DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
Amendment was filed on 04/30/2026 and has been entered and acknowledged.
Claims 1, 5, 7, 10, 14, and 17 have been amended
Claim 15 has been canceled.
Claims 1-14, 16 and 17 are pending
Claim Objections
Claim 14 objected to because of the following informalities: Claim 14 recites “generation module for generating a two-dimensional first inspection image (IIl )” , “ for generating at least one two-dimensional second inspection image (II1) and “evaluating the second inspection image (II2)” . The notation regarding first and second inspection image needs to be consistent. This may be a typo. Appropriate correction is required.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim
shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitations are: "volume generation module" , "image generation module", "evaluation module", in claim 14. and “scanning module” in claim 16
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Response to Arguments
In regards to prior actions 112(b) rejection. 112(b) rejection of claim 1 and subsequent claims are withdrawn due to claim 1’s amendment distinguishing a first and second neural network.
In regards to prior actions 101 rejection. 101 rejection has been withdrawn due to claim 17’s amendment which replaces “computer program product” with “non-transitory computer readable medium”.
Applicant’s arguments, see “Applicant Arguments/Remarks Made in an Amendment”, filed 04/30/2026, with respect to the rejection of claims 1-9, 11-14 16 and 17 under Chen US 20140185923 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, new grounds of rejection is made in view of Chen et al US 20140185923 in view of Chen et al US 20140185742 A1.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-9, 11-14, 16 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Chen et al (Chen hereinafter US 20140185923 A1) in view of Chen et al (Chen hereinafter US 20140185742 A1)
As per claim 1
Chen (US 20140185923 A1) teaches a method for checking items of luggage (L) in order to detect objects (Figure 4) generating a three-dimensional inspection volume comprising a plurality of voxels containing information about the objects (0) located in the item of luggage (Figure 4 box S42) generating a two-dimensional first inspection image (II1) from the three- dimensional inspection volume (IV) along a first projection direction (PD1) (Paragraph [0052] “After the rotation, the vertices are organized into a 3D surface for surface rendering, and the 3D model is observed vertically from top, thereby obtaining a 2D depth projection image I.sub.0.”), generating at least one two-dimensional second inspection image (II2) from the three-dimensional inspection volume along a second projection direction (PD2) which differs from the first projection direction (PD1) (Figure 4 label S43), evaluating the first inspection image (IIl) in order to detect objects (O) by means of a first neural network (NN) (Chen calculates (evaluates) metrics from the generated depth projection images including a metric of probability (Fig 10 S104 Fig 4 S44) and a metric of symmetry for each of the first-fourth depth projection images (Fig 4 S45, Fig 10 S105) and then generates a shape feature parameter based at least on those metrics (fig 4 S46 Fig 10 S106). Chen then determines whether the object is suspicious based on the generated shape feature parameter (S107). Chen further teaches that the classifier used to classify the object based on the shape feature parameter may be a neural network Paragraph [0074-0077]. Therefore Chen’s “evaluating” of first inspection image includes computing image derived metrics and features from the image and using a classifier (a neural network) based on those features to detect and classify the object.) evaluating at least one second inspection image (II) in order to detect objects (O) by means of a second neural network (NN) (Chen calculates a metric of symmetry for each of the first, second, third and fourth depth projection images (S45/S105). The shape feature parameter is generated based at least on metrics of symmetry of the first to fourth depth projection images (S46/S106), inspection image 2 contributes to the evaluation results used by the classifier. Therefore, the evaluation of inspection image 2 is also “by means of a neural network” because the classifier (which may be a neural network) operates on the shape feature parameter generated from the first-fourth image. In paragraph [0082] Chen states “the object in the inspected luggage is classified using a classifier based on a shape parameter. Then, if the object has a shape meeting the requirement of the shape parameter, the object is further classified using a classifier based on a physical property.” This shows a second classifier (which was previously stated by Chen can be a Neural Network) in direct sequence with the first which evaluates a second depth projection inspection image.) and outputting a result of the evaluation steps evaluation of the first inspection image (IIl) and the evaluation of the at least one second inspection image(II2). (Paragraph [0033} “output projection data of the inspected luggage 70 with respect to the x rays. …The computer data processor 60 is configured to process data collected by the data collector, reconstruct the data and output results.” The sequence classifiers presented in paragraphs [0082]-[0088] necessarily output an evaluation as this is the nature of a classifier.)
Chen (US 20140185923 A1) does not teach that comprising a plurality of voxels containing information about an item of luggage.
Chen (US 20140185742 A1) teaches A method for checking items of luggage (L) in order to detect objects (O), the method comprising: generating a three dimensional inspection volume comprising a plurality of voxels containing information about an item of luggage (Fig. 7 Paragraph [0075] “As shown in FIG. 7, at step S71, slice data of the inspected luggage are obtained in the CT system. At step S72, the 3D volume data of the luggage are generated by interpolating the slice data. The 3D volume data includes density volume data and atomic number volume data in the case of dual energy, while the 3D volume data includes linear attenuation coefficient in the case of mono energy.”)
Accordingly a person of ordinary skill in the art would have found it obvious at the time this invention was effectively filed to modify Chen (US 20140185923 A1) with the their concepts of not only object 3D reconstruction but the 3D reconstruction of the actual luggage itself as Chen teaches in (US 20140185742 A1) A person of ordinary skill would be motivated to do this because collecting the isosurfaces of the luggage that Chen (US 20140185742 A1) does before obtaining the geometries of the object establishes the exact boundaries and geometry of the luggage. This boundary makes an isolation of the scanning environment letting the system ignore background noise. This allows the CT system to define an exact internal volume eliminating outside empty space. Furthermore, a person of ordinary skill in the art may be aware that any metal on a luggage or bag will scatter X-ray projections. By finding the geometry of the luggage itself the system or person of ordinary skill in the art themselves can compensate for the exterior without the need of omitting data where the scatter may happen at dense pieces of metal such as a zipper or lock.
As per claim 2
Chen and Chen teach all claim limitations previously presented in claim 1.
Chen (US 20140185923 A1) teaches the three-dimensional inspection volume (IV) is generated on the basis of a plurality of two-dimensional inspection scans (Paragraph (006) “generating, from the slice data, 3-dimensional (3D) volume data of at least one object in the luggage; calculating,”)
As per claim 3
Chen (US 20140185923 A1) and Chen (US 20140185742 A1) teach all claim limitations previously presented in claim 1.
Chen (US 20140185923 A1) teaches wherein the result of the evaluation of the first inspection image (II1) and the result of the evaluation of the second inspection image (II2) are combined to form a combined inspection result (CIR). (Figure 10 last two boxes in flowchart)
As per claim 4
Chen (US 20140185923 A1) and Chen (US 20140185742 A1) teach all claim limitations previously presented in claim 1.
Chen (US 20140185923 A1) teaches at least one additional piece of information, in particular information about a material density of an object (O), is evaluated on the basis of the three-dimensional inspection volume (IV). (Paragraph [0032] “In some embodiments, the shape feature of an object is first extracted, and then used in combination with characteristics involved in typical methods, such as atomic number and density, to achieve more efficient detection of suspicious explosives.”)
As per claim 5
Chen (US 20140185923 A1) and Chen (US 20140185742 A1) teach all claim limitations previously presented in claim 1.
Chen (US 20140185923 A1) teaches the method according to claim 1, wherein particular the first neural network and the second neural network are a same neural network (Paragraph [0077] “various types of classifier may be used, such as a linear classifier, a support vector machine, a decision tree, a neural network, and/or ensemble classifiers.” Paragraph [0090] “The shape features of known objects obtained with the sample set may be manually labeled to create two classifiers for "suspicious explosive" and "non-explosive." For a new unknown object, its shape features may be first obtained, and then applied to the classifiers for shape recognition”
As per claim 6
Chen (US 20140185923 A1) and Chen (US 20140185742 A1) teach all claim limitations previously presented in claim 1.
Chen (US 20140185923 A1) teaches The method according to claim 1, wherein material luminescence images are generated as two-dimensional inspection images (IIl,II2).Paragraph [0038] “The projection data obtained by the detection and collection device 30 is stored in the computer 60 to reconstruct CT sections, and thus obtain slice data (CT slice) of the luggage 70. Then, the computer 60 executes software, for example, to extract 3D shape parameter for at least one object contained in the luggage 70 from the slice data for security inspection. According to a further embodiment, the above CT system may be a dual-energy CT system, that is, the x-ray source” Dual Energy Computed Tomography (DECT) create material luminescence images.
As per claim 7
Chen (US 20140185923 A1) and Chen (US 20140185742 A1) teach all claim limitations previously presented in claim 1
Chen teaches US 20140185923 A1) the method according to claim 1, wherein the orientation of the first projection direction (PD1) and/or the second projection direction (PD2) is adjustable (Figures 7-9, Paragraph [0051] View angles for I.sub.0.about.I.sub.3 are defined with reference to a coordinate system shown in FIGS. 6, 8 and 9. Assume that the object is horizontally placed, and six (6) viewing directions are defined as view angles 1.about.6. …Further, it is possible to obtain an "aligned" model by rotating and normalizing the model, as shown in FIG. 8. … The projection may be achieved by rotating the 3D model about y-axis perpendicular to the horizontal plane until the upper and lower halves of top-view projection are most symmetric.”)
As per claim 8
Chen (US 20140185923 A1) and Chen (US 20140185742 A1) teach all claim limitations previously presented in claim 1
Chen (US 20140185923 A1) teaches The method according to claim 1, wherein the number of second two-dimensional inspection images (II2) generated is adjustable. (Paragraph [0040] “image segmentation is performed on the slice data to segment them into multiple regions. Then, based on a relation between binary masks for the respective regions, the regions of different slice data are connected to obtain connected data for the object” Paragraph [0041] “the slice data may undergo preprocessing, such as thresholding the slice data with ranges of densities and atomic numbers of suspicious objects, which is followed by unsupervised image segmentation, connection of segmented regions across sections, and resolution normalization after the connection.”)
As per claim 9
Chen (US 20140185923 A1) and Chen (US 20140185742 A1) teach all claim limitations previously presented in claim .
Chen (US 20140185923 A1)teaches the steps of generating the two-dimensional inspection images are repeated at least once (Chen calculates based on the 3D volume data “a first depth projection image…and second third and fourth depth projection images” in other directions.(Paragraph [0048]), Chen also describes projecting the model at multiple angles “ by projecting at the view angles View1, View2, and View3” (Paragraph [0051]) to obtain multiple projections. Therefore Chen repeats the generation step at least once) and evaluating the generated two dimensional inspection are repeated at least once (Chen calculates “a metric of symmetry for each of the first, second, third, and fourth depth projection images” ,Paragraph [0056]) As previously stated Chen uses “various types of classifier may be used such as…a neural network” Therefore Chen repeats evaluation at least once because they evaluate multiple generated projection images and does this with the neural network classifier)where in at least one of the projection directions is changed for the repetition (As previously stated Chen expressly states projections are generated at different view angles .(Figure 6, Figure 8 , Figure 9). When Chen generates the next projection image at least one projection direction is changed for the repetition).
As per claim 11
Chen (US 20140185923 A1) and Chen (US 20140185742 A1) teach all claim limitations previously presented in claim 1.
Chen teaches the method according to claim 1, wherein the three-dimensional inspection volume (IV) is built up section by section. (Paragraph [0029] “Then, 3D volume data of at least one object in the luggage is generated from the slice data.” And Paragraph [0089] “2D slice images may be first analyzed on a section basis. A series of 2D binary masks… may be obtained through thresholding and image segmentation… 3D "object" data across the sections may be obtained by connecting regions that are overlapping between sections and have high similarity…”)
As per claim 12
Chen (US 20140185923 A1) and Chen (US 20140185742 A1) teach all claim limitations previously presented in claim 1.
Chen (US 20140185923 A1) teaches when generating the three-dimensional inspection volume (IV), the generation is carried out with at least two different energy levels, in particular on the basis of a plurality of two-dimensional inspection scans ( Paragraph [0038] “…the above CT system may be a dual-energy CT system, that is, the x-ray source 10 in the rack 10 emits two kinds of rays of high and low energy levels, and the detection and collection device 30 detects projection data of the different energy levels…”)
As per claim 13
Chen (US 20140185923 A1) and Chen (US 20140185742 A1) teach all claim limitations previously presented in claim 1.
Chen (US 20140185923 A1) teaches an alarm is issued as a result if at least one alarm object has been detected as an object ( Paragraph [0031] “If the shape feature parameter of the object meets certain shape requirement, material recognition is performed on the object. In this way, the false alarm rate can be reduced.”)
As per claim 14
Chen (US 20140185923 A1) and Chen (US 20140185742 A1) teach all claim limitations previously presented in claim 1.
Claim 14 is the device claim parallel to method claim 1 and will be rejected under the same premise. Figure 1-3 show Chen’s (US 20140185923 A1) device that performs method claim 1
As per claim 16
Chen (US 20140185923 A1) and Chen (US 20140185742 A1) teach all claim limitations previously presented in claim 14.
Chen (US 20140185923 A1) teaches the control device according to claim 14 wherein a scanning module is provided for the acquisition of input data, in particular in the form of two-dimensional inspection scans (IS). (Paragraph [0033]“…a detection and collection device 30. The bearing mechanism 40 bears an inspected luggage 70, and moves it to pass through a scanning region between the ray source 10 and the detection and collection device 30”…”)
As per claim 17
Chen (US 20140185923 A1) and Chen (US 20140185742 A1) teach all claim limitations previously presented in claim 1
Claim 17 is the parallel non-transitory computer readable medium claim of claim 1and will be rejected under the same premise.
Chen (US 20140185923 A1) teaches a non-transitory computer readable medium comprising instructions which, when executed, cause a processor to carry out the a method having according to claim 1. (Figure 1 and Figure 2, Paragraph [0093] “some aspects of the embodiments disclosed here, in part or as a whole, may be equivalently implemented in an integrated circuit, as one or more computer programs”
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Chen et Al (Chen hereinafter US-20140185923-A1) in view of Chen et al (Chen hereinafter US 20140185742 A1) in further view of Mery et al (X-Ray Testing by Computer Vision.)
As per claim 10
Chen (US 20140185923 A1) and Chen (US 20140185742 A1) teach all claim limitations previously presented in claim 9
Chen (US 20140185923 A1) nor Chen (US 20140185742 A1) teach The method according to claim 9, wherein a correlation between the result of the evaluation and the change in the at least one projection direction (PD 1, PD2) carried out is stored for future evaluations and/or changes.
Mery teaches a correlation between the result of the evaluation and the change in the at least one projection direction carried out (Figure 1, “applications on cargo inspection that employ active vision where a next best view is set according to the information of a single view” Here Mery shows evaluation information and results driving the change in view) is stored for future evaluations and/or changes. (Figure 1. The active vision mechanism described here requires storing the evaluation information long enough to drive the next view change. The relationship between the evaluation and results are integral for the “ next best view” decision)
Accordingly, a person of ordinary skill in the art would have been motivated to modify the Chen/Chen workflow with Mery’s active vison technique so that the system can adapt the next projection direction based on what the prior result indicates and retain that relationship for future iterations and inspections. This directly addresses the issue you could expect in baggage inspection where clutter and overlapping make single view interpretation unreliable. Viewpoint selection affects detectability. Mery even explains that baggage images are often “intricate…due to overlapping parts” This is why they use active vision to guide to poses where detection performance should be higher and that multiple views can confirm inspection and filter out false alarms. This modification yields a greater reduction in false alarms by choosing a more informative next projection direction when the current evaluation may be ambiguous. It also yields more objective and repeatable performance by using a systematic viewpoint selection loop and retaining the evaluation view change relationship for later uses.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHANE WRENSFORD CODRINGTON whose telephone number is (571)272-8130. The examiner can normally be reached 8:00am-5pm.
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/SHANE WRENSFORD CODRINGTON/Examiner, Art Unit 2667
/MATTHEW C BELLA/Supervisory Patent Examiner, Art Unit 2667
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