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 .
Prior arts cited in this office action:
Kim (US 20220222844 A1, hereinafter “Kim”)
Mohan et al. (US 6005959 A, hereinafter “Mohan”)
Deng et al. (CN 115294085 A, hereinafter “Deng”)
Liu et al. (CN 107945156 A, hereinafter “Liu”)
Sofue et al. (JP 2013146029 A, hereinafter “Sofue”)
Heusch et al. (WO 2014198315 A1, hereinafter “Heusch”)
Yang et al. (CN 115177015 A, hereinafter “Yang”)
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.
Claims 1-2, 13, 15-16 are rejected under 35 U.S.C. 103 as being unpatentable over Kim (US 20220222844 A1, hereinafter “Kim”) and in view of Mohan et al. (US 6005959 A, hereinafter “Mohan”).
Regarding claim 1:
Kim teaches a computer-implemented method of measuring throughput of a food
processing system (Kim [0003], where Kim teaches Even in places where food is served to multiple people, such as schools, companies, the military, government offices, hospitals, by measuring the amount of food served and the amount of leftover through measurement of the amount of food served and distributed to people, there are many advantages such as being able to conduct efficient food distribution in anticipation of the amount of demand and supply and to manage the calories of those who are served of the food), the method comprising:
capturing, by a computing system, an image of a portion of the food processing
system configured to carry one or more food product items (Kim [0048], where Kim teaches in some embodiments, when photographing the tableware 500 moving along a conveyor belt 270, the photographing unit 250 of the device for measuring food 200 may acquire depth data in addition to image data, even if it includes a single camera);
determining, by the computing system, a set of pixels of the image that depict one
or more food product items (Kim [0095]-[0097], where Kim teaches a calculation unit 140 calculates a volume of each food by using height information for each pixel (i.e., 3D distance data) of the extracted food image data at step S50.);
determining, by the computing system, an image area (Kim [0107],-[0108], [0138], [0215], where Kim teaches it may be possible to calculate volume of area where food (e.g., boiled rice) located on the lower side is covered by food (e.g., a fried egg) located on the upper side by calculating the area where the food (e.g., boiled rice) located on the lower side is covered by the food (e.g., a fried egg) located on the upper side and calculating a height of the area covered by the food (e.g., a fried egg) located on the upper side using the height of the area not covered by the food (e.g., a fried egg) located on the upper side among food (e.g., boiled rice) located on the lower side);;
determining, by the computing system, a total product weight based on the image area (Kim [0114], where Kim teaches the calculation unit 140 calculates meal information including the weight of each food by using the volume information and food information of each food calculated in the step S50 (step S60); and
determining, by the computing system, a throughput weight of the food processing
system based on the total product weight (Kim [0122], where Kim teaches In an embodiment, a restaurant may have a weighing device (not shown) capable of measuring the weight of the tableware 500, but it is not limited thereto, and it may further include a step that the receiving unit 110 receives the weight information of the tableware 500 measured from the weighing device (not shown) provided in the restaurant, and a step that the correction unit 150 corrects the weight of each food by matching the total weight of each food and the weight of the empty tableware calculated with the received weight information).
Kim fails to explicitly teach wherein determining, by the computing system, a pixel count of the set of pixels.
Although, the area determined can be considered as the determined pixel count of the set of pixels, for the sake of completeness one can turn to Mohan. Mohan teaches Segmenting refers to identifying those image pixels that are contained in the image of the object versus those that belong to the image of the background. The segmented object image is then the collection of pixels that comprise the object in the original image of the complete scene. The area of a segmented object image is the number of pixels in the object image (Mohan col. 2 lines 6-14, 31-48).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date, to count the number of pixels in a particular area that represent the area in the image a food is located and use that information to determine food related features such as weight.
Regarding claim 2:
Kim in view of Mohan teaches wherein the image is a two- dimensional image (Kim [0014]).
Regarding claim 13:
Kim in view of Mohan teaches further comprising:
separating pixels associated with the one or more food product items into individual
food product item areas;
determining pixel counts for the individual food product item areas; and
determining a product type based on the pixel counts for the individual food product
item areas (Kim [0135]; Mohan col. 2 lines 6-14, 31-48).
Regarding claim 15:
Kim teaches a food processing system, comprising:
a first conveyor portion configured to carry food product items; a digital camera positioned to capture images of the first conveyor portion (Kim [0048], where Kim teaches In some embodiments, when photographing the tableware 500 moving along a conveyor belt 270, the photographing unit 250 of the device for measuring food 200 may acquire depth data in addition to image data, even if it includes a single camera);
a computing system communicatively coupled to the digital camera and configured
to perform actions to determine a throughput weight of the food product items on the first
conveyor portion (Kim [0003], where Kim teaches Even in places where food is served to multiple people, such as schools, companies, the military, government offices, hospitals, by measuring the amount of food served and the amount of leftover through measurement of the amount of food served and distributed to people, there are many advantages such as being able to conduct efficient food distribution in anticipation of the amount of demand and supply and to manage the calories of those who are served of the food),, the actions comprising:
capturing, by the computing system using the digital camera, an image of
the first conveyor portion (Kim [0048], where Kim teaches in some embodiments, when photographing the tableware 500 moving along a conveyor belt 270, the photographing unit 250 of the device for measuring food 200 may acquire depth data in addition to image data, even if it includes a single camera);
determining, by the computing system, a set of pixels of the image that depict one or more food product items (Kim [0095]-[0097], where Kim teaches a calculation unit 140 calculates a volume of each food by using height information for each pixel (i.e., 3D distance data) of the extracted food image data at step S50.);
determining, by the computing system, a total product weight based on the
pixel count; and determining, by the computing system, a throughput weight of the food processing system based on the total product weight (Kim [0122], where Kim teaches In an embodiment, a restaurant may have a weighing device (not shown) capable of measuring the weight of the tableware 500, but it is not limited thereto, and it may further include a step that the receiving unit 110 receives the weight information of the tableware 500 measured from the weighing device (not shown) provided in the restaurant, and a step that the correction unit 150 corrects the weight of each food by matching the total weight of each food and the weight of the empty tableware calculated with the received weight information).
Kim fails to explicitly teach wherein determining, by the computing system, a pixel count of the set of pixels.
Although, the area determined can be considered as the determined pixel count of the set of pixels, for the sake of completeness one can turn to Mohan. Mohan teaches Segmenting refers to identifying those image pixels that are contained in the image of the object versus those that belong to the image of the background. The segmented object image is then the collection of pixels that comprise the object in the original image of the complete scene. The area of a segmented object image is the number of pixels in the object image (Mohan col. 2 lines 6-14, 31-48).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date, to count the number of pixels in a particular area that represent the area in the image a food is located and use that information to determine food related features such as weight.
Regarding claim 16:
Kim in view of Mohan teaches wherein the digital camera is positioned at an angle between zero degrees and forty-five degrees from a surface normal of the first conveyor portion (Kim [0048]; Mohan fig. 1 element 120).
Claims 3 and 4 are rejected under 35 U.S.C. 103 as being unpatentable over Kim (US 20220222844 A1, hereinafter “Kim”) and in view of Mohan et al. (US 6005959 A, hereinafter “Mohan”) and in view of Deng et al. (CN 115294085 A, hereinafter “Deng”).
Regarding claim 3:
Kim in view of Mohan fails to explicitly teach wherein determining the set
of pixels of the image that depict one or more food product items includes:
determining pixels having pixel values in a channel of a color space that satisfy a
threshold associated with the food product items.
However, Den teaches dividing the detected fruit based on pixel point, expanding the detected fruit original boundary frame size to one time of the original, so as to ensure the detection frame can cover the complete apple area; converting the cut detection area target image to CIELAB colour space, and using OTSU maximum inter-class variance method to separate apple outline in the L* channel. based on the fixed colour threshold of the a * channel and b * channel to remove the remaining leaves and branches and other related pixels, at last, expanding the divided image based on the morphological method, corrosion operation to obtain the complete fruit area. The specific flow of the fruit diameter estimation is as shown in the algorithm 1 (Deng [0049]-[0050]).
Therefore, taking the teachings of Kim, Mohan, and Deng as a whole, it would have been obvious to one of ordinary skill in the art before the effective filing date of the application to analyze the pixels color and by performing morphological processing on them to determine what object they represent, for example, any food or object. This technique is well-known in the art for object identification and classification and has been shown to provide good and predictable results
Regarding claim 4:
Kim in view of Mohan and in view of Deng teaches wherein determining the set of pixels of the image that depict one or more food product items includes performing a morphological closing operation on the channel of the color space (Deng [0049]-[0050]).
Claims 5-7 and 10-11 are rejected under 35 U.S.C. 103 as being unpatentable over Kim (US 20220222844 A1, hereinafter “Kim”) in view of Mohan et al. (US 6005959 A, hereinafter “Mohan”), in view of Deng et al. (CN 115294085 A, hereinafter “Deng”) and in view of Liu et al. (CN 107945156 A, hereinafter “Liu”).
Regarding claim 5:
Kim in view of Mohan and in view of Deng fails to teach further comprising: determining, by the computing system, whether a quality of the image is adequate for further processing; and
in response to determining that the quality of the image is not adequate for further
processing, discarding the image for use in determining the throughput.
However, Liu teaches By using said technical solution, capable of evaluating the image quality visually by the mark number, mark number if more than mark number threshold, it indicates that the digital image more blurred region, bad quality and does not need to be marked in the image quality checking, directly discarding; is not suitable for slicing a pathological diagnosis, re-scanning the pathological slice, if the mark number not exceed the mark number threshold, the marked image can be quality checked, if marked good in image quality, it can be used for slicing a pathological diagnosis; otherwise, if the image quality is bad, then it can be discarded, not for slice pathological diagnosis, re-scanning the pathological slice (Liu [0067], [0072]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the application to evaluate the quality of the image to determine if the quality is good enough for further processing and discard the image if it is determined the quality is not good enough for further processing. In doing so, help improve the system by avoiding allocating resources to process images that would not provide good result desired and only process image that are good enough to determine the feature desired.
Regarding claim 6:
Kim in view of Mohan in view of Deng and in view of Liu teaches wherein determining whether the quality of the image is adequate for further processing includes determining whether fog is present in the image (Liu [0067], [0072], where the blur can be considered fog as well).
Regarding claim 7:
Kim in view of Mohan in view of Deng and in view of Liu teaches wherein determining whether the quality of the image is adequate for further processing includes determining whether a foreign object is present in the image; and
wherein determining whether the foreign object is present in the image includes
detecting pixels having pixel values in a second channel of a second color space that satisfy
a second threshold associated with the foreign object (Kim [0113], [0120]-[0122]; Liu [0067], [0072]; Deng [0049]-[0050]).
Regarding claim 10:
Kim in view of Mohan in view of Deng and in view of Liu teaches wherein the portion of the food processing system configured to carry one or more food product items includes a portion of a food processing device configured to receive food product items from a conveyor and process the food product items according to a cyclical process;
wherein capturing the image includes capturing the image at a predetermined point
of the cyclical process; and
wherein determining the throughput of the food processing system based on the
total product weight includes determining the throughput based on the total product weight
and a length of the cyclical process (Kim 0048], [0095]-[0122]; Liu [0067], [0072]; Deng [0049]-[0050]).
Regarding claim 11:
Kim in view of Mohan in view of Deng and in view of Liu teaches wherein the portion of the food processing device configured to receive food product items from the conveyor and process the food product items according to the cyclical process includes one or more extractor cups of an extractor configured to process fruit according to a stroke cycle (Kim 0048], [0095]-[0122]; Liu [0067], [0072]; Deng [0049]-[0050]).
Claims 8 is rejected under 35 U.S.C. 103 as being unpatentable over Kim (US 20220222844 A1, hereinafter “Kim”) in view of Mohan et al. (US 6005959 A, hereinafter “Mohan”), in view of Deng et al. (CN 115294085 A, hereinafter “Deng”) and in view of Liu et al. (CN 107945156 A, hereinafter “Liu”), in view of Sofue et al. (JP 2013146029 A, hereinafter “Sofue”).
Regarding claim 8:
Kim in view of Mohan, and in view of Deng fails to teach wherein determining whether the quality of the image is adequate for further processing includes using pattern matching to determine whether a conveyor is present in the image.
However, Sofue teaches in the case of snow or heavy rain, snow and water drops are attached to the lens of the camera, the image quality of the image captured by the camera is reduced, image recognition (for example, pattern matching) the accuracy is reduced (Sofue [0011]-[0015]). In other words, the object detection using pattern matching is directly related to the quality of the image.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the application to determine whether pattern matching can be perform on the image to detect an object such as the conveyer belt, in order to determine whether the image is good enough to detect the food on the conveyer belt, since the belt is known in advance and does not change much thereby making it easy to perform the matching and if the matching cannot be performed on the conveyer belt it is most likely not going to be able to detect the food on it.
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Kim (US 20220222844 A1, hereinafter “Kim”) in view of Mohan et al. (US 6005959 A, hereinafter “Mohan”) and in view of Heusch et al. (WO 2014198315 A1, hereinafter “Heusch”).
Regarding claim 14:
Kim in view of Mohan fails to explicitly teach wherein separating the pixels associated with the one or more food product items into individual food product item areas includes using a watershed segmentation technique.
However, Heusch teaches an object 10 may be a product of a manufacturing process such as consumer products, food goods, beverage packs, cans and bottles, cigarette packages and other tobacco products, and the like. The objects are conveyed by a conveyor line 20 or any other suitable transporting means along the production line. The conveyor line may be in the form of a belt or any other suitable form for conveying objects. Another method to select interest points is the Maximally Stable Extremal Regions (MSER) approach, in which regions are extracted from an image with a watershed- like segmentation algorithm (see for example J. Matas , 0. Chum, M. Urban and T. Pajdla, "Robust Wide Baseline Stereo From Maximally Stable Extremal Regions" , British Machine Vision Conference , 2002) . Basically, it consists of growing regions within an intensity range . The method hence extracts homogeneous intensity regions which are stable over a wide range of thresholds. Since this algorithm retrieves regions and not interest points directly, the keypoints are extracted as the barycenter of each region (Heusch page 7 first paragraph, page 12 first full paragraph).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the application to segment each type of food using watershed technique since the watershed algorithm is a well-known technique for performing segmentation and when apply to food product provide predictable and good results.
Claims 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Kim (US 20220222844 A1, hereinafter “Kim”) in view of Mohan et al. (US 6005959 A, hereinafter “Mohan”) and in view of Yang et al. (CN 115177015 A, hereinafter “Yang”).
Regarding claim 17:
Kim in view of Mohan fails to explicitly teach wherein the first conveyor portion is configured to carry the food product items into a food processing device.
However, Yang teaches as shown in FIG. 1 to FIG. 6,the embodiment of the invention claims a food processing slurry coating powder device comprises a machine shell 10, a sizing unit 20 and a powder coating unit 30.
The sizing unit 20 for food sizing, the sizing unit 20 is detachably set on the shell 10, powder coating unit 30 for food coating powder, coating powder unit 30 located at the downstream end of the sizing unit 20, powder coating unit 30 set in the shell 10, coating powder unit 30 comprises a driving wheel 31, a mounting plate 32, a turning frame 33. rotating shaft 35 and transmission 34, mounting plate 32 and rotating shaft 35 number are two, two mounting plate 32 set on the upper slurry unit 20 downstream of the two sides, two mounting plate 32 of the bottom is fixedly set on the shell 10, turning frame 33 through rotating shaft 35 is rotatably set on the two mounting plate 32 the top end, any rotating shaft 35 passes through the mounting plate 32 and is fixedly connected with the driving wheel 31 (Yang [0018]-[0025], figs 1-6).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the application to have the first conveyor portion configured to carry the food product items into a food processing device, as taught by Yang, in order to avoid manually inputting food into the food processing device, such that safety and efficiency might be increased.
Regarding claim 18:
Kim in view of Mohan and in view of Yang teaches further wherein the food processing device is an oven, a freezer, or a portioner (Yang [0018]-[0025], where the frying device can be considered a frying oven).
Regarding claim 19:
Kim in view of Mohan and in view of Yang teaches further comprising:
a second conveyor portion configured to carry the food product items out of the
food processing device; and
a second camera positioned to capture images of the second conveyor portion;
wherein the computing system is further configured to:
determine a throughput weight of the food product items on the second
conveyor portion; and
compare the throughput weight of the food product items on the second
conveyor portion to the throughput weight of the food product items on the first conveyor
portion to determine a yield of the food processing device ((Kim [0095]-[0097]; Yang [0018]-[0025], fig. 1-6, where the same technique for measuring the food when coming in can be apply to the food as going out and the comparison of the results can be performed)
Regarding claim 20:
The combination above fails to teach wherein the first conveyor portion
is a return conveyor; and
wherein the computing system is further configured to:
compare the throughput weight of the food product items to a desired throughput range;
transmit a command to cause the amount of food product items provided to the first conveyor portion to be increased in response to determining that the throughput weight is below the desired throughput range; and
transmit a command to cause the amount of food product items provided to the first conveyor portion to be decreased in response to determining that the throughput weight is above the desired throughput range.
However, Kim teaches in recent years, as more people want to maintain a healthy diet such as well-being and diet, the demand for food measuring technology is increasing.
Even in places where food is served to multiple people, such as schools, companies, the military, government offices, hospitals, by measuring the amount of food served and the amount of leftover through measurement of the amount of food served and distributed to people, there are many advantages such as being able to conduct efficient food distribution in anticipation of the amount of demand and supply and to manage the calories of those who are served of the food (Kim [0002]-[0003], [0167]-[0172]).
Therefore, taking the teachings of Kim. Mohan and Yang as a whole, it would have been obvious to one of ordinary skill in the art before the effective filing date of the application to control the amount of food distributed, coming in and/or going out of the conveyer belt, by increasing or decreasing the among of food as needed, in order to avoid waste and be more efficient.
Allowable Subject Matter
Claims 8 is 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
Any inquiry concerning this communication or earlier communications from the examiner should be directed to WEDNEL CADEAU whose telephone number is (571)270-7843. The examiner can normally be reached Mon-Fri 9:00-5:00.
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/WEDNEL CADEAU/Primary Examiner, Art Unit 2632 September 4, 2026
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