DETAILED ACTION
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
2. 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.
In view of the new 2019 Revised Patent Subject Matter Eligibility Guidance (Federal Register Vol. 84, No. 4, January 7, 2019), the Examiner has considered the claims and has determined that under step 1, claims 1-7 are to a process, claims 8-13 are to an article of manufacture, and claims 14-20 are to a machine.
Next under the new step 2A prong 1 analysis, the claims are considered to determine if they recite an abstract idea (judicial exception) under the following groupings: (a) mathematical concepts, (b) certain methods of organizing human activity, or (c) mental processes. The independent claims contain at least the following bolded limitations (see representative independent claims) that fall into the grouping of mathematical concepts and/or mental processes:
1. A computer-implemented method for inferring spectra data, the method comprising:
selecting a first spectra dataset from a training dataset, the training dataset having a specified number of data points, the first spectra dataset having fewer data points than the specified number;
interpolating the first spectra dataset to generate a first interpolated dataset having the specified number of data points;
training an inference model to generate a first inferred spectra dataset from the first interpolated dataset; and
causing the trained inference model to generate a final spectrum inference dataset from the training dataset to predict a rough shape of a spectrum.
8. A computer program product comprising a computer-readable storage medium having a set of instructions stored therein which, when executed by a processor, causes the processor to infer spectra data by:
selecting a first spectra dataset from a training dataset, the training dataset having a specified number of data points, the first spectra dataset having fewer data points than the specified number;
interpolating the first spectra dataset to generate a first interpolated dataset having the specified number of data points;
training an inference model to generate a first inferred spectra dataset from the first interpolated dataset; and causing the trained inference model to generate a final spectrum inference dataset from the training dataset to predict a rough shape of a spectrum.
14. A computer system for inferring spectra data, the computer system comprising:
a processor set; and
a computer readable storage medium;
wherein:
the processor set is structured, located, connected, and/or programmed to run program instructions stored on the computer readable storage medium; and
the program instructions which, when executed by the processor set, cause the processor set to infer spectra data by:
selecting a first spectra dataset from a training dataset, the training dataset having a specified number of data points, the first spectra dataset having fewer data points than the specified number;
interpolating the first spectra dataset to generate a first interpolated dataset having the specified number of data points;
training an inference model to generate a first inferred spectra dataset from the first interpolated dataset; and
causing the trained inference model to generate a final spectrum inference dataset from the training dataset to predict a rough shape of a spectrum.
It is important to note that a mathematical concept need not be expressed in mathematical symbols, because "[w]ords used in a claim operating on data to solve a problem can serve the same purpose as a formula."(see MPEP 2106.04(a)(2) I.). The limitations of “inferring spectra data” amounts to a mental process to analyze and estimate additional spectra data points (given broadest reasonable interpretation, a spectra could be at a very basic level with mappable points), or a mathematical concept to perform a calculation to solve for additional spectra data points. The limitations of “selecting a first spectra dataset from a training dataset, the training dataset having a specified number of data points, the first spectra dataset having fewer data points than the specified number,” amounts to a mental process to select a subset of data points out of a larger training data set, which can be performed mentally by a person or on pen and paper by choosing data points out of a list of training data points. The limitations of “interpolating the first spectra dataset to generate a first interpolated dataset having the specified number of data points” amount to a mathematical concept to describe in words the calculation of intermediate interpolated data points between known data points. The limitations of “training an inference model to generate a first inferred spectra dataset from the first interpolated dataset” amount to a mathematical concept to build a mathematical model (e.g., see specification as originally presented 5/14/2024, paragraph [0060] describing training using a “loss function”) using the input of the interpolated dataset to generate an output of a first inferred spectra dataset. The limitations of “causing the trained inference model to generate a final spectrum inference dataset from the training dataset to predict a rough shape of a spectrum” amounts to a mathematical concept of applying the model to the inputs of the training dataset to solve for an output of a final spectrum inference dataset, where the prediction of a rough shape of a spectrum can be a mental process to map the data points (such as on pen and paper) to determine a rough shape of a spectrum. Patents that do no more than claim the application of generic machine learning to new data environments, without disclosing improvements to the machine learning models to be applied, are patent ineligible under § 101 (see Recentive Analytics, Inc. v. Fox Corp., 134 F.4th 1205 (Fed. Cir. 2025)). The bolded limitations describe a sequence of abstract idea algorithmic steps calculate an inference “dataset” and a predicted rough “shape” of a spectrum, which amounts to solving for an informational-based abstract result.
Next in step 2A prong 2, the independent claims are analyzed to determine whether there are additional elements or combination of elements that apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception such that it is more than a drafting effort designed to monopolize the exception, in order to integrate the judicial exception into a practical application. These limitations have been identified and underlined above, and are not indicative of integration into a practical application because the recitations for “a computer-implemented method,” “a computer program product comprising a computer-readable storage medium having a set of instructions stored therein which, when executed by a processor, causes the processor...”, and “a computer system... the computer system comprising: a processor set; and a computer readable storage medium; wherein: the processor set is structured, located, connected, and/or programmed to run program instructions stored on the computer readable storage medium; and the program instructions which, when executed by the processor set, cause the processor set...”, amount to mere instructions to implement an abstract idea on a computer or merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)). Next in step 2B, the independent claims are considered to determine if they recite additional elements that amount to an inventive concept (“significantly more”) than the recited judicial exception. These limitations have been identified and also underlined above, and do not add amount to an inventive concept because the recitations for “a computer-implemented method,” “a computer program product comprising a computer-readable storage medium having a set of instructions stored therein which, when executed by a processor, causes the processor...”, and “a computer system... the computer system comprising: a processor set; and a computer readable storage medium; wherein: the processor set is structured, located, connected, and/or programmed to run program instructions stored on the computer readable storage medium; and the program instructions which, when executed by the processor set, cause the processor set...”, amount to mere instructions to implement an abstract idea on a computer or merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)). The use of generic computer equipment is considered insignificant additional elements. As recited in the MPEP, 2106.07(b), merely adding a generic computer, generic computer components, or a programmed computer to perform generic computer functions does not automatically overcome an eligibility rejection (see Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 134 S. Ct. 2347, 2359-60, 110 USPQ2d 1976, 1984 (2014). See also OIP Techs. v. Amazon.com, 788 F.3d 1359, 1364, 115 USPQ2d 1090, 1093-94).
Dependent claims 2-7, 9-13, and 15-20 contain additional limitations that fall under the abstract idea grouping of a mental process or mathematical concept to describe further details of the abstract data evaluation/processing steps or mathematical calculations, without providing a further integration into a practical application or an inventive concept beyond the abstract data processing/calculations themselves.
3. An invention is not rendered ineligible for patent simply because it involves an abstract concept. Applications of such concepts "to a new and useful end" remain eligible for patent protection (see Alice Corp., 134 S. Ct. at 2354 (quoting Benson, 409 U.S. at 67)). However, "a claim for a new abstract idea is still an abstract idea" (see Synopsys v. Mentor Graphics Corp. _F.3d_, 120 U.S.P.Q. 2d1473 (Fed. Cir. 2016)). There needs to be additional elements or combination of additional elements in the claim to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception or render the claim as a whole to be significantly more than the exception itself in order to demonstrate “integration into a practical application” or an “inventive concept.” For instance, applying the judicial exception with or by use of a particular machine (see MPEP 2106.05(b)), or further physical applications (excluding any post-solution outputting) using the predicted rough shape of a spectrum to drive a transformation (see MPEP 2106.05(c)), change in physical operation or machine function (MPEP 2106.05(a)), or repair/maintenance of a technology or technical process for improved performance (MPEP 2106.05(a)) could provide integration into a practical application to demonstrate an improvement to the technology or technical field.
Allowable Subject Matter
4. Claims 1-20 would be allowable if rewritten or amended to overcome the rejection(s) under 35 U.S.C. 101, set forth in this Office action.
The following is a statement of reasons for the indication of allowable subject matter:
In regards to claim 1, the closest prior art, Martins et al. (US Pat. Pub. 2022/0223235, hereinafter “Martins”) at least teaches a computer-implemented method (Martins paragraph [0052] teaches a computer for executing processing operations and instructions) for inferring spectra data (Martins abstract and paragraph [0045] teach generating an inference model to detect at least one chemical from chemical spectra data), the method comprising:
selecting a first spectra dataset from a training dataset, the training dataset having a specified number of data points, the first spectra dataset having fewer data points than the specified number (Martins paragraph [0022] and [0028] teach selecting a subset (first spectra dataset) of training data from a training dataset to produce trained classification models, where the “subset” of training data by definition will have fewer data points than the number of data points of the training data).5. However, claim 1 contains allowable subject matter because the closest prior art, Martins et al. (US Pat Pub. 2022/0022325) fails to teach or suggest interpolating the first spectra dataset to generate a first interpolated dataset having the specified number of data points; training an inference model to generate a first inferred spectra dataset from the first interpolated dataset; and causing the trained inference model to generate a final spectrum inference dataset from the training dataset to predict a rough shape of a spectrum, in combination with the rest of the claim limitations as claimed and defined by the Applicant. Martins et al. (US Pat. Pub. 2022/0223235) does not provide any teaching or motivation for interpolating the subset of of training data up to a number of points that matches the specified number of points of the training dataset, and then further performing training of an inference model using that interpolated dataset, and then using the trained inference model to generate a final spectrum inference dataset from the training dataset.
Similarly, claim 8 contains allowable subject matter because the closest prior art, Martins et al. (US Pat Pub. 2022/0022325) fails to teach or suggest a computer program product comprising a computer-readable storage medium having a set of instructions stored therein which, when executed by a processor, causes the processor to infer spectra data by: interpolating the first spectra dataset to generate a first interpolated dataset having the specified number of data points; training an inference model to generate a first inferred spectra dataset from the first interpolated dataset; and causing the trained inference model to generate a final spectrum inference dataset from the training dataset to predict a rough shape of a spectrum, in combination with the rest of the claim limitations as claimed and defined by the Applicant.
Similarly, claim 14 contains allowable subject matter because the closest prior art, Martins et al. (US Pat Pub. 2022/0022325) fails to teach or suggest a computer system for inferring spectra data, the computer system comprising: the program instructions which, when executed by the processor set, cause the processor set to infer spectra data by: interpolating the first spectra dataset to generate a first interpolated dataset having the specified number of data points; training an inference model to generate a first inferred spectra dataset from the first interpolated dataset; and causing the trained inference model to generate a final spectrum inference dataset from the training dataset to predict a rough shape of a spectrum, in combination with the rest of the claim limitations as claimed and defined by the Applicant.
6. Dependent claims 2-7 depend from claim 1 and contain allowable subject matter for at least the same reasons as given for claim 1. Dependent claims 9-13 depend from claim 8 and contain allowable subject matter for at least the same reasons as given for claim 8. Dependent claims 15-20 depend from claim 1 and contain allowable subject matter for at least the same reasons as given for claim 14.
Pertinent Art
7. Applicants are directed to consider additional pertinent prior art included on the Notice of References Cited (PTOL 892) attached herewith. The Examiner has pointed out particular references contained in the prior art of record within the body of this action for the convenience of the Applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply. Applicant, in preparing the response, should consider fully the entire reference as potentially teaching all or part of the claimed invention, as well as the context of the of the passage as taught by the prior art or disclosed by the Examiner. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
A. Zhang et al. (US Pat. Pub. 2024/0044801) discloses Autochemometric Scientific Instrument Support Systems.
B. Lucas de Sousa Almeida et al. (US Pat. Pub. 2025/0209369) discloses Knowledge-Based Machine Learning Surrogate Models.
C. Finotello et al. (US Pat. Pub. 2025/0378343) discloses Method for Validating the Predictions of a Supervised Model for Multivariate Quantative Analysis of Spectral Data.
Conclusion
8. Any inquiry concerning this communication or earlier communications from the examiner should be directed to PAUL D LEE whose telephone number is (571)270-1598. The examiner can normally be reached on M to F, 9:30 am to 6 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Arleen Vazquez can be reached at 571-272-2619. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/PAUL D LEE/Primary Examiner, Art Unit 2857 9/25/2026
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