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 1-20 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, Claim 1 recites:
A system, comprising:
a memory that stores computer executable components; and
a processor that executes the computer executable components stored in the memory, wherein the computer executable components comprise:
a determination component that determines a known composition having a same component as a target composition; and
a generation component that, based on a synthesized spectrum of the known composition and on a first synthesized spectrum of the target composition, generates a second synthesized spectrum of the target composition.
While Claim 8 recites
A computer-implemented method, comprising:
synthesizing, by a system operatively coupled to a processor, a revised synthesized spectrum of a target composition, comprising:
generating, by the system, an intermediary synthesized relationship being a relationship defining a delta of a synthesized spectrum of a synthesized spectrum of a known composition and a first synthesized spectrum of the target composition;
aggregating, by the system, the intermediary synthesized relationship and a measured spectrum of the known composition; and
generating, by the system, an aggregated synthesized spectrum based on the aggregating and being the revised synthesized spectrum of the target composition.
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 (machine).
Additionally, the Examiner notes that Claim 15, is not directed toward one of the four statutory categories and is considered “software per se” as it does not have a physical or tangible form (see MPEP 2106.03 I. Products that do not have a physical or tangible form, such as information (often referred to as "data per se") or a computer program per se (often referred to as "software per se") when claimed as a product without any structural recitations; […] a product claim to a software program that does not also contain at least one structural limitation (such as a "means plus function" limitation) has no physical or tangible form, and thus does not fall within any statutory category). Claims 16-20 are rejected for their dependence on claim 15.
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 in claim 1, the step of “based on a synthesized spectrum of the known composition and on a first synthesized spectrum of the target composition, generates a second synthesized spectrum of the target composition (spectral analysis)” is treated by the Examiner as belonging to mathematical concept grouping, while the steps of “determines a known composition having a same component as a target composition (determination); and
based on a synthesized spectrum of the known composition and on a first synthesized spectrum of the target composition, generates a second synthesized spectrum of the target composition (output of analysis)” are treated as belonging to mental process grouping.
In claim 8, the step of “synthesizing a revised synthesized spectrum of a target composition (build spectrum based on desired composition), comprising:
generating an intermediary synthesized relationship being a relationship defining a delta of a synthesized spectrum of a synthesized spectrum of a known composition and a first synthesized spectrum of the target composition (determining a relationship); and
generating an aggregated synthesized spectrum based on the aggregating and being the revised synthesized spectrum of the target composition (spectral analysis)” is treated by the Examiner as belonging to mathematical concept grouping, while the steps of “determines a known composition having a same component as a target composition (determination); and
aggregating the intermediary synthesized relationship and a measured spectrum of the known composition (grouping information); and
generating an aggregated synthesized spectrum based on the aggregating and being the revised synthesized spectrum of the target composition (grouping information for spectral analysis)” are treated as belonging to mental process grouping.
Similar limitations comprise the abstract ideas of Claim 15.
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: a memory that stores computer executable components; and a processor that executes the computer executable components stored in the memory, wherein the computer executable components comprise: a determination component; and a generation component ;
Claim 8: a processor;
Claim 15: a processor.
The additional elements of a memory (generic memory) and a processor, a determination component; and a generation component (generic processors) 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 2-7, 9-14, and 16-20 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, 2, 7-9, 11, 12, 15 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Dasaratha et al. (US 20110153226 A1), hereinafter “Dasaratha”.
Regarding Claim 1, Dasaratha teaches a system, comprising:
a memory that stores computer executable components (Dasaratha [0043] the series of instructions are stored on a storage device, such as mass storage 406. However, the series of instructions can be stored on any suitable storage medium, such as a diskette, CD-ROM, ROM, EEPROM, DVD, Blu-ray disk, etc. Furthermore, the series of instructions need not be stored locally, and could be received from a remote storage device, such as server on a network, via network/communication interface 410. ); and
a processor that executes the computer executable components stored in the memory (Dasaratha [0043] In one embodiment, component identification process 408 described herein is implemented as a series of software routines run by hardware system 400. These software routines comprise a plurality or series of instructions to be executed by a processor in a hardware system, such as processor 402. […] The instructions are copied from the storage device, such as mass storage 406, into system library 404 and then accessed and executed by processor 402. See Fig. 4 402), wherein the computer executable components comprise:
a determination component that determines a known composition having a same component as a target composition (Dasaratha [0015] At step 102, the spectrum of the unknown mixture is compared with the spectrum of each of a first plurality of library compounds. The library may contain the spectrum of one or more known compounds. See Fig. 1 102); and
a generation component that, based on a synthesized spectrum of the known composition (Dasaratha [0019] At step 104, a model is generated for each of the candidate mixture combinations obtained in the step 102. The model is used to generate scaling factors for each of the compounds present in the candidate mixture combination. See Fig. 1 104) and on a first synthesized spectrum of the target composition, generates a second synthesized spectrum of the target composition (Dasaratha [0022] At step 106, a residual spectrum may be computed for each of the candidate mixture combinations by removing the fitted spectrum of the candidate mixture combination from the spectrum of the unknown mixture. See Fig. 1 106).
Regarding Claim 2, Dasaratha further teaches wherein the generation component further generates the second synthesized spectrum of the target composition based on a measured spectrum of the known composition (Dasaratha [0015] The library may contain the spectrum of one or more known compounds. In an embodiment of the invention, the library may be stored as various sub-libraries. The known spectra within the library must have been measured or modeled).
Regarding Claim 7, Dasaratha further teaches a database maintenance component that updates data in a database of known compositions, that includes the measured spectrum and the synthesized spectrum of the known composition and the second synthesized spectrum of the target composition, wherein the determination component determines the second synthesized spectrum of the target composition as a second known composition for determining a resultant synthesized spectrum of a second target composition (Dasaratha [0025] The selected compounds are hereinafter referred to as potential compounds. Thus, the potential compounds are identified based on the comparison of the residual spectrum of each of the candidate mixture combination with the spectrum of the second plurality of library compounds. Also see [0028] At step 112, the search algorithm checks a first termination condition and determines whether another iteration of the algorithm is required. And Figs. 2 and 3. The comparisons are made iteratively until error is satisfactorily reduced).
Regarding Claim 8, Dasaratha teaches a computer-implemented method (Dasaratha [0043] In one embodiment, component identification process 408 described herein is implemented as a series of software routines run by hardware system 400. These software routines comprise a plurality or series of instructions to be executed by a processor in a hardware system, such as processor 402. […] The instructions are copied from the storage device, such as mass storage 406, into system library 404 and then accessed and executed by processor 402.), comprising:
synthesizing, by a system operatively coupled to a processor, a revised synthesized spectrum of a target composition (Dasaratha [0015] At step 102, the spectrum of the unknown mixture is compared with the spectrum of each of a first plurality of library compounds. The library may contain the spectrum of one or more known compounds. [0019] At step 104, a model is generated for each of the candidate mixture combinations obtained in the step 102. The model is used to generate scaling factors for each of the compounds present in the candidate mixture combination. See Fig. 1 102-104. Determining spectrum of the expected mixture), comprising:
generating, by the system, an intermediary synthesized relationship being a relationship defining a delta of a synthesized spectrum of a synthesized spectrum of a known composition and a first synthesized spectrum of the target composition (Dasaratha [0022] At step 106, a residual spectrum may be computed for each of the candidate mixture combinations by removing the fitted spectrum of the candidate mixture combination from the spectrum of the unknown mixture. In an embodiment of the present invention, the residual spectrum may be computed by subtracting the fitted spectrum obtained in step 104 from the spectrum of the unknown mixture. In other embodiments of the present invention, peak-based subtraction may be used to remove the fitted spectrum of the candidate mixture combination from the spectrum of the unknown mixture.);
aggregating, by the system, the intermediary synthesized relationship and a measured spectrum of the known composition (Dasaratha [0023] At step 108, the residual spectrum corresponding to each candidate mixture combination is compared with the spectrum of each of a second plurality of library compounds. In an embodiment of the invention, the second plurality of library compounds used in this step is same as the first plurality of library compounds used in step 102. The spectra are iteratively compared to match each component of the total mixture); and
generating, by the system, an aggregated synthesized spectrum based on the aggregating and being the revised synthesized spectrum of the target composition (Dasaratha [0026] At step 110, the potential compounds are added to the candidate mixture combinations. In an embodiment of the present invention, one or more new candidate mixture combinations may be obtained by the addition of "children" potential compounds to the "parent" mixture combinations.).
Regarding Claim 9, Dasaratha further teaches wherein the known composition comprises a same component as the target composition, and wherein the same component comprises an atomic element, molecular element, phase of an atomic or molecular element, or combination thereof (Dasaratha [0015] The library may contain the spectrum of one or more known compounds. In an embodiment of the invention, the library may be stored as various sub-libraries. Further, the user may select one or more sub-libraries to be considered in the search algorithm. For instance, the user may pre-select a portion of the library that may be used as the first plurality of library compounds in the search algorithm. Alternatively, a pre-processing step may be employed to select a portion of the library based on the user input. In one embodiment of the present invention, the mixture may contain more than one compound. The unknown mixture is determined by figuring out and applying the features of the known compounds and components. The unknown mixture must be made up of components of known compounds).
Regarding Claim 11, Dasaratha further teaches wherein the generating the intermediary synthesized relationship comprises determining the intermediary synthesized relationship based on first quantities of photons of portions of the synthesized spectrum of the known composition and on second quantities of photons of portions of the first synthesized spectrum of the target composition (Dasaratha [0022] peak-based subtraction may be used to remove the fitted spectrum of the candidate mixture combination from the spectrum of the unknown mixture.).
Regarding Claim 12, Dasaratha further teaches wherein the aggregating further comprises aggregating third quantities of photons of portions of the intermediary synthesized relationship and fourth quantities of photons of portions of the measured spectrum of the known composition (Dasaratha [0022] peak-based subtraction may be used to remove the fitted spectrum of the candidate mixture combination from the spectrum of the unknown mixture. [0026] At step 110, the potential compounds are added to the candidate mixture combinations. [0028] At step 112, the search algorithm checks a first termination condition and determines whether another iteration of the algorithm is required. The method is iterative until the difference between the spectra of the unknown mixture and known compounds are acceptable).
Regarding Claim 15, Dasaratha teaches a computer program product facilitating a process for synthesizing a spectrum of a target composition, the program instructions executable by a processor (Dasaratha [0043] In one embodiment, component identification process 408 described herein is implemented as a series of software routines run by hardware system 400. These software routines comprise a plurality or series of instructions to be executed by a processor in a hardware system, such as processor 402. […] The instructions are copied from the storage device, such as mass storage 406, into system library 404 and then accessed and executed by processor 402.) to cause the processor to:
synthesize, by the processor, a spectrum of a target composition (Dasaratha [0015] At step 102, the spectrum of the unknown mixture is compared with the spectrum of each of a first plurality of library compounds. The library may contain the spectrum of one or more known compounds. [0019] At step 104, a model is generated for each of the candidate mixture combinations obtained in the step 102. The model is used to generate scaling factors for each of the compounds present in the candidate mixture combination. See Fig. 1 102-104. Determining spectrum of the expected mixture), wherein the synthesizing comprises causing the processor to:
determine, by the processor, a delta of first quantities of photons of portions of a synthesized spectrum of a known composition and of second quantities of photons of portions of a first synthesized spectrum of the target composition, resulting in an intermediary synthesized relationship (Dasaratha [0022] At step 106, a residual spectrum may be computed for each of the candidate mixture combinations by removing the fitted spectrum of the candidate mixture combination from the spectrum of the unknown mixture. In an embodiment of the present invention, the residual spectrum may be computed by subtracting the fitted spectrum obtained in step 104 from the spectrum of the unknown mixture. In other embodiments of the present invention, peak-based subtraction may be used to remove the fitted spectrum of the candidate mixture combination from the spectrum of the unknown mixture.);
aggregate, by the processor, third quantities of photons of portions of the intermediary synthesized relationship and fourth quantities of photons of portions of a measured spectrum of the known composition (Dasaratha [0023] At step 108, the residual spectrum corresponding to each candidate mixture combination is compared with the spectrum of each of a second plurality of library compounds. In an embodiment of the invention, the second plurality of library compounds used in this step is same as the first plurality of library compounds used in step 102. The spectra are iteratively compared to match each component of the total mixture); and
generate, by the processor, a second synthesized spectrum of the target composition based on a result of the aggregating (Dasaratha [0026] At step 110, the potential compounds are added to the candidate mixture combinations. In an embodiment of the present invention, one or more new candidate mixture combinations may be obtained by the addition of "children" potential compounds to the "parent" mixture combinations.).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 3-6, 10, 13, 14, and 16-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Dasaratha (as stated above) in view of Corbett et al. (US 20110144922 A1), hereinafter “Corbett”.
Regarding Claim 3, Dasaratha further teaches wherein the determination component determines the known composition as being a nearest neighbor of a set of known compositions, including the known composition, to the target composition (Dasaratha [0017] A similarity measure is computed for the first plurality of library compounds. […] Further, the first plurality of library compounds may be sorted by the similarity measure and ranked in a descending or ascending order depending on the similarity measure used).
Although Dasaratha teaches mixture spectra percentages (Dasaratha [0032] [0032] FIG. 2 illustrates an example embodiment of the present invention in which the components of an unknown mixture are identified using the search algorithm. Consider a synthetic test mixture spectra created by combining 33% of the Raman spectra of 1.3-Cyclooctadiene, 33% of the Raman spectra of 1.5-Hexadiene and 33% of the Raman spectra of 1.7-Octadiene for illustrating various steps involved in the search algorithm.), Dasaratha is not relied upon to explicitly teach wherein the nearest neighbor is a composition having component weight percentages closer to component weight percentages of the target composition than the other known compositions of the set of known compositions.
Corbett teaches the relationship between spectral percentages/ratios and weight percentages (Corbett [0013] If one measures a sample of pure iron and pure sulphur, the spectra of these elements can be overlaid onto the spectrum of pyrite appropriately. The result is shown in FIG. 4. In particular, FIG. 4 shows that scaling the iron spectrum 44 to 42.0% and the sulphur spectrum 46 to 41.5% allows them to fit the peaks in the pyrite spectrum 42. These numbers are the peak ratios of these elements, because they represent the ratio of the area of each peak relative to a pure sample of the element. They do not represent the weight percentage of the material.).
It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the instant application, to modify Dasaratha in view of Corbett to explicitly teach wherein the nearest neighbor is a composition having component weight percentages closer to component weight percentages of the target composition than the other known compositions of the set of known compositions, because the spectral percentages of Dasaratha can be converted to weight percentages and still be used to determine the closest composition (Corbett [0015] The peak ratios obtained previously can be converted to a weight percentage using a standard matrix correction algorithm such as ZAF corrections. ZAF corrections account for differences in the atomic number (Z) of the elements, the absorption factor (A) of x-rays travelling through the material, and fluorescence (F) of x-rays from elements stimulating the emission of x-rays from other elements.).
Regarding Claim 4, although the disclosure of Dasaratha (as stated above) teaches modeling mixtures according to their components (Dasaratha [0017] a principal component regression model, and a partial least squares model may be used to compute the similarity measure. Principal component regression models and partial least squares models are built using the library data and then applied to the unknown mixture to generate similarity measures. Also see [0015] In one embodiment of the present invention, the mixture may contain more than one compound), Dasaratha is not relied upon to explicitly teach a linear combination component that further determines the known composition based on identifying a linear combination of two or more other known compositions.
Corbett teaches a linear combination component that further determines the known composition based on identifying a linear combination of two or more other known compositions (Corbett [0094] The calculation to generate the optimal weights uses the standard linear least squares algorithm. The least squares algorithm solves an over-determined system of linear equations of the form Ax=b (Equation 1: Linear equation to be solved)).
It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the instant application, to modify Dasaratha (as stated above) in view of Corbett to explicitly teach a linear combination component that further determines the known composition based on identifying a linear combination of two or more other known compositions, because a mixture is made up of a combination of its components, whether that be a combination of a single substance or compounds, and therefore the mixture has to be the sum of its parts (Corbett [0081] The iterative least squares analysis is a technique which uses standard linear least squares curve fitting to find the optimal weights for each template in order to synthesise the spectrum of an unknown material.).
Regarding Claim 5, Dasaratha (as stated above) further teaches a synthesizing component that synthetically generates the first synthesized spectrum of the target composition by performing steps comprising:
determining a first sum of component percentages of the components of the target composition, based on percentage composition of the components of the target composition (Dasaratha [0032] [0032] FIG. 2 illustrates an example embodiment of the present invention in which the components of an unknown mixture are identified using the search algorithm. Consider a synthetic test mixture spectra created by combining 33% of the Raman spectra of 1.3-Cyclooctadiene, 33% of the Raman spectra of 1.5-Hexadiene and 33% of the Raman spectra of 1.7-Octadiene for illustrating various steps involved in the search algorithm. Also see [0040] FIG. 3 illustrates an example embodiment of the present invention in which the components of an unknown mixture are identified using the search algorithm. Consider a synthetic test mixture spectra created by combining 80% of the Raman spectra of D-Fructose, 10% of the Raman spectra of Picric Acid and 10% of the Raman spectra of Carbazole for illustrating various steps involved in the search algorithm. A mixture, whether its composition is known or unknown is inherently equal to the sum of its parts, which make up a ratio/.percentage of the entire mixture.).
Dasaratha is not relied upon to explicitly teach weight percentages; and
applying, to the weight percentage composition of the components of the target composition, scales for the components of the target composition based on a result of a matrix correction procedure on the first sum, wherein the applying causes generation of a scaled synthesized spectrum being the first synthesized spectrum of the target composition.
Corbett teaches weight percentages (Corbett [0013] If one measures a sample of pure iron and pure sulphur, the spectra of these elements can be overlaid onto the spectrum of pyrite appropriately. The result is shown in FIG. 4. In particular, FIG. 4 shows that scaling the iron spectrum 44 to 42.0% and the sulphur spectrum 46 to 41.5% allows them to fit the peaks in the pyrite spectrum 42. These numbers are the peak ratios of these elements, because they represent the ratio of the area of each peak relative to a pure sample of the element. They do not represent the weight percentage of the material.); and
applying, to the weight percentage composition of the components of the target composition, scales for the components of the target composition based on a result of a matrix correction procedure on the first sum, wherein the applying causes generation of a scaled synthesized spectrum being the first synthesized spectrum of the target composition (Ax=b (Equation 1: Linear equation to be solved) [0095] where: [0096] A is the matrix of known data points; [0097] b is final solution; and [0098] x is the set of unknowns which linearly scale the known matrix A to generate the vector b. The weights scale the spectrum).
It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the instant application, to modify Dasaratha (as stated above) in view of Corbett to explicitly teach weight percentages; and
applying, to the weight percentage composition of the components of the target composition, scales for the components of the target composition based on a result of a matrix correction procedure on the first sum, wherein the applying causes generation of a scaled synthesized spectrum being the first synthesized spectrum of the target composition, because the spectral percentages of Dasaratha can be converted to weight percentages and still be used to determine the closest composition (Corbett [0015] The peak ratios obtained previously can be converted to a weight percentage using a standard matrix correction algorithm such as ZAF corrections. ZAF corrections account for differences in the atomic number (Z) of the elements, the absorption factor (A) of x-rays travelling through the material, and fluorescence (F) of x-rays from elements stimulating the emission of x-rays from other elements.) and to apply the determined weights to properly scale the components of the spectrum (Corbett [0102] FIG. 23 shows each of the element templates after they've been scaled by the values shown in FIG. 22. The four main elements, oxygen, sodium, aluminium and silicon, stand out quite strongly for this mineral.).
Regarding Claim 6, Dasaratha (as stated above) further teaches a synthesizing component that synthetically generates the synthesized spectrum of the known composition by performing steps comprising:
determining a first sum of component percentages of the components of the known composition, based on weight percentage composition of the components of the known composition (Dasaratha [0032] [0032] FIG. 2 illustrates an example embodiment of the present invention in which the components of an unknown mixture are identified using the search algorithm. Consider a synthetic test mixture spectra created by combining 33% of the Raman spectra of 1.3-Cyclooctadiene, 33% of the Raman spectra of 1.5-Hexadiene and 33% of the Raman spectra of 1.7-Octadiene for illustrating various steps involved in the search algorithm. Also see [0040] FIG. 3 illustrates an example embodiment of the present invention in which the components of an unknown mixture are identified using the search algorithm. Consider a synthetic test mixture spectra created by combining 80% of the Raman spectra of D-Fructose, 10% of the Raman spectra of Picric Acid and 10% of the Raman spectra of Carbazole for illustrating various steps involved in the search algorithm. A mixture, whether its composition is known or unknown is inherently equal to the sum of its parts, which make up a ratio/.percentage of the entire mixture.).
Dasaratha is not relied upon to explicitly teach weight percentages; and
applying, to the weight percentage composition of the components of the known composition, scales for the components of the known composition based on a result of a matrix correction procedure on the first sum, wherein the applying causes generation of a scaled synthesized spectrum being the synthesized spectrum of the known composition.
Corbett teaches weight percentages (Corbett [0013] If one measures a sample of pure iron and pure sulphur, the spectra of these elements can be overlaid onto the spectrum of pyrite appropriately. The result is shown in FIG. 4. In particular, FIG. 4 shows that scaling the iron spectrum 44 to 42.0% and the sulphur spectrum 46 to 41.5% allows them to fit the peaks in the pyrite spectrum 42. These numbers are the peak ratios of these elements, because they represent the ratio of the area of each peak relative to a pure sample of the element. They do not represent the weight percentage of the material.); and
applying, to the weight percentage composition of the components of the target composition, scales for the components of the target composition based on a result of a matrix correction procedure on the first sum, wherein the applying causes generation of a scaled synthesized spectrum being the first synthesized spectrum of the target composition (Ax=b (Equation 1: Linear equation to be solved) [0095] where: [0096] A is the matrix of known data points; [0097] b is final solution; and [0098] x is the set of unknowns which linearly scale the known matrix A to generate the vector b. The weights scale the spectrum).
It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the instant application, to modify Dasaratha (as stated above) in view of Corbett to explicitly teach weight percentages; and
applying, to the weight percentage composition of the components of the known composition, scales for the components of the known composition based on a result of a matrix correction procedure on the first sum, wherein the applying causes generation of a scaled synthesized spectrum being the synthesized spectrum of the known composition, because the spectral percentages of Dasaratha can be converted to weight percentages and still be used to determine the closest composition (Corbett [0015] The peak ratios obtained previously can be converted to a weight percentage using a standard matrix correction algorithm such as ZAF corrections. ZAF corrections account for differences in the atomic number (Z) of the elements, the absorption factor (A) of x-rays travelling through the material, and fluorescence (F) of x-rays from elements stimulating the emission of x-rays from other elements.) and to apply the determined weights to properly scale the components of the spectrum (Corbett [0102] FIG. 23 shows each of the element templates after they've been scaled by the values shown in FIG. 22. The four main elements, oxygen, sodium, aluminium and silicon, stand out quite strongly for this mineral.).
Regarding Claim 10, Dasaratha further teaches determining, by the system, the known composition as being a nearest neighbor of a set of known compositions, including the known composition, to the target composition (Dasaratha [0017] A similarity measure is computed for the first plurality of library compounds. […] Further, the first plurality of library compounds may be sorted by the similarity measure and ranked in a descending or ascending order depending on the similarity measure used).
Although Dasaratha teaches mixture spectra percentages (Dasaratha [0032] [0032] FIG. 2 illustrates an example embodiment of the present invention in which the components of an unknown mixture are identified using the search algorithm. Consider a synthetic test mixture spectra created by combining 33% of the Raman spectra of 1.3-Cyclooctadiene, 33% of the Raman spectra of 1.5-Hexadiene and 33% of the Raman spectra of 1.7-Octadiene for illustrating various steps involved in the search algorithm.), Dasaratha is not relied upon to explicitly teach wherein the nearest neighbor is a composition having component weight percentages closer to component weight percentages of the target composition than the other known compositions of the set of known compositions.
Corbett teaches the relationship between spectral percentages/ratios and weight percentages (Corbett [0013] If one measures a sample of pure iron and pure sulphur, the spectra of these elements can be overlaid onto the spectrum of pyrite appropriately. The result is shown in FIG. 4. In particular, FIG. 4 shows that scaling the iron spectrum 44 to 42.0% and the sulphur spectrum 46 to 41.5% allows them to fit the peaks in the pyrite spectrum 42. These numbers are the peak ratios of these elements, because they represent the ratio of the area of each peak relative to a pure sample of the element. They do not represent the weight percentage of the material.).
It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the instant application, to modify Dasaratha in view of Corbett to explicitly teach wherein the nearest neighbor is a composition having component weight percentages closer to component weight percentages of the target composition than the other known compositions of the set of known compositions, because the spectral percentages of Dasaratha can be converted to weight percentages and still be used to determine the closest composition (Corbett [0015] The peak ratios obtained previously can be converted to a weight percentage using a standard matrix correction algorithm such as ZAF corrections. ZAF corrections account for differences in the atomic number (Z) of the elements, the absorption factor (A) of x-rays travelling through the material, and fluorescence (F) of x-rays from elements stimulating the emission of x-rays from other elements.).
Regarding Claim 13, Dasaratha (as stated above) is not relied upon to explicitly teach verifying, by the system, that a delta, of component weight percentages of the known composition and of component weight percentages of the target composition, satisfies a selected threshold defining a selected compositional relationship between the known composition and the target composition.
Corbett teaches verifying, by the system, that a delta, of component weight percentages of the known composition and of component weight percentages of the target composition, satisfies a selected threshold defining a selected compositional relationship between the known composition and the target composition (Corbett [0013] If one measures a sample of pure iron and pure sulphur, the spectra of these elements can be overlaid onto the spectrum of pyrite appropriately. The result is shown in FIG. 4. In particular, FIG. 4 shows that scaling the iron spectrum 44 to 42.0% and the sulphur spectrum 46 to 41.5% allows them to fit the peaks in the pyrite spectrum 42. These numbers are the peak ratios of these elements, because they represent the ratio of the area of each peak relative to a pure sample of the element. They do not represent the weight percentage of the material. […] [0015] The peak ratios obtained previously can be converted to a weight percentage using a standard matrix correction algorithm such as ZAF corrections. The difference between the generated material and the target material is determined so that the makeup of the generated material can be adjusted to match the target).
It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the instant application, to modify Dasaratha (as stated above) in view of Corbett to explicitly teach verifying, by the system, that a delta, of component weight percentages of the known composition and of component weight percentages of the target composition, satisfies a selected threshold defining a selected compositional relationship between the known composition and the target composition, because the spectral percentages are used to determine similarity and can be converted to weight percentages and used to determine the closest composition (Corbett [0015] The peak ratios obtained previously can be converted to a weight percentage using a standard matrix correction algorithm such as ZAF corrections. ZAF corrections account for differences in the atomic number (Z) of the elements, the absorption factor (A) of x-rays travelling through the material, and fluorescence (F) of x-rays from elements stimulating the emission of x-rays from other elements.).
Regarding Claim 14, Dasaratha in view of Corbett (as stated above) further teaches determining, by the system, the delta as an output of an error score of component weight percentage differences of components of the target composition and of components of the known composition (Dasaratha [0017] A spectral error parameter of a metric may be computed as a similarity measure for each of the first plurality of library compounds. Also see Corbett [0079] The difference between the measured pattern and the combination of the data templates and weighting factors, is referred to an error value. Mathematical algorithms are used to minimize the error value. One preferred algorithm to find weighting factors that minimize the error value is an iterative least squares method).
Regarding Claim 16, although Dasaratha teaches comparing spectra of known and unknown mixtures and compounds (Dasaratha [0015] in various embodiments of the present invention, a search algorithm is employed to identify one or more compounds that may be present in an unknown mixture. At step 102, the spectrum of the unknown mixture is compared with the spectrum of each of a first plurality of library compounds. The library may contain the spectrum of one or more known compounds. In an embodiment of the invention, the library may be stored as various sub-libraries.).
Dasaratha is not relied upon to further teach wherein the portions of the synthesized spectrum of the known composition represent different photon energy ranges of the synthesized spectrum of the known composition,
wherein the portions of the synthesized spectrum of the target composition represent different photon energy ranges of the synthesized spectrum of the target composition,
wherein the portions of the intermediary synthesized relationship represent different photon energy ranges of the intermediary synthesized relationship, and
wherein the portions of the measured spectrum of the known composition represent different photon energy ranges of the measured spectrum of the known composition.
Corbett teaches wherein the portions of the synthesized spectrum of the known composition represent different photon energy ranges of the synthesized spectrum of the known composition, wherein the portions of the synthesized spectrum of the target composition represent different photon energy ranges of the synthesized spectrum of the target composition, wherein the portions of the intermediary synthesized relationship represent different photon energy ranges of the intermediary synthesized relationship, and wherein the portions of the measured spectrum of the known composition represent different photon energy ranges of the measured spectrum of the known composition (Corbett [0013] If one measures a sample of pure iron and pure sulphur, the spectra of these elements can be overlaid onto the spectrum of pyrite appropriately. The result is shown in FIG. 4. In particular, FIG. 4 shows that scaling the iron spectrum 44 to 42.0% and the sulphur spectrum 46 to 41.5% allows them to fit the peaks in the pyrite spectrum 42. These numbers are the peak ratios of these elements, because they represent the ratio of the area of each peak relative to a pure sample of the element. They do not represent the weight percentage of the material. See Figs. 4-14. The spectra of the sample material is compared with that of its suspected parts).
It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the instant application, to modify Dasaratha (as stated above) in view of Corbett to explicitly teach wherein the portions of the synthesized spectrum of the known composition represent different photon energy ranges of the synthesized spectrum of the known composition, wherein the portions of the synthesized spectrum of the target composition represent different photon energy ranges of the synthesized spectrum of the target composition, wherein the portions of the intermediary synthesized relationship represent different photon energy ranges of the intermediary synthesized relationship, and wherein the portions of the measured spectrum of the known composition represent different photon energy ranges of the measured spectrum of the known composition, to explicitly show how the spectra of an unknown or target mixture is a makeup of the spectra of its components (Corbett [0011] Standard based spectral analysis is a term used to describe the process of comparing the spectrum of an unknown mineral or element with a set of known spectra to determine the composition of the unknown spectrum in terms of the known spectra. This approach generates a solution that represents a multiplication factor for each of the known templates, which are then added together to synthesize the unknown spectrum.).
Regarding Claim 17, although Dasaratha teaches comparing spectra of known and unknown mixtures and compounds (Dasaratha [0015] in various embodiments of the present invention, a search algorithm is employed to identify one or more compounds that may be present in an unknown mixture. At step 102, the spectrum of the unknown mixture is compared with the spectrum of each of a first plurality of library compounds. The library may contain the spectrum of one or more known compounds. In an embodiment of the invention, the library may be stored as various sub-libraries.).
Dasaratha is not relied upon to further teach wherein the determining the delta comprises determining a difference of a first array of numbers, indexed over a set of energy ranges and corresponding to the first quantities of photons of portions of the synthesized spectrum of the known composition, and a second array of numbers, indexed over the set of energy ranges and corresponding to the second quantities of photons of portions of the first synthesized spectrum of the target composition.
Corbett teaches wherein the determining the delta comprises determining a difference of a first array of numbers, indexed over a set of energy ranges and corresponding to the first quantities of photons of portions of the synthesized spectrum of the known composition, and a second array of numbers, indexed over the set of energy ranges and corresponding to the second quantities of photons of portions of the first synthesized spectrum of the target composition (Corbett [0013] If one measures a sample of pure iron and pure sulphur, the spectra of these elements can be overlaid onto the spectrum of pyrite appropriately. The result is shown in FIG. 4. In particular, FIG. 4 shows that scaling the iron spectrum 44 to 42.0% and the sulphur spectrum 46 to 41.5% allows them to fit the peaks in the pyrite spectrum 42. These numbers are the peak ratios of these elements, because they represent the ratio of the area of each peak relative to a pure sample of the element. They do not represent the weight percentage of the material. See Figs. 4-14. The spectra of the sample material is compared with that of its suspected parts).
It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the instant application, to modify Dasaratha (as stated above) in view of Corbett to explicitly teach wherein the determining the delta comprises determining a difference of a first array of numbers, indexed over a set of energy ranges and corresponding to the first quantities of photons of portions of the synthesized spectrum of the known composition, and a second array of numbers, indexed over the set of energy ranges and corresponding to the second quantities of photons of portions of the first synthesized spectrum of the target composition, to explicitly show how the spectra of an unknown or target mixture is a makeup of the spectra of its components (Corbett [0011] Standard based spectral analysis is a term used to describe the process of comparing the spectrum of an unknown mineral or element with a set of known spectra to determine the composition of the unknown spectrum in terms of the known spectra. This approach generates a solution that represents a multiplication factor for each of the known templates, which are then added together to synthesize the unknown spectrum.).
Regarding Claim 18, Dasaratha in view of Corbett (as stated above) further teaches wherein the aggregating comprises determining an aggregation of a third array of numbers, indexed over the set of energy ranges and corresponding to the third quantities of photons of portions of the intermediary synthesized relationship and a fourth array of numbers, indexed over the set of energy ranges and corresponding to the fourth quantities of photons of portions of the measured spectrum of the known composition (Dasaratha Fig. 1 showing iteration through the algorithm. Also see Corbett [0013] If one measures a sample of pure iron and pure sulphur, the spectra of these elements can be overlaid onto the spectrum of pyrite appropriately. The result is shown in FIG. 4. In particular, FIG. 4 shows that scaling the iron spectrum 44 to 42.0% and the sulphur spectrum 46 to 41.5% allows them to fit the peaks in the pyrite spectrum 42. These numbers are the peak ratios of these elements, because they represent the ratio of the area of each peak relative to a pure sample of the element. They do not represent the weight percentage of the material. See Figs. 4-14. The spectra of the sample material is compared with that of its suspected parts).
Regarding Claim 19, Dasaratha in view of Corbett (as stated above) further teaches wherein the measured spectrum has been measured from a sample of established providence of the known composition (Dasaratha [0015] The library may contain the spectrum of one or more known compounds. In an embodiment of the invention, the library may be stored as various sub-libraries. The known spectra within the library must have been measured or modeled).
Regarding Claim 20, Dasaratha further teaches to determine, by the processor, the known composition as being a nearest neighbor of a set of known compositions, including the known composition, to the target composition (Dasaratha [0017] A similarity measure is computed for the first plurality of library compounds. […] Further, the first plurality of library compounds may be sorted by the similarity measure and ranked in a descending or ascending order depending on the similarity measure used).
Although Dasaratha teaches mixture spectra percentages (Dasaratha [0032] [0032] FIG. 2 illustrates an example embodiment of the present invention in which the components of an unknown mixture are identified using the search algorithm. Consider a synthetic test mixture spectra created by combining 33% of the Raman spectra of 1.3-Cyclooctadiene, 33% of the Raman spectra of 1.5-Hexadiene and 33% of the Raman spectra of 1.7-Octadiene for illustrating various steps involved in the search algorithm.), Dasaratha is not relied upon to explicitly teach wherein the nearest neighbor is a composition having component weight percentages closer to component weight percentages of the target composition than the other known compositions of the set of known compositions.
Corbett teaches the relationship between spectral percentages/ratios and weight percentages (Corbett [0013] If one measures a sample of pure iron and pure sulphur, the spectra of these elements can be overlaid onto the spectrum of pyrite appropriately. The result is shown in FIG. 4. In particular, FIG. 4 shows that scaling the iron spectrum 44 to 42.0% and the sulphur spectrum 46 to 41.5% allows them to fit the peaks in the pyrite spectrum 42. These numbers are the peak ratios of these elements, because they represent the ratio of the area of each peak relative to a pure sample of the element. They do not represent the weight percentage of the material.).
It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the instant application, to modify Dasaratha in view of Corbett to explicitly teach wherein the nearest neighbor is a composition having component weight percentages closer to component weight percentages of the target composition than the other known compositions of the set of known compositions, because the spectral percentages of Dasaratha can be converted to weight percentages and still be used to determine the closest composition (Corbett [0015] The peak ratios obtained previously can be converted to a weight percentage using a standard matrix correction algorithm such as ZAF corrections. ZAF corrections account for differences in the atomic number (Z) of the elements, the absorption factor (A) of x-rays travelling through the material, and fluorescence (F) of x-rays from elements stimulating the emission of x-rays from other elements.).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Sexton et al. (US 20230093452 A1) discloses Assemblies And Methods For Enhancing Fluid Catalytic Cracking (FCC) Processes During The FCC Process Using Spectroscopic Analyzers.
Gao et al. (CN 117333683 A) discloses a Device And Method For Identifying Foreign Matter.
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/CHRISTIAN T BRYANT/Examiner, Art Unit 2863 11/19/2025
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