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 .
Missing Oath or Declaration
A properly executed inventor’s Oath or Declaration signed by the following inventor(s): Pierre Meriguet; Luis Tobias; Najib Gadi; Laurent Cohen; Jamal Atif, must be submitted no later than the expiration of Three-month time period set in the Notice of Allowability to avoid abandonment. (This is a supplemental reminder of the Notice to File Missing Parts communication from the U.S. Patent Office mailed to Applicant on September 6, 2024).
Priority
Receipt is acknowledged of papers submitted under 35 U.S.C. 119(a)-(d), which papers have been placed of record in the file.
Specification
The specification (including the abstract and claims), and any amendments for applications, except as provided for in 37 CFR 1.821 through 1.825, must have text written plainly and legibly either by a typewriter or machine printer in a nonscript type font (e.g., Arial, Times Roman, or Courier, preferably a font size of 12) lettering style having capital letters which should be at least 0.3175 cm. (0.125 inch) high, but may be no smaller than 0.21 cm. (0.08 inch) high (e.g., a font size of 6) in portrait orientation and presented in a form having sufficient clarity and contrast between the paper and the writing thereon to permit the direct reproduction of readily legible copies in any number by use of photographic, electrostatic, photo-offset, and microfilming processes and electronic capture by use of digital imaging and optical character recognition; and only a single column of text. See 37 CFR 1.52(a) and (b).
The application papers are objected to because the Specification on page 9, and also in claim 3, recite an equation, however the equation is not fully legible.
A legible substitute specification in compliance with 37 CFR 1.52(a) and (b) and 1.125 is required.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on August 23, 2024 complies with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
35 USC § 101 Statutory Analysis
The claims do not recite any of the judicial exceptions enumerated in the 2019 Revised Patent Subject Matter Eligibility Guidance. Further, the claims do not recite any method of organizing human activity, such as a fundamental economic concept or managing interactions between people. Finally, the claims do not recite a mathematical relationship, formula, or calculation. Thus, the claims are eligible because they do not recite a judicial exception.
Claim rejections - 35 U.S.C. §112(b)
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claim 17 is rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
Claim 17, lines 1-4 recites the limitation “A device configured to determine whether or not an object corresponding to a type of interest is present in an inspection image generated using penetrating radiation, the device comprising a memory storing a classifier generated by the method according to claim 1”, however the claim fails to recite a critical element described in the specification namely a processor or a computer system (see paragraphs [0035] through [0040]) in order to access the “memory storing a classifier generated by the method according to claim 1” and “determine whether or not an object corresponding to a type of interest is present in an inspection image generated using penetrating radiation”.
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.
Claim 17 is rejected under 35 U.S.C. §101 because the claimed invention is directed to non-statutory subject matter. The claim does not fall within at least one of the four categories of patent eligible subject matter because it is directed to a device (e.g. computer program product). The scope of a device (e.g. computer program product) is broad enough to include either a computer program by itself, and/or a signal per se, both of which are non-statutory.
The examiner suggests clarifying the claim to exclude such non-statutory signal embodiments, such as (but not limited to) by reciting “the device comprising a processor and a memory … ”, or equivalents.
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 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, 2, 5, 6 and 8-17 are rejected under 35 U.S.C. §102(a)(1) as being anticipated by Cinnamon et al. (U.S. Patent Application Publication No. US 2019/0057520 A1) (hereafter referred to as “Cinnamon”).
With regard to claim 1, Cinnamon describes generating training data comprising a plurality of training images comprising objects corresponding to the type of interest, using a generative adversarial network comprising a generator and a discriminator (refer for example to paragraphs [0019] through [0021] which discuss the plurality of training images, and to paragraphs [0032] and [0079] which discuss using a generative adversarial network comprising a generator and a discriminator for generating training data), wherein generating the training data comprises training the generator and the discriminator using initial training data including a plurality of observed images generated using penetrating radiation (refer for example to paragraph [0032] and to paragraphs [0080] through [0083]), one or more of the plurality of observed images including an object corresponding to the type of interest (refer for example to paragraphs [0023] and [0032]), and a representation of a container (refer for example to paragraphs [0023] and [0032]); and training the classifier by applying a machine learning algorithm, using the generated training data (refer for example to paragraphs [0021] and [0112]), wherein generating the training data further comprises generating synthetized objects corresponding to the type of interest, each synthetized object generated using at least a part of an object in the one or more observed images (refer for example to paragraphs [0037] and [0039]); and generating synthetized images using at least a part of a representation of the container in the one or more observed images, wherein generating the synthetized images further comprises using one or more of the synthetized objects (refer for example to paragraphs [0037] and [0039]).
As to claim 2, Cinnamon describes wherein the generator and the discriminator compete with each other based on a loss function, the loss function comprising at least one of a least mean square function and/or a combination of weighted Gaussian kernels (refer for example to paragraphs [0084], [0088] and [0089]).
In regard to claim 5, Cinnamon describes wherein the generator comprises one or more deconvolution layers and a transposed convolution layer (see Figure 5 and refer for example to paragraphs [0101] and [0102]).
As to claim 6, Cinnamon describes wherein the discriminator comprises one or more convolution layers and a fully connected linear activation layer (see Figure 5 and refer for example to paragraphs [0101] and [0102]).
With regard to claim 8, Cinnamon describes obtaining the observed images, irradiating, using penetrating radiation, one or more real objects corresponding to the type of interest and/or one or more real containers configured to contain cargo, and detecting radiation from the irradiated one or more real objects and/or the irradiated one or more real containers, wherein the irradiating and/or the detecting are performed using one or more devices configured to inspect real containers (refer for example to paragraphs [0105] through [0107]).
As to claim 9, Cinnamon describes wherein the classifier is configured to detect an object corresponding to a type of interest in an inspection image generated using penetrating radiation, the inspection image comprising one or more features at least similar to the training data used to generate the classifier by the machine learning algorithm (refer for example to paragraphs [0019], [0020], [0021] and [0112])).
In regard to claim 10, Cinnamon describes wherein the classifier comprises a plurality of output states (refer for example to paragraphs [0019] and [0020]).
With regard to claim 11, Cinnamon describes wherein the method is performed at a computer system separate from a device configured to inspect real containers (refer for example to paragraph [0041] which discusses the computer system, and to paragraphs [0105] through [0107] which discuss the detection devices which are separate from the computer system).
As to claim 12, Cinnamon describes obtaining an inspection image by irradiating, using penetrating radiation, one or more real containers configured to contain cargo (refer for example to paragraph [0023] and to paragraphs [0105] through [0107]); and detecting radiation from the irradiated one or more real containers (refer for example to paragraph [0023] and to paragraphs [0105] through [0107]); applying, to the obtained image, a classifier generated by the method according to claim 1 (see the rejection of claim 1 hereinabove); and determining whether or not an object corresponding to the type of interest is present in the inspection image, based on the applying (refer to paragraphs [0020] and [0025]).
With regard to claim 13, Cinnamon describes obtaining a classifier generated by the method according to claim 1 (see the rejection of claim 1 hereinabove); and storing the obtained classifier in a memory of the device, wherein the storing comprises transmitting the generated classifier to the device via a network, the device receiving and storing the classifier applying (refer to paragraphs [0041], [0119] and [0125]).
As to claim 14, Cinnamon describes wherein the classifier is generated, stored and/or transmitted in the form of one or more of a data representation of the classifier and/or executable code for applying the classifier to one or more inspection images (refer for example to paragraphs [0098] and [0104]).
In regard to claim 15, Cinnamon describes wherein the type of interest comprises at least one of a threat, such as a weapon and/or an explosive material and/or a radioactive material and/or a contraband product, such as drugs and/or cigarettes(refer for example to paragraphs [0019] and [0099]).
With regard to claim 16, Cinnamon describes wherein using penetrating radiation comprises irradiating by transmission (refer to paragraphs [0105] through [0107]).
As to claim 17, Cinnamon describes a device configured to determine whether or not an object corresponding to a type of interest is present in an inspection image generated using penetrating radiation (refer for example to paragraphs [0019] and [0099]), the device comprising a memory storing a classifier generated by the method according to claim 1 (refer to paragraphs [0041], [0119] and [0125]).
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 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(a) 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
Claim 7 is rejected under 35 U.S.C. §103(a) as being unpatentable over Cinnamon et al. (U.S. Patent Application Publication No. US 2019/0057520 A1) in view of Holt (U.S. Patent Application Publication No. US 2010/0310175 A1) (hereafter referred to as “Holt”).
The arguments advanced in section 13 above, as to the applicability of Cinnamon, are incorporated herein.
In regard to claim 7, although Cinnamon does not expressly describe the generating the synthetized images comprises using a Beer-Lambert law, such a technique is well known and widely utilized in the prior art.
Holt discloses a system for facilitate using fused images to identify materials (see abstract) which provides for capturing images of an object using penetrating x-ray radiation (refer for example to paragraph [0003]) and describes generating the synthetized images comprises using a Beer-Lambert law (refer for example to paragraphs [0045] and [0058]).
Given the teachings of the two references and the same environment of operation, namely that of systems that serve to provide views of objects that are otherwise occluded from visual inspection, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the Cinnamon system to generate synthetized images in the manner described by Holt according to known methods to yield predictable results and would have been motivated to do so with a reasonable expectation of success in order to provide for increased processing efficiency and higher accuracy as suggested by Holt (refer for example to paragraphs [0005] and [0014]), which fails to patentably distinguish over the prior art absent some novel and unexpected result.
Allowable Subject Matter
Claims 3 and 4 objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Relevant Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Perron, Wang, Fu and Jeong all disclose systems similar to applicant’s claimed invention.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Jose L. Couso whose telephone number is (571) 272-7388. The examiner can normally be reached on Monday through Friday from 5:30am to 1:30pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Matthew Bella, can be reached on 571-272-7778. The fax phone number for the organization where this application or proceeding is assigned is (571) 273-8300.
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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.
/JOSE L COUSO/Primary Examiner, Art Unit 2667
February 26, 2026