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
In the preliminary amendment dates 01/21/2025, the following occurred: The Specification was amended at para. [0099]-[0110], [0216], [0286]-[0306], [0312] and [0313]. The drawings have been amended at Figs. 39, 40A, 40B, 41, 42, 43, 44, 45, 46, 47, 48, 49A and 49B.
This is the first action on the merits. Claims 1-49 are currently pending.
Priority
This application claims priority from Provisional Application Nos. 63609802 dated 12/12/2023.
Information Disclosure Statement
The information disclosure statements (IDSs) submitted on 03/24/2025 and 07/31/2025 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
TVOC sensor,
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-49 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claims 1, 21, 34, 36 and 43 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites a method and a system for disease detection, which are within a statutory category.
Regarding claims 1, 21, 34, 36 and 43, the limitation of (claim 1 being representative) receive a first sensor data associated with a first set of detection animals that have been exposed to a biological sample of a patient; calculate, based on the first sensor data, a first confidence score corresponding to a disease category associated with the biological sample, wherein the disease category comprises a plurality of disease states, and wherein the first confidence score indicates a likelihood of at least one of the disease states of the disease category being present in the patient; responsive to the first confidence score being greater than a first threshold score, receive a second sensor data associated with a second set of detection animals that have been exposed to the biological sample of the patient; and calculate, based on the second sensor data, one or more second confidence scores corresponding to one or more disease states in the disease category associated with the biological sample, wherein each confidence score indicates a likelihood of a respective disease state in the disease category being present in the patient and regarding claim 21- the limitation receiving a test kit, wherein the test kit comprises a biological sample from a patient; exposing the biological sample to a first set of detection animals and regarding claim 43- the limitation training the detection animal to provide the conditioned response by providing the detection animal with a reward for identifying the target disease state; inputting a first sensor data corresponding to the detection animal, wherein the first sensor data is associated with presence of the target disease state; storing tangibly the first sensor data to obtain a dataset of detection events; and training to detect the disease state based on the dataset of detection events as drafted, is a process that, under the broadest reasonable interpretation, covers a method organizing human activity but for the recitation of generic computer components. That is other than reciting a disease-detection system, a memory and a computer processor, in claim 43, the claimed invention amounts to managing personal behavior or interaction between people (i.e., rules or instructions). For example, but for the disease-detection system, memory and computer processor, the claims encompass disease detection in the manner described in the identified abstract idea, supra. The Examiner notes that certain “method[s] of organizing human activity” includes a person’s interaction with a computer (see MPEP 2106.04(a)(2)(II)). If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or interactions between people, but for the recitation of generic computer components, then it falls within the “Certain Methods of Organizing Human Activity – Managing Personal Behavior Relationships, Interactions Between People (e.g. social activities, teaching, following rules or instructions)” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. Claims 1, 21, 34 and 36 are not tied to any particular technological environment that implements the identified abstract idea. In particular, claim 43 recites the additional elements of a disease-detection system, a memory and a computer processor. These additional elements are not exclusively defined by the applicant and are recited at a high-level of generality (i.e., a generic computer components for enabling access to medical information or for performing generic computer functions. See Spec. at para. [0122] and [0292]) such that they amounts to no more than mere instructions to apply the exception using a generic computer component. As set forth in MPEP 2106.04(d) “merely including instructions to implement an abstract idea on a computer” is an example of when an abstract idea has not been integrated into a practical application. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Claim 1 recites the additional elements of one or more trained machine learning-based (ML-based) disease-detection models. Claim 21 recites the additional elements of a trained first ML-based disease-detection model and a second trained ML-based disease-detection model. Claim 34 recites the additional element of a trained first ML-based disease-detection model. Claim 43 recites the additional element of a trained ML-based disease-detection system. These additional element are interpreted as (“apply it”) to the abstract idea. MPEP 2106.04(d)(I) indicates that merely saying “apply it” or equivalent to the abstract idea cannot provide a practical application. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of the disease-detection system, memory and computer processor to perform the noted steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept (“significantly more”). Moreover, using generic computer components to perform abstract ideas does not provide a necessary inventive concept. See Alice, 573 U.S. at 223 (“mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention”). Therefore, whether considered alone or in combination, the additional elements do not amount to significantly more than the abstract idea.
Also as discussed with respect to integration of the abstract idea into a practical application, the additional elements of one or more trained machine learning-based (ML-based) disease-detection models, the trained first ML-based disease-detection model, the second trained ML-based disease-detection model and the trained ML-based disease-detection system were determined to be (“apply it”) to the identified abstract idea. This has been re-evaluated under the “significantly more” analysis and has also been found insufficient to provide significantly more. MPEP2106.05(1)(A) indicates that merely saying “apply it’ or equivalent to the abstract idea cannot provide an inventive concept (“significantly more’). As such the claim is not patent eligible.
The examiner notes that: A well-known, general-purpose computer has been determined by the courts to be a well-understood, routine and conventional element (see, e.g., Alice Corp. v. CLS Bank; see also MPEP 2106.05(d)); Receiving and/or transmitting data over a network (“a communications network”) has also been recognized by the courts as a well - understood, routine and conventional function (see, e.g., buySAFE v. Google; MPEP 2016(d)(II)); and Performing repetitive calculations is/are also well-understood, routine and conventional computer functions when they are claimed in a merely generic manner (see, e.g., Parker v. Flook; MPEP 2016.05(d)).
Claims 2-20, 22-33, 35, 37-42 and 44-49 are similarly rejected because they either further define/narrow the abstract idea and/or do not further limit the claim to a practical application or provide as inventive concept such that the claims are subject matter eligible even when considered individually or as an ordered combination. Claim(s) 2, 22 further merely describe(s) the first sensor data. Claim(s) 3 further merely describe(s) the first sensor data and the second sensor data. Claim(s) 4 further merely describe(s) the one or more behavioral sensors. Claim(s) 5 further merely describe(s) the one or more physiological sensors. Claim(s) 6 further merely describe(s) the one or more environmental sensors. Claim(s) 7m 30 further merely describe(s) the first set of detection animals. Claim(s) 8 further merely describe(s) one or more breath sensors to detect volatile organic compounds (VOCs). Claim(s) 9 further merely describe(s) the one or more breath sensors. Claim(s) 10, 23 further merely describe(s) receive a third sensor data and calculate one or more confidence scores. Claim(s) 11, 24 further merely describe(s) the first and second sets of detection animals. Claim(s) 12 further merely describe(s) the sampling port. Claim(s) 13-15 further merely describe(s) the models. Claim(s) 16, 17, 28, 29, 47 and 49 further merely describe(s) the respective disease state. Claim(s) 18, 31 further merely describe(s) the biological sample. Claim(s) 19, 20, 32, 33, 41 and 42 further merely describe(s) the disease category. Claim(s) 25 further merely describe(s) determining which of a particular sample to expose to the first/second set of detection animals. Claim(s) 26 and 27 further merely describe(s) identifying the biological sample. Claim(s) 35 further merely describe(s) exposing the biological sample, accessing a second sensor data, processing the second sensor data. Claim(s) 37 further merely describe(s) accessing and processing a second sensor data. Claim(s) 38 further merely describe(s) receiving a prior test kit, exposing the prior biological sample to a set of detection animals, accessing a first sensor data, processing the first sensor data and identify the biological sample as associated with the first disease state. Claim(s) 39 further merely describe(s) the new biological sample. Claim(s) 40 further merely describe(s) the prior biological sample. Claim(s) 44 further merely describe(s) the conditioned response. Claim(s) 45 and 46 further merely describe(s) repeating each of the steps. Claim(s) 49 further merely describe(s) the first and second biological sample. Claims 2-20, 22-33, 35, 37-42 and 44-49 further define the abstract idea and are rejected for the same reason presented above with respect to claims 1, 21, 34, 36 and 43.
Claim(s) 3 also include the additional element of “one or more behavioral sensors, one or more physiological sensors, or one or more environmental sensors”, which are recited at a high level of generality (i.e. a general means to measure/output/receive/transmit data) and amount to extra solution activity. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application nor provide significantly more.
Claim(s) 4 also include the additional element of “one or more behavioral sensors” which is recited at a high level of generality (i.e. a general means to measure/output/receive/transmit data) and amounts to extra solution activity. Accordingly, even in combination, this additional elements does not integrate the abstract idea into a practical application nor provide significantly more.
Claim(s) 5 also include the additional element of “one or more heart rate sensors, one or more heart rate variability sensors, one or more temperature sensors, one or more breath rate sensors, one or more sweat rate sensors, one or more blood pressure sensors, one or more skin temperature sensors, one or more pupil size variability sensors, one or more salivary cortisol sensors, one or more galvanic skin response (GSR) sensors, one or more electroencephalogram (EEG) sensors, one or more functional near-infrared spectroscopy (fNIR) sensors, one or more functional magnetic resonance imaging (fMRI) scanners, one or more electromyography imaging (EMG) scanners, or one or more magnetic resonance imaging (MRI) scanners”, which are recited at a high level of generality (i.e. a general means to measure/output/receive/transmit data) and amount to extra solution activity. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application nor provide significantly more.
Claim(s) 6 also include the additional element of “one or more environmental sensors, one or more temperature sensors, one or more humidity sensors, one or more audio sensors, one or more gas sensors, or one or more air particulate sensors”, which are recited at a high level of generality (i.e. a general means to measure/output/receive/transmit data) and amount to extra solution activity. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application nor provide significantly more.
Claim(s) 8 also include the additional element of “one or more breath sensors”, which is recited at a high level of generality (i.e. a general means to measure/output/receive/transmit data) and amounts to extra solution activity. Accordingly, even in combination, this additional element does not integrate the abstract idea into a practical application nor provide significantly more.
Claim(s) 9 also include the additional element of “TVOC sensor, breath VOC sensor, relative humidity sensors, temperature sensor, photoionization detector (PID), flame ionization detector (FID), and metal oxide (MOX) sensor”, which are recited at a high level of generality (i.e. a general means to measure/output/receive/transmit data) and amount to extra solution activity. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application nor provide significantly more.
These additional elements, when considered alone or in combination, are recited at high level generality and amount to extra solution activity. They do not provide practical application or significantly more. MPEP 2106.04(d)(I) indicates that extra-solution data gathering activity cannot provide a practical application.
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 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 1-3, 5-10, 14, 15, 18-23, 26, 27 and 30-35 are rejected under 35 U.S.C. 103 as being unpatentable over Darling (US 2023/0060676) and in further view of Saito (US 2022/0020496).
REGARDING CLAIM 1
Darling discloses a system for disease detection comprising: one or more machine learning-based (ML-based) disease-detection models trained on a dataset of detection events, wherein the models operable to: receive a first sensor data associated with a first set of detection animals that have been exposed to a biological sample of a patient ([abstract] teaches diagnosing a disease or virus or other illness and capture and output biometric data corresponding to the subject. [0004] teaches a system that enables a patient or a subject to perform a method of combining different sensor data for higher reliable diagnosis information. [0018] teaches an olfactory sensor can essentially be a scent sensor that detects and analyzes molecules in the air that mimics the olfactory skills of dogs (interpreted by examiner as the detection animals) using artificial intelligence. [0143] teaches using machine-learning process (interpreted by examiner as the one or more machine learning-based disease detection model) that can identify the distinctive characteristics of the disease-bearing samples where it can utilize any combination of information and analysis derived from the various sensors including the sight, sound and/or olfactory sensors. Olfactory sensors are not just limited to sampling a “scent” from just a breath, but sources of samples including, but not limited to breath, urine, stools, sweat, sebum, saliva, ear wax, etc. (interpreted by examiner as receive a first sensor data associated with a first set of detection animals that have been exposed to a biological sample of a patient)); receive a second sensor data associated with a second set of detection animals that have been exposed to the biological sample of the patient ([0005] teaches the method can then combine any one among the first, second, third or fourth vital sign to provide a higher confidence level diagnostic and [0143] teaches using machine-learning process that can identify the distinctive characteristics of the disease-bearing samples where it can utilize any combination of information and analysis derived from the various sensors including the sight, sound and/or olfactory sensors (interpreted by examiner as receive a second sensor data associated with a second set of detection animals that have been exposed to the biological sample of the patient));
Darling does not explicitly disclose calculate, based on the first sensor data, a first confidence score corresponding to a disease category associated with the biological sample, wherein the disease category comprises a plurality of disease states, and wherein the first confidence score indicates a likelihood of at least one of the disease states of the disease category being present in the patient; responsive to the first confidence score being greater than a first threshold score, and calculate, based on the second sensor data, one or more second confidence scores corresponding to one or more disease states in the disease category associated with the biological sample, wherein each confidence score indicates a likelihood of a respective disease state in the disease category being present in the patient, however Saito discloses:
calculate, based on the first sensor data, a first confidence score corresponding to a disease category associated with the biological sample, wherein the disease category comprises a plurality of disease states, and wherein the first confidence score indicates a likelihood of at least one of the disease states of the disease category being present in the patient ([abstract] teaches the trained CNN (interpreted by examiner as the ML-based disease-detection models of Darling) outputs at least one of a probability of the positivity and/or the negativity for the disease in the digestive organ, a severity level of the disease (interpreted by examiner as the disease state), or a probability corresponding to the invasion depth (infiltration depth) of the disease, based on a second endoscopic image of the digestive organ of the disease. [0276] teaches the trained CNN system outputs a continuous number between 0 and 1 as a probability score for blood contents per image (interpreted by examiner as calculate a first confidence score corresponding to a disease category associated with the biological sample, wherein the disease category comprises a plurality of disease states, and wherein the first confidence score indicates a likelihood of at least one of the disease states of the disease category being present in the patient)); responsive to the first confidence score being greater than a first threshold score ([0276] teaches primary outcome of the CNN system included the area under the receiver operating characteristic curve (ROC-AUC), the sensitivity, the degree of specificity, and the accuracy of the discrimination capability by the CNN system between images of blood contents and those of normal mucosa. The ROC curve was plotted by varying a threshold of a probability score, and the AUC was calculated to assess the degree of discrimination. The higher the probability score, the more the CNN system had confidence that the image included blood contents and [0277] teaches for the final classification by the CNN system, the threshold of a probability score was simply set at 0.5 (interpreted by examiner as the first confidence score being greater than a first threshold score)), and calculate, based on the second sensor data, one or more second confidence scores corresponding to one or more disease states in the disease category associated with the biological sample, wherein each confidence score indicates a likelihood of a respective disease state in the disease category being present in the patient ([0051]-[0054] teaches the final diagnosis result of the positivity or the negativity for the disease in the digestive organ, a severity level, or an invasion depth of the disease, the final diagnosis result being corresponding to the first endoscopic image, in which the trained CNN system outputs at least one of a probability of the positivity and/or the negativity for the disease in the digestive organ, a probability of the past disease, the severity level of the disease, the invasion depth of the disease, and a probability corresponding to the site where the image is captured, based on a second endoscopic image of the digestive organ, [0063] teaches it is possible to make the confirmation diagnosis by subjecting the selected subject to a measurement of an anti-H. pylori IgG level in the blood or urine, coproantibody test, or a urea breath test (interpreted by examiner as calculate, based on the second sensor data, one or more second confidence scores corresponding to one or more disease states in the disease category associated with the biological sample, wherein each confidence score indicates a likelihood of a respective disease state in the disease category being present in the patient)).
It would have been obvious for one of the ordinary skill in the art before the effective filling date of the claimed invention to have modified the (ML-based) disease-detection models of Darling to incorporate calculating confidence scores as taught by Saito, with the motivation of improving the detection accuracy of the probability of the negativity or the positivity for the disease, the probability of the past disease and the severity level of the disease (Saito at [0057]).
REGARDING CLAIM 2
Darling and Saito disclose the limitation of claim 1.
Darling further discloses:
The system of Claim 1, wherein: the first sensor data comprises data associated with a conditioned response of the first set of detection animals; and the second sensor data comprises data associated with a conditioned response of the second set of detection animals (Darling at [abstract] teaches at least one camera or video sensor that can result in a diagnosis or a microphone or other acoustic sensor that can result in another diagnosis and [0018] teaches an olfactory sensor can essentially be a scent sensor that detects and analyzes molecules in the air that mimics the olfactory skills of dogs (interpreted by examiner as the detection animals) using artificial intelligence (interpreted by examiner as the first/second sensor data comprises data associated with a conditioned response of the first/second set of detection animals)).
REGARDING CLAIM 3
Darling and Saito disclose the limitation of claim 2.
Darling further discloses:
The system of Claim 2, wherein the first sensor data and the second sensor data comprise data received from one or more of: one or more behavioral sensors, one or more physiological sensors, or one or more environmental sensors (Darling at [0144] teaches the method can capture two or more among an image, an olfactory sample, an audio sample or an nth biometric (such as temperature, blood pressure, or blood oxygenation) (interpreted by examiner as the one or more physiological sensors)).
REGARDING CLAIM 5
Darling and Saito disclose the limitation of claim 3.
Darling further discloses:
The system of Claim 3, wherein the one or more physiological sensors comprises one or more of: one or more heart rate sensors, one or more heart rate variability sensors, one or more temperature sensors, one or more breath rate sensors, one or more sweat rate sensors, one or more blood pressure sensors, one or more skin temperature sensors, one or more pupil size variability sensors, one or more salivary cortisol sensors, one or more galvanic skin response (GSR) sensors, one or more electroencephalogram (EEG) sensors, one or more functional near-infrared spectroscopy (fNIR) sensors, one or more functional magnetic resonance imaging (fMRI) scanners, one or more electromyography imaging (EMG) scanners, or one or more magnetic resonance imaging (MRI) scanners (Darling at [0144] teaches biometric such as temperature, blood pressure, or blood oxygenation for a particular subject (interpreted by examiner as wherein the one or more physiological sensors comprises one or more of: one or more temperature sensors, one or more blood pressure sensors or one or more skin temperature sensors)).
REGARDING CLAIM 6
Darling and Saito disclose the limitation of claim 3.
Darling further discloses:
The system of Claim 3, wherein the one or more environmental sensors comprise one or more of: one or more temperature sensors, one or more humidity sensors, one or more audio sensors, one or more gas sensors, or one or more air particulate sensors (Darling at [0061] teaches determine confidence level of each sensor input based on factors such environmental factors impacting a particular input (e.g., acoustic noise levels in terms of decibels or sound pressure levels or particulate matter levels in parts per million) (interpreted by examiner as wherein the one or more environmental sensors comprise one or more of: one or more audio sensors or one or more air particulate sensors)).
REGARDING CLAIM 7
Darling and Saito disclose the limitation of claim 1.
Darling further discloses:
The system of Claim 1, wherein: the first set of detection animals is conditioned to detect the disease category of the biological sample; and the second set of detection animals is conditioned to detect the disease state of the biological sample (Darling at [0140] teaches an olfactory sensor can essentially be a scent sensor that detects and analyzes molecules in the air that mimics the olfactory skills of dogs using artificial intelligence. “Electronic noses” have been shown to be around 96% accurate in detecting lung cancer in patients. [0143] teaches the system can use a machine learning process that identifies the distinctive characteristics of the disease-bearing samples where it can utilize any combination of information and analysis derived from the various sensors including the sight, sound, and/or olfactory sensors. Olfactory sensor are not just limited to sampling a “scent” from just a breath, but sources of samples including, but not limited to breath, urine, stools, sweat, sebum, saliva, ear wax, etc. (interpreted by examiner as the first set of detection animals is conditioned to detect the disease category of the biological sample and the second set of detection animals is conditioned to detect the disease state of the biological sample)).
REGARDING CLAIM 8
Darling and Saito disclose the limitation of claim 1.
Darling further discloses:
The system of Claim 1, wherein: the system further comprises one or more breath sensors; and the models are further operable to: detect volatile organic compounds (VOCs) in the biological sample from the one or more breath sensors, wherein presence of the VOCs validates the biological sample as containing biological material from the patient (Darling at [0143] teaches olfactory sensor are not just limited to sampling a “scent” from just a breath, but sources of samples including, but not limited to breath, urine, stools, sweat, sebum, saliva, ear wax, etc. (interpreted by examiner as the system further comprises one or more breath sensors) [0141] teaches the olfactory sensor can be a sensor for measuring volatile organic compounds or VOCs. In some embodiments, the olfactory sensor can include a sensor that senses a relative value for total VOCs or an equivalent CO.sub.2 (eCO.sub.2). In some embodiments, VOCs can be further specified to be either endogenous or exogenous. Thus, embodiments can measure VOC emissions both emanating from within the body as well as VOC emissions due to external effects of the environment of the body (interpreted by examiner as detect volatile organic compounds (VOCs) in the biological sample from the one or more breath sensors, wherein presence of the VOCs validates the biological sample as containing biological material from the patient)).
REGARDING CLAIM 9
Darling and Saito disclose the limitation of claim 8.
Darling further discloses:
The system of Claim 8, wherein the one or more breath sensors are selected from the group comprising: TVOC sensor, breath VOC sensor, relative humidity sensors, temperature sensor, photoionization detector (PID), flame ionization detector (FID), and metal oxide (MOX) sensor (Darling at [0141] teaches a sensor that senses a relative value for total VOCs or an equivalent CO.sub.2 (eCO.sub.2). Thus, embodiments can measure VOC emissions both emanating from within the body and [0143] teaches the olfactory sensor sampling a “scent” from just a breath).
REGARDING CLAIM 10
Darling and Saito disclose the limitation of claim 1.
Darling further discloses:
The system of Claim 1, wherein the models are further operable to: receive a third sensor data associated with the first set of detection animals or the second set of detection animals that have been exposed to one or more of a service sample, and calculate, based on the third sensor data, one or more confidence scores, each corresponding to a positive control category or a negative control category (Darling at [0018] teaches an olfactory sensor can essentially be a scent sensor that detects and analyzes molecules in the air that mimics the olfactory skills of dogs using artificial intelligence. “Electronic noses” have been shown to be around 96% accurate in detecting lung cancer in patients. [0143] teaches olfactory sensor are not just limited to sampling a “scent” from just a breath, but sources of samples including, but not limited to breath, urine, stools, sweat, sebum, saliva, ear wax, etc. The system can use a machine learning process that identifies the distinctive characteristics of the disease-bearing samples where it can utilize any combination of information and analysis derived from the various sensors (interpreted by examiner as receive a third sensor data associated with the first set of detection animals or the second set of detection animals that have been exposed to one or more of a service sample) [0028] teaches a first confidence score determined from the image signal and a second confidence score determined from the acoustic information, a third confidence score determined from the olfactory information, an nth confidence score from an nth sensor, and a combined confidence score having a higher confidence score than the first confidence score or the other confidence scores (interpreted by examiner as calculate, based on the third sensor data, one or more confidence scores, each corresponding to a positive control category or a negative control category)).
REGARDING CLAIM 14
Darling and Saito disclose the limitation of claim 1.
Darling further discloses:
The system of Claim 1, wherein the models are further operable to: identify the biological sample as associated with at least one of the disease states of the disease category when the first confidence score is equal to or greater than a threshold value; or identify the biological sample as not associated with at least one of the disease states of the disease category when the first confidence score is less than the threshold value (Darling at [0145] teaches including an nth biometric sample capture of the subject where ultimately an estimate of an nth vital sign of the subjected is obtained corresponding to a diagnosis. Each estimate or a subset of the estimates can be combined to provide a higher confidence level of the diagnosis. If the overall diagnosis exceeds a predetermined threshold level for confidence, then an alert is triggered and the subject is retested, quarantined and/or administered medicine (interpreted by examiner as identify the biological sample as associated with at least one of the disease states of the disease category when the first confidence score is equal to or greater than a threshold value). [0064] teaches some sensors 101 provide confidence indicators explicitly, while others may provide confidence implicitly in that they do not provide any data when confidence is below determined thresholds. In yet other cases confidence indicators may not be provided at all such as when image or audio data of insufficient reliability is used (interpreted by examiner as identify the biological sample as not associated with at least one of the disease states of the disease category when the first confidence score is less than the threshold value)).
REGARDING CLAIM 15
Claim 15 is analogous to Claim 14 thus Claim 15 is similarly analyzed and rejected in a manner consistent with the rejection of Claim 14.
REGARDING CLAIM 18
Darling and Saito disclose the limitation of claim 1.
Darling further discloses:
The system of Claim 1, wherein the biological sample is one or more of breath, saliva, urine, stool, skin emanations, tissue, or blood (Darling at [0143] teaches sensor are not just limited to sampling a “scent” from just a breath, but sources of samples including, but not limited to breath, urine, stools, sweat, sebum, saliva, ear wax, etc.).
REGARDING CLAIM 19
Darling and Saito disclose the limitation of claim 1.
Darling further discloses:
The system of Claim 1, wherein the disease category is selected from a group consisting of: cancer, liver disease, gastrointestinal disease, neurological disease, metabolic disease, vascular disease, and infectious disease (Darling at [0054] teaches the innovations and improvements described herein are presented in terms of specific implementations that address disease or viral detection and pre-screening or diagnosis, particularly diseases such as COVID-19, Alzheimer's, dementia, breast cancer, lung cancer, prostate cancer, ovarian cancer, throat cancer, mouth cancer, gum cancer, tongue cancer, melanoma, skin cancers, eye disease and many others (interpreted by examiner as cancer and infectious disease)).
REGARDING CLAIM 20
Darling and Saito disclose the limitation of claim 1.
Darling further discloses:
The system of Claim 19, wherein the disease category is cancer, and the one or more disease states is selected from a group consisting of: breast cancer, lung cancer, prostate cancer, brain cancer, bladder cancer, ovarian cancer, skin cancer, colorectal cancer, kidney cancer, lower urinary tract cancer, a carcinoid, pancreatic cancer, cervical cancer, endometrial cancer, vulvar cancer, stomach cancer, oropharyngeal cancer, appendicular cancer, mesothelioma cancer, thymoma cancer, and thyroid cancer (Darling at [0054] teaches disease or viral detection and pre-screening or diagnosis, particularly diseases such as breast cancer, lung cancer, prostate cancer, ovarian cancer and many others).
REGARDING CLAIM 21
Claim 21 is analogous to Claim 1 thus Claim 21 is similarly analyzed and rejected in a manner consistent with the rejection of Claim 1.
Darling further discloses:
receiving a test kit, wherein the test kit comprises a biological sample from a patient (Darling at [0127] teaches an AI pre-screening tool that could test the whole world on a daily, or even hourly basis or essentially in real time and [0143] teaches analysis of olfactory information can identify distinctive characteristics of disease-bearing samples utilizing disease diagnostic tools and [0147] teaches COVID 19 test that an individual can perform (interpreted by examiner as test kit comprising biological sample from a patient)); exposing the biological sample to a first set of detection animals ([0018] teaches an olfactory sensor can essentially be a scent sensor that detects and analyzes molecules in the air that mimics the olfactory skills of dogs (interpreted by examiner as the detection animal) using artificial intelligence. The scent sensor can detect and identify tiny traces of different molecules and machine learning can help interpreting those molecules similar to how a dog infers patterns for scent and [0139] teaches these sensors can be combined with a machine-learning process that can identify the distinctive characteristics of the disease-bearing samples (interpreted by examiner as exposing the biological sample to a first set of detection animals)).
REGARDING CLAIM 22
Claim 22 is analogous to Claim 2 thus Claim 22 is similarly analyzed and rejected in a manner consistent with the rejection of Claim 2.
REGARDING CLAIM 23
Claim 23 is analogous to Claim 10 thus Claim 22 is similarly analyzed and rejected in a manner consistent with the rejection of Claim 10.
REGARDING CLAIM 26
Claim 26 is analogous to Claim 14 thus Claim 26 is similarly analyzed and rejected in a manner consistent with the rejection of Claim 14.
REGARDING CLAIM 27
Claim 27 is analogous to Claims 14 and 15 thus Claim 27 is similarly analyzed and rejected in a manner consistent with the rejection of Claims 14 and 15.
REGARDING CLAIM 30
Claim 30 is analogous to Claim 7 thus Claim 30 is similarly analyzed and rejected in a manner consistent with the rejection of Claim 7.
REGARDING CLAIM 31
Claim 31 is analogous to Claim 18 thus Claim 31 is similarly analyzed and rejected in a manner consistent with the rejection of Claim 18.
REGARDING CLAIM 32
Claim 32 is analogous to Claim 19 thus Claim 32 is similarly analyzed and rejected in a manner consistent with the rejection of Claim 19.
REGARDING CLAIM 33
Claim 33 is analogous to Claim 20 thus Claim 33 is similarly analyzed and rejected in a manner consistent with the rejection of Claim 20.
REGARDING CLAIMS 34 and 35
Claims 34 and 35 are analogous to Claims 1-3, 7, 10, 14, 15, 21-23, 26, 27 and 30 thus Claims 34 and 35 are similarly analyzed and rejected in a manner consistent with the rejection of Claims 1-3, 7, 10, 14, 15, 21-23, 26, 27 and 30.
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Darling (US 2023/0060676), in view of Saito (US 2022/0020496) and in further view of Kjellsen (US 2022/0087220).
REGARDING CLAIM 4
Darling and Saito disclose the limitation of claims 1 and 3.
Darling and Saito do not explicitly disclose wherein the one or more behavioral sensors of the detection animal comprises one or more of: a face gesture of the detection animal, tail movements of the detection animal, landmarks on a skeleton model of the detection animal a duration of a sniff from the detection animal, a sniff intensity, a number of repeated sniffs, a pose of the detection animal, whether the detection animal looks at its handler, a pressure of a nose of the detection animal against a sampling port, or auditory features of the sniff, however Kjellsen further discloses:
The system of Claim 3, wherein the one or more behavioral sensors of the detection animal comprises one or more of: a face gesture of the detection animal, tail movements of the detection animal, landmarks on a skeleton model of the detection animal a duration of a sniff from the detection animal, a sniff intensity, a number of repeated sniffs, a pose of the detection animal, whether the detection animal looks at its handler, a pressure of a nose of the detection animal against a sampling port, or auditory features of the sniff (Kjellsen at [0058] teaches when the canine finds the source of the target scent or odor, the canine performs an alert behavior. Passive alert behavior include the trained canine lying down, staring, or sitting in front of the source of the target odor. Passive and/or active alert behavior is detected by means of sensors which include canine body language detectors (interpreted by examiner as wherein the one or more behavioral sensors of the detection animal comprises auditory features of the sniff)).
It would have been obvious for one of the ordinary skill in the art before the effective filling date of the claimed invention to have modified the sensor data of Darling and Saito to incorporate the behavioral sensor including an auditory features of the sniff as taught by Kjellsen, with the motivation of quickly and accurately screening people for viruses (Kjellsen at [0032]).
Claims 11-13, 24 and 25 are rejected under 35 U.S.C. 103 as being unpatentable over Darling (US 2023/0060676), in view of Saito (US 2022/0020496) and in further view of Hall (US 2018/0092335).
REGARDING CLAIM 11
Darling and Saito disclose the limitation of claims 1 and 10.
Darling and Saito do not explicitly disclose wherein the first and second sets of detection animals are exposed to each of the biological sample and the service sample via a sampling port, however Hall further discloses:
The system of Claim 10, wherein the first and second sets of detection animals are exposed to each of the biological sample and the service sample via a sampling port (Hall at [0003] teaches that studies show that a variety of biological substances collected from a diseased human emit substances that animals distinguish from those of healthy humans, animals (interpreted by examiner as the detection animal of Darling and Saito) identifying disease include those in which the animal evaluated feces, urine, blood, and exhaled breath. Fig. 4A and [0047] show/teach deposited bodily waste (interpreted by examiner as the biological sample of Darling and Saito) into the medical toilet and animal is shown sniffing VOC’s emanating from the scent dispenser in the toilet (interpreted by examiner as the sampling port) and measuring an analyte in the bodily waste that is an indicator of the same disease that the animal is trained to associate with a specific scent (interpreted by examiner as the first and second sets of detection animals are exposed to each of the biological sample and the service sample via a sampling port)).
It would have been obvious for one of the ordinary skill in the art before the effective filling date of the claimed invention to have modified the first and second sets of detection animals of Darling and Saito to incorporate exposing the first and second sets of detection animals to the biological sample and the service sample via a sampling port as taught by Hall, with the motivation of diagnosing with more sensitivity and specificity than available laboratory assays. (Hall at [0003]).
REGARDING CLAIM 12
Darling and Saito disclose the limitation of claims 1 and 3.
Darling and Saito do not explicitly disclose wherein the sampling port is fluidly connected to one or more receptacles of a plurality of receptacles, each receptacle operable to hold the biological sample or the service sample, however Hall further discloses:
The system of Claim 11, wherein the sampling port is fluidly connected to one or more receptacles of a plurality of receptacles, each receptacle operable to hold the biological sample or the service sample (Hall at Figs. 4A and 4B show deposited bodily waste into the medical toilet and animal is shown sniffing VOC’s emanating from the scent dispenser in the toilet (interpreted by examiner as the sampling port is fluidly connected to one or more receptacles of a plurality of receptacles, each receptacle operable to hold the biological sample or the service sample)).
It would have been obvious for one of the ordinary skill in the art before the effective filling date of the claimed invention to have modified the systems of Darling and Saito to incorporate the sampling port as taught by Hall, with the motivation of providing a medical toilet comprising a medical device, a scent dispenser and a conduit to transfer volatile organic compounds from the toilet bowl to the environment outside the toilet, where an animal is trained to identify a scent that is associated with the disease and perform a defined act when the animal perceives scent associated with the disease. (Hall at [Claim 1]).
REGARDING CLAIM 13
Darling and Saito disclose the limitation of claims 1 and 3.
Darling further discloses:
The system of Claim 12, wherein the models are further operable to determine which of a particular sample to expose to the first set of detection animals or the second set of detection animals, wherein the particular sample is selected from a group consisting of: the biological sample from the patient and the service sample (darling at [0143] teaches the system can use a machine learning process that identifies the distinctive characteristics of the disease-bearing samples where it can utilize any combination of information and analysis derived from the various sensors, and that the olfactory sensor are not just limited to sampling a “scent” from just a breath, but sources of samples including, but not limited to breath, urine, stools, sweat, sebum, saliva, ear wax, etc. (interpreted by examiner as the particular sample is selected from a group consisting of: the biological sample from the patient and the service sample)).
REGARDING CLAIM 24
Claim 24 is analogous to Claim 11 thus Claim 24 is similarly analyzed and rejected in a manner consistent with the rejection of Claim 11.
REGARDING CLAIM 25
Claim 25 is analogous to Claim 13 thus Claim 24 is similarly analyzed and rejected in a manner consistent with the rejection of Claim 13.
Claims 16 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Darling (US 2023/0060676), in view of Saito (US 2022/0020496), in view of Hall (US 2018/0092335) and in further view of Namsaraev (US 2018/0092335).
REGARDING CLAIM 16
Darling, Saito and Hall disclose the limitation of claims 13.
Darling, Saito and Hall do not explicitly disclose wherein the respective disease state is identified with a sensitivity of at least approximately 90%, however Namsaraev further discloses:
The system of Claim 13, wherein the respective disease state is identified with a sensitivity of at least approximately 90% (Namsaraev at [0177] teaches sensitivity of at least 90% for a set of markers indicative of a tumor (interpreted by examiner as the respective disease state is identified with a sensitivity of at least approximately 90%)).
It would have been obvious for one of the ordinary skill in the art before the effective filling date of the claimed invention to have modified the identification of disease state of Darling, Saito and Hall to incorporate the respective disease state is identified with a sensitivity of at least approximately 90% as taught by Namsaraev, with the motivation of correctly identify a proportion of the population that truly has a condition. (Namsaraev at [0177]).
REGARDING CLAIM 17
Darling, Saito and Hall disclose the limitation of claims 13.
Darling, Saito and Hall do not explicitly disclose wherein the respective disease state is identified with a specificity of at least approximately 94%, however Namsaraev further discloses:
The system of Claim 13, wherein the respective disease state is identified with a specificity of at least approximately 94% (Namsaraev at [0184] teaches specificity of at least 94% for a set of markers indicative of a tumor (interpreted by examiner as the respective disease state is identified with a specificity of at least approximately 94%)).
It would have been obvious for one of the ordinary skill in the art before the effective filling date of the claimed invention to have modified the identification of disease state of Darling, Saito and Hall to incorporate the respective disease state is identified with a specificity of at least approximately 94% as taught by Namsaraev, with the motivation of correctly identify a proportion of the population that truly has a condition. (Namsaraev at [0177]).
Claims 28 and 29 are rejected under 35 U.S.C. 103 as being unpatentable over Darling (US 2023/0060676), in view of Saito (US 2022/0020496) and in further view of Namsaraev (US 2018/0092335).
REGARDING CLAIM 28
Darling and Saito disclose the limitation of claims 27.
Darling and Saito do not explicitly disclose wherein the respective disease state is identified with a sensitivity of at least approximately 90%, however Namsaraev further discloses:
The system of Claim 27, wherein the respective disease state is identified with a sensitivity of at least approximately 90% (Namsaraev at [0177] teaches sensitivity of at least 90% for a set of markers indicative of a tumor (interpreted by examiner as the respective disease state is identified with a sensitivity of at least approximately 90%)).
It would have been obvious for one of the ordinary skill in the art before the effective filling date of the claimed invention to have modified the identification of disease state of Darling and Saito to incorporate the respective disease state is identified with a sensitivity of at least approximately 90% as taught by Namsaraev, with the motivation of correctly identify a proportion of the population that truly has a condition. (Namsaraev at [0177]).
REGARDING CLAIM 29
Darling and Saito disclose the limitation of claims 27.
Darling and Saito do not explicitly disclose wherein the respective disease state is identified with a specificity of at least approximately 94%, however Namsaraev further discloses:
The system of Claim 27, wherein the respective disease state is identified with a specificity of at least approximately 94% (Namsaraev at [0184] teaches specificity of at least 94% for a set of markers indicative of a tumor (interpreted by examiner as the respective disease state is identified with a specificity of at least approximately 94%)).
It would have been obvious for one of the ordinary skill in the art before the effective filling date of the claimed invention to have modified the identification of disease state of Darling and Saito to incorporate the respective disease state is identified with a specificity of at least approximately 94% as taught by Namsaraev, with the motivation of correctly identify a proportion of the population that truly has a condition. (Namsaraev at [0177]).
Claims 36-49 are rejected under 35 U.S.C. 103 as being unpatentable over Darling (US 2023/0060676) and in further view of Mischley (US 2020/0008395).
REGARDING CLAIM 36
Darling discloses a method for determining a progression of a disease in a patient undergoing a treatment (Darling at [0131] teaches the MIT group has proven that COVID-19 can be discriminated with 98.5% accuracy using only a forced-cough and an AI biomarker focused approach that also creates an explainable diagnostic in the form of a disease progression saliency chart) comprising: accessing patient data indicating the patient previously tested positive for a first disease state in a disease category and has subsequently received treatment for the disease (Darling at [claim 19] teaches dashboard or presentation board providing the status for the subject with the higher confidence level diagnostic for a suspected disease or virus among Covid-19, Alzheimer's, tuberculosis, dementia, breast cancer, lung cancer, prostate cancer, ovarian cancer, throat cancer, mouth cancer, gum cancer, tongue cancer, melanoma, skin cancers, eye disease. [0130] teaches the MIT Open Voice Medicine architecture uses the same four biomarkers previously tested for the detection of Alzheimer's which achieved above state-of-the-art accuracy and [0131] teaches the model detected all of theCOVID-19 positive asymptomatic patients, 100% of them, a finding consistent with other approaches eliciting the diagnostic value of speech. [0134] further teaches when an individual takes a video and speaks or coughs into the mobile device, that video is sent to the cloud where the AI reads or analyzes the images and the voice to provide an accurate result of the individual's medical status (interpreted by examiner as accessing patient data indicating the patient previously tested positive for a first disease state in a disease category and has subsequently received treatment for the disease));
Darling does not explicitly disclose receiving a new test kit at a time after the patient has received the treatment for the disease, wherein the new test kit comprises a new biological sample from the patient; exposing the new biological sample to a set of detection animals; identifying the new biological sample as being associated with a second disease state; comparing the second disease state with the first disease state; and determining the progression of the disease in the patient after the treatment based on the comparing, however Mischley discloses:
receiving a new test kit at a time after the patient has received the treatment for the disease, wherein the new test kit comprises a new biological sample from the patient; exposing the new biological sample to a set of detection animals (Mischley at [0017] teaches a trained animal is provided a sample track that includes one or more test samples (e.g., duplicates or triplicates), one or more negative control samples derived from non-PD sources, and one or more positive or PD standard samples derived from one or more PD sources to improve confidence in the detection and [0043] teaches biological samples or excrements may be collected from the ear canal, anus, saliva, throat, skin, and urine of one or more PD sources. Thus, the types of excrements that may be used in the training sample include, for example, cerumen (ear wax), skin cells (e.g., taken from underarms, face, feet, groin, which include a combination of sebum, sweat, epithelial cells, and dermal microbiota), organisms and their metabolites from the dermal microbiome, stool (including intestinal microbiome, sloughed intestinal epithelial cells, byproducts of metabolism), urine, sebum, saliva, and exhaled breath (interpreted by examiner as receiving a new test kit at a time after the patient has received the treatment for the disease, wherein the new test kit comprises a new biological sample from the patient and exposing the new biological sample to a set of detection animals)); identifying the new biological sample as being associated with a second disease state (Mischley at [0016] teaches allowing a trained animal that has been trained to identify Parkinson-distinctive scent to smell the test subject or a test sample containing excrement extracted from the test subject and [0031] teaches PRO-PD is the Patient Reported Outcomes in Parkinson's Disease rating scale, which is used to describe PD disease severity among individuals with a clinical diagnosis of PD (interpreted by examiner as identifying the new biological sample as being associated with a second disease state)); comparing the second disease state with the first disease state (Mischley at [0032] and fig. 2 teach that the PRO-PD rating scale, could be used to screen and identify individuals with non-motor/nonspecific symptoms of PD, thereby increasing the likelihood of early detection. [0035] teaches people with PD, including pre-motor and prodromal PD, have “unique molecular profiles,” which are likely to overlap with clinical symptoms. These “unique molecular profiles” are associated with an “aromatic cloud,” also referred to as Parkinson-distinctive scent. At least in one incident, the distinct scent in PD was reportedly detectable by a human who, after having attended to her husband inflicted with PD, could identify PD sufferers by their smells and [0036] teaches capable of detecting Parkinson-distinct scent before an individual even exhibits motor symptoms or receives a PD clinical diagnosis (i.e., pre-motor/prodromal PD) and because it enables samples from individuals to be sent to a lab for detection (interpreted by examiner as means to compare the second disease state with the first disease state)); and determining the progression of the disease in the patient after the treatment based on the comparing (Mischley at [0031] teaches implementation of an intervention in the individual with pre-motor PD can reduce the mean rate of PD progression from 37 points/year to <16 on the PRO-PD scale. FIG. 1 further shows that the reduction in rate (i.e., slope) of disease progression means less when the treatment is initiated later in the course of the disease (interpreted by examiner as determining the progression of the disease in the patient after the treatment based on the comparing)).
It would have been obvious for one of the ordinary skill in the art before the effective filling date of the claimed invention to have modified the method for determining a progression of a disease in a patient of Darling to incorporate receiving a new test kit comprising a new biological sample from the patient, exposing the new biological sample to a set of detection animals, identifying the new biological sample as being associated with a second disease state, comparing the second disease state with the first disease state and determining the progression of the disease in the patient after the treatment based on the comparing as taught by Mischley, with the motivation of increasing the likelihood of early detection and improving personal and public health planning and preparedness. (Mischley at [0032] and [0034]).
REGARDING CLAIM 37
Darling and Mischley disclose the limitation of claims 36.
Darling further discloses:
The method of Claim 36, further comprising, accessing a second sensor data associated with the set of detection animals and processing, using a first ML-based disease-detection model trained on a first dataset of detection events ([abstract] teaches diagnosing a disease or virus or other illness and capture and output biometric data corresponding to the subject. [0004] teaches a system that enables a patient or a subject to perform a method of combining different sensor data for higher reliable diagnosis information. [0005] teaches the method can then combine any one among the first, second, third or fourth vital sign to provide a higher confidence level diagnostic. [0139] teaches sensors can be combined with a machine-learning process that can identify the distinctive characteristics of the disease-bearing samples and [0140] teaches an olfactory sensor can essentially be a scent sensor that detects and analyzes molecules in the air that mimics the olfactory skills of dogs using artificial intelligence. The scent sensor can detect and identify tiny traces of different molecules and machine learning can help interpreting those molecules similar to how a dog infers patterns for scent (interpreted by examiner as accessing a second sensor data associated with the set of detection animals and processing, using a first ML-based disease-detection model trained on a first dataset of detection events)), the second sensor data to calculate a second confidence score corresponding to the second disease state associated with the new biological sample ([abstract] teaches a sensor fusion component can receive and combine the biometric data and the various diagnoses results and further determine a confidence score and an event record creator compiles the biometric data and the confidence scores to create an event record having a higher confidence score with respect to a final diagnosis result. [0028] teaches a first confidence score determined from the image signal and a second confidence score determined from the acoustic information, a third confidence score determined from the olfactory information, an nth confidence score from an nth sensor, and a combined confidence score having a higher confidence score than the first confidence score or the other confidence scores (interpreted by examiner as the second sensor data to calculate a second confidence score corresponding to the second disease state associated with the new biological sample)).
REGARDING CLAIM 38
Claim 38 is analogous to Claims 36 and 37 thus Claim 38 is similarly analyzed and rejected in a manner consistent with the rejection of Claims 36 and 37.
Darling further discloses:
prior to accessing the patient data: receiving a prior test kit, wherein the prior test kit comprises a prior biological sample from the patient (Darling at [0146] teaches the engine could be used on each estimate or a portion of the vital sign estimates before one or more the vital sign estimates are combined (interpreted by examiner as prior to accessing the patient data) and [0130] teaches using the same four biomarkers previously tested for the detection of Alzheimer's which achieved above state-of-the-art accuracy (interpreted by examiner as receiving a prior test kit, wherein the prior test kit comprises a prior biological sample from the patient)); accessing a first sensor data associated with the set of detection animals (Darling at [0139] teaches sensors can be combined with a machine-learning process that can identify the distinctive characteristics of the disease-bearing samples); processing, using a first ML-based disease-detection model trained on a first dataset of detection events, the first sensor data to calculate a first confidence score corresponding to the first disease state associated with the prior biological sample (Darling at [abstract] teaches a sensor fusion component can receive and combine the biometric data and the various diagnoses results and further determine a confidence score and an event record creator compiles the biometric data and the confidence scores to create an event record having a higher confidence score with respect to a final diagnosis result. [0140] teaches the scent sensor can detect and identify tiny traces of different molecules and machine learning can help interpreting those molecules similar to how a dog infers patterns for scent and [0028] teaches a first confidence score determined from the image signal and a second confidence score determined from the acoustic information, a third confidence score determined from the olfactory information, an nth confidence score from an nth sensor, and a combined confidence score having a higher confidence score than the first confidence score or the other confidence scores (interpreted by examiner as processing, using a first ML-based disease-detection model trained on a first dataset of detection events, the first sensor data to calculate a first confidence score corresponding to the first disease state associated with the prior biological sample)); and identify the biological sample as associated with the first disease state when the first confidence score is equal to or greater than a threshold value (Darling at [0145] teaches including an nth biometric sample capture of the subject where ultimately an estimate of an nth vital sign of the subjected is obtained corresponding to a diagnosis. Each estimate or a subset of the estimates can be combined to provide a higher confidence level of the diagnosis. If the overall diagnosis exceeds a predetermined threshold level for confidence, then an alert is triggered and the subject is retested, quarantined and/or administered medicine (interpreted by examiner as identify the biological sample as associated the first disease states when the first confidence score is equal to or greater than a threshold value)).
REGARDING CLAIM 39
Darling and Mischley disclose the limitation of claims 36.
Darling further discloses:
The method of Claim 38, wherein: the new biological sample is one or more of breath, saliva, urine, stool, skin emanations, tissue, or blood; and the prior biological sample is one or more of breath, saliva, urine, stool, skin emanations, tissue, or blood (Darling at [0143] teaches sensor are not just limited to sampling a “scent” from just a breath, but sources of samples including, but not limited to breath, urine, stools, sweat, sebum, saliva, ear wax, etc.).
REGARDING CLAIM 40
Darling and Mischley disclose the limitation of claims 36.
Darling further discloses:
The method of Claim 39, wherein the prior biological sample and the new biological sample are of a same sample type (Darling at [0143] teaches sensor are not just limited to sampling a “scent” from just a breath, but sources of samples including, but not limited to breath, urine, stools, sweat, sebum, saliva, ear wax, etc. (interpreted by examiner as wherein the prior biological sample and the new biological sample are of a same sample type)).
REGARDING CLAIM 41
Darling and Mischley disclose the limitation of claims 36.
Darling further discloses:
The method of Claim 36, wherein the disease category is selected from a group consisting of: cancer, liver disease, gastrointestinal disease, neurological disease, metabolic disease, vascular disease, and infectious disease (Darling at [0054] teaches the innovations and improvements described herein are presented in terms of specific implementations that address disease or viral detection and pre-screening or diagnosis, particularly diseases such as COVID-19, Alzheimer's, dementia, breast cancer, lung cancer, prostate cancer, ovarian cancer, throat cancer, mouth cancer, gum cancer, tongue cancer, melanoma, skin cancers, eye disease and many others (interpreted by examiner as cancer and infectious disease)).
REGARDING CLAIM 42
Darling and Mischley disclose the limitation of claims 41.
Darling further discloses:
The method of Claim 41, wherein the disease category is cancer, and the disease state is selected from a group consisting of: breast cancer, lung cancer, prostate cancer, brain cancer, bladder cancer, ovarian cancer, skin cancer, colorectal cancer, kidney cancer, lower urinary tract cancer, a carcinoid, pancreatic cancer, cervical cancer, endometrial cancer, vulvar cancer, stomach cancer, oropharyngeal cancer, appendicular cancer, mesothelioma cancer, thymoma cancer, and thyroid cancer (Darling at [0054] teaches disease or viral detection and pre-screening or diagnosis, particularly diseases such as breast cancer, lung cancer, prostate cancer, ovarian cancer and many others).
REGARDING CLAIM 43
Darling discloses a method for training a detection animal to provide a conditioned response to be used with a machine learning-based (ML-based) disease-detection system comprising steps of: exposing a detection animal to a first biological sample from a subject having a target disease state ([abstract] teaches diagnosing a disease or virus or other illness. [0004] teaches combining different sensor data for higher reliable diagnosis information such as diagnosing for COVID-19, Alzheimer's, dementia, tuberculosis, breast cancer, lung cancer, prostate cancer, ovarian cancer, throat cancer, mouth cancer, gum cancer, tongue cancer, melanoma, skin cancers, eye disease or other viruses or diseases. [0018] teaches an olfactory sensor can essentially be a scent sensor that detects and analyzes molecules in the air that mimics the olfactory skills of dogs using artificial intelligence. The scent sensor can detect and identify tiny traces of different molecules and machine learning can help interpreting those molecules similar to how a dog infers patterns for scent. “Electronic noses” have been shown to be around 96% accurate in detecting lung cancer in patients and [0143] teaches the system can use a machine learning process that identifies the distinctive characteristics of the disease-bearing samples where it can utilize any combination of information and analysis derived from the various sensors including the sight, sound, and/or olfactory sensors); inputting, to the disease-detection system, a first sensor data corresponding to the detection animal, wherein the first sensor data is associated with presence of the target disease state (Darling at [0147] teaches a COVID19 test is required to prove they are not positive for COVID19 (interpreted by examiner as inputting, to the disease-detection system, a first sensor data corresponding to the detection animal, wherein the first sensor data is associated with presence of the target disease state)); storing tangibly, in a memory of a computer processor, the first sensor data to obtain a dataset of detection events (Darling at [0028] teaches a data storage device in the mobile device stores an event record indicating the identity of the subject (interpreted by examiner as storing tangibly, in a memory of a computer processor, the first sensor data to obtain a dataset of detection events)); and training the ML-based disease-detection system to detect the disease state based on the dataset of detection events ([0018] teaches an olfactory sensor can essentially be a scent sensor that detects and analyzes molecules in the air that mimics the olfactory skills of dogs using artificial intelligence. The scent sensor can detect and identify tiny traces of different molecules and machine learning can help interpreting those molecules similar to how a dog infers patterns for scent (interpreted by examiner as training the ML-based disease-detection system to detect the disease state based on the dataset of detection events)).
Darling does not explicitly disclose training the detection animal to provide the conditioned response by providing the detection animal with a reward for identifying the target disease state, however Mischley discloses:
training the detection animal to provide the conditioned response by providing the detection animal with a reward for identifying the target disease state (Mischley at [0007] teaches correct identification of Parkinson-distinctive scent requires positive reinforcement of the desired behavior (i.e., detection and signaling) during a multi-stage training process, in which an animal, typically a canine, begins the training with a positive association of the Parkinson-distinctive scent with a reward (e.g., a treat); followed by training the animal to discriminate between Parkinson-distinctive scent from other scents (controls) (interpreted by examiner as training the detection animal to provide the conditioned response by providing the detection animal with a reward for identifying the target disease state));
It would have been obvious for one of the ordinary skill in the art before the effective filling date of the claimed invention to have modified the method the training and the conditioned response of Darling to incorporate training the detection animal to provide the conditioned response by providing the detection animal with a reward for identifying the target disease state as taught by Mischley, with the motivation of training the animal to discriminate between the training sample and the control sample by allowing the animal to smell the training sample and the control sample in the absence of any treat and rewarding the animal with a treat only when the animal correctly identifies the training sample. (Mischley at [0012]).
REGARDING CLAIM 44
Darling and Mischley disclose the limitation of claims 36.
Darling does not explicitly disclose the conditioned response comprises a body pose of the detection animal, however Mischley further discloses:
The method of Claim 43, wherein the conditioned response comprises a body pose of the detection animal (Mischley at [0007] teaches correct identification of Parkinson-distinctive scent requires positive reinforcement of the desired behavior (i.e., detection and signaling) during a multi-stage training process, in which an animal, typically a canine, begins the training with a positive association of the Parkinson-distinctive scent with a reward (e.g., a treat); followed by training the animal to discriminate between Parkinson-distinctive scent from other scents (controls). Correct identification can take any form so long as the animal's response to the Parkinson-distinctive scent can be signaled to the human handler in a consistent and reliable manner. For example, the animal may bark or wag its tail at a sample having Parkinson-distinctive scent or lead the handler to the sample (interpreted by examiner as wherein the conditioned response comprises a body pose of the detection animal)).
It would have been obvious for one of the ordinary skill in the art before the effective filling date of the claimed invention to have modified the method the conditioned response of Darling to incorporate the conditioned response comprises a body pose of the detection animal as taught by Mischley, with the motivation of training the animal to discriminate between the training sample and the control sample by allowing the animal to smell the training sample and the control sample in the absence of any treat and rewarding the animal with a treat only when the animal correctly identifies the training sample. (Mischley at [0012]).
REGARDING CLAIM 45
Darling and Mischley disclose the limitation of claims 36.
Darling does not explicitly disclose repeating each of the steps until a threshold sensitivity is reached by the detection animal, however Darling Mischley further discloses:
The method of Claim 43, further comprising repeating each of the steps until a threshold sensitivity is reached by the detection animal (Mischley at [0082] teaches that there are numerous quality control measures in place. For instance, any dog that incorrectly signals to the Standards three times in a day will be retired for the day and is not permitted to return until he/she demonstrates a capacity to discern PD Standards from Control Standards with a sensitivity and specificity meeting published standards. Statistical analyses will be performed to determine the value added by running samples in duplicate or triplicate and the ultimate decision will be based on requirements of governing bodies (interpreted by examiner as repeating each of the steps until a threshold sensitivity is reached by the detection animal)).
It would have been obvious for one of the ordinary skill in the art before the effective filling date of the claimed invention to have modified the method of Darling to incorporate the conditioned response comprises a body pose of the detection animal as taught by Mischley, with the motivation of training the animal to discriminate between the training sample and the control sample by allowing the animal to smell the training sample and the control sample in the absence of any treat and rewarding the animal with a treat only when the animal correctly identifies the training sample. (Mischley at [0012]).
REGARDING CLAIM 46
Darling and Mischley disclose the limitation of claims 36.
Darling does not explicitly disclose repeating each of the steps until a threshold specificity is reached by the detection animal, however Mischley further discloses:
The method of Claim 43, further comprising repeating each of the steps until a threshold specificity is reached by the detection animal (Mischley at [0082] teaches that there are numerous quality control measures in place. For instance, any dog that incorrectly signals to the Standards three times in a day will be retired for the day and is not permitted to return until he/she demonstrates a capacity to discern PD Standards from Control Standards with a sensitivity and specificity meeting published standards. Statistical analyses will be performed to determine the value added by running samples in duplicate or triplicate and the ultimate decision will be based on requirements of governing bodies (interpreted by examiner as repeating each of the steps until a threshold specificity is reached by the detection animal)).
It would have been obvious for one of the ordinary skill in the art before the effective filling date of the claimed invention to have modified the method of Darling to incorporate the conditioned response comprises a body pose of the detection animal as taught by Mischley, with the motivation of training the animal to discriminate between the training sample and the control sample by allowing the animal to smell the training sample and the control sample in the absence of any treat and rewarding the animal with a treat only when the animal correctly identifies the training sample. (Mischley at [0012]).
REGARDING CLAIM 47
Claim 47 is analogous to Claim 41 thus Claim 47 is similarly analyzed and rejected in a manner consistent with the rejection of Claim 41.
REGARDING CLAIM 48
Claim 48 is analogous to Claim 42 thus Claim 48 is similarly analyzed and rejected in a manner consistent with the rejection of Claim 42.
REGARDING CLAIM 49
Claim 49 is analogous to Claim 39 thus Claim 49 is similarly analyzed and rejected in a manner consistent with the rejection of Claim 39.
Conclusion
The prior art made of record though not relied upon in the present basis of rejection are noted in the attached PTO 892 and include:
Alburty (US 2022/0125333) discloses multi-function face masks.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LIZA TONY KANAAN whose telephone number is (571)272-4664. The examiner can normally be reached on Mon-Thu 9:00am-6:00pm ET.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Robert Morgan can be reached on 571-272-6773. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from the Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docs for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/LIZA TONY KANAAN/Examiner, Art Unit 3683
/ROBERT W MORGAN/Supervisory Patent Examiner, Art Unit 3683