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 .
Claim Objections
Claims 1 and 11 are objected to because of the following informalities:
Claim 1, third limitation recites “that included portions of images”. There is a typographical error, it should read as “that includes portions of images” (emphasis added with underline).
Claim 11, fourth limitation recites “that included portions of images”. There is a typographical error, it should read as “that includes portions of images” (emphasis added with underline).
Appropriate correction is required.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
“a unit for processing the images acquired by said camera comprising…” and
“a module for computing a filling rate…” in claim 1.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof. The specifications recite the structure of the unit for processing acquired images as at least one processor, memories and software routine able to implement the various processing modules. The modules are further recited as a software element or a hardware element or a combination of a hardware element and a software sub-program.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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.
Claims 1 – 11 are 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 1, recites the limitation “the type of waste” in the third limitation. There is insufficient antecedent basis for this limitation in the claim as there is no prior definition for type of waste in the claim. Appropriate corrections are required.
Claim 11, recites the limitation “the type of waste” in the third limitation. There is insufficient antecedent basis for this limitation in the claim as there is no prior definition for type of waste in the claim. Appropriate corrections are required.
Claims 2 – 10 are objected for being dependent on rejected claim 1.
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.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1 – 2, 7 – 11 are rejected under 35 U.S.C. 103 as being unpatentable over Kurani et al. (US 20210188541 A1; hereafter referred to as Kurani) in view of Swaroop et al. (US 20210326658 A1; hereafter referred to as Swaroop).
Regarding Claim 1, Kurani teaches:
A system for monitoring at least one loose waste collection enclosure (Kurani, Fig. 3 – 5, Fig. 40 – 42, monitoring system 300, Kurani, [0451] “FIGS. 40, 41 and 42 illustrate an example waste and litter sensor 320, wherein the waste and litter sensor 320 implements, operates, and monitors waste, and the litter sensor implements, operates, detects, measures, and monitors a waste type, a waste volume, a litter type, a litter level, a biohazardous waste type, and a biohazardous waste level inside or surrounding the waste bin 600”), said system comprising:
an image acquisition camera configured to be able to acquire, at a predetermined time interval, an image of an area where each collection enclosure to be monitored is arranged (Kurani, Fig. 40 – 42, Kurani, [0139] “The camera takes photos of the waste bin interior with a wide-angle lens”; [0461] The waste and litter sensor 320 consists of a camera, a flash, and a digital image processing component to process photos and videos”; Kurani, [0462] “The waste 650 and litter 108 videos are used to increase the sensitivity of the results using video processing using images as the data format to store the video frames”);
a unit for processing the images acquired by said camera comprising at least the following modules (Kurani, Fig. 41, waste and litter image processing module 4110):
a module for detecting each collection enclosure present in said acquired images based on a first machine learning model trained to be able to detect collection enclosures, (Kurani, [0158] “Models are also created for the waste bin database structure to contain all the waste bin information”; Kurani, [0218] The cloud server 114 comprises a cloud sever memory, wherein the cloud server memory comprises a waste bin model, wherein the waste bin model comprises a set of waste bin attributes”; Kurani, [0462] “In processing step image feature extraction 4114, an initial set of the raw waste photo data and video frames is divided and reduced to more manageable groups. The input photo image and video frames are transformed into a reduced set of features. The processing step pattern recognition 4116 is the process of recognizing patterns by using a machine learning algorithm”);
a module for determining the type of waste present in each image portion detected by said enclosure detection module as being a collection enclosure, called enclosure images, based on a second trained machine learning model, with this second learning module having been trained by means of a library of training images, called waste library, that includes images of several types of waste likely to be collected in a collection enclosure (Kurani, Fig. 41 – 42, Kurani, [0462] “The image pattern recognition involves classification of feature extracted data in recognizing the waste and litter objects. In the processing step waste and litter classification 4118, waste and litter objects are classified into waste types, waste volume, litter types, litter level, biohazardous waste types, and biohazardous waste level information in the waste bin 600”); and
a module for computing a filling rate of each collection enclosure by analyzing said enclosure images detected by said enclosure detection module (Kurani, [0463] “The waste volume measurement working principle involves taking pictures and videos of the waste inside the waste bin 600 at a set frequency and producing a 3-dimensional (3D) depth map of the waste bin 600 content to further refine the waste bin 600 waste fill level and also provide waste type information. The waste volume can also be calculated by establishing a relationship between an image pixel and an area or virtual wireframe and waste in the waste bin 600”).
While Kurani teaches using machine learning algorithm for pattern recognition of the input images and transforming into reduced set of features wherein a waste bin model comprises of a wet of waste bin attributes, it does not explicitly recite:
a module for detecting each collection enclosure present in said acquired images based on a first machine learning model trained to be able to detect collection enclosures, with this first learning module having been trained by means of a library of training images, called enclosure library, that included portions of images labeled as reflecting the presence of a collection enclosure and portions of images labeled as reflecting the absence of a collection enclosure;
In the same field of endeavor, Swaroop teaches:
a module for detecting each collection enclosure present in said acquired images based on a first machine learning model trained to be able to detect collection enclosures, with this first learning module having been trained by means of a library of training images, called enclosure library, that included portions of images labeled as reflecting the presence of a collection enclosure and portions of images labeled as reflecting the absence of a collection enclosure (Swaroop, [0004] “a method for receiving a plurality of images … the plurality of images comprising a training set of images and a validation set of images; labeling each image of the plurality of images as including either an overfilled container or a not-overfilled container; pre-processing each image of the plurality of images to reduce bias of a machine learning model; training, and based on the labeling, the machine learning model using the plurality of images”; Swaroop, [0025] “the analysis module(s) 122 can include a classification engine 136, which can also be described as a classifier, a model, an image classifier, or an image classification engine. The engine 136 can be trained, using any suitable ML technique, to identify images that show a container having an overage condition. For example, the engine 136 can be trained to look for various pattern(s) and/or feature(s) within image(s) that indicate the presence, or absence, of an overage, such as a lid that is not completely shut, refuse that is visible through an opening between the lid and the body of the container, and so forth…, the engine 136 can be trained based on a (e.g., large) data set of images that have been tagged as exhibiting overages or not exhibiting overages, e.g., by an operator reviewing the image(s)”);
Kurani and Swaroop as considered analogous art as they are reasonably pertinent to the same field of endeavor of waste management system using machine learning models. Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Kurani with the method of classifying images as taught by Swaroop to make the invention that uses machine learning module trained by means of images based on labelling and reflecting if the image has presence or absence of waste containers; doing so can allow those models to efficiently identify overloaded containers with reduced computational latency and furthermore improve the accuracy of identification of overloaded containers by preprocessing images prior to such identification by the machine learning models (Swaroop,[0005]); thus one of the ordinary skill in the art would have been motivated to combine the references.
Regarding Claim 2, Kurani in view of Swaroop teaches the system as claimed in claim 1, wherein said module for computing the filling rate implements a third trained machine learning model, with this third learning model having been trained by means of a library of training images, called filling library, that comprises images of various enclosures accommodating various types of waste, each labeled with a filling level ranging between 0 and 100% full (Kurani, [0139] “The waste fill level is reported in the form of a percentage that runs from 0 to 100”; Kurani, [0151] “Machine learning, along with neural network algorithms, can continually learn to recognize new waste objects like paper, containers, food scraps, cardboard boxes, litter, hazardous waste, and such, from different angles and in various ranges from the photos and videos taken by the camera. It can also learn and predict when the waste bin will be full”; [0190] “The litter level measurement working principle involves measuring the density of litter surrounding the waste bin and assigning the litter level value based on a camera machine vision algorithm and neural net algorithm”).
Regarding Claim 7, Kurani in view of Swaroop teaches the system as claimed in claim 1, wherein said predetermined time interval between two acquisitions of images by said camera depends on the evolution of the filling rate computed by said module for computing the filling rate and/or on the type of waste determined by said module for determining the type of waste (Kurani, [0463]” The waste volume measurement working principle involves taking pictures and videos of the waste inside the waste bin 600 at a set frequency and producing a 3-dimensional (3D) depth map of the waste bin 600 content to further refine the waste bin 600 waste fill level and also provide waste type information”).
Regarding Claim 8, Kurani in view of Swaroop teaches the system as claimed in claim 1, further comprising a module for assessing a mass and volume balance of the waste present in said enclosure (Kurani, [0190] “The waste volume measurement working principle involves taking pictures and videos of the waste inside the waste bin at a set frequency and producing a 3-dimensional (3D) depth map of the waste bin content to further refine the waste bin waste fill level value, and also provide type of waste paper, container, food scraps, cardboard boxes, litter, biohazardous waste, and related information”).
Regarding Claim 9, Kurani in view of Swaroop teaches the system as claimed in claim 1, wherein said processing unit is formed by a server remote from said camera and in that it further comprises wireless communication means configured to transmit said images acquired by said camera to said processing unit (Kurani, [0134] “A cloud server can involve deploying groups of remote servers and/or software networks that allow centralized data storage and online access to computer application software or resources. These groups of remote servers and/or software networks can be a collection of remote computing services. A cloud server can contain algorithms, methods, and databases. Smart waste bin sensor device data is sent to the cloud server and stored in a database for further processing and can be accessed by the waste bin mobile application or waste collection facility application”).
Regarding Claim 10, Kurani in view of Swaroop teaches the system as claimed in claim 9, wherein said wireless communication means include at least 3G, 4G, 5G or Wi-Fi connectivity (Kurani, [0154] “the combination of accurate sensors, powerful processing, and wireless communication (for example, WiFi or Bluetooth) on a single IC; Kurani, [0201] “The waste bin mobile application allows users to access the smart waste bin sensor device data through Wi-Fi”; Kurani, [0210]).
Regarding Claim 11, Kurani teaches:
A method for monitoring at least one loose waste collection enclosure (Kurani, Fig. 3 – 5, Fig. 40 – 42, monitoring system 300, Kurani, [0451] “FIGS. 40, 41 and 42 illustrate an example waste and litter sensor 320, wherein the waste and litter sensor 320 implements, operates, and monitors waste, and the litter sensor implements, operates, detects, measures, and monitors a waste type, a waste volume, a litter type, a litter level, a biohazardous waste type, and a biohazardous waste level inside or surrounding the waste bin 600”), said system comprising:
acquiring images, at a predetermined time interval, of an area where each monitored collection enclosure is arranged (Kurani, Fig. 40 – 42, Kurani, [0139] “The camera takes photos of the waste bin interior with a wide-angle lens”; [0461] The waste and litter sensor 320 consists of a camera, a flash, and a digital image processing component to process photos and videos”; Kurani, [0462] “The waste 650 and litter 108 videos are used to increase the sensitivity of the results using video processing using images as the data format to store the video frames”);
processing the acquired images (Kurani, Fig. 41, waste and litter image processing module 4110);
characterized in that said image processing comprises:
detecting each collection enclosure present in said acquired images by inputting images into a first trained machine learning model, (Kurani, [0158] “Models are also created for the waste bin database structure to contain all the waste bin information”; Kurani, [0218] The cloud server 114 comprises a cloud sever memory, wherein the cloud server memory comprises a waste bin model, wherein the waste bin model comprises a set of waste bin attributes”; Kurani, [0462] “In processing step image feature extraction 4114, an initial set of the raw waste photo data and video frames is divided and reduced to more manageable groups. The input photo image and video frames are transformed into a reduced set of features. The processing step pattern recognition 4116 is the process of recognizing patterns by using a machine learning algorithm”);
determining the type of waste present in each image portion detected by said first learning model, by inputting each enclosure image into a second trained machine learning model, with this second learning model having been trained by means of a library of training images, called waste library, that includes images of several types of waste likely to be collected in a collection enclosure (Kurani, Fig. 41 – 42, Kurani, [0462] “The image pattern recognition involves classification of feature extracted data in recognizing the waste and litter objects. In the processing step waste and litter classification 4118, waste and litter objects are classified into waste types, waste volume, litter types, litter level, biohazardous waste types, and biohazardous waste level information in the waste bin 600”); and
computing a filling rate of each collection enclosure by analyzing said enclosure images detected by said enclosure detection module (Kurani, [0463] “The waste volume measurement working principle involves taking pictures and videos of the waste inside the waste bin 600 at a set frequency and producing a 3-dimensional (3D) depth map of the waste bin 600 content to further refine the waste bin 600 waste fill level and also provide waste type information. The waste volume can also be calculated by establishing a relationship between an image pixel and an area or virtual wireframe and waste in the waste bin 600”).
While Kurani teaches using machine learning algorithm for pattern recognition of the input images and transforming into reduced set of features wherein a waste bin model comprises of a wet of waste bin attributes, it does not explicitly recite:
detecting each collection enclosure present in said acquired images by inputting images into a first trained machine learning model, with this first learning model having been trained by means of a library of training images, called enclosure library, that included portions of images labeled as reflecting the presence of a collection enclosure and portions of images labeled as reflecting the absence of a collection enclosure, with the portions of images labeled as reflecting the presence of a collection enclosure including the limits of the collection enclosures;
In the same field of endeavor, Swaroop teaches:
detecting each collection enclosure present in said acquired images by inputting images into a first trained machine learning model, with this first learning model having been trained by means of a library of training images, called enclosure library, that included portions of images labeled as reflecting the presence of a collection enclosure and portions of images labeled as reflecting the absence of a collection enclosure, with the portions of images labeled as reflecting the presence of a collection enclosure including the limits of the collection enclosures (Swaroop, [0004] “a method for receiving a plurality of images … the plurality of images comprising a training set of images and a validation set of images; labeling each image of the plurality of images as including either an overfilled container or a not-overfilled container; pre-processing each image of the plurality of images to reduce bias of a machine learning model; training, and based on the labeling, the machine learning model using the plurality of images”; Swaroop, [0025] “the analysis module(s) 122 can include a classification engine 136, which can also be described as a classifier, a model, an image classifier, or an image classification engine. The engine 136 can be trained, using any suitable ML technique, to identify images that show a container having an overage condition. For example, the engine 136 can be trained to look for various pattern(s) and/or feature(s) within image(s) that indicate the presence, or absence, of an overage, such as a lid that is not completely shut, refuse that is visible through an opening between the lid and the body of the container, and so forth…, the engine 136 can be trained based on a (e.g., large) data set of images that have been tagged as exhibiting overages or not exhibiting overages, e.g., by an operator reviewing the image(s)”);
Kurani and Swaroop as considered analogous art as they are reasonably pertinent to the same field of endeavor of waste management system using machine learning models. Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Kurani with the method of classifying images as taught by Swaroop to make the invention that uses machine learning module trained by means of images based on labelling and reflecting if the image has presence or absence of waste containers; doing so can allow those models to efficiently identify overloaded containers with reduced computational latency and furthermore improve the accuracy of identification of overloaded containers by preprocessing images prior to such identification by the machine learning models (Swaroop,[0005]); thus one of the ordinary skill in the art would have been motivated to combine the references.
Claims 3 – 5 are rejected under 35 U.S.C. 103 as being unpatentable over Kurani et al. (US 20210188541 A1; hereafter referred to as Kurani) in view of Swaroop et al. (US 20210326658 A1; hereafter referred to as Swaroop) further in view of Murad et al. (US 20230136451 A1; hereafter referred to as Murad).
Regarding Claim 3, Kurani in view of Swaroop teaches the system as claimed in claim 1, but fails to explicitly recite: wherein said module for computing the filling rate compares at least one reference image corresponding to an empty enclosure with said image of said enclosure detected by said enclosure detection module.
In the same field of endeavor, Murad teaches:
wherein said module for computing the filling rate compares at least one reference image corresponding to an empty enclosure with said image of said enclosure detected by said enclosure detection module (Murad, [0126] The detection unit 120 uses trained neural networks 130 to process the data pipeline. The analytics unit 122 can generate insights on validated waste predictive data or insights. The data can include but is not limited to: type of waste item; item disposed in waste stream; product brand of waste item; dimensions of waste item; volume of waste item; public engagement metrics; updated diversion rates of waste items; individual receptacle fill level”; Murad, [0142] The analytics unit 122 can compute individual receptacle fill level metrics…The analytics unit 122 can build a set the volume of each receptacle”).
Kurani, Swaroop and Murad are considered analogous art as they are pertinent to the same field of endeavor of waste management method and system using machine learning models. Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Kurani in view of Swaroop with the method of computing the filling rate as taught by Murad to make the invention that compares at least one reference image corresponding to an empty enclosure with said image of said enclosure detected to compute filling rate (metric); doing so can efficiently monitor volume of each receptable based on the filling (Murad,[0169]); thus one of the ordinary skill in the art would have been motivated to combine the references.
Regarding Claim 4, Kurani in view of Swaroop further in view of Murad teaches the system as claimed in claim 3, wherein said comparison of said reference image with said enclosure image involves detecting and identifying similar key elements between said two images, called image descriptors (Kurani, [0463], Murad, [0127] “After scanning a captured frame, detection unit 120 locates the waste item within the image. The brand unit 128 can apply a brand image classifier in real time to recognize any of the brand logos that it is previously trained to identify. Brand logos and their variations can be continuously updated on system 100 and linked to corresponding waste items”).
Regarding Claim 5, Kurani in view of Swaroop teaches the system as claimed in claim 1, but fails to explicitly recite: further comprising a solar panel connected to said image acquisition camera in order to be able to supply it with electrical energy.
In the same field of endeavor, Murad teaches:
further comprising a solar panel connected to said image acquisition camera in order to be able to supply it with electrical energy (Murad, [0174] Diverter 2400 may include an imaging system (or integrate with system 100) to take an image of the waste item inserted into the waste bin 1000”; Murad, [0184] “The X-fin motors and other powered components of bin 1000 may be electrically powered. Power may be provided by sources such as batteries or solar panels”).
Kurani, Swaroop and Murad are considered analogous art as they are pertinent to the same field of endeavor of waste management method and system using machine learning models. Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Kurani in view of Swaroop with the method of using a solar pannel as taught by Murad to make the invention that uses a solar panel connected to said image acquisition camera in order to be able to supply it with electrical energy; doing so can yield predictable results by powering image source with solar panels similar to standard wall power (Murad,[0184]); thus one of the ordinary skill in the art would have been motivated to combine the references.
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Kurani et al. (US 20210188541 A1; hereafter referred to as Kurani) in view of Swaroop et al. (US 20210326658 A1; hereafter referred to as Swaroop) further in view of Charlet (WO 2020245547 A1; hereafter referred to as Charlet).
Regarding Claim 6, Kurani in view of Swaroop teaches the system as claimed in claim 1 and the camera positioned to visualize objects in front of the vehicle, such as container (Swaroop, [0034] “The vehicle 102 can also include one or more cameras 134 that capture images in proximity to the vehicle 102 and/or, in some instances, of the interior of the vehicle. In the example shown, a camera 134 is positioned to visualize objects in front of the vehicle 102, such as container(s) 130 being handled by the front-loading vehicle shown in the example. The camera(s) 134 may also be placed in other positions and/or orientations. For example, a side-loading vehicle 102 (e.g., with an ASL) can have a camera 134 affixed to its side to capture image(s) of container(s) being processed using the side-loading mechanism”), but fails to explicitly recite: further comprising a mast mounted in said area where said collection enclosure is arranged, with said mast supporting said image acquisition camera.
In the same field of endeavor, Charlet teaches:
further comprising a mast mounted in said area where said collection enclosure is arranged, with said mast supporting said image acquisition camera (Charlet, [0110] “the assembly further comprises a camera installed on a mast, overhanging the container, so as to capture images of the ground in the vicinity of the container”)
Kurani, Swaroop and Charlet are considered analogous art as they are pertinent to the same field of endeavor of waste management method and system using machine learning models. Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Kurani in view of Swaroop with the method of using a mast mounted in said area as taught by Charlet to make the invention that uses a mast mounted in said area connected to said image acquisition camera to capture images of the vicinity of the container; doing so can make it possible to obtain an image taken at a vertical distance from the top of the voluntary drop-off container, so as to simply obtain a Bird View type image (Charlet,[0110]); thus one of the ordinary skill in the art would have been motivated to combine the references.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20220331841 A1 METHODS AND ARRANGEMENTS TO AID RECYCLING: A waste stream is analyzed and sorted to segregate different items for recycling. Certain features of the technology improve the accuracy with which waste stream items are diverted to collection repositories. Other features concern adaptation of neural networks in accordance with context information sensed from the waste. Still other features serve to automate and simplify maintenance of machine vision systems used in waste sorting. Yet other aspects of the technology concern marking 2D machine readable code data on items having complex surfaces (e.g., food containers with integral ribbing for structural strength or juice pooling), to mitigate issues that such surfaces can introduce in code reading.
US 20240286832 A1 SYSTEMS AND METHODS FOR DETECTING WASTE RECEPTACLES A system can include a camera, a single-stage object detector, and one or more processors. The camera can capture image data that includes a waste receptacle. The single-stage object detector can detect waste receptables. The one or more processors can communicate with a waste-collection vehicle, the camera, and the single-stage object detector. The one or more processors can receive, from the camera, the image data. The one or more processors can provide, as an input, the image data to the single-stage object detector. The one or more processors can identify, based on an output of the single-stage object detector, the waste receptacle.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to VAISALI RAO KOPPOLU whose telephone number is (571)270-0273. The examiner can normally be reached Monday - Friday 8:30 - 5.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jennifer Mehmood can be reached at (571) 272-2976. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format.
For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
VAISALI RAO. KOPPOLU
Examiner
Art Unit 2664
/VAISALI RAO KOPPOLU/Examiner of Art Unit 2664
Prosecution Insights