterministically estimating whether the location of a computing device that is fixed or mobile is inside a fully enclosed building or not (e.g., fully or partially indoors/outdoors). Various environments are supported by the substance of the disclosure, including fully or partially indoor and outdoor environments. Specifically, Lukaszewski discloses that if a velocity value is over a threshold value (e.g., consistent with being in or on a powered vehicle), the confidence value corresponding with the computing device or sensor 132 being fully outdoors may be increased [0043].
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HAILEY R LE whose telephone number is (571)272-4910. The examiner can normally be reached 9:00 AM - 5:00 PM EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, WILLIAM J KELLEHER can be reached at (571) 272-7753. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/Hailey R Le/Examiner, Art Unit 3648 May 22, 2026
Prosecution Insights