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 .
DETAILED ACTION
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 12/12/2025 has been entered.
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 13, 21, 22, 25-33, 36, and 37 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim(s) recite(s) as follows: capturing a characteristic of each of the first heterogeneous mixture of materials with a sensor and assigning with a machine learning system a first classification (claim 13) and a sensor configured to capture one or more characteristics of each of the mixture of materials and a data processing system comprising an artificial intelligence neural network configured to classify certain ones of the mixture (claim 25).
This judicial exception is not integrated into a practical application because there is no mechanism for the type of data analysis being performed as the generically recited sensor does not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the high level analysis of characteristics could be performed in the human mind (mental process see MPEP 2106.04(a)(2)III). The fact that this is performed with a machine learning model/artificial intelligence neural network appears to be at the “apply it” level (see MPEP 2106.05(A)).
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 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 of this title, 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 13, 21, 22, 25-32, and 37 are rejected under 35 U.S.C. 103 as being unpatentable over Skaff (US Pub 2015/0144537 A1) in view of Talathi et al (US Pub 2015/0254532 A1).
Regarding claim 13, Skaff a method for handling a first heterogeneous mixture of separable materials comprising at least one of a first type of materials and at least one of a second type of materials, the method comprising: capturing a characteristic of each of the first heterogeneous mixture of materials with a sensor (paragraph 0034 and element 24); and assigning with a machine learning system a first classification to certain ones of the first heterogeneous mixture of materials as belonging to the first type of materials based on the captured characteristics of each of the first heterogeneous mixture of materials, wherein the first classification is based on a first knowledge base produced from a previously generated classification of one or more examples of the first type of materials (paragraphs 0043 where predetermined object classifiers classify based on an initial input and then further feedback from subsequent classification to obtain greater confidence level), but Skaff does not disclose the machine learning system comprises an artificial intelligence neural network. Talathi teaches machine learning system comprises an artificial intelligence neural network (paragraph 0067) for the purpose of image management by sorting images based on characteristics of the image. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to modify Skaff, as taught by Talathi, for the purpose of rapidly recognizing and sorting a plurality of materials.
Regarding claim 21, Skaff does not disclose the limitations of the claim. Talathi teaches the assignment of the first classification is performed by the artificial intelligence neural network (paragraph 0067) for the purpose of image management by sorting images based on characteristics of the image. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to modify Skaff, as taught by Talathi, for the purpose of rapidly recognizing and sorting a plurality of materials.
Regarding claim 22, Skaff does not disclose the limitations of the claim. Talathi teaches the artificial intelligence neural network includes a convolutional neural network (paragraph 0067) for the purpose of image management by sorting images based on characteristics of the image. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to modify Skaff, as taught by Talathi, for the purpose of rapidly recognizing and sorting a plurality of materials.
Regarding claim 25, Skaff discloses an apparatus for handling a first mixture of materials comprising different first and second classes of materials, the apparatus comprising: a sensor configured to capture one or more characteristics of each of the first mixture of materials (paragraph 0034 and element 24); and a data processing system (element 100) comprising a machine learning system (paragraph 0019) configured to classify certain ones of the mixture of materials as belonging in the first class of materials based on the one or more captured characteristics of the first mixture, wherein the classifying of certain ones of the first mixture is based on a previously generated first knowledge base of characteristics associated with the first class of materials (paragraphs 0043 where predetermined object classifiers classify based on an initial input and then further feedback from subsequent classification to obtain greater confidence level), but Skaff does not disclose the machine learning machine is an artificial intelligence neural network. Talathi teaches the machine learning machine is an artificial intelligence neural network (paragraph 0067) for the purpose of image management by sorting images based on characteristics of the image. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to modify Skaff, as taught by Talathi, for the purpose of rapidly recognizing and sorting a plurality of materials.
Regarding claim 26, Skaff discloses the sensor is a camera (paragraph 0034), and wherein the one or more captured characteristics were captured by the camera configured to capture images of the one or more samples of the first class of materials as they were conveyed past the camera (paragraph 0043).
Regarding claim 27, Skaff discloses the classifying of certain ones of the first mixture is based on a comparison of the previously generated first knowledge base to a second previously generated knowledge base of characteristics captured from one or more samples of the second class of materials (paragraph 0043).
Regarding claim 28, Skaff discloses a conveyor system (element 12) configured to convey the first mixture past the sensor; and a sorter (element 160) configured to sort the classified certain ones of the first mixture from the first mixture as a function of the classifying of certain ones of the first mixture.
Regarding claim 29, Skaff does not disclose the limitations of the claim. Talathi teaches the artificial intelligence neural network includes a convolutional neural network (paragraph 0067) for the purpose of image management by sorting images based on characteristics of the image. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to modify Skaff, as taught by Talathi, for the purpose of rapidly recognizing and sorting a plurality of materials.
Regarding claim 30, Skaff discloses the artificial intelligence neural network includes a first classifier associated with the first class of materials and different from a second classifier associated with the second class of materials, the data processing system being configured to test for (1) the first class of materials using the first classifier and (2) the second class of materials using the second classifier (paragraphs 0042-0043 material classifiers 151).
Regarding claim 31, Skaff does not disclose the limitations of the claim. Talathi teaches the sensor is configured to capture the one or more characteristics of each of the mixture of materials as an array of data values, and wherein the data processing system comprising the artificial intelligence neural network is configured to receive the array of data values as input to classify the certain ones of the mixture of materials as belonging in the first class of materials based on the one or more captured characteristics of the mixture (paragraph 0067 where different values of a taken image are used to manage characteristics of an image) for the purpose of image management by sorting images based on characteristics of the image. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to modify Skaff, as taught by Talathi, for the purpose of rapidly recognizing and sorting a plurality of materials.
Regarding claim 32, Skaff does not disclose the limitations of the claim. Talathi teaches the data processing system further comprises a segmentation neural network configured to determine that a subset array of data values from the array of data values belongs to one of the first class of materials or the second class of materials, the subset array of data values being smaller than the array of data values (paragraph 0067 where there are subset values of any particular image such as meta data, size, and type) for the purpose of image management by sorting images based on characteristics of the image. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to modify Skaff, as taught by Talathi, for the purpose of rapidly recognizing and sorting a plurality of materials.
Regarding claim 37, Skaff does not disclose the limitations of the claim. Talathi teaches the characteristic of each of the first heterogeneous mixture of materials is captured with the sensor as a plurality of data values that represents the characteristic, and wherein the plurality of data values is concurrently provided as input to the machine learning system to assign the first classification (paragraph 0067 where different values of a taken image are used to manage characteristics of an image) for the purpose of image management by sorting images based on characteristics of the image. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to modify Skaff, as taught by Talathi, for the purpose of rapidly recognizing and sorting a plurality of materials.
Claim 36 is rejected under 35 U.S.C. 103 as being unpatentable over Skaff/Talathi in view of Torek et al (US Pub 2013/0304254 A1).
Regarding claim 36, Skaff does not disclose the limitations of the claim. Talathi teaches the characteristic of each of the first heterogeneous mixture of materials is captured with the sensor as a plurality of pixel values that represents the characteristic, and wherein the plurality of pixel values is provided as input to the machine learning system to assign the first classification (paragraph 0036) for the purpose of data processing on the datasets to determine the category of metal. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to modify Skaff, as taught by Torek, for the purpose of data processing on the datasets to determine the category of metal.
Allowable Subject Matter
Claims 1-12, 14-20, 23, 24, 34, and 35 are allowed.
The following is an examiner’s statement of reasons for allowance:
The closest prior art discloses an apparatus and method for handling a mixture of materials. The closest prior art does not disclose or make obvious a sensor configured to capture data including a plurality of pixel values and representing one or more characteristics of each of the first mixture of materials; and a data processing system comprising a machine learning system configured to receive the captured data as input to classify certain ones of the first mixture as belonging in the first class of materials based on each pixel value from the plurality of pixel values in conjunction with the other structures in claim 1.
The closest prior art discloses an apparatus and method for handling a mixture of materials. The closest prior art does not disclose or make obvious capturing, with a sensor, data associated with a plurality of pixels representing a characteristic of each of the first heterogeneous mixture of materials; and providing the captured data as input to a machine learning system to assign a first classification to certain ones of the first heterogeneous mixture of materials as belonging to the first type of materials based on each pixel from the plurality of pixels in conjunction with the other structures in claim 7.
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
Response to Arguments
Applicant's arguments filed 12/12/2025 have been fully considered but they are not persuasive.
Rejection under USC 103
Regarding Applicant’s argument to claims 13 and 25,” Moreover, Skaff and Talathi, alone or in combination, do not make clear to a person having ordinary skill in the art ("PHOSITA") how the process of Skaff might be adapted to work with Talathi's DCN, as Skaff in view of Talathi is missing a step (e.g., converting concatenated coefficients into an image or vice versa). Consequently, Applicants respectfully assert that they have put forth sufficient evidence that there would not be a reasonable expectation that the combination would be successful, and a PHOSITA would have to resort to undue experimentation to combine the references to arrive at the claimed invention,” the Examiner disagrees. The Examiner asserts that the combination of Skaff and Talathi would have been obvious to a person having ordinary skill in the art. Primarily, the teaching of Talathi is used to teach an obvious use of artificial intelligence neural network for the purpose of managing images for sorting by characterisitcs. Even through each reference uses a separate machine learning system such as support vector machine (SVM) or deep convolutional network (DCN), these systems are not incompatible with each other. The combination of SVM and DCN would be an obvious combination to teach the enhancement of image classification tasks as taking information from the smallest datasets of individual images all the way to a multi-image analysis to gain a complete image classification.
Declaration under 37 CFR 1.132
The declaration under 37 CFR 1.132 filed 12/12/2025 is insufficient to overcome the rejection of claims 13 and 25 based upon USC 103 Skaff in view of Talathi as set forth in the last Office action because: As discussed above, the combination of SVM and DCN would be an obvious combination to teach the enhancement of image classification tasks as taking information from the smallest datasets of individual images all the way to a multi-image analysis to gain a complete image classification.
In response to applicant's argument that the SVM of Skaff and the DCN of Talathi receive completely different data structures as input (concatenated coefficients versus an image), the SVM and DCN also perform analysis along completely different dimensions, the test for obviousness is not whether the features of a secondary reference may be bodily incorporated into the structure of the primary reference; nor is it that the claimed invention must be expressly suggested in any one or all of the references. Rather, the test is what the combined teachings of the references would have suggested to those of ordinary skill in the art. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Kalyanavenkateshware Kumar whose telephone number is (571)272-8102. The examiner can normally be reached on M-F 08:00-16:30.
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/K.K./Examiner, Art Unit 3653
/MICHAEL MCCULLOUGH/Supervisory Patent Examiner, Art Unit 3653