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 Objections
Claim 15 objected to because of the following informalities:
“identifying” and “determining” at the beginning of the limitations should be “identify” and “determine”.
Appropriate correction is required.
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 (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Specifically, representative Claim 1 recites:
A method, comprising:
receiving, by a device, spectroscopic data associated with an iteration of a dynamic process;
generating, by the device and based on the spectroscopic data, a set of parameter profiles associated with the iteration of the dynamic process,
wherein each parameter profile in the set of parameter profiles corresponds to a respective parameter in a set of parameters of a physical signal associated with the iteration of the dynamic process; and
determining an end point of the iteration of the dynamic process based on the set of parameter profiles.
The claim limitations in the abstract idea have been highlighted in bold above; the remaining limitations are “additional elements”.
Under the Step 1 of the eligibility analysis, we determine whether the claims are to a statutory category by considering whether the claimed subject matter falls within the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: Process, machine, manufacture, or composition of matter. The above claim is considered to be in a statutory category (process).
Under the Step 2A, Prong One, we consider whether the claim recites a judicial exception (abstract idea). In the above claim, the highlighted portion constitutes an abstract idea because, under a broadest reasonable interpretation, it recites limitations that fall into/recite an abstract idea exceptions. Specifically, under the 2019 Revised Patent Subject matter Eligibility Guidance, it falls into the grouping of subject matter when recited as such in a claim limitation, that covers mathematical concepts (mathematical relationships, mathematical formulas or equations, mathematical calculations) and mental processes – concepts performed in the human mind including an observation, evaluation, judgement, and/or opinion.
For example, steps of “generating, by the device and based on the spectroscopic data, a set of parameter profiles associated with the iteration of the dynamic process, wherein each parameter profile in the set of parameter profiles corresponds to a respective parameter in a set of parameters of a physical signal associated with the iteration of the dynamic process (has a scope of multiplicative scatter correction which is mathematical data processing)” are treated by the Examiner as belonging to mathematical concept grouping, while the steps of “generating, by the device and based on the spectroscopic data, a set of parameter profiles associated with the iteration of the dynamic process, wherein each parameter profile in the set of parameter profiles corresponds to a respective parameter in a set of parameters of a physical signal associated with the iteration of the dynamic process (extracting desired subsets of data); and
determining an end point of the iteration of the dynamic process based on the set of parameter profiles (observation and determination)” are treated as belonging to mental process grouping.
Similar limitations comprise the abstract ideas of Claims 9 and 17.
Next, under the Step 2A, Prong Two, we consider whether the claim that recites a judicial exception is integrated into a practical application.
In this step, we evaluate whether the claim recites additional elements that integrate the exception into a practical application of that exception.
The above claims comprise the following additional elements:
Claim 1: receiving, by a device, spectroscopic data associated with an iteration of a dynamic process;
Claim 9: one or more memories; and one or more processors, coupled to the one or more memories, configured to: obtain spectroscopic data associated with an iteration of a dynamic process;
Claim 17: A non-transitory computer-readable medium storing a set of instructions, the set of instructions comprising: one or more instructions that, when executed by one or more processors of a device, cause the device to: receive spectroscopic data associated with an iteration of a dynamic process.
The additional element of “receiving/obtaining spectroscopic data associated with an iteration of a dynamic process” represents a mere data gathering step and only adds an insignificant extra-solution activity to the judicial exception. A non-transitory computer-readable medium or one or more memories (generic memory) and one or more processors (generic processor) are generally recited and are not qualified as particular machines.
In conclusion, the above additional elements, considered individually and in combination with the other claim elements do not reflect an improvement to other technology or technical field, and, therefore, do not integrate the judicial exception into a practical application. Therefore, the claims are directed to a judicial exception and require further analysis under the Step 2B.
However, the above claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception (Step 2B analysis).
The claims, therefore, are not patent eligible.
With regards to the dependent claims, claims 2-8, 10-16, and 18-20 provide additional features/steps which are part of an expanded algorithm, so these limitations should be considered part of an expanded abstract idea of the independent claims.
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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Sun et al. (US 20230243699 A1), hereinafter “Sun”.
Regarding Claim 1, Sun teaches a method, comprising:
receiving, by a device, spectroscopic data associated with an iteration of a dynamic process (Sun [0017] As shown in FIG. 1A by reference 102, the detection device 220 may receive spectroscopic data associated with a blending process. For example, as shown, the spectrometer 210 may measure spectroscopic data at a given time during the performance of the blending process, and may provide the spectroscopic data to the detection device 220.);
generating, by the device and based on the spectroscopic data, a set of parameter profiles associated with the iteration of the dynamic process (Sun [0019] In some implementations, the detection device 220 may preprocess the spectroscopic data. For example, the raw spectroscopic data may include some amount of noise, a scattering effect, an artifact, or other type of unwanted feature. Therefore, in some implementations, the detection device 220 may preprocess the spectroscopic data to reduce a presence of or remove such unwanted features from the spectroscopic data.),
wherein each parameter profile in the set of parameter profiles corresponds to a respective parameter in a set of parameters of a physical signal associated with the iteration of the dynamic process (Sun [0020] In some implementations, as shown by reference 104, the detection device 220 may generate a PCA model based on a first block of spectra from the spectroscopic data. In some implementations, a block of spectra comprises a time-series group of spectra from the spectroscopic data. For example, with reference to FIG. 1C, a block of spectra identified as Block 1 may comprise spectra S.sub.1 (e.g., spectra collected during a first revolution of a blender performing a blending process) through spectra S.sub.N (N>1) (e.g., spectra collected during an N.sup.th revolution of the blender performing the blending process).); and
determining an end point of the iteration of the dynamic process based on the set of parameter profiles (Sun [0021] the detection device 220 may use the PCA model in association with determining whether the dynamic process has reached an end point, as described herein.).
Regarding Claim 2, Sun teaches wherein the set of parameter profiles are generated using a multiplicative scatter correction (MSC) model that extracts the set of parameter profiles from the spectroscopic data (Sun [0019] the detection device 220 may preprocess the spectroscopic data using, for example, a derivative calculation technique, a standard normal variate (SNV) technique, or a multiplicative scatter correction (MSC) technique, among other examples.).
Regarding Claim 3, Sun teaches wherein the set of parameter profiles includes a profile of a parameter indicating an additive effect of light scattering during the iteration of the dynamic process (Sun [0019] In some implementations, the detection device 220 may preprocess the spectroscopic data. For example, the raw spectroscopic data may include some amount of noise, a scattering effect, an artifact, or other type of unwanted feature. Therefore, in some implementations, the detection device 220 may preprocess the spectroscopic data to reduce a presence of or remove such unwanted features from the spectroscopic data.).
Regarding Claim 4, Sun teaches wherein the set of parameter profiles includes a profile of a parameter indicative of a multiplicative effect of light scattering during the iteration of the dynamic process (Sun [0019] In some implementations, the detection device 220 may preprocess the spectroscopic data. For example, the raw spectroscopic data may include some amount of noise, a scattering effect, an artifact, or other type of unwanted feature. Therefore, in some implementations, the detection device 220 may preprocess the spectroscopic data to reduce a presence of or remove such unwanted features from the spectroscopic data. In some implementations, the detection device 220 may preprocess the spectroscopic data using, for example, a derivative calculation technique, a standard normal variate (SNV) technique, or a multiplicative scatter correction (MSC) technique, among other examples.).
Regarding Claim 5, Sun teaches wherein determining the end point of the iteration of the dynamic process comprises:
determining a set of trends, wherein each trend in the set of trends corresponds to a respective parameter profile in the set of parameter profiles (Sun [0023] In some implementations, the metric may include a Mahalanobis distance, a Hotelling's T.sup.2, a Q residual, or an ellipsoid volume, as described in further detail below with respect to FIGS. 1D-1G. In some implementations, the detection device 220 may determine values of multiple metrics. For example, the detection device 220 may determine a value of a first metric (e.g., a Mahalanobis distance) associated with the second block and may determine a value of a second metric (e.g., a Hotelling's T.sup.2) associated with the second block. In such an implementation. The profiles shown in Figs. 1D-1G are based on the metrics described);
generating, a set of slope profiles, wherein each slope profile in the set of slope profiles is associated with a respective parameter profile in the set of parameter profiles (Sun [0031] he detection device 220 may provide information associated with the end point to the user device 230 to, for example, enable visualization of the evolution of the dynamic process via the user device 230. See Figs. 1D-1F);
identifying sets of slope thresholds associated with the iteration of the dynamic process based on the set of trends and the set of slope profiles (Sun Figs. 1D-1F Threshold);
determining a set of candidate end points based on the set of slope profiles and the sets of slope thresholds, wherein each candidate end point in the set of candidate end points is associated with a respective slope profile in the set of slope profiles (Sun Figs. 1D-1F Endpoint. Each metric has its own endpoint); and
determining the end point of the iteration of the dynamic process based on the set of candidate end points (Sun [0023] In such an implementation, the detection device 220 may use the value of the first metric and/or the value of the second metric in association with determining whether the dynamic process has reached the end point, as described herein.).
Regarding Claim 6, Sun teaches wherein the end point is a first candidate end point, and the method further comprises:
determining a second candidate end point of the iteration of the dynamic process based on the spectroscopic data (Sun [0032] In some implementations, the detection device 220 may perform the above-described operations in association with utilizing multiple PCA models in association with determining whether the dynamic process has reached the end point. Multiple models produce multiple candidate endpoints),
wherein the second candidate end point is determined based on a chemical signal associated with the iteration of the dynamic process (Sun [0032] In some implementations, the detection device 220 may perform the above-described operations in association with utilizing multiple PCA models in association with determining whether the dynamic process has reached the end point. Multiple models produce multiple candidate endpoints); and
selecting either the first candidate end point or the second candidate end point as a final end point of the iteration of the dynamic process (Sun [0024] if the value of the metric satisfies the threshold, then the detection device 220 may determine that the dynamic process has reached the end point (e.g., that the dynamic process has reached the steady state). Also see [0025]-[0029] which disclose different ways the potential endpoints are analyzed to determine if they are actual endpoints.).
Regarding Claim 7, Sun teaches wherein determining the end point of the iteration of the dynamic process comprises:
identifying a set of parameter thresholds associated with the iteration of the dynamic process based on the set of parameter profiles (Sun [0024] the detection device 220 may determine whether the value of the metric associated with the second block satisfies a threshold associated with the metric.); and
determining the end point of the iteration of the dynamic process based on the set of parameter profiles and the set of parameter thresholds (Sun [0024] if the value of the metric satisfies the threshold, then the detection device 220 may determine that the dynamic process has reached the end point (e.g., that the dynamic process has reached the steady state).).
Regarding Claim 8, Sun teaches identifying, based on the spectroscopic data, a starting time point of the spectroscopic data to be used for determining the end point of the iteration of the dynamic process, wherein a starting time point associated with generating the set of parameter profiles is at or after the identified starting time point of the spectroscopic data (Sun [0028] In some implementations, prior to determining whether the dynamic process has reached the end point, the detection device 220 may identify a starting point for performing end point detection of the dynamic process.).
Regarding Claim 9, Sun teaches a device, comprising:
one or more memories (Sun [0004] The device may include one or more memories and one or more processors coupled to the one or more memories. Also see [0046] a processor 320, a memory 330. And [0048] memory 330 includes one or more memories that are coupled to one or more processors (e.g., processor 320)); and
one or more processors, coupled to the one or more memories (Sun [0004] The device may include one or more memories and one or more processors coupled to the one or more memories. Also see [0046] a processor 320, a memory 330. And [0048] memory 330 includes one or more memories that are coupled to one or more processors (e.g., processor 320)), configured to:
obtain spectroscopic data associated with an iteration of a dynamic process (Sun [0017] As shown in FIG. 1A by reference 102, the detection device 220 may receive spectroscopic data associated with a blending process. For example, as shown, the spectrometer 210 may measure spectroscopic data at a given time during the performance of the blending process, and may provide the spectroscopic data to the detection device 220.);
generate, based on the spectroscopic data, a set of parameter profiles associated with the iteration of the dynamic process (Sun [0019] In some implementations, the detection device 220 may preprocess the spectroscopic data. For example, the raw spectroscopic data may include some amount of noise, a scattering effect, an artifact, or other type of unwanted feature. Therefore, in some implementations, the detection device 220 may preprocess the spectroscopic data to reduce a presence of or remove such unwanted features from the spectroscopic data.),
wherein the set of parameter profiles are generated using a multiplicative scatter correction (MSC) model that extracts the set of parameter profiles from the spectroscopic data (Sun [0019] the detection device 220 may preprocess the spectroscopic data using, for example, a derivative calculation technique, a standard normal variate (SNV) technique, or a multiplicative scatter correction (MSC) technique, among other examples.); and
determine an end point of the iteration of the dynamic process based on the set of parameter profiles (Sun [0021] the detection device 220 may use the PCA model in association with determining whether the dynamic process has reached an end point, as described herein.).
Regarding Claim 10, Sun teaches wherein each parameter profile in the set of parameter profiles corresponds to a respective parameter in a set of parameters of a physical signal associated with the iteration of the dynamic process (Sun [0020] In some implementations, as shown by reference 104, the detection device 220 may generate a PCA model based on a first block of spectra from the spectroscopic data. In some implementations, a block of spectra comprises a time-series group of spectra from the spectroscopic data. For example, with reference to FIG. 1C, a block of spectra identified as Block 1 may comprise spectra S.sub.1 (e.g., spectra collected during a first revolution of a blender performing a blending process) through spectra S.sub.N (N>1) (e.g., spectra collected during an N.sup.th revolution of the blender performing the blending process).).
Regarding Claim 11, Sun teaches wherein the set of parameter profiles includes a profile of a parameter indicating an additive effect of light scattering during the iteration of the dynamic process (Sun [0019] In some implementations, the detection device 220 may preprocess the spectroscopic data. For example, the raw spectroscopic data may include some amount of noise, a scattering effect, an artifact, or other type of unwanted feature. Therefore, in some implementations, the detection device 220 may preprocess the spectroscopic data to reduce a presence of or remove such unwanted features from the spectroscopic data.).
Regarding Claim 12, Sun teaches wherein the set of parameter profiles includes a profile of a parameter indicative of a multiplicative effect of light scattering during the iteration of the dynamic process (Sun [0019] In some implementations, the detection device 220 may preprocess the spectroscopic data. For example, the raw spectroscopic data may include some amount of noise, a scattering effect, an artifact, or other type of unwanted feature. Therefore, in some implementations, the detection device 220 may preprocess the spectroscopic data to reduce a presence of or remove such unwanted features from the spectroscopic data. In some implementations, the detection device 220 may preprocess the spectroscopic data using, for example, a derivative calculation technique, a standard normal variate (SNV) technique, or a multiplicative scatter correction (MSC) technique, among other examples.).
Regarding Claim 13, Sun teaches wherein the one or more processors, to determine the end point of the iteration of the dynamic process, are configured to:
determine a set of trends, wherein each trend in the set of trends corresponds to a respective parameter profile in the set of parameter profiles (Sun [0023] In some implementations, the metric may include a Mahalanobis distance, a Hotelling's T.sup.2, a Q residual, or an ellipsoid volume, as described in further detail below with respect to FIGS. 1D-1G. In some implementations, the detection device 220 may determine values of multiple metrics. For example, the detection device 220 may determine a value of a first metric (e.g., a Mahalanobis distance) associated with the second block and may determine a value of a second metric (e.g., a Hotelling's T.sup.2) associated with the second block. In such an implementation. The profiles shown in Figs. 1D-1G are based on the metrics described);
generate, a set of slope profiles, wherein each slope profile in the set of slope profiles is associated with a respective parameter profile in the set of parameter profiles (Sun [0031] he detection device 220 may provide information associated with the end point to the user device 230 to, for example, enable visualization of the evolution of the dynamic process via the user device 230. See Figs. 1D-1F);
identify sets of slope thresholds associated with the iteration of the dynamic process based on the set of trends and the set of slope profiles (Sun Figs. 1D-1F Threshold);
determine a set of candidate end points based on the set of slope profiles and the sets of slope thresholds, wherein each candidate end point in the set of candidate end points is associated with a respective slope profile in the set of slope profiles (Sun Figs. 1D-1F Endpoint. Each metric has its own endpoint); and
determine the end point of the iteration of the dynamic process based on the set of candidate end points (Sun [0023] In such an implementation, the detection device 220 may use the value of the first metric and/or the value of the second metric in association with determining whether the dynamic process has reached the end point, as described herein.).
Regarding Claim 14, Sun teaches wherein the end point is a first candidate end point, and the one or more processors are further configured to:
determine a second candidate end point of the iteration of the dynamic process based on the spectroscopic data (Sun [0032] In some implementations, the detection device 220 may perform the above-described operations in association with utilizing multiple PCA models in association with determining whether the dynamic process has reached the end point. Multiple models produce multiple candidate endpoints),
wherein the second candidate end point is determined based on a chemical signal associated with the iteration of the dynamic process (Sun [0032] In some implementations, the detection device 220 may perform the above-described operations in association with utilizing multiple PCA models in association with determining whether the dynamic process has reached the end point. Multiple models produce multiple candidate endpoints); and
select either the first candidate end point or the second candidate end point as a final end point of the iteration of the dynamic process (Sun [0024] if the value of the metric satisfies the threshold, then the detection device 220 may determine that the dynamic process has reached the end point (e.g., that the dynamic process has reached the steady state). Also see [0025]-[0029] which disclose different ways the potential endpoints are analyzed to determine if they are actual endpoints.).
Regarding Claim 15, Sun teaches wherein the one or more processors, to determine the end point of the iteration of the dynamic process, are configured to:
identify a set of parameter thresholds associated with the iteration of the dynamic process based on the set of parameter profiles (Sun [0024] the detection device 220 may determine whether the value of the metric associated with the second block satisfies a threshold associated with the metric.); and
determine the end point of the iteration of the dynamic process based on the set of parameter profiles and the set of parameter thresholds (Sun [0024] if the value of the metric satisfies the threshold, then the detection device 220 may determine that the dynamic process has reached the end point (e.g., that the dynamic process has reached the steady state).).
Regarding Claim 16, Sun teaches to identify, based on the spectroscopic data, a starting time point of the spectroscopic data to be used for determining the end point of the iteration of the dynamic process, wherein a starting time point associated with generating the set of parameter profiles is at or after the identified starting time point of the spectroscopic data (Sun [0028] In some implementations, prior to determining whether the dynamic process has reached the end point, the detection device 220 may identify a starting point for performing end point detection of the dynamic process.).
Regarding Claim 17, Sun teaches a non-transitory computer-readable medium storing a set of instructions (Sun [0050] Device 300 may perform one or more operations or processes described herein. For example, a non-transitory computer-readable medium (e.g., memory 330) may store a set of instructions (e.g., one or more instructions or code) for execution by processor 320. Processor 320 may execute the set of instructions to perform one or more operations or processes described herein.), the set of instructions comprising:
one or more instructions that, when executed by one or more processors of a device (Sun [0050] execution of the set of instructions, by one or more processors 320, causes the one or more processors 320 and/or the device 300 to perform one or more operations or processes described herein.), cause the device to:
receive spectroscopic data associated with an iteration of a dynamic process (Sun [0017] As shown in FIG. 1A by reference 102, the detection device 220 may receive spectroscopic data associated with a blending process. For example, as shown, the spectrometer 210 may measure spectroscopic data at a given time during the performance of the blending process, and may provide the spectroscopic data to the detection device 220.);
generate, based on the spectroscopic data, a set of parameter profiles associated with the iteration of the dynamic process (Sun [0019] In some implementations, the detection device 220 may preprocess the spectroscopic data. For example, the raw spectroscopic data may include some amount of noise, a scattering effect, an artifact, or other type of unwanted feature. Therefore, in some implementations, the detection device 220 may preprocess the spectroscopic data to reduce a presence of or remove such unwanted features from the spectroscopic data.), wherein the set of parameter profiles includes at least one of:
a profile of a parameter indicating an additive effect of light scattering during the iteration of the dynamic process (Sun [0019] In some implementations, the detection device 220 may preprocess the spectroscopic data. For example, the raw spectroscopic data may include some amount of noise, a scattering effect, an artifact, or other type of unwanted feature. Therefore, in some implementations, the detection device 220 may preprocess the spectroscopic data to reduce a presence of or remove such unwanted features from the spectroscopic data.), or
a profile of a parameter indicative of a multiplicative effect of light scattering during the iteration of the dynamic process (Sun [0019] In some implementations, the detection device 220 may preprocess the spectroscopic data. For example, the raw spectroscopic data may include some amount of noise, a scattering effect, an artifact, or other type of unwanted feature. Therefore, in some implementations, the detection device 220 may preprocess the spectroscopic data to reduce a presence of or remove such unwanted features from the spectroscopic data. In some implementations, the detection device 220 may preprocess the spectroscopic data using, for example, a derivative calculation technique, a standard normal variate (SNV) technique, or a multiplicative scatter correction (MSC) technique, among other examples.); and
determine an end point of the iteration of the dynamic process based on the set of parameter profiles (Sun [0021] the detection device 220 may use the PCA model in association with determining whether the dynamic process has reached an end point, as described herein.).
Regarding Claim 18, Sun teaches wherein the set of parameter profiles are generated using a multiplicative scatter correction (MSC) model that extracts the set of parameter profiles from the spectroscopic data (Sun [0019] the detection device 220 may preprocess the spectroscopic data using, for example, a derivative calculation technique, a standard normal variate (SNV) technique, or a multiplicative scatter correction (MSC) technique, among other examples.).
Regarding Claim 19, Sun teaches wherein each parameter profile in the set of parameter profiles corresponds to a respective parameter in a set of parameters of a physical signal associated with the iteration of the dynamic process (Sun [0020] In some implementations, as shown by reference 104, the detection device 220 may generate a PCA model based on a first block of spectra from the spectroscopic data. In some implementations, a block of spectra comprises a time-series group of spectra from the spectroscopic data. For example, with reference to FIG. 1C, a block of spectra identified as Block 1 may comprise spectra S.sub.1 (e.g., spectra collected during a first revolution of a blender performing a blending process) through spectra S.sub.N (N>1) (e.g., spectra collected during an N.sup.th revolution of the blender performing the blending process).).
Regarding Claim 20, Sun teaches determine a set of trends, wherein each trend in the set of trends corresponds to a respective parameter profile in the set of parameter profiles (Sun [0023] In some implementations, the metric may include a Mahalanobis distance, a Hotelling's T.sup.2, a Q residual, or an ellipsoid volume, as described in further detail below with respect to FIGS. 1D-1G. In some implementations, the detection device 220 may determine values of multiple metrics. For example, the detection device 220 may determine a value of a first metric (e.g., a Mahalanobis distance) associated with the second block and may determine a value of a second metric (e.g., a Hotelling's T.sup.2) associated with the second block. In such an implementation. The profiles shown in Figs. 1D-1G are based on the metrics described);
generate, a set of slope profiles, wherein each slope profile in the set of slope profiles is associated with a respective parameter profile in the set of parameter profiles (Sun [0031] he detection device 220 may provide information associated with the end point to the user device 230 to, for example, enable visualization of the evolution of the dynamic process via the user device 230. See Figs. 1D-1F);
identify sets of slope thresholds associated with the iteration of the dynamic process based on the set of trends and the set of slope profiles (Sun Figs. 1D-1F Threshold);
determine a set of candidate end points based on the set of slope profiles and the sets of slope thresholds, wherein each candidate end point in the set of candidate end points is associated with a respective slope profile in the set of slope profiles (Sun Figs. 1D-1F Endpoint. Each metric has its own endpoint); and
determine the end point of the iteration of the dynamic process based on the set of candidate end points (Sun [0023] In such an implementation, the detection device 220 may use the value of the first metric and/or the value of the second metric in association with determining whether the dynamic process has reached the end point, as described herein.).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Hsiung et al. (US 20230204502 A1) discloses a Dynamic Process End Point Detection.
Hsiung et al. (US 20210224672 A1) discloses Endpoint Detection In Manufacturing Process By Near Infrared Spectroscopy And Machine Learning Techniques.
Shah (US 20210293715 A1) discloses an Application Of Raman Spectroscopy For The Manufacture Of Inhalation Powders.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHRISTIAN T BRYANT whose telephone number is (571)272-4194. The examiner can normally be reached Monday-Thursday and Alternate Fridays 7:00-4:30.
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, CATHERINE RASTOVSKI can be reached at (571) 270-0349. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/CHRISTIAN T BRYANT/Primary Examiner, Art Unit 2857
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