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 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 an abstract idea without significantly more.
Claim 1 recites:
processing the first set of data to obtain a second set of data in a second format different from the first format, the second set of data including the set of spectral metrics;
applying one or more trained models to at least one of the set of spectral metrics and a set of values based on the set of spectral metrics to obtain a result, the one or more trained models trained on a set of training samples for a particle of interest; based on the result which falls into the abstract idea grouping of mathematical concepts. Under the broadest and responsible interpretation, and light of applicant’s file specification, specifically para. [0099], the processing involves the normalizing or arranging the set of values received from the different spectral acquisition apparatuses. Normalization involves scaling numerical values, typical mapping data to a range or z-scores, which are mathematical formulas. The act of arranging/ordering involves sorting or re-indexing data based on a numerical, algebraic or logic rules. Further, applying trained models to datasets is fundamentally a mathematical process involving high-dimensional linear algebra, calculus, and statistics to transform input data into predictions. It operates by multiplying input vectors by learned weight matrices, applying non-linear activation functions, and minimizing error through optimization techniques to generate insights. Therefore, the identified abstract idea falls into the abstract idea grouping of mathematical concepts.
The claim further recites determining either a positive particle of interest detection or a negative particle of interest detection for the particle of interest for the sample. This step falls into the abstract idea grouping of mental concepts, as insofar as what is structurally recited, a user can observe the output of the trained model to make determinations regarding if a result indicates a positive particle of interest or a negative particle of interest. Therefore, the identified abstract idea falls into the abstract idea grouping of mental concepts.
Although the claim recites a non-transitory computer-readable medium, instructions and one or more processors, these generically claimed computer elements are merely acting as tools for performing the abstract ideas in a computer technology; as neither the performance or result of the abstract idea improves their respective operations. MPEP 2106.05(a)
This judicial exception is not integrated into a practical application because “receiving a first set of data in a first format, the first set of data including a set of spectral metrics, the first set of data provided by an apparatus that obtains the set of spectral metrics based on interactions of electromagnetic radiation with a sample” reads as an insignificant pre-solution activity related to data gathering. The receiving step merely provides the abstract ideas the needed data to perform the abstract idea; as the result neither improves the data gathering step or the gathered data. MPEP 2106.05(g)
The recited sample and electromagnetic radiation merely link the abstract ideas to a field of use, as neither the result or performance of the abstract ideas improves these generically recited elements. MPEP 2106.05(h)
Lastly, the recited step of “generating a particle of interest detection notification that indicates either the positive particle of interest detection or the negative particle of interest detection for the particle of interest for the sample; and providing the particle of interest detection notification” merely read as instructions to apply the exceptions, as generating a notification to provide a result of the abstract ideas is merely a generalized application of the judicial exceptions, as the outputted result does not confine the abstract ideas to a particular application of the abstract ideas. MPEP 2106.05(f)
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the receiving data related to a generically linked sample interacting with electromagnetic waves are not improved or bettered by the abstract idea. Further, generically claimed computer elements and their outputted notification does not provide significantly more because this type of recitation is equivalent to the words "apply it".
Note: claim 11 is rejected similar to claim 1.
Claims 2 recites “receiving metadata associated with at least one of the apparatuses, the sample, a date and time at which the apparatus obtains the set of spectral metrics; and storing the metadata in association with the result”. The recited steps further define the additional elements related to gathering data to perform the identified abstract ideas without providing significantly more or integrating the abstract ideas into a practical application; as neither the result or performance of the abstract idea improves the receiving or storing of that data. MPEP 2106.05(g)
Claims 12 and 23 are rejected similar to claim 2.
Claim 3 further defines the sample itself. The variously recited samples merely link the abstract idea to a field of use without providing significantly more or integrating the abstract idea into a practical application; as neither the result or performance of the abstract idea improves the sample. MPEP 2106.05(h)
Claim 13 is rejected similar to claim 3.
Claim 4 recites further recites “wherein the set of spectral metrics is a first set of spectral metrics, the apparatus is a first apparatus from a first manufacturer sited at a first location, the sample is a first sample, the set of values is a first set of values, the result is a first result, the positive particle of interest detection is a first positive particle of interest detection, the negative particle of interest detection is a first negative particle of interest detection, the particle of interest detection notification is a first particle of interest detection notification, and the method further comprises: receiving a third set of data in a third format, the third set of data including a second set of spectral metrics, the second set of spectral metrics provided by a second apparatus that obtains the second set of spectral metrics based on interactions of electromagnetic radiation with a second sample, and the second apparatus is sited at a second location different from the first location”. The identified additional elements further defined the insignificant pre-solution activity of mere data gathering while generically linking the abstract ideas to a field of use. Therefore, alone or in combination, these additional elements fail to provide significantly more or integrate the abstract ideas into a practical application; as neither the performance or result of the abstract ideas improve the sets of spectral metrics, apparatuses, manufacture site locations, samples or particles of interest. MPEP 2106.05(g) and (h)
The claim further defines the abstract idea falling into the abstract idea groupings of mathematical and mental concepts by reciting “ processing the third set of data to obtain a fourth set of data in the second format, the fourth set of data including the second set of spectral metrics; applying the one or more trained models to at least one of the second set of spectral metrics and a second set of values based on the second set of spectral metrics to obtain a second result; based on the second result, determining either a second positive particle of interest detection or a second negative particle of interest detection for the particle of interest in the second sample; generating a second particle of interest detection notification that indicates either the second positive particle of interest detection or the second negative particle of interest detection for the particle of interest in the second sample; and providing the second particle of interest detection notification” for the same reasons state in the rejection of claim 1.
Therefore, the claim, as a whole, fails to provide significantly more or integrate the abstract idea into a practical application.
Note: claim 14 is rejected similar to claim 4.
Claims 5 and 15 further define the abstract ideas falling into the abstract idea grouping of mathematical concepts, thereby failing to provide significantly more or integrate the abstract ideas into a practical application.
Claims 6, 10 and 20 and further defines training one or models and the trained model being a set of trained decision trees. As known, training models and trained models involve mathematical operations. Therefore, claims 6, 10 and 20 are considered to further define the abstract idea falling into the abstract idea grouping of mathematical concepts without providing significantly more or integrating the abstract idea into a practical application.
Note: claim 16 is rejected similarly to claim 6.
Claims 7, 17 and 24 further define the metric. These metrics read as an additional element related to the data gathered. Therefore, the claim further defines the insignificant pre-solution step of data gathering without providing significantly more or integrating the abstract ideas into a practical application. MPEP 2106.05(g)
Claims 8 and 18 further define the post solution step related to the abstract idea falling into the abstract idea grouping of mental concepts. As the human mind is capable of comparing the resulting data to a threshold to make a determination such that the result indicates a positive particle. Therefore, claims 8 and 18 are considered to further define the abstract idea falling into the abstract idea grouping of mental concepts without providing significantly more or integrating the abstract idea into a practical application.
Claims 9, 19 and 25 further define the insignificant pre-solution step of data gathering without providing significantly more or integrating the abstract ideas into a practical application; as the electromagnetic radiation including at least one of ultraviolet light, visible light, and infrared light is neither improved or bettered by the result of the abstract ideas. MPEP 2106.05(g)
Claim 21 recites:
process the first set of data to obtain a second set of data in a second format different from the first format, the second set of data including the set of spectral metrics; and a second computing device configured to: apply one or more trained models to at least one of the set of spectral metrics and a set of values based on the set of spectral metrics to obtain a result, the one or more trained models trained on a set of training samples for a particle of interest which falls into the abstract idea grouping of mathematical concepts. Under the broadest and responsible interpretation and light of applicant’s file specification, specifically para. [0099], the processing involves the normalize or arrange the set of values received from the different spectral acquisition apparatuses. Normalization involves scaling numerical values, typical mapping data to a range or z-scores, which are mathematical formulas. The act of arranging/ordering involves sorting or re-indexing data based on a numerical, algebraic or logic rules. Further, applying trained models to datasets is fundamentally a mathematical process involving high-dimensional linear algebra, calculus, and statistics to transform input data into predictions. It operates by multiplying input vectors by learned weight matrices, applying non-linear activation functions, and minimizing error through optimization techniques to generate insights. Therefore, the identified abstract idea falls into the abstract idea grouping of mathematical concepts.
The claim further recites based on the result, determine either a positive particle of interest detection or a negative particle of interest detection for the particle of interest for the sample; and transmit to the first computing device either the positive particle of interest detection or the negative particle of interest detection for the particle of interest for the sample, wherein the first computing device is further configured to: generate a particle of interest detection notification that indicates either the positive particle of interest detection or the negative particle of interest detection for the particle of interest for the sample; and provide the particle of interest detection notification. This step falls into the abstract idea grouping of mental concepts, as insofar as what is structurally recited, a user can observe the output of the trained model to make determinations regarding if a result indicates a positive particle of interest or a negative particle of interest. Therefore, the identified abstract idea falls into the abstract idea grouping of mental concepts.
Although the claim recites a first and second computing devices, these generically claimed computers are merely acting as tools for performing the abstract ideas in a computer technology; as neither the performance or result of the abstract improves their respective operations. MPEP 2106.05(a)
This judicial exception is not integrated into a practical application because transmitting the second set of data and receiving the second set of data reads as an insignificant pre-solution activity related to data gathering. The receiving and transmitting steps merely provide the abstract ideas the needed data to perform the abstract idea, as the result neither improves the data gathering step or the gathered data. MPEP 2106.05(g)
The recited sample and electromagnetic radiation merely link the abstract ideas to a field of use, as neither the result or performance of the abstract ideas improves these generically recited elements. MPEP 2106.05(h)
Lastly, the recited step of “generating a particle of interest detection notification that indicates either the positive particle of interest detection or the negative particle of interest detection for the particle of interest for the sample; and providing the particle of interest detection notification” merely read as instructions to apply the exceptions, as generating a notification to provide a result of the abstract ideas is merely a generalized application of the judicial exceptions, as the outputted result does not confine the abstract ideas to a particular application of the abstract ideas. MPEP 2106.05(f)
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the receiving data related to a generically linked sample interacting with electromagnetic waves are not improved or bettered by the abstract idea. Further, generically claimed computers and their outputted notification does not provide significantly more because this type of recitation is equivalent to the words "apply it".
Claim 22 further defines obtaining metrics based on interactions of electromagnetic radiation with a sample and providing the first set of data including the set of spectral metrics to the first computing device. The claim further defines the pre-solution insignificant activity of mere data gathering required to perform the abstract ideas in the defined computer environment. The claim fails to provide significantly more or integrate the abstract idea into a practical application because neither the result or performance of the abstract idea improves the data gathering steps or the computer. MPEP 2106.05(g)
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-3, 6, 7, 9, 11, 12, 13, 16, 17 and 21-25 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Trenholm (2016/0034809).
With respect to claims 1 and 11, Trenholm et al. teaches a non-transitory computer-readable medium [0053] comprising executable instructions (i.e. readable/executable instructions; [0053]), the executable instructions being executable by one or more processors [0053] to perform a method, the method comprising: receiving a first set of data in a first format (as Trenholm teaches receiving data via an interface module 240; [0072]), the first set of data including a set of spectral metrics (as Trenholm discloses a first set of data is collected from sensors 1414 that include features extracted using FFT or wavelet transformation; [0072]), the first set of data provided by an apparatus (i.e. an apparatus found in food manufacturing; [0120]) that obtains the set of spectral metrics (via the sensors like various optical inputs may be provided, such as an X-ray imaging input 1414; [0120]) based on interactions of electromagnetic radiation with a sample (i.e. x-rays, spectroscopic, etc.); processing the first set of data to obtain a second set of data in a second format different from the first format (as Trenholm teaches converting image data to difference image file format; [0129]), the second set of data including the set of spectral metrics (i.e. a set of spectral metric a model has been trained on); applying one or more trained models (i.e. as Trenholm teaches downloading a trained model according to its application; [0134]) to a set of values based on the set of spectral metrics (i.e. the converted set of spectral metrics) to obtain a result (i.e. a generated prediction; [0134]), the one or more trained models (as downloaded) trained on a set of training samples for a particle of interest (as Trenholm teaches these models being trained to classify input signals, as processed and converted, to detect pathogens present in a sample; [0152]); based on the result (i.e. predictions sent to computational modules), determining either a positive particle of interest detection or a negative particle of interest detection for the particle of interest for the sample (i.e. as the computational module, trained and receiving the converted data sets from the x-ray sensor, for example, to indicate the presence or absence of a substance; [0152]); generating a particle of interest detection notification that indicates either the positive particle of interest detection or the negative particle of interest detection for the particle of interest for the sample (as Trenholm teaches notifying a professional based on the indicated presence or absence of the pathogen substance; 0151]); and providing the particle of interest detection notification (as Trenholm teaches providing the profession the notification).
With respect to claims 2, 12 and 23, Trenholm et al. teaches the non-transitory computer-readable medium, wherein the method further comprises: receiving metadata associated with the apparatus (i.e. an origin of the data; [0072]), at which the apparatus (i.e. sensor of the apparatus) obtains the set of spectral metrics (sensed data); and storing the metadata in association with the result (as the overall process stores the data relative the result in the taught computer modules).
With respect to claims 3 and 13, Trenholm et al. teaches the non-transitory computer-readable medium wherein the sample is a sample of a food processing byproduct [0150], and the particle of interest is a foodborne pathogen [0152].
With respect to claims 6 and 16, Trenholm et al. teaches the non-transitory computer-readable medium wherein the method further comprises training one or more models on the set of training samples for the particle of interest to obtain the one or more trained models (as Trenholm et al. teaches using a trained dataset; [0157]).
With respect to claims 7 and 17, Trenholm et al. teaches the non-transitory computer-readable medium wherein spectral metrics in the set of spectral metrics reflectance metrics (SPIRIS; [0153]).
With respect to claims 9 and 19, Trenholm et al. teaches the non-transitory computer-readable medium wherein the electromagnetic radiation includes visible light (as Trenholm et al. teaches SPIRIS which uses visible light).
With respect to claim 21, Trenholm et al. teaches a system comprising: a first computing device configured to: receive a first set of data in a first format, the first set of data including a set of spectral metrics, the first set of data provided by an apparatus configured to obtain the set of spectral metrics based on interactions of electromagnetic radiation with a sample (para [0152]-[0153]); process the first set of data to obtain a second set of data in a second format different from the first format, the second set of data including the set of spectral metrics (para [0129]); and transmit the second set of data (para [0152]-[0153]); and a second computing device configured to: receive the second set of data (para [0152]-[0153]); apply one or more trained models to at least one of the set of spectral metrics and a set of values based on the set of spectral metrics to obtain a result, the one or more trained models trained on a set of training samples for a particle of interest (para [0129], [0150]); based on the result, determine either a positive particle of interest detection or a negative particle of interest detection for the particle of interest for the sample (para [0152]); and transmit to the first computing device either the positive particle of Interest detection or the negative particle of interest detection for the particle of interest for the sample, wherein the first computing device is further configured to: generate a particle of interest detection notification that indicates either the positive particle of interest detection or the negative particle of interest detection for the particle of interest for the sample (para [0156]); and provide the particle of interest detection notification (para [0156]).
With respect to claim 22, Trenholm et al. teaches the system of claim 21, further comprising the apparatus, wherein the apparatus is configured to: obtain the set of spectral metrics based on interactions of electromagnetic radiation with a sample (para [0152]-[0153], [0156]); and provide the first set of data including the set of spectral metrics to the first computing device (para [0152]-[0153], [0156]).
With respect to claim 24, Trenholm et al. teaches the system of claim 21 wherein spectral metrics in the set of spectral metrics are one of absorbance metrics, transmittance metrics, reflectance metrics, and scattering metrics (para [0153]).
With respect to claim 25 Trenholm et al. teaches the system of claim 21 wherein the electromagnetic radiation includes at least one of ultraviolet light, visible light, and infrared light (para [0153]).
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) 4, 10, 14 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Trenholm (2016/0034809) in view of Lefkofsky (2021/0118559).
With respect to claims 4 and 14, Trenholm et al. teaches all that is claimed in the above rejection of claims 1 and 11, including the non-transitory computer-readable medium wherein the set of spectral metrics is a first set of spectral metrics (as Trenholm teaches the sensors providing spectral imaging data; [0150], the apparatus is a first apparatus from a first manufacturer sited (as Trenholm teaches a first apparatus in food manufacturing including the taught sensors; [0150]) at a first location (i.e. at a specialized locations; [0134]), the sample is a first sample (i.e. a first sample under testing), the set of values is a first set of values (as converted), the result is a first result (related to the first sample), the positive particle of interest detection is a first positive particle of interest detection, the negative particle of interest detection is a first negative particle of interest detection (as the trained model and predication is feed data from the first sample to determine a positive or negative particle of interest), the particle of interest detection notification is a first particle of interest detection notification (as the professional will be notified if there is a positive or negative presence of the pathogen based on the results (para [0129]; "a neural network is applied to drive evolution of the choice of reaction substances provided for pathogen detection with a sample. Particularly, selection of a reaction substance may compensate for mutation, pleomorphism and polymorphism of pathogens to ensure that appropriate reaction substances are selected to maximize likelihood of detecting a pathogen. Accordingly, inputs including a combination of time series genomic data of the pathogen and/or human host cells, and data relating to a plurality of desired substances (e.g. disease biomarkers) may be provided to a neural network trained to provide an output indicating which reaction substance(s) should be selected," para [0158]); applying the one or more trained models to at least one of the second set of spectral metrics and a second set of values based on the second set of spectral metrics to obtain a second result (para [0129], [0150]); based on the second result, determining either a second positive particle of interest detection or a second negative particle of interest detection for the particle of interest in the second sample (para [0152]); generating a second particle of interest detection notification that indicates either the second positive particle of interest detection or the second negative particle of interest detection for the particle of interest in the second sample (para [0156]); and providing the second particle of interest detection notification (para [0156]) but remains silent regarding, and the method further comprises: receiving a third set of data in a third format, the third set of data including a second set of spectral metrics, the second set of spectral metrics provided by a second apparatus that obtains the second set of spectral metrics based on interactions of electromagnetic radiation with a second sample, and the second apparatus is sited at a second location different from the first location; processing the third set of data to obtain a fourth set of data in the second format, the fourth set of data including the second set of spectral metrics.
Lefkofsky teaches a similar method receiving a third set of data in a third format, the third set of data including a second set of spectral metrics, the second set of spectral metrics provided by a second apparatus that obtains the second set of spectral metrics based on interactions of electromagnetic radiation with a second sample, and the second apparatus is sited at a second location different from the first location; processing the third set of data to obtain a fourth set of data in the second format, the fourth set of data including the second set of spectral metrics ("[e]ach device may be in operative communication with one or more aspects of the system 101 in order to transmit information from the device into the system for processing and storage into the device dataset 270. Device dataset 270 may further include communication processes, application interfaces, and/or conversion parameters for receipt of the subject information from the devices included within the dataset autonomously. Such communication processes may include communication over the World Wide Web, Wi-Fi, Bluetooth, internet of things, or other communication mediums. Application interfaces may include the APIs and libraries needed to access the communication processes. Conversion parameters may include data formats of which the devices provide subject information and processes for converting the device subject information to the structured format of structured databases 210 and 220," para [0096]; "[o]ften, there are a large number of clinical trials being conducted at any given time, and typically the clinical trials relate to a wide range of diseases and conditions. In some instances, clinical trials are performed at multiple sites, such as hospitals, laboratories, and universities," para [0148]; "A clinical module (not shown) may comprise a feature collection associated with information derived from clinical records of a subject, which can include records from family members of the subject. These may be abstracted from unstructured clinical documents, EMR, EHR, or other sources of subject history. Information may include subject symptoms, diagnosis, treatments, medications, therapies, hospice, responses to treatments, laboratory testing results, medical history, geographic locations of each, demographics, or other features of the subject which may be found in the subject's medical record," para [0195]).
It would have been obvious to one of ordinary skill in the art to modify the spectroscopy system of Trenholm with the support for capturing and processing data at a pluralist of sites of Lefkofsky, because such systems and methods allow for conducting spectroscopic practices across multiple facilities (Lefkofsky: para [0096], [0145], [0195]), thereby improving the versatility of Trenholm, such that a third set of data comes from another site.
With respect to claims 10 and 20, Trenholm teaches the non-transitory computer-readable medium of claims 1 and 11 but fails to explicitly teach such a non-transitory computer-readable medium wherein the one or more trained models include a set of trained decision trees.
However, Lefkofsky teaches such a non-transitory computer-readable medium wherein the one or more trained models include a set of trained decision trees ("decision trees," para [0094]).
It would have been obvious to one of ordinary skill in the art to combine the spectroscopy system of Trenholm with the support for decision trees of Lefkofsky, because such systems and methods allow for the use of decision trees in making determinations using models for processing spectral data (Lefkofshy: para [0094]).
Claim(s) 5, 8, 15, and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Trenholm (2016/0034809) in view of Mahadevan et al. (2019/0250105).
With respect to claim 5, Trenholm et al. teaches the non-transitory computer-readable medium of claim 1, but fails to teach such a non-transitory computer-readable medium wherein the method further comprises normalizing each spectral metric in the set of spectral metrics to be between zero, inclusive, and one, inclusive, to obtain the set of values, and wherein applying the one or more trained models to at least one of the set of spectral metrics and the set of values based on the set of spectral metrics to obtain the result includes applying the one or more trained models to the set of values.
However, Mahadevan et al. teaches such a non-transitory computer-readable medium wherein the method further comprises normalizing each spectral metric in the set of spectral metrics to be between zero, inclusive, and one, inclusive, to obtain the set of values, and wherein applying the one or more trained models to at least one of the set of spectral metrics and the set of values based on the set of spectral metrics to obtain the result includes applying the one or more trained models to the set of values ("This statistical method uses a logistic regression to produce specific weights and frequencies for biochemical features that are important in classification of each bacteria based on a training data set," para [0176]; "To evaluate the importance of spectral features used for classification, a scaled version (from 0 to 1) of both the weight and how often spectral features were found from the SMLR was utilized. The product of these values is used to calculate the SMLR feature importance, which is a quantitative metric that considers both the biochemical differences across the three bacteria characterized in this study and spectral heterogeneity among the same bacteria," para [0260]).
It would have been obvious to one of ordinary skill in the art to combine the spectroscopy system of Trenholm et al. with the support for normalizing metrics to a 0-1 range of Mahadevan et al., because such systems and methods allow for scaling data to analyze data features (Mahadevan et al. para [0176], [0260]). Furthermore, both Trenholm et al. and Mahadevan et al. are directed to systems and methods for spectroscopy.
With respect to claim 8, Trenholm et al. teaches the non-transitory computer-readable medium of claim 1 but fails to explicitly teach such a non-transitory computer-readable medium wherein the result indicates the positive particle of interest detection if the result meets or exceeds a threshold.
However, Mahadevan et al. teaches such a non-transitory computer-readable medium wherein the result indicates the positive particle of interest detection if the result meets or exceeds a threshold ("After Raman spectra are collected from MEE samples, a preliminary statistical analysis approach such as a Student's t-test is calculated at each wavenumber and the significance threshold is calculated using a multiple comparison correction to identify Raman peaks that may be important in identifying the presence of bacteria," para [0206]).
It would have been obvious to one of ordinary skill in the art to combine the spectroscopy system of Trenholm et al. with the support for thresholding of Mahadevan et al., because such systems and methods allow for using thresholded data to determine the presence of a pathogen (Mahadevan et al.: para [0206]). Furthermore, both Trenholm et al. and Mahadevan et al. are directed to systems and methods for spectroscopy.
With respect to claim 15, Trenholm et al. teaches the method of claim 11, but fails to explicitly teach such a method further comprising normalizing each spectral metric in the set of spectral metrics to be between zero, inclusive, and one, inclusive, to obtain the set of values, and wherein applying the one or more trained models to at least one of the set of spectral metrics and the set of values based on the set of spectral metrics to obtain the result includes applying the one or more trained models to the set of values.
However, Mahadevan et al. teaches such a method further comprising normalizing each spectral metric in the set of spectral metrics to be between zero, inclusive, and one, inclusive, to obtain the set of values, and wherein applying the one or more trained models to at least one of the set of spectral metrics and the set of values based on the set of spectral metrics to obtain the result includes applying the one or more trained models to the set of values (para [0176], [0260]).
It would have been obvious to one of ordinary skill in the art to combine the spectroscopy system of Trenholm et al. with the support for normalizing metrics to a 0-1 range of Mahadevan et al., because such systems and methods allow for scaling data to analyze data features (Mahadevan et al.: para [0176], [0260]). Furthermore, both Trenholm et al. and Mahadevan et al. are directed to systems and methods for spectroscopy.
With respect to claim 18, Trenholm et al. teaches the method of claim 11 but fails to explicitly teach such a method wherein the result indicates the positive particle of interest detection if the result meets or exceeds a threshold.
However, Mahadevan et al. teaches such a method wherein the result indicates the positive particle of interest detection if the result meets or exceeds a threshold (para [0206]).
It would have been obvious to one of ordinary skill in the art to combine the spectroscopy system of Trenholm et al. with the support for thresholding of Mahadevan et al., because such systems and methods allow for using thresholded data to determine the presence of a pathogen (Mahadevan et al.: para [0206]). Furthermore, both Trenholm et al. and Mahadevan et al. are directed to systems and methods for spectroscopy.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Lee (2020/0306757) which teaches collects data from a sample to detect pathogens.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MATTHEW G MARINI whose telephone number is (571)272-2676. The examiner can normally be reached Monday-Friday 8am-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, Stephen Meier can be reached at 571-272-2149. 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.
/MATTHEW G MARINI/ Primary Examiner, Art Unit 2853