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 .
Applicant’s response to the last Office Action, filed 9/16/2025, has been entered and made of record.Applicant has amended claims 1,3-4. Claims 1,3-7 are currently pending.Applicants arguments filed 12/09/2025 have been fully considered but they are not persuasive.Applicant argues that the determination unit determines the amount of feces based on a surface area that is calculated from a length of feces in a dropping direction thereof on the feces image and a width of feces in a direction that intersects with the dropping direction thereof on the feces image, and a property of feces."
Kashyap, on the other hand, describes estimating a volume of stool (and/or urine) based on changes in water level and the flow rate. Ueda describes estimating a volume of stool by approximating as a volume of a truncated cone using line data and the height of the stool falling per time interval, and integrating the volume. Additionally applicant argues that the cited references both fail to teach or suggest at least a determination of an amount of feces based on the two-dimensional surface area calculated from the width and the length of the feces in the image.
In response, Ueda teaches “The determination unit 30 determines the nature of the stool based on the line data Ln that is the detection result detected by the detection unit 20. Specifically, the determination unit 30 extracts the feature amount of the stool to be detected from the obtained line data Ln. The feature amount to be extracted is specifically the number of pixels corresponding to stool in each line data Ln, the number of frames in which stool is detected, the number of high-luminance areas existing in one stool area, and the like. . These feature amounts serve as indices representing the width (diameter), length, and surface irregularities of the stool, respectively. And the determination part 30 determines the property of a stool based on these feature-values, figure 4) Therefore , Ueda teaches determination of an amount of feces based on the indices(paragraph [0024];[0029]). Additionally, Ueda teaches the stool drop is detected by the detection unit 20 until it is no longer detected. In the case of FIG. 6, stool starts to be detected in the line data Ln + 2, and no stool is detected in the line data Ln + 6. That is, in the case of FIG. 6, the line data Ln + 2 to the line data Ln + 5 are regarded as one bowel movement, and the characteristics of the stool are determined. In addition, each line data Ln is appropriately subjected to processing for clarifying a region where stool is detected, such as binarization processing and expansion / contraction processing. All remaining arguments are reliant on the aforementioned and addressed arguments and thus are considered to be wholly addressed herein.
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.
Claim(s) 1-7 are rejected under 35 U.S.C. 103 as being unpatentable over Kashyap et al ( 2018/0303466) in view of Ueda et al (JP 2018/0146244)
As to claim 1, Kashyap et al teaches the information processing system having:
a detection unit that has a sensor that is installed on a toilet (FIG. 1 illustrates an exemplary system of the present invention that consists of a toilet 200 where various sensors are integrated into a toilet seat, paragraph [0031]) where a bowl part that receives excrement is formed where a plurality of elements are linearly arranged to detect dropping feces (FIG. 15E shows the workflow for estimating the urine and stool voiding volume of a person, paragraph [0106]);
a feces image acquisition unit that acquires a feces image ( FIGS. 15A-15D are block diagrams showing exemplary image processing and classification methods for processing data from the devices of the present invention. FIG. 15A shows exemplary image pre-processing tasks; FIG. 15B shows one image classification method for classifying stool consistency; FIG. 15C shows one image classification method for detecting colors in the excreta; FIG. 15D is a set of labels for stool and urine classification; FIG. 15E shows the workflow for estimating the urine voiding volume of a person) that is based on information that is acquired in time series by the detection unit (The sum of the inter-frame capture time of pairs of frames that exhibit motion is factored with the constant flow rate to model the voiding volume. Motion sensing approaches that pertain to this are background subtraction based on sum-of-absolute differences, motion sensing based on background subtraction of averages, and motion sensing based on background subtraction Gaussian Mixture models. Approaches that do not rely on a background model can also be used, such as thresholding a sum-of-absolute differences of pairs of frames, paragraph [0106]]); and a determination unit that determines an amount of feces from the feces image (FIG. 15B is an image classification method for determining stool consistency. In embodiments where the image is captured in color, the first step is to convert the captured color image into a grayscale image. The gradient magnitude of the image is then computed using an operator such as Sobel-Feldman Operator. The gradient magnitudes are binned into a histogram of a fixed step size. Each image is encoded as features as a quantized histogram of gradients. These features are then fed into a pre-trained classifier, such as a support-vector machine (SVM), that classifies the feature into a label according to the Bristol stool scale, or other similar clinically accepted scales known in the art, paragraph [0104]).While Kashyap et al. teaches the limitation above, Kashyap et al. fails to teach”
wherein the determination unit determines the amount of feces based on a surface area that is calculated from a length of feces in a dropping direction thereof on the feces image and a width of feces in a direction that intersects with the dropping direction thereof on the feces image, and a property of feces.”
However, Ueda teaches in FIG. 4, the sensor 21 is arranged in the toilet seat 41 so as to face the inner side of the toilet bowl 11. The sensor 21 detects a falling stool excreted in the toilet bowl 11 provided on the lower surface of the toilet seat 41. As shown in FIGS. 3 and 4, the sensor 21 of the first embodiment is disposed at a position corresponding to the left side of the rear side of the toilet bowl 11 in a plan view. Moreover, the sensor 21 is arrange in a positioned by making the detection direction into diagonally downward. The sensor 21 detects the excretion during the fall, that is, from the time it is discharged outside the body until it reaches the water surface of the water reservoir 12, with the upper side of the water reservoir 12 as the detection range. Additionally, Ueda teaches a detection unit that includes a sensor in which a plurality of detection elements are linearly arranged, and detects falling feces excreted into the toilet bowl portion at a predetermined time interval; Ueda clearly teaches in paragraph [0024] .The determination unit 30 determines the property of feces based on the line data Ln, which is the detection result detected by the detection unit 20. Specifically, the determination unit 30 extracts the feature amount of the feces to be detected from the obtained line data Ln. Specifically, the feature amount to be extracted is the number of pixels corresponding to feces in each line data Ln, the number of frames in which feces is detected, the number of high-luminance regions existing in one feces region, or the like. These feature amounts serve as indices representing the lateral width (diameter), length, and surface roughness of feces, respectively. Then, the determination unit 30 determines the property of the feces based on these feature amounts note that These feature amounts serve as indices representing the lateral width (diameter), length, and surface roughness of feces, respectively, paragraph [0024];[0029]). It would have been obvious to one skilled in the art before filing of the claimed invention to use determination unit as taught by Ueda in order to detect feces characteristics while considering the privacy of the user. Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
The limitation of claim 3 has been addressed in claim 1 except the following “ corrects and determines the amount of feces based on a threshold of a length thereof in a dropping direction thereof on the feces image that corresponds to each of properties of the feces.” Kashyap et al teaches the information processing system according to claim 1, wherein the determination unit corrects and determines the amount of feces based on a threshold of a length thereof in a dropping direction thereof on the feces image that corresponds to each of properties of the feces (All pixels with a threshold close to zero are ignored from the classification, including glare spots and static artifacts that are specific to the environment. Furthermore, to be invariant to lighting changes histogram equalization is performed. This procedure improves contrast in the image and makes the classification robust across different lighting condition. Once defecation and/or urination is complete, which is detected through software-based image detection, the sample collection is disengaged and the images are processed locally or sent through access point 30 to networked computing resources in a cloud computing environment 50. Locally or remotely through memory 51 and processor 52 the images are then analyzed, paragraph [0100][0105]).
The limitation of claim 4 has been addressed in claim 1 except the following “ the property of feces that is one of two or more types of properties that are based on a hardness thereof.” Kashyap et al teaches the determination unit determines the amount of feces based on the property of feces that is one of two or more types of properties that are based on a hardness thereof ( The training of the SVM minimizes classification errors against these ground truth labels. This classification method determines the stool consistency from a range of hard and lumpy to completely unformed and liquid, using standard clinical labels used in clinical studies to assess bowel consistency, paragraph [0104]; FIG. 15E shows the workflow for estimating the urine and stool voiding volume of a person; FIG. 15E shows the workflow for estimating the urine and stool voiding volume of a person; paragraph [0106])
As to claim 5, Ueda et al teaches the information processing system according to claim 1,wherein the determination unit corrects the length in a case where the length is a predetermined length or greater, and determines the amount of feces depending on the length after correction(The number of consecutive scan lines of (1) is the number of line data Ln from the detection of feces the end of the detection. The number of consecutive scan lines serves as an index indicating the length of feces. For example, in the case of FIG. 15, feces is detected from the line L2 to the line L10. Therefore, the number of consecutive scan lines in FIG. 15 is 9. The average lateral width of (2) is an average of widths of regions where feces is detected in each line data Ln from when feces is detected to when feces is completely detected, and serves as an index indicating the lateral width of feces. For ex ample, in the case of FIG. 15, the widths of the regions where feces are detected in L2 are 3, L3 is 8, L4 is 12,Therefore, a value 9.33 obtained by averaging these values is the average width of each line data Ln in FIG. 15.) .
As to claim 6, Ueda et al teaches the information processing system according to claim 1,wherein the determination unit divides and derives, in a case where a plurality of bowel movements are included in a single act of excreting and a plurality of properties of feces are provided, amounts for respective properties thereof, and determines the amount of feces by using a total value of derived amounts (The number of consecutive scan lines of (1) is the number of line data Ln from the detection of feces the end of the detection. The number of consecutive scan lines serves as an index indicating the length of feces. For example, in the case of FIG. 15, feces is detected from the line L2 to the line L10. Therefore, the number of consecutive scan lines in FIG. 15 is 9. The average lateral width of (2) is an average of widths of regions where feces is detected in each line data Ln from when feces is detected to when feces is completely detected, and serves as an index indicating the lateral width of feces. For ex ample, in the case of FIG. 15, the widths of the regions where feces are detected in L2 are 3, L3 is 8, L4 is 12,Therefore, a value 9.33 obtained by averaging these values is the average width of each line data Ln in FIG. 15.) .
As to claim 7, Ueda et al teaches the information processing system according claim 1,wherein the determination unit corrects, in a case where a total length that is a total of lengths of a plurality of feces in a dropping direction thereof in a single act of(figure 8) excreting is a predetermined length or greater, the total length, and determines the amount of feces depending on the total length after correction((The number of consecutive scan lines of (1) is the number of line data Ln from the detection of feces the end of the detection. The number of consecutive scan lines serves as an index indicating the length of feces. For example, in the case of FIG. 15, feces is detected from the line L2 to the line L10. Therefore, the number of consecutive scan lines in FIG. 15 is 9. The average lateral width of (2) is an average of widths of regions where feces is detected in each line data Ln from when feces is detected to when feces is completely detected, and serves as an index indicating the lateral width of feces. For ex ample, in the case of FIG. 15, the widths of the regions where feces are detected in L2 are 3, L3 is 8, L4 is 12,Therefore, a value 9.33 obtained by averaging these values is the average width of each line data Ln in FIG. 15.)
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NANCY BITAR whose telephone number is (571)270-1041. The examiner can normally be reached Mon-Friday from 8:00 am to 5:00 p.m..
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NANCY . BITAR
Examiner
Art Unit 2664
/NANCY BITAR/Primary Examiner, Art Unit 2664