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 .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
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 Amendment
Applicant’s amendment filed 23 December, 2025 is acknowledged and has been entered.
The previous objection(s) to claim 6 has been overcome in view of the Applicant’s amendment to the claim(s).
The invocation under 112(f) no longer applies in view of the Applicant’s amendment to the claim(s).
Response to Arguments
Applicant’s remarks filed 23 December, 2025 have been fully considered but are moot in view of a new ground of rejection necessitated by Applicant’s amendment to the claim(s).
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim(s) 6 is/are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 6 recites “a second satellite signal measured in a second reception environment originating from a second” which renders the claim indefinite, because the feature “a second” is unclear. The scope of the claim would not be reasonably ascertainable by one of ordinary skill in the art.
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, 6-9, 15, and 19-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Werner et al. (US 2020/0049837 A1 previously cited “WERNER”), in view of Liu et al. (US 2016/0195620 A1 previously cited “LIU”), and further in view of Majjigi et al. (US 2019/0368884 A1 “MAJJIGI”).
Regarding claim 1, WERNER discloses (Examiner’s note: What WERNER does not disclose is ) a global navigation satellite system (GNSS) receiver comprising: one or more processors (one or more of a host processor 302 [0040]) configured to implement:
a signal processing module configured to receive a satellite signal from a satellite, to process the satellite signal GNSS positioning (e.g., via a GNSS receiver configured to receive signals from the GNSS satellites 104 a-104 d) [0044])
a classification module configured to: determine a reception environment of the satellite signal through a machine learning model (machine learning models are trained and tested for different environments (e.g., urban, suburban, rural) [WERNER 0062]); (the electronic device 102 may use a machine learning model (e.g., stored in local memory of the electronic device 102) in conjunction GNSS position estimates (e.g., position estimates determined based on signals received from the GNSS satellites 104 a-104 d) to estimate device location [0028]) by
extracting, from one or more sensors coupled to the GNSS receiver, a first plurality of features related to motion characteristics of a user (the electronic device 102 may implement an inertial navigation system (INS). The INS uses device sensor(s) (e.g., motion sensors such as accelerometers, gyroscope) to calculate device state (e.g., device position, velocity, attitude) and/or user state (e.g., user velocity, position) for supplementing location data [0030]) wearing the GNSS receiver (the electronic device 102 may be a wearable device [0025])
and extracting, from the input data, a second plurality of features related to the satellite (in the environment 100, the electronic device 102 may determine its location based on signals received from GNSS satellites 104a-104d [0027]), and output environment information indicating the determined reception environment (the training/testing data 408 may include parameters used by the GNSS receiver to determine the location estimates, such as a multipath indicator (e.g., a value of present, not present or unknown with respect to whether the signal provided by the GNSS satellite to the GNSS receiver is a multipath signal) [0050-0051])
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 (output of the machine learning model 412 may indicate a revised location estimation [0070]); (multipath flag [0085])
wherein the first plurality of features comprises a speed of the user, an acceleration of the user (the electronic device 102 may implement an inertial navigation system (INS). The INS uses device sensor(s) (e.g., motion sensors such as accelerometers, gyroscope) to calculate device state (e.g., device position, velocity, attitude) and/or user state (e.g., user velocity, position) for supplementing location data [0030]),
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 WERNER 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.
WERNER, as modified by LIU, discloses the invention as set forth above, but does not disclose a posture of the user performing an activity.
In a same or similar field of endeavor, MAJJIGI teaches that IMU wander states include: C0 (initial), C1 (straight) and C2 (twisty). C2 is a strong outdoor state because if the user is on a treadmill there will be no turning detected. The wearable computer uses IMU data (e.g., position, heading) to trigger a transition to another state. The IMU data is used to determine if the wearable computer is moving in a straight line or turning, as described further in reference to FIG. 10. When in state C0, the wander state transitions to C1 or C2 based on the IMU data. When in state C1, the wander state transitions to C2 based on IMU data [0028].
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 WERNER to include the teachings of LIU, because doing so would improve location determination, thereby conserving power consumption and providing more accurate fitness information to users, as recognized by MAJJIGI.
Regarding claim 2, WERNER/ LIU/ MAJJIGI 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 (parameters that may be included as part of the training/testing data 408 include: a satellite identifier (e.g., constellation, band, carrier frequency and/or satellite number) for the GNSS satellite; measurement latency; a carrier tracking state (e.g., tracking, cycle slip detected, no cycle slips); carrier tracking uncertainty; position fix uncertainty (e.g., horizontal, vertical components); number of satellites used in the position fix; and/or horizontal dilution of precision (HDOP) [WERNER 0052]).
Regarding claim 6, WERNER/ LIU/ MAJJIGI 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 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 (at multiple locations along the route, the operator may collect location estimates from the GNSS receiver as well as location estimates from the high-precision location sensor of the reference device [WERNER 0049]), and train the machine learning model based on the first information or the second information (these estimates may be included as part of the training/testing data 408 [WERNER 0049]). It is further noted that the limitation is in alternative form; therefore, only one alternative was given patentable weight.
Regarding claim 7, WERNER/ LIU/ MAJJIGI discloses the GNSS receiver of claim 1, wherein the machine learning model is configured to determine the reception environment by using any one of machine learning algorithms of a logistic regression, support vector machines (SVM), a kernel SVM, a decision tree, or a random forest (block 410 of the process 400 indicates training and testing of the machine learning model 412. Examples of the algorithms used for training and/or testing the machine learning model 412 include, but are not limited to, linear regression, boosted trees, multi-layer perceptron and/or random forest algorithms [WERNER 0057]). It is further noted that the limitation is in alternative form; therefore, only one alternative was given patentable weight.
Regarding claim 8, WERNER/ LIU/ MAJJIGI discloses the GNSS receiver of claim 1, wherein the reception environment is one of an open area or an urban area (machine learning models are trained and tested for different environments (e.g., urban, suburban, rural) [WERNER 0062], 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 9, WERNER/ LIU/ MAJJIGI discloses the GNSS receiver of claim 1, wherein the position calculation module is configured to: calculate a pseudo range between the satellite and the GNSS receiver by calculating a time difference between time information of the satellite and current time information (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 [WERNER 0018]), and calculate the position information based on the reception environment indicated by the calculated pseudo range and the environment information (the inputs to the machine learning model may be these pseudorange errors and range rate errors [WERNER 0021]).
Regarding claim 15, WERNER discloses a mobile device comprising: a first processor (processor 302 [0075]); a memory configured to store data processed by the first processor (memory 304 [0075]); and a global navigation satellite system (GNSS) receiver controlled by the first processor (a GNSS receiver [0044]), and wherein the GNSS receiver comprises one or more second processors configured to implement:
a signal processing module configured to: receive a satellite signal from a satellite, to process the satellite signal GNSS positioning (e.g., via a GNSS receiver configured to receive signals from the GNSS satellites 104 a-104 d) [0044])
a classification module configured to: determine a reception environment of the satellite signal through a machine learning model (machine learning models are trained and tested for different environments (e.g., urban, suburban, rural) [WERNER 0062]) by
extracting, from one or more sensors coupled to the GNSS receiver, a first plurality of features related to motion characteristics of a user (the electronic device 102 may implement an inertial navigation system (INS). The INS uses device sensor(s) (e.g., motion sensors such as accelerometers, gyroscope) to calculate device state (e.g., device position, velocity, attitude) and/or user state (e.g., user velocity, position) for supplementing location data [0030]) wearing the GNSS receiver (the electronic device 102 may be a wearable device [0025])
and extracting, from the input data, a second plurality of features related to the satellite (in the environment 100, the electronic device 102 may determine its location based on signals received from GNSS satellites 104a-104d [0027]), and output environment information indicating the determined reception environment (the training/testing data 408 may include parameters used by the GNSS receiver to determine the location estimates, such as a multipath indicator (e.g., a value of present, not present or unknown with respect to whether the signal provided by the GNSS satellite to the GNSS receiver is a multipath signal) [0050-0051])
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 (output of the machine learning model 412 may indicate a revised location estimation [0070]); (multipath flag [0085])
wherein the first plurality of features comprises a speed of the user, an acceleration of the user (the electronic device 102 may implement an inertial navigation system (INS). The INS uses device sensor(s) (e.g., motion sensors such as accelerometers, gyroscope) to calculate device state (e.g., device position, velocity, attitude) and/or user state (e.g., user velocity, position) for supplementing location data [0030]),
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 WERNER 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.
WERNER, as modified by LIU, discloses the invention as set forth above, but does not disclose a posture of the user performing an activity.
In a same or similar field of endeavor, MAJJIGI teaches that IMU wander states include: C0 (initial), C1 (straight) and C2 (twisty). C2 is a strong outdoor state because if the user is on a treadmill there will be no turning detected. The wearable computer uses IMU data (e.g., position, heading) to trigger a transition to another state. The IMU data is used to determine if the wearable computer is moving in a straight line or turning, as described further in reference to FIG. 10. When in state C0, the wander state transitions to C1 or C2 based on the IMU data. When in state C1, the wander state transitions to C2 based on IMU data [0028].
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 WERNER to include the teachings of LIU, because doing so would improve location determination, thereby conserving power consumption and providing more accurate fitness information to users, as recognized by MAJJIGI.
Claims 19-20 correspond to respective claims 6-7 sufficiently in scope and therefore are similarly rejected.
Claim(s) 3-5, 10-14, and 16-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over WERNER, in view of LIU and MAJJIGI, and further in view of Ali et al. (US previously cited 2017/0030716 A1 “ALI”).
Regarding claim 3, WERNER/ LIU/ MAJJIGI discloses the GNSS receiver of claim 1,
In a same or similar field of endeavor, ALI relates to a machine learning technique. Specifically, 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 WERNER 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 4, WERNER/ LIU/ MAJJIGI/ 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, WERNER/ LIU/ MAJJIGI/ 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 10, WERNER 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, processing the satellite signal GNSS positioning (e.g., via a GNSS receiver configured to receive signals from the GNSS satellites 104 a-104 d) [0044])
extracting, from one or more sensors coupled to the GNSS receiver, a first plurality of features related to motion characteristics of a user (the electronic device 102 may implement an inertial navigation system (INS). The INS uses device sensor(s) (e.g., motion sensors such as accelerometers, gyroscope) to calculate device state (e.g., device position, velocity, attitude) and/or user state (e.g., user velocity, position) for supplementing location data [0030]) wearing the GNSS receiver (the electronic device 102 may be a wearable device [0025]) and extracting, from the input data, a second plurality of features related to the satellite (in the environment 100, the electronic device 102 may determine its location based on signals received from GNSS satellites 104a-104d [0027])
outputting environment information indicating the reception environment using machine learning models are trained and tested for different environments (e.g., urban, suburban, rural) [WERNER 0062])
and calculating position information indicating a position of the GNSS receiver based on the satellite signal and the environment information (output of the machine learning model 412 may indicate a revised location estimation [0070]); (multipath flag [0085])
wherein the first plurality of features comprises a speed of the user, an acceleration of the user,
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 WERNER 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.
WERNER, 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; and 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.
In a same or similar field of endeavor, MAJJIGI teaches that IMU wander states include: C0 (initial), C1 (straight) and C2 (twisty). C2 is a strong outdoor state because if the user is on a treadmill there will be no turning detected. The wearable computer uses IMU data (e.g., position, heading) to trigger a transition to another state. The IMU data is used to determine if the wearable computer is moving in a straight line or turning, as described further in reference to FIG. 10. When in state C0, the wander state transitions to C1 or C2 based on the IMU data. When in state C1, the wander state transitions to C2 based on IMU data [0028].
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 WERNER to include the teachings of LIU, because doing so would improve location determination, thereby conserving power consumption and providing more accurate fitness information to users, as recognized by MAJJIGI.
WERNER, as modified by LIU and MAJJIGI, 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; and 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 relates to a machine learning technique. Specifically, 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 WERNER 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, WERNER/ LIU/ MAJJIGI/ 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 (parameters that may be included as part of the training/testing data 408 include: a satellite identifier (e.g., constellation, band, carrier frequency and/or satellite number) for the GNSS satellite; measurement latency; a carrier tracking state (e.g., tracking, cycle slip detected, no cycle slips); carrier tracking uncertainty; position fix uncertainty (e.g., horizontal, vertical components); number of satellites used in the position fix; and/or horizontal dilution of precision (HDOP) [WERNER 0052]).
Regarding claim 12, WERNER/ LIU/ MAJJIGI/ 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, WERNER/ LIU/ MAJJIGI/ 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, WERNER/ LIU/ MAJJIGI/ ALI discloses the method of claim 10, wherein the machine learning model is configured to determine the reception environment by using any one of machine learning algorithms of a logistic regression, support vector machines (SVM), a kernel SVM, a decision tree, or a random forest (block 410 of the process 400 indicates training and testing of the machine learning model 412. Examples of the algorithms used for training and/or testing the machine learning model 412 include, but are not limited to, linear regression, boosted trees, multi-layer perceptron and/or random forest algorithms [WERNER 0057]). It is further noted that the limitation is in alternative form; therefore, only one alternative was given patentable weight.
Claims 16-18 correspond to respective claims 3-5 sufficiently in scope and therefore are similarly rejected.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any 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.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Lukaszewski et al. (US 2023/0400826 A1 newly cited) discloses 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.
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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.
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/Hailey R Le/Examiner, Art Unit 3648 February 17, 2026
/William Kelleher/Supervisory Patent Examiner, Art Unit 3648