Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
Acknowledgement is made of Applicant’s claim of the present application claiming priority and benefit under 35 U.S.C. 119(a-d) to Israeli Patent Application No. IL310664 filed 02/05/2024.
Information Disclosure Statement
The information disclosure statements (“IDS”) filed 05/13/2025 and 09/17/2026 have been reviewed and the listed references were noted.
Drawings
The 17-page drawings have been considered and placed in the file.
Status of Claims
Claims 1-20 are pending.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter (an abstract idea without significantly more). The claims recite a system, and a non-transitory computer-readable medium for identifying objects in baggage. With respect to analysis of claim 1:
Step 1:
With regard to step 1, the instant claim is directed to a system; and therefore, the claim is directed to one of the statutory categories of inventions.
Step 2A, Prong One:
With regard to 2A, Prong One, the limitations of “use the image … to determine whether (i) or (ii) is met in the image: (i) at least one electronic device of a first category is present within, below, or on luggage of a second category; (ii) at least one electronic device of the first category is present, wherein said at least one electronic device is not located within, below, or on luggage of the second category; responsive to a determination that (i) is met, trigger an alarm of a first type; responsive to a determination that (ii) is met, use at least part of the image … to determine whether a suspicious element is present in the at least part of the image”, as drafted, recite an abstract idea, such as the combination of a system and using judgement/evaluation that, under their broadest reasonable interpretation, covers the performance of the limitation manually or in the mind of security personnel at a transportation station. That is, a skilled security technician may be able to review an image of luggage, and determine if an electronic device is within, below, or on the luggage. The skilled security technician then may be able to trigger an alert/alarm or determine if the device is suspicious based on their original determination. This is the concept that falls under the combination of the groupings of abstract ideas of mental processes, i.e., a concept performed in the human mind, evaluation, judgement, and/or opinion of a security technician to determine bag check compliance.
Step 2A, Prong Two:
The 2019 PEG defines the phrase “integration into a practical application” to require an
additional element or a combination of additional elements in the claim to apply, rely on, or use
the judicial exception. In the instant case, there are no additional elements/limitations in the
claims, with the exception of the insignificant extra-solution activities and general computing components, such as obtaining image data as input data and machine learning models and algorithms. The image acquisition limitation is regarded as insignificant extra-solution activity of acquiring medical image data, i.e., gathering input information, which may not be considered as an indication of integration of the judicial exception into a practical application. In regard to the ”machine learning model” and algorithm, these limitations, as claimed, do not recite more that generic computing components and may not be considered as an indication of integration of the judicial exception into a practical application. Accordingly, the above-mentioned additional element/limitation does not integrate the abstract idea into a practical application; and therefore, the claim recites an abstract idea.
Step 2B:
Because the claims fail under Step 2A, the claims are further evaluated under Step 2B.
The claims herein do not include additional elements that are sufficient to amount to
significantly more than the judicial exception, because as discussed above with respect to
integration of the abstract idea into practical application, the additional elements/limitations, amount to no more than insignificant routine and conventional elements. The abstract
operation/purpose of the apparatus as listed in Claim 1, cannot provide an inventive concept.
Therefore, independent Claim 1 is not patent eligible.
Furthermore, with regard to dependent claims 2-17, viewed individually, these
additional elements/limitations, under their broadest reasonable interpretation, are regarded
as either providing additional abstract ideas (i.e., judgement/evaluation or performing the
limitations in the mind) or adding limitations that may not be considered as significantly more
than the abstract idea, which do not provide meaningful limitations to transform the abstract
idea into a patent eligible application of the abstract idea such that the claims amount to
significantly more than the abstract idea itself. Accordingly, they are not patent eligible.
Independent claims 18 and 19 is evaluated in the same manner as claim 1 under 35 U.S.C. Using the same evaluation as claim 1 for claims 18 and 19, claims 18 and 19 are not patent eligible. Furthermore, with regard to dependent claims 2-17 and 20, viewed individually, these additional elements/limitations, under their broadest reasonable interpretation, are regarded as either providing additional abstract idea (i.e., judgement/evaluation or performing the limitations in the mind) or adding limitations that may not be considered as significantly more than the abstract idea; which do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself. Accordingly, they are not patent eligible.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-3, 5, 7-9, 13, 14, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Hong et al. (US 20110206240 A1 – IDS), in view of Rodriguez et al. (US 20080005804 A1 – IDS).
Regarding claim 1, Hong teaches, “A system comprising one or more processing circuitries configured to: obtain an image acquired by an acquisition device, use the image and a first machine learning model to determine whether (i) or (ii) is met in the image: (i) at least one electronic device of a first category is present within, below, or on luggage of a second category;” (Hong, Para. [0033] discloses; “In this example, the object segmentor 130 may segment the portable electronic device in an image of the luggage (e.g., separate the device from other objects inside the luggage, in the image) generated by the x-ray scanner or the CT scanner.” Examiner interprets the system of Hong to have the ability to determine if an electronic devis is present within, below, or on luggage by segmenting the image and separating potential objects.) “(ii) at least one electronic device of the first category is present, wherein said at least one electronic device is not located within, below, or on luggage of the second category;” (Hong, Para. [0033] discloses; “As another example, a portable electronic device may be taken out of a passenger's luggage and placed into a bin for scanning by an x-ray scanner (e.g., current FAA regulations require that laptop computers be removed from a passenger's luggage prior to screening) or a CT scanner. In this example, the object segmentor 130 may be able to segment the portable electronic device inside an image of the bin generated by the x-ray scanner or a CT scanner. It will be appreciated that, in this example environment, an object can also be imaged (e.g., scanned by an x-ray scanner or a CT scanner) directly, and may not be located inside a container (e.g., luggage or a bin).” Examiner interprets the system of Hong to have the ability to determine if an electronic device is present, wherein the device is not within, below, or on luggage by segmenting the image.) “(Hong, Para. [0038] discloses; “In the example environment 100, a threat determiner 126 can receive one or more layout feature vectors 158 for an object. The threat determiner 126 can be configured to compare the one or more layout feature vectors to one or more pre-determined boundaries, corresponding to the object's class, to determine whether the object contains a potential threat (e.g. explosives).” Examiner interprets the threat determiner of Hong to be an algorithm different from the first machine learning model that determines if there is a suspicious element in the image.) Hong does not explicitly teach, “responsive to a determination that (i) is met, trigger an alarm of a first type”. Since Hong does not explicitly disclose this limitation, Examiner relies on the teachings of Rodriguez in an analogous field of endeavor. Specifically, Rodriguez teaches, “responsive to a determination that (i) is met, trigger an alarm of a first type” (Rodriguez, Para. [0077] discloses; “Block 1005 illustrates a determination whether the RFID detected device is properly placed in a tray. If the RFID detected device is properly placed in a tray, then the process passes to block 1006. If the RFID detected device is not properly placed in a tray or other holder, then the process passes to block 1007. Block 1007 depicts providing an alert that an electronic device is detected in luggage or otherwise not properly placed in a tray or other holder, and the process ends.” Rodriguez, Para. [0007] discloses; “Thus, it would be advantageous to provide an x-ray screening that detects electronic devices within baggage that have not been separately presented for x-ray screening.” Rodriguez teaches triggering an alarm if there is an electronic device within, below, or on luggage.)
Hong and Rodriguez are considered to be analogous to the claimed invention because they are in the same field of detecting objects in luggage. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Hong to incorporate the teachings of Rodriguez in order to trigger an alarm if there is an electronic device in the scanned luggage. One of ordinary skill in the art would have been motivated to combine the previously described system of Hong with the teachings of Rodriguez to inform security personnel of an electronic device that is in luggage, as it is prohibited. (Rodriguez, Para. [0007] discloses; “Further, currently, there is not a system for ensuring that travelers produce all electronic devices. Thus, it would be advantageous to provide an x-ray screening that detects electronic devices within baggage that have not been separately presented for x-ray screening.”) It would be obvious to combine Hong and Rodriguez to raise an alarm when prohibited objects are in luggage.
Regarding claim 2, the combination of Hong and Rodriguez teaches, “The system of claim 1, configured to, responsive to a determination that (ii) is met, feed a sub-part of the image to the algorithm, wherein said sub-part corresponds to a location of the at least one electronic device in the image.” (Hong, Para. [0035] discloses; “In the example environment 100, object image data and class information 154 can be sent to an object partitioner 122, which can be configured to partition an object (e.g., a laptop computer) into object partitions (e.g., layers of the laptop computer) using Eigen analysis. As an example, a laptop computer can often be divided into three layers along a height direction. In this example, a screen and keyboard may be part of an upper layer, a hard drive, memory and an optical drive may be in a middle layer, and a battery and motherboard may be in a bottom layer. Typically, respective layers can have particular density distributions, and if a sheet or bulk explosive, for example, were introduced into a layer the density distribution of the layer may be different than expected. In this way, for example, information identifying a potential threat item concealed in an image representation of a segmented object may be determined by using object partition information.” See Para. [0036] of Hong as well.)
Regarding claim 3, the combination of Hong and Rodriguez teaches, “The system of claim 1, wherein, responsive to a determination of a presence of a suspicious element in the image, the system is configured to trigger an alarm of a second type, different from said first type.” (Hong, Para. [0041] discloses; “Information concerning whether a scanned object is potentially threat-containing 162 can be sent to a terminal 118 in the example environment 100, for example, comprising a display that can be viewed by security personal at a luggage screening checkpoint. In this way, in this example, real-time threat potential information can be retrieved for objects subjected to scanning by a security scanner 102.” Examiner interprets sending real-time threat information to security personnel as being an alarm.)
Regarding claim 5, the combination of Hong and Rodrigues teaches, “The system of claim 1, wherein the first category comprises at least one of: laptops, tablets, hairdryers, straighteners, speakers, cameras, docking stations, irons, e-readers, or game consoles.” (Hong, Para. [0034] discloses; “In the example environment 100, image data for objects 152 can be sent to an object class identifier 120, which can be configured to compare data of a segmented object 152 with pre-determined object class data (e.g., data for a number of classes of portable electronic devices, such as laptops, handheld video games, portable DVD players, etc.) to identify whether the segmented object belongs to a particular class of objects.”)
Regarding claim 7, the combination of Hong and Rodrigues teaches, “The system of claim 1, wherein: responsive to a determination that (ii) is met, the system is configured to transmit data informative of a location of the electronic device of the first category to the algorithm, wherein the algorithm is configured to use the data to determine whether a suspicious element is present in the image, or responsive to a determination that (ii) is met, and to a determination that at least one electronic device of the first category is present in the image, the system is configured to transmit data informative of a type of the electronic device of the first category to the algorithm, wherein the algorithm is configured to use the data to determine whether a suspicious element is present in the image.” (Hong, Figure 1 shows a flow chart of data through the system. Object 120 is the object class identifier, which determines the type of object in the image. This information eventually feeds into object 126 which determines if there is a threating (suspicious) object in the image. See para. [0034] for object class identifier, and para. [0038] for threat determiner. The claim language “or” is underlined to emphasize that the claim does not require both instances to be met.)
Regarding claim 8, the combination of Hong and Rodriguez teaches, “The system of claim 1, wherein, responsive to a determination that (i) and (ii) are met” (Hong discloses determining if (i) and/or (ii) is met. See rejection of claim 1.) “the system is configured to both trigger an alarm of the first type” (Rodriguez, Para. [0077] discloses; “Block 1005 illustrates a determination whether the RFID detected device is properly placed in a tray. If the RFID detected device is properly placed in a tray, then the process passes to block 1006. If the RFID detected device is not properly placed in a tray or other holder, then the process passes to block 1007. Block 1007 depicts providing an alert that an electronic device is detected in luggage or otherwise not properly placed in a tray or other holder, and the process ends.” Rodriguez, Para. [0007] discloses; “Thus, it would be advantageous to provide an x-ray screening that detects electronic devices within baggage that have not been separately presented for x-ray screening.” Rodriguez teaches triggering an alarm if there is an electronic device within, below, or on luggage.) “and use at least part of the image and the algorithm to determine whether a suspicious element is present in the at least part of the image.” (Hong, Para. [0038] discloses; “In the example environment 100, a threat determiner 126 can receive one or more layout feature vectors 158 for an object. The threat determiner 126 can be configured to compare the one or more layout feature vectors to one or more pre-determined boundaries, corresponding to the object's class, to determine whether the object contains a potential threat (e.g. explosives).” It would be obvious to a person of ordinary skill in the art to combine the alarm of Rodriguez with the determining of suspicious elements of Hong.) The proposed combination as well as the motivation for combining the Hong and Rodriguez references presented in the rejection of claim 1, apply to claim 8 and are incorporated herein by reference. Thus, the system recited in claim 8 is met by Hong and Rodriguez.
Regarding claim 9, the combination of Hong and Rodriguez teaches, “The system of claim 1, wherein: responsive to a determination that (ii) is met, and to a determination of a type of the at least one electronic device, the system is configured to use said type to select a given algorithm among a plurality of algorithms, and to use said given algorithm to determine whether a suspicious element is present in the image, or responsive to a determination that (ii) is met, and to a determination of a type of the at least one electronic device, the system is configured to use said type to select a given algorithm among a plurality of algorithms, and to use said given algorithm to determine whether a suspicious element is present in the image, wherein the given algorithm has been trained with images comprising electronic devices of said type.” (Hong, Figure 1 shows a flow chart of their object detection system. In the flow chart, box 120 is the object class identifier which determines the type of object in the image. Following the arrows, the system can decide to use the “sub-region extractor” algorithm or the “layout vector generator” algorithm. These algorithms are then input into the threat determinator. Therefore, the object is identified, an algorithm is chosen, and a threat (suspicious element) is determined.
PNG
media_image1.png
297
251
media_image1.png
Greyscale
The claim language “or” is underlined to emphasize that the claim does not require both instances to be met.)
Regarding claim 13, the combination of Hong and Rodriguez teaches, “The system of claim 1, configured to, responsive to determination that neither (i) nor (ii) are met, not raise an alarm of the first type and not feed the image to the algorithm.” (Hong, Claim 5 discloses; “if the object does not belong to the class of known object, the one or more image representations are not partitioned” Examiner interprets the object not belonging to a class as there to be no electronic device in the image. Examiner also interprets this disclosure as not feeding the image to the algorithm. Since the image does not get partitioned, then it will not be sent to the output terminal, meaning no alarm will be raised.)
Regarding claim 14, the combination of Hong and Rodriguez teaches, “The system of claim 1, configured to: use the image and the first machine learning model to determine whether (a) or (b) is met in the image: (a) at least one electronic device of a first category is present within, below, or on an object of a third category;” (Hong, Para. [0033] discloses; “In this example, the object segmentor 130 may segment the portable electronic device in an image of the luggage (e.g., separate the device from other objects inside the luggage, in the image) generated by the x-ray scanner or the CT scanner.” Examiner interprets the system of Hong to have the ability to determine if an electronic devis is present within, below, or on luggage by segmenting the image and separating potential objects) “(b) at least one electronic device of the first category is present, wherein said at least one electronic device is not located within, below, or on an object of the third category;” (Hong, Para. [0033] discloses; “As another example, a portable electronic device may be taken out of a passenger's luggage and placed into a bin for scanning by an x-ray scanner (e.g., current FAA regulations require that laptop computers be removed from a passenger's luggage prior to screening) or a CT scanner. In this example, the object segmentor 130 may be able to segment the portable electronic device inside an image of the bin generated by the x-ray scanner or a CT scanner. It will be appreciated that, in this example environment, an object can also be imaged (e.g., scanned by an x-ray scanner or a CT scanner) directly, and may not be located inside a container (e.g., luggage or a bin).” Examiner interprets the system of Hong to have the ability to determine if an electronic device is present, wherein the device is not within, below, or on luggage by segmenting the image.) “responsive to a determination that (a) is met, trigger an alarm;” (Rodriguez, Para. [0077] discloses; “Block 1005 illustrates a determination whether the RFID detected device is properly placed in a tray. If the RFID detected device is properly placed in a tray, then the process passes to block 1006. If the RFID detected device is not properly placed in a tray or other holder, then the process passes to block 1007. Block 1007 depicts providing an alert that an electronic device is detected in luggage or otherwise not properly placed in a tray or other holder, and the process ends.” Rodriguez, Para. [0007] discloses; “Thus, it would be advantageous to provide an x-ray screening that detects electronic devices within baggage that have not been separately presented for x-ray screening.” Rodriguez teaches triggering an alarm if there is an electronic device within, below, or on luggage.) “responsive to a determination that (b) is met, use at least part of the image and the algorithm to determine whether a suspicious element is present in the at least part of the image” (Hong, Para. [0038] discloses; “In the example environment 100, a threat determiner 126 can receive one or more layout feature vectors 158 for an object. The threat determiner 126 can be configured to compare the one or more layout feature vectors to one or more pre-determined boundaries, corresponding to the object's class, to determine whether the object contains a potential threat (e.g. explosives).” Examiner interprets the threat determiner of Hong to be an algorithm different from the first machine learning model that determines if there is a suspicious element in the image.) “wherein the third category includes objects operative to mask, at least partially, an electronic device of the first category in the image.” (Examiner submits that it would be obvious to a person of ordinary skill in the art that any baggage or luggage can be used to mask or hide an electronic device.) The proposed combination as well as the motivation for combining the Hong and Rodriguez references presented in the rejection of claim 1, apply to claim 14 and are incorporated herein by reference. Thus, the system recited in claim 14 is met by Hong and Rodriguez.
Claim 18 recites a computer-readable storage medium storing a program with instructions corresponding to the steps recited in Claim 1. Therefore, the recited programming instructions of this claim are mapped to the proposed combination in the same manner as the corresponding steps in its corresponding system claim. Additionally, the rationale and motivation to combine the Hong and Rodriguez references, presented in rejection of Claim 1, apply to this claim. Finally, the combination of Hong and Rodriguez references discloses a computer readable storage medium (Hong, Para. [0093] discloses; “Still another embodiment involves a computer-readable medium comprising processor-executable instructions configured to implement one or more of the techniques presented herein”).
Claims 4 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Hong et al. (US 20110206240 A1 – IDS), in view of Rodriguez et al. (US 20080005804 A1 – IDS), in further view of Ahmed et al. (US 20200394430 A1).
Regarding claim 4, the combination of Hong and Rodriguez does not explicitly teach, “The system of claim 1, wherein the algorithm comprises a second machine learning model, distinct from the first machine learning model.” Since the combination of Hong and Rodriguez does not explicitly disclose this limitation, Examiner relies on the teachings of Ahmed in an analogous field of endeavor. Specifically, Ahmed teaches, “The system of claim 1, wherein the algorithm comprises a second machine learning model, distinct from the first machine learning model.” (Ahmed Figure 7 shows a flow chart for detecting objects in luggage. Figure 7 shows a first neural network for identifying regions of interest, and a second neural network for classifying the shapes of the objects in the luggage.
PNG
media_image2.png
679
498
media_image2.png
Greyscale
Using 2 neural networks (machine learning models) for detecting and identifying objects in luggage is known in the art.)
Hong, Rodriguez, and Ahmed are considered to be analogous to the claimed invention because they are in the same field of detecting objects in luggage. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Hong and Rodriguez to incorporate the teachings of Ahmed in order to use 2 distinct machine learning models. One of ordinary skill in the art would have been motivated to combine the previously described system of Hong and Rodriguez with the teachings of Ahmed to be able to train the model to determine if there is a prohibited item. (Ahmed, Abstract discloses; “using a second trained neural network to determine whether any of the shapes correspond to a shape of a prohibited object known by the second neural network”) It would be obvious to combine Hong, Rodriguez, and Ahmed to have a second trained machine learning model.
Regarding claim 10, the combination of Hong, Rodriguez, and Ahmed teaches, “The system of claim 1, wherein, responsive to a determination that (i) is met, the system is configured to perform at least one of: outputting information on a type of the electronic device that has been detected, or outputting data informative of a location of the at least one electronic device that has been detected in the image, or outputting a total number of one or more electronic devices of the first category, each present in, below, or on luggage of the second category.” (Ahmed, Para. [0028] discloses; “For instance, the screening technique first scans the compartment with the imaging device 110 and then feeds the three-dimensional representation to a two-stage neural network to output a classification of whether the scanned compartment contains a prohibited object or not, and a likely location of the prohibited object within the compartment.”) The proposed combination as well as the motivation for combining the Hong, Rodriguez, and Ahmed references presented in the rejection of claim 4, apply to claim 10 and are incorporated herein by reference. Thus, the system recited in claim 10 is met by Hong, Rodriguez, and Ahmed.
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Hong et al. (US 20110206240 A1 – IDS), in view of Rodriguez et al. (US 20080005804 A1 – IDS), in further view of Heilmann et al. (US 20250329103 A1 w/ EFD of 03/15/2021)
Regarding claim 12, the combination of Hong and Rodriguez does not explicitly teach, “The system of claim 1, wherein, for an image comprising an electronic device of the first category, and luggage of the second category, the first machine learning model is configured to determine a first location of the electronic device, a second location of the luggage, wherein the first machine learning model or the system is configured to compare the first location with the second location to determine whether (i) or (ii) is met.” Since the combination of Hong and Rodriguez does not explicitly disclose these limitations, Examiner relies on the teachings of Heilmann in an analogous field of endeavor. Specifically, Heilmann teaches, “The system of claim 1, wherein, for an image comprising an electronic device of the first category, and luggage of the second category, the first machine learning model is configured to determine a first location of the electronic device, a second location of the luggage, wherein the first machine learning model or the system is configured to compare the first location with the second location to determine whether (i) or (ii) is met.” (Heilmann, Para. [0023] discloses; “While it is a fundamental core idea of the present disclosure to position the alarm object within the item of luggage, this embodiment ensures that the alarm object is actually visually located within the boundaries of the item of luggage. In other words, the method according to the disclosure is improved here in that a defined relative positioning of the alarm object in relation to the item of luggage is carried out. For example, it is possible to detect where the boundary of the respective item of luggage lies through abrupt changes in the density of the individual adjacent voxels or the individual adjacent pixels of the luggage scan. Once this boundary has been determined, the alarm object can be positioned relative to the item of luggage, within the item of luggage, in the desired way, so that unrealistic combination scans or, consequently, unrealistic combination volumes, in which the alarm object would be arranged outside of the boundaries of the item of luggage or even extending across such a boundary, can be avoided.”)
Hong, Rodriguez, and Heilmann are considered to be analogous to the claimed invention because they are in the same field of detecting objects in luggage. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Hong and Rodriguez to incorporate the teachings of Heilmann in order to determine if an electronic device is in the luggage based on comparing locations. One of ordinary skill in the art would have been motivated to combine the previously described system of Hong and Rodriguez with the teachings of Heilmann to ensure the item is actually within the borders of the luggage. (Heilmann, Para. [0023] discloses; “While it is a fundamental core idea of the present disclosure to position the alarm object within the item of luggage, this embodiment ensures that the alarm object is actually visually located within the boundaries of the item of luggage”) It would be obvious to combine Hong, Rodriguez, and Heilmann to locate the item and luggage, and compare the locations.
Claims 6, 11, 15-17, 19, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Hong et al. (US 20110206240 A1 – IDS), in view of Rodriguez et al. (US 20080005804 A1 – IDS), in further view of Liang et al. (“Toward Automatic Threat Recognition for Airport X-Ray Baggage Screening with Deep Convolutional Object Detection” – IDS).
Regarding claim 6, the combination of Hong and Rodriguez does not explicitly teach, “The system of claim 1, wherein the first machine learning model is configured to classify a scenario in which a laptop is located within, below, or on a bag specifically designed for laptops, as a scenario in which the laptop is not present within, below, or on luggage of the second category.” Since the combination of Hong and Rodriguez does not explicitly disclose this limitation, Examiner relies on the teachings of Liang in an analogous field of endeavor. Specifically, Liang teaches, “The system of claim 1, wherein the first machine learning model is configured to classify a scenario in which a laptop is located within, below, or on a bag specifically designed for laptops, as a scenario in which the laptop is not present within, below, or on luggage of the second category.” (Liang, Pg. 3, Para. 1 discloses; “To simulate the diversity of real-world traffic, a variety of host bags was used, including roller, laptop, and duffel bags.” Examiner interprets a laptop bag to be a bag specifically designed for laptops. Hong, Para. [0004] discloses; “As described above, these objects are often removed from luggage and placed either into bins or directly on conveyor belts prior to being scanned by an imaging device.” In light of Hong’s disclosure that laptops are often removed from luggage, it would be obvious to categorize a laptop bag as essentially not having a laptop in luggage.)
Hong, Rodriguez, and Liang are considered to be analogous to the claimed invention because they are in the same field of detecting objects in luggage. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Hong and Rodriguez to incorporate the teachings of Liang in order to classify luggage specifically for laptops, as a laptop not with luggage. One of ordinary skill in the art would have been motivated to combine the previously described system of Hong and Rodriguez with the teachings of Liang to not alarm security personnel when a laptop is in a laptop-specific bag. (Hong, Para. [0004] discloses; “As described above, these objects are often removed from luggage and placed either into bins or directly on conveyor belts prior to being scanned by an imaging device.” In light of Hong’s disclosure that laptops are often removed from luggage, it would be obvious to categorize a laptop bag as essentially not having a laptop in luggage.) It would be obvious to combine Hong, Rodriguez, and Liang to classify a laptop-specific bag in this way.
Regarding claim 11, the combination of Hong, Rodriguez, and Liang teaches, “The system of claim 1, wherein: the first machine learning model has been trained with training images including one or more electronic devices of the first category, and associated with a label indicative of a position of the one or more electronic devices of the first category, and training images including one or more luggage items of the second category, and associated with a label indicative of a position of the one or more luggage items of the second category, or the first machine learning model has been trained with training images including at least one electronic device of a first category present within, below, or on luggage of a second category, associated with a first label, and training images in which at least one electronic device of the first category is present, but which is not located within, below, or on luggage of the second category, associated with a second label, different from the first label.” (Liang, Pgs. 1-2 disclose; “Most deep learning methods require a large training dataset of labeled examples to achieve good performance [33]; for object detection, this means data comprising both images and bounding boxes with class labels. While many such datasets exist for Red-Green-Blue (RGB) natural scenes (e.g. [11, 23, 8]), none contain threats in X ray luggage, and so a sizable data collection effort was necessary for this endeavor. We assembled a large variety of cluttered bags (e.g. clothing, electronics, etc.) with hidden threats (firearms, sharps, blunts, LAGs), and scanned these with the Rapiscan 620DV. Each threat in the scans was then annotated with a tight bounding box and labeled according to class. This dataset was then used for training and evaluating object detection models.” Examiner interprets the bounding boxes to be labels indicative of position. A person of ordinary skill in the art would find it obvious to combine the training set of Liang with the electronic devices of Hong. The claim language “or” is underlined to emphasize that the claim does not require both instances to be met.)
Regarding claim 15, the combination of Hong and Rodriguez teaches, “The system of claim 1, configured to: use said first machine learning model, or another machine learning model, to determine whether the image comprises a Liquid, an Aerosol, or a Gel which does not comply with a security criterion” (Liang, Pg. 5, Section 5 discloses; “We initialize each model with pre-trained weights from the MSCOCO Object Detection Challenge [23] and then fine-tune them to detect each of the target classes (firearms, sharps, blunts, LAGs) simultaneously, which allows us to perform detection four times as fast as if we trained a separate algorithm for each.” Liang defines “LAGs” as liquids, aerosols, and gels with volumes exceeding the TSA-established thresholds.) “and responsive to detection of a Liquid, an Aerosol, or a Gel which does not comply with the security criterion, trigger an alarm.” (Liang, Figure 2 shows the process of the system. After object detection, the system displays the image and triggers an alarm. As shown above, the system detects liquids, aerosols, and gels. Thus, the alarm would trigger after detecting a liquid, aerosol, or gel.)
Regarding claim 16, the combination of Hong, Rodriguez, and Liang teaches, “The system of claim 1, configured to perform at least one of (i), (ii), (iii) or (iv): (i) use said first machine learning model, or another machine learning model, to detect presence of at least one of a Liquid, an Aerosol, or a Gel, which has a capacity above a threshold, responsive to a detection of at least one of a Liquid, an Aerosol, or a Gel, which has a capacity above the threshold, trigger an alarm; (ii) use said first machine learning model, or another machine learning model, to detect presence of at least one of a Liquid, an Aerosol, or a Gel, with a capacity equal to or below an authorized threshold, located within or below luggage of a fourth category, responsive to a detection of at least one of a Liquid, an Aerosol, or a Gel with a capacity equal to or below the authorized threshold, located within or below luggage of the fourth category, trigger an alarm; (iii) use said first machine learning model, or another machine learning model, to detect presence of at least one of a Liquid, an Aerosol, or a Gel, with a capacity equal to or below an authorized threshold, located within or below authorized luggage, wherein, responsive to a detection of at least one of a Liquid, an Aerosol, or a Gel with a capacity equal to or below the authorized threshold, located within or below authorized luggage, the system is configured to not trigger an alarm; (iv) use said first machine learning model, or another machine learning model, to detect presence of at least one of a Liquid, an Aerosol, or a Gel, and to differentiate between: a Liquid, an Aerosol, or a Gel with a capacity equal to or below an authorized threshold, and a Liquid, an Aerosol, or a Gel with a capacity above an authorized threshold; (v) use said first machine learning model, or another machine learning model, to detect presence of at least one of a Liquid, an Aerosol, or a Gel, of a certain type, and to not trigger an alarm for this certain type, wherein this certain type includes a lighter.” (Liang, Pg. 5, Section 5 discloses; “We initialize each model with pre-trained weights from the MSCOCO Object Detection Challenge [23] and then fine-tune them to detect each of the target classes (firearms, sharps, blunts, LAGs) simultaneously, which allows us to perform detection four times as fast as if we trained a separate algorithm for each.” Liang defines “LAGs” as liquids, aerosols, and gels with volumes exceeding the TSA-established thresholds. Liang, Figure 2 shows the process of the system. After object detection, the system displays the image and triggers an alarm. As shown above, the system detects liquids, aerosols, and gels. Thus, the alarm would trigger after detecting a liquid, aerosol, or gel. The claim language “at least one of” is underlined to emphasize that the claim only requires one instance to be met. Therefore, the claim is rejected because Liang teaches (i).)
Regarding claim 17, the combination of Hong, Rodriguez, and Liang teaches, “The system of claim 1, configured to use the image and the first machine learning model to determine whether (i), (ii), (iii) or (iv) is met in the image: (i) at least one electronic device of the first category is present within, below, or on luggage of the second category;” (Hong, Para. [0033] discloses; “In this example, the object segmentor 130 may segment the portable electronic device in an image of the luggage (e.g., separate the device from other objects inside the luggage, in the image) generated by the x-ray scanner or the CT scanner.” Examiner interprets the system of Hong to have the ability to determine if an electronic devis is present within, below, or on luggage by segmenting the image and separating potential objects.) “(ii) at least one electronic device of the first category is present in the image, which is not located within, below, or on luggage of the second category;” (Hong, Para. [0033] discloses; “As another example, a portable electronic device may be taken out of a passenger's luggage and placed into a bin for scanning by an x-ray scanner (e.g., current FAA regulations require that laptop computers be removed from a passenger's luggage prior to screening) or a CT scanner. In this example, the object segmentor 130 may be able to segment the portable electronic device inside an image of the bin generated by the x-ray scanner or a CT scanner. It will be appreciated that, in this example environment, an object can also be imaged (e.g., scanned by an x-ray scanner or a CT scanner) directly, and may not be located inside a container (e.g., luggage or a bin).” Examiner interprets the system of Hong to have the ability to determine if an electronic device is present, wherein the device is not within, below, or on luggage by segmenting the image.) “(iii) the image comprises a Liquid, an Aerosol or a Gel which does not comply with a security criterion;” (Liang, Pg. 5, Section 5 discloses; “We initialize each model with pre-trained weights from the MSCOCO Object Detection Challenge [23] and then fine-tune them to detect each of the target classes (firearms, sharps, blunts, LAGs) simultaneously, which allows us to perform detection four times as fast as if we trained a separate algorithm for each.” Liang defines “LAGs” as liquids, aerosols, and gels with volumes exceeding the TSA-established thresholds.) “(iv) no Liquid, an Aerosol, or a Gel is present, or all of one or more Liquids, Aerosols, or Gels which are present are such that their presence meets the security criterion.” (Liang, Pg. 5, Section 5 discloses; “We initialize each model with pre-trained weights from the MSCOCO Object Detection Challenge [23] and then fine-tune them to detect each of the target classes (firearms, sharps, blunts, LAGs) simultaneously, which allows us to perform detection four times as fast as if we trained a separate algorithm for each.” Liang defines “LAGs” as liquids, aerosols, and gels with volumes exceeding the TSA-established thresholds. Therefore, the system can determine that it does not detect LAGs.)
Claim 19 recites a computer-readable storage medium storing a program with instructions corresponding to the steps recited in Claim 15. Therefore, the recited programming instructions of this claim are mapped to the proposed combination in the same manner as the corresponding steps in its corresponding system claim. Additionally, the rationale and motivation to combine the Hong, Rodriguez, and Liang references, presented in rejection of Claim 6, apply to this claim. Finally, the combination of Hong, Rodriguez, and Liang references discloses a computer readable storage medium (Hong, Para. [0093] discloses; “Still another embodiment involves a computer-readable medium comprising processor-executable instructions configured to implement one or more of the techniques presented herein”).
Claim 20 recites a computer-readable storage medium storing a program with instructions corresponding to the steps recited in Claim 16. Therefore, the recited programming instructions of this claim are mapped to the proposed combination in the same manner as the corresponding steps in its corresponding system claim. Additionally, the rationale and motivation to combine the Hong, Rodriguez, and Liang references, presented in rejection of Claim 6, apply to this claim. Finally, the combination of Hong, Rodriguez, and Liang references discloses a computer readable storage medium (Hong, Para. [0093] discloses; “Still another embodiment involves a computer-readable medium comprising processor-executable instructions configured to implement one or more of the techniques presented herein”).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JUSTIN M. OAKES whose telephone number is (571)272-9379. The examiner can normally be reached 7:30am-5pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Amandeep Saini can be reached at (571) 272-3382. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/JUSTIN M OAKES/
Examiner, Art Unit 2662
/Siamak Harandi/Primary Examiner, Art Unit 2662
