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 .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 4/30/26 has been entered.
Response to Arguments
Applicant’s arguments with respect to claims 1, 8 and 15 have been read and considered but are moot because claims 1, 8-10, 15-17 and 20 are now rejected with Rosas-Maxemin (WO 2023/107584) and Westmacott (US 2016/0094793) in view of Weber (US 2013/0002879) due to amendments to claims 1, 8 and 15. Peruse the rejection below for elaboration.
Also, after the RCE filing, since claims 2 and 10 are canceled, dependent claims 5 and 13 are now rejected under 35 USC 112(b) because claim 5 is now dependent on canceled claim 2, and claim 13 is now dependent on canceled claim 10. Appropriate correction is required.
For prior art rejection purposes, claim 5 will be treated as being dependent on claim 1, and claim 13 will be treated as being dependent on claim 8.
Dependent claims 5, 6, 13, 14 and 18 are now rejected under 35 U.S.C. 103 as being unpatentable over Rosas-Maxemin (WO 2023/107584), Westmacott (US 2016/0094793) and Weber (US 2013/0002879) in view of Seaman (US 2019/0026915). Peruse the rejection below.
Dependent claims 7 and 19 are now rejected under 35 U.S.C. 103 as being unpatentable over Rosas-Maxemin (WO 2023/107584), Westmacott (US 2016/0094793) and Weber (US 2013/0002879) in view of Anderson (US 2020/0327472). Peruse the rejection below.
Regarding lines 25-26 on page 17 of Applicant's remarks filed 3/19/26, Applicant asserts that there is no motivation to combine Rosas-Maxemin and Westmacott. The Examiner respectfully disagrees. In response to applicant’s argument that there is no teaching, suggestion, or motivation to combine the references, the examiner recognizes that obviousness may be established by combining or modifying the teachings of the prior art to produce the claimed invention where there is some teaching, suggestion, or motivation to do so found either in the references themselves or in the knowledge generally available to one of ordinary skill in the art. See In re Fine, 837 F.2d 1071, 5 USPQ2d 1596 (Fed. Cir. 1988), In re Jones, 958 F.2d 347, 21 USPQ2d 1941 (Fed. Cir. 1992), and KSR International Co. v. Teleflex, Inc., 550 U.S. 398, 82 USPQ2d 1385 (2007). In this case, since Rosas-Maxemin discloses “determine, using the map of the intermodal container yard, a parking location of the shipping container”, and Westmacott discloses “…determine the determined current field of view of the imaging device in a parking lot”, by substitution, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Rosas-Maxemin and Westmacott together as a whole for ascertaining the limitation of “…determine, using the map of the intermodal container yard and the determined current field of view of the imaging device, a parking location of the shipping container" in order to accurately follow, track and locate objects of interest within a monitored scene.
Since Rosas-Maxemin pertains to tracking shipments and objects, and Westmacott pertains to tracking objects like vehicles and people, it is clear that both Rosas-Maxemin and Westmacott pertain to the tracking of objects, thus, the combination is reasonable to combine together for accurately following, tracking and locating objects of interest within a monitored scene.
The test for obviousness is not whether the features of a secondary reference may be bodily incorporated into the structure of the primary reference; nor is it that the claimed invention must be expressly suggested in any one or all of the references. Rather, the test is what the combined teachings of the references would have suggested to those of ordinary skill in the art. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981).
Regarding lines 4-5 on page 18 and lines 13-14 on page 19 of Applicant's remarks filed 3/19/26, Applicant asserts that there must be a reasonable expectation of success for the combination to be successful. The Examiner respectfully disagrees. Where there is a reason to modify or combine the prior art to achieve the claimed invention, the claims may be rejected as prima facie obvious provided there is also a reasonable expectation of success. The reasonable expectation of success requirement refers to "the likelihood of success" in combining or modifying prior art disclosures to meet the limitations of the claimed invention. See Elekta Ltd. v. ZAP Surgical Sys., Inc., 81 F.4th 1368, 1375, 2023 USPQ2d 1100 (Fed. Cir. 2023) and Intelligent Bio-Sys., Inc. v. Illumina Cambridge Ltd., 821 F.3d 1359, 1367, 119 USPQ2d 1171, 1176 (Fed. Cir. 2016). See MPEP § 2143. Thus, reasonable expectation of success can be implicitly shown via the prior art teachings or as part of the obviousness analysis. See Elekta Ltd. v. ZAP Surgical Sys., Inc., 81 F.4th 1368, 1376-77, 2023 USPQ2d 1100 (Fed. Cir. 2023). Conclusive proof of efficacy is not required to show a reasonable expectation of success. See OSI Pharm., LLC v. Apotex Inc., 939 F.3d 1375, 1385, 2019 USPQ2d 379681 (Fed. Cir. 2019). In this case, since Rosas-Maxemin discloses “determine, using the map of the intermodal container yard, a parking location of the shipping container”, and Westmacott discloses “…determine the determined current field of view of the imaging device in a parking lot”, by substitution, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Rosas-Maxemin and Westmacott together as a whole for ascertaining the limitation of “…determine, using the map of the intermodal container yard and the determined current field of view of the imaging device, a parking location of the shipping container" in order to accurately follow, track and locate objects of interest within a monitored scene.
Since Rosas-Maxemin pertains to tracking shipments and objects, and Westmacott pertains to tracking objects like vehicles and people, it is clear that both Rosas-Maxemin and Westmacott pertain to the tracking of objects, thus, the combination is reasonable to combine together for accurately following, tracking and locating objects of interest within a monitored scene.
Regarding lines 14-17 on page 18, lines 5-6 and lines 10-13 on page 19 of Applicant's remarks filed 3/19/26, Applicant asserts that was no adequate reason to combine the references. The Examiner respectfully disagrees. The examiner recognizes that obviousness may be established by combining or modifying the teachings of the prior art to produce the claimed invention where there is some teaching, suggestion, or motivation to do so found either in the references themselves or in the knowledge generally available to one of ordinary skill in the art. See In re Fine, 837 F.2d 1071, 5 USPQ2d 1596 (Fed. Cir. 1988), In re Jones, 958 F.2d 347, 21 USPQ2d 1941 (Fed. Cir. 1992), and KSR International Co. v. Teleflex, Inc., 550 U.S. 398, 82 USPQ2d 1385 (2007). In this case, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Rosas-Maxemin and Westmacott together as a whole for ascertaining the limitation of “…determine, using the map of the intermodal container yard and the determined current field of view of the imaging device, a parking location of the shipping container" in order to accurately follow, track and locate objects of interest within a monitored scene.
The test for obviousness is not whether the features of a secondary reference may be bodily incorporated into the structure of the primary reference; nor is it that the claimed invention must be expressly suggested in any one or all of the references. Rather, the test is what the combined teachings of the references would have suggested to those of ordinary skill in the art. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981).
Regarding lines 14-17 on page 19 of Applicant's remarks filed 3/19/26, Applicant asserts that the combination of Rosas-Maxemin and Westmacott is combined under impermissible hindsight reconstruction. The Examiner respectfully disagrees. In response to applicant's argument that the examiner's conclusion of obviousness is based upon improper hindsight reasoning, it must be recognized that any judgment on obviousness is in a sense necessarily a reconstruction based upon hindsight reasoning. But so long as it takes into account only knowledge which was within the level of ordinary skill at the time the claimed invention was made, and does not include knowledge gleaned only from the applicant's disclosure, such a reconstruction is proper. See In re McLaughlin, 443 F.2d 1392, 170 USPQ 209 (CCPA 1971).
Since Rosas-Maxemin discloses “determine, using the map of the intermodal container yard, a parking location of the shipping container”, and Westmacott discloses “…determine the determined current field of view of the imaging device in a parking lot”, by substitution, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Rosas-Maxemin and Westmacott together as a whole for ascertaining the limitation of “…determine, using the map of the intermodal container yard and the determined current field of view of the imaging device, a parking location of the shipping container" in order to accurately follow, track and locate objects of interest within a monitored scene.
Since Rosas-Maxemin pertains to tracking shipments and objects, and Westmacott pertains to tracking objects like vehicles and people, it is clear that both Rosas-Maxemin and Westmacott pertain to the tracking of objects, thus, the combination is reasonable to combine together for accurately following, tracking and locating objects of interest within a monitored scene.
The test for obviousness is not whether the features of a secondary reference may be bodily incorporated into the structure of the primary reference; nor is it that the claimed invention must be expressly suggested in any one or all of the references. Rather, the test is what the combined teachings of the references would have suggested to those of ordinary skill in the art. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981).
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.
Claims 5 and 13 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.
Dependent claims 5 and 13 are dependent on canceled claims 2 and 10, respectively, so thus the scope of the claims is unclear and unknown. The dependency of claims 5 and 13 needs to be changed to be dependent to independent claims 1 and 8, respectively. Appropriate correction is required.
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.
Claims 1, 8, 9, 15-17 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Rosas-Maxemin (WO 2023/107584) and Westmacott (US 2016/0094793) in view of Weber (US 2013/0002879).
Regarding claim 1, Rosas-Maxemin discloses a system comprising:
an imaging device (paragraph [17], fig.2, Rosas-Maxemin discloses camera 210);
a communications interface (paragraph [21], Rosas-Maxemin discloses a communication system 230 can be utilized for transmission of data, wherein paragraph [38], Rosas-Maxemin discloses communication interface that includes wireless or wired transmission like Bluetooth, RFID (radio frequency identification), near field communications (NFC), WiFi, WLAN, VLC (visible light communication), WiMAx, IR (infrared) communication wireless signal, PTSN, ISDN, 3G/4G/5G/LTE cellular data wireless, ultraviolet, microwave, etc; paragraph [39], Rosas-Maxemin discloses GNSS (Global Navigation Satellite System) and GPS (Global Positioning System) can also be utilized for communication);
a Global Positioning System (GPS) module (paragraph [17] and [39], Rosas-Maxemin discloses implementation of a Global Positioning System (GPS) for determining GPS coordinates of the system located on imaging system);
one or more memory units storing a map of an intermodal container yard (paragraph [17], Rosas-Maxemin discloses locator system 200 determines the location of shipping containers and prevent the containers from being lost within the yard, and also, locator system comprises mapping of the container yard stored in computer 220, wherein paragraph [36], Rosas-Maxemin discloses that a computer comprises system memory 715 like ROM and RAM, as well as high-speed memory 712); and
one or more computer processors (paragraph [18], Rosas-Maxemin discloses computer 220 comprises machine learning and computer vision algorithms stored in memory on computer 220, and paragraph [36], Rosas-Maxemin discloses that computer comprises system memory 715 (ie.ROM, RAM, high speed memory 712) linked to the processor of computer, paragraph [37], Rosas-Maxemin discloses memory stores software instructions to be executed by a processor of a computer) communicatively coupled to the one or more memory units and configured to:
access a plurality of images captured by the imaging device (paragraph [19], Rosas-Maxemin discloses computer 220 access plural images captured by camera 210 for performing machine learning and computer vision algorithms on the accessed images);
determine, by analyzing the plurality of images using a machine-learning module (paragraph [25], Rosas-Maxemin discloses a computer 200 utilizes machine learning and computer vision algorithms on the received image and video data captured by the camera for determining the identification and position information of the shipping containers), that a shipping container is depicted within at least one of the plurality of images (paragraph [25], Rosas-Maxemin discloses a computer 200 utilizes machine learning and computer vision algorithms on the received image and video data captured by the camera for determining the identification and position information of the shipping containers);
determine current GPS coordinates of the system from the GPS module (paragraph [17] and [39], Rosas-Maxemin discloses implementation of a Global Positioning System (GPS) for determining GPS coordinates of the system located on imaging system);
determine, using the map of the intermodal container yard (paragraph [17], ln. 19-20, Rosas-Maxemin discloses locator system 200 comprises a mapping of the container yard is stored within computer 220), a parking location of the shipping container (paragraph [17], ln.19-20, Rosas-Maxemin discloses locator system 200 comprises a mapping of the container yard is stored within computer 220, and paragraph [17], ln.11-14, Rosas-Maxemin discloses the coordinates or location of the shipping containers and parking spots are stored in memory of computer); and
in response to determining that the shipping container is depicted within at least one of the plurality of images (paragraph [15], Rosas-Maxemin discloses that when machine learning and computer vision algorithms identify a specific shipping container, the locator system can send location information of an identified shipping container and transmit the location information to an external location such as a cloud or to a remote location for informing a user of the specific location of the shipping container), electronically communicate across a communications network (paragraph [21], Rosas-Maxemin discloses a communication system 230 can be utilized for transmission of data, wherein paragraph [38], Rosas-Maxemin discloses communication interface that includes wireless or wired transmission like Bluetooth, RFID (radio frequency identification), near field communications (NFC), WiFi, WLAN, VLC (visible light communication), WiMAx, IR (infrared) communication wireless signal, PTSN, ISDN, 3G/4G/5G/LTE cellular data wireless, ultraviolet, microwave, etc; paragraph [39], Rosas-Maxemin discloses GNSS (Global Navigation Satellite System) and GPS (Global Positioning System) can also be utilized for communication), using the communications interface (paragraph [21], Rosas-Maxemin discloses a communication system 230 can be utilized for transmission of data, wherein paragraph [38], Rosas-Maxemin discloses communication interface that includes wireless or wired transmission like Bluetooth, RFID (radio frequency identification), near field communications (NFC), WiFi, WLAN, VLC (visible light communication), WiMAx, IR (infrared) communication wireless signal, PTSN, ISDN, 3G/4G/5G/LTE cellular data wireless, ultraviolet, microwave, etc; paragraph [39], Rosas-Maxemin discloses GNSS (Global Navigation Satellite System) and GPS (Global Positioning System) can also be utilized for communication), a message comprising data about the shipping container (paragraph [15], Rosas-Maxemin discloses that when machine learning and computer vision algorithms identify a specific shipping container, the locator system can send location information of an identified shipping container and transmit the location information to an external location such as a cloud or to a remote location for informing a user of the specific location of the shipping container),
the data comprising:
the determined parking location of the shipping container (paragraph [15], Rosas-Maxemin discloses that when machine learning and computer vision algorithms identify a specific shipping container, the locator system can send location information of an identified shipping container and transmit the location information to an external location such as a cloud or to a remote location for informing a user of the specific location of the shipping container; paragraph [17], ln.11-14, Rosas-Maxemin discloses the coordinates or location of the shipping containers and parking spots are stored in memory of computer); and
one or more identification markings of the shipping container (paragraph [18], Rosas-Maxemin discloses shipping container identification comprises a trailer ID, decals, shipping container customizations, etc.).
Rosas-Maxemin does not disclose determine a current field of view of the imaging device, and determine, using the map of the intermodal container yard and the determined current field of view of the imaging device, a parking location of the shipping container.
However, Westmacott discloses determine a current field of view of the imaging device (paragraph [34], Westmacott discloses camera systems 104-1 to 104-n are camera systems that comprise cameras for obtaining different fields of view, wherein paragraph [76], Westmacott discloses that camera system’s current field of view is obtained to ascertain the next current camera field of view for tracking or follow objects of interest, and paragraph [67], Westmacott discloses that the object tracker permits the camera system to track the object of interest by following the movement of the object of interest via maintaining the objects within the current field of view of the camera system), and determine the determined current field of view of the imaging device in a parking lot (paragraph [70], Westmacott discloses that the monitoring system of cameras can be applied for monitoring a parking lot, and paragraph [34], Westmacott discloses camera systems 104-1 to 104-n are camera systems that comprise cameras for obtaining different fields of view, wherein paragraph [76], Westmacott discloses that camera system’s current field of view is obtained to ascertain the next current camera field of view for tracking or follow objects of interest, and paragraph [67], Westmacott discloses that the object tracker permits the camera system to track the object of interest by following the movement of the object of interest via maintaining the objects within the current field of view of the camera system).
Since Rosas-Maxemin discloses “determine, using the map of the intermodal container yard, a parking location of the shipping container”, and Westmacott discloses “…determine the determined current field of view of the imaging device in a parking lot”, therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Rosas-Maxemin and Westmacott together as a whole for ascertaining the limitation of “…determine, using the map of the intermodal container yard and the determined current field of view of the imaging device, a parking location of the shipping container" in order to accurately follow, track and locate objects of interest within a monitored scene.
Rosas-Maxemin and Westmacott do not disclose determine, by analyzing the current GPS coordinates of the system, a current field of view of the imaging device. However, Weber teaches determine, by analyzing the current GPS coordinates of the system (paragraph [67], Weber discloses processor utilizes GPS coordinate info received from GPS sensors 22a, for identified tag 20a to track article or item's location 26a, wherein GPS coordinates are utilized to select video segments from live camera feed CF1-CF4 to provide a field of view from GPS coordinates as analyzed from processor; paragraph [49], Weber discloses that processor 36 is configured to select one of the surveillance cameras 30 to produce a view or field of view (FOV) from the selected camera to include image or video segment from the selected surveillance camera 30, in that image segments or video segments can be selected based on the GPS data, and that when the item or article 26 is monitored by the system to keep track of the article 26 within the monitored environment by constantly analyzing the GPS coordinates data associated with the tag 20, in that tag 20 is relaying GPS coordinates data to processor 36, wherein the processor 36 processes and utilizes the received GPS coordinates to select from one of the plurality of cameras 30 to provide a field of view or the segment that comprises the real time visual information of the article 26, and paragraph [42], fig.3A, Weber discloses that processor module 36 comprises a processor 302 that can analyze data received from individual sensors and data from communication interface 308 which includes data from GPS (ie. GPS coordinates) and video feeds (ie. live real-time camera field of view data), and that processor 302 is constantly organizing and analyzing data received from communication interface 308 including GPS coordinates data, and paragraph [81], Weber discloses that processor module 36 is utilized to detect anomalies and generate suspicious activity reports data that include analyzing composite log data that include reports from data involving the analysis of report data including GPS location data during the shipment and/or storage of items or articles of commodities), a current field of view of the imaging device (paragraph [67], Weber discloses processor utilizes GPS coordinate info received from GPS sensors 22a, for identified tag 20a to track article or item's location 26a, wherein GPS coordinates are utilized to select video segments from live camera feed CF1-CF4 to provide a field of view from GPS coordinates as analyzed from processor; paragraph [49], Weber discloses that processor 36 is configured to select one of the surveillance cameras 30 to produce a view or field of view (FOV) from the selected camera to include image or video segment from the selected surveillance camera 30, in that image segments or video segments can be selected based on the GPS data as analyzed by processor, and that when the item or article 26 is monitored by the system to keep track of the article 26 within the monitored environment by constantly analyzing the GPS coordinates data associated with the tag 20, in that tag 20 is relaying GPS coordinates data to processor 36, wherein the processor 36 processes and utilizes the received GPS coordinates to select from one of the plurality of cameras 30 to provide a field of view or the segment that comprises the real time visual information of the article 26, thus, a determined field of view is made based on GPS coordinates information as analyzed by processor; paragraph [42], fig.3A, Weber discloses that processor module 36 comprises a processor 302 that can analyze data received from individual sensors and data from communication interface 308 which includes data from GPS (ie. GPS coordinates) and video feeds (ie. live real-time camera field of view data), and that processor 302 is constantly organizing and analyzing data received from communication interface 308 including GPS coordinates data, and paragraph [81], Weber discloses that processor module 36 is utilized to detect anomalies and generate suspicious activity reports data that include analyzing composite log data that include reports from data involving the analysis of report data including GPS location data during the shipment and/or storage of items or articles of commodities, thus, Weber discloses the analysis of GPS location data or GPS coordinates data is performed to determine a current field of view of the imaging device).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Rosas-Maxemin, Westmacott and Weber together as a whole for providing a low cost, high performance electronic article surveillance system for properly tracking items and objects in order to track inventory of items and objects within a monitored space (Weber’s paragraph [11]).
Regarding claim 8, Rosas-Maxemin discloses a method by a computing system (paragraph [18], Rosas-Maxemin discloses computer 220 comprises machine learning and computer vision algorithms stored in memory on computer 220, and paragraph [36], Rosas-Maxemin discloses that computer comprises system memory 715 (ie.ROM, RAM, high speed memory 712) linked to the processor of computer, paragraph [37], Rosas-Maxemin discloses memory stores software instructions to be executed by a processor of a computer), the method comprising:
accessing a plurality of images captured by an imaging device (paragraph [19], Rosas-Maxemin discloses computer 220 access plural images captured by camera 210 for performing machine learning and computer vision algorithms on the accessed images), wherein the imaging device and the computing system are coupled to a vehicle that moves within an intermodal container yard (paragraph [17], Rosas-Maxemin discloses that locator system 200 can be attached to a yard rig or drone, wherein yard rig and drones are movable within the container yard with permitting the implementation of machine learning and computer vision algorithms for capturing images of the container yard that includes shipping containers and parking spots);
determining, by analyzing the plurality of images using a machine-learning module (paragraph [25], Rosas-Maxemin discloses a computer 200 utilizes machine learning and computer vision algorithms on the received image and video data captured by the camera for determining the identification and position information of the shipping containers), that a shipping container is depicted within at least one of the plurality of images (paragraph [25], Rosas-Maxemin discloses a computer 200 utilizes machine learning and computer vision algorithms on the received image and video data captured by the camera for determining the identification and position information of the shipping containers);
determining current Global Positioning System (GPS) coordinates of the computing system from a GPS module (paragraph [17] and [39], Rosas-Maxemin discloses implementation of a Global Positioning System (GPS) for determining GPS coordinates of the system located on imaging system);
determining, using a map of the intermodal container yard (paragraph [17], ln. 19-20, Rosas-Maxemin discloses locator system 200 comprises a mapping of the container yard is stored within computer 220), a parking location of the shipping container (paragraph [17], ln.19-20, Rosas-Maxemin discloses locator system 200 comprises a mapping of the container yard is stored within computer 220, and paragraph [17], ln.11-14, Rosas-Maxemin discloses the coordinates or location of the shipping containers and parking spots are stored in memory of computer); and
in response to determining that the shipping container is depicted within at least one of the plurality of images (paragraph [15], Rosas-Maxemin discloses that when machine learning and computer vision algorithms identify a specific shipping container, the locator system can send location information of an identified shipping container and transmit the location information to an external location such as a cloud or to a remote location for informing a user of the specific location of the shipping container), electronically communicating (paragraph [21], Rosas-Maxemin discloses a communication system 230 can be utilized for transmission of data, wherein paragraph [38], Rosas-Maxemin discloses communication interface that includes wireless or wired transmission like Bluetooth, RFID (radio frequency identification), near field communications (NFC), WiFi, WLAN, VLC (visible light communication), WiMAx, IR (infrared) communication wireless signal, PTSN, ISDN, 3G/4G/5G/LTE cellular data wireless, ultraviolet, microwave, etc; paragraph [39], Rosas-Maxemin discloses GNSS (Global Navigation Satellite System) and GPS (Global Positioning System) can also be utilized for communication), across a communications network (paragraph [21], Rosas-Maxemin discloses a communication system 230 can be utilized for transmission of data, wherein paragraph [38], Rosas-Maxemin discloses communication interface that includes wireless or wired transmission like Bluetooth, RFID (radio frequency identification), near field communications (NFC), WiFi, WLAN, VLC (visible light communication), WiMAx, IR (infrared) communication wireless signal, PTSN, ISDN, 3G/4G/5G/LTE cellular data wireless, ultraviolet, microwave, etc; paragraph [39], Rosas-Maxemin discloses GNSS (Global Navigation Satellite System) and GPS (Global Positioning System) can also be utilized for communication), a message comprising data about the shipping container (paragraph [15], Rosas-Maxemin discloses that when machine learning and computer vision algorithms identify a specific shipping container, the locator system can send location information of an identified shipping container and transmit the location information to an external location such as a cloud or to a remote location for informing a user of the specific location of the shipping container),
the data comprising:
the determined parking location of the shipping container (paragraph [15], Rosas-Maxemin discloses that when machine learning and computer vision algorithms identify a specific shipping container, the locator system can send location information of an identified shipping container and transmit the location information to an external location such as a cloud or to a remote location for informing a user of the specific location of the shipping container; paragraph [17], ln.11-14, Rosas-Maxemin discloses the coordinates or location of the shipping containers and parking spots are stored in memory of computer); and
one or more identification markings of the shipping container (paragraph [18], Rosas-Maxemin discloses shipping container identification comprises a trailer ID, decals, shipping container customizations, etc.).
Rosas-Maxemin does not disclose determining a current field of view of the imaging device, and determining, using the map of the intermodal container yard and the determined current field of view of the imaging device, a parking location of the shipping container.
However, Westmacott discloses determining a current field of view of the imaging device (paragraph [34], Westmacott discloses camera systems 104-1 to 104-n are camera systems that comprise cameras for obtaining different fields of view, wherein paragraph [76], Westmacott discloses that camera system’s current field of view is obtained to ascertain the next current camera field of view for tracking or follow objects of interest, and paragraph [67], Westmacott discloses that the object tracker permits the camera system to track the object of interest by following the movement of the object of interest via maintaining the objects within the current field of view of the camera system), and determining the determined current field of view of the imaging device in a parking lot (paragraph [70], Westmacott discloses that the monitoring system of cameras can be applied for monitoring a parking lot, and paragraph [34], Westmacott discloses camera systems 104-1 to 104-n are camera systems that comprise cameras for obtaining different fields of view, wherein paragraph [76], Westmacott discloses that camera system’s current field of view is obtained to ascertain the next current camera field of view for tracking or follow objects of interest, and paragraph [67], Westmacott discloses that the object tracker permits the camera system to track the object of interest by following the movement of the object of interest via maintaining the objects within the current field of view of the camera system).
Since Rosas-Maxemin discloses “determining, using the map of the intermodal container yard, a parking location of the shipping container”, and Westmacott discloses “…determining the determined current field of view of the imaging device in a parking lot”, therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Rosas-Maxemin and Westmacott together as a whole for ascertaining the limitation of “…determining, using the map of the intermodal container yard and the determined current field of view of the imaging device, a parking location of the shipping container" in order to accurately follow, track and locate objects of interest within a monitored scene.
Rosas-Maxemin and Westmacott do not disclose determining, by analyzing the current GPS coordinates of the system, a current field of view of the imaging device. However, Weber teaches determining, by analyzing the current GPS coordinates of the system (paragraph [67], Weber discloses processor utilizes GPS coordinate info received from GPS sensors 22a, for identified tag 20a to track article or item's location 26a, wherein GPS coordinates are utilized to select video segments from live camera feed CF1-CF4 to provide a field of view from GPS coordinates as analyzed from processor; paragraph [49], Weber discloses that processor 36 is configured to select one of the surveillance cameras 30 to produce a view or field of view (FOV) from the selected camera to include image or video segment from the selected surveillance camera 30, in that image segments or video segments can be selected based on the GPS data, and that when the item or article 26 is monitored by the system to keep track of the article 26 within the monitored environment by constantly analyzing the GPS coordinates data associated with the tag 20, in that tag 20 is relaying GPS coordinates data to processor 36, wherein the processor 36 processes and utilizes the received GPS coordinates to select from one of the plurality of cameras 30 to provide a field of view or the segment that comprises the real time visual information of the article 26, and paragraph [42], fig.3A, Weber discloses that processor module 36 comprises a processor 302 that can analyze data received from individual sensors and data from communication interface 308 which includes data from GPS (ie. GPS coordinates) and video feeds (ie. live real-time camera field of view data), and that processor 302 is constantly organizing and analyzing data received from communication interface 308 including GPS coordinates data, and paragraph [81], Weber discloses that processor module 36 is utilized to detect anomalies and generate suspicious activity reports data that include analyzing composite log data that include reports from data involving the analysis of report data including GPS location data during the shipment and/or storage of items or articles of commodities), a current field of view of the imaging device (paragraph [67], Weber discloses processor utilizes GPS coordinate info received from GPS sensors 22a, for identified tag 20a to track article or item's location 26a, wherein GPS coordinates are utilized to select video segments from live camera feed CF1-CF4 to provide a field of view from GPS coordinates as analyzed from processor; paragraph [49], Weber discloses that processor 36 is configured to select one of the surveillance cameras 30 to produce a view or field of view (FOV) from the selected camera to include image or video segment from the selected surveillance camera 30, in that image segments or video segments can be selected based on the GPS data as analyzed by processor, and that when the item or article 26 is monitored by the system to keep track of the article 26 within the monitored environment by constantly analyzing the GPS coordinates data associated with the tag 20, in that tag 20 is relaying GPS coordinates data to processor 36, wherein the processor 36 processes and utilizes the received GPS coordinates to select from one of the plurality of cameras 30 to provide a field of view or the segment that comprises the real time visual information of the article 26, thus, a determined field of view is made based on GPS coordinates information as analyzed by processor; paragraph [42], fig.3A, Weber discloses that processor module 36 comprises a processor 302 that can analyze data received from individual sensors and data from communication interface 308 which includes data from GPS (ie. GPS coordinates) and video feeds (ie. live real-time camera field of view data), and that processor 302 is constantly organizing and analyzing data received from communication interface 308 including GPS coordinates data, and paragraph [81], Weber discloses that processor module 36 is utilized to detect anomalies and generate suspicious activity reports data that include analyzing composite log data that include reports from data involving the analysis of report data including GPS location data during the shipment and/or storage of items or articles of commodities, thus, Weber discloses the analysis of GPS location data or GPS coordinates data is performed to determine a current field of view of the imaging device).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Rosas-Maxemin, Westmacott and Weber together as a whole for providing a low cost, high performance electronic article surveillance system for properly tracking items and objects in order to track inventory of items and objects within a monitored space (Weber’s paragraph [11]).
Regarding claim 9, Rosas-Maxemin discloses wherein the vehicle comprises: a container delivery vehicle (paragraph [16], Rosas-Maxemin discloses a yard rig for delivering containers); an aerial vehicle (paragraph [16], Rosas-Maxemin discloses a drone or aerial vehicle); an automobile; a truck; a golf cart; an all-terrain vehicle (ATV); an autonomous vehicle; a motorcycle; or a remote-control vehicle (paragraph [16], Rosas-Maxemin discloses a drone or aerial vehicle that can be controlled from a remote location).
Regarding claim 15, Rosas-Maxemin discloses a system for imaging shipping containers in an intermodal container yard, the system comprising:
a first inventory imaging system coupled to a container delivery vehicle (paragraph [16], in fig.1, Rosas-Maxemin discloses an embodiment with a yard rig 120 (ie. container delivery vehicle) and a drone 150, wherein the yard rig 120 comprises a locator system 110 and the drone 150 also comprises a locator system 110, in that locator subsystem 110 comprises computer vision and machine learning algorithms to function as inventory imaging system, and paragraph [17], Rosas-Maxemin discloses the details of locator system 110 of fig.1 in locator system 200 of fig.2, wherein locator system 200 comprises a camera 210, and a computer 220, wherein paragraph [36], Rosas-Maxemin discloses that a computer comprises system memory 715 (ie.ROM, RAM, high speed memory 712) linked to the processor of computer), the first inventory imaging system comprising one or more first computer processors (paragraph [16], in fig.1, Rosas-Maxemin discloses an embodiment with a yard rig 120 (ie. container delivery vehicle) and a drone 150, wherein the yard rig 120 comprises a locator system 110 and the drone 150 also comprises a locator system 110, in that locator subsystem 110 comprises computer vision and machine learning algorithms to function as inventory imaging system, and paragraph [17], Rosas-Maxemin discloses the details of locator system 110 of fig.1 in locator system 200 of fig.2, wherein locator system 200 comprises a camera 210, and a computer 220, wherein paragraph [36], Rosas-Maxemin discloses that a computer comprises system memory 715 (ie.ROM, RAM, high speed memory 712) linked to the processor of computer) configured to:
access a first plurality of images that are captured by the first inventory imaging system (paragraph [19], Rosas-Maxemin discloses computer 220 access plural images captured by camera 210 for performing machine learning and computer vision algorithms on the accessed images) while the container delivery vehicle moves shipping containers around the intermodal container yard (paragraph [17], Rosas-Maxemin discloses that locator system 200 can be attached to a yard rig, wherein yard rig moves within the container yard with permitting the implementation of machine learning and computer vision algorithms for capturing images of the container yard that includes shipping containers and parking spots, and also, yard rig can move shipping containers around the container yard);
determine, by analyzing the first plurality of images using a first machine-learning module (paragraph [25], Rosas-Maxemin discloses a computer 200 utilizes machine learning and computer vision algorithms on the received image and video data captured by the camera for determining the identification and position information of the shipping containers), that a first shipping container is depicted within at least one of the first plurality of images (paragraph [25], Rosas-Maxemin discloses a computer 200 utilizes machine learning and computer vision algorithms on the received image and video data captured by the camera for determining the identification and position information of the shipping containers);
determine a parking location of the first shipping container (paragraph [17], ln.19-20, Rosas-Maxemin discloses locator system 200 comprises a mapping of the container yard is stored within computer 220, and paragraph [17], ln.11-14, Rosas-Maxemin discloses the coordinates or location of the shipping containers and parking spots are stored in memory of computer); and
in response to determining that the first shipping container is depicted within at least one of the first plurality of images (paragraph [15], Rosas-Maxemin discloses that when machine learning and computer vision algorithms identify a specific shipping container, the locator system can send location information of an identified shipping container and transmit the location information to an external location such as a cloud or to a remote location for informing a user of the specific location of the shipping container), electronically communicate (paragraph [21], Rosas-Maxemin discloses a communication system 230 can be utilized for transmission of data, wherein paragraph [38], Rosas-Maxemin discloses communication interface that includes wireless or wired transmission like Bluetooth, RFID (radio frequency identification), near field communications (NFC), WiFi, WLAN, VLC (visible light communication), WiMAx, IR (infrared) communication wireless signal, PTSN, ISDN, 3G/4G/5G/LTE cellular data wireless, ultraviolet, microwave, etc; paragraph [39], Rosas-Maxemin discloses GNSS (Global Navigation Satellite System) and GPS (Global Positioning System) can also be utilized for communication) a first message comprising data about the first shipping container to a remote computing system (paragraph [15], Rosas-Maxemin discloses that when machine learning and computer vision algorithms identify a specific shipping container, the locator system can send location information of an identified shipping container and transmit the location information to an external location such as a cloud or to a remote location for informing a user of the specific location of the shipping container);
a second inventory imaging system coupled to a dedicated imaging vehicle (paragraph [16], in fig.1, Rosas-Maxemin discloses an embodiment with a yard rig 120 (ie. container vehicle) and a drone 150 (ie. dedicated imaging vehicle), wherein the yard rig 120 comprises a locator system 110 and the drone 150 also comprises a locator system 110, in that locator subsystem 110 comprises computer vision and machine learning algorithms to function as inventory imaging system, and thus, the drone 150 is the dedicated imaging vehicle, and also, that one or more yard rigs (ie. a second yard rig) for delivering containers can be utilized as a dedicated imaging vehicle, and paragraph [17], Rosas-Maxemin discloses the details of locator system 110 of fig.1 in locator system 200 of fig.2, wherein locator system 200 comprises a camera 210, and a computer 220, wherein paragraph [36], Rosas-Maxemin discloses that a computer comprises system memory 715 (ie.ROM, RAM, high speed memory 712) linked to the processor of computer), the second inventory imaging system comprising one or more second computer processors (paragraph [16], in fig.1, Rosas-Maxemin discloses an embodiment with a yard rig 120 (ie. container delivery vehicle) and a drone 150 (ie.dedicated imaging vehicle), wherein the yard rig 120 comprises a locator system 110 and the drone 150 also comprises a locator system 110, in that locator subsystem 110 comprises computer vision and machine learning algorithms to function as inventory imaging system, and thus, the drone 150 is the dedicated imaging vehicle, and paragraph [17], Rosas-Maxemin discloses the details of locator system 110 of fig.1 in locator system 200 of fig.2, wherein locator system 200 comprises a camera 210, and a computer 220, wherein paragraph [36], Rosas-Maxemin discloses that a computer comprises system memory 715 (ie.ROM, RAM, high speed memory 712) linked to the processor of computer) configured to:
access a second plurality of images that are captured by the second inventory imaging system (paragraph [19], Rosas-Maxemin discloses computer 220 access plural images captured by camera 210 for performing machine learning and computer vision algorithms on the accessed images) while the dedicated imaging vehicle drives a dedicated route to image the intermodal container yard (paragraph [17], Rosas-Maxemin discloses that locator system 200 can be attached to a drone, wherein the drone is movable within the container yard with permitting the implementation of machine learning and computer vision algorithms for capturing images of the container yard that includes shipping containers and parking spots);
determine, by analyzing the second plurality of images using a second machine-learning module (paragraph [25], Rosas-Maxemin discloses a computer 200 utilizes machine learning and computer vision algorithms on the received image and video data captured by the camera for determining the identification and position information of the shipping containers), that a second shipping container is depicted within at least one of the second plurality of images (paragraph [25], Rosas-Maxemin discloses a computer 200 utilizes machine learning and computer vision algorithms on the received image and video data captured by the camera for determining the identification and position information of the shipping containers);
determine a parking location of the second shipping container (paragraph [17], ln.19-20, Rosas-Maxemin discloses locator system 200 comprises a mapping of the container yard is stored within computer 220, and paragraph [17], ln.11-14, Rosas-Maxemin discloses the coordinates or location of the shipping containers and parking spots are stored in memory of computer); and
in response to determining that the second shipping container is depicted within at least one of the second plurality of images, electronically communicate (paragraph [21], Rosas-Maxemin discloses a communication system 230 can be utilized for transmission of data, wherein paragraph [38], Rosas-Maxemin discloses communication interface that includes wireless or wired transmission like Bluetooth, RFID (radio frequency identification), near field communications (NFC), WiFi, WLAN, VLC (visible light communication), WiMAx, IR (infrared) communication wireless signal, PTSN, ISDN, 3G/4G/5G/LTE cellular data wireless, ultraviolet, microwave, etc; paragraph [39], Rosas-Maxemin discloses GNSS (Global Navigation Satellite System) and GPS (Global Positioning System) can also be utilized for communication) a second message comprising data about the second shipping container to the remote computing system (paragraph [15], Rosas-Maxemin discloses that when machine learning and computer vision algorithms identify a specific shipping container, the locator system can send location information of an identified shipping container and transmit the location information to an external location such as a cloud or to a remote location for informing a user of the specific location of the shipping container).
Rosas-Maxemin does not disclose determine, using a field of view of a first imaging device of the first inventory imaging system, a parking location of the first shipping container, and determine, using a field of view of a second imaging device of the second inventory imaging system, a parking location of the second shipping container.
However, Westmacott teaches determine, using a field of view of a first imaging device (paragraph [34], Westmacott discloses camera systems 104-1 to 104-n are camera systems that comprise cameras for obtaining different fields of view in that camera system 104-1 can be considered a first imaging device to capture an object of interest at a first parking location and camera system 104-2 can be considered a second imaging device to capture an object at a second parking location, and camera system 104-n can be considered an n-th imaging device to capture an object of interest at an n-th parking location, etcetera), a first parking location (paragraph [70], Westmacott discloses that the monitoring system of cameras can be applied for monitoring a parking lot, and paragraph [34], Westmacott discloses camera systems 104-1 to 104-n are camera systems that comprise cameras for obtaining different fields of view in that camera system 104-1 can be considered a first imaging device to capture an object of interest at a first parking location and camera system 104-2 can be considered a second imaging device to capture an object at a second parking location, and camera system 104-n can be considered an n-th imaging device to capture an object of interest at an n-th parking location, etcetera, wherein paragraph [76], Westmacott discloses that camera system’s current field of view is obtained to ascertain the next current camera field of view for tracking or follow objects of interest, and paragraph [67], Westmacott discloses that the object tracker permits the camera system to track the object of interest by following the movement of the object of interest via maintaining the objects within the current field of view of the camera system), and determine, using a field of view of a second imaging device (paragraph [34], Westmacott discloses camera systems 104-1 to 104-n are camera systems that comprise cameras for obtaining different fields of view in that camera system 104-1 can be considered a first imaging device to capture an object of interest at a first parking location and camera system 104-2 can be considered a second imaging device to capture an object at a second parking location, and camera system 104-n can be considered an n-th imaging device to capture an object of interest at an n-th parking location, etcetera), a second parking location (paragraph [70], Westmacott discloses that the monitoring system of cameras can be applied for monitoring a parking lot, and paragraph [34], Westmacott discloses camera systems 104-1 to 104-n are camera systems that comprise cameras for obtaining different fields of view in that camera system 104-1 can be considered a first imaging device to capture an object of interest at a first parking location and camera system 104-2 can be considered a second imaging device to capture an object at a second parking location, and camera system 104-n can be considered an n-th imaging device to capture an object of interest at an n-th parking location, etcetera, wherein paragraph [76], Westmacott discloses that camera system’s current field of view is obtained to ascertain the next current camera field of view for tracking or follow objects of interest, and paragraph [67], Westmacott discloses that the object tracker permits the camera system to track the object of interest by following the movement of the object of interest via maintaining the objects within the current field of view of the camera system).
Since Rosas-Maxemin discloses “a first imaging system”, “determine a parking location of the first shipping container”, “a second imaging system” and “determine a parking location of the second shipping container”, and Westmacott discloses “determine, using a field of view of a first imaging device, a first parking location” and “determine, using a field of view of a second imaging device, a second parking location”, therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Rosas-Maxemin and Westmacott together as a whole for ascertaining the limitations of “…determine, using a field of view of a first imaging device of the first inventory imaging system, a parking location of the first shipping container" and “…determine, using a field of view of a second imaging device of the second inventory imaging system, a parking location of the second shipping container” in order to accurately follow, track and locate objects of interest within a monitored scene.
Rosas-Maxemin and Westmacott do not disclose wherein the field of view of the first imaging device is determined by analyzing current GPS coordinates of the first inventory imaging system, and wherein the field of view of the second imaging device is determined by analyzing current GPS coordinates of the second inventory imaging system.
However, Weber teaches wherein the field of view of the first imaging device is determined by analyzing current GPS coordinates of the first inventory imaging system (paragraph [67], Weber discloses processor utilizes GPS coordinate info received from GPS sensors 22a, for identified tag 20a to track article or item's location 26a, wherein GPS coordinates are utilized to select video segments from live camera feed CF1-CF4 to provide a field of view from GPS coordinates as analyzed from processor; paragraph [49], Weber discloses that processor 36 is configured to select one of the surveillance cameras 30 to produce a view or field of view (FOV) from the selected camera to include image or video segment from the selected surveillance camera 30, in that image segments or video segments can be selected based on the GPS data as analyzed by processor, and that when the item or article 26 is monitored by the system to keep track of the article 26 within the monitored environment by constantly analyzing the GPS coordinates data associated with the tag 20, in that tag 20 is relaying GPS coordinates data to processor 36, wherein the processor 36 processes and utilizes the received GPS coordinates to select from one of the plurality of cameras 30 to provide a field of view or the segment that comprises the real time visual information of the article 26, thus, a determined field of view is made based on GPS coordinates information as analyzed by processor; paragraph [42], fig.3A, Weber discloses that processor module 36 comprises a processor 302 that can analyze data received from individual sensors and data from communication interface 308 which includes data from GPS (ie. GPS coordinates) and video feeds (ie. live real-time camera field of view data), and that processor 302 is constantly organizing and analyzing data received from communication interface 308 including GPS coordinates data, and paragraph [81], Weber discloses that processor module 36 is utilized to detect anomalies and generate suspicious activity reports data that include analyzing composite log data that include reports from data involving the analysis of report data including GPS location data during the shipment and/or storage of items or articles of commodities, thus, Weber discloses the analysis of GPS location data or GPS coordinates data is performed to determine a current field of view of the imaging device), and wherein the field of view of the second imaging device is determined by analyzing current GPS coordinates (paragraph [67], Weber discloses processor utilizes GPS coordinate info received from GPS sensors 22a, for identified tag 20a to track article or item's location 26a, wherein GPS coordinates are utilized to select video segments from live camera feed CF1-CF4 from first imaging device, second imaging device, third imaging device, and fourth imaging device to provide a field of view from GPS coordinates as analyzed from processor; paragraph [49], Weber discloses that processor 36 is configured to select one of the surveillance cameras 30 (ie. cameras 30a, 30b, 30c and 30d) to produce a view or field of view (FOV) from the selected camera to include image or video segment from the selected surveillance camera 30, in that image segments or video segments can be selected based on the GPS data as analyzed by processor, and that when the item or article 26 is monitored by the system to keep track of the article 26 within the monitored environment by constantly analyzing the GPS coordinates data associated with the tag 20, in that tag 20 is relaying GPS coordinates data to processor 36, wherein the processor 36 processes and utilizes the received GPS coordinates to select from one of the plurality of cameras 30 to provide a field of view or the segment that comprises the real time visual information of the article 26, thus, a determined field of view is made based on GPS coordinates information as analyzed by processor; paragraph [42], fig.3A, Weber discloses that processor module 36 comprises a processor 302 that can analyze data received from individual sensors and data from communication interface 308 which includes data from GPS (ie. GPS coordinates) and video feeds (ie. live real-time camera field of view data), and that processor 302 is constantly organizing and analyzing data received from communication interface 308 including GPS coordinates data, and paragraph [81], Weber discloses that processor module 36 is utilized to detect anomalies and generate suspicious activity reports data that include analyzing composite log data that include reports from data involving the analysis of report data including GPS location data during the shipment and/or storage of items or articles of commodities, thus, Weber discloses the analysis of GPS location data or GPS coordinates data is performed to determine a current field of view of the imaging device).
Since Rosas-Maxemin teaches implementing “second inventory imaging system”, and Weber discloses “wherein the field of view of the first imaging device is determined by analyzing current GPS coordinates of the first inventory imaging system” and “wherein the field of view of the second imaging device is determined by analyzing current GPS coordinates”, therefore, by simple substitution of Rosas-Maxemin’s “second inventory imaging system” into Weber’s teaching of determining a field of view by analyzing GPS coordinates, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Rosas-Maxemin, Westmacott and Weber together as a whole for ascertaining the limitation “…wherein the field of view of the second imaging device is determined by analyzing current GPS coordinates of the second inventory imaging system” in order to provide a low cost, high performance electronic article surveillance system for properly tracking items and objects in order to track inventory of items and objects within a monitored space (Weber’s paragraph [11]).
Regarding claim 16, Rosas-Maxemin discloses wherein the dedicated imaging vehicle comprises: a second container delivery vehicle (paragraph [16], Rosas-Maxemin discloses one or more yard rigs for delivering containers); an aerial vehicle (paragraph [16], Rosas-Maxemin discloses a drone or aerial vehicle); an automobile; a truck; a golf cart; an all-terrain vehicle (ATV); an autonomous vehicle; a motorcycle; or a remote-control vehicle (paragraph [16], Rosas-Maxemin discloses a drone or aerial vehicle that can be controlled from a remote location).
Regarding claim 17, Rosas-Maxemin discloses wherein: the data about the first shipping container comprises: the determined parking location of the first shipping container (paragraph [15], Rosas-Maxemin discloses that when machine learning and computer vision algorithms identify a specific shipping container, the locator system can send location information of an identified shipping container and transmit the location information to an external location such as a cloud or to a remote location for informing a user of the specific location of the shipping container; paragraph [17], ln.11-14, Rosas-Maxemin discloses the coordinates or location of the shipping containers and parking spots are stored in memory of computer); and one or more identification markings of the first shipping container (paragraph [18], Rosas-Maxemin discloses shipping container identification comprises a trailer ID, decals, shipping container customizations, etc.); and the data about the second shipping container comprises: the determined parking location of the second shipping container (paragraph [15], Rosas-Maxemin discloses that when machine learning and computer vision algorithms identify a specific shipping container(s), the locator system can send location information of an identified shipping container and transmit the location information to an external location such as a cloud or to a remote location for informing a user of the specific location of the shipping container; paragraph [17], ln.11-14, Rosas-Maxemin discloses the coordinates or location of the shipping containers and parking spots are stored in memory of computer); and one or more identification markings of the second shipping container (paragraph [18], Rosas-Maxemin discloses shipping container(s) identification comprises a trailer ID, decals, shipping container customizations, etc.).
Regarding claim 20, Rosas-Maxemin discloses wherein the dedicated route driven by the dedicated imaging vehicle traverses the entire intermodal container yard (paragraph [20], ln.27-30, Rosas-Maxemin discloses the yard rig or drone traverses the entire yard).
Claims 5, 6, 13, 14 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Rosas-Maxemin (WO 2023/107584), Westmacott (US 2016/0094793) and Weber (US 2013/0002879) in view of Seaman (US 2019/0026915).
Regarding claim 5, Rosas-Maxemin, Westmacott and Weber do not disclose wherein: the one or more computer processors are further configured to determine whether the system is currently located within a geofence of the intermodal container yard by comparing the current GPS coordinates of the system from the GPS module to GPS coordinates of the geofence of the intermodal container yard; and the one or more computer processors are prevented from analyzing the plurality of images using the machine-learning module when it is determined that the system is not currently located within the geofence of the intermodal container yard.
However, Seaman teaches the one or more computer processors are further configured to determine whether the system is currently located within a geofence of the intermodal container yard by comparing the current GPS coordinates of the system from the GPS module to GPS coordinates of the geofence of the intermodal container yard (paragraph [65], Seaman disclose utilizing an image sensor apparatus on a shipping container, wherein a determination is done to see if the shipping container has stopped moving for a period of time within the geofence area, then an image processing or image capture process is performed, and that global positioning system data is also taken into account for determining the triggering conditions of the image capture processing, wherein paragraph [64], Seaman discloses rough GPS fixes and comparison of images over time is dynamically updated on a map for determine or finding a particular container); and the one or more computer processors are prevented from analyzing the plurality of images using the machine-learning module when it is determined that the system is not currently located within the geofence of the intermodal container yard (paragraph [65], Seaman disclose utilizing an image sensor apparatus on a shipping container, wherein a determination is done to see if the shipping container has stopped moving for a period of time within the geofenced area, then an image processing or image capture process is performed, and if the container with the image sensor apparatus is not located within the geofenced area, then no image processing or image capture process will be performed, and that global positioning system data is also taken into account for determining the triggering conditions of the image capture processing, wherein paragraph [64], Seaman discloses rough GPS fixes and comparison of images over time is dynamically updated on a map for determine or finding a particular container).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Rosas-Maxemin, Westmacott, Weber and Seaman together as a whole for permitting accurate pinpointing of the location of shipping containers when one needs to find a particular shipping container in a large area for storage and retrieval purposes.
Regarding claim 6, Rosas-Maxemin, Westmacott and Weber do not disclose wherein the one or more identification markings of the shipping container are determined by the one or more computer processors using optical character recognition on the plurality of images. However, Seaman teaches wherein the one or more identification markings of the shipping container are determined by the one or more computer processors using optical character recognition on the plurality of images (paragraph [72], Seaman discloses implementing optical character recognition for identification of specific shipping containers to find the specific shipping container location). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Rosas-Maxemin, Westmacott, Weber and Seaman together as a whole for permitting accurate pinpointing of the location of shipping containers when one needs to find a particular shipping container in a large area for storage and retrieval purposes.
Regarding claim 13, Rosas-Maxemin, Westmacott and Weber do not disclose further comprising determining whether the computing system is currently located within a geofence of the intermodal container yard by comparing the current GPS coordinates of the system from the GPS module to GPS coordinates of the geofence of the intermodal container yard, wherein the machine-learning module is prevented from analyzing the plurality of images when it is determined that the computing system is not currently located within the geofence of the intermodal container yard.
However, Seaman teaches determining whether the computing system is currently located within a geofence of the intermodal container yard by comparing the current GPS coordinates of the system from the GPS module to GPS coordinates of the geofence of the intermodal container yard (paragraph [65], Seaman disclose utilizing an image sensor apparatus on a shipping container, wherein a determination is done to see if the shipping container has stopped moving for a period of time within the geofence area, then an image processing or image capture process is performed, and that global positioning system data is also taken into account for determining the triggering conditions of the image capture processing, wherein paragraph [64], Seaman discloses rough GPS fixes and comparison of images over time is dynamically updated on a map for determine or finding a particular container), wherein the machine-learning module is prevented from analyzing the plurality of images when it is determined that the computing system is not currently located within the geofence of the intermodal container yard (paragraph [65], Seaman disclose utilizing an image sensor apparatus on a shipping container, wherein a determination is done to see if the shipping container has stopped moving for a period of time within the geofenced area, then an image processing or image capture process is performed, and if the container with the image sensor apparatus is not located within the geofenced area, then no image processing or image capture process will be performed, and that global positioning system data is also taken into account for determining the triggering conditions of the image capture processing, wherein paragraph [64], Seaman discloses rough GPS fixes and comparison of images over time is dynamically updated on a map for determine or finding a particular container; paragraph [118], Seaman discloses machine-readable instructions are stored in machine-readable storage media to be executed by machines or computers).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Rosas-Maxemin, Westmacott, Weber and Seaman together as a whole for permitting accurate pinpointing of the location of shipping containers when one needs to find a particular shipping container in a large area for storage and retrieval purposes.
Regarding claim 14, Rosas-Maxemin, Westmacott and Weber do not disclose wherein the one or more identification markings of the shipping container are determined by the one or more computer processors using optical character recognition on the plurality of images. However, Seaman teaches wherein the one or more identification markings of the shipping container are determined by the one or more computer processors using optical character recognition on the plurality of images (paragraph [72], Seaman discloses implementing optical character recognition for identification of specific shipping containers to find the specific shipping container location). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Rosas-Maxemin, Westmacott, Weber and Seaman together as a whole for permitting accurate pinpointing of the location of shipping containers when one needs to find a particular shipping container in a large area for storage and retrieval purposes.
Regarding claim 18, Rosas-Maxemin, Westmacott and Weber do not disclose wherein: the one or more identification markings of the first shipping container are determined by the one or more first computer processors using optical character recognition on the first plurality of images; and the one or more identification markings of the second shipping container are determined by the one or more second computer processors using optical character recognition on the second plurality of images. However, Seaman teaches wherein: the one or more identification markings of the first shipping container are determined by the one or more first computer processors using optical character recognition on the first plurality of images (paragraph [72], Seaman discloses implementing optical character recognition for identification of specific shipping containers to find the specific shipping container location); and the one or more identification markings of the second shipping container are determined by the one or more second computer processors using optical character recognition on the second plurality of images (paragraph [72], Seaman discloses implementing optical character recognition for identification of specific shipping containers to find the specific shipping container location). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Rosas-Maxemin, Westmacott, Weber and Seaman together as a whole for permitting accurate pinpointing of the location of shipping containers when one needs to find a particular shipping container in a large area for storage and retrieval purposes.
Claims 7 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Rosas-Maxemin (WO 2023/107584), Westmacott (US 2016/0094793) and Weber (US 2013/0002879) in view of Anderson (US 2020/0327472).
Regarding claim 7, Rosas-Maxemin discloses wherein the determined parking location of the shipping container comprises spot identification (paragraph [18], Rosas-Maxemin discloses open parking spot identification can be realized by identifying open parking spot identification features with alphanumeric identifiers and parking lot striping features).
Rosas-Maxemin, Westmacott and Weber do not disclose wherein the determined parking location of the shipping container comprises: a lot identification; a row identification; and a spot identification. However, Anderson teaches wherein the determined parking location of the shipping container comprises: a lot identification (paragraph [42], Anderson discloses that parking spot can be identified by lot, row and parking space (ie. spot)); a row identification (paragraph [42], Anderson discloses that parking spot can be identified by lot, row and parking space (ie. spot)); and a spot identification (paragraph [42], Anderson discloses that parking spot can be identified by lot, row and parking space (ie. spot)). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Rosas-Maxemin, Westmacott, Weber and Anderson together as a whole for clearly identifying parking locations so as to find and locate containers in a quick, seamless manner.
Regarding claim 19, Rosas-Maxemin discloses wherein the determined parking locations of the first and second shipping containers comprise spot identification (paragraph [18], Rosas-Maxemin discloses open parking spot identification can be realized by identifying open parking spot identification features with alphanumeric identifiers and parking lot striping features).
Rosas-Maxemin, Westmacott and Weber do not disclose wherein the determined parking locations of the first and second shipping containers each comprise: a lot identification; a row identification; and a spot identification. However, Anderson teaches the determined parking locations of the first and second shipping containers each comprise: a lot identification (paragraph [42], Anderson discloses that parking spot can be identified by lot, row and parking space (ie. spot)); a row identification (paragraph [42], Anderson discloses that parking spot can be identified by lot, row and parking space (ie. spot)); and a spot identification (paragraph [42], Anderson discloses that parking spot can be identified by lot, row and parking space (ie. spot)). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Rosas-Maxemin, Westmacott, Weber and Anderson together as a whole for clearly identifying parking locations so as to find and locate containers in a quick, seamless manner.
Allowable Subject Matter
Claims 3-4 and 11-12 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter: the prior art does not disclose “…determining, using the determined current field of view of the imaging device and the stored map of the intermodal container yard, a plurality of possible parking locations of the intermodal container yard that are within the field of view of the imaging device; determining, from the stored map of the intermodal container yard, GPS coordinates of each of the possible parking locations that are within the field of view of the imaging device…, and determining the parking location of the shipping container by comparing the GPS coordinates of each of the possible parking locations with the GPS coordinates of the shipping container” of claims 3 and 11.
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/ALLEN C WONG/Primary Examiner, Art Unit 2488
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