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 .
DETAILED ACTION
The United States Patent & Trademark Office appreciates the application that is submitted by the inventor/assignee. The United States Patent & Trademark Office reviewed the following application and has made the following comments below.
Priority
This application claims benefit of foreign priority under 35 U.S.C. 119(a)-(d) of PCT/KR2022/095142, filed in Korea on 10/19/2022, which claims priority to KR10-2022-0117347, filed in Korea on 09/16/2022 and KR10-2022-0134365 filed in Korea on 10/18/2022.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 01/27/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Specification
The disclosure is objected to because of the following informalities:
In paragraph [0075], line 3, “ay” should read “may”.
Appropriate correction is required.
Claim Objections
Claims 1, 3, 11 and 13 are objected to because of the following informalities:
In Claim 1, line 6, the Examiner recommends replacing “receiving an inputted image for a diagnostic kit” with “receiving an inputted image of a diagnostic kit”.
In Claim 3, line 3, the Examiner recommends revising “concentration of a detection target included in a test subject” to improve clarity. It is unclear whether the concentration of the “detection target” is determined in the test subject (human, animal, plant).
In Claim 11, line 3, the Examiner recommends replacing “receiving an inputted image for a diagnostic kit” with “receiving an inputted image of a diagnostic kit”.
In Claim 13, line 3, the Examiner recommends revising “concentration of a detection target included in a test subject” to improve clarity. It is unclear whether the concentration of the “detection target” is determined in the test subject (human, animal, plant).
Appropriate correction is required.
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 2-5, 8-9 and 12-14 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 pre-AIA the applicant regards as the invention. The Examiner strongly suggested that appropriate corrections be made to clarify the claim scope.
With respect to Claim 2, the claim recites the following, each of which renders the claim indefinite:
“the infection state” on line 2 (unclear antecedent basis); it is unclear as to whether “the infection state” recited in line 2 of claim 2 is the same or different from “infection state information” recited in lines 1, 3, 4, and 11 of claim 1.
With respect to Claim 3, the claim recites the following, each of which renders the claim indefinite:
“the concentration of a detection target” on lines 2-3 (unclear antecedent basis); it is unclear what “the concentration of a detection target” is referring to. The Examiner recommends replacing “the” with “a”: “a concentration of a detection target.”
With respect to Claim 4, the claim recites the following, each of which renders the claim indefinite:
“the infection state trend” on line 5 (unclear antecedent basis); it is unclear what “infection state trend” is referring to. The Examiner recommends replacing “the” with “a/an”: “a/an infection state trend.”
With respect to Claim 8, the claim recites the following, each of which renders the claim indefinite:
“the control line” on line 3, (unclear antecedent basis); it is unclear what “the control line” is referring to. The Examiner recommends replacing “the” with “a”: “a control line.”
With respect to Claim 12, the claim recites the following, each of which renders the claim indefinite:
“the infection state” on lines 2-3, (unclear antecedent basis); it is unclear whether “the infection state” recited in lines 2-3 of claim 12 is referring to “infection state information” recited in lines 1, 2 and 8 of claim 11.
With respect to Claim 13, the claim recites the following, each of which renders the claim indefinite:
“the concentration of a detection target” on line 3 (unclear antecedent basis); it is unclear what “the concentration of a detection target” is referring to. The Examiner recommends replacing “the” with “a”: “a concentration of a detection target.”
With respect to Claim 14, the claim recites the following, each of which renders the claim indefinite:
“the control line” on line 3, (unclear antecedent basis); it is unclear what “the control line” is referring to. The Examiner recommends replacing “the” with “a”: “a control line.”
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 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(a) which forms the basis for all obviousness rejections set forth in this Office action:
(a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102 of this title, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negatived by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103(a) 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 1-3, 6, 8, 10-14 are rejected under 35 U.S.C. 103(a) as being unpatentable over Sia et al. (U.S. Patent Pub. No. 2023/0274538, hereafter referred to as Sia) in view of Harder et al. (U.S. Patent Pub No. 2022/0299431, hereafter referred to as Harder).
[AltContent: arrow]Regarding Claim 1, Sia teaches a computing device for generating infection state information on the basis of image information (Paragraphs [0014], [0138], Figs. 2A & 5, Sia teaches a smart phone with software that may be implemented on the smart phone for automated image interpretation of rapid test kits.),
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the computing device comprising a memory that stores one or more programs for generating the infection state information (Paragraph [0138], Sia teaches computer components include one or more non-transitory computer-readable media and one or more programs.); and one or more processors that perform operations for generating the infection state information according to the one or more programs (Paragraph [0138], Sia teaches computer components including one or more processors and one or more programs.), wherein the operations performed by the [AltContent: arrow]processor comprise: a step of receiving an inputted image for a diagnostic kit (Paragraph [0049], Fig. 1, Sia teaches after self-administering the test, the patient may input an image of the rapid test kit to a system for automated interpretation of the image. For example, the patient may use a smart phone to take a picture of the rapid test kit and send that picture for automated interpretation.)
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to which a sample extracted from a test subject is applied (Paragraph [0048], Sia teaches a sample, for example saliva or blood, is applied to the test kit.); (Paragraph [0071], Fig. 2D, Sia teaches cropping individual zones (i.e., regions in the membrane corresponding to bands and a portion of surrounding area) from the membrane. The Examiner interprets the “main region” to be the test line. Therefore, as shown in Fig. 2D below, the cropped zones “surround” the main region (test line) to include it.);
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and a step of applying the sub-region to a machine learned model (Paragraphs [0062], [0071], Fig. 2D “Feature extractor”, Sia teaches the membrane is cropped from the kit, and the individual zones (control and test) are extracted. Next, images of zones enter a feature-extraction network.) to generate (Paragraphs [0062], [0064], [0071], Sia teaches the kit status (positive, negative, invalid) is predicted using the binary values from the classifier and a look-up table with a list of matched values for the multiple zones.) and output the infection state information for the test subject (Paragraphs [0109], [0049], Fig. 2A, Sia teaches the model classifies each zone as positive or negative, and provides an overall assay result on the screen of the smartphone.).
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Sia does not explicitly disclose a step of determining a main region related to the test subject in the inputted image.
Harder is in the same field of art of localization of test and control regions of interest on an assay membrane used for diagnostic applications, such as detecting infectious disease organisms. Further, Harder teaches a step of determining a main region related to the test subject in the inputted image (Paragraph [0011], Fig. 2B, Fig. 3, Harder teaches determining contours of the test region by imaging the localization label and the background around the region of interest and comparing intensity of the background of the assay membrane to intensify at the region of interest. Fig. 2B below shows one test region in a region of interest on an assay membrane. The Examiner interprets “a main region” to be a test line in light of Applicant’s disclosure, specifically paragraph [0082], which states “determine a main region related to the test subject by detecting a region corresponding to the test line (411) of a window (410) in which a test result of a diagnostic kit appears” and Applicant’s Fig. 2 reference character “411”. Also, paragraphs [0011], [0083] of Applicant’s specification.).
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Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Sia by first localizing the test region/line that is taught by Harder, to make the invention that localizes/identifies the test region (test line) prior to determining and extracting a sub-region (cropped region) to be applied to the machine learning model; thus, one of ordinary skilled in the art would be motivated to combine the references since using pre-localization of regions of interest (test region) ensures that image analysis is performed at the location where the bound species is known to be on the assay membrane (Paragraph [0092], Harder). In addition, pre-localization of the test region ensures the test region is included in the extracted sub-region that is sent to the feature extractor, and the extracted features are sent to a binary classifier that predicts whether the indicia of interest is present or absent, resulting in zone classification of kit status (positive, negative, invalid) (Sia, Paragraph [0062]).
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.
[AltContent: arrow]In regards to Claim 2, Sia in view of Harder discloses the computing device according to claim 1, wherein the main region is a region comprising a test line (Paragraphs [0087-88], Fig. 2B, Harder teaches the test region in a region of interest on a flow assay membrane. The region of interest (ROI) 28 is indicated by the dashed lines.)
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whose color changes depending on the infection state of the test subject (Paragraphs [0087], [0123], [0125], Sia teaches the approach may be used on test kits beyond signals of single colors (for example, some urinalysis kits with color-based readouts). The self-supervised method is effective for recognizing zone images that are of faint colors.).
In regards to Claim 3, Sia in view of Harder discloses the computing device according to claim 2, wherein the infection state information includes infection information indicating whether the test subject is infected (Paragraph [0049], Sia teaches the system interprets the image to determine a classification, e.g., positive, negative, or invalid, and then returns that interpretation as a result to the patient and/or to a care provider.) or the concentration of a detection target included in the test subject predicted on the basis of the color change of the test line (Under Broadest Reasonable Interpretation (BRI), the Examiner interprets “or” to mean only one of either an indication of whether the test subject is infected or the concentration of a detection target is required to meet the claim limitation.).
In regards to Claim 6, Sia in view of Harder discloses the computing device according to claim 1, wherein the machine learned model is learned using an augmented data set including a plurality of training data (Paragraph [0097], Sia teaches pretraining the model on a training subset of 50 images of the base kit with a learning rate of 5E-5. Then, the model was finetuned on the new assay kits with a learning rate of 5E-6 using 10 training images and evaluated the performance on 10 evaluation images. Train-time augmentations were performed.) and transformation training data that is transformed on the basis of the plurality of training data (Paragraph [0097], Sia teaches the following train-time augmentations were performed: (i) horizontal flip, (ii) scaling, (iii) aspect-ratio modification, (iv) brightness adjustment, (v) contrast adjustment, (vi) hue adjustment, (vii) saturation adjustment, (viii) color distortion, (ix) jitter addition, (x) cropping, (xi) padding, and (xii) Gaussian noise addition.).
In regards to Claim 8, Sia in view of Harder discloses the computing device according to claim 2, wherein the determining of the sub-region is determining to include a region corresponding to the test line and exclude a region corresponding to the control line (Paragraph [0071], Fi. 2A, Sia teaches cropping individual zones (i.e., regions in the membrane corresponding to bands and a portion of surrounding area from the membrane. As shown in Fig. 2A “Zone-level interpretation”, the control (top) and test (bottom) bands/zones are cropped individually. The Examiner interprets the extracted test zone does not include the control line/zone.).
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In regards to Claim 10, Sia in view of Harder discloses the computing device according to claim 1, wherein the processor (Paragraph [0138], Sia teaches a processor.) activates a mobile application and performs the operations through the activated mobile application (Paragraphs [0014], [0112], Figs. 2A & 5, Sia teaches the system for automated image interpretation of rapid test kits, wherein the system comprises software that may be implemented on a user’s smart phone. Fig. 5(b) shows app guided image capture of the rapid test kit.).
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In regards to Claim 11, Sia discloses a method for generating infection state information (Paragraph [0019], Sia teaches a method for automatic image interpretation of rapid test kits (determine a classification, e.g., positive, negative, or invalid, and then returns that interpretation as a result to the patient and/or care provider).) performed by a computing device (Paragraph [0138], Sia teaches the method may be implemented with computer components. Components such as neural network(s) may reside on smart phones.) for generating infection state information on the basis of image information (Paragraph [0049], Sia teaches interpreting the image to determine a classification, e.g., positive, negative, or invalid, and then returning that interpretation as a result to the patient and/or care provider.), the method comprising: a step of receiving an inputted image for a diagnostic kit (Paragraph [0049], Fig. 1, Sia teaches after self-administering the test, the patient may input an image of the rapid test kit to a system for automated interpretation of the image. For example, the patient may use a smart phone to take a picture of the rapid test kit and send that picture for automated interpretation.) to which a sample extracted from a test subject is applied (Paragraph [0048], Sia teaches a sample, for example saliva or blood, is applied to the test kit.); (Paragraph [0071], Fig. 2D, Sia teaches cropping individual zones (control and test) (i.e., regions in the membrane corresponding to bands and a portion of surrounding area) from the membrane.); and a step of applying the sub-region to a machine learned model (Paragraphs [0062], [0071], Fig. 2D “Feature extractor”, Sia teaches the membrane is cropped from the kit, and the individual zones (control and test) are extracted. Next, images of zones enter a feature-extraction network.) to generate (Paragraphs [0062], [0064], [0071], Sia teaches the kit status (positive, negative, invalid) is predicted using the binary values from the classifier and a look-up table with a list of matched values for the multiple zones.) and output the infection state information for the test subject (Paragraphs [0109], [0049], Sia teaches the model classifies each zone as positive or negative, and provides an overall assay result on the screen of the smartphone.).
Sia does not explicitly disclose a step of determining a main region related to the test subject in the inputted image.
Harder is in the same field of art of localization of test and control regions of interest on an assay membrane used for diagnostic applications, such as detecting infectious disease organisms. Further, Harder teaches a step of determining a main region related to the test subject in the inputted image (Paragraph [0011], Fig. 2B, Fig. 3, Harder teaches determining contours of the test region by imaging the localization label and the background around the region of interest and comparing intensity of the background of the assay membrane to intensify at the region of interest. Fig. 2B shows one test region in a region of interest on an assay membrane. The Examiner interprets “a main region” to be a test line in light of Applicant’s disclosure, specifically paragraph [0082], which states “determine a main region related to the test subject by detecting a region corresponding to the test line (411) of a window (410) in which a test result of a diagnostic kit appears” and Applicant’s Fig. 2 reference character “411”. Also, see paragraphs [0011], [0083] of Applicant’s specification.).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Sia by first localizing the test region/line that is taught by Harder, to make the invention that localizes/identifies the test region (test line) prior to determining and extracting a sub-region (cropped region) to be applied to the machine learning model; thus, one of ordinary skilled in the art would be motivated to combine the references since using pre-localization of regions of interest (test region) ensures that image analysis is performed at the location where the bound species is known to be on the assay membrane (Paragraph [0092], Harder). In addition, pre-localization of the test region ensures the test region is included in the extracted sub-region that is sent to the feature extractor, and the extracted features are sent to a binary classifier that predicts whether the indicia of interest is present or absent, resulting in zone classification of kit status (positive, negative, invalid) (Sia, Paragraph [0062]).
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.
In regards to Claim 12, Sia in view of Harder discloses the method for generating infection state information according to claim 11, wherein the main region is a region comprising a test line (Paragraphs [0087-88], Fig. 2B, Harder teaches the test region in a region of interest on a flow assay membrane. The region of interest (ROI) 28 is indicated by the dashed lines.) whose color changes depending on the infection state of the test subject (Paragraphs [0087], [0123], [0125], Sia teaches the approach may be used on test kits beyond signals of single colors (for example, some urinalysis kits with color-based readouts). The self-supervised method is effective for recognizing zone images that are of faint colors.).
In regards to Claim 13, Sia in view of Harder discloses the method for generating infection state information according to claim 12, wherein the infection state information includes infection information indicating whether the test subject is infected (Paragraph [0049], Sia teaches the system interprets the image to determine a classification, e.g., positive, negative, or invalid, and then returns that interpretation as a result to the patient and/or to a care provider.) or the concentration of a detection target included in the test subject predicted on the basis of the color change of the test line (Under Broadest Reasonable Interpretation (BRI), the Examiner interprets “or” to mean only one of either an indication of whether the test subject is infected or the concentration of a detection target is required to meet the claim limitation.).
In regards to Claim 14, Sia in view of Harder discloses the method for generating infection state information according to claim 12, wherein the determining of the sub-region is determining to include a region corresponding to the test line and exclude a region corresponding to the control line (Paragraph [0071], Fig. 2A, Sia teaches cropping individual zones (i.e., regions in the membrane corresponding to bands and a portion of surrounding area from the membrane. As shown in Fig. 2A “Zone-level interpretation”, the control (top) and test (bottom) bands/zones are cropped individually. The Examiner interprets the extracted test zone does not include the control line/zone.).
Claim 4 is rejected under 35 U.S.C. 103(a) as being unpatentable over Sia et al. (U.S. Patent Pub. No. 2023/0274538, hereafter referred to as Sia) in view of Harder et al. (U.S. Patent Pub No. 2022/0299431, hereafter referred to as Harder) in further view of Divaraniya et al. (U.S. Patent Pub. No. 2021/0264604, hereafter referred to as Divaraniya).
Regarding Claim 4, Sia in view of Harder discloses the computing device according to claim 3, wherein the receiving of the inputted image for a diagnostic kit is receiving a plurality of inputted images (Paragraph [0019], Sia teaches inputting a set of images of first rapid test kits.) including an inputted image captured at a current time point (Paragraph [0049], Sia teaches the patient may input an image of the rapid test kit to a system for automated interpretation of the image.)
Sia in view of Harder does not explicitly disclose an inputted image captured at a previous time point, and wherein the step of generating and outputting the infection state information is generating and outputting information on the infection state trend using the concentrations of the plurality of detection targets generated on the basis of the plurality of inputted images.
Divaraniya is in the same field of art of processing an image of an indicator (such as a lateral flow device) associated with a health status of a subject (e.g., the status of a disease or condition). Further, Divaraniya teaches an inputted image captured at a previous time point (Paragraphs [0170], [0005], Divaraniya teaches quantifying an analyte at a first time point. The Examiner interprets the first time point to be a previous time point since it occurs before the second time point. The quantity of an analyte is determined using image processing by processing pixel intensities of a hue, saturation, value, or lightness color space of an image of said analyte.), and wherein the step of generating and outputting the infection state information is generating and outputting information on the infection state trend (Paragraphs [0171], [0135], Divaraniya teaches comparing the quantity of an analyte at a first time point and a second time point, wherein the comparing provides an indication of a change in quantity of the analyte. For example, an analyte can be quantified from one day to the next, before and after treatment, or over the course of several months (e.g., to determine the progression/regression of a disease.) A report on the subject’s health based on quantification of a target analyte may be provided. For example, the report may be an alert. The Examiner interprets an indication of a change in quantity of an analyte to be information that indicates a trend.) using the concentrations of the plurality of detection targets (Paragraph [0135], Divaraniya teaches quantifying an analyte at two time points to determine a change in analyte quantity overtime.) generated on the basis of the plurality of inputted images (Abstract, Paragraph [0004], Divaraniya teaches quantifying analyte levels using image processing of pixels. For example, processing pixel intensities in a hue, saturation, value, or lightness color space of an image of an indicator associated with a health status of a subject, thereby quantifying said indicator.).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Sia in view of Harder by comparing the concentrations of analyte at two different time points that is taught by Divaraniya, to make the invention that determines a change in analyte quantity over time; thus, one of ordinary skilled in the art would be motivated to combine the references since tracking the quantity of analyte over a period of time, such as over the course of several months can determine the progression or regression of a disease (Divaraniya, Paragraph [0135]) and therefore, provide insight into the health status of the subject and determine future steps required in terms of medical care and treatment.
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.
Claim 5 is rejected under 35 U.S.C. 103(a) as being unpatentable over Sia et al. (U.S. Patent Pub. No. 2023/0274538, hereafter referred to as Sia) in view of Harder et al. (U.S. Patent Pub No. 2022/0299431, hereafter referred to as Harder) in further view of Divaraniya et al. (U.S. Patent Pub. No. 2021/0264604, hereafter referred to as Divaraniya) in further view of Sato (U.S. Patent Pub. No. 2024/0203541, hereafter referred to as Sato).
Regarding Claim 5, Sia in view of Harder in further view of Divaraniya discloses the computing device according to claim 4.
Sia in view of Harder in further view of Divaraniya does not explicitly disclose wherein the step of generating and outputting the infection state information: provides a first message about the infection state at the current time point if the concentration of the detection target at the current time point has increased compared to the concentration at the previous time point; and provides a second message about the infection state at the current time point if the concentration of the detection target at the current time point has decreased compared to the concentration at the previous time point.
Sato is in the same field of art of providing a user with a test result of an infection based on detection data. Further, Sato teaches wherein the step of generating and outputting the infection state information (Paragraphs [0136-137], Fig. 10, Sato teaches generating test result information, which may include the calculation result, the infection phase information, and the plot image. The test result may display a moving image or still image associated with the calculation result or the infection phase information): provides a first message about the infection state at the current time point (Paragraphs [0124], [0068], Sato teaches the test result output unit may generate infection phase information about the infection phase. The infection phase is estimated for the user based on a first detection result and a second detection result. The second detection result is obtained by detecting particles of the virus contained in another specimen collected from a user at a second time that is different from the first time. The second time is intended to be any time after the first time. The Examiner interprets the second test result to be the infection state at the “current time point”.) if the concentration of the detection target at the current time point has increased compared to the concentration at the previous time point (Paragraphs [0124-125], [0129], [0068], Sato teaches estimating the infection phase for the user based on a first detection result and a second detection result and generating infection phase information about the infection phase. The plurality of infection phases includes at least one of the phases (1) through (8). For example, phase (4), the “exacerbation period” in which the virus count contained in the specimen collected from the user tends to increase. The Examiner interprets the first test result to be the infection state at the “previous time point” (see Para. [0068]).) and provides a second message about the infection state at the current time point (Paragraphs [0124], [0068], Sato teaches the test result output unit may generate infection phase information about the infection phase. The infection phase is estimated for the user based on a first detection result and a second detection result. The second detection result is obtained by detecting particles of the virus contained in another specimen collected from a user at a second time that is different from the first time. The second time is intended to be any time after the first time. The Examiner interprets the second test result to be the infection state at the “current time point”.) if the concentration of the detection target at the current time point has decreased compared to the concentration at the previous time point (Paragraphs [0124-125], [0131], [0068], Sato teaches estimating the infection phase for the user based on a first detection result and a second detection result and generating infection phase information about the infection phase. The plurality of infection phases includes at least one of the phases (1) through (8). For example, phase (6), the “recovery period” in which the virus count for the specimen collected from the user tends to decrease. The Examiner interprets the first test result to be the infection state at the “previous time point” (see Para. [0068]).)
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Sia in view of Harder in further view of Divaraniya by comparing the infection state information at two different time points that is taught by Sato, to make the invention that tracks the progression and/or phases of the infection; thus, one of ordinary skilled in the art would be motivated to combine the references since information regarding a change over time in the number of particles of the virus in the specimen is useful for determining the infection state of the user (Sato, Paragraph [0070]) and provide useful information to the user such as how long the user needs to stay home (until they are no longer viral) and when the infection will be completely cured (Sato, Paragraph [0135]).
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.
Claims 7 and 15 are rejected under 35 U.S.C. 103(a) as being unpatentable over Sia et al. (U.S. Patent Pub. No. 2023/0274538, hereafter referred to as Sia) in view of Harder et al. (U.S. Patent Pub No. 2022/0299431, hereafter referred to as Harder) in further view of Kumar et al. (U.S. Patent Pub. No. 2023/0146924, hereafter referred to as Kumar).
Regarding Claim 7, Sia in view of Harder discloses the computing device according to claim 6, wherein the transformation training data includes at least one selected from the group consisting of: first transformation training data generated by blurring the training data (Paragraph [0097], Sia teaches train-time augmentations such as gaussian noise addition to the training images. The Examiner interprets adding gaussian noise to the image to be blurring the image since it introduces noise to the image and reduces sharpness of the image.), second transformation training data generated by adjusting the size of the training data (Paragraph [0097], Sia teaches cropping the training images. The Examiner interprets cropping the training data effectively adjusts the size of the training images.), third transformation training data generated by distorting the training data (Paragraph [0097], Sia teaches performing color distortion on the training images. The Examiner interprets color distortion is a type of distortion applied to the training images.), fourth transformation training data generated by rotating the training data (Paragraph [0100], Sia teaches performing random rotation transformations.), fifth transformation training data generated by adjusting the brightness of the training data (Paragraph [0097], Sia teaches brightness adjustment on the training images.),
Sia in view of Harder does not explicitly disclose sixth transformation training data generated by changing the surrounding environment of the training data, and seventh transformation training data generated by adjusting the color temperature of the training data.
Kumar is in the same field of art of training a neural network machine to analyze images of lateral flow assay test strips for qualitative and quantitative detection of specific antigens and antibodies. Further, Kumar discloses sixth transformation training data generated by changing the surrounding environment of the training data (Paragraph [0018], Kumar teaches the training images may also vary their respective imaging conditions, such as imaging locations, test strip backgrounds to generate a representative dataset.), and seventh transformation training data generated by adjusting the color temperature of the training data (Paragraph [0067], Fig. 5, Kumar teaches training the neural network on images that vary in color temperature (e.g., 1000K, 2000K, 2500K, 3000K, 3500K, 4000K, 5000K, 5200K, 6000K, 6500K, 7000K, 8000K, 9000K, 10,000K, or any suitable combination thereof).).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Sia in view of Harder by performing augmentations such as changing the surrounding environment and adjusting the color temperature to training data that is taught by Kumar, to make the invention that augments the training data with artificially simulated variations; thus, one of ordinary skilled in the art would be motivated to combine the references since training the neural network model on a diverse set of training images and imaging conditions may facilitate improved performance of the trained neural network in analyzing images of test strips (Kumar, Paragraph [0074]).
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.
In regards to Claim 15, Sia in view of Harder discloses (The Examiner states see rejection of Claim 11, method of combining references.) on a computer (Paragraph [0138], Sia teaches methods may be implemented with computer components).
Sia in view of Harder does not explicitly disclose a computer program stored in a non-transitory computer-readable recording medium.
Kumar is in the same field of art of training a neural network machine to analyze images of lateral flow assay test strips for qualitative and quantitative detection of specific antigens and antibodies. Further, Kumar discloses a computer program stored in a non-transitory computer-readable recording medium (Paragraph [0076], Kumar teaches instructions (e.g., software, program, an application, an applet, an app, or other executable code) on a machine readable medium (e.g., a non-transitory machine-readable medium).).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Sia in view of Harder by implementing the method via a program stored on a non-transitory computer readable medium that is taught by Kumar, to make the invention that allows the computing system to read and execute the instructions to perform the method of generating infection state information; thus, one of ordinary skilled in the art would be motivated to combine the references to automate the method and enable the processor(s) to quickly execute the instructions of the method, reducing the need for manual intervention and speeding up computation.
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.
Claim 9 is rejected under 35 U.S.C. 103(a) as being unpatentable over Sia et al. (U.S. Patent Pub. No. 2023/0274538, hereafter referred to as Sia) in view of Harder et al. (U.S. Patent Pub No. 2022/0299431, hereafter referred to as Harder) in further view of Rowe et al. (U.S. Patent Pub. No. 2023/0296600, hereafter referred to as Rowe).
Regarding Claim 9, Sia in view of Harder discloses the computing device according to claim 8.
Sia in view of Harder does not explicitly disclose wherein the step of generating and outputting the infection state information is judging whether the diagnostic kit is normal on the basis of the region corresponding to the control line, and generating and outputting information on whether the diagnostic kit is normal.
Rowe is in the same field of art of analyzing a diagnostic test result by capturing an image of the diagnostic test. Further, Rowe discloses wherein the step of generating and outputting the infection state information is judging whether the diagnostic kit is normal on the basis of the region corresponding to the control line (Paragraph [0078], Fig. 7B, Rowe teaches receiving a first image of one or more control markings and computer vision techniques may assess whether all or a sufficient portion of the control markings may be detected in the first image.), and generating and outputting information on whether the diagnostic kit is normal (Paragraph [0078], Fig. 7B, Rowe teaches if not all of the control markings are detected in an image, then the user may be notified of the error.).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Sia in view of Harder by identifying whether or not the control line is present in the image that is taught by Rowe, to make the invention that first determines that the control line is present before outputting the infection result; thus, one of ordinary skilled in the art would be motivated to combine the references since improper assay operation could produce absent control bands (Sia, Paragraph [0067]). In addition, verifying detection of one or more control markings on a scan surface may be used to assess camera quality used to obtain an image for diagnostic test analysis (Rowe, Paragraph [0075]).
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.
Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Satish et al. (U.S. Patent Pub. No. 2023/0351754 A1) teaches a device configured to function as an image-based analyzer for one or more test kits. The device guides the user in capturing an image of a test kit. The device analyzes the test kit, recognizes features of the test kit in the image, and obtains results indicated by the recognized features. The device provides the results via displaying then on a display screen, audibly, etc.
Rothberg et al. (U.S. Patent Pub. No. 2021/0293805 A1) a method which comprises obtaining a sample from the subject, processing the sample, analyzing the sample with a detection component of the diagnostic test, and performing a computer-implemented method comprising accessing information representing the detection component of a diagnostic test, and determining, based at least in part on the information representing the detection component of the diagnostic test, results of the diagnostic test.
Conclusion
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/SYDNEY L BLACKSTEN/Examiner, Art Unit 2674
/ONEAL R MISTRY/Supervisory Patent Examiner, Art Unit 2674
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