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 Amendment
The Amendment filed 04/20/2026 has been entered. Claims 1 and 19-20 have been amended. Claims 1-20 remain pending in the application.
Response to Arguments
Applicant’s arguments with respect to claims 1-20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-4, 9-11 and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Khare et al. (US 20250240602 A1). In view of Fei et al. (CN 117630989 A).
Regarding claim 1, Khare teaches a network device (second device 220 of Fig. 1; device 700 of Fig. 7 implementing the method of Fig. 2), comprising:
a processor (processor 710); at least one network interface controller configured to provide access to a network (communication module 730); and a memory communicatively coupled to the processor (memory 720), wherein the memory comprises a management logic (program 740) that is configured to:
collect a first global navigation satellite system (GNSS) dataset associated with a first network device (the second device 220 may subscribes to different network functions, such as an application function (AF) or an Analytics Data Repository Function (ARDF) to obtain relevant information in order to determine the requested the indoor or outdoor analytics information of the UE 101. For example, the obtained relevant information may be shown in Table 1 such as GNSS measurements, satellite visibility patterns satellite system (GNSS), [0049]); and
determine an indoor-or-outdoor status of the first network device based on the collected first GNSS dataset (the device 220 may determine the indoor or outdoor analytics information of the UE 101 based on AI or ML and additionally based on at least one of: GNSS measurements of the UE 101, [0050]).
However, Khare does not clearly teach determine one or more angle ranges within which at least one satellite is detectable; and analyze the collected GNSS dataset to identify the one or more angles at which the network device can detect satellites wherein the determined angle ranges provide data regarding visibility of the sky.
In an analogous art, Fei teaches determine one or more angle ranges within which at least one satellite is detectable (each set of satellite characteristic information may include one or more parameters such as target carrier-to-noise ratio, satellite elevation angle, and satellite azimuth angle, page 4, par 11); and analyze the collected GNSS dataset to identify the one or more angles at which the network device can detect satellites (the coverage range may be determined according to the satellite azimuth corresponding to each satellite signal received at the current detection time, and the coverage range may be the sum of the satellite azimuths corresponding to each satellite signal, or may be the union of the ranges of the satellite azimuths corresponding to each satellite signal, page 9, par 4) wherein the determined angle ranges provide data regarding visibility of the sky (when the coverage range is less than the second angle threshold, it means that the range of satellites that the electronic device can detect and track at the current detection time is narrow, page 6, par 4).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the indoor/outdoor state detection of Khare with the satellite angle of Fei to provide a method to accurately recognize the positioning scene of the electronic equipment, are small in calculated amount, lower in cost and higher in applicability as suggested, Fei Abstract.
Regarding claim 2, Khare as modified by Fei teaches the network device of claim 1, wherein to determine the indoor-or-outdoor status of the first network device, the management logic is further configured to identify one or more features in the collected first GNSS dataset, and the indoor-or-outdoor status of the first network device is determined based on applying a machine learning process to the identified one or more features (Then, based on the location and time, the device 220 may learn the orientation or satellite visibility pattern based on AI/ML. On this basis, when the satellite visibility pattern of the UE 101 matches a certain learnt pattern, the UE the device 220 may determine that the UE 101 should be indoors or outdoors, [0051]).
Regarding claim 3, Khare as modified by Fei teaches the network device of claim 2, wherein the one or more features comprise one or more of a signal attribute, a carrier-to-noise density ratio (C/NO), a number of observable satellites, a number of decodable satellites, pseudorange measurement noise statistics, or a pseudorange residual (the device 220 may determine the indoor or outdoor analytics information of the UE 101 based on the GNSS satellite visibility patterns. In this case, for each building or house, it has relatively fixed orientations. The UE 101 inside the building/house may show certain pattern of satellite visibility, [0051]).
Regarding claim 4, Khare as modified by Fei teaches the network device of claim 2 wherein the machine learning process is associated with a model (These images can be provided by CRM or AF (police agency) Area/Building layout AF Area map, building 3G model of the area, [0049], table 1).
9. The network device of claim 1, wherein to determine the indoor-or-outdoor status of the first network device, the management logic is further configured to determine zero, one, or more angle ranges within which at least one satellite is detectable at the first network device based on the collected first GNSS dataset (when the UE 101 has visibility of satellite of various directions, it may be most likely that the UE 101 is outdoors, otherwise, the UE 101 is indoors. Then, based on the location and time, the device 220 may learn the orientation or satellite visibility pattern based on AI/ML. On this basis, when the satellite visibility pattern of the UE 101 matches a certain learnt pattern, the UE the device 220 may determine that the UE 101 should be indoors or outdoors, [0051]), and the indoor-or-outdoor status of the first network device is determined based on the zero, one, or more angle ranges (Then, based on the location and time, the device 220 may learn the orientation or satellite visibility pattern based on AI/ML. On this basis, when the satellite visibility pattern of the UE 101 matches a certain learnt pattern, the UE the device 220 may determine that the UE 101 should be indoors or outdoors, [0051]).
Regarding claim 10, Khare as modified by Fei teaches the network device of claim 9, wherein the determined indoor-or-outdoor status of the first network device comprises an outdoor environment status if the zero, one, or more angle ranges comprises at least one angle range that satisfies a criterion (when the UE 101 has visibility of satellite of various directions, it may be most likely that the UE 101 is outdoors, otherwise, the UE 101 is indoors. Then, based on the location and time, the device 220 may learn the orientation or satellite visibility pattern based on AI/ML. On this basis, when the satellite visibility pattern of the UE 101 matches a certain learnt pattern, the UE the device 220 may determine that the UE 101 should be indoors or outdoors, [0051]).
Regarding claim 11, Khare as modified by Fei teaches the network device of claim 9, wherein the determined indoor-or-outdoor status of the first network device comprises an indoor environment status if the zero, one, or more angle ranges comprises one or more angle ranges a widest of which is less than a threshold (For example, when the UE 101 has visibility of satellite of various directions, it may be most likely that the UE 101 is outdoors, otherwise, the UE 101 is indoors. Then, based on the location and time, the device 220 may learn the orientation or satellite visibility pattern based on AI/ML. On this basis, when the satellite visibility pattern of the UE 101 matches a certain learnt pattern, the UE the device 220 may determine that the UE 101 should be indoors or outdoors, [0051]).
Regarding claim 17, Khare as modified by Fei teaches the network device of claim 1, wherein the network device and the first network device are co-located at a same device (Alternatively, the LCS client, the LMF 103, an AMF, a GMLC, an application function, a network function, or a fifth generation core network function may be enhanced to determine the indoor or outdoor analytics information of the UE, [0061]).
Regarding claim 18, Khare as modified by Fei teaches the network device of claim 1, wherein the network device and the first network device are separate devices (the first device 210 may comprise a 5G core network function, and the second device 220 may comprise the NWDAF 105, a MDAS or an analytics function, [0060]).
Regarding claim 19, Khare teaches a network device (second device 220 of Fig. 2, device 700 of Fig. 7 implementing the method of Fig. 2), comprising: a processor (processor 710); at least one network interface controller configured to provide access to a network (communication module 730); and a memory communicatively coupled to the processor (memory 720), wherein the memory comprises a management logic (program 740) that is configured to:
collect a first global navigation satellite system (GNSS) dataset associated with a first network device (the second device 220 may subscribes to different network functions, such as an application function (AF) or an Analytics Data Repository Function (ARDF) to obtain relevant information in order to determine the requested the indoor or outdoor analytics information of the UE 101. For example, the obtained relevant information may be shown in Table 1 such as GNSS measurements, satellite visibility patterns satellite system (GNSS), [0049]);
identify one or more features in the collected first GNSS dataset (Then, based on the location and time, the device 220 may learn the orientation or satellite visibility pattern based on AI/ML. On this basis, when the satellite visibility pattern of the UE 101 matches a certain learnt pattern, the UE the device 220 may determine that the UE 101 should be indoors or outdoors, [0051]); and
determine an indoor-or-outdoor status of the first network device based on applying a machine learning process to the identified one or more features (the device 220 may determine the indoor or outdoor analytics information of the UE 101 based on AI or ML and additionally based on at least one of: GNSS measurements of the UE 101, [0050]).
However, Khare does not clearly teach determine one or more angle ranges within which at least one satellite is detectable; and analyze the collected GNSS dataset to identify the one or more angles at which the network device can detect satellites wherein the determined angle ranges provide data regarding visibility of the sky.
In an analogous art, Fei teaches determine one or more angle ranges within which at least one satellite is detectable (each set of satellite characteristic information may include one or more parameters such as target carrier-to-noise ratio, satellite elevation angle, and satellite azimuth angle, page 4, par 11); and analyze the collected GNSS dataset to identify the one or more angles at which the network device can detect satellites (the coverage range may be determined according to the satellite azimuth corresponding to each satellite signal received at the current detection time, and the coverage range may be the sum of the satellite azimuths corresponding to each satellite signal, or may be the union of the ranges of the satellite azimuths corresponding to each satellite signal, page 9, par 4) wherein the determined angle ranges provide data regarding visibility of the sky (when the coverage range is less than the second angle threshold, it means that the range of satellites that the electronic device can detect and track at the current detection time is narrow, page 6, par 4).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the indoor/outdoor state detection of Khare with the satellite angle of Fei to provide a method to accurately recognize the positioning scene of the electronic equipment, are small in calculated amount, lower in cost and higher in applicability as suggested, Fei Abstract.
Regarding claim 20, Khare teaches a method for managing a network device (method of Fig. 2), comprising: collecting a first global navigation satellite system (GNSS) dataset associated with the network device ((the second device 220 may subscribes to different network functions, such as an application function (AF) or an Analytics Data Repository Function (ARDF) to obtain relevant information in order to determine the requested the indoor or outdoor analytics information of the UE 101. For example, the obtained relevant information may be shown in Table 1 such as GNSS measurements, satellite visibility patterns satellite system (GNSS), [0049]); and determine an indoor-or-outdoor status of the network device based on the collected first GNSS dataset (the second device 220 may subscribes to different network functions, such as an application function (AF) or an Analytics Data Repository Function (ARDF) to obtain relevant information in order to determine the requested the indoor or outdoor analytics information of the UE 101. For example, the obtained relevant information may be shown in Table 1 such as GNSS measurements, satellite visibility patterns satellite system (GNSS), [0049]).
However, Khare does not clearly teach determine one or more angle ranges within which at least one satellite is detectable; and analyze the collected GNSS dataset to identify the one or more angles at which the network device can detect satellites wherein the determined angle ranges provide data regarding visibility of the sky.
In an analogous art, Fei teaches determine one or more angle ranges within which at least one satellite is detectable (each set of satellite characteristic information may include one or more parameters such as target carrier-to-noise ratio, satellite elevation angle, and satellite azimuth angle, page 4, par 11); and analyze the collected GNSS dataset to identify the one or more angles at which the network device can detect satellites (the coverage range may be determined according to the satellite azimuth corresponding to each satellite signal received at the current detection time, and the coverage range may be the sum of the satellite azimuths corresponding to each satellite signal, or may be the union of the ranges of the satellite azimuths corresponding to each satellite signal, page 9, par 4) wherein the determined angle ranges provide data regarding visibility of the sky (when the coverage range is less than the second angle threshold, it means that the range of satellites that the electronic device can detect and track at the current detection time is narrow, page 6, par 4).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the indoor/outdoor state detection of Khare with the satellite angle of Fei to provide a method to accurately recognize the positioning scene of the electronic equipment, are small in calculated amount, lower in cost and higher in applicability as suggested, Fei Abstract.
Claims 5-8 are rejected under 35 U.S.C. 103 as being unpatentable over Khare in view of Fei and further in view of Venkatraman et al. (US 20160066844 A1).
Regarding claim 5, Khare as modified by Fei teaches the network device of claim 4.
However, Khare and Fei do not teach wherein the model comprises a classifier.
In an analogous art, Venkatraman teaches wherein the model comprises a classifier (The machine learning system may then return a stroke classification for each detected stroke, [0358]).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the indoor/outdoor state detection of Khare and Fei with the indoor/outdoor classification of Venkatraman to provide a method to determine whether the lighting conditions indicate that the biometric monitoring device is likely indoors as opposed to outdoors as suggested, Venkatraman [0466].
Regarding claim 6, Khare as modified by Fei teaches the network device of claim 4.
However, Khare and Fei do not teach wherein the model is pretrained based on supervised learning.
In an analogous art, Venkatraman teaches wherein the model is pretrained based on supervised learning (The extracted features may then be put through a machine learning system where the system coefficients are computed off-line (supervised learning) or are adapted as the user uses the biometric monitoring device (unsupervised learning). The machine learning system may then return a stroke classification for each detected stroke, [0358]).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the indoor/outdoor state detection of Khare and Fei with the indoor/outdoor classification of Venkatraman to provide a method to determine whether the lighting conditions indicate that the biometric monitoring device is likely indoors as opposed to outdoors as suggested, Venkatraman [0466].
Regarding claim 7, Khare as modified by Fei teaches the network device of claim 4.
However, Khare and Fei do not teach wherein the model is pretrained based on unsupervised learning.
In an analogous art, Venkatraman teaches wherein the model is pretrained based on unsupervised learning (The extracted features may then be put through a machine learning system where the system coefficients are computed off-line (supervised learning) or are adapted as the user uses the biometric monitoring device (unsupervised learning). The machine learning system may then return a stroke classification for each detected stroke, [0358].
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the indoor/outdoor state detection of Khare and Fei with the indoor/outdoor classification of Venkatraman to provide a method to determine whether the lighting conditions indicate that the biometric monitoring device is likely indoors as opposed to outdoors as suggested, Venkatraman [0466].
Regarding claim 8, Khare as modified by Fei teaches the network device of claim 4.
However, Khare and Fei do not teach wherein the management logic is further configured to update the model based on locally collected data.
In an analogous art, Venkatraman teaches wherein the management logic is further configured to update the model based on locally collected data (a database of SSID's and their associated vehicles may be created or updated with the user of a biometric monitoring device or through portable communication device data, [0418].
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the indoor/outdoor state detection of Khare and Fei with the indoor/outdoor classification of Venkatraman to provide a method to determine whether the lighting conditions indicate that the biometric monitoring device is likely indoors as opposed to outdoors as suggested, Venkatraman [0466].
Claims 12-16 are rejected under 35 U.S.C. 103 as being unpatentable over Khare in view of Fei and further in view of Chhokra et al. (US 20190094379 A1).
Regarding claim 12, Khare as modified by Fei teaches the network device of claim 9.
However, Khare and Fei do not teach wherein the determined indoor-or-outdoor status of the first network device comprises an indoor environment status if the zero, one, or more angle ranges comprises zero angle range.
In an analogous art, Chhokra teaches wherein the determined indoor-or-outdoor status of the first network device comprises an indoor environment status if the zero, one, or more angle ranges comprises zero angle range (If the user's observation point for testing satellite visibility is at a higher altitude, the satellite visibility may change. In the example shown, the elevation mask angle θ.sub.M1 calculated at street level is greater than the elevation mask angle θ.sub.M2 calculated on the 10.sup.th floor of building 307, [0036]).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the indoor/outdoor state detection of Khare and Fei with the indoor/outdoor classification of Chhokra to provide a method using 3D city maps and shadow mapping to improve altitude fixes in urban environments a suggested, Chhokra [0003].
Regarding claim 13, Khare as modified by Fei teaches the network device of claim 1.
However, Khare and Fei do not teach wherein to determine the indoor-or-outdoor status of the first network device, the management logic is further configured to: collect a second GNSS dataset associated with a second network device; identify one or more satellites that are simultaneously detectable by the first network device and the second network device based on the collected first GNSS dataset and the collected second GNSS dataset; determine one or more unobstructed zones based on each of the one or more satellites being simultaneously detectable by the first network device and the second network device; and construct an obstacle profile based on a plurality of unobstructed zones, the plurality of unobstructed zones comprising the one or more unobstructed zones determined based on each of the one or more satellites, wherein the indoor-or-outdoor status of the first network device is determined based on the constructed obstacle profile.
In an analogous art, Chhokra teaches wherein to determine the indoor-or-outdoor status of the first network device, the management logic is further configured to:
collect a second GNSS dataset associated with a second network device (The location of mobile device 102a can be estimated precisely using GNSS signals if observable… GNSS signals 108b, 109b (albeit weaker) can still be received by mobile device 102b when operating on the fourth floor of building, [0019]);
identify one or more satellites that are simultaneously detectable by the first network device and the second network device based on the collected first GNSS dataset and the collected second GNSS dataset (It is noted that mobile device 102a may observe or receive NLOS GNSS signals, which are diffracted or reflected off surfaces of buildings and other objects in GNSS signal environment 100, [0018] and Fig. 1), each of the one or more satellites being simultaneously detectable by the first network device and the second network device over a respective period of time (we assume that GNSS signals 108b, 109b (albeit weaker) can still be received by mobile device 102b when operating on the fourth floor of building 103. For example, mobile device 102b may be operating close to a window, or in an open-air atrium, terrace or balcony, [0019]);
determine one or more unobstructed zones based on each of the one or more satellites being simultaneously detectable by the first network device and the second network device (If the user's observation point for testing satellite visibility is at a higher altitude, the satellite visibility may change, [0036]); and
construct an obstacle profile based on a plurality of unobstructed zones, the plurality of unobstructed zones comprising the one or more unobstructed zones determined based on each of the one or more satellites (3D diffraction zone can be modeled for building boundaries both horizontally and vertically by modelling the buildings as lower and narrower than their actual height and width, [0037]),
wherein the indoor-or-outdoor status of the first network device is determined based on the constructed obstacle profile (If the improved position fix P.sub.est2 has an estimated altitude below a threshold value (e.g., zero), location analyzer 202 assumes that the mobile device is located outdoors, [0044]).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the indoor/outdoor state detection of Khare and Fei with the indoor/outdoor classification of Chhokra to provide a method using 3D city maps and shadow mapping to improve altitude fixes in urban environments a suggested, Chhokra [0003].
Regarding claim 14, Khare as modifies by Fei and Chhokra teaches the network device of claim 13, wherein based on each of the one or more satellites being simultaneously detectable by the first network device and the second network device, the one or more unobstructed zones are determined based further on satellite location data, a distance between the first network device and the second network device, and a triangulation technique (To identify candidate buildings where the mobile device is most likely located, in an embodiment, in an embodiment location analyzer 202 determines the distance between the improved position fix P.sub.est2 and the locations of each building that is abutting the footprint of the 3D CPG, such as the candidate positions (including candidate position 303) that are shaded in FIG. 3A. In an embodiment, the building determined to be nearest to the improved position fix P.sub.est2 can be selected as the building where the mobile device is most likely located. In our example, this would be building 301a, [0045]).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the indoor/outdoor state detection of Khare and Fei with the indoor/outdoor classification of Chhokra to provide a method using 3D city maps and shadow mapping to improve altitude fixes in urban environments a suggested, Chhokra [0003].
Regarding claim 15, Khare as modifies by Fei and Chhokra teaches the network device of claim 14, wherein the distance between the first network device and the second network device is based on a radio frequency ranging measurement between the first network device and the second network device (the mobile device may cache a history of frequented locations by the user, which can be used to adjust the score. If the candidate building is a frequented location by the user, the weight of this factor can be higher, [0046]).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the indoor/outdoor state detection of Khare and Fei with the indoor/outdoor classification of Chhokra to provide a method using 3D city maps and shadow mapping to improve altitude fixes in urban environments a suggested, Chhokra [0003].
Regarding claim 16, Khare as modifies by Fei and Chhokra teaches the network device of claim 13, wherein the obstacle profile is constructed based further on one or more first satellites that are successively but not simultaneously detectable by the first network device and the second network device (see SV 101a, 101b and 101c of Fig. 1).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the indoor/outdoor state detection of Khare and Fei with the indoor/outdoor classification of Chhokra to provide a method using 3D city maps and shadow mapping to improve altitude fixes in urban environments a suggested, Chhokra [0003].
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Aoyama (US 20200292716 A1): An indoor/outdoor determination program, etc., whereby an indoor or outdoor state can be determined with higher precision than by a determination method based on satellite reception strength in which a threshold value is difficult to set. The indoor/outdoor determination program according to the present invention causes a step to be executed for determining whether a mobile terminal is present indoors or outdoors on the basis of satellite elevation angle information and/or satellite azimuth angle information acquired directly or indirectly from a satellite receiver provided to the mobile terminal.
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.
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/NICOLE M LOUIS-FILS/ Examiner, Art Unit 2641
/CHARLES N APPIAH/Supervisory Patent Examiner, Art Unit 2641
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