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 .
Claims 1, 17 and 19 have been amended. Claims 1-26 are pending for examination.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 05/11/2026 has been entered.
Response to Arguments
Applicant’s arguments, filed 05/11/2026, with respect to the rejections of claims 1, 17, 19-20 under 35 U.S.C. 112(a) have been fully considered and are persuasive. The rejections of claims 1, 17, 19-20 has been withdrawn.
Applicant’s arguments, filed 05/11/2026, with respect to the rejections of claims 1-7, 11-15 and 17-26 under 35 U.S.C. 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of in view of Casella (US 20070219862 A1).
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 1-7, 11-15 and 17-26 are rejected under 35 U.S.C. 103 as being unpatentable over JAPUNTICH (CA 2558906 A1) in view of Poss (US 20130278067 A1) further in view of Casella (US 20070219862 A1).
Regarding claim 1, JAPUNTICH teaches a computer-implemented method of managing waste contained by a plurality of waste containers, the method comprising, for each waste container of the plurality of waste containers:
receiving, via a processor, an image of content within the waste container (Imaging systems optionally use video cameras or image detectors such as charged-coupled devices (CCD), CMOS imagers, or particle detectors. [0071]);
estimating, via the processor, a time at which the volume parameter will exceed a threshold value (discusses a mathematical algorithm for predicting when a waste container will be full. [0107]); and
optimizing, via the processor, a waste management process for the waste container based on the estimation (In this manner, the data analysis computer 66 permits a user to optimize container usage efficiency and save money by changing out only those containers 22 that need to be changed. [0062]).
JAPUNTICH does not explicitly a teach a machine learning model or characterizing, via the machine learning model executed by the processor, the content based on the received image.
In an analogous art, Poss teaches a machine learning model to execute the aforementioned steps (visual sensors such as cameras, CCDs and ultrasound echo (sonographic) equipment … These are, in some instances, associated with software and/or artificial intelligence software. [0054]) and
characterizing, via the machine learning model executed by the processor, the content based on the received image (Operational functions are activities performed by electrical components including sensors to determine waste deposits characteristics and contents [0001]).
It would have been obvious for a person of ordinary skill in the art, before the effective filling date of the claimed invention, to take the teachings of Poss and apply them to JAPUNTICH. One would be motivated as such so that optimal waste capacity and location of each bin on a collection route can be recommended, reducing wasted fuel and time stemming from collections rendered on bins that are not full (Poss: [0101]).
Japuntich does not explicitly teach the following limitations with optical recognition content assessment. However, in analogous art, Casella teaches extracting a set of content parameters based on the received images ([0044] and [0079] Teach extracting content and quality information from scanned container images); and
characterizing the content based on the received image extracted content parameters, wherein characterizations comprise a volume parameter ([0079] teaches automated content characterization from optically recognized image data).
It would have been obvious for a person of ordinary skill in the art, before the effective filling date of the claimed invention, to take the teachings of Casella and apply them to Japuntich. One would be motivated as such to promote, based on incentive, improved recycling behavior.
Regarding claim 2, JAPUNTICH in view of Poss and Casella teaches the method of claim 1. JAPUNTICH teaches capturing, via a monitoring system, the image of content within the waste container (Imaging systems optionally use video cameras or image detectors such as charged-coupled devices (CCD), CMOS imagers, or particle detectors.Radiographic imaging may also be employed using a detector of radioactivity and a source of radioactivity, either imposed on the system or emanating directly from the contents of container 22. [0071]).
Regarding claim 3, JAPUNTICH in view of Poss and Casella teaches the method of claim 2. JAPUNTICH teaches transmitting, via the monitoring system, the captured image to a remote system, wherein the remote system comprises the processor (The message is transmitted according to a messaging protocol and contains information representative of the sensed state of the waste container. [0011]).
Regarding claim 4, JAPUNTICH in view of Poss and Casella teaches the method of claim 2. JAPUNTICH teaches wherein the monitoring system comprises the processor (The sensor 24 in one embodiment includes the transmitter 32 as well as a memory 42 and a microprocessor 44. [0037]).
Regarding claim 5, JAPUNTICH in view of Poss and Casella teaches the method of claim 2. JAPUNTICH teaches detecting, via the monitoring system, a trigger event (The emitter and detector may be positioned so that when the container is filled with sharps up to a predetermined level the beam is interrupted, thus triggering the notification. [0074]).
Regarding claim 6, JAPUNTICH in view of Poss and Casella teaches the method of claim 5. JAPUNTICH teaches wherein capturing the image of content within the waste container is based on the detection of the trigger event (An alternative embodiment involves a video fill level sensing system. One embodiment may employ a camera to continuously detect an intensity of light exiting container 22 from the source. [0096]).
Regarding claim 7, JAPUNTICH in view of Poss and Casella teaches the method of claim 5. JAPUNTICH teaches wherein detection of the trigger event is based on an input from an ambient light sensor (In some embodiments, a fill level sensing system employing optical sources and detectors can include an additional photodetector that is generally configured to measure changes in "ambient" light within the system in order to appropriately adjust the readings from the detector arrays measuring fill level. [0091]).
Regarding claim 11, JAPUNTICH in view of Poss and Casella teaches the method of claim 5. JAPUNTICH teaches wherein detection of the trigger event is based on an input from a timer (Such a "waste presence" sensor may be used in combination with a timer to determine a replacement schedule for a particular container based on a maximum acceptable dwell time for a particular waste item in a container. [0077]).
Regarding claim 12, JAPUNTICH in view of Poss and Casella teaches the method of claim 1. Poss teaches wherein optimizing the waste management process comprises optimizing, via the processor, a route for a set of waste removal vehicles to retrieve content from the waste container (there is software code configured to determine useful statistics, such as optimal routes for collections or servicing each bin, the optimal configuration of bins of different volumes, so that optimal waste capacity and location of each bin on a collection route can be recommended, reducing wasted fuel and time stemming from collections rendered on bins that are not full [0101]).
The same motivation used to combine JAPUNTICH in view of Poss and Casella in claim 1 is applicable.
Regarding claim 13, JAPUNTICH in view of Poss and Casella teaches the method of claim 12. JAPUNTICH does not explicitly teach the following limitations, however, in an analogous art, Poss teaches routing the set of waste removal vehicles to retrieve content from the waste container based on the optimized route (there is software code configured to determine useful statistics, such as optimal routes for collections or servicing each bin, the optimal configuration of bins of different volumes, so that optimal waste capacity and location of each bin on a collection route can be recommended, reducing wasted fuel and time stemming from collections rendered on bins that are not full [0101]).
The same motivation used to combine JAPUNTICH in view Poss in claim 1 is applicable.
Regarding claim 14, JAPUNTICH in view of Poss and Casella teaches the method of claim 1. Poss teaches wherein optimizing the waste management process comprises at least one of increasing a sorting efficiency at a receiving facility, increasing an accuracy of monetary charges for waste collection, determining an ideal time for waste collection, tailoring a marketing campaign, or increasing a monetary value of collected waste, or combinations thereof (there is software code configured to determine useful statistics, such as optimal routes for collections or servicing each bin, the optimal configuration of bins of different volumes, so that optimal waste capacity and location of each bin on a collection route can be recommended, reducing wasted fuel and time stemming from collections rendered on bins that are not full [0101]).
The same motivation used to combine JAPUNTICH in view of Poss and Casella in claim 1 is applicable.
Regarding claim 15, JAPUNTICH in view of Poss and Casella teaches the method of claim 1. Poss teaches determining, via the processor, an economic parameter, and wherein optimizing the waste management process is based on the economic parameter (there is software code configured to determine useful statistics, such as optimal routes for collections or servicing each bin, the optimal configuration of bins of different volumes, so that optimal waste capacity and location of each bin on a collection route can be recommended, reducing wasted fuel and time stemming from collections rendered on bins that are not full [0101]).
The same motivation used to combine JAPUNTICH in view of Poss and Casella in claim 1 is applicable.
Regarding claim 17, JAPUNTICH teaches a system configured to manage waste of content within a waste container, the system comprising:
a processor (Fig. 4: 44 CPU); and
a memory configured to store instructions that, when executed by the processor (Fig. 4: 42), are configured to cause the monitoring system to:
receive an image of the content within the waste container (Imaging systems optionally use video cameras or image detectors such as charged-coupled devices (CCD), CMOS imagers, or particle detectors. [0071]);
estimate a time at which the volume parameter will exceed a threshold value (discusses a mathematical algorithm for predicting when a waste container will be full. [0107]); and
optimize a waste management process for the waste container based on the estimation (In this manner, the data analysis computer 66 permits a user to optimize container usage efficiency and save money by changing out only those containers 22 that need to be changed. [0062]).
JAPUNTICH does not explicitly a teach a machine learning model or characterizing, via the machine learning model executed by the processor, the content based on the received image.
In an analogous art, Poss teaches a machine learning model to execute the aforementioned steps (visual sensors such as cameras, CCDs and ultrasound echo (sonographic) equipment … These are, in some instances, associated with software and/or artificial intelligence software. [0054]) and
characterize the content based on the received image (Operational functions are activities performed by electrical components including sensors to determine waste deposits characteristics and contents [0001]).
It would have been obvious for a person of ordinary skill in the art, before the effective filling date of the claimed invention, to take the teachings of Poss and apply them to JAPUNTICH. One would be motivated as such so that optimal waste capacity and location of each bin on a collection route can be recommended, reducing wasted fuel and time stemming from collections rendered on bins that are not full (Poss: [0101]).
Japuntich does not explicitly teach the following limitations with optical recognition content assessment. However, in analogous art, Casella teaches extracting a set of content parameters based on the received images ([0044] and [0079] Teach extracting content and quality information from scanned container images); and
characterizing the content based on the received image extracted content parameters, wherein characterizations comprise a volume parameter ([0079] teaches automated content characterization from optically recognized image data).
It would have been obvious for a person of ordinary skill in the art, before the effective filling date of the claimed invention, to take the teachings of Casella and apply them to Japuntich. One would be motivated as such to promote, based on incentive, improved recycling behavior.
Regarding claim 18, JAPUNTICH in view of Poss and Casella. teaches the system of claim 17. JAPUNTICH teaches an optical sensor communicably coupled to the processor, wherein the optical sensor is configured to capture the image of the content within the waste container (Imaging systems optionally use video cameras or image detectors such as charged-coupled devices (CCD), CMOS imagers, or particle detectors.Radiographic imaging may also be employed using a detector of radioactivity and a source of radioactivity, either imposed on the system or emanating directly from the contents of container 22. [0071]).
Regarding claim 19, JAPUNTICH teaches a computer-implemented method of managing waste contained by a plurality of waste containers, the method comprising, for each waste container of the plurality of waste containers:
receiving, via a processor, an image of content within the waste container (Imaging systems optionally use video cameras or image detectors such as charged-coupled devices (CCD), CMOS imagers, or particle detectors. [0071]);
optimizing, via the processor, a waste management process for the waste container based on the determined content parameter (In this manner, the data analysis computer 66 permits a user to optimize container usage efficiency and save money by changing out only those containers 22 that need to be changed. [0062]).
JAPUNTICH does not explicitly a teach a machine learning model or characterizing, via the machine learning model executed by the processor, the content based on the received image.
In an analogous art, Poss teaches a machine learning model to execute the aforementioned steps (visual sensors such as cameras, CCDs and ultrasound echo (sonographic) equipment … These are, in some instances, associated with software and/or artificial intelligence software. [0054]) and
characterizing, via the machine learning model executed by the processor, the content based on the received image (Operational functions are activities performed by electrical components including sensors to determine waste deposits characteristics and contents [0001]).
It would have been obvious for a person of ordinary skill in the art, before the effective filling date of the claimed invention, to take the teachings of Poss and apply them to JAPUNTICH. One would be motivated as such so that optimal waste capacity and location of each bin on a collection route can be recommended, reducing wasted fuel and time stemming from collections rendered on bins that are not full (Poss: [0101]).
Japuntich does not explicitly teach the following limitations with optical recognition content assessment. However, in analogous art, Casella teaches extracting a set of content parameters based on the received images ([0044] and [0079] Teach extracting content and quality information from scanned container images); and
characterizing the content based on the received image extracted content parameters, wherein characterizations comprise a volume parameter ([0079] teaches automated content characterization from optically recognized image data).
It would have been obvious for a person of ordinary skill in the art, before the effective filling date of the claimed invention, to take the teachings of Casella and apply them to Japuntich. One would be motivated as such to promote, based on incentive, improved recycling behavior.
Regarding claim 20, JAPUNTICH in view of Poss and Casella teaches the method of claim 1. JAPUNTICH teaches wherein the content parameter comprises at least one of an object boundary, a brand identifier, or a content status, or combinations thereof (Fig. 2: level).
Regarding claim 21, JAPUNTICH in view of Poss and Casella teaches the method of claim 1. JAPUNTICH teaches wherein characterizing the content comprises predicting, via the processor, a future change in the content (the present invention include fullness prediction algorithms [0107]).
Regarding claim 22, JAPUNTICH in view of Poss and Casella teaches the method of claim 21. JAPUNTICH teaches wherein the future change comprises a volume change (a mathematical algorithm for predicting when a waste container will be full. [0107]).
Regarding claim 23, JAPUNTICH in view of Poss and Casella teaches the method of claim 21. Poss teaches wherein the future change comprises a material distribution change (Then the PLC compares such levels to the previous time periods (e.g., the previous one or two weeks). Examiner note: the level is an indicative of distribution). The same motivation used to combine JAPUNTICH in view of Poss in claim 1 is applicable.
Regarding claim 24, JAPUNTICH in view of Poss and Casella teaches the method of claim 21. Poss teaches wherein the future change comprises a spatial distribution change (Then the PLC compares such levels to the previous time periods (e.g., the previous one or two weeks).). The same motivation used to combine JAPUNTICH in view Poss in claim 1 is applicable.
Regarding claim 25, JAPUNTICH in view of Poss and Casella teaches the method of claim 21. JAPUNTICH teaches wherein the future change comprises a physical change to the content (Signal and data processing technologies embodying aspects of the present invention include fullness prediction algorithms [0107] Examiner’s note: fullness includes physical change).
Regarding claim 26, JAPUNTICH in view of Poss and Casella teaches the method of claim 21. JAPUNTICH teaches wherein characterizing the content based on the received image comprises comparing the received image to other images taken across a time period to determine a change in waste volume over the time period ([0095] Once steady state is reached, the inference engine compares the values of the detector readings and ultimately derives a final fill value).
Claims 8-10 are rejected under 35 U.S.C. 103 as being unpatentable over JAPUNTICH in view of Poss and Casella further in view of Nemat-Nasser (US 20140210625 A1).
Regarding claim 8, JAPUNTICH in view of Poss and Casella teaches the method of claim 5. JAPUNTICH in view of Poss does not explicitly teach the following limitations, however, in an analogous art, Nemat-Nasser teaches wherein detection of the trigger event is based on an input from a laser beam break sensor (sensors 508 comprise video recorders, audio recorders, accelerometers, gyroscopes, vehicle state sensors, GPS, outdoor temperature sensors, moisture sensors, laser line tracker sensors, or any other appropriate sensors. [0046] Examiner note: Nemat-Nasser discloses well-known event triggering techniques).
It would have been obvious for a person of ordinary skill in the art, before the effective filling date of the claimed invention, to take the teachings of Nemat-Nasser and apply them to JAPUNTICH in view of Poss and Casella. One would be motivated as such as to improve detection of triggering events accuracy (Nemat-Nasser: [0078]).
Regarding claim 9, JAPUNTICH in view of Poss and Casella teaches the method of claim 5. JAPUNTICH in view of Poss and Casella does not explicitly teach the following limitations, however, in an analogous art, Nemat-Nasser teaches wherein detection of the trigger event is based on an input from an accelerometer (sensors 508 comprise video recorders, audio recorders, accelerometers, gyroscopes, vehicle state sensors, GPS, outdoor temperature sensors, moisture sensors, laser line tracker sensors, or any other appropriate sensors. [0046] Examiner note: Nemat-Nasser discloses well-known event triggering techniques).
It would have been obvious for a person of ordinary skill in the art, before the effective filling date of the claimed invention, to take the teachings of Nemat-Nasser and apply them to JAPUNTICH in view of Poss and Casella. One would be motivated as such as to improve detection of triggering events accuracy (Nemat-Nasser: [0078]).
Regarding claim 10, JAPUNTICH in view of Poss and Casella teaches the method of claim 5. JAPUNTICH in view of Poss and Casella does not explicitly teach the following limitations, however, in an analogous art, Nemat-Nasser teaches wherein detection of the trigger event is based on an input from a gyroscope (sensors 508 comprise video recorders, audio recorders, accelerometers, gyroscopes, vehicle state sensors, GPS, outdoor temperature sensors, moisture sensors, laser line tracker sensors, or any other appropriate sensors. [0046] Examiner note: Nemat-Nasser discloses well-known event triggering techniques).
It would have been obvious for a person of ordinary skill in the art, before the effective filling date of the claimed invention, to take the teachings of Nemat-Nasser and apply them to JAPUNTICH in view of Poss and Casella. One would be motivated as such as to improve detection of triggering events accuracy (Nemat-Nasser: [0078]).
Claim 16 is rejected under 35 U.S.C. 103 as being unpatentable over JAPUNTICH in view of Poss and Casella further in view of Hand (US 20110231217 A1).
Regarding claim 16, JAPUNTICH in view of Poss and Casella teaches the method of claim 1. JAPUNTICH in view of Poss and Casella does not explicitly teach the following limitations, however, in an analogous art, Hand teaches wherein optimizing the waste management process is based on predicted weather ([0036] In the example, route generator 320 analyzes streets within a pre-defined spray/fog zone and automatically calculates and creates an optimized Fog Route for each chemical delivery vehicle. In one embodiment, route generator 320 utilizes vehicle specifications, plume specifications, real-time and forecasted weather conditions, no chemical zones 110, and temporal data to account for plume coverage during the creation of fog routes and attempts to avoid both duplication of coverage and gaps in coverage: Examiner note: predicted weather is a well-known route optimizing parameter as taught in Hand).
It would have been obvious for a person of ordinary skill in the art, before the effective filling date of the claimed invention, to take the teachings of Hand and apply them to JAPUNTICH in view of Poss and Casella. One would be motivated as such as to commence automatic route creation and optimization (Hand: [0034]).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HESHAM K ABOUZAHRA whose telephone number is (571)270-0425. The examiner can normally be reached M-F 8-5.
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/HESHAM K ABOUZAHRA/ Primary Examiner, Art Unit 2486
