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 .
This action is in response to the Request for Continued Examination filed 2 January 2026. Claims 1, 4-5, 7-810, 13, 15-16, 18-19, 21, 23-25, 33 have been amended. Claim 2-3, 9, 11, 14, 29 have been canceled. Claims 1, 4-8, 10, 12-13, 15-28, 30-35 are pending and have been considered below.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 2 January 2026 has been entered.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1, 4-8, 10, 13, 17-25, 27-28, 32-33 is/are rejected under 35 U.S.C. 103 as being unpatentable over Howley et al. (US 2010/0179934 A1) in view of Bobroff et al. (US 2019/0180199 A1) and further in view of Thaler (US 2007/0094172 A1).
Claim 1. Howley discloses a method for operating a spectral data processing system, comprising:
processing spectral data of a chemical sample at least partly using a machine learning processing model to provide the processing result, the presence of chemicals in a mixture is detected by first building a classification model on a training set of spectra of samples of known composition, using a kernel-based classification technique that incorporates a WS kernel (P 0092) the kernel-based learner uses the spectral database to build a prediction model, which can be used for the quantification or classification of unknown mixtures by using the WS kernel to calculate the similarity of pairs of sample spectra from the spectral database (P 0115) and comparing the spectrum of an unknown material or chemical with a database of spectra of known materials (P 0151),
wherein the processing includes performing one or more of the following using the machine learning processing model: spectral signal segmentation; spectral peak detection; spectral peak deconvolution; and chemical component related information determination, in sample X, point xi is positioned at the crest of a peak in the spectral response, and the points immediately neighbouring point x, are located on the sides of the peak about point xi (P 0129) It is clear that the classification model is trained on known samples and those are stored in a database, then the unknown sample are processed with the machine learning model, Bobroff has been combined with Howley for the user input,
receiving a[n] … input associated with the processing, in building weight spectral (WS) kernel-based classification or regression models, parameters of the kernel method and WS kernel itself may be optimised for a training set, by selecting parameter values that result in the best accuracy on that set (P 0092),
of the spectral data, detecting the presence of chemicals in a mixture by building a classification model on a training set of spectra of samples of known composition to classify the spectrum of an unidentified sample (P 0093) using kernel-based machine learning for both the classification of chemicals and the quantification of properties of chemicals based on spectral data (P 0113),
the machine learning processing model being arranged in a machine learning controller of the spectral data processing system, the spectral data is applied to a spectral data processing unit (P 0115); and
storing the received … input for training the machine learning processing model based on the received … input, the kernel-based learner uses a spectral database to build a prediction model, which can be used for the quantification or classification of unknown mixtures using a WS kernel (P 0115 Fig 1) an unknown sample spectrum is classified or quantified by the kernel model incorporating the WS Kernel (P 0116 Fig 2) the kernel compares two spectral samples, x and z, as inputs (P 0117) a storage device stores at least a first spectrum and a second spectrum (Claim 26) It is clear that the first spectrum inputs are stored in the WS kernel model and then the input spectrums that are used by the kernel based learner are stored in a database.
Howley does not disclose receiving a user input, as disclosed in the claims. However, in the same field of invention, Bobroff discloses training data can include the data themselves as well as a description of the data and/or what the data represents, including spectral signatures, or the like (P 0035) one or more model relations are updated based on user feedback or other suitable information (P 0045) a user provides input (P 0078). Therefore, considering the teachings of Howley and Bobroff, one having ordinary skill in the art before the effective filing date of the invention would have been motivated to combine receiving a user input with the teachings of Howley with the motivation to allow a user to have more control over the spectrographic analysis process and the Supreme Court in KSR International Co. v. Teleflex Inc. identified applying a known technique to a known device (method, or product) ready for improvement to yield predictable results as a rationale to support a conclusion of obviousness which is consistent with the proper “functional approach” to the determination of obviousness as laid down in Graham.
Howley does not disclose wherein the user input includes feedback associated with a processing result based on the processing of the spectral data, as disclosed in the claims. However, Bobroff discloses one or more model relations are updated based on user feedback or other suitable information (P 0045). Therefore, considering the teachings of Howley and Bobroff, one having ordinary skill in the art before the effective filing date of the invention would have been motivated to combine wherein the user input includes feedback associated with a processing result based on the processing of the spectral data with the teachings of Howley and Bobroff with the motivation to allow a user to have more control over the spectral data input to the system of Howley for future consideration (Bobroff: P 0058).
Howley does not disclose receiving storing the received user input … based on the received user input, as disclosed in the claims. However, Bobroff discloses the user can then provide feedback in response to a provided suggestion, e.g., at the time the suggestion is made and/or in connection with deployment of a model in accordance with the suggestion and the learning component can refine distances and/or other model relations (P 0046) a user can then provide feedback regarding the suggestion and/or deployment of the model suggested, if the feedback indicates changes to distance profiles associated with one or more models stored at the model database(s), those distance profiles can be updated and stored (P 0053) the nature of user feedback is recorded (P 0058). Therefore, considering the teachings of Howley and Bobroff, one having ordinary skill in the art before the effective filing date of the invention would have been motivated to combine storing the received user input … based on the received user input with the teachings of Howley and Bobroff with the motivation to allow a user to have more control over the spectrographic analysis process and the Supreme Court in KSR International Co. v. Teleflex Inc. identified applying a known technique to a known device (method, or product) ready for improvement to yield predictable results as a rationale to support a conclusion of obviousness which is consistent with the proper “functional approach” to the determination of obviousness as laid down in Graham.
Howley does not disclose wherein the user input represents a negative feedback or a positive feedback on the processing result, and the spectral data associated with the user input that represents the negative feedback is given a reduced weight compared to the spectral data associated with the user input that represents the positive feedback, as disclosed in the claims. However, Bobroff discloses respective pairwise distances can be adjusted based on user feedback wherein a user can be provided with the ability to provide feedback regarding the model relations and if the user feedback indicates that the pairwise distances are inaccurate, the pairwise distances are adjusted the nature of the user feedback is recorded, e.g., whether the feedback was positive or negative, to influence future annotations (P 0058). Therefore, considering the teachings of Howley and Bobroff, one having ordinary skill in the art before the effective filing date of the invention would have been motivated to combine wherein the user input represents a negative feedback or a positive feedback on the processing result, and the spectral data associated with the user input that represents the negative feedback is given a reduced weight compared to the spectral data associated with the user input that represents the positive feedback with the teachings of Howley and Bobroff with the motivation to allow a user to have more control over the spectrographic analysis process to influence future considerations (Bobroff: P 0058) and the Supreme Court in KSR International Co. v. Teleflex Inc. identified applying a known technique to a known device (method, or product) ready for improvement to yield predictable results as a rationale to support a conclusion of obviousness which is consistent with the proper “functional approach” to the determination of obviousness as laid down in Graham.
Howley does not disclose training, based on receiving a predetermined number of user inputs and to increase an accuracy of the machine learning processing model for analyzing the spectral data, the machine learning processing model to generate a trained machine learning processing model, as disclosed in the claims. However, Howley discloses the presence of chemicals in a mixture is detected by first building a classification model on a training set of spectra of samples of known composition (P 0092). In the same field of invention, Thaler discloses a greater the number of exemplary ratings input by the user results in more accurate training data for the system, which will generally produce faster training, in a neural network (P 0032). Therefore, considering the teachings of Howley, Bobroff and Thaler, one having ordinary skill in the art before the effective filing date of the invention would have been motivated to combine training, based on receiving a predetermined number of user inputs and to increase an accuracy of the machine learning processing model for analyzing the spectral data, the machine learning processing model to generate a trained machine learning processing model with the teachings of Howley and Bobroff with the motivation to allow a user to have more control over the spectrographic analysis process to influence future considerations (Bobroff: P 0058) and the Supreme Court in KSR International Co. v. Teleflex Inc. identified applying a known technique to a known device (method, or product) ready for improvement to yield predictable results as a rationale to support a conclusion of obviousness which is consistent with the proper “functional approach” to the determination of obviousness as laid down in Graham.
Howley discloses executing the trained machine learning processing model to further analyze the spectral data of the chemical sample, a method of determining the similarity of a first spectrum and a second spectrum, each spectrum representing a result of spectral analysis of a material or chemical, each spectrum comprising a set of m spectral attributes distributed across a spectral range (P 0015) but Howley does not disclose modifying, based on an output of the trained machine learning processing model, a weight value and a bias value associated with node connections of an artificial neural network associated with the trained machine learning processing model, as disclosed in the claims. However, Howley discloses weighting values are used in calculations (P 0025, 0034). Thaler discloses neural networks are sized to correspond to the relevant fields associated with the records within a database and to construct their internal connection weights based on the training rating pattern provided by the user's inputting of a few exemplary ratings (P 0029). Therefore, considering the teachings of Howley, Bobroff and Thaler, one having ordinary skill in the art before the effective filing date of the invention would have been motivated to combine modifying, based on an output of the trained machine learning processing model, a weight value and a bias value associated with node connections of an artificial neural network associated with the trained machine learning processing model with the teachings of Howley, Bobroff and Thaler with the motivation to allow a user to have more control over the spectrographic analysis process to influence future considerations (Bobroff: P 0058) and the Supreme Court in KSR International Co. v. Teleflex Inc. identified applying a known technique to a known device (method, or product) ready for improvement to yield predictable results as a rationale to support a conclusion of obviousness which is consistent with the proper “functional approach” to the determination of obviousness as laid down in Graham.
Claim 2-3. Canceled.
Claim 4. Howley, Bobroff and Thaler disclose the method of claim 1, and Howley discloses wherein the chemical component related information determination is performed based on the spectral signal segmentation, spectral peak detection, and/or the spectral peak deconvolution, the process of classifying and quantification of chemicals (P 0113) includes spectral peak analysis (P 0129).
Claim 5. Howley, Bobroff and Thaler disclose the method of claim 1, and Howley discloses wherein the chemical component related information determination includes one or more of: chemical component class identification; chemical component type identification; chemical component identification; and chemical component concentration determination, the process includes classifying and quantification of chemicals (P 0113) including the measurement of concentration of an analyte mixture based on the Raman spectrum of the mixture (P 0159).
Claim 6. Howley, Bobroff and Thaler disclose the method of claim 1, and Howley discloses prior to receiving the user input: providing the processing result of the processing of the spectral data, wherein providing the processing result includes providing at least one of: a graphical representation of at least part of the spectral data; and information associated with at least one chemical component contained in the chemical sample, the spectrum of a known sample mixture is displayed in graphical form comprising a range of attributes (P 0115 Fig 1) the spectrum of an unknown sample mixture is displayed in graphical form comprising a range of attributes (P 0116 Fig 2).
Claim 7. Howley, Bobroff and Thaler disclose the method of claim 6, and Howley discloses wherein the information associated with the at least one chemical component includes: identity of the least one chemical component and/or concentration of the at least one chemical component, the process includes classifying and quantification of chemicals (P 0113) identifying the unknown sample mixture (P 0115) including the measurement of concentration of an analyte mixture based on the Raman spectrum of the mixture (P 0159).
Claim 8. Howley, Bobroff and Thaler disclose the method of claim 1, and Howley discloses prior to the processing: selecting the machine learning processing model from a plurality of machine learning processing models arranged in the machine learning controller, wherein each respective one of the plurality of machine learning processing models is associated with a respective type or class of the chemical sample, and the selection is based on a type or class of the chemical sample, a determination is made of the best machine learning method from among a plurality of different methods, e.g. Support Vector Machines and Nearest Neighbour algorithms, which use are kernel-based (P 0113) wherein different embodiments incorporate different kernel-based learners (P 0115) for use in different spectral analysis applications (P 0144) a number of different models are evaluated based on weighted spectrum kernel settings and the best model is selected (P 0155-0156) parameters of both the kernel method and WS kernel may be tuned to achieve the best model of the training data (P 0163).
Claim 9. Canceled.
Claim 10. Howley, Bobroff and Thaler disclose the method of claim 1, and Howley in view of Bobroff discloses training of the machine learning processing model based on the received user input, which comprises: training the machine learning processing model based on the spectral data and the processing result, detecting the presence of chemicals in a mixture by building a classification model on a training set of spectra of samples of known composition to classify the spectrum of an unidentified sample (P 0092) a number of different models are evaluated based on weighted spectrum kernel settings and the best model is selected (P 0155-0156) Bobroff has been combined with Howley for the limitations directed to user input.
Claim 11. Canceled.
Claim 13. Howley, Bobroff and Thaler disclose the method of claim 11, and Howley discloses weighting values are used in calculations (P 0025, 0034) detecting the presence of chemicals in a mixture by building a classification model on a training set of spectra of samples of known composition to classify the spectrum of an unidentified sample (P 0092) and Bobroff discloses training data can include the data themselves as well as a description of the data and/or what the data represents, including spectral signatures, or the like (P 0035) one or more model relations are updated based on user feedback or other suitable information (P 0045) a user provides input (P 0078). Thaler discloses neural networks are sized to correspond to the relevant fields associated with the records within a database and to construct their internal connection weights based on the training rating pattern provided by the user's inputting of a few exemplary ratings (P 0029). Therefore, considering the teachings of Howley, Bobroff and Thaler, one having ordinary skill in the art before the effective filing date of the invention would have been motivated to combine wherein the user input is associated with an adjustment on the spectral data, and the method further comprises: processing the adjusted spectral data at least partly using the machine learning processing model to determine an updated processing result; and wherein the training of the machine learning processing model to generate the trained machine learning processing model further comprises: training the machine learning processing model based on the adjusted spectral data and the updated processing result with the teachings of Howley, Bobroff and Thaler with the motivation to allow a user to have more control over the spectrographic analysis process and the Supreme Court in KSR International Co. v. Teleflex Inc. identified applying a known technique to a known device (method, or product) ready for improvement to yield predictable results as a rationale to support a conclusion of obviousness which is consistent with the proper “functional approach” to the determination of obviousness as laid down in Graham.
Claim 14. Canceled.
Claim 17. Howley, Bobroff and Thaler disclose the method of claim 1, and Howley discloses the ANNs are used to classify components from their mass spectra (P 0005) detecting the presence of chemicals in a mixture by building a classification model on a training set of spectra of samples of known composition to classify the spectrum of an unidentified sample (P 0092). Therefore, considering the teachings of Howley, Bobroff and Thaler, one having ordinary skill in the art before the effective filing date of the invention would have been motivated to combine wherein the spectral data is data of a chromatogram or a mass spectrum, and wherein the spectral data processing system is associated with a chemical analysis system with the teachings of Howley, Bobroff and Thaler with the motivation to allow a user to have more control over the spectrographic analysis process and the Supreme Court in KSR International Co. v. Teleflex Inc. identified applying a known technique to a known device (method, or product) ready for improvement to yield predictable results as a rationale to support a conclusion of obviousness which is consistent with the proper “functional approach” to the determination of obviousness as laid down in Graham.
Claim 18. Howley, Bobroff and Thaler disclose the method of claim 17, and Hawley discloses wherein the chemical analysis system comprises a gas chromatograph or a liquid chromatograph, the purpose of this system is to classify an unknown sample of a chemical or material, which can be in the form of a solid, liquid or gas (P 0116). Hawley further discloses the ANNs are used to classify components from their mass spectra (P 0005) detecting the presence of chemicals in a mixture by building a classification model on a training set of spectra of samples of known composition to classify the spectrum of an unidentified sample (P 0092). Therefore, considering the teachings of Howley, Bobroff and Thaler, one having ordinary skill in the art before the effective filing date of the invention would have been motivated to combine the spectral data comprises data of a chromatogram of a chemical sample; or wherein the chemical analysis system comprises a mass spectrometer, and the spectral data comprises data of a mass spectrum of the chemical sample with the teachings of Howley, Bobroff and Thaler with the motivation to allow a user to have more control over the spectrographic analysis process and the Supreme Court in KSR International Co. v. Teleflex Inc. identified applying a known technique to a known device (method, or product) ready for improvement to yield predictable results as a rationale to support a conclusion of obviousness which is consistent with the proper “functional approach” to the determination of obviousness as laid down in Graham.
Claim 19. Howley discloses a spectral data processing system, comprising: one or more processors arranged to:
processing spectral data of a chemical sample at least partly using a machine learning processing model to provide the processing result, the presence of chemicals in a mixture is detected by first building a classification model on a training set of spectra of samples of known composition, using a kernel-based classification technique that incorporates a WS kernel (P 0092) the kernel-based learner uses the spectral database to build a prediction model, which can be used for the quantification or classification of unknown mixtures by using the WS kernel to calculate the similarity of pairs of sample spectra from the spectral database (P 0115) and comparing the spectrum of an unknown material or chemical with a database of spectra of known materials (P 0151),
wherein the processing includes performing one or more of the following using the machine learning processing model: spectral signal segmentation; spectral peak detection; spectral peak deconvolution; and chemical component related information determination, in sample X, point xi is positioned at the crest of a peak in the spectral response, and the points immediately neighbouring point x, are located on the sides of the peak about point xi (P 0129) It is clear that the classification model is trained on known samples and those are stored in a database, then the unknown sample are processed with the machine learning model, Bobroff has been combined with Howley for the user input,
receive a[n] … input associated with the processing, in building weight spectral (WS) kernel-based classification or regression models, parameters of the kernel method and WS kernel itself may be optimised for a training set, by selecting parameter values that result in the best accuracy on that set (P 0092),
of the spectral data of, detecting the presence of chemicals in a mixture by building a classification model on a training set of spectra of samples of known composition to classify the spectrum of an unidentified sample (P 0092), using kernel-based machine learning for both the classification of chemicals and the quantification of properties of chemicals based on spectral data (P 0113); and
train the machine learning processing model … , in building weight spectral (WS) kernel-based classification or regression models, parameters of the kernel method and WS kernel itself may be optimised for a training set, by selecting parameter values that result in the best accuracy on that set (P 0092) the kernel-based learner uses a spectral database to build a prediction model, which can be used for the quantification or classification of unknown mixtures using a WS kernel (P 0115 Fig 1) an unknown sample spectrum is classified or quantified by the kernel model incorporating the WS Kernel (P 0116 Fig 2) the kernel compares two spectral samples, x and z, as inputs (P 0117) a storage device stores at least a first spectrum and a second spectrum (Claim 26).
Howley does not disclose receive a user input associated with processing, as disclosed in the claims. However, in the same field of invention, Bobroff discloses training data can include the data themselves as well as a description of the data and/or what the data represents, including spectral signatures, or the like (P 0035) one or more model relations are updated based on user feedback or other suitable information (P 0045) a user provides input (P 0078). Therefore, considering the teachings of Howley and Bobroff, one having ordinary skill in the art before the effective filing date of the invention would have been motivated to combine receive a user input associated with processing with the teachings of Howley with the motivation to allow a user to have more control over the spectrographic analysis process and the Supreme Court in KSR International Co. v. Teleflex Inc. identified applying a known technique to a known device (method, or product) ready for improvement to yield predictable results as a rationale to support a conclusion of obviousness which is consistent with the proper “functional approach” to the determination of obviousness as laid down in Graham.
Howley does not disclose wherein the user input is associated with an adjustment on the spectral data and/or an adjustment on a processing result based on the processing of the spectral data, as disclosed in the claims. However, Howley discloses detecting the presence of chemicals in a mixture by building a classification model on a training set of spectra of samples of known composition to classify the spectrum of an unidentified sample (P 0092), and Bobroff discloses one or more model relations are updated based on user feedback or other suitable information (P 0045). Therefore, considering the teachings of Howley and Bobroff, one having ordinary skill in the art before the effective filing date of the invention would have been motivated to combine wherein the user input is associated with an adjustment on the spectral data and/or an adjustment on a processing result based on the processing of the spectral data with the teachings of Howley and Bobroff with the motivation to allow a user to have more control over the spectral data input to the system of Howley for future consideration (Bobroff: P 0058).
Howley does not disclose train the machine learning processing model based on receiving a predetermined number of user inputs and to increase an accuracy of the machine learning processing model for analyzing the spectral data to generate a trained machine learning processing model, as disclosed in the claims. However, Howley discloses in building weight spectral (WS) kernel-based classification or regression models, parameters of the kernel method and WS kernel itself may be optimised for a training set, by selecting parameter values that result in the best accuracy on that set (P 0092) and Bobroff discloses training data is used to train a model (P 0035) for a machine learning model (P 0036). In the same field of invention, Thaler disclose a greater the number of exemplary ratings input by the user results in more accurate training data for the system, which will generally produce faster training, in a neural network (P 0032). Therefore, considering the teachings of Howley, Bobroff and Thaler, one having ordinary skill in the art before the effective filing date of the invention would have been motivated to combine train the machine learning processing model based on receiving a predetermined number of user inputs and to increase an accuracy of the machine learning processing model for analyzing the spectral data to generate a trained machine learning processing model with the teachings of Howley and Bobroff with the motivation to allow a user to have more control over the spectral data input to the system of Howley for future consideration (Bobroff: P 0058).
Howley does not disclose wherein the user input represents a negative feedback or a positive feedback on the processing result, and the spectral data associated with the user input that represents the negative feedback is given a reduced weight compared to the spectral data associated with the user input that represents the positive feedback, as disclosed in the claims. However, Bobroff discloses respective pairwise distances can be adjusted based on user feedback wherein a user can be provided with the ability to provide feedback regarding the model relations and if the user feedback indicates that the pairwise distances are inaccurate, the pairwise distances are adjusted the nature of the user feedback is recorded, e.g., whether the feedback was positive or negative, to influence future annotations (P 0058). Therefore, considering the teachings of Howley, Bobroff and Thaler, one having ordinary skill in the art before the effective filing date of the invention would have been motivated to combine wherein the user input represents a negative feedback or a positive feedback on the processing result, and the spectral data associated with the user input that represents the negative feedback is given a reduced weight compared to the spectral data associated with the user input that represents the positive feedback with the teachings of Howley, Bobroff and Thaler with the motivation to allow a user to have more control over the spectrographic analysis process to influence future considerations (Bobroff: P 0058) and the Supreme Court in KSR International Co. v. Teleflex Inc. identified applying a known technique to a known device (method, or product) ready for improvement to yield predictable results as a rationale to support a conclusion of obviousness which is consistent with the proper “functional approach” to the determination of obviousness as laid down in Graham.
Howley discloses execute the trained machine learning processing model to further analyze the spectral data of the chemical sample, a method of determining the similarity of a first spectrum and a second spectrum, each spectrum representing a result of spectral analysis of a material or chemical, each spectrum comprising a set of m spectral attributes distributed across a spectral range (P 0015) but Howley does not disclose modifying, based on an output of the trained machine learning processing model, a weight value and a bias value associated with node connections of an artificial neural network associated with the trained machine learning processing model, as disclosed in the claims. However, Howley discloses weighting values are used in calculations (P 0025, 0034). Thaler discloses neural networks are sized to correspond to the relevant fields associated with the records within a database and to construct their internal connection weights based on the training rating pattern provided by the user's inputting of a few exemplary ratings (P 0029). Therefore, considering the teachings of Howley, Bobroff and Thaler, one having ordinary skill in the art before the effective filing date of the invention would have been motivated to combine modifying, based on an output of the trained machine learning processing model, a weight value and a bias value associated with node connections of an artificial neural network associated with the trained machine learning processing model with the teachings of Howley, Bobroff and Thaler with the motivation to allow a user to have more control over the spectrographic analysis process to influence future considerations (Bobroff: P 0058) and the Supreme Court in KSR International Co. v. Teleflex Inc. identified applying a known technique to a known device (method, or product) ready for improvement to yield predictable results as a rationale to support a conclusion of obviousness which is consistent with the proper “functional approach” to the determination of obviousness as laid down in Graham.
Claim(s) 22, 23, 27, 28, 32, 33 is/are directed to spectral data processing system claim(s) similar to the method claim(s) of Claim(s) 6, 8, 13, 10, 17, 18 and is/are rejected with the same rationale.
Claim 20. Howley, Bobroff and Thaler disclose the spectral data processing system of claim 19, and Howley discloses a machine learning controller with the machine learning processing model, the machine learning controller including the one or more processors, the spectral data is applied to a spectral data processing unit (P 0115).
Claim 21. Howley, Bobroff and Thaler disclose the spectral data processing system of Claim 19, and Howley discloses wherein the chemical component related information determination includes one or more of: chemical component class identification; chemical component type identification; chemical component identification; and chemical component concentration determination, the presence of chemicals in a mixture is detected by first building a classification model on a training set of spectra of samples of known composition, using a kernel-based classification technique that incorporates a WS kernel (P 0092) the kernel-based learner uses the spectral database to build a prediction model, which can be used for the quantification or classification of unknown mixtures by using the WS kernel to calculate the similarity of pairs of sample spectra from the spectral database (P 0115) in sample X, point xi is positioned at the crest of a peak in the spectral response, and the points immediately neighbouring point x, are located on the sides of the peak about point xi (P 0129) and comparing the spectrum of an unknown material or chemical with a database of spectra of known materials (P 0151). It is clear that the classification model is trained on known samples and those are stored in a database, then the unknown sample are processed with the machine learning model. Bobroff has been combined with Howley for the user input.
Claim 24. Howley, Bobroff and Thaler disclose the spectral data processing system of claim 19, and Howley discloses weighting values are used in calculations (P 0025, 0034) detecting the presence of chemicals in a mixture by building a classification model on a training set of spectra of samples of known composition to classify the spectrum of an unidentified sample (P 0092) and Bobroff discloses training data can include the data themselves as well as a description of the data and/or what the data represents, including spectral signatures, or the like (P 0035) one or more model relations are updated based on user feedback or other suitable information (P 0045) respective pairwise distances can be adjusted based on user feedback wherein a user can be provided with the ability to provide feedback regarding the model relations and if the user feedback indicates that the pairwise distances are inaccurate, the pairwise distances are adjusted the nature of the user feedback is recorded, e.g., whether the feedback was positive or negative, to influence future annotations (P 0058) a user provides input (P 0078). Thaler discloses neural networks are sized to correspond to the relevant fields associated with the records within a database and to construct their internal connection weights based on the training rating pattern provided by the user's inputting of a few exemplary ratings (P 0029). Therefore, considering the teachings of Howley, Bobroff and Thaler, one having ordinary skill in the art before the effective filing date of the invention would have been motivated to combine wherein the user input represents the positive feedback on the processing result, and wherein the one or more processors are arranged to train the machine learning processing model to generate the trained machine learning processing model by, at least, training the machine learning processing model based on the spectral data and the processing result with the teachings of Howley, Bobroff and Thaler with the motivation to allow a user to have more control over the spectrographic analysis process and the Supreme Court in KSR International Co. v. Teleflex Inc. identified applying a known technique to a known device (method, or product) ready for improvement to yield predictable results as a rationale to support a conclusion of obviousness which is consistent with the proper “functional approach” to the determination of obviousness as laid down in Graham.
Claim 25. Howley, Bobroff and Thaler disclose the spectral data processing system of claim 19, and Bobroff discloses respective pairwise distances can be adjusted based on user feedback wherein a user can be provided with the ability to provide feedback regarding the model relations and if the user feedback indicates that the pairwise distances are inaccurate, the pairwise distances are adjusted the nature of the user feedback is recorded, e.g., whether the feedback was positive or negative, to influence future annotations (P 0058). Therefore, considering the teachings of Howley and Bobroff, one having ordinary skill in the art before the effective filing date of the invention would have been motivated to combine wherein the user input represents the negative feedback on the processing result, and wherein the user input is associated with an adjustment on the spectral data and/or an adjustment on the processing result with the teachings of Howley and Bobroff with the motivation to allow a user to have more control over the spectrographic analysis process to influence future considerations (Bobroff: P 0058) and the Supreme Court in KSR International Co. v. Teleflex Inc. identified applying a known technique to a known device (method, or product) ready for improvement to yield predictable results as a rationale to support a conclusion of obviousness which is consistent with the proper “functional approach” to the determination of obviousness as laid down in Graham.
Claim(s) 12, 35 is/are rejected under 35 U.S.C. 103 as being unpatentable over Howley et al. (US 2010/0179934 A1) in view of Bobroff et al. (US 2019/0180199 A1) and Thaler (US 2007/0094172 A1) and further in view of Weber et al. (US 2021/0110841 A1).
Claim 12. Howley, Bobroff and Thaler disclose the method of claim 1, and but Howley does not disclose wherein the user input is associated with an adjustment on the spectral data and/or an adjustment on the processing result, wherein the user input includes at least one of: an adjusted peak start time; or an adjusted peak end time, as disclosed in the claims. However, in the same field of invention, Weber discloses peak-time amplitude values can then be modified/edited or changed using a user interface (P 0013) a user interface allows changing and/or editing the time-amplitude values of the received audio signal for one or more frequency bands, including editing/changing the time amplitude values of the received audio signal for one or more frequency bands to alter the time-amplitude envelope of one or more frequency bands (P 0072) the graphical user interface can modify, edit, add or delete the time-frequency envelopes comprising time-frequency values (P 0144 Fig 9). Therefore, considering the teachings of Howley, Bobroff, Thaler and Weber, one having ordinary skill in the art before the effective filing date of the invention would have been motivated to combine wherein the user input is associated with an adjustment on the spectral data and/or an adjustment on the processing result, wherein the user input includes at least one of: an adjusted peak start time; or an adjusted peak end time with the teachings of Howley, Bobroff and Thaler with the motivation to provide a more efficient process for editing complex filtering of frequency range data (Weber: P 0002-0003).
Claim 35. Howley, Bobroff and Thaler disclose the method of claim 1, but Howley does not disclose wherein the user input is associated with an adjustment on the spectral data and/or an adjustment on the processing result, wherein the user input includes an adjusted background subtraction, as disclosed in the claims. However, in the same field of invention, Weber discloses the signal conditioner reduces unwanted noise, distortion, and non-linearity (P 0099) the residual noise editor allows the editing of the noise component received from the data reduction by reshaping the noise curve, or by editing the time-amplitude-frequency values (P 0146). Therefore, considering the teachings of Howley, Bobroff, Thaler and Weber, one having ordinary skill in the art before the effective filing date of the invention would have been motivated to combine wherein the user input is associated with an adjustment on the spectral data and/or an adjustment on the processing result, wherein the user input includes an adjusted peak baseline with the teachings of Howley, Bobroff and Thaler with the motivation to provide an improved system for solving the technical issues for analyzing spectrometry data over/across samples Lafew (P 0009).
Claim(s) 15, 30, 31 is/are rejected under 35 U.S.C. 103 as being unpatentable over Howley et al. (US 2010/0179934 A1) in view of Bobroff et al. (US 2019/0180199 A1) and Thaler (US 2007/0094172 A1) and further in view of Kulkarni et al. (US 2003/0055921 A1).
Claim 15. Howley, Bobroff and Thaler disclose the method of claim 1, but Howley does not disclose prior to the processing: determining a format of the spectral data; and if it is determined that the format of the spectral data is a proprietary format, converting the format of the spectral data from the proprietary format to an open format, as disclosed in the claims. However, in the same field of invention, Kulkarni discloses an adapter transforms data from a closed (private) environment of an associated legacy system to an open (public) environment such that the language format inherent to the legacy systems is converted to an open non-proprietary format (P 0028). Therefore, considering the teachings of Howley, Bobroff, Thaler and Kulkarni, one having ordinary skill in the art before the effective filing date of the invention would have been motivated to combine prior to the processing: determining a format of the spectral data; and if it is determined that the format of the spectral data is a proprietary format, converting the format of the spectral data from the proprietary format to an open format with the teachings of Howley, Bobroff and Thaler with the motivation to increase and enhance reliable system performance without requiring significant system redevelopment (Kulkarni: P 0010).
Claim(s) 30 is/are directed to spectral data processing system claim(s) similar to the method claim(s) of Claim(s) 15 and is/are rejected with the same rationale.
Claim 31. Howley, Bobroff, Thaler and Kulkarni disclose the spectral data processing system of claim 30, and Howley discloses wherein the one or more processors are arranged to train the machine learning processing model periodically, in building weight spectral (WS) kernel-based classification or regression models, parameters of the kernel method and WS kernel itself may be optimised for a training set, by selecting parameter values that result in the best accuracy on that set (P 0092) in the building of WS kernel-based classification or regression models, parameters of the kernel method and WS kernel itself may be optimised for a training set, e.g. by selecting parameter values that result in the best accuracy on that set (P 0092) parameters of both the kernel method and WS kernel may be tuned to achieve the best model of the training data (P 0145) a number of classification or regression models using a kernel-based method that incorporates a WS kernel are generated by changing parameters of the WS kernel (P 0155) the WS kernel settings that achieve the best accuracy (classification or regression) on the training dataset are selected (P 0156) the different models are generated by changing parameters of the WS kernel (P 0168) the WS kernel settings that achieved the best accuracy (classification or regression) on the training dataset are selected (P 0169) Bobroff has been combined with Hawley for limitations directed to user input.
Claim(s) 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Howley et al. (US 2010/0179934 A1) in view of Bobroff et al. (US 2019/0180199 A1) and Thaler (US 2007/0094172 A1) and further in view of Collister et al. (US 2020/0166398 A1).
Claim 16. Howley, Bobroff and Thaler disclose the method of claim 1, and Howley in view of Bobroff discloses receiving one or more further user inputs, each associated with a respective processing of a respective spectral data of a respective chemical sample using the machine learning processing model; storing the one or more received further user inputs for training the machine learning processing model based on the one or more received further user inputs; training the machine learning processing model based on the one or more received further user inputs, in building weight spectral (WS) kernel-based classification or regression models, parameters of the kernel method and WS kernel itself may be optimised for a training set, by selecting parameter values that result in the best accuracy on that set (P 0092) in the building of WS kernel-based classification or regression models, parameters of the kernel method and WS kernel itself may be optimised for a training set, e.g. by selecting parameter values that result in the best accuracy on that set (P 0092) parameters of both the kernel method and WS kernel may be tuned to achieve the best model of the training data (P 0145) a number of classification or regression models using a kernel-based method that incorporates a WS kernel are generated by changing parameters of the WS kernel (P 0155) the WS kernel settings that achieve the best accuracy (classification or regression) on the training dataset are selected (P 0156) the different models are generated by changing parameters of the WS kernel (P 0168) the WS kernel settings that achieved the best accuracy (classification or regression) on the training dataset are selected (P 0169) Bobroff has been combined with Hawley for limitations directed to user input.
Howely does not disclose wherein the training the machine learning processing model comprises: training the machine learning processing model periodically at specified time intervals, as disclosed in the claims. However, in the same field of invention, Collister discloses an artificial intelligence engine is trained during a first time period (P 0006). Therefore, considering the teachings of Howley, Bobroff, Thaler and Collister, one having ordinary skill in the art before the effective filing date of the invention would have been motivated to combine wherein the training the machine learning processing model comprises: training the machine learning processing model periodically at specified time intervals with the teachings of Howley, Bobroff and Thaler with the motivation to provide a rigorous and organized method to provide more accurate model development.
Claim(s) 26, 34 is/are rejected under 35 U.S.C. 103 as being unpatentable over Howley et al. (US 2010/0179934 A1) in view of Bobroff et al. (US 2019/0180199 A1) and Thaler (US 2007/0094172 A1) and further in view of Lafew et al. (US 2020/0312642 A1).
Claim 26. Howley, Bobroff and Thaler disclose the spectral data processing system of claim 25, but Howley does not disclose wherein the user input includes an adjusted retention time, as disclosed in the claims. However, in the same field of invention, Lafew discloses modifying, via the user interface, the range of characteristic retention times of each of the one or more ions determined to be present in the sample (P 0010). Therefore, considering the teachings of Howley, Bobroff, Thaler and Lafew, one having ordinary skill in the art before the effective filing date of the invention would have been motivated to combine wherein the user input includes an adjusted retention time with the teachings of Howley, Bobroff and Thaler with the motivation to provide an improved system for solving the technical issues for analyzing spectrometry data over/across samples Lafew (P 0009).
Claim 34. Howley, Bobroff and Thaler disclose the method of claim 1, but Howley does not discloses wherein the user input is associated with an adjustment on the spectral data and/or an adjustment on the processing result, wherein the user input includes an adjusted peak baseline, as disclosed in the claims. However, in the same field of invention, Lafew discloses any detected ion intensity peaks/maxima is identified, and then the predictions/expectations of retention times for the ion peaks/maxima are iteratively updated or modified with the new data to conform to any unexpected shift or drift of the sample data (P 0058). Therefore, considering the teachings of Howley, Bobroff, Thaler and Lafew, one having ordinary skill in the art before the effective filing date of the invention would have been motivated to combine wherein the user input is associated with an adjustment on the spectral data and/or an adjustment on the processing result, wherein the user input includes an adjusted peak baseline with the teachings of Howley, Bobroff and Thaler with the motivation to provide an improved system for solving the technical issues for analyzing spectrometry data over/across samples Lafew (P 0009).
Response to Arguments
Applicant’s arguments, see Applicant Arguments/Remarks Made in an Amendment, filed 25 November 2025, with respect to Claims 1, 19 have been fully considered and are persuasive. The 35 USC § 101 of Claim 1, 19 has been withdrawn.
The processing of spectral data of a chemical sample using a machine learning processing model that includes one or more of spectral signal segmentation, spectral peak detection, spectral peak deconvolution, and chemical component related information determination overcomes the 35 USC § 101 rejection directed to an abstract idea without significantly more or without being integrated in a practical application because the operations of spectral signal segmentation, spectral peak detection, spectral peak deconvolution, and chemical component related information determination requires the use of computing devices beyond the ability of the human mind.
Applicant's arguments filed 25 November 2025 have been fully considered but they are not persuasive.
The applicant argues:
These distances described by Bobroff are clearly not "weights" in the context of claim 1 that recites that the "negative feedback is given a reduced weight compared to the spectral data associated with the user input that represents the positive feedback". In this regard, Bobroff does not teach or suggest that the distances correspond to negative or positive feedback, but instead, that the distances correspond to "distances between respective model components of the new model 910 and corresponding model components of other models stored in one or more model databases 640". Bobroff also does not teach or suggest that negative feedback results in a reduced distance compared to positive feedback. Further, at most paragraph [0058] of Bobroff appears to describe "annotations created by the annotation component 830 are provided to an adaptation component 1010 that can adjust the respective pairwise distances computed by the annotation component 830 based on user feedback." The annotations are also clearly not "weights" in the context of claim 1. Instead, at most, the annotations appear to be indicators of the distances. Bobroff also does not teach or suggest that negative feedback results in a reduced annotation compared to positive feedback. Thus, Bobroff fails to teach or suggest, at least, "wherein the user input represents a negative feedback or a positive feedback on the processing result, and the spectral data associated with the user input that represents the negative feedback is given a reduced weight compared to the spectral data associated with the user input that represents the positive feedback", as recited in claim 1, as amended.
The examiner respectfully disagrees. Howley discloses claims directed to weights, “weighting values are used in the calculations” (P 0025). Bobroff discloses “respective pairwise distances can be adjusted based on user feedback … to influence future annotations” (P 0058). While the adjustable pairwise distances are not disclosed explicitly as weights, the fact that their values influence future annotations indicates they function as weights.
The applicant argues:
However, Thaler clearly does not teach or suggest modification of any type of bias value associated with node connections of the STANNOs.
The examiner respectfully disagrees. In Thaler, the connections between fields in database records are clearly weighted (P 0029). The examiner has equated the weighted connections in Thaler with the claimed bias values associated with node connections.
Conclusion
Any inquiry concerning this communication should be directed to JOHN M HEFFINGTON at telephone number (571)270-1696.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOHN M HEFFINGTON whose telephone number is (571)270-1696. The examiner can normally be reached on Monday through Friday from 9:30 am to 5:30 pm Eastern.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Cesar B Paula, can be reached at telephone number (571)270-1696. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of an application may be obtained from Patent Center. Status information for published applications may be obtained from Patent Center. Status information for unpublished applications is available through Patent Center for authorized users only. Should you have questions about access to Patent Center, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free).
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) Form at https://www.uspto.gov/patents/uspto-automated- interview-request-air-form.
/J.M.H/Examiner, Art Unit 2145 3/21/2026
/CESAR B PAULA/Supervisory Patent Examiner, Art Unit 2145