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
The amendment filed on January 20, 2026 cancelled no claims. Claims 1-3, 6, and 11-16 were amended and new claims 18-19 were added. Thus, the currently pending claims addressed below are claims 1-19.
Claim Objections
The amendment filed on January 20, 2026 has corrected the issues of Claims 2-7, 11, 13-17 that were objected to in the Office Action dated October 21, 2026. Thus, the objections are hereby withdrawn.
Claim 19 is objected to because of the following informalities: Newly added claim 19 recites “The RV system of claim 1”. However, claim 1 is not directed to an RV system. Claim 1 recites “A method for using a reverse vending (RV) system” and, as such, is a method claim and not a system claim. The examiner assumes this a mere typographical error and that the applicant intended claim 19 to recite “The method of claim 1, wherein the verifying the at least one container based on the comparison of the at least one container depicted in the captured image to the at least one previous image associated with the scanned barcode within the DDB further comprises: …”. Appropriate correction is required.
Claim Rejections - 35 USC § 112
The amendment filed on January 20, 2026 has overcome the 35 U.S.C. 112(b) rejections of claims 1-12. Thus, the rejections are hereby withdrawn.
The amendment filed on January 20, 2026 has overcome the 35 U.S.C. 112(b) rejections of claims 13-17. Thus, the rejections are hereby withdrawn.
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-19 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claim 1 has been amended to recite: A method for using a reverse vending (RV) system comprising the steps of:
receiving at least one container to be recycled;
scanning the at least one container for available barcodes with a barcode scanning device;
capturing at least one image of the at least one container;
sharing the captured image with a distributed database (DDB); and
identifying the an unidentified container of the at least one container according to information received in one or both of steps (ii) and (iii),
verifying the at least one container based on a comparison of the at least one container depicted in the captured image to at least one previous image associated with a scanned barcode within the DDB: and
accepting or rejecting the at least one container based on the verifying:
whereby steps (ii) and (iii) can be conducted in any order or in parallel, wherein the at least one image is designated to capture specific and unique parameters of a container to be identified, and wherein the DDB is shared by at least one more RV system, and wherein operation (ii) - (v) are controlled by a controller.
Claim 13 has been amended to recite A reverse vending (RV) system comprising:
an information capture system capable of capturing barcode information and an at least one image of a deposited container's specific and unique parameters;
a controller, wherein the controller is in communication with the information capture system and with a distributed database (DDB); and
wherein the controller is configured to verify the deposited based on a comparison of the deposited depicted in the captured image to at least one previous image associated with a scanned barcode within the DDB; and accept or reject the distributed container based on the verifying.
The examiner has been unable to find support for either of these inventions in the applicant’s disclosure. The only support in the applicant’s disclosure for accepting or rejecting the at least one container based on the verifying is found in figure 1 and on page 18 of the applicant’s specification where it states “If the result of any of the decision points in FIG. 1 is negative, the analysis arrives at operation 119, wherein the inserted item(s) is rejected”. As such, the only support in the applicant’s disclosure for rejecting the at least one container based on the verifying is when the decision path in figure 1 required to be followed and result in landing in the box identified as 119. According to figure 1 and pages 16-18, a user must insert an object (101) or objects, at this point two processes are initiated. The first process is associated with a barcode on object (101) and the second process is associated with a captured image of the object 101. These two processes are used to determine whether object (101) passes fraud detection phase 1 (107).
In regards to the first process is a barcode scanning device (102) searches for a barcode on the object (101) and a determination is made regarding the presence of a barcode(103). If there is no barcode, object (101) is rejected (119). If a barcode is present, a scanned barcode is obtained and the system searches for the scanned barcode in a database which stores a plurality of barcodes (104). If a barcode matching the scanned barcode is not in DDB (105), object (101) is rejected (119). If a match with a barcode in the DDB is found, the scanned barcode is considered legal (106).
In regards to the second process a camera (111) captures an image of object (101) and an analysis (112) is made to determine whether object (101) is the only object in the captured image. If there are any other objects in the image the insertion is rejected (119). If object (101) is the only object in the captured image, the captured image is analyzed to determine whether the object in the captured image is a specific type of object (113). If the object is not the specific type of object, object (101) is rejected.
According to figure 1 and page 17 of the applicant’s disclosure, after performing the above steps for both processes in parallel and obtaining the result of the scanned barcode matches a barcode in the DDB and that object (101) is a specific type of object is the analysis able to move on to object (101) passes fraud detection phase (107).
The next phase of the invention requires the invention to use scanned barcode to retrieve a plurality of images from the DDB, that are associated the barcode that matches the scanned barcode (108) and the retrieved images are compared to the captured image to determine a match (115). Only is a match is found does the insertion pass fraud detection phase 2 (109) and object (101) is accepted (110).
One or ordinary skill in the art will note that in order to perform the claimed invention the following steps are required to occur:
the scanning of the at least one container must result in obtaining a scanned barcode because, if there is no barcode, the container (i.e., object (101)) is rejected irrespective of whether the claimed verification step is performed;
after capturing the image of the at least one unidentified container, a check must be made to determine whether there is more than a single container in the captured image because, if there is more than one container in the image, the container (i.e., object (101)) is rejected irrespective of whether the claimed verification step is performed;
identifying the container (i.e., object (101)) is only able to be performed using the captured imaged and, even then, its is an identification of the type of container (e.g., bottle) rather than the container itself (e.g., a bottle of wine vs bottle of nail polish), wherein if the type of container is not able to be identified the container (i.e., object (101)) is rejected;
only if the scanned barcode is determined to be legal in the manner required and the object is the required specific type of object is the container (i.e., object (101)) determined to pass a first phase of fraud detection;
the container (i.e., object (101)) must have both a scanned barcode that is legal in order and a captured image of the container that matches a specific type of container to determine whether it passes the second phase of fraud detection because it is the barcode that is used to obtain a plurality of images of container associated with the barcode and the matching of these retrieved images is compared to the captured image to determine a match and a determination that the container in the captured image passes the second phase of fraud detection.
However, claim 1, as currently amended, does not follow the only fact patter supported by the applicant’s disclosure. According the claims as amended, the scanning of the barcode need not result in obtaining a scanned barcode; the identifying of the container can be base solely or partly on the information obtained from scanning the barcode; there is no determination regarding the number of containers in the capture images, there is no determination that the container in the captured image is a specific type of container; and the verifying of the at least one container using previous images associated with a scanned barcode does not first require a determination regarding the number of containers in the capture images, a determination that the container in the captured image is a specific type of container, nor that the scanned barcode is a legal barcode. As such, it is clear that independent claim 1, as currently amended, failing to comply with the written description requirement.
Likewise, claim 13, as currently amended, appears to be able to capture a deposited container’s specific and unique parameters before a verification process. However, the applicant’s disclosure merely supports capturing an image. According to the applicant’s disclosure a ML model is trained to identify any unique and distinctive parameters associated with a container in the captured image. As such, the applicant’s specification does not support capturing a deposited container’s specific and unique parameters. It is an image that is captures and the ML model identifies any unique and distinctive parameters. However, clam 13 can apparently be done with a ML model and the captured image need only contain specific and unique parameters of the container rather than an image of the container. Likewise, the invention of claim 13 can apparently verify the deposit based solely on a comparison of the deposited depicted in the capture image to at least one previous image associated with a scanned barcode and accept or reject the container based on this type of verification. The applicant’s disclosure does not support such an invention. As indicated, in the rejection of claim 1 above, there are a number of required steps must be performed by the applicant’s invention before the claimed verifying can be accomplished and the acceptance or rejection performed based on said verifying. There is absolutely no support in the applicant’s specification of the verification being performed in the manner claims without each and every step identified above being performed first. As such, it is clear that claim 13, as amended, fails to comply with the written description requirement.
Dependent claims 2-12 and 14-19 fail to correct the deficiencies of the claims from which they depend and, as such, are rejected by virtue of dependency.
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 1-19 are directed to a method and an apparatus which would be classified under one of the listed statutory classifications (i.e., 2019 Revised Patent Subject Matter Eligibility Guidance (hereinafter “PEG”) “PEG” Step 1=Yes).
However, claims 1-19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claim(s) 1, 10, 13-14, and 17 recite(s) the following abstract idea: (Examiner note: the at least one container, the distributed database (DDB) and the at least one more RV system have been included as part of the abstract idea itself because they are outside the scope of the claimed RV system performing the claimed steps and, as such, cannot be considered “additional elements” of the claimed invention itself.)
scanning the at least one deposited container for available barcodes;
capturing at least one image of the at least one deposited container, the image including specific and unique parameters of the deposited container, wherein the at least one image is designated to capture the specific and unique parameters of the container to be identified;
sharing the captured image with a distributed database (DDB), wherein the sharing is performed by transmitting captured image to the DDB; and
identifying the container of the at least one deposited container according to information received based in one or both of the scanning or the capturing steps;
verifying the at least one deposited container based on a comparison of the at least one deposited container depicted in the captured image to at least one previous image associated with a scanned barcode within the DDB;
accepting or rejecting the at least one container based on the verifying;
wherein the scanning and the capturing steps can be performed in parallel; and
wherein the DDB is shared by at least one more RV system;
The limitations as detailed above, as drafted, falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas namely managing personal behavior or relationships or interactions between people and/or fundamental economic principles because the steps are directed to performing actions in order to determine whether to accept or reject an object provided by a person for the purpose of recycling (i.e. “PEG” Revised Step 2A Prong One=Yes).
This judicial exception is not integrated into a practical application because the claim only recites the additional elements of:
a computer (i.e., information capture system) with a processor (i.e., controller), a barcode scanning device, and a camera (e.g., a general-purpose computer with generic computer components);
receiving at least one container to be recycled (e.g., a required and standard step in the recycling of any physical object); and
a designated processor to reduce the volume of a recycled product as part of a recycling process (a standard step in the process of the recycling of any physical object).
Considered individually, the steps amount to no more than a general-purpose computer with generic computer components which is merely used to apply an abstract idea; a standard recycling step which is merely used in applying the abstract idea; and a standard recycling device which is merely used to apply the abstract idea. Considered in combination, the additional elements amount to no more than a general-purpose computer with generic computer component upon which an abstract idea is merely applied and standard recycling steps and/or machines which are merely used as tools in applying the abstract idea.
The following limitations, if removed from the abstract idea and considered additional elements, merely perform generic computer function of processing, storing, communicating (e.g., transmitting and receiving), and displaying data and, as such, are insignificant extra-solution activities (see MPEP 2016.05(d)(II) and MPEP 2106.05(g)):
scanning the at least one deposited container for available barcodes (receiving data);
capturing at least one image of the at least one deposited container, the image including specific and unique parameters of the deposited container, wherein the at least one image is designated to capture the specific and unique parameters of the container to be identified; (receiving data) and
sharing the captured image with a distributed database (DDB), wherein the sharing is performed by transmitting captured image to the DDB (transmitting data).
The additional technical elements above are recited at a high-level of generality (i.e., as a generic processor and generic computer components performing a generic computers function of processing, communicating and displaying) such that it amounts to no more than mere instructions to apply the exception using one or more general-purpose computers and generic computer components. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional technical elements above do not integrate the abstract idea/judicial exception into a practical application because it does not impose any meaningful limits on practicing the abstract idea. More specifically, the additional elements fail to include (1) improvements to the functioning of a computer or to any other technology or technical field (see MPEP 2106.05(a)), (2) applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition (see Vanda memo), (3) applying the judicial exception with, or by use of, a particular machine (see MPEP 2106.05(b)), (4) effecting a transformation or reduction of a particular article to a different state or thing (see MPEP 2106.05(c)), or (5) applying or using 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 more than a drafting effort designed to monopolize the exception (see MPEP 2106.05(e) and Vanda memo).
Rather, the limitations merely add the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on one or more computers, or merely uses computers as a tool to perform an abstract idea (see MPEP 2106.05(f)), or generally link the use of the judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)).
Thus, the claim is “directed to” an abstract idea (i.e. “PEG” Revised Step 2A Prong Two=Yes)
When considering Step 2B of the Alice/Mayo test, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims do not amount to significantly more than the abstract idea.
More specifically, as discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using: at least one received containers as a tool to implement a recycling abstract idea; a computer (i.e., information capture system) with a processor (i.e., controller), a barcode scanning device, and a camera (e.g., a general-purpose computer with generic computer components) as a tool to apply the recycling abstract idea; and a designated processor for crushing the at least one received containers as a tool to implement the recycling abstract idea to reduce the volume of a recycled product as part of a recycling process to perform the claimed functions amounts to no more than mere instructions to apply the exception using one or more general-purpose computers and one or more generic computer component in the process of performing standard recycling operations as a tool to apply the exception.
“Generic computer implementation” is insufficient to transform a patent-ineligible abstract idea into a patent-eligible invention (See Affinity Labs, _F.3d_, 120 U.S.P.Q.2d 1201 (Fed. Cir. 2016), citing Alice, 134 S. Ct. at 2352, 2357) and more generally, “simply appending conventional steps specified at a high level of generality” to an abstract idea does not make that idea patentable (See Affinity Labs, _F.3d_, 120 U.S.P.Q.2d 1201 (Fed. Cir. 2016), citing Mayo, 132 S. Ct. at 1300). Moreover, “the use of generic computer elements like a microprocessor or user interface do not alone transform an otherwise abstract idea into patent-eligible subject matter (See FairWarning, 120 U.S.P.Q.2d. 1293, citing DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1256 (Fed. Cir. 2014)). As such, the additional elements of the claim do not add a meaningful limitation to the abstract idea because they would be generic computer functions in any computer implementation. Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of the computer or improves any other technology. Their collective functions merely provide generic computer implementation.
The Examiner notes simply implementing an abstract concept on one or more computers, without meaningful limitations to that concept, does not transform a patent-ineligible claim into a patent-eligible one (See Accenture, 728 F.3d 1336, 108 U.S.P.Q.2d 1173 (Fed. Cir. 2013), citing Bancorp, 687 F.3d at 1280), limiting the application of an abstract idea to one field of use does not necessarily guard against preempting all uses of the abstract idea (See Accenture, 728 F.3d 1336, 108 U.S.P.Q.2d 1173 (Fed. Cir. 2013), citing Bilski, 130 S. Ct. at 3231), and further the prohibition against patenting an abstract principle “cannot be circumvented by attempting to limit the use of the [principle] to a particular technological environment” (See Accenture, 728 F.3d 1336, 108 U.S.P.Q.2d 1173 (Fed. Cir. 2013), citing Flook, 437 U.S. at 584), and finally merely limiting the field of use of the abstract idea to a particular existing technological environment does not render the claims any less abstract (See Affinity Labs, _F.3d_, 120 U.S.P.Q.2d 1201 (Fed. Cir. 2016), citing Alice, 134 S. Ct. at 2358; Mayo, 132 S. Ct. at 1294; Bilski v. Kappos, 561 U.S. 593, 612 (2010); Content Extraction & Transmission LLC v. Wells Fargo Bank, Nat’l Ass’n, 776 F.3d 1343, 1348 (Fed. Cir. 2014); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355 (Fed. Cir. 2014).
Applicant herein only requires one or more general-purpose computer and generic computer components (as evidenced from page 7, lines 1-13 which disclose that the controller can be any computer device; page 2, lines 3 through page 4, line 6 which discloses that barcode identification database; barcode readers, image capture devices, and image processing by machine learning to identify objects that are beverage containers were all are well-understood, routine and conventional before the effective filing date of the invention, as well as qrc.co.uk, The History of Can Recycling, December 12, 2021, https://www.qcr.co.uk/news/the-history-of-can-recycling/, pages 1-2 which discloses that receiving a recyclable container and can crushers for crushing the container were well-understood, routine, and conventional by at least 2021); therefore, there does not appear to be any alteration or modification to the generic activities indicated, and they are also therefore recognized as insignificant activity with respect to eligibility. Finally, the following limitations, if removed from the abstract idea and considered additional elements, would be considered insignificant extra solution activity as they are directed to merely receiving, displaying, storing, and/or transmitting data (see MPEP 2016.05(d)(II) and MPEP 2106.05(g)):
scanning the at least one deposited container for available barcodes (receiving data);
capturing at least one image of the at least one deposited container, the image including specific and unique parameters of the deposited container, wherein the at least one image is designated to capture the specific and unique parameters of the container to be identified; (receiving data) and
sharing the captured image with a distributed database (DDB), wherein the sharing is performed by transmitting captured image to the DDB (transmitting data).
Thus, taken individually and in combination, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea) (i.e., “PEG” Step 2B=No).
The dependent claims 2-9, 11-12, and 15-19 appear to merely further limit the abstract idea and/or introduce new additional elements that merely amount to applying the abstract idea using a generic machine learning model as follows: adding an additional processing and analyzing of the image step; adding a step of training an algorithmic model to identify parameters; adding a step of training the algorithmic model to identify the type of the container in the image; further limiting the images used to train the algorithmic model; adding a training step where the algorithmic model is trained to determine the number of object in the image; and adding a training step regarding the algorithmic model being trained to identify whether the image corresponds to images in the DDB, all ow which are considered part of the abstract idea itself; the claims also add the additional limitation of a deep neural network machine learning model which is a generic machine learning model which is insufficient to transform an abstract idea into a practical application and insufficient to be considered significantly more than the abstract idea as per the Recentive Analytics decision (Claims 2-7 and 15-16); further limiting the types of containers that are received which places further limits on an insignificant extra-solution activity but make said activity no less insignificant (Claims 8-9); further limiting the barcodes scanned which is considered part of the abstract idea (Claim 11); further limiting the identification which is considered part of the abstract idea (Claim 12), and further limiting the verifying which is considered part of the abstract idea (Claims 18-19), and therefore only further limit the abstract idea (i.e. “PEG” Revised Step 2A Prong One=Yes), does/do not include any new additional elements that are sufficient to amount to significantly more than the judicial exception, and as such are “directed to” said abstract idea (i.e. “PEG” Step 2A Prong Two=Yes); and do not add significantly more than the idea (i.e. “PEG” Step 2B=No)..
Thus, based on the detailed analysis above, claims 1-19 are not patent eligible.
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 (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 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.
Claim(s) 1-9 and 11-19 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Odom et al. (PGPUB: 2021/0035146).
Claims 1, 13-14, 17. A reverse vending (RV) system, and a method for using a reverse vending (RV) system (Paragraph 26: the system is a reverse vending system because rewards are given for depositing waste items in the correct waste receptacle comprising the steps of:
receiving at least one container to be recycled (Paragraph 11: provide an instruction to a user to hold the waste item within a field of view of the camera of the device so that the device may capture the one or more images, video, and/or real-time video feed of the waste item; Paragraph 14: a waste item is presented within a field of view of a sensor);
scanning, by a controller in communication with a barcode scanning device that is an image capturing device, the at least one container for available barcodes; (Paragraph 14: the device may be able to identify that a waste item is included within a field of view of one or more of its sensors using any number of methods; as a first example, the waste item may include a physical code printed on it, such as a bar code, quick response (QR) code, or any other type of computer-readable code or marker)
capturing, by the controller in communication with the barcode scanning device that is the image capturing device, at least one image of the at least one container, wherein the at least one image is designated to capture specific and unique parameters of the at least one container; (Paragraph 14: the waste item may include an identifier other than a computer-readable code, such as a waste receptacle logo (for example, recycling logo), a food item type logo, waste item logo, textual information, or the like; the waste item may have its name printed in text on a portion of the waste item; the device may capture information through its sensors until it determines that one of these types of computer readable codes (or other types of identifiers) is identified in any one of one or more images, video, and/or a real-time video feed being captured by the device and determine the type of waste item; based on one or more images, video, and/or a real-time video feed of a field of view of a sensor of the device, a computer vision algorithm may be able to perform object classification of objects identified within the one or more images, video, and/or a real-time video feed)
sharing, by the controller, the captured image with a distributed database (DDB), wherein the distributed database is shared by at least one more RV system, and (Paragraph 54: a remote cloud system with a database in communication with one or more mobile devices, one or more stationary devices, and/or one or more waste receptacles, wherein the database includes the previously-captured images, videos, and/or real-time video feeds of waste items)
identifying, by the controller, the at least one container according to information received in one or both of the scanning or the capturing steps. (Paragraph 14: the device may capture information through its sensors until it determines that one of these types of computer readable codes (or other types of identifiers) is identified in any one of one or more images, video, and/or a real-time video feed being captured by the device; in such instances, the device may be able to decipher the code to determine the type of waste item);
verifying the at least one container based on a comparison of the at least one container depicted in the captured image to at least one previous image associated with a scanned barcode within the DDB (Paragraph 14: as a second example, the device may use computer vision methods to identify that a waste item is presented within a field of view of a sensor; a computer vision algorithm may be able to perform object classification of objects identified within the one or more images, video, and/or a real-time video feed; through this object classification, the computer vision algorithm may be able to identify that an object is a waste item); and
accepting or rejecting the at least one container based on the verifying. (Paragraphs 12 and 22: the device may then indicate (as may be described below) which waste receptacles are appropriate for some or all of the items included in the tray. For example, an indication may be provided that the soda can may be recycled and the paper products may be deposited in the trash bin, which is an example of accepting the soda can, and rejecting the paper product)
Claims 2-3 and 15-16: Odom discloses the method of claim 1 and the system of claim 13, wherein the at least one captured image of the unidentified deposited container is processed and analyzed by a deep neural network (DNN) machine learning (ML) model installed on a controller trained to identify unique and distinctive parameters of the unidentified deposited container. (Paragraph 17: determining the waste item captured using artificial intelligence (for example, machine learning, deep learning, fuzzy logic, etc.); the artificial intelligence algorithm may be trained with one or more previously-captured images, videos, and/or real-time video feeds of waste items in order to train the artificial intelligence algorithm; every time a new waste item is presented by a user, the one or more images, video, and/or real-time video feed of the waste item, along with the determined of the type of waste item that is made, may be stored along with the previously-stored images, videos, and/or real-time video feeds; thus, every time a user presents a waste item to the device, the artificial intelligence algorithm may improve)
Claim 4-6: Odom discloses the method of claim 2, wherein the training of the ML model is conducted by utilizing a training dataset configured to identify each type of container according to its at least one captured image, wherein the training dataset comprises images of deformed/crushed containers, wherein the ML model is trained to identify whether the image or images of the unidentified container corresponds to a single object or to multiple objects. (Paragraph 17: determining the waste item captured using artificial intelligence (for example, machine learning, deep learning, fuzzy logic, etc.); the artificial intelligence algorithm may be trained with one or more previously-captured images, videos, and/or real-time video feeds of waste items in order to train the artificial intelligence algorithm; every time a new waste item is presented by a user, the one or more images, video, and/or real-time video feed of the waste item, along with the determined of the type of waste item that is made, may be stored along with the previously-stored images, videos, and/or real-time video feeds; thus, every time a user presents a waste item to the device, the artificial intelligence algorithm may improve; Paragraphs 21 and 42: the determination may not only be based on the type of waste item, but also may be based on its state; the state of the waste item may include factors such as how clean or dirty the waste item is, a level of deformation or damage to the waste item; the state of the item is determined in the same manner as determination of the type of waste item such as computer vision; Paragraph 12: multiple items are received as a bundle based on a single identifier, and each item is identified)
Claim 7: Odom discloses the method of claim 2, wherein the ML model is trained to identify whether the at least one image of the unidentified container corresponds to the container in the DDB associated with the scanned barcode. (Paragraph 17: determining the waste item captured using artificial intelligence (for example, machine learning, deep learning, fuzzy logic, etc.); the artificial intelligence algorithm may be trained with one or more previously-captured images, videos, and/or real-time video feeds of waste items in order to train the artificial intelligence algorithm; every time a new waste item is presented by a user, the one or more images, video, and/or real-time video feed of the waste item, along with the determined of the type of waste item that is made, may be stored along with the previously-stored images, videos, and/or real-time video feeds; thus, every time a user presents a waste item to the device, the artificial intelligence algorithm may improve; Paragraph 54: the database of the remote cloud system stores the previously-captured images, videos, and/or real-time video feeds of waste items)
Claim 8: Odom discloses the method of claim 1, wherein multiple containers are received as a bundle by the RV system. (Paragraph 12: multiple items are received as a bundle based on a single identifier, and each item is identified)
Claim 9: Odom discloses the method of claim 1, wherein each container is individually received by the RV system. (Paragraph 11: a waste item is held within a field of view of the camera of the device so that the device may capture the one or more images, video, and/or real-time video feed of the waste item; Paragraph 14: a waste item is presented within a field of view of a sensor)
Claim 11: Odom discloses the method of claim 1, wherein barcode scanning includes also QR code scanning. (Paragraph 14: the device may be able to identify that a waste item is included within a field of view of one or more of its sensors using any number of methods; as a first example, the waste item may include a physical code printed on it, such as a bar code, quick response (QR) code, or any other type of computer-readable code or marker)
Claim 12: Odom discloses the method of claim 1, wherein the identification of unidentified containers is conducted according solely to the image captured. (Paragraph 14: as a second example, the device may use computer vision methods to identify that a waste item is presented within a field of view of a sensor; a computer vision algorithm may be able to perform object classification of objects identified within the one or more images, video, and/or a real-time video feed; through this object classification, the computer vision algorithm may be able to identify that an object is a waste item.)
Claims 18-19: The RV system of claim 13, and the method of claim 1, wherein verifying the at least one container includes a two-phase fraud detection scheme comprising: comparing a scanned barcode to a barcode in the DDB, and comparing the image of the container having the scanned barcode to an image in the DDB corresponding to the scanned barcode. (Paragraph 14: the device may capture information through its sensors until it determines that one of these types of computer readable codes (or other types of identifiers) is identified in any one of one or more images, video, and/or a real-time video feed being captured by the device; in such instances, the device may be able to decipher the code to determine the type of waste item; as a second example, the device may use computer vision methods to identify that a waste item is presented within a field of view of a sensor; a computer vision algorithm may be able to perform object classification of objects identified within the one or more images, video, and/or a real-time video feed; through this object classification, the computer vision algorithm may be able to identify that an object is a waste item; Paragraph 21: The status of the waste item, in some instances, may be determined based on some of the same or similar methods used to determine the type of waste item (for example, computer vision, manual indication by the user, etc.); The examiner notes that the applicant’s disclosure does not have support for identifying the contained according to the captured image and/or according to the scanned barcode, as well as, verifying the container based on the claimed two-phase fraud detection including comparing the scanned barcode to a barcode in a database and comparing the captured image with previously stored images, as such, the examiner is interpreting the limitations of claims 18-19, as such, the examiner is interpreting the limitation to recite wherein the identifying and the verifying steps are considered a two-phased fraud based detection scheme)
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.
Claim(s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Odom et al. (PGPUB: 2021/0035146) in view of Cole et al. (PGPUB: 2018/0194554).
Claim 10. The method of claim 1, wherein once a container has been identified, it is processed in a designated processor in order to reduce its volume, as part of a recycling process.
Odom discloses the method of claim 1, wherein once a container has been identified it is place in a bin, such as a recycling bin in at least paragraphs 31-32.
Odom does not disclose that the recycling bin has a mechanism to process the recycled item in order to reduce its volume.
The analogous art of Cole discloses that it is known for a recycling bin to have a mechanism to process the recycled item in order to reduce its volume in at least paragraphs 39-41.
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the invention to modify the recycling bin or Odom to include a mechanism to process the recycled item in order to reduce its volume as disclosed by Cole.
The motivation for doing so is to reduce the servicing costs associated with the recycling bin (Cole – Paragraphs 3-5)
Response to Arguments
Applicant's arguments filed January 20, 2026 have been fully considered but they are not persuasive.
The applicant’s arguments, with respect to Step 2a, Prong 1 of the 35 USC 101 rejections are moot. The applicant has amended the claims to include steps that cannot be done in the human mind. After reviewing the newly added limitations including at least the actual scanning of the barcodes and the accepting or rejecting of the container received, the examiner has determined that both independent claim 1 and independent claim 13, as amended, now fall within the Certain Methods of Organizing Human Activity bucket of abstract idea namely managing personal behavior or relationships or interactions between people and/or fundamental economic principles because the steps are directed to performing actions in order to determine whether to accept or reject an object provided by a person for the purpose of recycling. As such, whether the claims, as amended, fall within the Mental Process bucket is a moot point. Therefore, the applicant’s arguments are not convincing.
The applicant argues, with respect to Step 2a, Prong 2 of the 35 USC 101, that the claims as amended recite a practical application which provides a technical solution to a technical problem, wherein the invention recites a filtering mechanism that verifies the container being entered into the RV system to ensure it is proper, and then accepts or rejects the container. The examiner disagrees. First, it appears that the applicant is reading limitation into the claim which are not currently present in the claim limitations as currently amended. There is no limitation in the claims disclosing an RV system with any type of structure (i.e. “additional elements”) which would allow an object to be entered into the RV system. As currently claimed, the RV system either merely receives a container or captures an image of a deposited container. As such, the claimed RV system is broad enough to encompass a general-purpose computer performing the steps based on someone depositing an object within the field of view of sensors of the general-purpose computer. Thus, it is clear, that the claims as currently written do not result in the argued improvement. Second, the claims recite no physical mechanisms (i.e., “additional elements”) for performing the verification and accepting or rejecting of the container. Thus, it is clear, that the claims as currently written do not result in the argued improvement.
Instead, the only “additional elements” in the claims are a general-purpose computer (i.e., RV system) with general-purpose components (i.e., controller, barcode scanner, and camera) executing software (e.g., a generic deep neural network machine learning model) and a receiving a container to be recycled which is standard step when performing a recycling actions. Thus, the claims merely recite using these “additional elements” as a tool to merely apply the abstract idea which is insufficient to transform an abstract idea into a practical application under Step 2a, Prong 2. “Additional elements” are defined as the elements of a claim which are not part of the abstract idea itself. Thus, the argued improvement of “a filtering mechanism that verifies the container being entered into the RV system to ensure it is proper, and then accepts or rejects the container” is rooted solely in the abstract idea itself which is merely being applied by the general-purpose computer with generic computer components. Improvements of this nature are improvements to an abstract idea which are improvements in ineligible subject matter (see SAP v. Investpic decision: Page 2, line 22 through Page 3, line 13 - Even assuming that the algorithms claimed are groundbreaking, innovative or even brilliant, the claims are ineligible because their innovation is an innovation in ineligible subject matter because they are nothing but a series of mathematical algorithms based on selected information and the presentation of the results of those algorithms. Thus, the advance lies entirely in the realm of abstract ideas, with no plausible alleged innovation in the non-abstract application realm. An advance of this nature is ineligible for patenting; and Page 10, lines 18-24 - Even if a process of collecting and analyzing information is limited to particular content, or a particular source, that limitations does not make the collection and analysis other than abstract.). In order to overcome a 35 USC 101 rejection under Step 2a, Prong 2, the purported technical improvement must be rooted in the “additional elements” of a claim in a manner other than using the “additional elements” as a tool to merely apply the abstract idea. Since the argued technical improvement is not rooted in the “additional elements” of the claim in a manner other than merely using the “additional elements” as a tool to apply the abstract idea, the argued improvement is not capable of transforming an abstract idea into a practical application under Step 2a, Prong 2. As such, the applicant’s arguments are not convincing.
The applicant argues, with respect to the 35 USC 102 rejections, that Odom does not teach the newly added limitations of “verifying the at least one container based on a comparison of the at least one container depicted in the captured image to at least one previous image associated with a scanned barcode within the DDB” or “accepting or rejecting the at least one container based on the verifying”. The examiner disagrees. First, the applicant’s disclosure does not support the claims as currently amended as clearly outlined in the 35 USC 112(a) rejection above. As such, it is immaterial whether Odom teaches these limitations. Second, the applicant has provided no arguments with regards to why Odom does not teach these newly added limitations. As such, the argument is a merely allegations that the claims as amended do not teach these limitations. Thus, the applicant's arguments fail to comply with 37 CFR 1.111(b) because they amount to a general allegation that the claims define a patentable invention without specifically pointing out how the language of the claims patentably distinguishes them from the references. Therefore, the applicant’s argument is not convincing. Finally, Odom does teach the argued limitations in at least paragraphs 12, 14 and 22, as indicated in the 35 USC 102 rejection above. Therefore, the applicant’s arguments are not persuasive.
The applicant argues, with respect to the 35 USC 103 rejection of claim 10, that Cole fails to remedy the deficiencies of Odom and, as such, the combination of Odom and Cole cannot be used to reject claim 10. The examiner disagrees. As clearly indicated in the response to arguments above, with respect to the newly added limitations, even if the applicant has submitted an amendment which was supported by the applicant’s disclosure and provided arguments that were more than mere allegations, Odom in fact teach the argued limitations. Thus, the applicant’s argument with regard to 35 USC 103 rejection is not persuasive.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Arnold et al. (PGPUB: 2021/0217265) which discloses a reverse vending machine that receives cans or bottles for recycling, identifies a can through a barcode, takes an image of the can or bottle, uses machine learning to automate the acceptance and/or rejection of the can or bottle for return of the deposit, and crushes the can or bottle.
Kavli et al. (PGPUB: 2014/0147005) which discloses a reverse vending machine, including: a chamber adapted to receive an object returned to the reverse vending machine and a plurality of cameras arranged around the perimeter of the chamber for viewing said object, wherein a first feature extraction algorithm identifies barcodes to identify the product and a second feature extraction algorithm is applied to images to determine the shape of the object, wherein the barcode and shape are used to identify the object.
Dong (CN112270788A) which discloses bottles being received in a bin, a code scanning module that scans the identification code of the bottle, a retrieval module for matching the identification code to an identification code in a database, an image processing module for acquiring bottle image data, a reasoning module for obtaining bottle information associated with a bottle similar to the bottle the image, a comparison module for identifying a match between the bottle information and the bottle image data.
Yoo et al. (Dual Image-Based CNN Ensemble Model for Waste Classification in Reverse Vending Machine, November 22, 2021, Applied Sciences Vol. 11, No. 11051, pages 1-19) which discloses a reverse vending machine that acquires images from multiple different views, and classifies an object placed into the reverse vending machine using a trained deep neural network model trained on multiple image views of objects.
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 JOHN W VAN BRAMER whose telephone number is (571)272-8198. The examiner can normally be reached Monday-Thursday 5:30 am - 4 pm EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Spar Ilana can be reached at 571-270-7537. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/John Van Bramer/Primary Examiner, Art Unit 3622
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