DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Status
Claims 1-30 are currently pending and under examination herein.
Claims 1-30 are rejected.
Priority
The instant application claims priority to US Provisional Application 63381269 filed 27 October 2022. In this action, claims 1-30 are examined as though they had an effective filing date of 27 October 2022. In future actions, the effective filing date of one or more claims may change, due to amendments to the claims, or further analysis of the disclosure(s) of the priority application(s).
Information Disclosure Statement
The information disclosure statement(s) (IDS) submitted on 16 April 2024 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Drawings
The drawings filed 27 October 2023 are accepted.
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.
Claims 1-30 are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
Claims 1, 6-8, 10, 17, 22-24, 26, and 30 recite an “angle separating” the “optical sensor and the apparatus” in multiple limitations. Claims 1, 17, and 30, from which the other cited claims depend, indicate the optical sensor is part of the apparatus (an apparatus configured to generate first light, detect the first light that has passed through at least a first portion of a sample, and measure intensity of the first light by at least one optical sensor). It is therefore unclear what specific angle is being referred to as the sensors are not separated from the apparatus, as currently claimed. Are there sensors that are part of and not part of the apparatus? The metes and bounds of the claims are therefore unclear rendering the claims indefinite. Claims 2-16 and 18-29 depend from claims 1 and 17, and thus contain the above issues due to said dependence.
Claims 1, 10, 13, 17, 26, 29, and 30 recites “the set of wavelengths”. Claims 1, 17, and 30, from which the other cites claims depend, recite “a set of wavelengths”, “the first set of wavelengths”, and “the set of wavelengths”. It is unclear if there are multiple sets of wavelength or only one. The metes and bounds of the “the set of wavelengths” is therefore unclear rendering the claims indefinite. Claims 2-16 and 18-29 depend from claims 1 and 17, and thus contain the above issues due to said dependence.
Claims 8, 9, 24, and 25 recite the term “substantially” to refer to both “180 degrees” and “the same”. The term “substantially” in these claim is a relative term which renders the claim indefinite. The term “substantially” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. The metes and bounds of the claims are there for unclear rendering the claims indefinite.
Claims 10 and 26 recites “detect the light” and “the portion of the sample”. Claim 1 and 17, upon which these depend, recites “first light”, “second light”, “first portion of a sample”, and “second portion of the sample”. It is unclear if these limitations in claims 10 and 26 are refereeing previously mentioned lights and portions. The metes and bounds of the claims are therefore unclear rendering the claim indefinite.
Claims 11 and 27 recite “a particular food processing facility”. The term “particular” in these claim is a relative term which renders the claim indefinite. The term “particular” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. The metes and bounds of the claims are there for unclear rendering the claims indefinite.
Claim 11 and 27 recite “wherein at least some training samples of the set of training samples”. Claims 1 and 17, from which Claim 11 and 27 depend, to not recite “set of training samples” or training of any kind. Previously claims only refer to using models that are already trained. The metes and bounds of the limitations are therefore unclear rendering the claims indefinite.
Claim 12 and 28 recite “the result”. There are multiple results recited in claims 1 and 17 (first and second), upon which claims 12 and 28 depend. It is unclear which result these claims are referring to. The metes and bounds of the limitations are unclear rendering the claims indefinite.
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 1-30 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. In accordance with MPEP § 2106, claims found to recite statutory subject matter (Step 1: YES) are then analyzed to determine if the claims recite any concepts that equate to an abstract idea or natural law (Step 2A, Prong 1). Claims 1-29 are directed to systems and Claim 30 is directed to a system. In the instant application, the claims recite the following limitations that equate to an abstract idea:
Claim 1 recite the limitation - applying a first trained model to the first set of intensity values to obtain a first result; applying a second trained model to the second set of intensity values to obtain a second result, the first and second trained model being different from each other; applying the first and second result to a trained multi-model to determine either a positive pathogen detection or a negative pathogen detection for the pathogen in the sample. Based on the broadest reasonable interpretation, applying machine learning models encompasses equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea. The claim also recites based on the first result, determining a first subset of wavelengths of the first set of wavelengths; generating a pathogen detection notification that indicates either the positive pathogen detection or the negative pathogen detection for the pathogen in the sample; and providing the pathogen detection notification. Based on the broadest reasonable interpretation, determining and providing information could practically be done by the human mind. This draws the limitation to a mental process, which classifies the limitation as an abstract idea.
Claim 2 recites the limitation - based on the output from the trained multi-model, determining a confidence level of the positive pathogen detection or the negative pathogen detection of the pathogen in the sample. Based on the broadest reasonable interpretation, determining a confidence vale associated with a model prediction encompasses equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea.
Claim 3 recites the limitation - based on the output from the trained multi-model, determining a distribution of pathogen particle size in the sample. Based on the broadest reasonable interpretation, determining a distribution associated with a model prediction encompasses equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea.
Claim 4 recites the limitation - based on the output from the trained multi-model, determining a concentration of the pathogen in the sample. Based on the broadest reasonable interpretation, determining a concertation associated with a model prediction encompasses equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea.
Claim 10 recites the limitation - applying a third trained model to the third set of intensity values to obtain a third result, wherein the determining either the positive pathogen detection or the negative pathogen detection for the pathogen in the sample is based on the output of the multi-model from the input of the first, second, and third results. Based on the broadest reasonable interpretation, applying a machine learning models to determine information encompasses equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea.
Claim 11 recites the limitation - wherein at least some training samples of the set of training samples correspond to a particular food processing facility, a region that includes multiple food processing facilities, or one or more classes of food processing facilities. Based on the broadest reasonable interpretation, training a machine learning model encompasses equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea.
Claim 12 recites the limitation - wherein the result indicates a positive foodborne pathogen detection if the result meets or exceeds a threshold. Based on the broadest reasonable interpretation, comparing information to a threshold encompasses equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea.
Claim 14 recites the limitation - the second trained model being a trained to analyze scattered radiation and the first trained model being trained to analyze absorbed radiation. Based on the broadest reasonable interpretation, training a machine learning model encompasses equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea.
Claim 16 recites the limitation - wherein the first trained model includes a first decision tree and the second trained model includes a second decision tree. This limitation specifies the machine learning model in the judicial exception of claim 1. The refined limitation indicated still represents a judicial expectation.
Claim 17 recites the limitation - apply a first trained model to the first set of intensity values to obtain a first result; apply a second trained model to the second set of intensity values to obtain a second result, the first and second trained model being different from each other; and applying the first and second result to a trained multi-model to determine either a positive pathogen detection or a negative pathogen detection for the pathogen in the sample. Based on the broadest reasonable interpretation, applying machine learning models encompasses equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea. The claim also recites based on the first result, determine a first subset of wavelengths of the first set of wavelengths; generate a pathogen detection notification that indicates either the positive pathogen detection or the negative pathogen detection for the pathogen in the sample; and provide the pathogen detection notification. Based on the broadest reasonable interpretation, determining and providing information could practically be done by the human mind. This draws the limitation to a mental process, which classifies the limitation as an abstract idea.
Claim 18 recites the limitation - determine a confidence level of the positive pathogen detection or the negative pathogen detection of the pathogen in the sample, based on the output from the trained multi-model. Based on the broadest reasonable interpretation, determining a confidence vale associated with a model prediction encompasses equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea.
Claim 19 recites the limitation - generate the second set of values based on the first set of values; and determine a distribution of pathogen particle size in the sample based on the output from the trained multi-model. Based on the broadest reasonable interpretation, determining a distribution associated with a model prediction and generating values based on other values encompasses equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea.
Claim 20 recites the limitation - determine a concentration of the pathogen in the sample based on the output from the trained multi-model. Based on the broadest reasonable interpretation, determining a concertation associated with a model prediction encompasses equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea.
Claim 26 recites the limitation - apply a third trained model to the third set of intensity values to obtain a third result, wherein the determining either the positive pathogen detection or the negative pathogen detection for the pathogen in the sample is based on the output of the multi-model from the input of the first, second, and third results. Based on the broadest reasonable interpretation, applying a machine learning models to determine information encompasses equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea.
Claim 27 recites the limitation - wherein at least some training samples of the set of training samples correspond to a particular food processing facility, a region that includes multiple food processing facilities, or one or more classes of food processing facilities. Based on the broadest reasonable interpretation, training a machine learning model encompasses equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea.
Claim 28 recites the limitation - wherein the result indicates a positive foodborne pathogen detection if the result meets or exceeds a threshold. Based on the broadest reasonable interpretation, comparing information to a threshold encompasses equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea.
Claim 30 recites the limitation - applying a first trained model to the first set of intensity values to obtain a first result; applying a second trained model to the second set of intensity values to obtain a second result, the first and second trained model being different from each other; and applying the first and second result to a trained multi-model to determine either a positive pathogen detection or a negative pathogen detection for the pathogen in the sample. Based on the broadest reasonable interpretation, applying machine learning models encompasses equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea. The claim also recites based on the first result, determining a first subset of wavelengths of the first set of wavelengths; generating a pathogen detection notification that indicates either the positive pathogen detection or the negative pathogen detection for the pathogen in the sample; and providing the pathogen detection notification. Based on the broadest reasonable interpretation, determining and providing information could practically be done by the human mind. This draws the limitation to a mental process, which classifies the limitation as an abstract idea.
These limitations recite concepts of using and training generic machine learning models and determining, providing, and comparing information that are so generically recited that they can be practically performed in the human mind as claimed, which falls under the “Mental processes” and “Mathematical concepts” grouping of abstract ideas. A mathematical concept need not be expressed in mathematical symbols, because words used in a claim operating on data to solve a problem can serve the same purpose as a formula (MPEP 2106.04(a)(2)). Additionally, both product claims and process claims may recite mental processes, which can include a claim that requires a computer (MPEP 2106.04(a)(2)). Therefore, these limitations fall under the “Mental process” and “Mathematical concepts” groupings of abstract ideas. As such, claims 1-30 recite an abstract idea (Step 2A, Prong 1: YES).
Claims found to recite a judicial exception under Step 2A, Prong 1 are then further analyzed to determine if the claims as a whole integrate the recited judicial exception into a practical application or not (Step 2A, Prong 2). These judicial exceptions are not integrated into a practical application because the claims do not recite an additional element that reflects an improvement to technology (MPEP § 2106.04(d)(1)). Rather, the claims provide insignificant extra-solution activity (MPEP § 2106.05(g)) and provide mere instructions to apply a judicial exception (MPEP § 2106.05(f)). Specifically, the claims recite the following additional elements:
Claim 1 recites a non-transitory computer-readable medium comprising executable instructions, one or more processors; receiving a first set of intensity values, the first set of intensity values being based on a first set of intensity measurements for a set of wavelengths, the first set of intensity measurements obtained by an apparatus configured to generate first light, detect the first light that has passed through at least a first portion of a sample, and measure intensity of the first light by at least one optical sensor, a first angle separating the at least one optical sensor and the apparatus configured to generate light; receiving a second set of intensity values, the second set of intensity values being based on a second set of intensity measurements for the set of wavelengths, the second set of intensity measurements obtained by the apparatus configured to generate second light, detect the second light that has passed through at least a second portion of the sample, and measure intensity of the second light by the at least one optical sensor, a second angle separating the at least one optical sensor and the apparatus configured to generate light, the first angle being different than the second angle.
Claim 5 recites the apparatus includes at least one incoherent light source.
Claim 6 recites the apparatus includes a plurality of light emitting diodes (LEDs) and wherein the first angle separating at least one optical sensor and the apparatus utilizes one of the plurality of LEDs and the second angle separating at least one optical sensor and the apparatus utilizes another of the plurality of LEDs.
Claim 7 recites wherein the at least one optical sensor includes at least two optical sensors and wherein the first angle separating at least one optical sensor and the apparatus utilizes a first optical sensor and the second angle separating at least one optical sensor and the apparatus is achieve utilizes a second optical sensor.
Claim 8 recites wherein the first angle separating the at least one optical sensor and the apparatus configured to generate light is substantially 180 degrees.
Claim 9 recites wherein the first angle and the second angle are substantially the same and wherein the first set of intensity measurements is obtained by positioning the sample in a first position and the second set of intensity measurements is obtained by positioning the sample in a second position, the first position being different from the second position.
Claim 10 recites receiving a third set of intensity values, the third set of values based on a third set of intensity measurements for the set of wavelengths, the third set of intensity measurements obtained by the apparatus configured to generate light, detect the light that has passed through at least the portion of the sample, and measure intensity of light at least one optical sensor, a third angle separating the at least one optical sensor and the apparatus configured to generate light.
Claim 13 recites wherein the set of wavelengths of light includes wavelengths of light in ultraviolet, visible, and infrared spectrums.
Claim 14 recites wherein the second set of intensity values is based on scattered radiation from a source.
Claim 15 recites wherein the at least one optical sensor comprises a first optical sensor that measures the intensity of the first light and a second optical sensor that measures the intensity of the second light.
Claim 17 recites a system comprising at least one processor and memory; receive a first set of intensity values, the first set of intensity values being based on a first set of intensity measurements for a set of wavelengths, the first set of intensity measurements obtained by an apparatus configured to generate first light, detect the first light that has passed through at least a first portion of a sample, and measure intensity of the first light by at least one optical sensor, a first angle separating the at least one optical sensor and the apparatus configured to generate light; and receive a second set of intensity values, the second set of intensity values being based on a second set of intensity measurements for the set of wavelengths, the second set of intensity measurements obtained by the apparatus configured to generate second light, detect the second light that has passed through at least a second portion of the sample, and measure intensity of the second light by the at least one optical sensor, a second angle separating the at least one optical sensor and the apparatus configured to generate light, the first angle being different than the second angle.
Claim 21 recites wherein the apparatus includes at least one incoherent light source.
Claim 22 recites wherein the apparatus includes a plurality of light emitting diodes (LEDs) and wherein the first angle separating at least one optical sensor and the apparatus utilizes one of the plurality of LEDs and the second angle separating at least one optical sensor and the apparatus utilizes another of the plurality of LEDs.
Claim 23 recites wherein the at least one optical sensor includes at least two optical sensors and wherein the first angle separating at least one optical sensor and the apparatus utilizes a first optical sensor and the second angle separating at least one optical sensor and the apparatus is achieve utilizes a second optical sensor.
Claim 24 recites wherein the first angle separating the at least one optical sensor and the apparatus configured to generate light is substantially 180 degrees.
Claim 25 recites wherein the first angle and the second angle are substantially the same and wherein the first set of intensity measurements is obtained by positioning the sample in a first position and the second set of intensity measurements is obtained by positioning the sample in a second position, the first position being different from the second position.
Claim 26 recites receive a third set of intensity values, the third set of values based on a third set of intensity measurements for the set of wavelengths, the third set of intensity measurements obtained by the apparatus configured to generate light, detect the light that has passed through at least the portion of the sample, and measure intensity of light at least one optical sensor, a third angle separating the at least one optical sensor and the apparatus configured to generate light.
Claim 29 recites wherein the set of wavelengths of light includes wavelengths of light in ultraviolet, visible, and infrared spectrums.
Claim 30 recites receiving a first set of intensity values, the first set of intensity values being based on a first set of intensity measurements for a set of wavelengths, the first set of intensity measurements obtained by an apparatus configured to generate first light, detect the first light that has passed through at least a first portion of a sample, and measure intensity of the first light by at least one optical sensor, a first angle separating the at least one optical sensor and the apparatus configured to generate light; and receiving a second set of intensity values, the second set of intensity values being based on a second set of intensity measurements for the set of wavelengths, the second set of intensity measurements obtained by the apparatus configured to generate second light, detect the second light that has passed through at least a second portion of the sample, and measure intensity of the second light by the at least one optical sensor, a second angle separating the at least one optical sensor and the apparatus configured to generate light, the first angle being different than the second angle.
There are no limitations that indicate that the claimed using and training generic machine learning models and determining, providing, and comparing information require anything other than generic computing systems. As such, these limitations equate to mere instructions to implement the abstract idea on a generic computer that the courts have stated does not render an abstract idea eligible in Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984. There is no indication that these steps are affected by the judicial exception in any way and thus do not integrate the recited judicial exception into a practical application. As such, claims 1-30 are directed to an abstract idea (Step 2A, Prong 2: NO).
Claims found to be directed to a judicial exception are then further evaluated to determine if the claims recite an inventive concept that provides significantly more than the judicial exception itself (Step 2B). The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims recite conventional additional elements that equate to mere instructions to apply the recited exception in a generic way or in a generic computing environment. The claims also recite conventional additional elements that represent insignificant extra-solution activities.
As discussed above, there are no additional limitations to indicate that the claimed using and training generic machine learning models and determining, providing, and comparing information require anything other than generic computer components in order to carry out the recited abstract idea in the claims. Claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea or natural law eligible. Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984. MPEP 2106.05(f) discloses that mere instructions to apply the judicial exception cannot provide an inventive concept to the claims. As specified in MPEP 2106.05(g), extra-solution activities can be understood as incidental to the primary process or product that are merely a nominal or tangential addition to the claim. Insignificant extra-solution activities include mere data gathering, selecting a particular data source or type of data to be manipulated, and displaying information. Additionally, Isikman et al. (2012, IEEE Journal of Selected Topics In Quantum Electronics, Vol. 18: 1-14) teach utilizing multiple light intensity values (Page 2, Column 2, Paragraph 1: the detected intensity at the sensor plane can thus be written as Equation 3); multiple lights, including LEDs at multiple angles, including 180[Symbol font/0xB0] (Page 9, Figure 1); multiple light sensors (Page 2, Figure 1); providing pathogen detecting information (Page 4, Column 2, Paragraph 3: we also investigated the use of our lensless microscopes to image and detect pathogenic protozoan parasites; Page 5, Figure 6); and altering position of a sample (Page 10, Column 2, Paragraph 1: microscopy systems can be integrated to mobile devices) were well-understood, routine, and conventional at the time of the effective filing date.
The additional elements do not comprise an inventive concept when considered individually or as an ordered combination that transforms the claimed judicial exception into a patent-eligible application of the judicial exception. Therefore, the claims do not amount to significantly more than the judicial exception itself (Step 2B: No). As such, Claims 1-30 are not patent eligible.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-10, 12, 14-26, 28, and 30 are rejected under 35 U.S.C. 103 as being unpatentable over Tandon et al. (US 20180211380 A1), in view of Garcia et al. (US 20170268977 A1). Italicized text from reference art.
Applicable claims include:
Claim 1. A non-transitory computer-readable medium comprising executable instructions, the executable instructions being executable by one or more processors to perform a method, the method comprising: (Claim 1.i) receiving a first set of intensity values, the first set of intensity values being based on a first set of intensity measurements for a set of wavelengths, the first set of intensity measurements obtained by an apparatus configured to generate first light, detect the first light that has passed through at least a first portion of a sample, and measure intensity of the first light by at least one optical sensor, a first angle separating the at least one optical sensor and the apparatus configured to generate light; (Claim 1.ii) applying a first trained model to the first set of intensity values to obtain a first result; (Claim 1.iii) based on the first result, determining a first subset of wavelengths of the first set of wavelengths; (Claim 1.iv) receiving a second set of intensity values, the second set of intensity values being based on a second set of intensity measurements for the set of wavelengths, the second set of intensity measurements obtained by the apparatus configured to generate second light, detect the second light that has passed through at least a second portion of the sample, and measure intensity of the second light by the at least one optical sensor, a second angle separating the at least one optical sensor and the apparatus configured to generate light, the first angle being different than the second angle; (Claim 1.v) applying a second trained model to the second set of intensity values to obtain a second result, the first and second trained model being different from each other; (Claim 1.vi) applying the first and second result to a trained multi-model to determine either a positive pathogen detection or a negative pathogen detection for the pathogen in the sample; (Claim 1.vii) generating a pathogen detection notification that indicates either the positive pathogen detection or the negative pathogen detection for the pathogen in the sample; and (Claim 1.viii) providing the pathogen detection notification.
Claim 2. The non-transitory computer-readable medium of claim 1, the method further comprising: based on the output from the trained multi-model, determining a confidence level of the positive pathogen detection or the negative pathogen detection of the pathogen in the sample.
Claim 3. The non-transitory computer-readable medium of claim 1, the method further comprising based on the output from the trained multi-model, determining a distribution of pathogen particle size in the sample.
Claim 4. The non-transitory computer-readable medium of claim 1, the method further comprising based on the output from the trained multi-model, determining a concentration of the pathogen in the sample.
Claim 5. The non-transitory computer-readable medium of claim 1, wherein the apparatus includes at least one incoherent light source.
Claim 6. The non-transitory computer-readable medium of claim 1, (Claim 6.i) wherein the apparatus includes a plurality of light emitting diodes (LEDs) and (Claim 6.ii) wherein the first angle separating at least one optical sensor and the apparatus utilizes one of the plurality of LEDs and the second angle separating at least one optical sensor and the apparatus utilizes another of the plurality of LEDs.
Claim 7. The non-transitory computer-readable medium of claim 1, (Claim 7.i) wherein the at least one optical sensor includes at least two optical sensors and (Claim 7.ii) wherein the first angle separating at least one optical sensor and the apparatus utilizes a first optical sensor and the second angle separating at least one optical sensor and the apparatus is achieve utilizes a second optical sensor.
Claim 8. The non-transitory computer-readable medium of claim 1, wherein the first angle separating the at least one optical sensor and the apparatus configured to generate light is substantially 180 degrees.
Claim 9. The non-transitory computer-readable medium of claim 1, (Claim 9.i) wherein the first angle and the second angle are substantially the same and (Claim 9.ii) wherein the first set of intensity measurements is obtained by positioning the sample in a first position and the second set of intensity measurements is obtained by positioning the sample in a second position, the first position being different from the second position.
Claim 10. The non-transitory computer-readable medium of claim 1, the method further comprising: (Claim 10.i) receiving a third set of intensity values, the third set of values based on a third set of intensity measurements for the set of wavelengths, the third set of intensity measurements obtained by the apparatus configured to generate light, detect the light that has passed through at least the portion of the sample, and measure intensity of light at least one optical sensor, a third angle separating the at least one optical sensor and the apparatus configured to generate light; and (Claim 10.ii) applying a third trained model to the third set of intensity values to obtain a third result, wherein the determining either the positive pathogen detection or the negative pathogen detection for the pathogen in the sample is based on the output of the multi-model from the input of the first, second, and third results.
Claim 12. The non-transitory computer-readable medium of claim 1, wherein the result indicates a positive foodborne pathogen detection if the result meets or exceeds a threshold.
Claim 14. The non-transitory computer-readable medium of claim 1, wherein the second set of intensity values is based on scattered radiation from a source, the second trained model being a trained to analyze scattered radiation and the first trained model being trained to analyze absorbed radiation.
Claim 15. The non-transitory computer-readable medium of claim 1, wherein the at least one optical sensor comprises a first optical sensor that measures the intensity of the first light and a second optical sensor that measures the intensity of the second light.
Claim 16. The non-transitory computer-readable medium of claim 1, wherein the first trained model includes a first decision tree and the second trained model includes a second decision tree.
Claim 17. A system comprising at least one processor and memory containing executable instructions, the executable instructions being executable by the at least one processor to:(Claim 17.i) receive a first set of intensity values, the first set of intensity values being based on a first set of intensity measurements for a set of wavelengths, the first set of intensity measurements obtained by an apparatus configured to generate first light, detect the first light that has passed through at least a first portion of a sample, and measure intensity of the first light by at least one optical sensor, a first angle separating the at least one optical sensor and the apparatus configured to generate light;(Claim 17.ii) apply a first trained model to the first set of intensity values to obtain a first result;(Claim 17.iii) based on the first result, determine a first subset of wavelengths of the first set of wavelengths;(Claim 17.iv) receive a second set of intensity values, the second set of intensity values being based on a second set of intensity measurements for the set of wavelengths, the second set of intensity measurements obtained by the apparatus configured to generate second light, detect the second light that has passed through at least a second portion of the sample, and measure intensity of the second light by the at least one optical sensor, a second angle separating the at least one optical sensor and the apparatus configured to generate light, the first angle being different than the second angle;(Claim 17.v) apply a second trained model to the second set of intensity values to obtain a second result, the first and second trained model being different from each other;(Claim 17.vi) applying the first and second result to a trained multi-model to determine either a positive pathogen detection or a negative pathogen detection for the pathogen in the sample;(Claim 17.vii) generate a pathogen detection notification that indicates either the positive pathogen detection or the negative pathogen detection for the pathogen in the sample; and (Claim 17.iii) provide the pathogen detection notification.
Claim 18. The system of claim 17, wherein the executable instructions being executable by the at least one processor to determine a confidence level of the positive pathogen detection or the negative pathogen detection of the pathogen in the sample, based on the output from the trained multi-model.
Claim 19. The system of claim 17, wherein the executable instructions being executable by the at least one processor to generate the second set of values based on the first set of values include executable instructions being executable by the at least one processor to determine a distribution of pathogen particle size in the sample based on the output from the trained multi-model.
Claim 20. The system of claim 17, wherein the executable instructions being executable by the at least one processor to determine a concentration of the pathogen in the sample based on the output from the trained multi-model.
Claim 21. The system of claim 17, wherein the apparatus includes at least one incoherent light source.
Claim 22. The system of claim 17, (Claim 6.i) wherein the apparatus includes a plurality of light emitting diodes (LEDs) and (Claim 6.ii) wherein the first angle separating at least one optical sensor and the apparatus utilizes one of the plurality of LEDs and the second angle separating at least one optical sensor and the apparatus utilizes another of the plurality of LEDs.
Claim 23. The system of claim 17, (Claim 23.i) wherein the at least one optical sensor includes at least two optical sensors and (Claim 23.i) wherein the first angle separating at least one optical sensor and the apparatus utilizes a first optical sensor and the second angle separating at least one optical sensor and the apparatus is achieve utilizes a second optical sensor.
Claim 24. The system of claim 17, wherein the first angle separating the at least one optical sensor and the apparatus configured to generate light is substantially 180 degrees.
Claim 25. The system of claim 17, (Claim 25.i) wherein the first angle and the second angle are substantially the same and (Claim 25.ii) wherein the first set of intensity measurements is obtained by positioning the sample in a first position and the second set of intensity measurements is obtained by positioning the sample in a second position, the first position being different from the second position.
Claim 26. The system of claim 17, wherein the executable instructions being executable by the at least one processor to: (Claim 26.i) receive a third set of intensity values, the third set of values based on a third set of intensity measurements for the set of wavelengths, the third set of intensity measurements obtained by the apparatus configured to generate light, detect the light that has passed through at least the portion of the sample, and measure intensity of light at least one optical sensor, a third angle separating the at least one optical sensor and the apparatus configured to generate light; and (Claim 26.i) apply a third trained model to the third set of intensity values to obtain a third result, wherein the determining either the positive pathogen detection or the negative pathogen detection for the pathogen in the sample is based on the output of the multi-model from the input of the first, second, and third results.
Claim 28. The system of claim 17, wherein the result indicates a positive foodborne pathogen detection if the result meets or exceeds a threshold.
Claim 30. A method comprising: (Claim 30.i) receiving a first set of intensity values, the first set of intensity values being based on a first set of intensity measurements for a set of wavelengths, the first set of intensity measurements obtained by an apparatus configured to generate first light, detect the first light that has passed through at least a first portion of a sample, and measure intensity of the first light by at least one optical sensor, a first angle separating the at least one optical sensor and the apparatus configured to generate light; (Claim 30.ii) applying a first trained model to the first set of intensity values to obtain a first result; (Claim 30.iii) based on the first result, determining a first subset of wavelengths of the first set of wavelengths; (Claim 30.iv) receiving a second set of intensity values, the second set of intensity values being based on a second set of intensity measurements for the set of wavelengths, the second set of intensity measurements obtained by the apparatus configured to generate second light, detect the second light that has passed through at least a second portion of the sample, and measure intensity of the second light by the at least one optical sensor, a second angle separating the at least one optical sensor and the apparatus configured to generate light, the first angle being different than the second angle; (Claim 30.v) applying a second trained model to the second set of intensity values to obtain a second result, the first and second trained model being different from each other; (Claim 30.vi) applying the first and second result to a trained multi-model to determine either a positive pathogen detection or a negative pathogen detection for the pathogen in the sample; (Claim 30.vii) generating a pathogen detection notification that indicates either the positive pathogen detection or the negative pathogen detection for the pathogen in the sample; and (Claim 30.viii) providing the pathogen detection notification.
Regarding Claim 1, 17, and 30, Tandon et al. teach (Claim 1.i) receiving a first set of intensity values, the first set of intensity values being based on a first set of intensity measurements for a set of wavelengths, the first set of intensity measurements obtained by an apparatus configured to generate first light, detect the first light that has passed through at least a first portion of a sample, and measure intensity of the first light by at least one optical sensor, a first angle separating the at least one optical sensor and the apparatus configured to generate light (Paragraph 0004: receive the one or more images of the biological sample captured by the camera; Paragraph 0042: the liquid biological sample onto the substrate and place the biological sample into the capillary tube where it forms a smear suitable for imaging; Paragraph 0145: The optical axis is an imaginary line that defines the path along which light propagates through the system; Paragraph 0211, Table 1: a backlight module; Paragraph 0018: the angle formed between the first dimension and a focal axis of the camera is in the range from about 45 degrees to 90 degrees). An image is a collection of spectral data that includes intensity (Paragraph 0266: A luminosity approach may be employed to generate the intensity map as shown in equation 1). The smear can be a monolayer allowing light to pass through (Paragraph 0048: the capillary tube is configured to produce a monolayer of the biological sample). Light inherently contains a set a wavelengths. Additionally, this type of language is interpreted as product by process (MPEP 2113: Product-by-process claims are not limited to the manipulations of the recited steps, only the structure implied by the steps). Tandon et al. teach (Claim 1.ii) applying a first trained model to the first set of intensity values to obtain a first result (Paragraph 0166: the image analysis technique utilizes Otsu clustering for thresholding and segmentation, using, e.g., a labeled dataset of smear cells to train a random forests ensemble; Paragraph 0196: a classification model has two phases: segmentation and classification. Segmentation takes in a raw sample image (e.g., an image of smear) and identifies sample features (e.g., single cells or organisms) by producing cellular artifacts from an image of a smear). The modeling involved in the segmentation (i.e. isolating part of the dataset) are the first set of models. Tandon et al. teach (Claim 1.iv) receiving a second set of intensity values, the second set of intensity values being based on a second set of intensity measurements for the set of wavelengths, the second set of intensity measurements obtained by the apparatus configured to generate second light, detect the second light that has passed through at least a second portion of the sample, and measure intensity of the second light by the at least one optical sensor, a second angle separating the at least one optical sensor and the apparatus configured to generate light, the first angle being different than the second angle (Paragraph 0203: a plurality of images is obtained when the camera moves along the range. The processors process the plurality of images of the cellular artifacts to obtain a plurality of measures of the plurality of images). The modeling is based on using multiple datasets (i.e. multiple images) taken at different positions. Tandon et al. teach (Claim 1.v) applying a second trained model to the second set of intensity values to obtain a second result, the first and second trained model being different from each other (Paragraph 0202: the system determine that the at least one measure of the one or more images of the sample feature is in the first range, and control the one or more actuators to move the camera and/or the stage in a first direction in the first dimension. The movement of the first direction can be based on a determination that the at least one measure). The first model involves a comparison to another value. Therefore, it is different that the second model with does not mention a comparison. Tandon et al. teach (Claim 1.vi) applying the first and second result to a trained multi-model to determine either a positive pathogen detection or a negative pathogen detection for the pathogen in the sample (Paragraph 0167: random forests (RF) classification to automatically identify parasites in blood smears or automatically identify other conditions detectable through image analysis of a biological sample; Paragraph 0196: a classification model has two phases: segmentation and classification. Segmentation takes in a raw sample image (e.g., an image of smear) and identifies sample features (e.g., single cells or organisms) by producing cellular artifacts from an image of a smear. Classification is conducted using, e.g., a deep learning neural network). This shows how the results from the first models (determine what is important for predicting the class) flow into the second model to make the classification determination. Tandon et al. teach (Claim 1.vii) generating a pathogen detection notification that indicates either the positive pathogen detection or the negative pathogen detection for the pathogen in the sample (Paragraph 0164: the disclosed systems include parasite detection). Tandon et al. teach (Claim 1.viii) providing the pathogen detection notification (Paragraph 0163: The image processing and computing module may output the results). Additionally, Tandon et al. teach the methods are caried out by a computer, and NTCRM, which contain memory and at least on processor (Paragraph 0038: non-transitory computer-readable medium storing computer-readable program code to be executed by one or more processors; Paragraph 0049: Computer program products and computer systems for implementing any of the methods mentioned above are also provided). Claim 17 recites the limitations of claim 1 directed to another system and claim 30 recites the limitations of claims 1 directed to a method.
Regarding Claim 2 and 18, Tandon et al. teach based on the output from the trained multi-model, determining a confidence level of the positive pathogen detection or the negative pathogen detection of the pathogen in the sample (Paragraph 00391: Table 1 calculates an accuracy score as a percent which is interpreted as equivalent to the confidence level).
Regarding Claim 7 and 23, Tandon et al. teach (Claim 7.i) wherein the at least one optical sensor includes at least two optical sensors (Paragraph 0195: the camera includes a CMOS sensor and a CCD sensor). Tandon et al. suggest (Claim 7.ii) the first angle separating at least one optical sensor and the apparatus utilizes a first optical sensor and the second angle separating at least one optical sensor and the apparatus is achieve utilizes a second optical sensor (data is taken by the camera at multiple angles – see claim 1).
Regarding Claim 8 and 24, Tandon et al. teach the first angle separating the at least one optical sensor and the apparatus configured to generate light is 180 degrees (Paragraph 0196: the camera is positioned above the stage. The stage includes an area for receiving a biological sample. The biological sample can be a smear of the biological sample position on a transparent portion of a slide or other test strip). The system includes a backlight (see regarding claim 1).
Regarding Claim 9 and 25, Tandon et al. teach (Claim 9.i) the first angle and the second angle are substantially the same (Paragraph 0199: the angle formed by the first dimension (axis X) and the optical axis of the camera (axis Z) is in a range between about 45° to 90°. Similarly, the angle formed between axis Y and axis Z is in the range between about 45° to 90°). Tandon et al. teach (Claim 9.ii) the first set of intensity measurements is obtained by positioning the sample in a first position and the second set of intensity measurements is obtained by positioning the sample in a second position, the first position being different from the second position (Paragraph 0196: The system automatically moves the stage so that the camera can capture one or more images of the biological sample to change the relative positions of the biological samples on the stage).
Regarding Claim 10 and 26, Tandon et al. teach (Claim 10.i) receiving a third set of intensity values, the third set of values based on a third set of intensity measurements for the set of wavelengths, the third set of intensity measurements obtained by the apparatus configured to generate light, detect the light that has passed through at least the portion of the sample, and measure intensity of light at least one optical sensor, a third angle separating the at least one optical sensor and the apparatus configured to generate light (Paragraph 0203: The processors are configured to analyze the plurality of measures to determine a second range smaller than the first range in which to position the camera and/or the stage). Tandon et al. describes imaging and modeling the image data at least three times. Tandon et al. suggest (Claim 10.ii) applying a third trained model to the third set of intensity values to obtain a third result, wherein the determining either the positive pathogen detection or the negative pathogen detection for the pathogen in the sample is based on the output of the multi-model from the input of the first, second, and third results. Tandon et al. teach taking multiple image datasets and combining the information to identify the pathogen. Additionally, the claim is interpreted as repeating steps of claim 1.Repeating this step (same as second data receiving in claim) is obvious (collecting more data from other position to enhance the decision).
Regarding Claim 12 and 28, Tandon et al. teach the result indicates a positive foodborne pathogen detection if the result meets or exceeds a threshold (Paragraph 0090: The terms “threshold” herein refer to any number that is used as, e.g., a cutoff to classify a sample feature as particular type of parasite; Paragraph 0108: Bacterial examples include: Listeria monocytogenes; Paragraph 0166: the image analysis technique utilizes Otsu clustering for thresholding and segmentation). Additionally, this is interpreted as a contingent limitation, which requires only those steps that must be performed (MPEP 2111.04).
Regarding Claim 14, Tandon et al. teach the second set of intensity values is based on scattered radiation from a source and the second trained model being a trained to analyze scattered radiation (see claim 1 - light that passes through a sample is inherently scattered).
Regarding Claim 16, Tandon et al. teach the first trained model includes a first decision tree and the second trained model includes a second decision tree (Paragraph 0167: random forests (RF) classification to automatically identify parasites in blood smears or automatically identify other conditions detectable through image analysis of a biological sample).
Tandon et al. do not explicitly teach determining a subset of wavelengths (Claim 1, 17, and 30.iii). Tandon et al. do explicitly teach estimating particle size (Claim 3 and 19) or concertation (Claim 4 and 20). Tandon et al. do explicitly teach an incoherent light source (Claim 5 and 21) or LEDs (Claim 6 and 22). Tandon et al. do explicitly teach utilizing multiple different lights (Claim 15).
Regarding Claim 1, 17, and 30, Garcia et al. teach (Claim 1.iii) based on the first result, determining a first subset of wavelengths of the first set of wavelengths (Paragraph 0032: the wavelength of the source light being selected).
Regarding Claim 3 and 19, Garcia et al. teach based on the output from the trained multi-model (Paragraph 0037: The autocorrelation function, which is used to determine instrument parameters and sizes of unknown particles in samples, can then be applied for the detection of the diacaustic with the same goal of calibrating the system to track solid particle size changes with time or determine the static particle size distribution of a sample droplet; Paragraph 0067: Autocorrelation is the cross-correlation of a signal with itself. Informally, it is the similarity between observations as a function of the time separation between them. It is a mathematical tool for finding repeating patterns, such as the presence of a periodic signal which has been buried under noise, or identifying the missing fundamental frequency in a signal implied by its harmonic frequencies). Autocorrelation function is a machine leaning model (calibrating = training). Garcia et al. teach determining a distribution of pathogen particle size in the sample (Paragraph 0055: disease can be monitored for measuring particle size). Additionally, it would be obvious to combine for the machine leaning modeling of Tandon et al (see reason to combine below).
Regarding Claim 4 and 20, Garcia et al. teach based on the output from the trained multi-model (Paragraph 0037: The autocorrelation function, which is used to determine instrument parameters and sizes of unknown particles in samples, can then be applied for the detection of the diacaustic with the same goal of calibrating the system to track solid particle size changes with time or determine the static particle size distribution of a sample droplet; Paragraph 0067: Autocorrelation is the cross-correlation of a signal with itself. Informally, it is the similarity between observations as a function of the time separation between them. It is a mathematical tool for finding repeating patterns, such as the presence of a periodic signal which has been buried under noise, or identifying the missing fundamental frequency in a signal implied by its harmonic frequencies). Autocorrelation function is a machine leaning model (calibrating = training). Garcia et al. teach determining a concentration of the pathogen in the sample (Paragraph 0055: disease can be monitored for measuring concentration). Additionally, it would be obvious to combine for the machine leaning modeling of Tandon et al.
Regarding Claim 5 and 21, Garcia et al. teach the apparatus includes at least one incoherent light source (Paragraph 0013: An instrument is used with a very strong LED).
Regarding Claim 6 and 22, Garcia et al. teach (Claim 6.i) the apparatus includes a plurality of light emitting diodes (LEDs) (Paragraph 0076: using a near ultraviolet wavelength light emitting diode (LED) paired with a detector that collects light from this LED (primarily at 400 nm) and for a pair of 640 nm light emitting diodes). Garcia et al. suggest (Claim 6.ii) the first angle separating at least one optical sensor and the apparatus utilizes one of the plurality of LEDs and the second angle separating at least one optical sensor and the apparatus utilizes another of the plurality of LEDs. The LEDs are paired with different sensors (see above). When combined with Tandon et al. the different leds would be used with the different sensors based on the different angles of the device of Tandon et al.
Regarding Claim 7 and 23, Garcia et al. teach multiple sensors placed based on angles (Paragraph 0040: A detector is placed at 180 degrees from the light source. A detector is placed at the paraxial focus).
Regarding Claim 8 and 24, Garcia et al. teach the first angle separating the at least one optical sensor and the apparatus configured to generate light is 180 degrees (Paragraph 0032: placing a detector at 180 degrees from the light source and centered at the paraxial focus).
Regarding Claim 14, Garcia et al. teach intensity values is based on scattered radiation from a source (Paragraph 0035: detection of light scattered at between 80-90 degrees is possible). Garcia et al. teach analyzing absorbed radiation (Paragraph 0091: device can be used to measure absorbance of light at different wavelengths).
Regarding Claim 15, Garcia et al. the at least one optical sensor comprises a first optical sensor that measures the intensity of the first light and a second optical sensor that measures the intensity of the second light (Paragraph 0076: using a near ultraviolet wavelength light emitting diode (LED) paired with a detector that collects light from this LED (primarily at 400 nm) and for a pair of 640 nm light emitting diodes). Tandon et al. teach the use of different sensors (Paragraph 0195: the camera includes a CMOS sensor and a CCD sensor).
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date to combine Garcia et al. with Tandon et al. Garcia et al. teach the methods associated with their device allow it to be low cost and portable (Paragraph 0043: The optical caustic product is a portable and low cost alternative that exploits communication device, phone, and Smart phone software for signal interpretation; Paragraph 0038: The simple instrument design due to the optical caustic leads to the detection of changes in particle size of a sample in non-laboratory or field settings as a means of testing for a particular component in a sample). Furthermore, one of ordinary skill in the art would predict that the methods could be readily combined with a reasonable expectation of success because both are within the same technical field – capturing and analyzing light data to determine pathogen information about a sample.
Claims 1-12, 14-28, and 30, are rejected under 35 U.S.C. 103 as being unpatentable over Tandon et al., in view of Garcia et al., as applied above to Claims 1-10, 12, 14-26, 28, and 30, and in further view of Amini et al. (US 20190204317 A1). Italicized text from reference art.
Applicable claims include:
Claims 1-10, 12, 14-26, 28, and 30 included above
Claim 11. The non-transitory computer-readable medium of claim 1, wherein at least some training samples of the set of training samples correspond to a particular food processing facility, a region that includes multiple food processing facilities, or one or more classes of food processing facilities.
Claim 27. The system of claim 17, wherein at least some training samples of the set of training samples correspond to a particular food processing facility, a region that includes multiple food processing facilities, or one or more classes of food processing facilities.
Regarding Claims 1-10, 12, 14-26, 28, and 30, these limitations are taught by Tandon et al. and Garcia et al. as indicated above.
Tandon et al. and Garcia et al. do not explicitly teach the application of their device/methods to samples from food processing facilities (Claim 11 and 27).
Regarding Claim 11 and 27, Amini et al. teach training samples of the set of training samples correspond to a particular food processing facility, a region that includes multiple food processing facilities, or one or more classes of food processing facilities (Paragraph 0004: sample is a surface swab or a surface rinse of said one or more food processing facilities; Paragraph 0110: Gather a training set. These can include, for example, samples that are from a food or environment contaminated or not contaminated with a particular microbe. The training samples are used to “train” the classifier).
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date to combine Amini et al. with Garcia et al. and Tandon et al. Garcia et al. suggest application of their methods/device to food applications (Paragraph 0042: Industrial (food) laboratories measure particle suspensions for a variety of reasons such as quality control, monitoring changes in particle sizes, and characterizing their synthesized particle products. These users desire a simpler, low-cost; and portable system to provide useful information on particle size distributions). Amini et al. teach their modeling applied to classifying pathogens represent an improvement within the field (Paragraph 0107: One of skill will appreciate that these or other classifiers, including improvements of any of these, are contemplated within the scope of the invention). Furthermore, one of ordinary skill in the art would predict that the methods could be readily combined with a reasonable expectation of success because both are within the same technical field – using machine learning models to determine pathogen information about a sample.
Claims 1-10, 12-26, and 28-30, are rejected under 35 U.S.C. 103 as being unpatentable over Tandon et al., in view of Garcia et al., as applied above to Claims 1-10, 12, 14-26, 28, and 30, and in further view of Mu et al. (2018, Chemometrics and Intelligent Laboratory Systems, Vol 179: 46-53). Italicized text from reference art.
Applicable claims include:
Claims 1-10, 12, 14-26, 28, and 30 included above
Claim 13. The non-transitory computer-readable medium of claim 1, wherein the set of wavelengths of light includes wavelengths of light in ultraviolet, visible, and infrared spectrums.
Claim 29. The system of claim 17, wherein the set of wavelengths of light includes wavelengths of light in ultraviolet, visible, and infrared spectrums.
Regarding Claims 1-10, 12, 14-26, 28, and 30, these limitations are taught by Tandon et al. and Garcia et al. as indicated above.
Regrading Claim 13 and 29, Garcia et al. teach the wavelengths includes ultraviolet and visible light (Paragraph 0045: wavelength range (0.001 nm to 750 nm)).
Tandon et al. and Garcia et al. do not explicitly teach the use of infrared light (Claim 13)
Regarding Claim 2 and 18, Mu et al. teach determining a confidence level of the positive pathogen detection based on the model output (Page 50, Column 1, Paragraph 3: the overall correct classification rates for both calibration and prediction were both above 96%).
Regrading Claim 13 and 29, Mu et al. teach wavelengths include infrared light and a sensor to detect these wavelengths (Page 47, Column 1, Paragraph 5: equipped with an InGaAs detector was employed for spectral acquisition; Page 47, Column 2, Paragraph 2: Interaction between light and samples changes the intensity and direction of light and such information is covered by the acquired near infrared spectra).
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date to combine Mu et al. with Garcia et al. and Tandon et al. Modeling was able to show 100% accuracy when predicting pathogens within a samples (Page 52, Column 1, Paragraph 2: The results showed that CARS together with SVM output the best OCCR of 100% for prediction. It was therefore demonstrated that RF was a powerful method that can be utilized to achieve good and concise classification of bacterial pathogens thus can provide effective references for potential microbial control in water systems). Furthermore, one of ordinary skill in the art would predict that the methods could be readily combined with a reasonable expectation of success because both are within the same technical field – capturing and analyzing light data to determine pathogen information about a sample.
Double Patenting
No double patenting rejections are issued.
Conclusion
No claims are allowed.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BLAKE H ELKINS whose telephone number is (571)272-2649. The examiner can normally be reached Monday-Friday 8-5PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Karlheinz Skowronek can be reached at (571) 272-9047. 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 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/docx 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.
/B.H.E./Examiner, Art Unit 1687
/Karlheinz R. Skowronek/Supervisory Patent Examiner, Art Unit 1687
Prosecution Insights