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 .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Claim Rejections - 35 USC § 101
The claims as presented were analyzed for compliance with 35 U.S.C. 101. While the claims recite an abstract idea, the steps of generating a first domesticated fowl image and generating a second domesticated fowl image are not considered to be abstract ideas or generic data gathering and outputting. The claims pass at Step 2A Prong Two/Step 2B.
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.
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.
Claim(s) 1-5 and 7-14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhang in Foreign Patent Document CN111539937A (IDS 12 DEC 2024; see machine translation), and further in view of Li et al. in Foreign Patent Document CN205537893U (IDS 12 DEC 2024; see machine translation) and Wang et al. in Foreign Patent Document CN 107549049 (see machine translation).
Regarding claim 1, Zhang teaches:
A domesticated fowl health monitoring system (see “livestock, such as…poultry…chicken, duck, goose…”, p. 5), comprising:
a learning calibration module (see “means of machine learning”, Abstract; p. 4) including a weighing structure (see “detecting…livestock weight”, Abstract) and a first camera (see “the camera device”, p. 4);
a computing core (see “a memory 1503 for storing a computer program; processor 1501, for executing the computer program stored on the memory 1503”, p. 6) coupled to the learning calibration module (see “means of machine learning”, Abstract; p. 4) and configured to receive the weight value and the first domesticated fowl image (see “obtaining the image”, p. 2) to analyze a number of the at least one first domesticated fowl in the first domesticated fowl image (see “…inputting the…image into the pre-rained segmentation model…extracting the characteristic vector…determine index information...”, p. 2) and generate at least one domesticated fowl image feature (see “…the sample image comprises at least one object…tag image corresponding to one part of the target object…determining the shape parameter…”, p. 2-3) and an image-to-weight formula corresponding to each of the at least one domesticated fowl image feature (see “obtaining the depth image…into the pre-trained segmentation model; segmenting the depth image…extracting a feature vector…based on the back image; determining the weight…according to the characteristic vector…”, p. 3 the segmentation model is the means for obtaining weight information from image and thus is understood to comprise an image-to-weight formula), wherein the image-to-weight formula includes a relative relationship between an image feature value and a weight (see “obtaining the depth image…into the pre-trained segmentation model; segmenting the depth image…extracting a feature vector…based on the back image; determining the weight…according to the characteristic vector…”, p. 3 the segmentation model is the means for obtaining weight information from image and thus is understood to comprise an image-to-weight formula);
a cloud module coupled to the computing core (see “point cloud data”, p. 5 means for obtaining and manipulating point cloud data is understood o be a cloud module) and configured to store the at least one domesticated fowl image feature and the image-to-weight formula (see “the depth image through coordinate conversion can be calculated as point cloud data; the point cloud data with rule and necessary information also can be inversely calculated as depth image data”, p. 5). Additionally, Zhang teaches a weight regression model (p. 7-8).
Zhang differs from the claimed invention in that it does not expressly teach wherein the weighing structure is configured to detect a weight value of at least one first domesticated fowl on the weighing structure, and the first camera is arranged in the weighing structure and configured to generate a first domesticated fowl image of the at least one first domesticated fowl on the weighing structure; a monitoring module coupled to the cloud module and including a second camera, wherein the second camera is configured to generate a second domesticated fowl image presenting at least one second domesticated fowl, wherein the cloud module is further configured to obtain a unit weight of the at least one second domesticated fowl based on the second domesticated fowl image and the image-to-weight formula.
Li et al. teaches “an automatically poultry weigh weighing system” (Abstract) comprising a weighing structure (see “weight weighing system”, claim 1) and a first camera (see “computer image processing system”, p. 5; see “image sensor and camera”, claim 1), wherein the weighing structure is configured to detect a weight value of at least one first domesticated fowl on the weighing structure (see “…the pressure sensor collects the weight data…the weight data with the poultry only number, aver weight data is obtained…”, p. 5), and the first camera is arranged in the weighing structure and configured to generate a first domesticated fowl image of the at least one first domesticated fowl on the weighing structure (see “EthoVision XT image processing system identifies the number of poultry in the photographs and combines the weight data to derive an average weight, the analysis recording terminal records the weight data acquired by the pressure sensor over time and the EthoVisionXT image processing system analyzes the number of poultry obtained”, p. 2 the pressure sensor records the weight (i.e. The weight bearing structure is used to sense the weight value of the at least one first poultry among the plurality of poultry carried by the load bearing structure) and transmits the information over a data line to an analytics recording terminal, the simultaneous analysis recording terminal triggers the image sensor to take a picture). One of ordinary skill in the art would have been capable of determining the unit weight using the estimated weight of the chickens, given that it is well-known that unit weight is determined by dividing the estimated weight by the volume (quantity) of chickens present.
Wang et al. teaches a cage chicken health condition automatic monitoring device (Abstract) comprising a second camera (see “a camera, a thermal imaging camera”, Abstract), wherein the second camera is configured to generate a second domesticated fowl image presenting at least one second domesticated fowl (see “…thermal imaging camera is arranged on the inspection device…cameras for collecting image…individual infrared thermal image…”, p. 2 generate a second domesticated fowl image presenting at least one second domesticated fowl). Further, Wang et al. teaches “an image processing module for collecting the camera chicken individual amount image using image recognition technology calculated the number of living chicken cage…alarm for occurrence of the dead chicken…” (p. 2).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the monitoring and weighing techniques of Li et al. and Wang et al. with Zhang to improve Zhang with a reasonable expectation that it would result in improving the automation of weighting and measuring the poultry, thereby resulting in a more efficient monitoring process.
Regarding claim 2, Zhang, Li et al. and Wang et al. teaches the limitations as claimed above. As disclosed above, Zhang teaches “obtaining the depth image…into the pre-trained segmentation model; segmenting the depth image…extracting a feature vector…based on the back image; determining the weight…according to the characteristic vector…” ( p. 3). That is, the segmentation model is the means for obtaining weight information from image and thus is understood to comprise an image-to-weight formula. Gaitlan et al. teaches multiple or second camera(s) and analyzing image data from the second cameras [0057]; [0096]). From the teachings or suggestions of the prior art, one of ordinary skill in the art would have been capable of modifying the prior art such that the unit weight of the at least one second domesticated fowl is obtained based on the at least one domesticated fowl image feature. There would have a reasonable expectation that the modification would result in successfully improving the ability of the system to accurately determine weight and, subsequently the health, of the birds.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the monitoring and weighing systems of Li et al. and Wang et al. with Zhang to improve Zhang with a reasonable expectation that it would result in improving the automation of weighting and measuring the poultry, thereby resulting in a more efficient monitoring process.
Regarding claim 3, Zhang, Li et al. and Wang et al. teaches the limitations as claimed above. Further, Wang et al. teaches further including an early warning analysis module, wherein the early warning analysis module (see “alarm are electrically connected”, Abstract; Fig. 1, p. 3) is configured to output at least one of a statistical report and a warning message (see “alarm for occurrence of the dead chicken or chicken to acousto-optic alarm”, p. 2, p. 4) based on at least one of the unit weight and an activity value (see “detects the chicken individual quantity and temperature”, Abstract; see “calculated the number of living chicken”, p. 2 living chicken have a different activity value than dead chicken). Coupling the warning analysis module of Wang et al. to the cloud module of Zhang would have been within the ordinary ability of one of ordinary skill in the art to ensure the functionality of the system and to do so would be nothing more than an obvious matter of design choice.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the monitoring and weighing systems of Li et al. and Wang et al. with Zhang to improve Zhang with a reasonable expectation that it would result in improving the automation of weighting and measuring the poultry, thereby resulting in a more efficient monitoring process.
Regarding claim 4, Zhang, Li et al. and Wang et al. teaches the limitations as claimed above. Further, Zhang teaches further including a mobile communication platform (see “electronic device”, p. 5, Fig. 6; see “completely performed on the remote computing device or server”, p. 10) wirelessly (see “any suitable medium, including but not limited to wireless, wired, optical cable, RF, and the like”, p. 10). Further, Wang et al. teaches further including an early warning analysis module, wherein the early warning analysis module (see “alarm are electrically connected”, Abstract; Fig. 1, p. 3) is configured to output at least one of a statistical report and a warning message (see “alarm for occurrence of the dead chicken or chicken to acousto-optic alarm”, p. 2, p. 4) based on at least one of the unit weight and an activity value (see “detects the chicken individual quantity and temperature”, Abstract; see “calculated the number of living chicken”, p. 2 living chicken have a different activity value than dead chicken).
Coupling the warning analysis module of Wang et al. to the mobile communication platform of Zhang would have been within the ordinary ability of one of ordinary skill in the art to ensure the functionality of the system, providing a user readable output, and to do so would be nothing more than an obvious matter of design choice.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the monitoring and weighing systems of Li et al. and Wang et al. with Zhang to improve Zhang with a reasonable expectation that it would result in improving the automation of weighting and measuring the poultry, thereby resulting in a more efficient monitoring process.
. Regarding claim 5, Zhang, Li et al. and Wang et al. teaches the limitations as claimed above. Further, Zhang teaches wherein the mobile communication platform includes one of a workstation, a server, a desktop computer, a notebook computer, a tablet computer, a personal digital assistant or a smart phone (see “electronic device”, p. 5, Fig. 6; see “the remote computing device or server”, p. 10).
Regarding claim 7, Zhang, Li et al. and Wang et al. teaches the limitations as claimed above. Further, Zhang teaches a server (see “server”, p. 10). Adding a cloud database is coupled to the server and configured to store at least one of the at least one domesticated fowl image feature, the image-to-weight formula, the unit weight, and the activity value would be a matter of rearranging the parts disclosed in the prior art without modifying the intended operation of the system. It would have been nothing more than an obvious matter of design choice. See MPEP 2144.04 VI E.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the monitoring and weighing systems of Li et al. and Wang et al. with Zhang to improve Zhang with a reasonable expectation that it would result in improving the automation of weighting and measuring the poultry, thereby resulting in a more efficient monitoring process.
Regarding claim 8, Zhang, Li et al. and Wang et al. teaches the limitations as claimed above. Further, Zhang teaches “including but not limited to wireless, wired, optical cable, RF, and the like” (p. 10) and connection “device through any kind of network, including a local area network (LAN) or a wide area network (WAN),” (p. 10). One of ordinary skill in the art would have been capable of modifying the prior art to couple the server to the cloud database through one of narrowband internet of things (NB-IoT), LoRa WAN, LTE and Wi-Fi without modifying the intended operation of the system. It would have been nothing more than an obvious matter of design choice and the results would have been predictable.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the monitoring and weighing systems of Li et al. and Wang et al. with Zhang to improve Zhang with a reasonable expectation that it would result in improving the automation of weighting and measuring the poultry, thereby resulting in a more efficient monitoring process.
Regarding claim 9, Zhang, Li et al. and Wang et al. teaches the limitations as claimed above. Further, Li et al. teaches that poultry walks over from the pressure sensor (see “when the door is opened. in the process of poultry direction B side of the door, from the pressure sensor, pressure sensor and recording the weight and lines to the information through the data analyzing and recording terminal”, p. 2; Abstract i.e. The load-bearing platform carrying the at least one first poultry) while the analysis recording terminal triggers the image sensor to take a picture (see “will trigger the image sensor, an image sensor (image sensor can be Keyence IV series image sensor) sends the image information to the computer”, p. 2 i.e. The first camera) during the course of walking towards the B-side door. An arrangement of intermediate platform is arranged above the weighing platform, and the first camera is arranged under the intermediate platform would be a matter of rearranging the parts disclosed in the prior art without modifying the intended operation of the system. It would have been nothing more than an obvious matter of design choice. See MPEP 2144.04 VI E.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the monitoring and weighing systems of Li et al. and Wang et al. with Zhang to improve Zhang with a reasonable expectation that it would result in improving the automation of weighting and measuring the poultry, thereby resulting in a more efficient monitoring process.
Regarding claim 9, Zhang, Li et al. and Wang et al. teaches the limitations as claimed above. Further, Li et al. teaches that poultry walks over from the pressure sensor (see “when the door is opened. in the process of poultry direction B side of the door, from the pressure sensor, pressure sensor and recording the weight and lines to the information through the data analyzing and recording terminal”, p. 2; Abstract i.e. the weighing platform). The prior art differs from the claimed invention in that it does not expressly teach the weighing platform is coupled to the intermediate platform through at least two column bodies. Adding at least two column bodies such that the weighing platform is coupled to the intermediate platform through the at least two column bodies would have been possible by one of ordinary skill in the art without modifying the intended operation of the system. It would have been nothing more than an obvious matter of design choice and the results would have been predictable.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the monitoring and weighing systems of Li et al. and Wang et al. with Zhang to improve Zhang with a reasonable expectation that it would result in improving the automation of weighting and measuring the poultry, thereby resulting in a more efficient monitoring process.
Regarding claim 11, Zhang teaches:
A domesticated fowl health monitoring method (see “livestock, such as…poultry…chicken, duck, goose…”, p. 5), comprising:
detecting a weight value of at least one first domesticated fowl (see “detecting…livestock weight”, Abstract);
generating a first domesticated fowl image of the at least one first domesticated fowl (see “the camera device”, p. 4);
analyzing a number of the at least one first domesticated fowl in the first domesticated fowl image (see “…inputting the…image into the pre-rained segmentation model…extracting the characteristic vector…determine index information...”, p. 2) and generating at least one domesticated fowl image feature and an image-to-weight formula corresponding to each of the at least one domesticated fowl image feature (see “…the sample image comprises at least one object…tag image corresponding to one part of the target object…determining the shape parameter…”, p. 2-3);
storing (see “a memory 1503 for storing a computer program; processor 1501, for executing the computer program stored on the memory 1503”, p. 6) the at least one domesticated fowl image feature and the image-to-weight formula (see “obtaining the depth image…into the pre-trained segmentation model; segmenting the depth image…extracting a feature vector…based on the back image; determining the weight…according to the characteristic vector…”, p. 3 the segmentation model is the means for obtaining weight information from image and thus is understood to comprise an image-to-weight formula).
Zhang differs from the claimed invention in that it does not expressly teach generating a second domesticated fowl image presenting at least one second domesticated fowl; and obtaining a unit weight of the at least one second domesticated fowl based on the second domesticated fowl image and the image-to-weight formula.
Li et al. teaches “an automatically poultry weigh weighing system” (Abstract) comprising a weighing structure (see “weight weighing system”, claim 1) and a first camera (see “computer image processing system”, p. 5; see “image sensor and camera”, claim 1), wherein the weighing structure is configured to detect a weight value of at least one first domesticated fowl on the weighing structure (see “…the pressure sensor collects the weight data…the weight data with the poultry only number, aver weight data is obtained…”, p. 5), and the first camera is arranged in the weighing structure and configured to generate a first domesticated fowl image of the at least one first domesticated fowl on the weighing structure (see “EthoVision XT image processing system identifies the number of poultry in the photographs and combines the weight data to derive an average weight, the analysis recording terminal records the weight data acquired by the pressure sensor over time and the EthoVisionXT image processing system analyzes the number of poultry obtained”, p. 2 the pressure sensor records the weight (i.e. The weight bearing structure is used to sense the weight value of the at least one first poultry among the plurality of poultry carried by the load bearing structure) and transmits the information over a data line to an analytics recording terminal, the simultaneous analysis recording terminal triggers the image sensor to take a picture). One of ordinary skill in the art would have been capable of determining the unit weight using the estimated weight of the chickens, given that it is well-known that unit weight is determined by dividing the estimated weight by the volume (quantity) of chickens present.
Wang et al. teaches a cage chicken health condition automatic monitoring device (Abstract) comprising a second camera (see “a camera, a thermal imaging camera”, Abstract), wherein the second camera is configured to generate a second domesticated fowl image presenting at least one second domesticated fowl (see “…thermal imaging camera is arranged on the inspection device…cameras for collecting image…individual infrared thermal image…”, p. 2 generate a second domesticated fowl image presenting at least one second domesticated fowl). Further, Wang et al. teaches “an image processing module for collecting the camera chicken individual amount image using image recognition technology calculated the number of living chicken cage…alarm for occurrence of the dead chicken…” (p. 2).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the monitoring and weighing techniques of Li et al. and Wang et al. with Zhang to improve Zhang with a reasonable expectation that it would result in improving the automation of weighting and measuring the poultry, thereby resulting in a more efficient monitoring process.
Regarding claim 12, Zhang, Li et al. and Wang et al. teaches the limitations as claimed above. As disclosed above, Zhang teaches “obtaining the depth image…into the pre-trained segmentation model; segmenting the depth image…extracting a feature vector…based on the back image; determining the weight…according to the characteristic vector…” ( p. 3). That is, the segmentation model is the means for obtaining weight information from image and thus is understood to comprise an image-to-weight formula. Gaitlan et al. teaches multiple or second camera(s) and analyzing image data from the second cameras [0057]; [0096]). From the teachings or suggestions of the prior art, one of ordinary skill in the art would have been capable of modifying the prior art such that the unit weight of the at least one second domesticated fowl is obtained based on the at least one domesticated fowl image feature. There would have a reasonable expectation that the modification would result in successfully improving the ability of the system to accurately determine weight and, subsequently the health, of the birds.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the monitoring and weighing techniques of Li et al. and Wang et al. with Zhang to improve Zhang with a reasonable expectation that it would result in improving the automation of weighting and measuring the poultry, thereby resulting in a more efficient monitoring process.
Regarding claim 13, Zhang, Li et al, and Wang et al. teaches the limitations as claimed above. Further, Zhang teaches wherein the image-to-weight formula includes a relative relationship between an image feature value and a weight (see “obtaining the depth image…into the pre-trained segmentation model; segmenting the depth image…extracting a feature vector…based on the back image; determining the weight…according to the characteristic vector…”, p. 3 the segmentation model is the means for obtaining weight information from image and thus is understood to comprise an image-to-weight formula).
Regarding claim 14, Zhang, Li et al. and Wang et al. teaches the limitations as claimed above. Further, Wang et al. teaches outputting at least one of a statistical report and a warning message (see “alarm are electrically connected”, Abstract; Fig. 1, p. 3; see “alarm for occurrence of the dead chicken or chicken to acousto-optic alarm”, p. 2, p. 4) based on at least one of the unit weight and an activity value (see “detects the chicken individual quantity and temperature”, Abstract; see “calculated the number of living chicken”, p. 2 living chicken have a different activity value than dead chicken).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the monitoring and weighing systems of Li et al. and Wang et al. with Zhang to improve Zhang with a reasonable expectation that it would result in improving the automation of weighting and measuring the poultry, thereby resulting in a more efficient monitoring process.
Claim(s) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhang in Foreign Patent Document CN111539937A (IDS 12 DEC 2024; see machine translation), Li et al. in Foreign Patent Document CN205537893U (IDS 12 DEC 2024; see machine translation) and Wang et al. in Foreign Patent Document CN 107549049 (see machine translation) as applied to claim 1 above, and further in view of Brownlee in NPL “A Gentle Introduction to Pooling Layers for Convolutional Neural Networks.
Regarding claim 6 Zhang, Li et al, and Wang et al. teaches the limitations as claimed above. Further, Zhang teaches “means of machine learning” (Abstract; p. 4). Deep learning is a subset of machine learning.
Zhang, Li et al, and Wang et al. differs from the claimed invention in that they do not expressly teach at least one convolution layer and at least one pooling layer.
Brownlee teaches “how the pooling operation works and how to implement it in convolutional neural networks.” (p. 1) and discloses “Convolutional layers in a convolutional neural network summarize the presence of features in an input image… Pooling layers provide an approach to down sampling feature maps by summarizing the presence of features in patches of the feature map. Two common pooling methods are average pooling and max pooling that summarize the average presence of a feature and the most activated presence of a feature respectively” (p. 1). Brownlee establishes the use of convolutional layers and pooling layers applied to images as part of the knowledge base of one of ordinary skill in the art.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the image analysis using convolutional and pooling layers as taught in Brownlee in the monitoring and weighing systems of Zhang, Li et al. and Wang et al. to improve Zhang, Li et al. and Wang et al. with a reasonable expectation that it would result in improving the precision of the image analysis, thereby resulting in a more efficient and effective health monitoring process.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Gaitlan et al. in Foreign Patent Document WO2024/176006 teaches an aviary monitoring system (Abstract) comprising a monitoring module coupled to the cloud module and including a second camera (see “a camera of a monitoring device fixedly coupled to a ceiling of the broiler aviary to capture image data”, Abstract; see “cameras (e.g., an image camera, a video camera) operably coupled…to an aviary monitoring device..”, [0057]; [0096]), wherein the second camera is configured to generate a second domesticated fowl image presenting at least one second domesticated fowl (see “The image data may be analyzed”, [0058]), wherein the cloud module is further configured to obtain a unit weight of the at least one second domesticated fowl based on the second domesticated fowl image and the image-to-weight formula (see “The image data may be analyzed… an estimate of the weight of the chickens…”, [0058]; [0066]).
Ii et al. in U.S. Patent Publication 20230354781 teaches “a full-cycle health detection system for a dairy cow based on visual recognition, including: electronic chips provided with dairy cow identifiers (IDs), and fixed on dairy cows; an online acquisition device configured to acquire an image of a to-be-detected dairy cow online and recognize a dairy cow ID, and transmit an acquired image of the to-be-detected dairy cow and the dairy cow ID to a master control module; the master control module provided with an image recognition algorithm and a health determination algorithm; and the storage module configured to store full-cycle growth information of dairy cows corresponding to all of the dairy cow IDs and standard full-cycle growth information of the dairy cows. The present disclosure can use visual recognition instead of the manual measurement, and can determine growth and health states of the dairy cow quickly and accurately” (Abstract).
Mellata et al. in U.S. Patent Publication 20220338448 teaches “an exemplary poultry cage-free housing, wherein a camera is included to monitor the health of the birds kept therein” ([0038]).
Pan et al. in U.S. Patent Publication 20220217951 teaches “a charge-coupled device (CCD) camera and a lighting device are installed in the machine-vision inspection device, both the CCD camera and the lighting device are located above the conveyor belt and face the conveyor belt, a motor drives the conveyor belt to run, and the motor and the CCD camera are connected to a computer; allowing eggs laid by hens in the plurality of coops to fall onto the conveyor belt from a bottom of the plurality of coops, and after the hens in the plurality of coops have laid all eggs, activating the conveyor belt, recording a time of activating the conveyor belt, and conveying, by the conveyor belt, the eggs to the machine-vision inspection device; and after the eggs enter the machine-vision inspection device, triggering the CCD camera to capture an image of a surface of the conveyor belt carrying the eggs, obtaining contours of the eggs in the image through image processing, statistically counting a quantity of the eggs, and obtaining a coop position, a color, a size, and a weight for each egg through further analysis and processing, to implement real-time monitoring” (claim 1).
Klein et al. in U.S. Patent Publication 2019/0166801 teaches “A computer-implemented method of estimating a weight of an animal, the computer-implemented method comprising: activating a camera to obtain a three-dimensional digital image of the animal when a presence of the animal is detected at a selected location” (claim 1).
Wang et al. in Foreign Patent Document CN 118298460 A teaches “the invention claims a broiler health monitoring method, system, storage medium and device based on excrement image identification, belonging to the technical field of cultivation monitoring. The invention solves the problem that the existing health monitoring method for breeding broiler chicken has bad effect.” (Abstract).
Shen et al. in Foreign Patent Document CN 114898405 A teaches “The invention claims a portable broiler chicken monitoring system based on edge computing, comprising: a data collecting and processing module, for collecting the RGB image data of the broiler chicken, the environment temperature distribution condition of the henhouse, and comprehensive image data pre-processing module returns the identification information to broiler chicken the abnormal analysis and judgment; an image data pre-processing module, using deep convolution neural network model, quickly identifying and classifying the target in the image, fast piling and identifying the chicken group and identifying the chicken excrement; a client, for displaying the analysis judging result of the data collecting and processing module. The application model claims a portable cage-based chicken image data collecting module based on edge computing In order to adapt to domestic cage chicken coop with narrow and complex environment, light and sensitive, stable operation, high cost performance, can be widely used in cage breeding poultry breeding house.” (Abstract).
Hu et al. in Foreign Patent Document CN 106305491 A teaches “a new type henhouse with good illumination condition, simulating natural environment scatter breeding chicken growth environment, and provides automatic detection in the house environment, rational ventilation, ensures that the temperature in the house is relatively constant and creates proper chicken growing environment while monitoring the health condition of the chicken and find the chicken, and to the environment to carry out disinfection treatment. avoid the massive infection state occurs.” (Abstract).
Pan et al. in Foreign Patent Document CN 105766789 B teaches “ a chicken physique health detection method and system. extracting chicken in falling experiment morphology characteristic parameter, establishing chicken physique health index after analyzing and processing the data. building a height adjustable platform, platform middleware as a electromagnetic valve by the electromagnetic door, a manual switch on the control platform. the side of the platform, the top provided with a high-definition camera. putting the chicken at the electromagnetic door, fall down from the same height, high-definition camera recording the video, obtaining the vibration number of chickens in the process of falling, falling back weighing instrument for measuring the chicken weight, recording the chicken day age, extracting chicken physique health characteristic data with the established chicken physique health index contrast.” (Abstract).
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/MI'SCHITA' HENSON/ Primary Examiner, Art Unit 2857
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