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 .
Status of the Claims
This is in response to a letter for a patent filed 30 September 2024 in which claims 1-28 were presented for examination. Claims 1-28 are currently pending.
Claim Objections
Claims 11 and 22 are objected to because of the following informalities:
Claim 1, line 6, insert “and” after “;” and
Claim 22, line 7, insert “and” after “;”.
Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-28 are rejected under 35 U.S.C. 101 because the claimed invention recites an abstract idea without significantly more.
Step 1
Claims 1-11 are directed to a method (i.e., a process). Claims 12-22 are directed to a system (i.e., a machine); Claims 23-27 are directed to a non-transitory computer-readable medium (i.e., a manufacture). Claim 28 is directed to a method (i.e., a process). Therefore, Claims 1-28 all fall within the one of the four statutory categories of invention.
Step 2A Prong 1 (additional elements omitted)
Independent claim 1 substantially recites:
receiving [ ] information of an area, the information being indicative of a type of the area and a map of the area;
capturing [ ] an image of one or more objects within the area;
deriving a location and orientation [ ] during capture of the image based on the information and data [ ] ;
detecting a first label associated with a first object, the first label having one or more identifiers;
processing the first label associated with the first object;
determining a location of the processed first label associated with the first object within the area based on the derived location and orientation [ ]; and
determining a location of the first object within the area based on the determined location of the processed first label. These limitations as a whole recite a method or organizing human activity including “Managing Personal Behavior or Relationships or Interactions Between People” (which further includes social activities, teaching, and following rules or instructions).
Independent claim 12 substantially recites:
receive information of an area, the information being indicative of a type of the area and a map of the area;
receive an image, captured [ ] of one or more objects within the area;
derive a location and orientation [ ] during capture of the image based on the information and data [ ];
detect a first label associated with a first object, the first label having one or more identifiers;
process the first label associated with the first object;
determine a location of the processed first label associated with the first object within the area based on the derived location and orientation[ ]; and
determine a location of the first object within the area based on the determined location of the processed first label. These limitations as a whole recite a method or organizing human activity including “Managing Personal Behavior or Relationships or Interactions Between People” (which further includes social activities, teaching, and following rules or instructions).
Independent claim 23 substantially recites:
receive information of an area, the information being indicative of a type of the area and a map of the area;
receive [ ] an image of one or more objects within the area;
derive a location and orientation of the device during capture of the image based on the information and data [ ];
detect a first label associated with a first object, the first label having one or more identifiers;
process the first label associated with the first object;
determine a location of the processed first label associated with the first object within the area based on the derived location and orientation of the device; and
determine a location of the first object within the area based on the determined location of the processed first label.
These limitations as a whole recite a method or organizing human activity including “Managing Personal Behavior or Relationships or Interactions Between People” (which further includes social activities, teaching, and following rules or instructions).
Step 2A Prong 2
This judicial exception is not integrated into a practical application. In particular, claim 1 recites the additional elements: “a device” and “at least one sensor” to perform the “receiving,” “capturing,” “deriving,” “detecting,” “processing,” “determining,” and determining” steps; claim 12 recites the additional elements: “a device,” “an imaging assembly,” “at least one sensor,” “one or more processors,” “a non-transitory computer-readable memory,” and “instructions” to perform the “receive,” “receive,” “derive,” “detect,” “process,” “determine,” and “determine” steps; and claim 23 recites the additional elements: “a non-transitory computer-readable memory,” “instructions,” “one or more processors,” “an imaging assembly,” and “at least one sensor” to perform the “receive,” “receive,” “derive,” “detect,” “process,” “determine,” and “determine” “predicting” steps. The claimed computer components in the steps of claims 1, 12, and 23 are recited at a high-level of generality and are merely invoked as a tool to perform the abstract idea (i.e., “a device” and “at least one sensor” in claim 1 to perform the generic functions of “receiving,” “capturing,” “deriving,” “detecting,” “processing,” “determining,” and “determining” steps; “a device,” “an imaging assembly,” “at least one sensor,” “one or more processors,” “a non-transitory computer-readable memory,” and “instructions” in claim 12 to perform the generic functions of “receive,” “receive,” “derive,” “detect,” “process,” “determine,” and “determine” steps; and “a non-transitory computer-readable memory,” “instructions,” “one or more processors,” “an imaging assembly,” and “at least one sensor” in claim 23 to perform the generic functions of “receive,” “receive,” “derive,” “detect,” “process,” “determine,” and “determine”) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Each of the additional limitations is no more than mere instructions to apply the exception using the generic computer components recited above. The combination of these additional elements is no more than mere instructions to apply the exception using a generic computer component as recited above. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Thus, the claims are not patent eligible.
Step 2B
The independent claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using the “device” and “at least one sensor” in claim 1 to perform the “receiving,” “capturing,” “deriving,” “detecting,” “processing,” “determining,” and “determining” steps; “device,” “imaging assembly,” “at least one sensor,” “one or more processors,” “non-transitory computer-readable memory,” and “instructions” in claim 12 to perform the “receive,” “receive,” “derive,” “detect,” “process,” “determine,” and “determine” steps; and “non-transitory computer-readable memory,” “instructions,” “one or more processors,” “an imaging assembly,” and “at least one sensor” in claim 23 to perform the “receive,” “receive,” “derive,” “detect,” “process,” “determine,” and “determine” steps amount to no more than mere instructions to apply the exception using a generic computer component. Thus, even when viewed as a whole, nothing in the claims add significantly more (i.e. inventive concept) to the abstract idea. The claims are patent ineligible.
As per Dependent claims 2 and 13, the recitations of “partitioning the area into a plurality of zones based on the map”; “determining a zone of the processed first label associated with the first object within the area based on the derived location and orientation…”; and “determining a zone of the first object within the area based on the determined zone of the processed first label” are further directed to a method of organizing human activity as described in claim 1. Similar to claims 1 and 12, respectively, the recitations do not provide a practical application of the abstract idea, or significantly more than the abstract idea. The claim does not add any additional elements to evaluate at Step 2A Prong 2 and Step 2B.
As per Dependent claims 3 and 14, the recitations of “partitioning the image when the one or more objects are within more than one zone of the plurality of zones based on a number of zones present in the image” is further directed to a method of organizing human activity as described in claims 1 and 12, respectively. Similar to claims 1 and 12, the recitation does not provide a practical application of the abstract idea, or significantly more than the abstract idea. The claim does not add any additional elements to evaluate at Step 2A Prong 2 and Step 2B.
As per Dependent claim 4, the recitation of “receiving information of the area calibrates…to the area by deriving an initial location…” is further directed to a method of organizing human activity as described in claim 1. Similar to claim 1, the recitation does not provide a practical application of the abstract idea, or significantly more than the abstract idea. The claim does not add any additional elements to evaluate at Step 2A Prong 2 and Step 2B.
As per dependent claims 5 and 16, the limitations merely narrow the previously recited abstract idea limitations. Dependent claims 5 and 16 recite wherein the area is an interior of a container, and the container is one of a storage unit affixed to or stored in a vehicle including a box affixed to a box truck, a trailer affixed to a platform having one or more sets of wheels and a hitch assembly for towing by the vehicle, or a unit loading device (ULD) stored in an aircraft, or a storage area integrated in at least a portion of a vehicle including a sports utility vehicle (SUV), a van, a cargo van, a commercial van, a sprinter van, or a step van. For the reasons described above with respect to claims 5 and 16, this judicial exception is not meaningfully integrated into a practical application, or significantly more than the abstract idea.
As per dependent claims 6 and 17, recitations of “one of a mobile computer, a heads up display, a tablet, a smartphone, or a wearable computing device; and the at least one sensor is one or more of an accelerometer, a gyroscope, a magnetometer, or a proximity sensor” are other computer components recited at a high-level of generality and is merely invoked as a tool to perform the abstract idea. Similar to claims 1 and 12, the recitation does not provide a practical application of the abstract idea, or significantly more than the abstract idea. The claim does not add any additional elements to evaluate at Step 2A Prong 2 and Step 2B.
As per Dependent claims 7, 18, and 24, the recitations of “wherein processing the first label associated with the first object comprises: determining whether the one or more identifiers are indicative of a barcode”; “responsive to determining the one or more identifiers are indicative of a barcode, decoding the one or more identifiers”; and “selecting a decoded identifier corresponding to a predetermined symbology” are further directed to a method of organizing human activity as described in claims 1, 12, and 23. Similar to claims 1, 12, and 23, the recitation does not provide a practical application of the abstract idea, or significantly more than the abstract idea. The claim does not add any additional elements to evaluate at Step 2A Prong 2 and Step 2B.
As per Dependent claims 8, 19, and 25, the recitations of “wherein processing the first label associated with the first object comprises: determining whether the one or more identifiers are indicative of a barcode”; “responsive to determining the one or more identifiers are not indicative of a barcode, utilizing character recognition to recognize the one or more identifiers; and “selecting a recognized identifier corresponding to a predetermined character string structure”” are further directed to a method of organizing human activity as described in claims 1, 12, and 23. Similar to claims 1, 12, and 23, the recitation does not provide a practical application of the abstract idea, or significantly more than the abstract idea. The claim does not add any additional elements to evaluate at Step 2A Prong 2 and Step 2B.
As per Dependent claims 9, 20, and 26, the recitations of “determining a location of the first object within the area based on the determined location of the first processed label comprises one or more of: generating a realogram of the area based on the determined location of the first object”; “generating a record of the determined location of the first object”; or “modifying an entry of a log associated with the area based on the determined location of the first object, the log being indicative of an inventory of the one or more objects within the area” are further directed to a method of organizing human activity as described in claims 1, 12, and 23. Similar to claims 1, 12, and 23, the recitation does not provide a practical application of the abstract idea, or significantly more than the abstract idea. The claim does not add any additional elements to evaluate at Step 2A Prong 2 and Step 2B.
As per Dependent claims 10 and 21, the recitation of “transmitting an indication indicative of the determined location of the first object to a user associated with the area” is further directed to a method of organizing human activity as described in claims 1 and 12. Similar to claims 1 and 12, the recitation does not provide a practical application of the abstract idea, or significantly more than the abstract idea. The claim does not add any additional elements to evaluate at Step 2A Prong 2 and Step 2B.
As per Dependent claims 11, 22, and 27, the recitation of “detecting a second label associated with a second object, the second label having one or more identifiers”; “processing the second label associated with the second object”; “determining a location of the processed second label associated with the second object within the area based on the derived location and orientation…”; “determining a location of the second object within the area based on the determined location of the processed second label” are further directed to a method of organizing human activity as described in claims 1, 12, and 23. Similar to claims 1, 12, and 23, the recitation does not provide a practical application of the abstract idea, or significantly more than the abstract idea. The claim does not add any additional elements to evaluate at Step 2A Prong 2 and Step 2B.
As per Dependent claim 15, the recitation of “…calibrate…to the area by deriving an initial location…” is further directed to a method of organizing human activity as described in claim 1. Similar to claim 1, the recitation does not provide a practical application of the abstract idea, or significantly more than the abstract idea. The claim does not add any additional elements to evaluate at Step 2A Prong 2 and Step 2B.
Dependent Claims 2-11, 13-22, and 24-27 have been given the full two part analysis including analyzing the additional limitations both individually and in combination. Dependent Claims 2-11, 13-22, and 24-27, when analyzed individually, and in combination, are also held to be patent ineligible under 35 U.S.C. 101. The dependent claims fail to establish that the claims do not recite an abstract idea because the additional recited limitations of the dependent claims merely further narrow the abstract idea of the independent claims. The dependent claims recite no additional elements that would integrate the judicial exception into a practical application or amount to significantly more than the judicial exception. Simply implementing the abstract idea on generic computer components is not a practical application of the judicial exception and does not amount to significantly more than the judicial exception. The claims are not patent eligible.
Claim Rejections - 35 USC § 102
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 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.
Claims 1, 4-12, and 15-28 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Gil et al. (US PG Pub. 2021/0383320 A1).
As per claim 1, Gil et al. discloses a method, comprising:
receiving, by a device, information of an area, the information being indicative of a type of the area and a map of the area (Gil et al.: [0082], shown in FIGS. 4 and 5, an information gathering device may be provided via a combination of the image camera 116 that is mounted on the device component 114 and/or the three- dimensional depth sensors 119); (Gil et al.: [0061] the control system 100 may be generally configured to maintain and/or update a defined location map associated with a facility or warehouse in which the user device (s) will be operated. This may be maintained for provision to the user device(s) upon calibration or initial "environment mapping" (see FIG. 8) via the user device (s)); (Gil et al.: [0093], the location 400 may include one or more vehicles (e.g., aircraft, tractor-trailer, cargo container, local delivery vehicles, and/or the like), pallets, identified areas within a building, bins, chutes, conveyor belts, shelves, and/or the like.);
capturing, by the device, an image of one or more objects within the area (Gil et al.: [0075], The user device 110 may also detect markers and/or target objects. For example, the user device 110 may include readers, scanners, cameras, sensors, and/or the like for detecting when a marker and/or target object and/or a pattern of unique colors within its point-of-view (POV)/field-of-view (FOV) of the real world environment/area.);
deriving a location and orientation of the device during capture of the image based on the information and data of at least one sensor of the device (Gil et al.: [0075], The user device 110 may also detect markers and/or target objects. For example, the user device 110 may include readers, scanners, cameras, sensors, and/or the like for detecting when a marker and/or target object and/or a pattern of unique colors within its point-of-view (POV)/field-of-view (FOV) of the real world environment/area.); (Gil et al.: [0074], the user device 110 may include outdoor and/or environmental positioning aspects, such as a location module adapted to acquire, for example, latitude, longitude, geocode, course, direction, heading, speed, universal time (UTC), date, and/or various other information/data); (Gil et al.: [0076], the user device 110 may include accelerometer circuitry for detecting movement, pitch, bearing, orientation, and the like of the user device 110. This information/data may be used to determine which area of the augmented/mixed environment/area corresponds to the orientation/bearing of the user device 110 (e.g., X, y, and Z axes), so that the corresponding environment/area of the augmented/mixed environment/area may be displayed via the display along with a displayed image) ;
detecting a first label associated with a first object, the first label having one or more identifiers (Gil et al.: [0075], The user device 110 may also detect markers and/or target objects); and (Gil et al.: [0099], the control system 100 may determine an asset identifier from the scanning information (which could be determined through image recognition that identifies a label on an asset and/or via wireless sensors detecting an RFID signal, or the like).;
processing the first label associated with the first object (Gil et al.: [0075], The user device 110 may also detect markers and/or target objects); (Gil et al.: [0099], the control system 100 may determine an asset identifier from the scanning information (which could be determined through image recognition that identifies a label on an asset and/or via wireless sensors detecting an RFID signal, or the like); (Gil et al.: [0126], For instance, the control system 100 can analyze the scanning information for distinguishing characteristics of the asset, such as a distinct visual aspect associated with the asset (such as an alphanumeric code, QR code, dimensions of the asset, symbols, and the like) or a wireless signal (e.g., an RFID signal communicating an asset identifier);
determining a location of the processed first label associated with the first object within the area based on the derived location and orientation of the device (Gil et al.: [0075], The user device 110 may also detect markers and/or target objects. For example, the user device 110 may include readers, scanners, cameras, sensors, and/or the like for detecting when a marker and/or target object and/or a pattern of unique colors within its point-of-view (POV)/field-of-view (FOV) of the real world environment/area); (Gil et al.: [0074], the user device 110 may include outdoor and/or environmental positioning aspects, such as a location module adapted to acquire, for example, latitude, longitude, geocode, course, direction, heading, speed, universal time (UTC), date, and/or various other information/data); (Gil et al.: [0076], the user device 110 may include accelerometer circuitry for detecting movement, pitch, bearing, orientation, and the like of the user device 110. This information/data may be used to determine which area of the augmented/mixed environment/area corresponds to the orientation/bearing of the user device 110 (e.g., X, y, and Z axes), so that the corresponding environment/area of the augmented/mixed environment/area may be displayed via the display along with a displayed image); (Gil et al.: [0099], Pattern recognition, machine learning, and/ or Al-based algorithms may be utilized to identify, from the scanning information, the shape, size, and position of each asset stored; the control system 100 may determine an asset identifier from the scanning information (which could be determined through image recognition that identifies a label on an asset and/ or via wireless sensors detecting an RFID signal, or the like)…the control system 100 may determine an asset identifier from the scanning information (which could be determined through image recognition that identifies a label on an asset and/or via wireless sensors detecting an RFID signal, or the like) while FIGS. 14A-18 describe a location 400 with respect to the storage area of a delivery vehicle, the location 400 can be any physical environment); and (Gil et al.:. [0126], For instance, the control system 100 can analyze the scanning information for distinguishing characteristics of the asset, such as a distinct visual aspect associated with the asset (such as an alphanumeric code, QR code, dimensions of the asset, symbols, and the like) or a wireless signal (e.g., an RFID signal communicating an asset identifier). Based on scanning information, the control system 100 can determine that a particular asset is located within the cargo container; the control system 100 can also determine a particular location for the particular asset.); and
determining a location of the first object within the area based on the determined location of the processed first label. (Gil et al.: [0075], The user device 110 may also detect markers and/or target objects); (Gil et al.: [0099], Pattern recognition, machine learning, and/or Al-based algorithms may be utilized to identify, from the scanning information, the shape, size, and position of each asset stored; the control system 100 may determine an asset identifier from the scanning information (which could be determined through image recognition that identifies a label on an asset and/or via wireless sensors detecting an RFID signal, or the like)… the control system 100 may determine an asset identifier from the scanning information (which could be determined through image recognition that identifies a label on an asset and/or via wireless sensors detecting an RFID signal, or the like) while FIGS. 14A-18 describe a location 400 with respect to the storage area of a delivery vehicle, the location 400 can be any physical environment); and (Gil et al.: [0126], For instance, the control system 100 can analyze the scanning information for distinguishing characteristics of the asset, such as a distinct visual aspect associated with the asset (such as an alphanumeric code, QR code, dimensions of the asset, symbols, and the like) or a wireless signal (e.g., an RFID signal communicating an asset identifier). Based on scanning information, the control system 100 can determine that a particular asset is located within the cargo container; the control system 100 can also determine a particular location for the particular asset.).
As per claim 4, Gil et al. discloses the method of claim 1, wherein receiving, by the device, information of the area calibrates the device to the area by deriving an initial location of the device within the area based on the information and the data (Gil et al.: [0152], In Block 501, an initialization or calibration of the user device 110 may be conducted; it need only be conducted periodically, for example upon initial use of the user device and/or upon receipt from the control system 100- of a notification that an environment in which the user device operates has been altered or updated. As described herein above, per block 501 an environment that a user is located in is mapped based at least in part on generating a multidimensional (e.g., 3-D) graphical representation of the environment); and (Gil et al.: [0061], the control system 100 may be generally configured to maintain and/or update a defined location map associated with a facility or warehouse in which the user device (s) will be operated. This may be maintained for provision to the user device (s) upon calibration or initial "environment mapping" (see FIG. 8) via the user device (s)).
As per claim 5, Gil et al. discloses the method of claim 1, wherein the area is an interior of a container, and the container is one of a storage unit affixed to or stored in a vehicle including a box affixed to a box truck, a trailer affixed to a platform having one or more sets of wheels and a hitch assembly for towing by the vehicle, or a unit loading device (ULD) stored in an aircraft, or a storage area integrated in at least a portion of a vehicle including a sports utility vehicle (SUV), a van, a cargo van, a commercial van, a sprinter van, or a step van (Gil et al.: [0126], At Block 1820, it can be determined that an asset is located within a cargo container); (Gil et al.: [0093], the location 400 may include may include one or more vehicles (e.g., aircraft, tractor-trailer, cargo container, local delivery vehicles, and/or the like), pallets, identified areas within a building, bins, chutes, conveyor belts, shelves, and/or the like; the location 400 includes a plurality of shelves onto which the assets 10 may be placed and/or removed from; For example, the location 400 can be a cargo container associated with the delivery vehicle or within the delivery vehicle itself.).
As per claim 6, Gil et al. discloses the method of claim 1, wherein the device is one of a mobile computer, a heads up display, a tablet, a smartphone, or a wearable computing device; and the at least one sensor is one or more of an accelerometer, a gyroscope, a magnetometer, or a proximity sensor (Gil et al.: [0063], the user device 110 is a hands-free type device, including wearable items/devices (e.g., the user devices of FIGS. 4-5), head-mounted displays (HMDs) (e.g., Oculus Rift, Sony HMZ-T3 W, and the like), and the like.); and the at least one sensor is one or more of an accelerometer, a gyroscope, a magnetometer, or a proximity sensor); and (Gil et al.: [0065], the user device 110 may include speakers, head phones, or other electronic hardware for audio output, a plurality of display devices, one or more position sensors (e.g., gyroscopes, global positioning system receivers, and/or accelerometers), battery packs, beacons for external sensors (e.g., infrared lamps), or the like.).
As per claim 7, Gil et al. discloses the method of claim 1, wherein processing the first label associated with the first object comprises:
determining whether the one or more identifiers are indicative of a barcode (Gil et al.: [0075], The user device 110 may also detect markers and/or target objects For example, readers, scanners, cameras, sensors, and/or the like may include RFID readers/interrogators to read RFID tags, scanners and cameras to capture visual patterns and/or codes (e.g., text, barcodes, character strings, Aztec Codes, Maxi Codes, information/ data Matrices, QR Codes, electronic representations, and/or the like), and sensors to detect beacon signals transmitted from target objects or the environment/area in which target objects are located); (Gil et al.: [0099], the control system 100 may determine an asset identifier from the scanning information (which could be determined through image recognition that identifies a label on an asset and/or via wireless sensors detecting an RFID signal, or the like); and (Gil et al.: [0126], For instance, the control system 100 can analyze the scanning information for distinguishing characteristics of the asset, such as a distinct visual aspect associated with the asset (such as an alphanumeric code, QR code, dimensions of the asset, symbols, and the like) or a wireless signal (e.g., an RFID signal communicating an asset identifier);
responsive to determining the one or more identifiers are indicative of a barcode, decoding the one or more identifiers (Gil et al.: [0075], The user device 110 may also detect markers and/or target objects, For example, readers, scanners, cameras, sensors, and/or the like may include RFID readers/ interrogators to read RFID tags, scanners and cameras to capture visual patterns and/or codes (e.g., text, barcodes, character strings, Aztec Codes, Maxi Codes, information/data Matrices, QR Codes, electronic representations, and/or the like), and sensors to detect beacon signals transmitted from target objects or the environment/area in which target objects are located); (Gil et al.: [0099], the control system 100 may determine an asset identifier from the scanning information (which could be determined through image recognition that identifies a label on an asset and/or via wireless sensors detecting an RFID signal, or the like); and (Gil et al.: [0126], For instance, the control system 100 can analyze the scanning information for distinguishing characteristics of the asset, such as a distinct visual aspect associated with the asset (such as an alphanumeric code, QR code, dimensions of the asset, symbols, and the like) or a wireless signal (e.g., an RFID signal communicating an asset identifier); and
selecting a decoded identifier corresponding to a predetermined symbology (Gil et al.: [0075], The user device 110 may also detect markers and/or target objects For example, readers, scanners, cameras, sensors, and/or the like may include RFID readers/interrogators to read RFID tags, scanners and cameras to capture visual patterns and/or codes (e.g., text, barcodes, character strings, Aztec Codes, Maxi Codes, information/ data Matrices, QR Codes, electronic representations, and/or the like), and sensors to detect beacon signals transmitted from target objects or the environment/area in which target objects are located); (Gil et al.: [0099], the control system 100 may determine an asset identifier from the scanning information (which could be determined through image recognition that identifies a label on an asset and/or via wireless sensors detecting an RFID signal, or the like); and (Gil et al.: [0126], For instance, the control system 100 can analyze the scanning information for distinguishing characteristics of the asset, such as a distinct visual aspect associated with the asset (such as an alphanumeric code, QR code, dimensions of the asset, symbols, and the like) or a wireless signal (e.g., an RFID signal communicating an asset identifier).
As per claim 8, Gil et al. discloses the method of claim 1, wherein processing the first label associated with the first object comprises:
determining whether the one or more identifiers are indicative of a barcode (Gil et al.: [0075], The user device 110 may also detect markers and/or target objects For example, readers, scanners, cameras, sensors, and/or the like may include RFID readers/interrogators to read RFID tags, scanners and cameras to capture visual patterns and/or codes (e.g., text, barcodes, character strings, Aztec Codes, Maxi Codes, information/ data Matrices, QR Codes, electronic representations, and/or the like), and sensors to detect beacon signals transmitted from target objects or the environment/area in which target objects are located); (Gil et al.: [0099], the control system 100 may determine an asset identifier from the scanning information (which could be determined through image recognition that identifies a label on an asset and/or via wireless sensors detecting an RFID signal, or the like); and (Gil et al.: [0126], For instance, the control system 100 can analyze the scanning information for distinguishing characteristics of the asset, such as a distinct visual aspect associated with the asset (such as an alphanumeric code, QR code, dimensions of the asset, symbols, and the like) or a wireless signal (e.g., an RFID signal communicating an asset identifier);
responsive to determining the one or more identifiers are not indicative of a barcode, utilizing character recognition to recognize the one or more identifiers (Gil et al.: [0075], The user device 110 may also detect markers and/or target objects For example, readers, scanners, cameras, sensors, and/or the like may include RFID readers/interrogators to read RFID tags, scanners and cameras to capture visual patterns and/ or codes (e.g., text, barcodes, character strings, Aztec Codes, Maxi Codes, information/data Matrices, QR Codes, electronic representations, and/or the like), and sensors to detect beacon signals transmitted from target objects or the environment/area in which target objects are located); Gil et al.: [0099], the control system 100 may determine an asset identifier from the scanning information (which could be determined through image recognition that identifies a label on an asset and/or via wireless sensors detecting an RFID signal, or the like); and (Gil et al.: [0126], For instance, the control system 100 can analyze the scanning information for distinguishing characteristics of the asset, such as a distinct visual aspect associated with the asset (such as an alphanumeric code, QR code, dimensions of the asset, symbols, and the like) or a wireless signal (e.g., an RFID signal communicating an asset identifier); and
selecting a recognized identifier corresponding to a predetermined character string structure (Gil et al.: [0075], The user device 110 may also detect markers and/or target objects For example, readers, scanners, cameras, sensors, and/or the like may include RFID readers/interrogators to read RFID tags, scanners and cameras to capture visual patterns and/or codes (e.g., text, barcodes, character strings, Aztec Codes, Maxi Codes, information/data Matrices, QR Codes, electronic representations, and/or the like), and sensors to detect beacon signals transmitted from target objects or the environment/area in which target objects are located); (Gil et al.: [0099], the control system 100 may determine an asset identifier from the scanning information (which could be determined through image recognition that identifies a label on an asset and/or via wireless sensors detecting an RFID signal, or the like); and (Gil et al.: [0126], For instance, the control system 100 can analyze the scanning information for distinguishing characteristics of the asset, such as a distinct visual aspect associated with the asset (such as an alphanumeric code, QR code, dimensions of the asset, symbols, and the like) or a wireless signal (e.g., an RFID signal communicating an asset identifier)).
As per claim 9, Gil et al. discloses the method of claim 1, wherein determining a location of the first object within the area based on the determined location of the first processed label comprises one or more of:
generating a realogram of the area based on the determined location of the first object;
generating a record of the determined location of the first object (Gil et al.: [0075], The user device 110 may also detect markers and/or target objects); (Gil et al.: [0099], the control system 100 may determine an asset identifier from the scanning information (which could be determined through image recognition that identifies a label on an asset and/or via wireless sensors detecting an RFID signal, or the like); (Gil et al.:. [0126], For instance, the control system 100 can analyze the scanning information for distinguishing characteristics of the asset, such as a distinct visual aspect associated with the asset (such as an alphanumeric code, QR code, dimensions of the asset, symbols, and the like) or a wireless signal (e.g., an RFID signal communicating an asset identifier). Based on scanning information, the control system 100 can determine that a particular asset is located within the cargo container; the control system 100 can also determine a particular location for the particular asset); and (Gil et al.: [0008], methods for maintaining accurate records of the location of an asset in a sort and/or pick process, while also providing to carrier personnel improved instructions and/or guidance for the automated handling of the packages within various environments); or
modifying an entry of a log associated with the area based on the determined location of the first object, the log being indicative of an inventory of the one or more objects within the area.
As per claim 10, Gil et al. discloses the method of claim 1, further comprising transmitting an indication indicative of the determined location of the first object to a user associated with the area (Gil et al.: [0112], the projector 900 can illuminate a portion of the environment of the delivery vehicle so as to guide the user 5 to the particular asset); and (Gil et al.: [0113], the light source can be activated, by the control system 100, to illuminate a portion of the environment to identify the physical location of the asset to be pulled).
As per claim 11, Gil et al. discloses the method of claim 1, further comprising:
detecting a second label associated with a second object, the second label having one or more identifiers (Gil et al.: [0075], The user device 110 may also detect markers and/or target objects); (Gil et al.: [0099], the control system 100 may determine an asset identifier from the scanning information (which could be determined through image recognition that identifies a label on an asset and/or via wireless sensors detecting an RFID signal, or the like); and (Gil et al.: [0126], For instance, the control system 100 can analyze the scanning information for distinguishing characteristics of the asset, such as a distinct visual aspect associated with the asset (such as an alphanumeric code, QR code, dimensions of the asset, symbols, and the like) or a wireless signal (e.g., an RFID signal communicating an asset identifier);
processing the second label associated with the second object (Gil et al.: [0075], The user device 110 may also detect markers and/or target objects); (Gil et al.: [0099], the control system 100 may determine an asset identifier from the scanning information (which could be determined through image recognition that identifies a label on an asset and/or via wireless sensors detecting an RFID signal, or the like); and (Gil et al.: [0126], For instance, the control system 100 can analyze the scanning information for distinguishing characteristics of the asset, such as a distinct visual aspect associated with the asset (such as an alphanumeric code, QR code, dimensions of the asset, symbols, and the like) or a wireless signal (e.g., an RFID signal communicating an asset identifier);
determining a location of the processed second label associated with the second object within the area based on the derived location and orientation of the device (Gil et al.: [0075], The user device 110 may also detect markers and/or target objects. For example, the user device 110 may include readers, scanners, cameras, sensors, and/or the like for detecting when a marker and/or target object and/or a pattern of unique colors within its point-of-view (POV)/field-of-view (FOV) of the real world environment/area); (Gil et al.: [0074], the user device 110 may include outdoor and/or environmental positioning aspects, such as a location module adapted to acquire, for example, latitude, longitude, geocode, course, direction, heading, speed, universal time (UTC), date, and/or various other information/data); (Gil et al.: [0076], the user device 110 may include accelerometer circuitry for detecting movement, pitch, bearing, orientation, and the like of the user device 110. This information/data may be used to determine which area of the augmented/mixed environment/area corresponds to the orientation/bearing of the user device 110 (e.g., X, y, and Z axes), so that the corresponding environment/area of the augmented/mixed environment/area may be displayed via the display along with a displayed image); (Gil et al.:. [0099], The scanning device 1500 can be used to capture the size and shape of asset 10 as it is loaded into the storage area and placed onto shelves (i.e., specific locations); Pattern recognition, machine learning, and/or Al-based algorithms may be utilized to identify, from the scanning information, the shape, size, and position of each asset stored; the control system 100 may determine an asset identifier from the scanning information (which could be determined through image recognition that identifies a label on an asset and/or via wireless sensors detecting an RFID signal, or the like) while FIGS. 14A-18 describe a location 400 with respect to the storage area of a delivery vehicle, the location 400 can be any physical environment); and (Gil et al.: [0126], For instance, the control system 100 can analyze the scanning information for distinguishing characteristics of the asset, such as a distinct visual aspect associated with the asset (such as an alphanumeric code, QR code, dimensions of the asset, symbols, and the like) or a wireless signal (e.g., an RFID signal communicating an asset identifier). Based on scanning information, the control system 100 can determine that a particular asset is located within the cargo container; the control system 100 can also determine a particular location for the particular asset.);
determining a location of the second object within the area based on the determined location of the processed second label (Gil et al.: [0075], The user device 110 may also detect markers and/or target objects); (Gil et al.: [0099], the control system 100 may determine an asset identifier from the scanning information (which could be determined through image recognition that identifies a label on an asset and/or via wireless sensors detecting an RFID signal, or the like); (Gil et al.: [0099], The scanning device 1500 can be used to capture the size and shape of asset 10 as it is loaded into the storage area and placed onto shelves (i.e., specific locations); Pattern recognition, machine learning, and/or Al-based algorithms may be utilized to identify, from the scanning information, the shape, size, and position of each asset stored; the control system 100 may determine an asset identifier from the scanning information (which could be determined through image recognition that identifies a label on an asset and/or via wireless sensors detecting an RFID signal, or the like) while FIGS. 14A-18 describe a location 400 with respect to the storage area of a delivery vehicle, the location 400 can be any physical environment); and (Gil et al.: [0126], For instance, the control system 100 can analyze the scanning information for distinguishing characteristics of the asset, such as a distinct visual aspect associated with the asset (such as an alphanumeric code, QR code, dimensions of the asset, symbols, and the like) or a wireless signal (e.g., an RFID signal communicating an asset identifier). Based on scanning information, the control system 100 can determine that a particular asset is located within the cargo container; the control system 100 can also determine a particular location for the particular asset.)..
As per claim 12, Gil et al. discloses a device (Abstract); (Gil et al. [0075], The user device 110 may also detect markers and/or target objects), comprising:
an imaging assembly (Gil et al.: [0075], The user device 110 may also detect markers and/or target objects. For example, the user device 110 may include readers, scanners, cameras, sensors, and/or the like for detecting when a marker and/or target object and/or a pattern of unique colors within its point-of-view (POV)/field- of-view (FOV) of the real world environment/area.);
at least one sensor (Gil et al.: [0075], The user device 110 may also detect markers and/or target objects. For example, the user device 110 may include readers, scanners, cameras, sensors, and/or the like for detecting when a marker and/or target object and/or a pattern of unique colors within its point-of-view (POV)/field- of-view (FOV) of the real world environment/area);
one or more processors (Gil et al.: [0067], the user device 110 can include an antenna 312 (e.g., the antenna 115 of FIG. 5), a transmitter 304 (e.g., radio), a receiver 306 (e.g., radio), and a processing element 308 (e.g., CPLDs, microprocessors, multi-core processors, co-processing entities, ASIPs, microcontrollers, and/or controllers) that provides signals to and receives signals from the transmitter 304 and receiver 306, respectively); and
a non-transitory computer-readable memory coupled to the one or more processors, the memory storing instructions thereon that, when executed by the one or more processors, cause the one or more processors (Gil et al.: [0057], the volatile storage or memory media may be used to store at least portions of the databases, database instances, database management systems, data, applications, programs, program modules, scripts, source code, object code, byte code, compiled such statement code, interpreted code, machine code, executable instructions, and/or the like being executed by, for example, the processing element 205) to:
receive information of an area, the information being indicative of a type of the area and a map of the area (Gil et al.: [0082], shown in FIGS. 4 and 5, an information gathering device may be provided via a combination of the image camera 116 that is mounted on the device component 114 and/or the three-dimensional depth sensors 119); (Gil et al.:. [0061], the control system 100 may be generally configured to maintain and/or update a defined location map associated with a facility or warehouse in which the user device (s) will be operated. This may be maintained for provision to the user device (s) upon calibration or initial "environment mapping" (see FIG. 8) via the user device (s); and (Gil et al.: [0093], the location 400 may include may include one or more vehicles (e.g., aircraft, tractor-trailer, cargo container, local delivery vehicles, and/or the like), pallets, identified areas within a building, bins, chutes, conveyor belts, shelves, and/or the like);
receive an image, captured by the imaging assembly, of one or more objects within the area (Gil et al.: [0075], The user device 110 may also detect markers and/or target objects. For example, the user device 110 may include readers, scanners, cameras, sensors, and/or the like for detecting when a marker and/or target object and/or a pattern of unique colors within its point-of-view (POV)/field-of-view (FOV) of the real world environment/area);
derive a location and orientation of the device during capture of the image based on the information and data of the at least one sensor (Gil et al.: [0075], the user device 110 may include readers, scanners, cameras, sensors, and/or the like for detecting when a marker and/or target object and/or a pattern of unique colors within its point-of-view (POV)/field-of-view (FOV) of the real world environment/area); (Gil et al.:. [0074], the user device 110 may include outdoor and/or environmental positioning aspects, such as a location module adapted to acquire, for example, latitude, longitude, geocode, course, direction, heading, speed, universal time (UTC), date, and/or various other information/data); and (Gil et al.:. [0076], the user device 110 may include accelerometer circuitry for detecting movement, pitch, bearing, orientation, and the like of the user device 110. This information/data may be used to determine which area of the augmented/mixed environment/area corresponds to the orientation/bearing of the user device 110 (e.g., X, y, and 7. axes), so that the corresponding environment/area of the augmented/mixed environment/area may be displayed via the display along with a displayed image.);
detect a first label associated with a first object, the first label having one or more identifiers (Gil et al.: [0075], The user device 110 may also detect markers and/or target objects); (Gil et al.: [0099], the control system 100 may determine an asset identifier from the scanning information (which could be determined through image recognition that identifies a label on an asset and/or via wireless sensors detecting an RFID signal, or the like);
process the first label associated with the first object (Gil et al.: [0075], The user device 110 may also detect markers and/or target objects); (Gil et al.: [0099], the control system 100 may determine an asset identifier from the scanning information (which could be determined through image recognition that identifies a label on an asset and/or via wireless sensors detecting an RFID signal, or the like); and (Gil et al.: [0126], For instance, the control system 100 can analyze the scanning information for distinguishing characteristics of the asset, such as a distinct visual aspect associated with the asset (such as an alphanumeric code, QR code, dimensions of the asset, symbols, and the like) or a wireless signal (e.g., an RFID signal communicating an asset identifier);
determine a location of the processed first label associated with the first object within the area based on the derived location and orientation of the device (Gil et al.: [0075], The user device 110 may also detect markers and/or target objects. For example, the user device 110 may include readers, scanners, cameras, sensors, and/or the like for detecting when a marker and/or target object and/or a pattern of unique colors within its point-of-view (POV)/field-of-view (FOV) of the real world environment/area); (Gil et al.: [0074], the user device 110 may include outdoor and/or environmental positioning aspects, such as a location module adapted to acquire, for example, latitude, longitude, geocode, course, direction, heading, speed, universal time (UTC), date, and/or various other information/data); (Gil et al.: [0076], the user device 110 may include accelerometer circuitry for detecting movement, pitch, bearing, orientation, and the like of the user device 110. This information/data may be used to determine which area of the augmented/mixed environment/area corresponds to the orientation/bearing of the user device 110 (e.g., X, y, and Z axes), so that the corresponding environment/area of the augmented/mixed environment/area may be displayed via the display along with a displayed image); (Gil et al.: [0099], Pattern recognition, machine learning, and/ or Al-based algorithms may be utilized to identify, from the scanning information, the shape, size, and position of each asset stored; the control system 100 may determine an asset identifier from the scanning information (which could be determined through image recognition that identifies a label on an asset and/ or via wireless sensors detecting an RFID signal, or the like); the control system 100 may determine an asset identifier from the scanning information (which could be determined through image recognition that identifies a label on an asset and/or via wireless sensors detecting an RFID signal, or the like) while FIGS. 14A-18 describe a location 400 with respect to the storage area of a delivery vehicle, the location 400 can be any physical environment); and (Gil et al.: [0126], For instance, the control system 100 can analyze the scanning information for distinguishing characteristics of the asset, such as a distinct visual aspect associated with the asset (such as an alphanumeric code, QR code, dimensions of the asset, symbols, and the like) or a wireless signal (e.g., an RFID signal communicating an asset identifier). Based on scanning information, the control system 100 can determine that a particular asset is located within the cargo container; the control system 100 can also determine a particular location for the particular asset.); and
determine a location of the first object within the area based on the determined location of the processed first label. determine a location of the first object within the area based on the determined location of the processed first label (Gil et al.: [0075], The user device 110 may also detect markers and/or target objects); (Gil et al.: [0099], Pattern recognition, machine learning, and/or Al-based algorithms may be utilized to identify, from the scanning information, the shape, size, and position of each asset stored; the control system 100 may determine an asset identifier from the scanning information (which could be determined through image recognition that identifies a label on an asset and/or via wireless sensors detecting an RFID signal, or the like); the control system 100 may determine an asset identifier from the scanning information (which could be determined through image recognition that identifies a label on an asset and/or via wireless sensors detecting an RFID signal, or the like) while FIGS. 14A-18 describe a location 400 with respect to the storage area of a delivery vehicle, the location 400 can be any physical environment); and (Gil et al.: [0126], For instance, the control system 100 can analyze the scanning information for distinguishing characteristics of the asset, such as a distinct visual aspect associated with the asset (such as an alphanumeric code, QR code, dimensions of the asset, symbols, and the like) or a wireless signal (e.g., an RFID signal communicating an asset identifier). Based on scanning information, the control system 100 can determine that a particular asset is located within the cargo container; the control system 100 can also determine a particular location for the particular asset.).
As per claim 15, Gil et al. discloses the device of claim 12, wherein the instructions, when executed, further cause the one or more processors to calibrate the device to the area by deriving an initial location of the device within the area based on the information and the data (Gil et al.: [0152], In Block 501, an initialization or calibration of the user device 110 may be conducted; it need only be conducted periodically, for example upon initial use of the user device and/or upon receipt from the control system 100- of a notification that an environment in which the user device operates has been altered or updated. As described herein above, per block 501 an environment that a user is located in is mapped based at least in part on generating a multidimensional (e.g., 3-D) graphical representation of the environment); and (Gil et al.: [0061], the control system 100 may be generally configured to maintain and/or update a defined location map associated with a facility or warehouse in which the user device (s) will be operated. This may be maintained for provision to the user device (s) upon calibration or initial "environment mapping" (see FIG. 8) via the user device (s)).
As per claim 16, Gil et al. discloses the device of claim 12, wherein the area is an interior of a container, and the container is one of a storage unit affixed to or stored in a vehicle including a box affixed to a box truck, a trailer affixed to a platform having one or more sets of wheels and a hitch assembly for towing by the vehicle, or a unit loading device (ULD) stored in an aircraft, or a storage area integrated in at least a portion of a vehicle including a sports utility vehicle (SUV), a van, a cargo van, a commercial van, a sprinter van, or a step van. (Gil et al.: [0126], At Block 1820, it can be determined that an asset is located within a cargo container); and (Gil et al.: [0093], the location 400 may include may include one or more vehicles (e.g., aircraft, tractor-trailer, cargo container, local delivery vehicles, and/or the like), pallets, identified areas within a building, bins, chutes, conveyor belts, shelves, and/or the like; the location 400 includes a plurality of shelves onto which the assets 10 may be placed and/or removed from; For example, the location 400 can be a cargo container associated with the delivery vehicle or within the delivery vehicle itself.).
As per claim 17, Gil et al. disclose the device of claim 12, wherein the device is one of a mobile computer, a heads up display, a tablet, a smartphone, or a wearable computing device; and the at least one sensor is one or more of an accelerometer, a gyroscope, a magnetometer, or a proximity sensor (Gil et al.: [0063], the user device 110 is a hands-free type device, including wearable items/devices (e.g., the user devices of FIGS. 4-5), head-mounted displays (HMDs) (e.g., Oculus Rift, Sony HMZ-T3 W, and the like), and the like.); and the at least one sensor is one or more of an accelerometer, a gyroscope, a magnetometer, or a proximity sensor (Gil et al.: [0065], the user device 110 may include speakers, head phones, or other electronic hardware for audio output, a plurality of display devices, one or more position sensors (e.g., gyroscopes, global positioning system receivers, and/or accelerometers), battery packs, beacons for external sensors (e.g., infrared lamps), or the like).
As per claim 18, Gil et al. discloses the device of claim 12, wherein the instructions, when executed, cause the one or more processors to process the first label associated with the first object by:
determining whether the one or more identifiers are indicative of a barcode (Gil et al.: [0075], The user device 110 may also detect markers and/or target objects For example, readers, scanners, cameras, sensors, and/or the like may include RFID readers/interrogators to read RFID tags, scanners and cameras to capture visual patterns and/or codes (e.g., text, barcodes, character strings, Aztec Codes, Maxi Codes, information/ data Matrices, QR Codes, electronic representations, and/or the like), and sensors to detect beacon signals transmitted from target objects or the environment/area in which target objects are located; Gil et al.: [0099], the control system 100 may determine an asset identifier from the scanning information (which could be determined through image recognition that identifies a label on an asset and/or via wireless sensors detecting an RFID signal, or the like); Gil et al.: [0126], For instance, the control system 100 can analyze the scanning information for distinguishing characteristics of the asset, such as a distinct visual aspect associated with the asset (such as an alphanumeric code, QR code, dimensions of the asset, symbols, and the like) or a wireless signal (e.g., an RFID signal communicating an asset identifier);
responsive to determining the one or more identifiers are indicative of a barcode, decoding the one or more identifiers (Gil et al.: [0075], The user device 110 may also detect markers and/or target objects For example, readers, scanners, cameras, sensors, and/or the like may include RFID readers/ interrogators to read RFID tags, scanners and cameras to capture visual patterns and/or codes (e.g., text, barcodes, character strings, Aztec Codes, Maxi Codes, information/data Matrices, QR Codes, electronic representations, and/or the like), and sensors to detect beacon signals transmitted from target objects or the environment/area in which target objects are located); (Gil et al.: [0099], the control system 100 may determine an asset identifier from the scanning information (which could be determined through image recognition that identifies a label on an asset and/or via wireless sensors detecting an RFID signal, or the like); and (Gil et al.: [0126], For instance, the control system 100 can analyze the scanning information for distinguishing characteristics of the asset, such as a distinct visual aspect associated with the asset (such as an alphanumeric code, QR code, dimensions of the asset, symbols, and the like) or a wireless signal (e.g., an RFID signal communicating an asset identifier); and
selecting a decoded identifier corresponding to a predetermined symbology (Gil et al.: [0075], The user device 110 may also detect markers and/or target objects For example, readers, scanners, cameras, sensors, and/or the like may include RFID readers/interrogators to read RFID tags, scanners and cameras to capture visual patterns and/or codes (e.g., text, barcodes, character strings, Aztec Codes, Maxi Codes, information/ data Matrices, QR Codes, electronic representations, and/or the like), and sensors to detect beacon signals transmitted from target objects or the environment/area in which target objects are located; Gil et al.: [0099], the control system 100 may determine an asset identifier from the scanning information (which could be determined through image recognition that identifies a label on an asset and/or via wireless sensors detecting an RFID signal, or the like); and (Gil et al.: [0126], For instance, the control system 100 can analyze the scanning information for distinguishing characteristics of the asset, such as a distinct visual aspect associated with the asset (such as an alphanumeric code, QR code, dimensions of the asset, symbols, and the like) or a wireless signal (e.g., an RFID signal communicating an asset identifier).
As per claim 19, Gil et al. discloses the device of claim 12, wherein the instructions, when executed, cause the one or more processors to process the first label associated with the first object by:
determining whether the one or more identifiers are indicative of a barcode (Gil et al.: [0075], The user device 110 may also detect markers and/or target objects For example, readers, scanners, cameras, sensors, and/or the like may include RFID readers/interrogators to read RFID tags, scanners and cameras to capture visual patterns and/or codes (e.g., text, barcodes, character strings, Aztec Codes, Maxi Codes, information/ data Matrices, QR Codes, electronic representations, and/or the like), and sensors to detect beacon signals transmitted from target objects or the environment/area in which target objects are located); (Gil et al.: [0099], the control system 100 may determine an asset identifier from the scanning information (which could be determined through image recognition that identifies a label on an asset and/or via wireless sensors detecting an RFID signal, or the like); and (Gil et al.: [0126], For instance, the control system 100 can analyze the scanning information for distinguishing characteristics of the asset, such as a distinct visual aspect associated with the asset (such as an alphanumeric code, QR code, dimensions of the asset, symbols, and the like) or a wireless signal (e.g., an RFID signal communicating an asset identifier);
responsive to determining the one or more identifiers are not indicative of a barcode, utilizing character recognition to recognize the one or more identifiers (Gil et al.: [0075], The user device 110 may also detect markers and/or target objects For example, readers, scanners, cameras, sensors, and/or the like may include RFID readers/interrogators to read RFID tags, scanners and cameras to capture visual patterns and/ or codes (e.g., text, barcodes, character strings, Aztec Codes, Maxi Codes, information/data Matrices, QR Codes, electronic representations, and/or the like), and sensors to detect beacon signals transmitted from target objects or the environment/area in which target objects are located); (Gil et al.: [0099], the control system 100 may determine an asset identifier from the scanning information (which could be determined through image recognition that identifies a label on an asset and/or via wireless sensors detecting an RFID signal, or the like); and (Gil et al.: [0126], For instance, the control system 100 can analyze the scanning information for distinguishing characteristics of the asset, such as a distinct visual aspect associated with the asset (such as an alphanumeric code, QR code, dimensions of the asset, symbols, and the like) or a wireless signal (e.g., an RFID signal communicating an asset identifier); and
selecting a recognized identifier corresponding to a predetermined character string structure (Gil et al.: [0075], The user device 110 may also detect markers and/or target objects For example, readers, scanners, cameras, sensors, and/or the like may include RFID readers/interrogators to read RFID tags, scanners and cameras to capture visual patterns and/or codes (e.g., text, barcodes, character strings, Aztec Codes, Maxi Codes, information/data Matrices, QR Codes, electronic representations, and/or the like), and sensors to detect beacon signals transmitted from target objects or the environment/area in which target objects are located); (Gil et al.: [0099], the control system 100 may determine an asset identifier from the scanning information (which could be determined through image recognition that identifies a label on an asset and/ or via wireless sensors detecting an RFID signal, or the like); and (Gil et al.: [0126], For instance, the control system 100 can analyze the scanning information for distinguishing characteristics of the asset, such as a distinct visual aspect associated with the asset (such as an alphanumeric code, QR code, dimensions of the asset, symbols, and the like) or a wireless signal (e.g., an RFID signal communicating an asset identifier).
As per claim 20, Gil et al. discloses the device of claim 12, wherein the instructions, when executed, cause the one or more processors to determine the location of the first object within the area based on the determined location of the processed first label by one or more of:
generating a realogram of the area based on the determined location of the first object;
generating a record of the determined location of the first object); (Gil et al.: [0099], the control system 100 may determine an asset identifier from the scanning information (which could be determined through image recognition that identifies a label on an asset and/or via wireless sensors detecting an RFID signal, or the like); (Gil et al.: [0126], For instance, the control system 100 can analyze the scanning information for distinguishing characteristics of the asset, such as a distinct visual aspect associated with the asset (such as an alphanumeric code, QR code, dimensions of the asset, symbols, and the like) or a wireless signal (e.g., an RFID signal communicating an asset identifier). Based on scanning information, the control system 100 can determine that a particular asset is located within the cargo container; the control system 100 can also determine a particular location for the particular asset); and (Gil et al.: [0008], methods for maintaining accurate records of the location of an asset in a sort and/or pick process, while also providing to carrier personnel improved instructions and/or guidance for the automated handling of the packages within various environments); or
modifying an entry of a log associated with the area based on the determined location of the first object, the log being indicative of an inventory of the one or more objects within the area.
As per claim 21, Gil et al. discloses the device of claim 12, wherein the instructions, when executed, further cause the one or more processors to transmit an indication indicative of the determined location of the first object to a user associated with the area (Gil et al.: [0112], the projector 900 can illuminate a portion of the environment of the delivery vehicle SO as to guide the user 5 to the particular asset); and (Gil et al.: [0113], the light source can be activated, by the control system 100, to illuminate a portion of the environment to identify the physical location of the asset to be pulled).
As per claim 22, Gil et al.: discloses the device of claim 12, wherein the instructions, when executed, further cause the one or more processors to:
detect a second label associated with a second object, the second label having one or more identifiers (Gil et al.:0075], The user device 110 may also detect markers and/or target objects); (Gil et al.: [0099], the control system 100 may determine an asset identifier from the scanning information (which could be determined through image recognition that identifies a label on an asset and/or via wireless sensors detecting an RFID signal, or the like); and (Gil et al.: [0126], For instance, the control system 100 can analyze the scanning information for distinguishing characteristics of the asset, such as a distinct visual aspect associated with the asset (such as an alphanumeric code, QR code, dimensions of the asset, symbols, and the like) or a wireless signal (e.g., an RFID signal communicating an asset identifier);
process the second label associated with the second object (Gil et al.: [0075], The user device 110 may also detect markers and/or target objects); (Gil et al.: [0099], the control system 100 may determine an asset identifier from the scanning information (which could be determined through image recognition that identifies a label on an asset and/or via wireless sensors detecting an RFID signal, or the like); and (Gil et al.: [0126], For instance, the control system 100 can analyze the scanning information for distinguishing characteristics of the asset, such as a distinct visual aspect associated with the asset (such as an alphanumeric code, QR code, dimensions of the asset, symbols, and the like) or a wireless signal (e.g., an RFID signal communicating an asset identifier);
determine a location of the processed second label associated with the second object within the area based on the derived location and orientation of the device (Gil et al.: [0075], The user device 110 may also detect markers and/or target objects. For example, the user device 110 may include readers, scanners, cameras, sensors, and/or the like for detecting when a marker and/or target object and/or a pattern of unique colors within its point-of-view (POV)/field-of-view (FOV) of the real world environment/area); and (Gil et al.: [0074], the user device 110 may include outdoor and/or environmental positioning aspects, such as a location module adapted to acquire, for example, latitude, longitude, geocode, course, direction, heading, speed, universal time (UTC), date, and/or various other information/data); (Gil et al.: [0076], the user device 110 may include accelerometer circuitry for detecting movement, pitch, bearing, orientation, and the like of the user device 110. This information/data may be used to determine which area of the augmented/mixed environment/area corresponds to the orientation/bearing of the user device 110 (e.g., X, y, and 7. axes), so that the corresponding environment/area of the augmented/mixed environment/area may be displayed via the display along with a displayed image); (Gil et al.: [0099], The scanning device 1500 can be used to capture the size and shape of asset 10 as it is loaded into the storage area and placed onto shelves (i.e., specific locations); Pattern recognition, machine learning, and/or Al-based algorithms may be utilized to identify, from the scanning information, the shape, size, and position of each asset stored; the control system 100 may determine an asset identifier from the scanning information (which could be determined through image recognition that identifies a label on an asset and/or via wireless sensors detecting an RFID signal, or the like) while FIGS. 14A-18 describe a location 400 with respect to the storage area of a delivery vehicle, the location 400 can be any physical environment); and (Gil et al.: [0126], For instance, the control system 100 can analyze the scanning information for distinguishing characteristics of the asset, such as a distinct visual aspect associated with the asset (such as an alphanumeric code, QR code, dimensions of the asset, symbols, and the like) or a wireless signal (e.g., an RFID signal communicating an asset identifier). Based on scanning information, the control system 100 can determine that a particular asset is located within the cargo container; the control system 100 can also determine a particular location for the particular asset.);
determine a location of the second object within the area based on the determined location of the processed second label. (Gil et al.: [0075], The user device 110 may also detect markers and/or target objects); (Gil et al.: [0099], the control system 100 may determine an asset identifier from the scanning information (which could be determined through image recognition that identifies a label on an asset and/or via wireless sensors detecting an RFID signal, or the like); (Gil et al.: [0099], The scanning device 1500 can be used to capture the size and shape of asset 10 as it is loaded into the storage area and placed onto shelves (i.e., specific locations); Pattern recognition, machine learning, and/or Al-based algorithms may be utilized to identify, from the scanning information, the shape, size, and position of each asset stored; the control system 100 may determine an asset identifier from the scanning information (which could be determined through image recognition that identifies a label on an asset and/or via wireless sensors detecting an RFID signal, or the like) while FIGS. 14A-18 describe a location 400 with respect to the storage area of a delivery vehicle, the location 400 can be any physical environment); and (Gil et al.: [0126], For instance, the control system 100 can analyze the scanning information for distinguishing characteristics of the asset, such as a distinct visual aspect associated with the asset (such as an alphanumeric code, QR code, dimensions of the asset, symbols, and the like) or a wireless signal (e.g., an RFID signal communicating an asset identifier). Based on scanning information, the control system 100 can determine that a particular asset is located within the cargo container; the control system 100 can also determine a particular location for the particular asset.).
As per claim 23, Gil et al. discloses a non-transitory computer-readable medium storing instructions thereon that, when executed by one or more processors, cause the one or more processors (Gil et al.: [0057], the volatile storage or memory media may be used to store at least portions of the databases, database instances, database management systems, data, applications, programs, program modules, scripts, source code, object code, byte code, compiled code, interpreted code, machine code, executable instructions, and/or the like being executed by, for example, the processing element 205) to:
receive information of an area, the information being indicative of a type of the area and a map of the area (Gil et al.: [0082], shown in FIGS. 4 and 5, an information gathering device may be provided via a combination of the image camera 116 that is mounted on the device component 114 and/or the three-dimensional depth sensors 119); (Gil et al.: [0061], the control system 100 may be generally configured to maintain and/or update a defined location map associated with a facility or warehouse in which the user device (s) will be operated. This may be maintained for provision to the user device (s) upon calibration or initial "environment mapping" (see FIG. 8) via the user device (s)); (Gil et al.: [0093], the location 400 may include may include one or more vehicles (e.g., aircraft, tractor-trailer, cargo container, local delivery vehicles, and/or the like), pallets, identified areas within a building, bins, chutes, conveyor belts, shelves, and/or the like.); receive, from an imaging assembly, an image of one or more objects within the area (Gil et al.: [0075], The user device 110 may also detect markers and/or target objects. For example, the user device 110 may include readers, scanners, cameras, sensors, and/or the like for detecting when a marker and/or target object and/or a pattern of unique colors within its point-of-view (POV)/field-of-view (FOV) of the real world environment/area);
receive, from an imaging assembly, an image of one or more objects within the area;
derive a location and orientation of the device during capture of the image based on the information and data of at least one sensor of the device (Gil et al.: [0075], the user device 110 may include readers, scanners, cameras, sensors, and/or the like for detecting when a marker and/or target object and/or a pattern of unique colors within its point-of-view (POV)/field-of-view (FOV) of the real world environment/area); (Gil et al.: [0074], the user device 110 may include outdoor and/or environmental positioning aspects, such as a location module adapted to acquire, for example, latitude, longitude, geocode, course, direction, heading, speed, universal time (UTC), date, and/or various other information/data); and (Gil et al.: [0076], the user device 110 may include accelerometer circuitry for detecting movement, pitch, bearing, orientation, and the like of the user device 110. This information/data may be used to determine which area of the augmented/mixed environment/area corresponds to the orientation/bearing of the user device 110 (e.g., X, y, and Z axes), so that the corresponding environment/area of the augmented/mixed environment/area may be displayed via the display along with a displayed image);
detect a first label associated with a first object, the first label having one or more identifiers; (Gil et al.: [0075], The user device 110 may also detect markers and/or target objects; Gil et al.: [0099], the control system 100 may determine an asset identifier from the scanning information (which could be determined through image recognition that identifies a label on an asset and/or via wireless sensors detecting an RFID signal, or the like);
process the first label associated with the first object (Gil et al.: [0099], the control system 100 may determine an asset identifier from the scanning information (which could be determined through image recognition that identifies a label on an asset and/or via wireless sensors detecting an RFID signal, or the like); and (Gil et al.: [0126], For instance, the control system 100 can analyze the scanning information for distinguishing characteristics of the asset, such as a distinct visual aspect associated with the asset (such as an alphanumeric code, QR code, dimensions of the asset, symbols, and the like) or a wireless signal (e.g., an RFID signal communicating an asset identifier);
determine a location of the processed first label associated with the first object within the area based on the derived location and orientation of the device (Gil et al.: [0075], The user device 110 may also detect markers and/or target objects. For example, the user device 110 may include readers, scanners, cameras, sensors, and/or the like for detecting when a marker and/or target object and/or a pattern of unique colors within its point-of-view (POV)/field-of-view (FOV) of the real world environment/area); (Gil et al.: [0074], the user device 110 may include outdoor and/or environmental positioning aspects, such as a location module adapted to acquire, for example, latitude, longitude, geocode, course, direction, heading, speed, universal time (UTC), date, and/or various other information/data); (Gil et al.: [0076], the user device 110 may include accelerometer circuitry for detecting movement, pitch, bearing, orientation, and the like of the user device 110. This information/data may be used to determine which area of the augmented/mixed environment/area corresponds to the orientation/bearing of the user device 110 (e.g., X, y, and Z axes), so that the corresponding environment/area of the augmented/mixed environment/area may be displayed via the display along with a displayed image); (Gil et al.: [0099], Pattern recognition, machine learning, and/ or Al-based algorithms may be utilized to identify, from the scanning information, the shape, size, and position of each asset stored; the control system 100 may determine an asset identifier from the scanning information (which could be determined through image recognition that identifies a label on an asset and/ or via wireless sensors detecting an RFID signal, or the like); the control system 100 may determine an asset identifier from the scanning information (which could be determined through image recognition that identifies a label on an asset and/or via wireless sensors detecting an RFID signal, or the like) while FIGS. 14A-18 describe a location 400 with respect to the storage area of a delivery vehicle, the location 400 can be any physical environment); and ( Gil et al.: [0126], For instance, the control system 100 can analyze the scanning information for distinguishing characteristics of the asset, such as a distinct visual aspect associated with the asset (such as an alphanumeric code, QR code, dimensions of the asset, symbols, and the like) or a wireless signal (e.g., an RFID signal communicating an asset identifier). Based on scanning information, the control system 100 can determine that a particular asset is located within the cargo container; the control system 100 can also determine a particular location for the particular asset); and
determine a location of the first object within the area based on the determined location of the processed first label (Gil et al.: [0075], The user device 110 may also detect markers and/or target objects. For example, the user device 110 may include readers, scanners, cameras, sensors, and/or the like for detecting when a marker and/or target object and/or a pattern of unique colors within its point-of-view (POV)/field-of-view (FOV) of the real world environment/area); (Gil et al.: [0074], the user device 110 may include outdoor and/or environmental positioning aspects, such as a location module adapted to acquire, for example, latitude, longitude, geocode, course, direction, heading, speed, universal time (UTC), date, and/or various other information/data); (Gil et al.: [0076], the user device 110 may include accelerometer circuitry for detecting movement, pitch, bearing, orientation, and the like of the user device 110. This information/data may be used to determine which area of the augmented/mixed environment/area corresponds to the orientation/bearing of the user device 110 (e.g., X, y, and Z axes), so that the corresponding environment/area of the augmented/mixed environment/area may be displayed via the display along with a displayed image); (Gil et al.: [0099], Pattern recognition, machine learning, and/ or Al-based algorithms may be utilized to identify, from the scanning information, the shape, size, and position of each asset stored; the control system 100 may determine an asset identifier from the scanning information (which could be determined through image recognition that identifies a label on an asset and/ or via wireless sensors detecting an RFID signal, or the like); the control system 100 may determine an asset identifier from the scanning information (which could be determined through image recognition that identifies a label on an asset and/or via wireless sensors detecting an RFID signal, or the like) while FIGS. 14A-18 describe a location 400 with respect to the storage area of a delivery vehicle, the location 400 can be any physical environment); (Gil et al.: [0126], For instance, the control system 100 can analyze the scanning information for distinguishing characteristics of the asset, such as a distinct visual aspect associated with the asset (such as an alphanumeric code, QR code, dimensions of the asset, symbols, and the like) or a wireless signal (e.g., an RFID signal communicating an asset identifier). Based on scanning information, the control system 100 can determine that a particular asset is located within the cargo container; the control system 100 can also determine a particular location for the particular asset.); and
determine a location of the first object within the area based on the determined location of the processed first label (Gil et al.: [0075], The user device 110 may also detect markers and/or target objects (Gil et al.: [0099], Pattern recognition, machine learning, and/or Al-based algorithms may be utilized to identify, from the scanning information, the shape, size, and position of each asset stored; the control system 100 may determine an asset identifier from the scanning information (which could be determined through image recognition that identifies a label on an asset and/or via wireless sensors detecting an RFID signal, or the like); the control system 100 may determine an asset identifier from the scanning information (which could be determined through image recognition that identifies a label on an asset and/or via wireless sensors detecting an RFID signal, or the like) while FIGS. 14A-18 describe a location 400 with respect to the storage area of a delivery vehicle, the location 400 can be any physical environment); (Gil et al.: [0126], For instance, the control system 100 can analyze the scanning information for distinguishing characteristics of the asset, such as a distinct visual aspect associated with the asset (such as an alphanumeric code, QR code, dimensions of the asset, symbols, and the like) or a wireless signal (e.g., an RFID signal communicating an asset identifier). Based on scanning information, the control system 100 can determine that a particular asset is located within the cargo container; the control system 100 can also determine a particular location for the particular asset.).
As per claim 24, Gil et al. discloses the non-transitory computer-readable medium of claim 23, wherein the instructions, when executed, cause the one or more processors to process the first label associated with the first object by:
determining whether the one or more identifiers are indicative of a barcode (Gil et al.: [0075], The user device 110 may also detect markers and/or target objects For example, readers, scanners, cameras, sensors, and/or the like may include RFID readers/interrogators to read RFID tags, scanners and cameras to capture visual patterns and/or codes (e.g., text, barcodes, character strings, Aztec Codes, Maxi Codes, information/data Matrices, QR Codes, electronic representations, and/or the like), and sensors to detect beacon signals transmitted from target objects or the environment/area in which target objects are located); (Gil et al.: [0099], the control system 100 may determine an asset identifier from the scanning information (which could be determined through image recognition that identifies a label on an asset and/ or via wireless sensors detecting an RFID signal, or the like); and (Gil et al.: [0126], For instance, the control system 100 can analyze the scanning information for distinguishing characteristics of the asset, such as a distinct visual aspect associated with the asset (such as an alphanumeric code, QR code, dimensions of the asset, symbols, and the like) or a wireless signal (e.g., an RFID signal communicating an asset identifier);
responsive to determining the one or more identifiers are indicative of a barcode, decoding the one or more identifiers (Gil et al.: [0075], The user device 110 may also detect markers and/or target objects For example, readers, scanners, cameras, sensors, and/or the like may include RFID readers/ interrogators to read RFID tags, scanners and cameras to capture visual patterns and/or codes (e.g., text, barcodes, character strings, Aztec Codes, Maxi Codes, information/data Matrices, QR Codes, electronic representations, and/or the like), and sensors to detect beacon signals transmitted from target objects or the environment/area in which target objects are located); (Gil et al.: [0099], the control system 100 may determine an asset identifier from the scanning information (which could be determined through image recognition that identifies a label on an asset and/or via wireless sensors detecting an RFID signal, or the like); and (Gil et al.: [0126], For instance, the control system 100 can analyze the scanning information for distinguishing characteristics of the asset, such as a distinct visual aspect associated with the asset (such as an alphanumeric code, QR code, dimensions of the asset, symbols, and the like) or a wireless signal (e.g., an RFID signal communicating an asset identifier); and
selecting a decoded identifier corresponding to a predetermined symbology (Gil et al.: [0075], The user device 110 may also detect markers and/or target objects For example, readers, scanners, cameras, sensors, and/or the like may include RFID readers/interrogators to read RFID tags, scanners and cameras to capture visual patterns and/or codes (e.g., text, barcodes, character strings, Aztec Codes, Maxi Codes, information/ data Matrices, QR Codes, electronic representations, and/or the like), and sensors to detect beacon signals transmitted from target objects or the environment/area in which target objects are located); (Gil et al.: [0099], the control system 100 may determine an asset identifier from the scanning information (which could be determined through image recognition that identifies a label on an asset and/or via wireless sensors detecting an RFID signal, or the like); and (Gil et al.: [0126], For instance, the control system 100 can analyze the scanning information for distinguishing characteristics of the asset, such as a distinct visual aspect associated with the asset (such as an alphanumeric code, QR code, dimensions of the asset, symbols, and the like) or a wireless signal (e.g., an RFID signal communicating an asset identifier).
As per claim 25, Gil et al. discloses the non-transitory computer-readable medium of claim 23, wherein the instructions, when executed, cause the one or more processors to process the first label associated with the first object by:
determining whether the one or more identifiers are indicative of a barcode (Gil et al.: [0075], The user device 110 may also detect markers and/or target objects For example, readers, scanners, cameras, sensors, and/or the like may include RFID readers/interrogators to read RFID tags, scanners and cameras to capture visual patterns and/or codes (e.g., text, barcodes, character strings, Aztec Codes, Maxi Codes, information/ data Matrices, QR Codes, electronic representations, and/or the like), and sensors to detect beacon signals transmitted from target objects or the environment/area in which target objects arc located); (Gil et al.: [0099], the control system 100 may determine an asset identifier from the scanning information (which could be determined through image recognition that identifies a label on an asset and/or via wireless sensors detecting an RFID signal, or the like); and (Gil et al.: [0126], For instance, the control system 100 can analyze the scanning information for distinguishing characteristics of the asset, such as a distinct visual aspect associated with the asset (such as an alphanumeric code, QR code, dimensions of the asset, symbols, and the like) or a wireless signal (e.g., an RFID signal communicating an asset identifier);
responsive to determining the one or more identifiers are not indicative of a barcode, utilizing character recognition to recognize the one or more identifiers (Gil et al.: [0075], The user device 110 may also detect markers and/or target objects For example, readers, scanners, cameras, sensors, and/or the like may include RFID readers/interrogators to read RFID tags, scanners and cameras to capture visual patterns and/ or codes (e.g., text, barcodes, character strings, Aztec Codes, Maxi Codes, information/data Matrices, QR Codes, electronic representations, and/or the like), and sensors to detect beacon signals transmitted from target objects or the environment/area in which target objects are located); (Gil et al.: [0099], the control system 100 may determine an asset identifier from the scanning information (which could be determined through image recognition that identifies a label on an asset and/or via wireless sensors detecting an RFID signal, or the like); and (Gil et al.: [0126], For instance, the control system 100 can analyze the scanning information for distinguishing characteristics of the asset, such as a distinct visual aspect associated with the asset (such as an alphanumeric code, QR code, dimensions of the asset, symbols, and the like) or a wireless signal (e.g., an RFID signal communicating an asset identifier); and
selecting a recognized identifier corresponding to a predetermined character string structure. (Gil et al.: [0075], The user device 110 may also detect markers and/or target objects For example, readers, scanners, cameras, sensors, and/or the like may include RFID readers/interrogators to read RFID tags, scanners and cameras to capture visual patterns and/or codes (e.g., text, barcodes, character strings, Aztec Codes, Maxi Codes, information/data Matrices, QR Codes, electronic representations, and/or the like), and sensors to detect beacon signals transmitted from target objects or the environment/area in which target objects are located); (Gil et al.: [0099], the control system 100 may determine an asset identifier from the scanning information (which could be determined through image recognition that identifies a label on an asset and/ or via wireless sensors detecting an RFID signal, or the like); and (Gil et al.: [0126], For instance, the control system 100 can analyze the scanning information for distinguishing characteristics of the asset, such as a distinct visual aspect associated with the asset (such as an alphanumeric code, QR code, dimensions of the asset, symbols, and the like) or a wireless signal (e.g., an RFID signal communicating an asset identifier).
As per claim 26, Gil et al. discloses the non-transitory computer-readable medium of claim 23, wherein the instructions, when executed, cause the one or more processors to determine the location of the first object within the area based on the determined location of the processed first label by one or more of:
generating a realogram of the area based on the determined location of the first object;
generating a record of the determined location of the first object (Gil et al.: [0075], The user device 110 may also detect markers and/or target objects); (Gil et al.: [0099], the control system 100 may determine an asset identifier from the scanning information (which could be determined through image recognition that identifies a label on an asset and/or via wireless sensors detecting an RFID signal, or the like); (Gil et al.: [0126], For instance, the control system 100 can analyze the scanning information for distinguishing characteristics of the asset, such as a distinct visual aspect associated with the asset (such as an alphanumeric code, QR code, dimensions of the asset, symbols, and the like) or a wireless signal (e.g., an RFID signal communicating an asset identifier). Based on scanning information, the control system 100 can determine that a particular asset is located within the cargo container; the control system 100 can also determine a particular location for the particular asset); and (Gil et al.: [0008], methods for maintaining accurate records of the location of an asset in a sort and/or pick process, while also providing to carrier personnel improved instructions and/or guidance for the automated handling of the packages within various environments.); or
modifying an entry of a log associated with the area based on the determined location of the first object, the log being indicative of an inventory of the one or more objects within the area.
As per claim 27, Gil et al. discloses the non-transitory computer-readable medium of claim 23, wherein the instructions, when executed, further cause the one or more processors to:
detect a second label associated with a second object, the second label having one or more identifiers (Gil et al.: [0075], The user device 110 may also detect markers and/or target objects); (Gil et al.: [0099], the control system 100 may determine an asset identifier from the scanning information (which could be determined through image recognition that identifies a label on an asset and/or via wireless sensors detecting an RFID signal, or the like); and (Gil et al.: [0126], For instance, the control system 100 can analyze the scanning information for distinguishing characteristics of the asset, such as a distinct visual aspect associated with the asset (such as an alphanumeric code, QR code, dimensions of the asset, symbols, and the like) or a wireless signal (e.g., an RFID signal communicating an asset identifier);
process the second label associated with the second object (Gil et al.: [0075], The user device 110 may also detect markers and/or target objects; Gil et al.: [0099], the control system 100 may determine an asset identifier from the scanning information (which could be determined through image recognition that identifies a label on an asset and/or via wireless sensors detecting an RFID signal, or the like); and (Gil et al.: [0126], For, the control system 100 can analyze the scanning information for distinguishing characteristics of the asset, such as a distinct visual aspect associated with the asset (such as an alphanumeric code, QR code, dimensions of the asset, symbols, and the like) or a wireless signal (e.g., an RFID signal communicating an asset identifier);
determine a location of the processed second label associated with the second object within the area based on the derived location and orientation of the device (Gil et al.: [0075], The user device 110 may also detect markers and/or target objects. For example, the user device 110 may include readers, scanners, cameras, sensors, and/or the like for detecting when a marker and/or target object and/or a pattern of unique colors within its point-of-view (POV)/field-of-view (FOV) of the real world environment/area); (Gil et al.: [0074], the user device 110 may include outdoor and/or environmental positioning aspects, such as a location module adapted to acquire, for example, latitude, longitude, geocode, course, direction, heading, speed, universal time (UTC), date, and/or various other information/data); (Gil et al.: [0076], the user device 110 may include accelerometer circuitry for detecting movement, pitch, bearing, orientation, and the like of the user device 110. This information/data may be used to determine which area of the augmented/mixed environment/area corresponds to the orientation/bearing of the user device 110 (e.g., X, y, and Z axes), SO that the corresponding environment/area of the augmented/mixed environment/area may be displayed via the display along with a displayed image); (Gil et al.: [0099], The scanning device 1500 can be used to capture the size and shape of asset 10 as it is loaded into the storage area and placed onto shelves (i.e., specific locations); Pattern recognition, machine learning, and/or Al-based algorithms may be utilized to identify, from the scanning information, the shape, size, and position of each asset stored; the control system 100 may determine an asset identifier from the scanning information (which could be determined through image recognition that identifies a label on an asset and/or via wireless sensors detecting an RFID signal, or the like) while FIGS. 14A-18 describe a location 400 with respect to the storage area of a delivery vehicle, the location 400 can be any physical environment); and (Gil et al.: [0126], For instance, the control system 100 can analyze the scanning information for distinguishing characteristics of the asset, such as a distinct visual aspect associated with the asset (such as an alphanumeric code, QR code, dimensions of the asset, symbols, and the like) or a wireless signal (e.g., an RFID signal communicating an asset identifier). Based on scanning information, the control system 100 can determine that a particular asset is located within the cargo container; the control system 100 can also determine a particular location for the particular asset);
determine a location of the second object within the area based on the determined location of the processed second label (Gil et al.: [0099], the control system 100 may determine an asset identifier from the scanning information (which could be determined through image recognition that identifies a label on an asset and/or via wireless sensors detecting an RFID signal, or the like); (Gil et al.: [0099], The scanning device 1500 can be used to capture the size and shape of asset 10 as it is loaded into the storage area and placed onto shelves (i.e., specific locations); Pattern recognition, machine learning, and/or Al-based algorithms may be utilized to identify, from the scanning information, the shape, size, and position of each asset stored; the control system 100 may determine an asset identifier from the scanning information (which could be determined through image recognition that identifies a label on an asset and/or via wireless sensors detecting an RFID signal, or the like) while FIGS. 14A-18 describe a location 400 with respect to the storage area of a delivery vehicle, the location 400 can be any physical environment); (Gil et al.: [0126], For instance, the control system 100 can analyze the scanning information for distinguishing characteristics of the asset, such as a distinct visual aspect associated with the asset (such as an alphanumeric code, QR code, dimensions of the asset, symbols, and the like) or a wireless signal (e.g., an RFID signal communicating an asset identifier). Based on scanning information, the control system 100 can determine that a particular asset is located within the cargo container; the control system 100 can also determine a particular location for the particular asset.)..
As per claim 28, Gil et al. discloses a method (An asset identifier for an asset located within a physical environment is obtained based on received scanning information (Abstract).), comprising:
deriving a location and orientation of a device during capture of an image of one or more objects present in an area based on information of the area and data of at least one sensor of the device, the information being indicative of a type of the area and a map of the area (Gil et al.: [0075], the user device 110 may include readers, scanners, cameras, sensors, and/or the like for detecting when a marker and/or target object and/or a pattern of unique colors within its point-of-view (POV)/field-of-view (FOV) of the real world environment/area); (Gil et al.: [0074], the user device 110 may include outdoor and/or environmental positioning aspects, such as a location module adapted to acquire, for example, latitude, longitude, geocode, course, direction, heading, speed, universal time (UTC), date, and/or various other information/ data); (Gil et al.: [0076], the user device 110 may include accelerometer circuitry for detecting movement, pitch, bearing, orientation, and the like of the user device 110. This information/data may be used to determine which area of the augmented/mixed environment/area corresponds to the orientation/bearing of the user device 110 (e.g., X, y, and Z axes), so that the corresponding environment/area of the augmented/mixed environment/area may be displayed via the display along with a displayed image); (Gil et al.: [0082], shown in FIGS. 4 and 5, an information gathering device may be provided via a combination of the image camera 116 that is mounted on the device component 114 and/or the three-dimensional depth sensors 119); (Gil et al.: [0061], the control system 100 may be generally configured to maintain and/or update a defined location map associated with a facility or warehouse in which the user device (s) will be operated. This may be maintained for provision to the user device (s) upon calibration or initial "environment mapping" (see FIG. 8) via the user device (s)); (Gil et al.: [0093], the location 400 may include may include one or more vehicles (e.g., aircraft, tractor-trailer, cargo container, local delivery vehicles, and/or the like), pallets, identified areas within a building, bins, chutes, conveyor belts, shelves, and/or the like.);
detecting a first label associated with a first object, the first label having one or more identifiers (Gil et al.: [0075], The user device 110 may also detect markers and/or target objects; Gil et al.: [0099], the control system 100 may determine an asset identifier from the scanning information (which could be determined through image recognition that identifies a label on an asset and/or via wireless sensors detecting an RFID signal, or the like);
processing the first label associated with the first object (Gil et al.: [0075], The user device 110 may also detect markers and/or target objects; Gil et al.: [0099], the control system 100 may determine an asset identifier from the scanning information (which could be determined through image recognition that identifies a label on an asset and/or via wireless sensors detecting an RFID signal, or the like); and (Gil et al.:. [0126], For instance, the control system 100 can analyze the scanning information for distinguishing characteristics of the asset, such as a distinct visual aspect associated with the asset (such as an alphanumeric code, QR code, dimensions of the asset, symbols, and the like) or a wireless signal (e.g., an RFID signal communicating an asset identifier);
determining a location of the processed first label associated with the first object within the area based on the derived location and orientation of the device (Gil et al.: [0075], The user device 110 may also detect markers and/or target objects. For example, the user device 110 may include readers, scanners, cameras, sensors, and/or the like for detecting when a marker and/or target object and/or a pattern of unique colors within its point-of-view (POV)/field-of-view (FOV) of the real world environment/area); ( Gil et al.: [0074], the user device 110 may include outdoor and/or environmental positioning aspects, such as a location module adapted to acquire, for example, latitude, longitude, geocode, course, direction, heading, speed, universal time (UTC), date, and/or various other information/data); (Gil et al.: [0076], the user device 110 may include accelerometer circuitry for detecting movement, pitch, bearing, orientation, and the like of the user device 110. This information/data may be used to determine which area of the augmented/mixed environment/area corresponds to the orientation/bearing of the user device 110 (e.g., X, y, and Z axes), so that the corresponding environment/area of the augmented/mixed environment/area may be displayed via the display along with a displayed image); (Gil et al.: [0099], Pattern recognition, machine learning, and/ or Al-based algorithms may be utilized to identify, from the scanning information, the shape, size, and position of each asset stored; the control system 100 may determine an asset identifier from the scanning information (which could be determined through image recognition that identifies a label on an asset and/ or via wireless sensors detecting an RFID signal, or the like); the control system 100 may determine an asset identifier from the scanning information (which could be determined through image recognition that identifies a label on an asset and/or via wireless sensors detecting an RFID signal, or the like) while FIGS. 14A-18 describe a location 400 with respect to the storage area of a delivery vehicle, the location 400 can be any physical environment); and (Gil et al.: [0126], For instance, the control system 100 can analyze the scanning information for distinguishing characteristics of the asset, such as a distinct visual aspect associated with the asset (such as an alphanumeric code, QR code, dimensions of the asset, symbols, and the like) or a wireless signal (e.g., an RFID signal communicating an asset identifier). Based on scanning information, the control system 100 can determine that a particular asset is located within the cargo container; the control system 100 can also determine a particular location for the particular asset.); and
determining a location of the first object within the area based on the determined location of the processed first label. (Gil et al.: [0075], The user device 110 may also detect markers and/or target objects); (Gil et al.: [0099], Pattern recognition, machine learning, and/or Al-based algorithms may be utilized to identify, from the scanning information, the shape, size, and position of each asset stored; the control system 100 may determine an asset identifier from the scanning information (which could be determined through image recognition that identifies a label on an asset and/or via wireless sensors detecting an RFID signal, or the like); the control system 100 may determine an asset identifier from the scanning information (which could be determined through image recognition that identifies a label on an asset and/or via wireless sensors detecting an RFID signal, or the like) while FIGS. 14A-18 describe a location 400 with respect to the storage area of a delivery vehicle, the location 400 can be any physical environment); and ( Gil et al.: [0126], For instance, the control system 100 can analyze the scanning information for distinguishing characteristics of the asset, such as a distinct visual aspect associated with the asset (such as an alphanumeric code, QR code, dimensions of the asset, symbols, and the like) or a wireless signal (e.g., an RFID signal communicating an asset identifier). Based on scanning information, the control system 100 can determine that a particular asset is located within the cargo container; the control system 100 can also determine a particular location for the particular asset.).
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 2-3 and 13-14 are rejected under 35 U.S.C. 103 as being unpatentable over Gil et al. (US PG Pub. 2021/0383320 A1) in view of Rodriguez et al. (US PG Pub. 2022/0261567 A1).
As per claim 2, Gil et al. discloses the method of claim 1. Gil et al. does not further disclose, however, Rodriguez et al. discloses:
partitioning the area into a plurality of zones based on the map (Rodriguez et al.: [0398], Data collected by any of the foregoing arrangements can be compiled and presented in map form); and (Rodriguez et al.: [0503], First, the detected foreground region is expanded to a square window, encompassing all the foreground pixels. Then the square foreground region is divided into equally spaced zones (e.g., one, four, nine, etc. - whichever yields zones most similar in size to the 15 smaller blocks of the B17 pattern). The foreground pixels (i.e., incoming pixel values from the camera, minus averages of corresponding pixels in previous frames) inside each zone are summed together);
determining a zone of the processed first label associated with the first object within the area based on the derived location and orientation of the device (Rodriguez et al.: [0143], the plenoptic information captured by camera 50 is processed to yield a multitude of different focal planes of image information The resulting sets of image information are then analyzed for product identification information (e.g., by applying to watermark decoder, barcode decoder, fingerprint identification module, etc.). Depending on the location and orientation of the item surfaces within the examined volume, different of these planes can reveal different product identification information); (Rodriguez et al.: [0494], For this camera system, where the input is a video stream, we have found that background subtraction from moving averages of previous frames is a computationally efficient and effective method to extract the fast moving foreground objects); (Rodriguez et al.: [0503], First, the detected foreground region is expanded to a square window, encompassing all the foreground pixels. Then the square foreground region is divided into equally spaced zones (e.g., one, four, nine, etc. - whichever yields zones most similar in size to the 15 smaller blocks of the B17 pattern). The foreground pixels (i.e., incoming pixel values from the camera, minus averages of corresponding pixels in previous frames) inside each zone are summed together); and (Rodriguez et al.: [0504], If the zones are ranked according to their illumination An ordered ranking of the remaining zones is established, and pixels for these zones are sent in that order to the watermark decoder for processing); and
determining a zone of the first object within the area based on the determined zone of the processed first label (Rodriguez et al.: [0143], the plenoptic information captured by camera 50 is processed to yield a multitude of different focal planes of image information The resulting sets of image information are then analyzed for product identification information (e.g., by applying to watermark decoder, barcode decoder, fingerprint identification module, etc.). Depending on the location and orientation of the item surfaces within the examined volume, different of these planes can reveal different product identification information); (Rodriguez et al.: [0494], For this camera system, where the input is a video stream, we have found that background subtraction from moving averages of previous frames is a computationally efficient and effective method to extract the fast moving foreground objects); (Rodriguez et al.: [0503], First, the detected foreground region is expanded to a square window, encompassing all the foreground pixels. Then the square foreground region is divided into equally spaced zones (e.g., one, four, nine, etc. - whichever yields zones most similar in size to the 15 smaller blocks of the B17 pattern). The foreground pixels (i.e., incoming pixel values from the camera, minus averages of corresponding pixels in previous frames) inside each zone are summed together; Rodriguez et al.: [0504], If the zones are ranked according to their illumination An ordered ranking of the remaining zones is established, and pixels for these zones are sent in that order to the watermark decoder for processing); (Rodriguez et al.: [0103], By reference to the discerned first and second 3D spatial orientation information, the system determines identification information for the item. In such arrangement, the identification information is typically based on at least a portion of the first patch and a portion of the second patch. In the case of a barcode, for example, it may span both patches.). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Gil et al. to include partitioning images as taught by Rodriguez et al. for the purpose of determining products in locations, as well as, providing the user the ability to match image fingerprint information to reference fingerprint information in a product database (Rodriguez et al.: [0102]).
As per claim 3, Gil et al. in view of Rodriguez et al. discloses the method of claim 2. Gil et al. does not further disclose, however, Rodriguez et al. discloses :
comprising partitioning, by the device, the image when the one or more objects are within more than one zone of the plurality of zones based on a number of zones present in the image (Rodriguez et al.: product identification (Abstract); (Rodriguez et al.: [0143], the plenoptic information captured by camera 50 is processed to yield a multitude of different focal planes of image information The resulting sets of image information are then analyzed for product identification information (e.g., by applying to watermark decoder, barcode decoder, fingerprint identification module, etc.). Depending on the location and orientation of the item surfaces within the examined volume, different of these planes can reveal different product identification information); (Rodriguez et al.: [0494], For this camera system, where the input is a video stream, we have found that background subtraction from moving averages of previous frames is a computationally efficient and effective method to extract the fast moving foreground objects); (Rodriguez et al.: [0503], First, the detected foreground region is expanded to a square window, encompassing all the foreground pixels. Then the square foreground region is divided into equally spaced zones (e.g., one, four, nine, etc. - whichever yields zones most similar in size to the 15 smaller blocks of the B17 pattern). The foreground pixels (i.e., incoming pixel values from the camera, minus averages of corresponding pixels in previous frames) inside each zone are summed together); (Rodriguez et al.: [0504], If the zones are ranked according to their illumination An ordered ranking of the remaining zones is established, and pixels for these zones are sent in that order to the watermark decoder for processing; Rodriguez et al.: [0103], By reference to the discerned first and second 3D spatial orientation information, the system determines identification information for the item. In such arrangement, the identification information is typically based on at least a portion of the first patch and a portion of the second patch. In the case of a barcode, for example, it may span both patches.). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Gil et al. to include partitioning images as taught by Rodriguez et al. for the purpose of determining products in locations, as well as, providing the user the ability to match image fingerprint information to reference fingerprint information in a product database (Rodriguez et al.: [0102]).
As per claim 13, Gil et al. in view of Rodriguez et al. discloses the device of claim 12. Gil et al. does not further disclose, however, Rodriguez et al. discloses, wherein the instructions, when executed, further cause the one or more processors to:
partition the area into a plurality of zones based on the map (Rodriguez et al.: [0398], Data collected by any of the foregoing arrangements can be compiled and presented in map form; Rodriguez et al.: [0503], First, the detected foreground region is expanded to a square window, encompassing all the foreground pixels. Then the square foreground region is divided into equally spaced zones (e.g., one, four, nine, etc. - whichever yields zones most similar in size to the 15 smaller blocks of the B17 pattern). The foreground pixels (i.e., incoming pixel values from the camera, minus averages of corresponding pixels in previous frames) inside each zone arc summed together);
determine a zone of the processed first label associated with the first object within the area based on the derived location and orientation of the device (Rodriguez et al.: [0143], the plenoptic information captured by camera 50 is processed to yield a multitude of different focal planes of image information The resulting sets of image information are then analyzed for product identification information (e.g., by applying to watermark decoder, barcode decoder, fingerprint identification module, etc.). Depending on the location and orientation of the item surfaces within the examined volume, different of these planes can reveal different product identification information; Rodriguez et al.: [0494], For this camera system, where the input is a video stream, we have found that background subtraction from moving averages of previous frames is a computationally efficient and effective method to extract the fast moving foreground objects; Rodriguez et al.: [0503], First, the detected foreground region is expanded to a square window, encompassing all the foreground pixels. Then the square foreground region is divided into equally spaced zones (e.g., one, four, nine, etc. - whichever yields zones most similar in size to the 15 smaller blocks of the B17 pattern). The foreground pixels (i.e., incoming pixel values from the camera, minus averages of corresponding pixels in previous frames) inside each zone are summed together; Rodriguez et al.: [0504], If the zones are ranked according to their illumination An ordered ranking of the remaining zones is established, and pixels for these zones are sent in that order to the watermark decoder for processing.); and
determine a zone of the first object within the area based on the determined zone of the processed first label (Rodriguez et al.: [0143], the plenoptic information captured by camera 50 is processed to yield a multitude of different focal planes of image information The resulting sets of image information are then analyzed for product identification information (e.g., by applying to watermark decoder, barcode decoder, fingerprint identification module, etc.). Depending on the location and orientation of the item surfaces within the examined volume, different of these planes can reveal different product identification information); Rodriguez et al.: [0494], For this camera system, where the input is a video stream, we have found that background subtraction from moving averages of previous frames is a computationally efficient and effective method to extract the fast moving foreground objects; Rodriguez et al.: [0503], First, the detected foreground region is expanded to a square window, encompassing all the foreground pixels. Then the square foreground region is divided into equally spaced zones (e.g., one, four, nine, etc. - whichever yields zones most similar in size to the 15 smaller blocks of the B17 pattern). The foreground pixels (i.e., incoming pixel values from the camera, minus averages of corresponding pixels in previous frames) inside each zone are summed together; Rodriguez et al.: [0504], If the zones are ranked according to their illumination An ordered ranking of the remaining zones is established, and pixels for these zones are sent in that order to the watermark decoder for processing); (Rodriguez et al.: [0103], By reference to the discerned first and second 3D spatial orientation information, the system determines identification information for the item. In such arrangement, the identification information is typically based on at least a portion of the first patch and a portion of the second patch. In the case of a barcode, for example, it may span both patches). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Gil et al. to include determining zones as taught by Rodriguez et al. for the purpose of determining products in locations, as well as, providing the user the ability to match image fingerprint information to reference fingerprint information in a product database (Rodriguez et al.: [0102]).
As per claim 14, Gil et al. in view of Rodriguez et al. discloses the device of claim 13. Gil et al. does not further disclose, however, Rodriguez et al. discloses, wherein the instructions, when executed, further cause the one or more processors to:
partition the image when the one or more objects are within more than one zone of the plurality of zones based on a number of zones present in the image (Rodriguez et al.. is in the field of product identification (Abstract)); (Rodriguez et al.: [0143], the plenoptic information captured by camera 50 is processed to yield a multitude of different focal planes of image information The resulting sets of image information are then analyzed for product identification information (e.g., by applying to watermark decoder, barcode decoder, fingerprint identification module, etc.). Depending on the location and orientation of the item surfaces within the examined volume, different of these planes can reveal different product identification information); (Rodriguez et al.: [0494], For this camera system, where the input is a video stream, we have found that background subtraction from moving averages of previous frames is a computationally efficient and effective method to extract the fast moving foreground objects); (Rodriguez et al.: [0503], First, the detected foreground region is expanded to a square window, encompassing all the foreground pixels. Then the square foreground region is divided into equally spaced zones (e.g., one, four, nine, etc. - whichever yields zones most similar in size to the 15 smaller blocks of the B17 pattern). The foreground pixels (i.e., incoming pixel values from the camera, minus averages of corresponding pixels in previous frames) inside each zone are summed together; Rodriguez et al.: [0504], If the zones are ranked according to their illumination An ordered ranking of the remaining zones is established, and pixels for these zones are sent in that order to the watermark decoder for processing; Rodriguez et al.: [0103], By reference to the discerned first and second 3D spatial orientation information, the system determines identification information for the item. In such arrangement, the identification information is typically based on at least a portion of the first patch and a portion of the second patch. In the case of a barcode, for example, it may span both patches). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Gil et al. to include partitioning images as taught by Rodriguez et al. for the purpose of determining products in locations, as well as, providing the user the ability to match image fingerprint information to reference fingerprint information in a product database (Rodriguez et al.: [0102]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
1) Ibanez et al. (US PG Pub. 20250252752 A1) discloses an intelligent real-time information ingestion system and method used to analyze a plurality of images using a machine-learning module to determine that a shipping container is depicted within at least one of the plurality of images thereby determining a parking location of the shipping container and one or more identification markings of the shipping container.
2) Zhang et al. (CN 118506229 A) discloses Method and system for identifying multi-dimensional perception of goods in carriage relating to logistics and cargo detection by using a laser radar and a camera to collect three-dimensional point cloud data and fault image data containing cargo information in a container.
3) Oleksandr Kondakov et al. “Object Detection and Object Tracking Explained [Real Examples]”, 26 June 2023, lembergsolutions.com, 19 pages discloses the difference between object tracking vs object detection, where object tracking identifies objects and tracks them during series of frames on the footage or video stream, and object detection is a part of the object tracking process, wherein an initial stage when a neural network finds an object on the video or image and identifies it as the target one.
While object detection and object tracking are used to analyze visual data to identify objects' locations, there are key differences between them. Object detection identifies target objects on an image or frame, while object tracking follows a target object's movement across multiple frames.
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/F.A.N/Examiner, Art Unit 3628
/SHANNON S CAMPBELL/Supervisory Patent Examiner, Art Unit 3628
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