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 .
Election/Restrictions
Applicant’s election without traverse of Group II, corresponding to claims 16-20, in the reply filed on July 23rd, 2025 is acknowledged.
Response to Amendment
The amendment filed January 12th, 2026 has been entered. Claims 16-20 have been amended. Claims 16-20 remain pending. Applicant’s amendments to the claims overcome some of the 112(b) rejections previously set forth in the Non-Final Office Action mailed August 13th, 2025.
Claim Objections
Claims 17-19 are objected to because of the following informalities:
In claim 17, “obtaining said library of images of pre-consumer recyclable products and post-consumer recyclable products multiple sources” should read “obtaining said library of images of pre-consumer recyclable products and post-consumer recyclable products from multiple sources”
In claim 18, “recyclable products and are comprised” should read “recyclable products are comprised”
In claim 19, “are at one of a group consisting of” should read “are at least one of a group consisting of”
Appropriate correction is required.
Claim Rejections - 35 USC § 112
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.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 20 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 20 recites the limitation "for the Deposit Return Scheme (DRS)." There is potentially insufficient antecedent basis for this limitation in the claim, as claim 16 previously recited “collecting and reporting recognized recyclable products in said feedstock as recycling data for at least one of a group consisting of: Extended Producer Responsibility (EPR), Deposit Return Scheme (DRS) compliance, and other regulatory requirements”.
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 16-20 are rejected under 35 U.S.C. 101 because the claim is directed to an abstract idea without significantly more.
Step I- Claim 16 is directed to a computer implemented method, which is considered a process, therefore claim 16 is within one of the four statutory categories: process, machine, manufacture, or composition of matter.
Step 2A, Prong 1: In Prong 1, the examiner must evaluate whether the claim recites a judicial exception (i.e. law of nature, natural phenomenon, or abstract idea.)
Claim 16 is provided below and the abstract idea limitations are highlighted in bold:
16. (Original) A computer-implemented method to identify a recyclable product in a feedstock of mixed recyclable products, the method comprising the steps of:
generating a training dataset from a library of images of pre-consumer recyclable products and post-consumer recyclable products;
training a neural network to recognize at least one of a group consisting of: the branding, the logo, the shape and the geometry in an identifying image of a recyclable product;
continuously conveying a feedstock of recyclable products;
continuously taking corresponding identifying images of each recyclable product of said feedstock of recyclable products concurrent with the step of conveying the feedstock;
recognizing at least one of a group consisting of: the branding, the logo, the shape, and the geometry in corresponding identifying images of each recyclable product of said feedstock of recyclable products with said neural network so as to match each corresponding identifying image with a respective image of said library of images;
matching each corresponding identifying image and the respective image of said library of images to at least one recyclable product in a group consisting of the pre-consumer recyclable products and the post-consumer recyclable products so as to determine recognized recyclable products in said feedstock; and
collecting and reporting recognized recyclable products in said feedstock as recycling data for at least one of a group consisting of: Extended Producer Responsibility (EPR), Deposit Return Scheme (DRS) compliance, and other regulatory requirements.
These bolded limitations, as drafted, are a process that, under its broadest reasonable interpretation, cover performance of the limitations in the mind, or by a human using pen and paper, and therefore recite mental processes. For example, a person can take the digital images collected and look for a match from the images of the recyclable products. A person can review a set of data and recognize branding or shape from observation.
Step 2A, Prong 2: In Prong 2, the examiner must evaluate whether the claim, as a whole, recites additional elements that integrate the exception into a practical application of that exception. In the above claim, these elements have been underlined. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application”. The recitation of the computer and a training data set with a library of images and the training of a neural network is recited at a high level of generality and represents no more than mere instructions to apply the judicial exceptions on a computer, using generic computing components. The computer implementation thus does not integrate the judicial exception into a practical application. The continuously conveyed feedstock is a mere indication of the field of use or technological environment in which the judicial exception is performed. (see MPEP 2106.05(h)). Additionally, this limitation and that of continuously taking a digital image is insignificant extra solution activity because it merely gathers data for use in implementation of the abstract idea of matching digital images to recyclable products and recognizing them. See, e.g., MPEP 2106.05(g) (citing OIPTechs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015); Ultramercial, Inc. v.Hulu, LLC, 772 F.3d 709, 715 (Fed. Cir. 2014); Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55 (Fed. Cir. 2016) which describe that gathering data and collecting and reporting data is insignificant extra solution activity. The taking of the digital image is described at a high level of generality and amounts mere data gathering as well. Further looking at the additional limitations in ordered combination, there is nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception to effect a particular identification of a recyclable product, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than a drafting effort designed to monopolize the exception (MPEP § 2106.05). Accordingly, the additional limitation(s) do not integrate the abstract into a practical application.
Step 2B: With respect to Step 2B, claim 16 does not include additional elements that are sufficient to amount to significantly more than the judicial exception for the same reasons as those discussed above. Further, a conclusion that an additional element is insignificant extra solution activity in Step 2A should be re-evaluated in Step 2B to determine if they are more than what is well understood, routine conventional activity in the field. As described above, these limitations are data gathering limitations for use in implementation of the abstract idea of matching digital images to recyclable products and recognizing them. Those additionally recited limitations of this claim fail to amount to significantly more than the judicial exception because the courts have found mere data gathering to be well-understood, routine, and conventional activity. See, e.g., MPEP 2106.05(d) (citing buy SAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355 (Fed. Cir. 2014); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1362-1363 (Fed. Cir. 2015)).
With respect to the dependent claims, claim 17 recites limitations with respect to further details about the data provided in the library of images and as such fails to provide an additional element that integrates the abstract idea into a practical application.
The recitation of the library of images being obtained from multiple sources is recited at a high level of generality and represents no more than mere instructions to apply the judicial exceptions on a computer, using generic computing components. The computer implementation thus does not integrate the judicial exception into a practical application. Additionally, the images being captured from an integrated camera and from external images is insignificant extra solution activity because it merely gathers data for use in implementation of the abstract idea of matching digital images to recyclable products and recognizing them. See, e.g., MPEP 2106.05(g) (citing OIPTechs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015); Ultramercial, Inc. v.Hulu, LLC, 772 F.3d 709, 715 (Fed. Cir. 2014); Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55 (Fed. Cir. 2016) which describe that gathering data is insignificant extra solution activity. The images being captured from an integrated camera and from external images is described at a high level of generality and amounts mere data gathering as well.
Step 2B: With respect to Step 2B, claim 17 does not include additional elements that are sufficient to amount to significantly more than the judicial exception for the same reasons as those discussed above.
With respect to claims 18 and 19, training images of post-consumer recyclable products which depict recyclable products which are damaged, crushed or distorted is a mere indication of the field of use or technological environment in which the judicial exception is performed (claim 18) and the further limitation regarding the location of the step for taking the digital image being a conveyed feedstock at a MRF, PRF, or materials processing facility is also directed to the field of use or technological environment. (see MPEP 2106.05(h)).
Step 2B: With respect to Step 2B, claim 18 does not include additional elements that are sufficient to amount to significantly more than the judicial exception for the same reasons as those discussed above.
With respect to claim 20, the step of redeeming of a deposit based on recycling data for a Deposit Return Scheme is directed to an abstract idea of the type considered certain methods of organizing human activity because it is a commercial or economic practice and as such does not integrate the claims into a practical application. (See MPEP 2106.04(a)(2) II.
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.
Claims 16-17 and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Wolff et al. (US 2010/0185506) in view of Torek et al. (US 9785851).
Regarding claim 16, Wolff et al. (US 2010/0185506) teaches a computer-implemented method to identify a recyclable product in a feedstock of mixed recyclable products (Paragraph 0035 lines 1-9), the method comprising the steps of:
generating a dataset from a library of images of pre-consumer recyclable products and post-consumer recyclable products (Paragraph 0042 lines 1-18, Paragraph 0063 lines 6-7), each image of said library of images having a branding (Paragraph 0054 lines 1-7), a logo, a shape and a geometry (Paragraph 0076 lines 12-14, Paragraph 0077 lines 11-12);
continuously conveying a feedstock of recyclable products (Paragraph 0077 lines 5-10);
continuously taking corresponding identifying images of each recyclable product (Paragraph 0063 lines 1-7, Paragraph 0077 lines 10-15) of said feedstock of recyclable products concurrent with the step of conveying the feedstock (Paragraph 0077 lines 5-15);
recognizing at least one of a group consisting of: the branding, the logo, the shape, and the geometry in corresponding identifying images of each recyclable product of said feedstock of recyclable products (Paragraph 0063 lines 1-7, Paragraph 0077 lines 10-15) so as to match each corresponding identifying image with a respective image of said library of images (Paragraph 0063 lines 1-7);
matching each corresponding identifying image and the respective image of said library of images to at least one recyclable product (Paragraph 0063 lines 1-7) in a group consisting of the pre-consumer recyclable products and the post-consumer recyclable products so as to determine recognized recyclable products in said feedstock (Paragraph 0042 lines 1-18, Paragraph 0063 lines 6-7); and
collecting and reporting recognized recyclable products in said feedstock as recycling data for at least one of a group consisting of: Extended Producer Responsibility (EPR), Deposit Return Scheme (DRS) compliance, and other regulatory requirements (Paragraph 0076 lines 5-12).
Wolff et al. (US 2010/0185506) lacks teaching generating a training dataset from a library of images of pre-consumer recyclable products and post-consumer recyclable products; training a neural network to recognize at least one of a group consisting of: the branding, the logo, the shape and the geometry in an identifying image of a recyclable product; and recognizing at least one of a group consisting of: the branding, the logo, the shape, and the geometry in corresponding identifying images of each recyclable product of said feedstock of recyclable products with said neural network.
Torek et al. (US 9785851) teaches a computer-implemented method to identify a recyclable product in a feedstock of mixed recyclable products (Col. 1 lines 5-6, Col. 3 lines 16-28), the method comprising the steps of:
generating a training dataset from a library of images of recyclable products (Col. 17 line 61-Col. 18 line 3, “input data” in “supervised learning”);
training a neural network to recognize at least one of a group consisting of: the branding, the logo, the shape and the geometry in an identifying image of a recyclable product (Col. 17 line 61-Col. 18 line 3, Col. 18 lines 58-62); and recognizing at least one of a group consisting of: the branding, the logo, the shape, and the geometry in corresponding identifying images of each recyclable product (Col. 17 lines 45-61, Col. 18 lines 58-62) of said feedstock of recyclable products with said neural network (Col. 17 lines 56-Col. 18 line 3, Col. 18 lines 58-62).
Torek et al. (US 9785851) explains that once developed, the neural network may incorporate information from a multitude of inputs into the decision making process to categorize particles in an efficient manner (Col. 19 lines 4-13). Torek et al. (US 9785851) additionally explains that when sorting several categories of particle that have similar constituents, the method may use a complex classification regime such as a neural network to categorize the particles (Col. 12 lines 27-34).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify Wolff et al. (US 2010/0185506) to include generating a training dataset from a library of images of pre-consumer recyclable products and post-consumer recyclable products; training a neural network to recognize at least one of a group consisting of: the branding, the logo, the shape and the geometry in an identifying image of a recyclable product; and recognizing at least one of a group consisting of: the branding, the logo, the shape, and the geometry in corresponding identifying images of each recyclable product of said feedstock of recyclable products with said neural network as taught by Torek et al. (US 9785851) in order to provide a complex classification regime which may classify several categories of materials with similar properties, and to classify the material in an efficient manner.
Regarding claim 17, Wolff et al. (US 2010/0185506) teaches the computer-implemented method of claim 16, further comprising the step of:
obtaining said library of images of pre-consumer recyclable products and post-consumer recyclable products (Paragraph 0052 lines 1-27, Paragraph 0063 lines 6-7) multiple sources (Paragraph 0052 lines 1-11), said multiple sources being comprised of camera images captured from an integrated camera (Paragraph 0052 lines 1-16 “the unique identifier…along with other relevant data for such item is stored in the database” when the item is presented at a recycling center, Paragraph 0063 lines 1-4) and external images of classified pre-consumer recyclable products and post-consumer recyclable products (Paragraph 0052 lines 1-11, Paragraph 0063 lines 6-7).
Regarding claim 19, Wolff et al. (US 2010/0185506) teaches the computer-implemented method of claim 16, wherein the step of continuously conveying a feedstock of recyclable products (Paragraph 0077 lines 5-10) and the step of continuously taking corresponding identifying images of each recyclable product of said feedstock of recyclable products concurrent with the step of conveying the feedstock (Paragraph 0063 lines 1-7, Paragraph 0077 lines 10-15) are at one of a group consisting of: a Materials Recovery Facility (MRF), Plastics Recovery Facility (PRF), and a materials processing facility (Paragraph 0076 lines 1-5).
Regarding claim 20, Wolff et al. (US 2010/0185506) teaches the computer-implemented method of claim 16, further comprising the step of: redeeming a deposit (Paragraph 0058 lines 12-16, Paragraph 0072 lines 1-22) based on said recycling data, after the step of collecting and reporting recognized recyclable product in said feedstock for the Deposit Return Scheme (DRS) (Paragraph 0063 lines 1-7, Paragraph 0072 lines 1-22, Paragraph 0076 lines 1-24).
Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over Wolff et al. (US 2010/0185506) in view of Torek et al. (US 9785851) and further in view of Kumar (US 2018/0243800).
Regarding claim 18, Wolff et al. (US 2010/0185506) lacks teaching the computer-implemented method of claim 16, wherein said library of images of pre-consumer recyclable products and are comprised of images of damaged recyclable products, crushed recyclable products and distorted recyclable products.
Kumar (US 2018/0243800) teaches a computer-implemented method to identify a recyclable product in a feedstock of mixed recyclable products (Paragraph 0002 lines 1-4), wherein said library of images of pre-consumer recyclable products (Paragraph 0091 lines 1-17) and are comprised of images of damaged recyclable products, crushed recyclable products and distorted recyclable products (Paragraph 0091 lines 21-41; see damaged, crushed and distorted recyclable products in Figs. 36A-I, 37A-I).
Kumar (US 2018/0243800) explains that the materials to be sorted may have irregular shapes and sizes, for example such material may have been previously run through a shredding mechanism (Paragraph 0050 lines 1-12). Kumar (US 2018/0243800) explains that the vision system may be trained using images of homogenous sets of scrap pieces (i.e. having the same material composition), where features from the images are extracted to create a knowledge base for classification of scrap pieces (Paragraph 0091 lines 21-41). Kumar (US 2018/0243800) explains that during the training stage, a plurality of scrap pieces of a particular classification (type) of material may be delivered so that the machine learning algorithms detect, extract, and learn what features visually represent such a type of material (Paragraph 0092 lines 1-22).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify Wolff et al. (US 2010/0185506) to include wherein said library of images of pre-consumer recyclable products and are comprised of images of damaged recyclable products, crushed recyclable products and distorted recyclable products as taught by Kumar (US 2018/0243800) in order to detect, extract and learn identifying features of the scrap material to be sorted.
Response to Arguments
Applicant's arguments filed January 12th, 2026 have been fully considered but they are not persuasive.
Regarding the Applicant’s argument that the present invention is no longer a judicial exception and there are additional elements that recite a practical application under 35 USC 101, the Examiner would like to clarify that the continuously conveyed feedstock is a mere indication of the field of use or technological environment in which the judicial exception is performed. (see MPEP 2106.05(h)). Additionally, this limitation and that of continuously taking a digital image is insignificant extra solution activity because it merely gathers data for use in implementation of the abstract idea of matching digital images to recyclable products and recognizing them. See, e.g., MPEP 2106.05(g) (citing OIPTechs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015); Ultramercial, Inc. v.Hulu, LLC, 772 F.3d 709, 715 (Fed. Cir. 2014); Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55 (Fed. Cir. 2016) which describe that gathering data and collecting and reporting data is insignificant extra solution activity. The taking of the digital image is described at a high level of generality and amounts mere data gathering as well.
Regarding the Applicant’s argument that there is no disclosure, teaching, suggestion or motivation to modify the one-at-a-time recycling method of the Wolff publication, the Examiner would like to clarify that as Wolff states “receiving co-mingled recyclable products from a recycling truck and placing the recyclable products on a moving conveyor belt, which distributes the material for inspection and separation” (Paragraph 0077 lines 6-12), Wolff teaches the continuously conveyed feedstock of recyclable products.
Regarding the Applicant’s argument that the prior art combination has no step of collecting and reporting recycling data based on the recognized recyclable products in the feedstock for regulatory compliance, the Examiner would like to clarify that as Wolff states “According to at least one embodiment, various industrial control systems are interfaced to authenticate the capture and sort of various material streams. The data captured by this interface may be used for reporting based upon route logistics, material streams, CPG, consumer versus commercial and other data, along with and reports valued by the recycling entity, its partners, federal and state governments and consumer groups.” (Paragraph 0076 lines 5-14), Wolff teaches that the data captured regarding the recognized recyclable products may be reported to federal and state governments as well as consumer groups.
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 Molly K Devine whose telephone number is (571)270-7205. The examiner can normally be reached Mon-Fri 7:00-4:00.
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/MOLLY K DEVINE/ Examiner, Art Unit 3653