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 .
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-4, 9-11 and 17-20 are rejected under 35 U.S.C. 102(a)(1)/(a)(2) as being anticipated by Khare et al. (US 20250240602 A1).
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]).
Regarding claim 2, Khare 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 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 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 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 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 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 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]).
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]).
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 5-8 are rejected under 35 U.S.C. 103 as being unpatentable over Khare in view of Venkatraman et al. (US 20160066844 A1).
Regarding claim 5, Khare teaches the network device of claim 4.
However, Khare does 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 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 teaches the network device of claim 4.
However, Khare does 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 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 teaches the network device of claim 4.
However, Khare does 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 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 teaches the network device of claim 4.
However, Khare does 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 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 Chhokra et al. (US 20190094379 A1).
Regarding claim 12, Khare teaches the network device of claim 9.
However, Khare does 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 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 teaches the network device of claim 1.
However, Khare does 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 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 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 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 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 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 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 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. Funk et al. (US 20130332064 A1): A system and method for recognizing features for location correction in Simultaneous Localization And Mapping operations, thus facilitating longer duration navigation, is provided. The system may detect features from magnetic, inertial, GPS, light sensors, and/or other sensors that can be associated with a location and recognized when revisited. Feature detection may be implemented on a generally portable tracking system, which may facilitate the use of higher sample rate data for more precise localization of features, improved tracking when network communications are unavailable, and improved ability of the tracking system to act as a smart standalone positioning system to provide rich input to higher level navigation algorithms/systems. The system may detect a transition from structured (such as indoors, in caves, etc.) to unstructured (such as outdoor) environments and from pedestrian motion to travel in a vehicle.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NICOLE M LOUIS-FILS whose telephone number is (571)270-0671. The examiner can normally be reached Monday-Friday.
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, Charles Appiah can be reached at 571-272-7904. 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.
/NICOLE M LOUIS-FILS/Examiner, Art Unit 2641
/CHARLES N APPIAH/Supervisory Patent Examiner, Art Unit 2641