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 Status
Claims 1-4 are pending for examination in the application filed 11/08/2023.
Priority
Acknowledgement is made of Applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent application CN202210449548.9 filed on 04/26/2022. Acknowledgement is additionally made of the present application as a national stage entry of PCT/CN2022/109950, international filing date: 08/03/2022.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 11/08/2023 has been considered by the examiner.
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 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 and 4 are rejected under 35 U.S.C. 103 as being unpatentable over Kotwaliwale (Kotwaliwale, Nachiket, et al. "Non-destructive quality determination of pecans using soft X-rays." Postharvest Biology and Technology 45.3 (2007): 372-380) in view of van Dael (van Dael, Mattias, et al. "A segmentation and classification algorithm for online detection of internal disorders in citrus using X-ray radiographs." Postharvest Biology and Technology 112 (2016): 205-214) and Susanto (Susanto, S., D. Hermansah, and F. Amanda. "The growth and quality of fruit of three pummelo (Citrus maxima (Burn.) Merr.) accessions." IOP Conference Series: Earth and Environmental Science. Vol. 196. No. 1. IOP Publishing, 2018).
Regarding claim 1, Kotwaliwale teaches a method for detecting a fruit flesh mass edible ratio based on X-ray images ([pg. 373 para. 3-4] In pecans, the radiography technique can determine the extent of internal damage, and also estimate the volume and weight of nutmeat. In this study we have determined appropriate imaging parameters for pecan radiographs and assessed whole nut quality on the basis of nut-fill and the quality and quantity of nutmeat), wherein the method comprises following steps:
1) image acquisition: acquiring, through an X-ray image acquisition apparatus, original X- ray images A of a fruit in two directions of a vertical direction and a transversal direction ([pg. 375 para. 1] X-ray images of fabricated pecan samples were acquired at eight levels of X-ray tube voltage (15–50 kVp in steps of 5 kVp), five levels of current (0.1, 0.25, 0.5, 0.75, and 1.0 mA), and two orientations of nut (plane joining two pecan cotyledons, horizontal or vertical). In total, 800 images were acquired);
2) image enhancement: performing grayscale stretching on the acquired original X-ray images A in the two directions of the vertical direction and the transversal direction, and obtaining enhanced X-ray images B in the two directions of the vertical direction and the transversal direction ([Abstract] Defects and insects were clearly differentiated in X-ray images after applying contrast stretching or high-frequency emphasis techniques);
3) two-dimensional (2D) flesh mass acquisition: processing both the vertical and transversal enhanced X-ray images B and respectively obtaining a vertical 2D flesh mass and a transversal 2D flesh mass ([pg. 378 para. 2] Volume of nutmeat was estimated for each pecan radiographed at two orientations. Weight was estimated by multiplying the volume by the mean nutmeat density of 950 kg/m3);
4) based on the vertical 2D flesh mass and the transversal 2D flesh mass, building a prediction model of an actual fruit flesh mass, inputting a vertical 2D flesh mass and a transversal 2D flesh mass of a fruit to be actually measured into the prediction model, and obtaining a predicted flesh mass ([pg. 379 para. 4] Imaging performance at these conditions was confirmed by capturing strong-contrast images for 30 pecans with unknown internal conditions. [pg. 376 para. 4] X-ray images of fabricated pecans were analyzed without modifying the image contrast. Shape and size of nutmeat inside the shell, physical damage to shell and nutmeat, absence of nutmeat, and weevil presence were distinctly visible in the images. Threshold values for different quality features were determined on the images of ‘fabricated samples’ and then these values were used on images of 30 ‘test samples’);
5) weighing a fruit mass of the entire fruit next (See Table 1: Weight of Nutmeat (g) and Nut (g)).
Kotwaliwale does not teach performing noise reduction on the acquired original X-ray images A in the two directions of the vertical direction and the transversal direction, and obtaining enhanced X-ray images B in the two directions of the vertical direction and the transversal direction.
van Dael, in the same field of endeavor of x-ray fruit image analysis, teaches performing noise reduction on the acquired original X-ray images A in the two directions of the vertical direction and the transversal direction, and obtaining enhanced X-ray images B in the two directions of the vertical direction and the transversal direction (See Fig. 1 [pg. 207 para. 2] The aim of this article was to develop a robust and fast image processing and classification algorithm to detect granulation in oranges and endoxerosis in lemons using X-ray radiographs. To this end, the radiographs will be segmented first using an automatic threshold. In a second stage, several features are derived from the segmented images. [pg. 206 para. 4] the reconstruction algorithm intrinsically removes noise).
Therefore, it would have been obvious to a person of ordinary skill in the art at the time that the invention was made to modify the method of Kotwaliwale with the teachings of van Dael to remove noise because "Noise reduced the classification accuracy and for oranges, the difference is 1.3% for the naive Bayesian classifier and 3.8% for the kNN classifier" [van Dael pg. 212 para. 4].
Kotwaliwale does not teach the fruit is a pomelo and calculating, combined with the fruit mass of the entire pomelo, a pomelo fruit mass edible ratio: pomelo fruit mass edible ratio = predicted flesh mass/fruit mass of the entire pomelo.
Susanto, in the same field of endeavor of pomelo edible mass determination, teaches the fruit is a pomelo and calculating, combined with the fruit mass of the entire pomelo, a pomelo fruit mass edible ratio: pomelo fruit mass edible ratio = predicted flesh mass/fruit mass of the entire pomelo ([pg. 2 para. 6] Edible part was obtained from the ratio of fruit flesh weight (g) to fruit weight (g). [pg. 2 para. 2] The plant materials used in this research is 4-year-old of three different accessions of pummelo cultivated with spacing of 4 m x 3 m).
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Therefore, it would have been obvious to a person of ordinary skill in the art at the time that the invention was made to modify the method of Kotwaliwale with the teachings of Susanto to calculate the pomelo fruit edible mass because "Efforts in development of pummelo plantation are directed at the availability of superior cultivars that produce adequate flowering to ensure high productivity and fruit quality. Superior cultivars can be obtained through germplasm selection, crossing and biotechnology utilization. Identification of differences among pummelo accessions can be seen based on the characteristics of the fruit, including size and shape of the fruit, seed number, color and texture of flavedo (epicarp), thickness and color of albedo (mesocarp), color and flavor fruit flesh, and aroma of essential oil" [Susanto pg. 2 para. 1].
Regarding claim 2, Kotwaliwale, van Dael, and Susanto teach the method of claim 1. Kotwaliwale further teaches wherein in step 3), threshold segmentation is performed on both the vertical and transversal enhanced X-ray images B, predetermined thresholds are used to segment each image into three parts of a background area, a peel area, and a flesh area ([pg. 374 para. 4] Preprocessing was performed to segment the region of interest (ROI), i.e., pecan samples, from the image background…Ideally, all pixels not representing the sample should have a value of 1.0, but to account for variation in detector response, a threshold of 0.9 was used to segment the ROI from background. A binary matrix (L) of the size of the image was thus obtained wherein elements representing the sample pixel had a value of ‘one’ while those representing background had ‘zero’. The morphological “open” operator was applied to L, and the resulting binary matrix was multiplied element-wise with the sample image matrix. In this the intensity value of all background pixels was zero, whereas all ROI pixels had the same intensity value as assigned by the camera. [pg. 375 para. 2] Two regions of interest (ROI) were segmented for each image: (1) region that excludes the shell and central separator, thus representing nutmeat and air gap (ROI-1) and (2) region that represents nutmeat only (ROI-2)), specific image processing is performed on the extracted flesh area to obtain a 2D flesh mass, and 2D flesh masses obtained corresponding to the vertical and transversal enhanced X-ray images B are respectively treated as the vertical 2D flesh mass and the transversal 2D flesh mass ([pg. 378 para. 2] Volume of nutmeat was estimated for each pecan radiographed at two orientations. Weight was estimated by multiplying the volume by the mean nutmeat density of 950 kg/m3…In Orientation-2, this woody separator was parallel to the image plane and could not be segmented from the nutmeat portion. Its presence reduced the pixel intensity in the ROI and thus increased the thickness estimates. Also, the woody separator made the task of segmenting nutmeat from shell and air gaps difficult and may have led to over sizing the segmented area).
Regarding claim 4, Kotwaliwale, van Dael, and Susanto teach the method of claim 1. Kotwaliwale further teaches wherein after step 4), different grading ranges are set for the fruit flesh mass in the method, and a flesh mass grade of the fruit is determined according to the obtained predicted flesh mass ([pg. 372 para. 2] Pecan quality evaluation is important for establishing price at various stages in marketing. Size, weight, density, kernel color, appearance, and physical and pathological damage are the major factors influencing pecan quality. [pg. 379 para. 2] Estimate of weights, correlation between true and estimated weights, and means of deviation of estimates from their corresponding actual values for the test samples were calculated… If the samples accepted on percent area occupancy and mean pixel intensity were considered separately, the nutmeat weight estimation error was comparable to that calculated for the fabricated samples. [pg. 377 para. 2] Some samples, which were considered for rejection due to shuck adherence to the shell or a small insect hole had enough nutmeat inside to be accepted as good).
Kotwaliwale does not teach the fruit is a pomelo.
Susanto teaches the fruit is a pomelo ([pg. 2 para. 2] The plant materials used in this research is 4-year-old of three different accessions of pummelo cultivated with spacing of 4 m x 3 m).
Therefore, it would have been obvious to a person of ordinary skill in the art at the time that the invention was made to modify the method of Kotwaliwale with the teachings of Susanto to evaluate pomelos because "Pummelo grows well in the tropics so it is potential to be developed in various area in Indonesia. The other advantage of the pummelo as having a bigger size, a fresh taste, and longer shelf life up to 4 months. However, pummelo development area is still very limited. Nationally, pummelo production is still low in 2014 and 2015 reached 141,296 tons and 111,753 tons, respectively, or around 5% of national citrus production" [Susanto pg. 1 para. 2].
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Kotwaliwale in view of van Dael, Susanto, and Han (CN1719238A).
Regarding claim 3, Kotwaliwale, van Dael, and Susanto teach the method of claim 1. Kotwaliwale further teaches wherein in step 3), a grayscale value Gi of each pixel in the flesh area is calculated ([pg. 377 para. 3] Mean and standard deviation of pixel intensity for the area inside the shell (ROI-1) for all the test pecan samples were determined. If threshold values identified for fabricated samples are applied, samples 4, 5, 11 through 19, and 27 can be identified as pecans with less than desired amount of nutmeat (Fig. 6). A threshold value of 1500 gray-level was used at 30 kVp 1 mA as shown in Fig. 6).
Kotwaliwale does not teach the fruit is a pomelo.
Susanto teaches the fruit is a pomelo ([pg. 2 para. 2] The plant materials used in this research is 4-year-old of three different accessions of pummelo cultivated with spacing of 4 m x 3 m).
Therefore, it would have been obvious to a person of ordinary skill in the art at the time that the invention was made to modify the method of Kotwaliwale with the teachings of Susanto to evaluate pomelos because "Pummelo grows well in the tropics so it is potential to be developed in various area in Indonesia. The other advantage of the pummelo as having a bigger size, a fresh taste, and longer shelf life up to 4 months. However, pummelo development area is still very limited. Nationally, pummelo production is still low in 2014 and 2015 reached 141,296 tons and 111,753 tons, respectively, or around 5% of national citrus production" [Susanto pg. 1 para. 2].
Kotwaliwale does not teach a grayscale value Gi of each pixel in the flesh area is calculated as a sum of logarithms with e as a base as a 2D flesh mass of the pomelo, and the calculation formula is as follows;
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wherein Gi represents the grayscale value of the ith pixel in the flesh area, and n represents the total number of pixels in the flesh area.
Han, in the same field of endeavor of imaging fruit quality determination, teaches a grayscale value Gi of each pixel in the flesh area is calculated as a sum of logarithms with e as a base as a 2D flesh mass of the fruit, and the calculation formula is as follows;
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wherein Gi represents the grayscale value of the ith pixel in the flesh area, and n represents the total number of pixels in the flesh area.
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Therefore, it would have been obvious to a person of ordinary skill in the art at the time that the invention was made to modify the method of Kotwaliwale with the teachings of Han to determine the grayscale value of each pixel in the flesh area for "determining the grade of the apple being tested according to the range of the honey index" [Han pg. 4].
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Zhang (Zhang, Chenghui, Wang, Qun. “Detection of Internal Quality of Rambutan Fruit by Using X-ray Imaging”, Chinese Journal of Tropical Crops, March 2005, pp.103-108) teaches image analysis of x-rays of fruit to determine the edible portion of fruit and internal quality.
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/JACQUELINE R ZAK/Examiner, Art Unit 2666
/EMILY C TERRELL/Supervisory Patent Examiner, Art Unit 2666