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 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 9, 11, 20, and 22 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.
Claims 9 and 20 recite calculating a total meal amount based, in part, on “an average frame of the image data” It is unclear what is meant by “average frame” including whether the term refers to an average number of frames, a representative frame, an average frame rate, or another quantity. The Specification further appears to use an average frames per second value in the corresponding calculation. Accordingly, the claimed parameter and resulting calculation are unclear.
Claims 11 and 22 recite that “multiple pieces of the image data are calculated being recorded for a preset time and at a predetermined interval.” The grammatical relationship between “calculated” and “being recorded” is unclear, such that it cannot be determined whether the image data are calculated, recorded, generated by recording, or whether a calculated quantity is recorded. Accordingly, the scope of the claimed operation is indefinite.
Claim Rejections - 35 USC § 101
Claim(s) 1-6, 11-17, and 22-23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The limitations, under their broadest reasonable interpretation, cover mental process (concept performed in a human mind, including as observation, evaluation, judgment, and opinion) and mathematical calculations.
Claim 1 recites analyzing livestock image information and mathematically calculating movement distance, weight, meal amount, and feed conversion ratio. The claims is directed to a mental process and mathematical calculations. The additional processor, memory, camera data, and user terminal output merely implement the analysis using generic computing components and data collection/output.
Claim 2 further recites frame based livestock recognition using a preset recognition model and assigning tracking identifiers. These limitations merely use computer vision technology as a tool to perform the claimed data analysis and do not recite an improvement to the recognition model or computer functionality.
Claim 3 further recites determining livestock count from the image containing the greatest number of recognized livestock, which constitutes additional evaluation and selection of information.
Claim 4 further recites calculating movement distance from tracked livestock positions across successive images, which constitutes mathematical calculation based on collected position information.
Claim 5 further recites calculating basal metabolic rate and activity amount, which constitutes additional mathematical analysis of livestock data.
Claim 6 further recites calculating total and average activity amounts using movement data, livestock count, and a coefficient, which constitutes additional mathematical calculations.
Claim 11 further recites periodically acquiring image data and calculating statistical values of meal amount, weight, and feed conversion ratio, which constitutes data gathering and statistical analysis.
Claims 12–17 recite substantially corresponding method limitations to claims 1–6 and are ineligible for substantially the same reasons.
Claim 22 recites substantially corresponding limitations to claim 11 and is ineligible for substantially the same reasons.
Claim 23 recites a non-transitory computer-readable medium containing a program for performing the method of claim 12. Merely implementing the ineligible method on a generic computer-readable medium does not integrate the judicial exception into a practical application or provide significantly more.
Claims 7–10 and 18–21 are not rejected under §101 because they recite a more particular image based physical measurement technique, including determining livestock weight using pixel information, an image to physical scale based on an actual dimension within the livestock shed, and a weight coefficient.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1, 2, 4, 12, 13, and 15, and 23 are rejected under 35 U.S.C. 103 as being unpatentable over Myung et al. (the machine English translation of KR 20220068711, “Myung”) in view of Song et al. (the machine English translation of CN 116019023 A, “Song”).
Regarding claim 1:
Myung discloses: a device for calculating a feed conversion ratio of livestock, the device comprising: a memory storing a program for calculating the feed conversion ratio of the livestock; and a processor configured to execute the program for calculating the feed conversion ratio of the livestock (See FIG. 1 and ¶¶ 23-24));
wherein the program for calculating the feed conversion ratio of the livestock receives image data from at least one camera capturing images of an inside of a livestock shed (Myung teaches camera 10 that may be assigned to each livestock room 1 to monitor a plurality of livestock rooms 1 provided in the livestock barn and may transmit an image captured at a predetermined angle of view within the search area to the data transmission/reception module 120. (See FIG. 1 and ¶ 25));
recognizes livestock from the image data (Myung further teaches inputting images collected through at least one camera 10 assigned to each livestock room into the livestock entity detection model, and accumulates and stores the center coordinates of each detected livestock entity. The trained model detect each livestock from the input image (See ¶29). Myung further teaches that both conventional object detection models such as YoLo, RetinaNet, SDD, and RCNN, as well as object detection models in which the neural network and loss function are changed to improve accuracy for each type of livestock, can be utilized. (See ¶ 39));
calculates a movement distance and a weight of the livestock (Myung further teaches that livestock object tracking unit 133 accumulates and stores the center coordinates of each detected livestock entity to track the movement state of each livestock entity, and assigns an identification number (ID) to each livestock entity. (See ¶57). Myung further teaches the trajectory information for each ID is recorded by measuring the total moving distance in 24 hours and the moving distance in 1 hour units. (See ¶67 and Eq. 1));
calculates a meal amount of the livestock based on a time period in which the livestock stays in a preset eating region (Myung sets a preset feeding area as a geofence, determines livestock entry into and departure from the feeding area based on livestock coordinates, defines the entry/departure pair as a feeding event, and accumulates the time of the feeding event to infer the amount of food consumed. (See ¶¶73-77));
Myung does not specifically teach: calculating the feed conversion ratio based on the weight and meal amount of the livestock.
Nonetheless, in the same field of endeavor, Song teaches: calculates the feed conversion ratio based on the weight and meal amount of the livestock (Song teaches an analysis unit that analyzes images captured by a camera to determine the weight of each pig. Song further teaches that the determined pig weight is compared with the pig’s food intake to obtain the feed conversion ratio (rate). (See ¶¶12, 21, 42-45 and 62));
Myung further teaches provides the calculated feed conversion ratio to a user terminal (Myung teaches the feeding status information may be provided to the user through a user interface displayed on a web page, a TV screen, or a smartphone screen. (See ¶78))
Therefore, it would have been obvious to a person of ordinary skill in the art prior to the effective filing date of the claimed invention to have modified Myung to incorporate the teachings of Song by including: calculating the feed conversion ratio based on the weight and meal amount of the livestock in order to accomplish reducing the workload of the staff and avoiding the stress response of the pigs induced by manual weighing, which affects the pigs subsequent healthy growth as disclosed by Song in ¶43.
Regarding claim 2:
Myung further teaches: wherein, in the process of recognizing the livestock, multiple images are generated by dividing the image data into frame units (Myung teaches that images from various angles of each livestock are collected, extracted in frame units and imaged. (See ¶48)); ,
the livestock is recognized by applying a preset image-based recognition model to each image (Myung further teaches that both conventional object detection models such as YoLo, RetinaNet, SDD, and RCNN, as well as object detection models in which the neural network and loss function are changed to improve accuracy for each type of livestock, can be utilized. (See ¶ 39)), and
a tracking identifier is assigned to each of the recognized livestock (Myung further teaches tracking unit 133 accumulates and stores the center coordinates of each detected livestock entity to track the movement state of each livestock entity, and assigns an identification number (ID) to each livestock entity, it is possible to track each livestock entity using the degree to which the center coordinates of each identified livestock entity are adjacent to each other. (See ¶57)).
Regarding claim 4:
Myung further teaches: wherein, the movement distance is calculated from continuous images based on position information of the livestock to which a same tracking identifier is assigned (Myung further teaches tracking unit 133 accumulates and stores the center coordinates of each detected livestock entity to track the movement state of each livestock entity, and assigns an identification number (ID) to each livestock entity, it is possible to track each livestock entity using the degree to which the center coordinates of each identified livestock entity are adjacent to each other. (See ¶57). Myung further determine whether the detection result between the two frames is of the same object by using a conventional object tracking algorithm such as a simple online and realtime tracking algorithm (SORT), Deep SORT, and correlation-based tracking based on the Hungarian algorithm. (See ¶58). Myung further teaches the trajectory information for each ID is recorded by measuring the total moving distance in 24 hours and the moving distance in 1 hour units. (See ¶67), and that the trajectory (coordinate) of the livestock object being tracked is accumulated and stored for every frame. (See ¶69)).
Regarding claims 12, 13, and 15: the claims limitations are similar to those of claims 1, 2, and 4; therefore, rejected in the same manner as applied above.
Regarding claim 23: Myung teaches the CRM in ¶86.
Claim(s) 7 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Myung in view of Song as applied in claim 2, and further in view of Inaba et al. (the machine English translation of WO 2022181132 A1, “Inaba”).
Regarding claim 7:
Myung in view of Song does not specifically teach: the weight of the livestock is calculated based on a specification of a pixel corresponding to the livestock in one image among the multiple images, a scale calculated based on the one image and an actual length of the inside of the livestock shed, and a preset weight coefficient.
However, in the same field of endeavor, Inaba teaches: the weight of the livestock is calculated based on a specification of a pixel corresponding to the livestock in one image among the multiple images (Inaba teaches in FIG. 6, the estimating unit 24b calculates the correction coefficient k from the measured data in which the sizes of the pigs 80 and the measured weights of the pigs 80 are associated with each of the pigs 1 to N for N pigs. The weight of the pig 80 is estimated using the correction coefficient k and parameters. The correction factor k is a factor that converts the volume of the pig 80 (expressed in pixels, for example) to its weight. The parameters are the length of the minor axis D and the major axis L through the center of gravity. Specifically, the estimating unit 24b regards the number of image regions n (n is a natural number) as the number of pigs 80, and uses the correction coefficient k to calculate the estimated weight M of each pig 80 using the calculation formula M = k x D2 x L. (See ¶41)),
a scale calculated based on the one image and an actual length of the inside of the livestock shed (Inaba teaches the lengths of the major axis and the minor axis are the lengths expressed in pixels on the image, but a specific length on the image (e.g., a feeder or known dimensions of a slatted floor, etc.) ) as a reference, and may be expressed as a relative size. (See ¶33));
and a preset weight coefficient (Inaba teaches in FIG. 6, the estimating unit 24b calculates the correction coefficient k from the measured data in which the sizes of the pigs 80 and the measured weights of the pigs 80 are associated with each of the pigs 1 to N for N pigs. The weight of the pig 80 is estimated using the correction coefficient k and parameters. The correction factor k is a factor that converts the volume of the pig 80 (expressed in pixels, for example) to its weight. (See ¶41)).
Therefore, it would have been obvious to a person of ordinary skill in the art prior to the effective filing date of the claimed invention to have modified Myung in view of Song to employ the image based weight estimation technique of Inaba in order to estimate livestock weight easily and accurately from images without human interventions or direct physical weighting.
Regarding claim 18: the claim limitations are similar to those of claim 7; therefore, rejected in the same manner as applied above.
Claim(s) 3 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Myung in view of Song as applied in claim 2, and further in view of Schobernd et al. ("Examining the utility of alternative video monitoring metrics for indexing reef fish abundance." Canadian Journal of Fisheries and Aquatic Sciences 71, no. 3 (2014), “Schobernd”).
Regarding claim 3:
Myung in view of Song does not specifically teach: a number of the livestock is calculated based on an image in which the most livestock are recognized among the multiple images.
However, in a related field, Schobernd teaches: a number of the livestock is calculated based on an image in which the most livestock are recognized among the multiple images (Schobernd teaches MaxN (i.e., the maximum number of fish in a single frame during the viewing interval). See abstract p.2, ll. 29-30. and p.4 ll. 79-80).
Therefore, it would have been obvious to a person of ordinary skill in the art prior to the effective filing date of the claimed invention to have modified Myung in view of Song to apply MaxN video counting technique as taught by Schobernd in order to provide a reliable livestock count estimate and reducing errors associated with individual animals being absent, obscured, or otherwise not recognized in particular frames and avoiding repeated counting across frames.
Regarding claim 14: the claim limitations are similar to those of claim 3; therefore, rejected in the same manner as applied above.
Claim(s) 5 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Myung in view of Song as applied in claim 2, and further in view of Itoh et al. (US 20010041647 A1, “Itoh”).
Regarding claim 5:
Myung in view of Song does not specifically teach: a number of the livestock is calculated based on an image in which the most livestock are recognized among the multiple images.
However, in a related field, Itoh teaches: the movement distance is calculated from continuous images based on position information of the livestock to which a same tracking identifier is assigned (Itoh teaches determining motion to measure number of steps or a distance covered. See ¶25. And receiving data including weight. See ¶21, and further traches calculating an amount of energy consumed by basal metabolism based on the motion data and weight. See ¶26. Itoh further teaches calculating an amount of energy consumed by living activities based on the measures motion and weight. See¶26. See also ¶28 describing an amount of exercise and amount of daily motion)
Therefore, it would have been obvious to a person of ordinary skill in the art prior to the effective filing date of the claimed invention to have modified Myung in view of Song to apply the known metabolic and activity calculation as taught by Itoh to the livestock monitoring system to provide additional physiological activity information concerning the monitored livestock.
Regarding claim 16: the claim limitations are similar to those of claim 5; therefore, rejected in the same manner as applied above.
Claim(s) 11 and 22 are rejected under 35 U.S.C. 103 as being unpatentable over Myung in view of Song as applied in claim 2, and further in view of Yasuhiko (the machine English translation of JP 2021111069 A).
Regarding claim 11:
Myung in view of Song does not specifically teach: wherein multiple pieces of the image data are calculated being recorded for a preset time and at a predetermined interval, and statistical values of the meal amount, the weight, and the feed conversion ratio of the livestock calculated for each of the multiple pieces of the image data are calculated..
However, in a related field, Yasuhiko teaches: wherein multiple pieces of the image data are calculated being recorded for a preset time and at a predetermined interval (Yasuhiko teaches that imaging device randomly images a plurality of livestock existing in this area. Imaging can be done several to hundreds of times a day. The computer estimates the average weight of multiple livestock. See ¶26), and
statistical values of the meal amount, the weight, and the feed conversion ratio of the livestock calculated for each of the multiple pieces of the image data are calculated. (Yasuhiko teaches as shown in FIG. 9, in this example, the lot identification number, the average body weight, the average weight gain, the average feed consumption, the feed conversion ratio, the average water supply, and the number of animals raised are calculated and displayed. See ¶¶26-35 and 61-63).
Therefore, it would have been obvious to a person of ordinary skill in the art prior to the effective filing date of the claimed invention to have modified Myung in view of Song to periodically acquire and process livestock image data to calculate statistical values in order to monitor livestock growth and feeding efficiency over time and timely identify changes in breeding condition.
Regarding claim 22: the claim limitations are similar to those of claim 11; therefore, rejected in the same manner as applied above.
Allowable Subject Matter
Claims 8, 10, 19 and 21 objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. US 20240224947 teaches characterization of livestock feeding function including networked cameras are configured to capture images of a livestock feed area and machine-vision logic circuitry characterizes, based on the captured images, an amount of available feed and the presence of livestock in the livestock feed area depicted in the captured image over time. Feed-control logic circuitry may assign time-based condition values each respective feed area characterized by the cameras based on the characterized amount of available feed and the characterized presence of livestock provided via the machine-vision logic circuitry.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to WASSIM MAHROUKA whose telephone number is (571)272-2945. The examiner can normally be reached Monday-Thursday 8:00-5:00 EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Stephen Koziol can be reached at (408) 918-7630. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/WASSIM MAHROUKA/Primary Examiner, Art Unit 2665
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