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 .
Status of Claims
The claim set received 03 July 2023 has been entered into the application.
Claims 1-25 are pending.
Priority
This Application claims benefit to U.S Provisional Patent Application 63/367,717, filed 05 July 2022.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 08 December 2023 has been considered by the examiner.
Drawings
The drawings were received on 03 July 2023. These drawings are accepted.
Specification
The Specification received 03 July 2023 has been entered into the application.
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 1-25 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Step I - Process, Machine, Manufacture or Composition
Claims 1-13 are drawn to a computer-readable media (CRM), so a manufacture.
Claims 14-24 are drawn to a system, so a machine.
Claim 25 is drawn to a method, so a process.
Step 2A Prong I - Identification of an Abstract Idea
Claim 1 is drawn to a manufacture, and claim 14 is drawn to a system while claim 25 is drawn to a process. However, claims 1, 14, and 25 encompass similar limitations and are therefore examined similarly
.
Claims 1, 14, and 25 recite:
generating a second set of values based on the first set of values
This step can be performed in the human mind by observing and evaluating the first set of values of generate a second set of values and is therefore an abstract idea.
applying a set of trained decision trees to the second set of values to obtain a result, the set of trained decision trees trained on a set of training samples, a first subset of training samples of the set of training samples containing a foodborne pathogen at a first concentration and a second subset of training samples of the set of training samples containing the foodborne pathogen at a second concentration different from the first concentration
This step encompasses using trained decision trees (DT) which encompasses utilizing mathematics concepts [Spec pages 35-36 para 0160-0161] which reads abstract ideas. Here, the decision trees are generically recited and read on abstract ideas. Additionally, the step encompasses taking information (i.e., samples containing a foodborne pathogen at a first concentration and samples of the set of training samples containing the foodborne pathogen at a second concentration) and manipulating the data via mathematical correlation (i.e., DT) for organizing the data into a different form (i.e., obtain a result) which reads on abstract ideas. See MPEP 2106.04(a)(2)(III)(A)(iv) and 2106.04(a)(2)(III)(C)(1-3).
based on the result, determining either a positive foodborne pathogen detection or a negative foodborne pathogen detection for the foodborne pathogen in the sample of the food processing byproduct
This step can be performed in the human mind by observing and evaluating the results to determine a positive foodborne pathogen detection or a negative foodborne pathogen detection in the sample of the food processing byproduct and is therefore an abstract idea.
generating a foodborne pathogen detection notification that indicates either the positive foodborne pathogen detection or the negative foodborne pathogen detection for the foodborne pathogen in the sample of the food processing byproduct
This step can be performed in the human mind by organizing information to generate a food borne pathogen notification indicating either positive foodborne pathogen detection or the negative foodborne pathogen detection for the foodborne pathogen in the sample of the food processing byproduct and is therefore an abstract idea.
Claims 2, 4-6, 7, 9-13 and 15, and 17-24 are further drawn to limitations that describe the abstract ideas of claim 1 and are therefore also abstract ideas.
Claims 10 and 22 recite “wherein the sample of the food processing byproduct is mixed with a reagent.” Here, the claimed limitations are interpreted as merely describing the food sample as mixed with a reagent which reads on data because claim 1, from which claim 10 is dependent, is drawn to CRM while claim 14 from which claim 22 is dependent, is drawn to a computer system. As such, because the claims are drawn to computer systems/software that processes food sample data, the claimed steps are interpreted as limitations that further the describe abstract ideas, not a physical byproducts and/or physical mixing step.
Step 2A Prong II - Consideration of Practical Application
Here, in the instant case, claims 1, 14, and 25 merely set forth a method of data analysis using a system for providing food borne pathogen detection notifications. As such, practicing the claims merely results in providing a notification. Such a result only produces information and does not provide for a practical application in the physical-realm of physical things and acts, i.e., the claims do not utilize the data generated by the judicial exception to affect any type of change. See MPEP 2106.04(a)(2)(A)(iv).
Claims 1, 14, and 25 recite “applying a set of trained decision trees to the second set of values to obtain a result, the set of trained decision trees trained on a set of training samples, a first subset of training samples of the set of training samples containing a foodborne pathogen at a first concentration and a second subset of training samples of the set of training samples containing the foodborne pathogen at a second concentration different from the first concentration”. Here, even though the claimed steps utilize a “decision trees (DT)”, the DT is broadly and generically recited and reads on mere instructions to implement an abstract idea using generic computer and reads on mathematical/statistical computations. See 2024 Subject Matter Eligibility Update (AI) [Example 47 Claim 2] and MPEP 2106.04(a)(2)(III)(C)(1-3) and 2106.05(f).
Furthermore, and for sake of compact prosecution, even if the DT’s of claims 1, 14, and 25 are also considered additional element, the DT’s is used to generally apply the abstract idea without limiting how the trained DT’s functions. The DT is described at a high level such that it amounts to using a computer with a generic DT to apply the abstract idea for obtaining results. These limitations only recite the outcomes for “obtaining results using training sets (i.e., first and second training samples of different concentrations” without any details about how DT utilizes the sample concentration datasets to obtain results which does not integrate the judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it". See 2024 Subject Matter Eligibility Update (AI) [Example 47 Claim 2] and MPEP 2106.05(f).
Dependent claim(s):
Similarly, claims 3 and 16 recite “applying the set of trained decision trees to the second set of values to obtain the result further obtains a confidence value for the foodborne pathogen in the sample of the food processing byproduct…” Here, even though the claimed steps utilize a “decision trees (DT)”, the DT is broadly and generically recited and reads on mere instructions to implement an abstract idea on a generic computer and reads on mathematical/statistical computations. See 2024 Subject Matter Eligibility Update (AI) [Example 47 Claim 2] and MPEP 2106.04(a)(2)(III)(C)(1-3) and 2106.05(f).
Furthermore, and for sake of compact prosecution, even if the DT’s of claims 3 and 16 are also considered additional element, the DT’s is used to generally apply the abstract idea without limiting how the trained DT’s functions. Here, the DT’s are described at a high level such that it amounts to using a computer with a generic DT to apply the abstract idea for obtaining results (i.e., a confidence value for the foodborne pathogen). These limitations only recite the outcomes for “…obtaining a confidence value for the foodborne pathogen” without any details about how DT utilizes the sample datasets to obtain results (i.e., confidence values) which does not integrate the judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it". See 2024 Subject Matter Eligibility Update (AI) [Example 47 Claim 2] and MPEP 2106.05(f).
Similarly, claims 4 and 17 recite “apply a second set of trained decision trees to the second set of values to obtain a second result, the second set of trained decision trees trained on a second set of training samples, a third subset of training samples containing a second foodborne pathogen at a third concentration and a fourth subset of training samples containing the second foodborne pathogen at a fourth concentration different from the third concentration, the second foodborne pathogen different from the first foodborne pathogen” Here, even though the claimed steps utilize a “decision trees (DT)”, the DT is broadly and generically recited and reads on mere instructions to implement an abstract idea on a generic computer and reads on mathematical/statistical computations. See 2024 Subject Matter Eligibility Update (AI) [Example 47 Claim 2] and MPEP 2106.04(a)(2)(III)(C)(1-3) and 2106.05(f).
Furthermore, and for sake of compact prosecution, even if the DT’s of claims 4 and 17 are also considered additional element, the DT’s is used to generally apply the abstract idea without limiting how the trained DT’s functions. Here, the DT’s are described at a high level such that it amounts to using a computer with a generic DT to apply the abstract idea for obtaining results (i.e., second results). These limitations only recite the outcomes for “…obtaining a second results using a DT trained using the second, third, and fourth training sample concentrations” without any details about how the DT utilizes the sample datasets to obtain the second result which does not integrate the judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it". See 2024 Subject Matter Eligibility Update (AI) [Example 47 Claim 2] and MPEP 2106.05(f).
Claims 7 and 19 recite “training a set of decision trees on the set of training samples to obtain the set of trained decision trees.” Here, even though the claims recite “training” DT’s, the DT’s is broadly and generically recited and reads on mere instructions to implement an abstract idea on a generic computer and reads on mathematical/statistical computations. For example, the step of training encompasses taking information (i.e., set of training samples) and manipulating the data via mathematical correlation (i.e., DT) for organizing the data into a different form (i.e., obtain a result) which reads on abstract ideas. See MPEP 2106.04(a)(2)(III)(A)(iv) and 2106.04(a)(2)(III)(C)(1-3) and 2024 Subject Matter Eligibility Update (AI) [Example 47 Claim 2].
Here, the DT’s of claims 7 and 19 used to generally apply the abstract idea without limiting how the trained DT’s functions. Here, the DT’s are described at a high level such that it amounts to using a computer with a generic DT to apply the abstract idea to obtain trained DT’s. These limitations only recite the outcomes for training a set of decision trees on the set of training samples to obtain the set of trained decision trees without any details about how DT utilizes the training samples to obtain trained DT’s which does not integrate the judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it". See 2024 Subject Matter Eligibility Update (AI) [Example 47 Claim 2] and MPEP 2106.05(f).
This judicial exception is not integrated into a practical application because the claims do not meet any of the following criteria:
An additional element reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field;
an additional element that applies or uses a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition;
an additional element implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim;
an additional element effects a transformation or reduction of a particular article to a different state or thing; and
an additional element applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception.
Step 2B: Consideration of Additional Elements and Significantly More
The claimed method also recites "additional elements" that are not limitations drawn to an abstract idea.
The recited additional elements using computer process, components, and equipment (i.e., CRM of claim 1, system/machine of claim 14) of claims 1-24 does not add significantly more than the recited judicial exception because using computers to processes, store, and evaluate abstract ideas is merely tangential to the claimed method and is deemed routine and conventional. See MPEP 2105.06(d)(II) and 2106.05(g). To provide evidence of conventionality of using computer elements, Amini et al. (Amini) discloses utilizing decision trees and random forest as classifiers for processing sample data (i.e., food or environmental samples [Amini, pages 20 left para 0177]) [Amini, pages 19-20 para 0174] (U.S Patent Pub. No.: US 2021/0193266, Patent Pub. Date: 24 June 2021).
To provide further evidence of conventionality, Kirby et al. (Kirby) discloses an apparatus that measures light spectra utilizes statistical analysis models (i.e., decision trees) for analytical learning [Kirby, page 26, para 0111] (International (Intl’) Patent Pub. No.: WO2021/067170, Intl’ Patent Pub Date: 08 April 2021).
To provide further evidence of conventionality of using computer processes, Meisel et al. (Meisel) using the R studio program “GNU R” for statistical analysis [page 38 right col]. Meisel also teaches using decision tree (i.e., classification trees) for classifying food pathogens [page 40 fig 3] from meat and poultry [page 41 table 2] (Food microbiology, 2014-04, Vol.38, p.36-43).
The recited additional elements of data gathering by receiving data of claims 1, 5, 14, and 25 does not add significantly more than the recited judicial exception because receiving data from an apparatus or device that measures the light spectra of pathogens in a sample that is subsequently analyzed by the abstract ideas is deemed routine and conventional. See MPEP 2106.05(g).
The recited additional elements of data outputting by providing notifications of claims 1, 14, and 25 does not add significantly more than the recited judicial exception because providing data output (i.e., notifications) is deemed routine and conventional. See MPEP 2106.05(g).
The recited additional elements of using apparatuses/machines configured to generate, detect, and measure light for light intensity data of pathogens food samples of claims 1, 14, and 25 does not add significantly more than the recited judicial exception because utilizing apparatuses/machines configured to generate, detect, and measure light for light intensity data for gathering and analyzing light intensity data is deemed routine and conventional. MPEP 2105.06(d)(II). To provide evidence of conventionality of using apparatuses/machines configured to generate, detect, and measure light for light intensity data of pathogens food samples, McGoverin et al. (McGoverin) review optical methods for detection and characterization of bacteria in samples [abstract]. McGoverin teaches bacteria concentrations of interests include food, beverage, and water safety [McGoverin, page 080903-4]. McGoverin reviews different fluorescence/optical methods for analyzing bacterial samples [McGoverin, page 080903-4 and page 080903-19] (APL Photonics, 2021-08, Vol.6 (8), p.080903-080903-25).
To provide evidence of conventionality of using apparatuses/machines configured to generate, detect, and measure light for light intensity data of pathogens food samples, Radhakrishnan et al. (Radhakrishnan) discloses a device for non-invasive, automatic, and in-situ detection and classification of pathogen that comprises an imagining module, light sources, detectors [Radhakrishnan, claim 1]. Radhakrishnan discloses in case of food contamination Salmonella typhi might be of relevance [Radhakrishnan, page 6 right col para 0070]. Radhakrishnan discloses the sample of the invention can comprise consumable commodity (i.e., food sample) [Radhakrishnan, page 6 right para 0071] (U.S Patent Pub No.: US 2021/0106231, Patent Pub Date: 15 April 2021).
In conclusion, and when viewed as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea recited in the instantly presented claims into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself. Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
Claim(s) 1, 3, 6-7, 10-11, 14, 16, 18-19, 22-23, and 25 are rejected under 35 U.S.C. 103 as being unpatentable over Gupta et al. (Transactions of the ASABE, 2006-07, Vol.49 (4), p.1249-1255) in view of Meisel et al. (Food microbiology, 2014-04, Vol.38, p.36-43) in view of Weidemaier et al. (International journal of food microbiology, 2015-04, Vol.198, p.19-27).
Claim 1 is drawn to a manufacture, and claim 14 is drawn to a system while claim 25 is drawn to a process. However, claims 1, 14, and 25 encompass similar limitations and are therefore examined similarly
Claim 1 recites receiving a first set of values, the first set of values based on a set of intensity measurements for a set of wavelengths of light, the set of intensity measurements for the set of wavelengths of light obtained by an apparatus configured to generate light, detect the light that has passed through at least a portion of a sample of a food processing byproduct, and measure intensities of the light to obtain the set of intensity measurements for the set of wavelengths of light.
Claim 1 recites generating a second set of values based on the first set of values.
Claim 1 recites applying a set of trained decision trees to the second set of values to obtain a result, the set of trained decision trees trained on a set of training samples, a first subset of training samples of the set of training samples containing a foodborne pathogen at a first concentration and a second subset of training samples of the set of training samples containing the foodborne pathogen at a second concentration different from the first concentration.
Claim 1 recites based on the result, determining either a positive foodborne pathogen detection or a negative foodborne pathogen detection for the foodborne pathogen in the sample of the food processing byproduct; generating a foodborne pathogen detection notification that indicates either the positive foodborne pathogen detection or the negative foodborne pathogen detection for the foodborne pathogen in the sample of the food processing byproduct;
Claim 1 recites providing the foodborne pathogen detection notification.
Meisel et al.
Meisel et al. (Meisel) teaches spiking minced chicken and/or beef with bacterial solutions for subsequent Raman spectroscopy which allows measurements of single-cell Raman spectra with an excitation light. Meisel teaches the Raman spectroscopy device that measure light intensity of single cells. Meisel teaches the device uses Olympus MPLFLN to focus the Raman excitation light onto the sample [Meisel, page 38 right col 2.4]. Meisel teaches Raman spectral profiles of 18 different microbial species (24 strains) (i.e., first set of values based on intensity measurements) were collected and analyzed to build an elaborate database [Meisel, page 39 left col, 3], as in claim 1 receiving a first set of values, the first set of values based on a set of intensity measurements for a set of wavelengths of light, the set of intensity measurements for the set of wavelengths of light obtained by an apparatus configured to generate light, detect the light that has passed through at least a portion of a sample of a food processing byproduct, and measure intensities of the light to obtain the set of intensity measurements for the set of wavelengths of light.
Meisel teaches performing hierarchical cluster analysis (HCA) on spectra data (i.e., first set of values based on intensity measurements) to organize the spectra according to intra-spectral similarities (i.e., a second set of values based on the first set of values) that is subsequently analyzed using a three-level classification approach [Meisel, page 39, let col, section 3, last para], as in claim 1 generating a second set of values based on the first set of values.
Meisel teaches subsequent to a first classifier that differentiates between Raman spectra of Gram-positive and Gram-negative bacteria, two decision knots regarding bacterial genus and species follow [Meisel, abstract]. Meisel teaches a structure of the classification tree for classification pathogens of the database [Meisel, page 40 fig 3], as in claim 1 applying a set of trained decision trees to the second set of values to obtain a result, the set of trained decision trees trained on a set of training samples. Here, although Meisel does not directly teach using a decision tree for the classification tree of figure 3, Meisel teaches using decision knots in the classification tree which is an inherent attribute of a decision tree/classification tree. Therefore, the classification tree of Meisel reads on the decision tree of the instant claims.
Dependent claim(s): 6-7 and 18-19
Meisel teaches a pre-processing of the multivariate data that provides normalization methods: Each spectrum was divided by its area, which was calculated as the Euclidean distance of the spectrum to the zero-spectrum [Meisel, page 39 left col top para], as in claims 6 and 18.
Meisel teaches training steps used in the three-level classification (i.e., classification tree/decision tree) approach [Meisel, page 39 right col and page 40 figure 3], as in claim 7 and 19. Here, it is inherent that the steps of the classification of Meisel [fig 3] is trained or contains steps of training.
Meisel does not teach:
Meisel does not teach claim 1 applying step a first subset of training samples of the set of training samples containing a foodborne pathogen at a first concentration and a second subset of training samples of the set of training samples containing the foodborne pathogen at a second concentration different from the first concentration.
Meisel does not teach claim 1 based on the result, determining either a positive foodborne pathogen detection or a negative foodborne pathogen detection for the foodborne pathogen in the sample of the food processing byproduct.
Meisel does not teach claim 1 generating a foodborne pathogen detection notification that indicates either the positive foodborne pathogen detection or the negative foodborne pathogen detection for the foodborne pathogen in the sample of the food processing byproduct;
Meisel does not teach claim 1 providing the foodborne pathogen detection notification.
Meisel does not teach claims 3, 10-11, 16, and 22-23.
Gupta
Gupta et al. (Gupta) teach a method resulting seven different concentrations and using four of them for subsequent FTIR measurements [Gupta, page 1250 FTIR measurements]. Gupta teaches mixtures of plain broths and respective food matrices in corresponding dilution were measured with FTIR [Gupta, page 1250 FTIR measurements]. Gupta teaches different training sets containing different suspension matrices [Gupta, page 1253 table 4], as in claim 1 applying step a first subset of training samples of the set of training samples containing a foodborne pathogen at a first concentration and a second subset of training samples of the set of training samples containing the foodborne pathogen at a second concentration different from the first concentration. Thus, it is obvious any one of these concentration class datasets could be utilized as either a first or second set of training samples.
Weidemaier
Weidemaier et al. (Weidemaier) teaches a Synthesis of Raman Active (“SERS”) nanoparticle reader and workflow that determines and reports (i.e., notification) to the user as positive whenever the pathogen concentration within the detection vial reaches the test's analytical limit of detection (i.e., generates the report/notification) [Weidemaier, page 25, right col, 4.2] and reports to the user as negative detected at the end of the protocol [Weidemaier, page 25, right col, 4.2, page 23 fig 2], as in claim 1 based on the result, determining either a positive foodborne pathogen detection or a negative foodborne pathogen detection for the foodborne pathogen in the sample of the food processing byproduct, claim 1 generating a foodborne pathogen detection notification that indicates either the positive foodborne pathogen detection or the negative foodborne pathogen detection for the foodborne pathogen in the sample of the food processing byproduct, and claim 1 providing the foodborne pathogen detection notification.
Dependent claim(s): 10 and 22
Weidemaier teaches the method sample is mixed with a reagent [Weidemaier, page 23, fig 2], as in claim 10 and 22
Weidemaier teaches using most probable number (MPN) that is used as a concentration threshold of pathogen (i.e., 1 CFU/sample) to determine whether the sample reported positive or negative for designated sample food matrix [Weidemaier, page 24 table 2]. Weidemaier illustrates the implementation of thresholding the MPN concentration to 0.3 or < 0.3 for indicating whether a pathogen was reported in said sample(s) [Weidemaier, page 24 table 2], as in claims 11 and 23.
Obvious claim(s): 3 and 16
Meisel teaches using percentages to represent similarities between pathogen spectra data in a database and experimental spectra data, classifying into pathogens gram positive and negative pathogen, and assigning the pathogens species level labels (i.e., bacteria: Ecol, Lmon, Styp), for example [Meisel, page 40 fig 3 and page 41 table 2]. Meisel teaches pathogens were 92.8% and 90.2% correctly classified [Meisel, page 40 fig 3]. Weidemaier teaches using most probable number (MPN) that is used as a concentration threshold of pathogen (i.e., 1 CFU/sample) to determine whether the sample reported positive or negative for designated sample food matrix [Weidemaier, page 24 table 2], as in claims 3 and 16. Here, although Meisel and Weidemaier do not directly teach the notification indicates a confidence values, it would be obvious confidence values or other quantitative values are used to determine if a pathogen is or is not present in a sample because Weidemaier provides table the illustrates the implementation of thresholding the MPN 0.3 or < 0.3 for indicating whether a pathogen was reported in said sample(s).
It would be obvious to one of ordinary skill in the art by the effective filing date of the claimed invention to modify Meisel in view of Gupta because Gupta teaches a method of identifying and quantifying foodborne pathogens using FTIR techniques and machine learning elements [abstract]. One of ordinary skill in the art would recognize that while Gupta does not provide notifications or reports to display positive and negative pathogen results, Gupta does teach measuring different concentrations food samples (i.e., milk, orange juice, broth, food matrices) with pathogens using FTIR and subsequently applying classifying methods using an artificial neural network for identifying pathogens [Gupta, page 1250 left col]. Furthermore, and similar to Meisel, Gupta teaches a method for classifying and/or confirming bacteria in food samples as pathogens using absorbance spectra fingerprint regions to develop ANN models to differentiate and quantify the various food pathogens in different food matrices, but Meisel utilized classification tress and support vector machines. Here, one of ordinary skill in the art would be motivated to combine Meisel in view of Gupta because Gupta teaches producing 60% to 100% accuracy using four different concentrations for each sample which the samples are not taught by Meisels’ constant suspension of 10-7 cells/ml [Meisel, page 38 left col]. As such, because Gupta teaches yielding a 100% accuracy when identifying pathogens from food samples at different concentrations, one of ordinary skill in the art would be further motivated to use the sample concentration data of Gupta. Here, one of ordinary skill would recognize the data of Gupta could be reconfigured that so that it can be evaluated using the decision knots and classification trees of Meisel. Therefore, utilizing the Top-level classifier of Meisel to evaluate sample concentration data of Gupta would yield a predictable method step for applying decision trees to training data comprising different sample concentrations for determining the presence or absence of pathogens in a food sample to provide foodborne pathogen detection notifications.
It would be obvious to one of ordinary skill in the art by the effective filing date of the claimed invention to modify Meisel in view of Gupta in view of Weidemaier because Weidemaier teaches pathogen monitoring with respect to food safety that uses a protocol involving samples mixed with reagents that are subsequently loaded into a SER reader and scanned by a Raman spectrometer to obtain sample spectra data to determine whether the sample is positive or negative for the foodborne pathogen. One of ordinary skill in the art would recognize that similar to Meisel and Gupta Weidemaier teaches foodborne pathogen detection, but Weidemaier teaches a system and method for real-time foodborne pathogen detection. Thus, one of ordinary skill in the art would be motivated to combine the teachings of Meisel in view of Gupta in view of Weidemaier because Weidemaier teaches their method provides specific detection of pathogens without interfering with bacterial growth by incorporating pathogen detection reagents into the culture enrichment which enables real-time monitoring of pathogen levels and flagging of positive samples as soon as they reach the limit of detection which could lead to faster positive detection and prompt notifications/reports of pathogens contained within food samples. Therefore, combining the classification methods of Meisel and sample concentration data of Gupta with the real-time pathogen detection workflow of Weidemaier would yield predictable method steps for detecting pathogens in food samples in real-time in a variety of food and/or environmental samples [Weidemaier, page 26 right col summary] and providing reports/notifications regarding food samples verified as containing pathogens.
Claim(s) 9, 13, and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Gupta in view of Meisel in view of Weidemaier, as applied to claims 1, 3, 6-7, 10-11, 14, 16, 18-19, 22-23, and 25 above, and in further view of Radhakrishnan et al. (U.S Patent Pub No.: US 2021/0106231, U.S Patent Pub Date: 15 April 2021).
Meisel in view of Gupta in view of Weidemaier teach claims 1, 3, 6-7, 10-11, 14, 16, 18-19, 22-23, and 25.
Meisel in view of Gupta in view of Weidemaier teach a methods using light spectra data for analyzing food pathogens for generating and providing notification that a sample contains or does not contain a pathogen.
Meisel in view of Gupta in view of Weidemaier do not teach claims 9, 12-13, 21, and 24.
In addition to Gupta using FITR absorbance data [Gupta, page 1251 right col quantification of the foodborne pathogens], Radhakrishnan et al. (Radhakrishnan) also discloses using absorption coefficient (i.e., absorbance values) and contains transmittance spectra [Radhakrishnan, page 5 right col para 0062], as in instant claims 9 and 21.
Radhakrishnan discloses the excitation wavelength ranges from 300nm to 1100nm [Radhakrishnan, page 3, right col para 0046], as in instant claims 13.
It would be obvious to one of ordinary skill in the art by the effective filing date of the claimed invention to modify Meisel in view of Gupta in view Weidemaier, and in further view of Radhakrishnan because Radhakrishnan discloses methods for detecting and classifying pathogens in varying different kinds of samples such as consumable commodities (i.e., food samples) [Radhakrishnan, pages 6-7 para 0070-0071]. Here, one of ordinary skill in the art would be motivated to combine Meisel in view of Gupta in view Weidemaier, and in further view of Radhakrishnan because the optical system for analyzing samples for pathogens that uses absorption coefficient (i.e., absorbance values) and transmittance spectra, ranges/thresholds for limiting nm spectrum to 33-4000nm [Radhakrishnan, page 3, para 0046 and page 4 para 0051], for measuring both steady state and time-resolved fluorescence of Radhakrishnan are implemented to improve the specificity and sensitivity of pathogen detection and pathogen classification accuracy [Radhakrishnan, page 4 para 0052]. Thus, one of ordinary would have a reasonable expectation of success combine Meisel in view of Gupta in view Weidemaier, and in further view of Radhakrishnan because, while Meisel in view of Gupta in view Weidemaier do not teach a combination using absorbance and transmittance and thresholds limiting nm spectrum ranges with respect to detecting in pathogen samples, the disclosure of Radhakrishnan does disclose those limitations and discloses those limitation facilitate with the improvements for accurate in detection and pathogen classification. Therefore, combining Meisel in view of Gupta in view Weidemaier, and in further view of Radhakrishnan would yield predictable method steps encompassing absorbance and transmittance spectra data and thresholds limiting nm spectrum ranges for improving pathogen classification accuracy so that notifications can be provided when pathogens are detected in food sample(s).
Claim(s) 8, 12, 20, and 24 are rejected under 35 U.S.C. 103 as being unpatentable over Gupta in view of Meisel in view of Weidemaier, as applied to claims 1, 3, 6-7, 10-11, 14, 16, 18-19, 22-23, and 25 above, and in further view of Amini et al. (U.S Patent Pub. No.: US 2021/0193266, Patent Pub. Date: 24 June 2021).
Meisel in view of Gupta in view of Weidemaier teach claims 1, 3, 6-7, 10-11, 14, 16, 18-19, 22-23, and 25.
Meisel in view of Gupta in view of Weidemaier teach a methods using light spectra data for analyzing food pathogens for generating and providing notification that a sample contains or does not contain a pathogen.
Meisel in view of Gupta in view of Weidemaier do not teach claims 8 and 20.
Amini et al. (Amini) disclose “the term "food processing facility" includes facilities that manufacture, process, pack, or hold food in any location globally. A food processing facility can, for example, determine the location and source of an outbreak of food-borne illness or a potential bioterrorism incident.” [Amini, page 4 left col para 0055], as in instant claims 8 and 20.
Amini discloses the transmittance, absorption, reflection or refraction of visible, infrared, microwave, or ultraviolet light sources may be measured using embedded optical ports [Amini, page 15, left col para 0139], as in instant claims 12 and 24.
It would be obvious to one of ordinary skill in the art by the effective filing date of the claimed invention to modify Meisel in view of Gupta in view of Weidemaier, and in view of Amini because Amini discloses analyzing food samples for foodborne pathogens using a foodborne pathogen detection apparatus [Amini, page 16 para 0146] with embedded optical ports that can measure the transmittance, absorption, reflection or refraction of visible, infrared, microwave, or ultraviolet light sources [Amini page 15 para 0139]. Here, one of ordinary skill in the art would recognize that although Amini discloses nucleic analysis, Amini acknowledges the system can be used to gather data related to optical measurements of a food sample. Thus, one of ordinary skill in the art would be motivated to combine the classification system of Meisel, the sample concentration data of Gupta, and providing detection reports of Weidemaier with the food processing facility samples and visible, infrared, microwave, or ultraviolet light sources measurements of Amini because the detection system of Amini can optically evaluate food samples from food processing facilities with higher than 95% precision and 97% accuracy for pathogen detection using spectra data (i.e., transmittance, absorption, reflection or refraction of visible, infrared, microwave, or ultraviolet light sources). Therefore, combining the teachings of Meisel in view of Gupta in view of Weidemaier in further view of Amini would yield an accurate and precise improved predictable system that can optically evaluate food samples from food processing facilities to determine if pathogen contamination is present.
Conclusion
Claims 1-25 are rejected.
No claims are allowed.
Finality
This Office action is a Non-Final action. A shortened statutory period for reply to this action is set to expire THREE MONTHS from the mailing date of this action.
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/J.C.P./Examiner, Art Unit 1687
/Anna Skibinsky/
Primary Examiner, AU 1635
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