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 1-20 are currently pending and under exam herein.
Claims 1-20 are rejected.
Claims 3-6 and 14-17 are objected to.
Priority
The instant application does not claim benefit to any preceding applications. Therefore, the effective filing date of claims 1-20 is 8 November 2022.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 14 November 2022 complies with 37 CFR 1.98. Accordingly, all references listed have been considered by the examiner.
Drawings
The drawing filed on 8 November 2022 have been received and are accepted.
Claim Objections
Claims 3-6 and 14-17 are objected to because of the following informalities:
Claims 3, 5-6, 14, and 16-17 recite the limitation “at difference instances.” This limitation should read “at different instances.”
Claims 4 and 15 recite “wherein the first lot of the pharmaceutical drug is at an expiration date of the pharmaceutical drug,” “wherein the second lot of the pharmaceutical drug is prior to the expiration date of the pharmaceutical drug,” and “wherein the third lot of the pharmaceutical drug is after the expiration date of the pharmaceutical drug.” For clarification, these limitations should be amended to explicitly state that the expiration date of the lot is at/prior to/after the expiration date of the pharmaceutical drug.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 3-6 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Claims 3 and 5 recite the limitation “the spectrometer output data” after reciting a first and second spectrometer. This renders the claims indefinite because it is unclear whether “the spectrometer output data” is obtained from the first or second spectrometer. Claims 4 and 6 are similarly rejected due to their dependency on claims 3 and 5. This rejection can be overcome by amending claims 3 and 5 to make clear which spectrometer the data is obtained from.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (abstract ideas and natural phenomenon) without significantly more. Under MPEP § 2106, subject matter is patent eligible when the claimed invention is to one of the four statutory categories of invention [Step 1], and the claim is not directed to a judicial exception [Step 2A] unless the claim as a whole includes additional limitations amounting to significantly more than the exception [Step 2B].
Step 1
Claims 1-20 describe inventions that are to one of the statutory categories. In Step 1, a claim must fall within one of the four enumerated categories of statutory subject matter (process, machine, manufacture, or composition of matter); a claim falling outside these categories is ineligible without further analysis. See MPEP § 2106.03. Claims 1-12 are properly to one of the four statutory categories because the claimed invention is a system, which falls into the machine category [Step 1: Yes]. Claims 13-19 are properly to one of the four statutory categories because the claimed invention is a method, which falls into the process category [Step 1: Yes]. Claim 20 is properly to one of the four statutory categories because the claimed invention is a non-transitory computer-readable medium storing processor-executable instructions, which fall into the manufacture category [Step 1: Yes].
Step 2A
Under Step 2A, a claim is directed to a judicial exception if, under the broadest reasonable interpretation, it recites an abstract idea, law of nature, or natural phenomena [Prong One] without the claim as a whole integrating the exception into a practical application [Prong Two]. Abstract ideas include mathematical concepts, mental processes, and certain methods of organizing human activity. Mathematical concepts encompass mathematical relationships, formulas, equations, and mathematical calculations. See MPEP § 2106.04(a)(2)(I). Mental processes involve concepts that can be performed in the human mind or by a human with the aid of pen and paper, such as observations, evaluations, judgments, or opinions. See MPEP § 2106.04(a)(2)(III). Certain methods of organizing human activity include fundamental economic principles, commercial or legal interactions, and managing personal behavior or relationships. See MPEP § 2106.04(a)(2)(II). Laws of nature and natural phenomena, include naturally occurring principles/relations and nature-based products that are naturally occurring or that do not have markedly different characteristics compared to what occurs in nature. See MPEP § 2106.04(b)-(c).
Prong One
A claim recites a judicial exception when it sets forth or describes a law of nature, natural phenomenon, or abstract idea. Claims 1-20 recite abstract ideas that fall into the groupings of mathematical concepts and mental processes.
Independent claims 1, 13, and 20 recite the following limitations, which describe abstract ideas within the mathematical concepts or mental processes groupings:
obtain at least one expired drug machine learning - artificial intelligence (ML - AI) model associated with a pharmaceutical drug;
input the drug expiration information into the at least one expired drug ML - Al model to determine usability information associated with the sample of the pharmaceutical drug;
The limitation of obtaining an expired drug ML-AI model encompasses the abstract idea of merely selecting a pre-trained model for later evaluation, which constitutes a mental process. The limitation of inputting the drug expiration information involves using a mathematical model/algorithm to evaluate drug potency or usability, which constitutes an abstract idea within the mathematical concepts grouping.
Dependent claims 2-3, 5-10, 14, and 16-19 recite the following limitations, which describe abstract ideas within the mathematical concepts or mental processes groupings:
Claim 2 recites train the at least one expired drug ML - Al model.
Claims 3 and 14 recite wherein the at least one expired drug ML - Al model comprises a spectrometer ML - Al model, and training the spectrometer ML - Al model using the spectrometer ML - Al training information.
Claims 5 and 16 recite wherein the at least one expired drug ML - Al model further comprises a signal to noise (SNR) ML - Al model; determining, based on the spectrometer output data, SNR ML - Al training information of the pharmaceutical drug at difference instances within the life expectancy of the pharmaceutical drug; and training the SNR ML - Al model using the SNR ML - Al training information.
Claims 6 and 17 recite wherein the at least one expired drug ML - Al model further comprises an olfactory ML - Al model; training the olfactory ML - Al model using the olfactory ML - Al training information.
Claims 7 and 18 recite wherein the at least one expired drug ML - Al model comprises a spectrometer ML - Al model, a SNR ML - Al model, and an olfactory ML - Al model.
Claims 8 and 19 recite inputting the spectrometer data into the spectrometer ML - Al model to determine spectrometer usability information; inputting the SNR data into the SNR ML - Al model to determine SNR usability information; inputting the olfactory data into the olfactory ML - Al model to determine olfactory usability information; and determining the usability information based on the spectrometer usability information, the SNR usability information, and the olfactory usability information.
Claim 9 recites wherein the spectrometer usability information is a first usability confidence value that is output by the spectrometer ML - Al model, the SNR usability information is a second usability confidence value that is output by the SNR ML - Al model, and the olfactory usability information is a third usability confidence value that is output by the olfactory ML - Al model.
Claim 10 recites determining the usability information as a weighted average of the first usability confidence value, the second usability confidence value, and the third usability confidence value.
The limitations related to training a model involve optimization via linear algebra, calculus, and statistics, which constitutes an abstract idea within the mathematical concepts grouping. The limitations specifying the expired drug model merely narrow the abstract ideas of the independent claims. The limitation of determining SNR training data involves dividing the magnitude of the signal by the fluctuations in the baseline, which is an abstract idea within the mathematical concepts and mental processes groupings. The limitations of inputting data involve using a mathematical model/algorithm to evaluate drug potency or usability, which constitutes an abstract idea within the mathematical concepts grouping. The limitations of determining the usability information involves multi-factor evaluation or ensemble judgment, which constitutes and abstract idea within the mathematical concepts and mental processes groupings. The limitation of claim 9 merely specifies the form of the model output as a confidence value, which is a mathematical output of an algorithm, constituting an abstract idea within the mathematical concepts grouping. Claims 4, 11-12, and 15 do not recite any judicial exceptions, but inherit the exceptions from the claims upon which they depend.
Therefore, claims 1-20 recite abstract ideas – namely mathematical concepts and mental processes [Step 2A, Prong One: Yes].
Prong Two
Claims 1-20 as a whole do not integrate the recited judicial exception into a practical application. A claim that recites a judicial exception [Prong One] is deemed to be directed to a judicial exception [Step 2A] unless the claim as a whole contains additional elements that integrate the exception into a practical application [Prong Two]. A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, 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. See MPEP §§ 2106.04(d) and 2106.05(e). A claim does not integrate a judicial exception into a practical application by reciting insignificant extra-solution activity, generally linking the exception to a particular technological environment or field of use, merely reciting to apply the exception, merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea. See MPEP § 2106.04(d)(I). Insignificant extra-solution activities are nominal or tangential additions to a claim that are incidental to the primary process or product, including both pre-solution and post-solution activity (e.g. pre-solution data gathering for use in a process). If integrated into a practical application, the claim is eligible; otherwise, it is directed to the judicial exception, necessitating further analysis at Step 2B.
Independent claims 1, 13, and 20 recite the following limitations, which are additional elements:
a first expired drug usability device, comprising: a first spectrometer; and one or more first processors;
obtain drug expiration information of a sample of the pharmaceutical drug, wherein the drug expiration information comprises spectrometer data associated with using the first spectrometer on the sample;
perform one or more actions based on the usability information.
The limitation of a first expired drug usability device is a generic device because it is recited at a high level of generality that merely limits the field of use and does not integrate the judicial exceptions into a practical application. See MPEP §§ 2106.05(b)(I)-(III) & 2106.05(h). Similarly, the limitation of claim 2 of a second expired drug usability device merely limits the field of use and does not integrate the judicial exceptions into a practical application. The limitation of obtaining drug expiration information constitutes insignificant extra-solution activity that does not integrate the judicial exceptions into a practical application because it is a mere data gathering step that does not transform the nature of the claim into a patent-eligible application of the judicial exception. See MPEP § 2106.04(g). Additionally, this limitation is a data gathering step that is limited to a particular type of data, which merely indicates a field of use or technological environment in which to apply a judicial exception and cannot integrate a judicial exception into a practical application. See MPEP § 2106.04(h). The limitation of perform one or more actions based on the usability information is insignificant post-solution activity recited at such a high level of generality that it is equivalent to the words “apply it” and does not integrate the judicial exceptions into a practical application. See MPEP §§ 2106.04(f)-(g).
Dependent claims 2-4, 6-7, 14-15, and 17-18 recite the following limitations, which are additional elements:
Claim 2 recites provide the at least one expired drug ML - Al model to the first expired drug usability device.
Claims 3 and 14 recite obtaining spectrometer ML - Al training information comprising spectrometer output data of the pharmaceutical drug at difference instances within a life expectancy of the pharmaceutical drug.
Claims 4 and 15 recite obtaining first spectrometer output data of the second spectrometer associated with testing a first lot of the pharmaceutical drug, wherein the first lot of the pharmaceutical drug is at an expiration date of the pharmaceutical drug; obtaining second spectrometer output data of the second spectrometer associated with testing a second lot of the pharmaceutical drug, wherein the second lot of the pharmaceutical drug is prior to the expiration date of the pharmaceutical drug; and obtaining third spectrometer output data of the second spectrometer associated with testing a third lot of the pharmaceutical drug, wherein the third lot of the pharmaceutical drug is after the expiration date of the pharmaceutical drug.
Claims 6 and 17 recite obtaining olfactory ML - Al training information comprising olfactory sensor output data of the pharmaceutical drug at difference instances within the life expectancy of the pharmaceutical drug.
Claims 7 and 18 recite wherein the drug expiration information of the sample of the pharmaceutical drug comprises the spectrometer data of the sample of the pharmaceutical drug, SNR data of the sample of the pharmaceutical drug, and olfactory data of the sample of the pharmaceutical drug.
Claim 11 recites wherein the first spectrometer is a liquid spectrometer.
Claim 12 recites wherein the first spectrometer is a near infrared (NIR) spectrometer.
The limitation of providing the model encompasses generic data transmission recited at such a high level of generality that it is equivalent to the words “apply it” and does not integrate the judicial exceptions into a practical application. See MPEP § 2106.04(f). The limitations of obtaining data/information constitute insignificant extra-solution activity that merely indicate a field of use or technological environment in which to apply a judicial exception and cannot integrate a judicial exception into a practical application. See MPEP §§ 2106.04(g)-(h). The limitations specifying the spectrometer narrow the data gathering steps of claim 1 by indicating a particular data source, which does not integrate the judicial exceptions into a practical application. See MPEP § 2106.04(h). Finally, claims 5, 8-10, and 16 do not include any additional elements.
The claims as a whole merely recite insignificant extra-solution activities and abstract ideas implemented on generic devices without meaningful limitations that tie it to a specific technological improvement. Therefore, claims 1-20 do not contain additional elements that integrate the recited abstract ideas into a practical application [Step 2A, Prong Two: No].
Step 2B
Claims 1-20 do not include additional elements, whether considered individually or in combination, that are sufficient to amount to significantly more than the judicial exception itself. Under Step 2B, the claim is analyzed to determine whether there are any additional elements that, individually or in combination, constitute an “inventive concept" sufficient to ensure that the claim, as a whole, amounts to significantly more than the judicial exception itself. See MPEP § 2106.05; and Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. 208, 217-18, 110 USPQ2d 1976, 1981 (2014).
Independent claims 1, 13, and 20 recite the following limitations, which are additional elements:
a first expired drug usability device, comprising: a first spectrometer; and one or more first processors;
obtain drug expiration information of a sample of the pharmaceutical drug, wherein the drug expiration information comprises spectrometer data associated with using the first spectrometer on the sample;
perform one or more actions based on the usability information.
The limitation of a first expired drug usability device is a generic device that is conventional because it is recited at a high level of generality that merely limits the field of use and does not add significantly more than the judicial exception itself. See MPEP §§ 2106.05(b)(I)-(III) & 2106.05(h); and Haneen Banjar et al., An Intelligent System for Proper Management and Disposal of Unused and Expired Medications, 19(5) Int J Environ Res Public Health, Abstract (1 March 2022). Similarly, the limitation of claim 2 of a second expired drug usability device merely limits the field of use and does not integrate the judicial exceptions into a practical application. Id. The limitation of obtaining drug expiration information constitutes conventional insignificant extra-solution activity that merely indicates a field of use and does not add significantly more than the judicial exception itself. See MPEP §§ 2106.05(g)-(h); and Hui Chen et al., Express detection of expired drugs based on near-infrared spectroscopy and chemometrics: A feasibility study, 220 Spectrochim. Acta - A: Mol. Biomol. Spectrosc. Abstract (5 September 2019). The limitation of perform one or more actions based on the usability information is insignificant post-solution activity recited at such a high level of generality that it is equivalent to the words “apply it” and does not add significantly more than the judicial exception itself. See MPEP §§ 2106.04(f)-(g); and Haneen Banjar, at 3 para.3.
Dependent claims 2-4, 6-7, 14-15, and 17-18 recite the following limitations, which are additional elements:
Claim 2 recites provide the at least one expired drug ML - Al model to the first expired drug usability device.
Claims 3 and 14 recite obtaining spectrometer ML - Al training information comprising spectrometer output data of the pharmaceutical drug at difference instances within a life expectancy of the pharmaceutical drug.
Claims 4 and 15 recite obtaining first spectrometer output data of the second spectrometer associated with testing a first lot of the pharmaceutical drug, wherein the first lot of the pharmaceutical drug is at an expiration date of the pharmaceutical drug; obtaining second spectrometer output data of the second spectrometer associated with testing a second lot of the pharmaceutical drug, wherein the second lot of the pharmaceutical drug is prior to the expiration date of the pharmaceutical drug; and obtaining third spectrometer output data of the second spectrometer associated with testing a third lot of the pharmaceutical drug, wherein the third lot of the pharmaceutical drug is after the expiration date of the pharmaceutical drug.
Claims 6 and 17 recite obtaining olfactory ML - Al training information comprising olfactory sensor output data of the pharmaceutical drug at difference instances within the life expectancy of the pharmaceutical drug.
Claims 7 and 18 recite wherein the drug expiration information of the sample of the pharmaceutical drug comprises the spectrometer data of the sample of the pharmaceutical drug, SNR data of the sample of the pharmaceutical drug, and olfactory data of the sample of the pharmaceutical drug.
Claim 11 recites wherein the first spectrometer is a liquid spectrometer.
Claim 12 recites wherein the first spectrometer is a near infrared (NIR) spectrometer.
The limitation of providing the model encompasses generic data transmission recited at such a high level of generality that it is equivalent to the words “apply it” and does not add significantly more than the judicial exception itself. See MPEP § 2106.04(f); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016); and OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015). The limitations of obtaining data/information constitute conventional insignificant extra-solution activity that merely indicate a field of use and does not add significantly more than the judicial exception itself. See MPEP §§ 2106.04(g)-(h); Shuoyang Zhang et al., Raman spectroscopy and mapping technique for the identification of expired drugs, 224 Spectrochim. Acta - A: Mol. Biomol. Spectrosc. 2 col.2 para.3 (5 January 2020) (obtaining spectrometer data); Mariana Valente Farraia et al., The electronic nose technology in clinical diagnosis: A systematic review, 4(4) Porto Biomed J. Abstract (6 June 2019) (obtaining olfactory data); and John W. Dolan, The Role of the Signal-to-Noise Ratio in Precision and Accuracy, 19(1) LCGC Europe para.2 (1 January 2006) (obtaining SNR data). The limitations specifying the spectrometer type narrow the data gathering steps of claim 1 by indicating a particular data source that is conventional, which does not add significantly more than the judicial exception itself. See MPEP § 2106.04(h); Hui Chen, at 2 col.2 para.2; and James J Pitt, Principles and Applications of Liquid Chromatography-Mass Spectrometry in Clinical Biochemistry, 30(1) Clin Biochem Rev. 19, Abstract (February 2009).
Overall, claims 1-20 amount to no more than conventional insignificant extra-solution activities and implementing the abstract ideas on conventional devices in a routine way. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception itself because the claims recite additional elements that equate to insignificant extra-solution activity that indicate a field of use and mere instructions to apply the recited abstract ideas in a generic way or in a generic computing environment. Therefore, claims 1-20 are rejected for failing to set forth patent eligible subject matter under 35 U.S.C. 101 because the claimed invention recites abstract ideas [Step 2A, Prong One: Yes] and the additional elements do not integrate the judicial exception into a practical application [Step 2A, Prong Two: No] and do not amount to claiming significantly more than the recited exception [Step 2B: No].
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 20 is rejected under 35 U.S.C. 102(a)(1) as being anticipated by Opoku-Ansah (Jerry Opoku-Ansah et al., 14 Anal. Methods 2405-14 (10 May 2022)), as evidenced by C3.ai. (Infrastructure: Machine Learning Hardware Requirements (15 May 2021)). The italicized text within parenthesis corresponds to the instant claim limitations.
Regarding claim 20, Opoku-Ansah discloses a framework using hand-held near-infrared (NIR) spectrometers with multivariate algorithms for the rapid identification of unexpired drugs from expired ones. At 2413 col.1 para.4. Opoku-Ansah discloses developing identification models for pharmaceutical drugs with multivariate algorithms, including random forest (RF), partial least square discriminant analysis (PLS-DA), and support vector machine (SVM). At 2407 col.1 para.3 – col.2 para.1 (obtaining at least one expired drug machine learning - artificial intelligence (ML - AI) model associated with a pharmaceutical drug). Opoku-Ansah teaches using a hand-held near-infrared (NIR) spectrometer to obtain the spectral profiles of pharmaceutical drug samples. At 2406 col.2 para.2 (obtaining drug expiration information of a sample of the pharmaceutical drug, wherein the drug expiration information comprises spectrometer data associated with using a spectrometer on the sample). Following pre-processing techniques, Opoku-Ansah uses the NIR spectrometer data as input data for modelling to determine whether the drug is expired. At 2408 col.1 para.1; 2410 col.1 para.2 (inputting the drug expiration information into the at least one expired drug ML - Al model to determine usability information associated with the sample of the pharmaceutical drug). Based on the identification of expired or not expired, Opoku-Ansah further went on to find out the major peaks that contributed to the separation of the two different categories of drugs. At 2411 col.2 para.2 (performing one or more actions based on the usability information). While Opoku-Ansah does not explicitly disclose a non-transitory computer-readable medium having processor-executable instructions stored thereon, Opoku-Ansah discloses a machine learning model, which necessarily involves a non-transitory computer-readable medium with instructions stored thereon to be executed by a processor. See C3.ai., §§ Processors:CPUs, GPUs, TPUs, and FPGAs & Memory and Storage.
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.
Claims 1-3 and 12-14 are rejected under 35 U.S.C. 103 as being unpatentable over Opoku-Ansah (Jerry Opoku-Ansah et al., 14 Anal. Methods 2405-14 (10 May 2022)), as evidenced by Keough (Allie Keough, Smartphone Processors: What Do They Do?, SmartphonesPlus (29 August 2023)).
Regarding independent claims 1 and 13, Opoku-Ansah discloses a framework using hand-held near-infrared (NIR) spectrometers with multivariate algorithms for the rapid identification of unexpired drugs from expired ones. At 2413 col.1 para.4. Opoku-Ansah discloses developing identification models for pharmaceutical drugs with multivariate algorithms, including random forest (RF), partial least square discriminant analysis (PLS-DA), and support vector machine (SVM). At 2407 col.1 para.3 – col.2 para.1. Opoku-Ansah teaches using a hand-held NIR spectrometer to obtain the spectral profiles of pharmaceutical drug samples. At 2406 col.2 para.2 (claim 1: obtain drug expiration information of a sample of the pharmaceutical drug, wherein the drug expiration information comprises spectrometer data associated with using the first spectrometer on the sample; claim 13: obtaining, by the expired drug usability device, drug expiration information of a sample of the pharmaceutical drug, wherein the drug expiration information comprises spectrometer data associated with using a spectrometer on the sample). Following pre-processing techniques, Opoku-Ansah uses the NIR spectrometer data as input data for modelling to determine whether the drug is expired. At 2408 col.1 para.1; 2410 col.1 para.2.
While Opoku-Ansah’s disclosure is focused on the development of the framework, Opoku-Ansah discloses that the framework is executed using hand-held NIR spectrometers with multivariate algorithms coupled to a smartphone. At 2413 col.1 para.4 (claim 1: a system, comprising: a first expired drug usability device, comprising: a first spectrometer; and one or more first processors [smartphones inherently have at least one processor, as evidenced by Keough, para.1]). Opoku-Ansah notes that the appropriate multivariate algorithm model should be selected for reliable identification of unexpired drugs from expired ones. At 2413 col.1 para.4. A person having ordinary skill in the art would understand that in executing the framework, a user would select the multivariate algorithm model appropriate for the pharmaceutical drug of interest (claim 1: obtain at least one expired drug machine learning - artificial intelligence (ML - AI) model associated with a pharmaceutical drug; claim 13: obtaining, by an expired drug usability device, at least one expired drug machine learning - artificial intelligence (ML - AI) model associated with a pharmaceutical drug). After selecting the appropriate model and obtaining the spectral profiles of pharmaceutical drug samples using the hand-held NIR spectrometer, one of ordinary skill in the art would understand that the spectrometer data would be inputted into the selected model to determine whether the drug is expired. At 2406 col.2 para.2; 2408 col.1 para.1; 2410 col.1 para.2 (claim 1: input the drug expiration information into the at least one expired drug ML - Al model to determine usability information associated with the sample of the pharmaceutical drug; claim 13: inputting, by the expired drug usability device, the drug expiration information into the at least one expired drug ML - Al model to determine usability information associated with the sample of the pharmaceutical drug). Opoku-Ansah teaches that this framework is intended to aid in reducing drug fraud in developing countries by identifying fake, expired, and substandard drugs that can result in treatment failure, untoward adverse events, and even death. At 2405 col.1 para.1. A person having ordinary skill in the art would know that to avoid such undesirable outcomes, the framework must provide a notification to the user based on the identified status of the drug to allow the user to use or dispose of the drug (claim 1: perform one or more actions based on the usability information; claim 13: performing, by the expired drug usability device, one or more actions based on the usability information). One of ordinary skill in the art would reasonably expect success in the application of Opoku-Ansah’s framework because all the identification algorithms disclosed gave an optimum performance above 95%. At 2410 col.1 para.2; 2411 col.1 para.2; Tables 3 & 4. Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention is likely to be obvious. See KSR International Co. v. Teleflex Inc., 550 U.S. 398, 415-421, USPQ2d 1385, 1395 – 97 (2007); and MPEP § 2143, G.
Regarding claim 2, Opoku-Ansah develops identification models for pharmaceutical drugs with multivariate algorithms, which are intended to be coupled to a smartphone for execution of the framework. At 2407 col.1 para.3 – col.2 para.1; 2413 col.1 para.4 (train the at least one expired drug ML - Al model). Opoku-Ansah discloses that the modeling was performed using in-house written MATLAB R2020a codes for Windows 10. At 2406 col.2 para.2. Opoku-Ansah does not explicitly disclose a second expired drug usability device, comprising a second spectrometer and one or more second processors configured to provide the at least one expired drug ML - Al model to the first expired drug usability device. However, a person having ordinary skill in the art would understand that the models were developed and tested on a computer, not a smartphone, which inherently includes one or more processors. See C3.ai., § Processors: CPUs, GPUs, TPUs, and FPGAs. Additionally, one of ordinary skill in the art would understand that the computer upon which the models were developed and tested (second expired drug usability device) must then provide the models to the smartphone (first expired drug usability device). Moreover, one of ordinary skill in the art would understand that the computer and smartphone use separate spectrometers when the first expired drug usability device is intended for use by a third party and the second expired drug usability device was used in developing the framework to be deployed by the third party.
Regarding claims 3 and 14, Opoku-Ansah discloses obtaining spectrometer data from unexpired and expired drug tablets. At 2406 col.2 para.1 (obtaining spectrometer ML - Al training information comprising spectrometer output data of the pharmaceutical drug at difference instances within a life expectancy of the pharmaceutical drug). Opoku-Ansah uses the spectrometer data to develop and validate the models. At 2410 col.1 para.2; 2411 col.1 para.2 (training the spectrometer ML - Al model using the spectrometer ML - Al training information).
Regarding claim 12, Opoku-Ansah discloses using hand-held NIR spectrometers. At 2413 col.1 para.4 (wherein the first spectrometer is a near infrared (NIR) spectrometer).
Claims 4 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Opoku-Ansah as applied to claims 1-3 and 12-14 above, and further in view of Sharma (Sushil Sharma et al., 78(Suppl 1) Med J Armed Forces India 194-200 (3 July 2021)).
Regarding claims 4 and 15, Opoku-Ansah discloses obtaining spectrometer data from unexpired drug tablets and drug tablets that were expired by three months. At 2406 col.2 para.1 (obtaining second spectrometer output data of the second spectrometer associated with testing a second lot of the pharmaceutical drug, wherein the second lot of the pharmaceutical drug is prior to the expiration date of the pharmaceutical drug; and obtaining third spectrometer output data of the second spectrometer associated with testing a third lot of the pharmaceutical drug, wherein the third lot of the pharmaceutical drug is after the expiration date of the pharmaceutical drug). Opoku-Ansah fails to teach obtaining first spectrometer output data of the second spectrometer associated with testing a first lot of the pharmaceutical drug, wherein the first lot of the pharmaceutical drug is at an expiration date of the pharmaceutical drug. However, Sharma investigates the change in the physical, chemical, and efficacy parameters of drugs within the last three months of their shelf life, immediately after their expiry date, and every six months till two years after the expiry date of each drug. At abstract; 195 col.2 para.1. Sharma demonstrates that there is a statistical difference in the dissolution times at expiry as compared to post expiry. At 197 col.2 para.3. A person having ordinary skill in the art would recognize that Sharma’s method of analyzing drugs before, on, and after the expiration date could be applied to Opoku-Ansah’s framework for distinguishing expired drugs from unexpired drugs by incorporating data from drugs on the expiration date. One of ordinary skill in the art would recognize that the application would predictably yield an improved system for distinguishing expired drugs from unexpired drugs because incorporating data from drugs on the expiration date better accounts for the statistical difference in the dissolution times. Applying a known technique to a known device (method, or product) ready for improvement to yield predictable results is likely to be obvious. See KSR, 550 U.S. at 415-421, USPQ2d at 1395-97; and MPEP § 2143, D.
Claims 5, 11, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Opoku-Ansah as applied to claims 1-3 and 12-14 above, and further in view of Wu (Ke Wu et al., 93(3) Anal. Chem. 1377-82 (30 December 2020)), as evidenced by van Kollenburg (Geert H. van Kollenburg et al., 223(2) Talanta (1 February 2021)).
Regarding claims 5 and 16, Opoku-Ansah discloses pre-processing the spectrometer data to eliminate noise and background information that could influence the modelling of the data. At 2407 col.1 para.2. Opoku-Ansah fails to disclose a signal to noise model. However, Wu discloses a deep neural network (DN-Unet) devised to suppress noise in liquid-state NMR spectra to enhance signal-to-noise ratio (SNR). At Abstract. Wu discloses obtaining training data for the SNR model where the inputs are spectra with white Gaussian noise and the labels are corresponding noiseless spectra. At 1378 col.2 para.2 (determining, based on the spectrometer output data, SNR ML - Al training information of the pharmaceutical drug ). Wu discloses utilizing the data to train the proposed SNR model, DN-Unet. Id (training the SNR ML - Al model using the SNR ML - Al training information). Wu teaches that DN-Unet demonstrates excellent ability in differentiating between signal and noise and suppressing noise, achieving larger than 200-fold increase in SNR while retaining weak peaks even submerged in noise and well suppressing spurious peaks. At 1379 col.1 para.1; 1381 col.2 para.1. Wu notes that since DN-Unet is based on data postprocessing, it is universal for a variety of samples and NMR platforms. At Abstract. Opoku-Ansah discloses a base framework for distinguishing expired drugs from unexpired drugs where handheld spectrometer data is pre-processed to eliminate noise and background information, which is used as input into multivariate algorithm models. Wu provides a universal post-processing solution to dramatically boost SNR on spectrometer data and recover weak signals that could be critical for detecting subtle degradation changes in expired drugs. A person having ordinary skill in the art would recognize that DN-Unet could be applied to the framework of Opoku-Ansah as a replacement for the pre-processing of Opoku-Ansah because DN-Unet is a universal post-processing solution that dramatically boosts SNR. One of ordinary skill in the art would recognize that the combination would predictably yield an improved, more automated system by reducing reliance on hand-crafted pre-processing. Applying a known technique to a known device (method, or product) ready for improvement to yield predictable results is likely to be obvious. See KSR, 550 U.S. at 415-421, USPQ2d at 1395-97; and MPEP § 2143, D.
Regarding claim 11, Wu teaches using liquid-state nuclear magnetic resonance spectroscopy. At 1378 col.1 paras.1-2 (wherein the first spectrometer is a liquid spectrometer). While Opoku-Ansah does not specify using a liquid spectrometer, Opoku-Ansah discloses using a SCiO handheld NIR spectrometer, at 2406 col.2 para.2, which can operate as a liquid spectrometer, see van Kollenburg, at 1 col.2 para.2.
Claims 6-10 and 17-19 are rejected under 35 U.S.C. 103 as being unpatentable over Opoku-Ansah and Wu as applied to claims 5, 11, and 16 above, and further in view of Miao (Jiacheng Miao et al., 8 Anal. Methods 1265-73 (4 January 2016)) and Zhu (Limin Zhu et al., 34(3) J Pharm Biomed Anal. 453-61 (18 February 2004)).
Regarding claims 6 and 17, Opoku-Ansah and Wu fail to teach an olfactory model. However, Miao discloses a hybrid system consisting of a metal-oxide-sensor based homemade electronic nose (E-nose) and near-infrared reflectance spectroscopy (NIRS). At 1272 col.1 para.1. Miao discloses obtaining olfactory sensor data to train and optimize a machine learning classification model. At 1267 col.1 para.2; 1268 col.1 paras.2 & 4 (obtaining olfactory ML - Al training information comprising olfactory sensor output data of the pharmaceutical drug ). Miao demonstrates that the olfactory-NIRS hybrid system outperforms both an individual olfactory system and an individual NIRS system. At 1272 col.1 para.1. Additionally, Zhu demonstrates that an electronic-nose can be used to monitor the shelf-life of a pharmaceutical drug because drugs often release more metal ions over time due to degradation, chemical changes, or environmental factors. At Abstract; 459 col.2 para.1; 460 col.2 para.2. A person having ordinary skill in the art would be motivated to modify the framework of Opoku-Ansah and Wu by incorporating the olfactory model of Miao to detect degradation, chemical changes, or environmental factors effecting pharmaceutical drugs. One of ordinary skill in the art would reasonably expect success in this combination because an olfactory-NIRS hybrid system outperforms an individual NIRS system. Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention is likely to be obvious. See KSR, 550 U.S. at 415-421, USPQ2d at 1395-97; and MPEP § 2143, G.
Regarding claims 7 and 18, the combination of Opoku-Ansah, Wu, and Miao comprises a spectrometer model, a SNR model, and an olfactory model. See Opoku-Ansah, at 2407 col.1 para.3 – col.2 para.1; Wu at 1378 col.2 para.2; and Miao, at 1268 col.1 paras.2 & 4 (wherein the at least one expired drug ML - Al model comprises a spectrometer ML - Al model, a SNR ML - Al model, and an olfactory ML - Al model). In combining the references, the input data related to the drug comprises spectrometer data, SNR data derived from the spectrometer data, and olfactory data. See Opoku-Ansah, at 2406 col.2 para.2; Wu at 1378 col.2 para.2; and Miao, at 1267 col.1 para.2 (wherein the drug expiration information of the sample of the pharmaceutical drug comprises the spectrometer data of the sample of the pharmaceutical drug, SNR data of the sample of the pharmaceutical drug, and olfactory data of the sample of the pharmaceutical drug).
Regarding claims 8 and 19, in combining Opoku-Ansah, Wu, and Miao, one of ordinary skill in the art would understand that the spectrometer data would be used as input for the selected spectrometer model to determine whether the drug is expired. Opoku-Ansah, at 2406 col.2 para.2; 2408 col.1 para.1; 2410 col.1 para.2 (inputting the spectrometer data into the spectrometer ML - Al model to determine spectrometer usability information). Similarly, the SNR data derived from the spectrometer data would be used as input for the SNR model to determine whether the drug is expired, Wu, at 1378 col.2 para.2, and the E-nose data would be used as input for the olfactory model to determine whether the drug is expired, Miao, at 1268 col.1 para.4 (inputting the SNR data into the SNR ML - Al model to determine SNR usability information; inputting the olfactory data into the olfactory ML - Al model to determine olfactory usability information). One of ordinary skill in the art would understand that the outputs of the three models should be used to determine the potency/expiration status of the drug. Miao, at 1268 col.2 para.2 (determining the usability information based on the spectrometer usability information, the SNR usability information, and the olfactory usability information).
Regarding claim 9, Miao discloses that multi-probability predictions are obtained from the E-nose model and the spectrometer model. At 1268 col.2 para.3 (the system of claim 8, wherein the spectrometer usability information is a first usability confidence value that is output by the spectrometer ML - Al model, and the olfactory usability information is a third usability confidence value that is output by the olfactory ML - Al model). Miao discloses that the signal to noise ratio is an enormous obstacle encountered in data fusion, and Miao increases the weight of spectrometer data to overcome this. At 1270 col.2 paras.2-3. In combining the teachings of Opoku-Ansah, Wu, and Miao, one of ordinary skill in the art would understand that the SNR model would similarly output a probability prediction to be used in the data fusion process. One of ordinary skill would reasonably expect success in this combination because the SNR model probability prediction remedies the signal to noise ratio obstacle encountered in data fusion.
Regarding claim 10, Miao discloses determining the category by a weighted average of the output probabilities from the E-nose model and the spectrometer model. At 1268, col.2 paras. 2-4 (the system of claim 9, wherein the one or more first processors configured to determine the usability information by: determining the usability information as a weighted average of the first usability confidence value, ). In combining the teachings of Opoku-Ansah, Wu, and Miao, one of ordinary skill in the art would understand that the weighted average should incorporate the probability output from the SNR model.
Conclusion
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/E.A.D./Examiner, Art Unit 1686
/LARRY D RIGGS II/Supervisory Patent Examiner, Art Unit 1686
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