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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 5/12/2026 has been entered.
Response to Arguments
Applicant’s arguments with respect to claim(s) 1 and 5 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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) 1,3-7 are rejected under 35 U.S.C. 103 as being unpatentable over Kashyap et al ( 2018/0303466) in view of Aoyama et al (US 2022/0375080)
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”
calculates a surface area 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, selects, from among a plurality of relational expressions that are predefined for respective properties of feces and that indicate a relationship between the surface area and the amount of feces, a relational expression corresponding to the property of the determined feces, and determines the amount of feces.”
However, AOYAMA teaches he analysis by the analysis unit 12 is to estimate the items of determination regarding the excrement based on the image related to the excretion. In the following description, the item of determination regarding the excrement may be referred to as a determination item. The determination item may be at least related to the excrement, for example, the characteristic and amount of stool. The characteristic of the excrement includes the shape and color of the excrement. The amount of excrement includes the size of the excrement ( paragraph [0023]). AOYAMA clearly teaches he learned model is created using, for example, a deep learning method. In the following description, deep learning is abbreviated as DL. DL is a machine learning method using a deep neural network configured of a multi-layer neural network. In the following description, the deep neural network is abbreviated as DNN. DNN is realized by a network inspired by the principle of predictive coding in neuroscience, and is constructed by a function that mimics the neurotransmission network. However, the learned model is not limited to DNN. The learned model may be at least a model that has learned the correspondence relationship between the image and the determination result ( paragraph [0026]) Moreover, AOYAMA The determination items regarding the excrement are the characteristic and amount of excrement. The determination unit 13 extracts a target image in which the time-dependent change rate in the amount of excrement is larger than the predetermined slope K regarding the relation between the imaging time of each target image and the amount of excrement. The predetermined slope K as the time-dependent change rate of the amount of excrement is an example of the “second threshold value”. The determination unit 13 determines the characteristic and amount representing the excrement based on the characteristic and amount of excrement determined in the extracted target image. As a result, it is possible for the determination device 10 to determine the characteristic and amount of stool based on the image in which the state before the excrement collapses or melts is captured. It is possible for the determination device 10 to accurately determine the characteristic and amount of excrement, paragraph [0064]]). It would have been obvious to one skilled in the art before filing of the claimed invention to use the characteristic of the excrement as taught by AOYAMA in order to correctly understand the stool characteristic and increase the stool health literacy. 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, AOYAMA 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 determination device 10 determines each of a plurality of indexes of excrement, and quantitatively determines the characteristic of stool by combining the determination results, paragraph [0078].) .
As to claim 6, AOYAMA 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 determination unit 13 determines the characteristic of the excrement based on the combination of the results of determining the texture, continuity, and edge of the excrement. Paragraph [0078-0080]) .
As to claim 7, AOYAMA 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 excreting is a predetermined length or greater, the total length, and determines the amount of feces depending on the total length after correction( if the determination device 10 quantitatively determines the characteristic of the learning image based on a plurality of indexes, it is possible to generate the high-quality teacher data with reduced variation in the determination. A learned model that has learned the high-quality teacher data is able to accurately determine the characteristic of stool even if the characteristic are near the boundary between type B2 and type B3, paragraph [0080].)
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NANCY . BITAR
Examiner
Art Unit 2664
/NANCY BITAR/Primary Examiner, Art Unit 2664
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