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 .
This action is responsive to communications: Preliminary amendment filed on 12/15/2023.
Claims 2-4, 8-9, 18, 30-31, 34, 36, 44, 56-57, 62, 69, 74-75, 79-82, and 84 are pending. Claims 2, 34, and 84 are independent. Claims 1, 5-7, 10-17, 19-29, 32-33, 35, 37-43, 45-55, 58-61, 63-68, 70-73, 76-78, 83, 85.
Claim Objections
Claim 84 objected to because of the following informalities: “c) providing” (line 15) and “d) using information” (line 20) should be “e) providing” and “f) using information”. Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 2-4, 8-9, 18, 30-31, 34, 36, 44, 56-57, 62, 69, 74-75, 79-82, and 84 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step2A prong1
Claim 2 recites
a) obtaining optical information from the sample;
b) using the optical information to provide an input dataset that comprises information indicative of the spectral data characteristics associated with the sample;
c) causing a computer processor to access an Al model stored in a computer memory and to use the Al model to carry out steps comprising transforming information comprising the input dataset to provide a reconstructed dataset, said transforming comprising compressing and decompressing a flow of data derived from the information comprising the input dataset, wherein a reconstruction error associated with the input data set and the reconstructed dataset is indicative of whether the sample is in the class; and
d) using information comprising the reconstruction error to determine if the sample is in the class.
The broadest reasonable interpretation of the bolded limitations above are directed to a mental process able to be performed in the human mind by a human using pen and paper. Further, compression and decompression are mathematical concepts. A human can generate an input data set, carry out steps to transform an input dataset, and interpreting a reconstruction error mentally or with pen and paper.
Step 2 A prong 1 (Yes)
Step 2A Prong 2
The additional elements in this claim are “obtaining optical information” and “causing a computer processor to access an AI model”. The element “obtaining optical information” is interpreted as mere data gathering. The element “causing a computer processor to access an AI model” is recited at a high level of generality and thus is a generic computer component performing computer functions. Thus these are mere instructions to apply the exception using generic computer components. See MPEP 2106.05(f).
Step 2B
As explained with respect to Step 2A, the only additional elements ”obtaining optical information” and “causing a computer processor to access an AI model” which at best is mere instructions to apply the abstract ides and cannot provide an inventive concept, even when considered in combination. See MPEP 2106.05(f).
Step 2B (No).
Claim 1 is ineligible.
Claims 3-4, 8-9, 18, and 30-31: These claims only recite further abstract ideas (mental processes) and thus are ineligible.
Step2A prong1
Claim 34 recites
A method of making a system that determines information indicative of whether a sample is in a class, comprising the steps of:
a) providing a training sample set comprising at least one plurality of training samples associated with the class;
b) providing an input dataset for each of the training samples, wherein each input dataset characterizes a corresponding training sample of the training sample set;
c) providing an artificial intelligence (AI) model that transforms the input dataset of each training sample into an associated reconstructed dataset, wherein the transforming comprises compressing a flow of data and decompressing or expanding a flow of data, and wherein a reconstruction error associated with each reconstructed dataset characterizes differences between the input dataset for each training sample and the associated reconstructed dataset; and
d) using information comprising the input datasets, the reconstructed datasets, and the reconstructions errors to train the AI model such that the reconstruction errors are indicative that the training samples are in the class.
The broadest reasonable interpretation of the bolded limitations above are directed to a mental process able to be performed in the human mind by a human using pen and paper. Further compression and decompression are mathematical concepts. A human can generate an input data set from training samples, carry out steps to transform an input dataset, and interpreting a reconstruction error mentally or with pen and paper.
Step 2 A prong 1 (Yes)
Step 2A Prong 2
The additional elements in this claim are “providing a training sample set” and “providing an AI model”. The element “providing a training sample set” is interpreted as mere data gathering. The element “providing an AI model” is recited at a high level of generality and thus is a generic computer component performing computer functions. Thus these are mere instructions to apply the exception using generic computer components. See MPEP 2106.05(f).
Step 2B
As explained with respect to Step 2A, the only additional elements “providing a training sample set” and “providing an AI model” which at best is mere instructions to apply the abstract ides and cannot provide an inventive concept, even when considered in combination. See MPEP 2106.05(f).
Step 2B (No).
Claim 34 is ineligible.
Claims 36, 44, 56-57, 62, 69, 74-75, and 79-82: These claims only recite further abstract ideas (mental processes) and thus are ineligible.
Step2A prong1
Claim 84 recites
A method of making a system that determines information indicative of whether a sample is in a class associated with an authentic taggant system, comprising the steps of.
a) providing the authentic taggant system, wherein the authentic taggant system exhibits spectral characteristics associated with an authentic spectral signature;
b) providing a plurality of training samples, wherein each training sample comprises the authentic taggant system, and wherein the authentic taggant system exhibits spectral characteristics associated with an authentic spectral signature;
c) obtaining the spectral characteristics of the authentic spectral signature from each of the training samples;
d) providing using the spectral characteristics obtained from the training samples to provide an input dataset for each of the training samples, wherein each of the input datasets comprises information indicative of the spectral characteristics exhibited by the authentic taggant system;
c) providing an artificial intelligence (AI) model that compresses and decompresses a flow of data from each of the input datasets to provide an associated, reconstructed dataset, wherein a reconstruction error associated with each of the reconstructed data sets characterizes differences between each input dataset and the associated reconstructed dataset; and
d) using information comprising the input dataset datasets, the reconstructed datasets, and the reconstruction errors to train the AI model such that the reconstruction errors are indicative that the training samples are in the associated class.
The broadest reasonable interpretation of the bolded limitations above are directed to a mental process able to be performed in the human mind by a human using pen and paper. A human can provide an input dataset from training samples, carry out steps to transform an input dataset, and interpreting a reconstruction error mentally or with pen and paper.
Step 2 A prong 1 (Yes)
Step 2A Prong 2
The additional elements in this claim are “providing the authentic taggant system”, “providing a training sample set” and “providing an AI model”. The element “providing a plurality of training sets” is interpreted as mere data gathering. The element “providing an AI model” is recited at a high level of generality and thus is a generic computer component performing computer functions. Thus these are mere instructions to apply the exception using generic computer components. See MPEP 2106.05(f).
Step 2B
As explained with respect to Step 2A, the only additional elements “providing the authentic taggant system”, “providing a training sample set” and “providing an AI model” which at best is mere instructions to apply the abstract ides and cannot provide an inventive concept, even when considered in combination. See MPEP 2106.05(f).
Step 2B (No).
Claim 84 is ineligible.
Claim Rejections - 35 USC § 102
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)(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) 2-4, 8, 18, and 31 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Wang et al. ("Auto-AD: Autonomous Hyperspectral Anomaly Detection Network Based on Fully Convolutional Autoencoder".)
In regards to claim 2, Wang et al. discloses a method for determining whether a sample is in a class, comprising the steps of:
a) obtaining optical information from the sample (Wang et al. pg2 section I para6, obtains optical information from convolutional image generator);
b) using the optical information to provide an input dataset that comprises information indicative of the spectral data characteristics associated with the sample (Wang et al. pg1 section1 para1, Hyperspectral imaging provides rich spectral information, which makes it possible to precisely distinguish different materials);
c) causing a computer processor to access an Al model stored in a computer memory and to use the Al model to carry out steps comprising transforming information comprising the input dataset to provide a reconstructed dataset, said transforming comprising compressing and decompressing a flow of data derived from the information comprising the input dataset, wherein a reconstruction error associated with the input data set and the reconstructed dataset is indicative of whether the sample is in the class (Wang et al. fig. 1 pg2 section I para6, fully convolutional autoencoder (AI model) is used to encode (compress) and decode (decompress) data to identify anomalies (class) from reconstruction error); and
d) using information comprising the reconstruction error to determine if the sample is in the class (Wang et al. pg2 section I para6, As a result, large reconstruction errors indicate potential anomalous pixels).
In regards to claim 3, Wang et al. discloses the method of claim 2, wherein the transforming comprises compressing the input dataset in one or more compression stages to provide compressed data and then decompressing the compressed data in one or more stages to provide the reconstructed dataset (Wang et al. fig. 1 pg3 section III.A para2 the network architecture is composed of an encoder and a decoder).
In regards to claim 4, Wang et al. discloses the method of claim 2, wherein the transforming comprises expanding the input dataset in one or more expansion stages to provide expanded data and then compressing the expanded data in one or more stages to provide the reconstructed dataset (Wang et al. pg5 section III.A para2, the reconstruction errors are fed backwards).
In regards to claim 8, Wang et al. discloses the method of claim 2, wherein said transforming comprises using a trained, specialized Al model associated with the class to transform the input dataset into the reconstructed dataset (Wang et al. pg4 section III.B para1, When the training process is complete, the map of reconstruction errors, which is composed of the reconstruction errors of all the image pixels, can be directly utilized for the anomaly detection).
In regards to claim 18, Wang et al. discloses the method of claim 2, wherein the input dataset comprises intensity values for a spectrum as a function of wavelength over a wavelength range (Wang et al. pg6 section IV.A.1 para1, The spectral range of this data set is 40-2500 nm).
In regards to claim 30, Wang et al. discloses the method of claim 3, wherein the number of compression stages is different than the number of decompression (Wang et al. pg3-4, section III.A. 1-2, encoder contains 15 convolution layers, decoder contains 11 convolution layers).
In regards to claim 31, Wang et al. discloses the method of claim 4, wherein the number of compression stages is different than the number of decompression stages (Wang et al. pg3-4, section III.A. 1-2, encoder contains 15 convolution layers, decoder contains 11 convolution layers).
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.
Claim(s) 9, 34, 44, 56, 57, 62, 69, 74, 75, 79-82 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. in view of Nakachi et al. (US2022/0343691).
In regards to claim 9, Wang et al. discloses the method of claim 2. Wang et al. does not explicitly disclose wherein the method comprises determining whether the sample is in a class of a plurality of classes, and wherein the method further comprises the step of providing a plurality of trained, specialized Al models associated with the plurality of classes, respectively, and wherein step c) is repeated in a manner such that each Al model is used to transform the input dataset into an associated reconstructed dataset and such that a reconstruction error is determined for each of the reconstructed datasets, and wherein step d) comprises using information comprising the reconstruction errors to determine if the sample is in a class associated with any of the trained, specialized Al models.
However Nakachi et al. substantially discloses wherein the method comprises determining whether the sample is in a class of a plurality of classes, and wherein the method further comprises the step of providing a plurality of trained, specialized Al models associated with the plurality of classes, respectively, and wherein step c) is repeated in a manner such that each Al model is used to transform the input dataset into an associated reconstructed dataset and such that a reconstruction error is determined for each of the reconstructed datasets, and wherein step d) comprises using information comprising the reconstruction errors to determine if the sample is in a class associated with any of the trained, specialized Al models (Nakachi et al. para[0114], the class which gives the smallest reconstruction error is determined as a class which the test sample Y belongs).
It would have been obvious to one of ordinary skill in the art before the filing date of the invention to have combined the detection method of Wang et al. with the image recognition method of Nakachi et al. in order to improve computational efficiency in object recognition (Nakachi et al. para[0004]).
In regards to claim 34, Wang et al. discloses a method of making a system that determines information indicative of whether a sample is in a class, comprising the steps of:
a) providing a training sample set comprising a plurality of training samples associated with the class (Wang et al. pg2 section II.B para1, provides samples of background and anomalous part of images);
c) providing an artificial intelligence (AI) model that transforms the input dataset of each training sample into an associated reconstructed dataset, wherein the transforming comprises compressing a flow of data and decompressing or expanding a flow of data, and wherein a reconstruction error associated with each reconstructed dataset characterizes differences between the input dataset for each training sample and the associated reconstructed dataset (Wang et al. fig. 1 pg2 section I para6, fully convolutional autoencoder (AI model) is used to encode (compress) and decode (decompress) data to identify anomalies (class) from reconstruction error).
Wang et al. does not explicitly disclose b) providing an input dataset for each of the training samples, wherein each input dataset characterizes a corresponding training sample of the training sample set;
d) using information comprising the input datasets, the reconstructed datasets, and the reconstructions errors to train the AI model such that the reconstruction errors are indicative that the training samples are in the class.
However Nakachi discloses b) providing an input dataset for each of the training samples, wherein each input dataset characterizes a corresponding training sample of the training sample set (Nakachi et al. para[0120], provides training samples for L different classes);
d) using information comprising the input datasets, the reconstructed datasets, and the reconstructions errors to train the AI model such that the reconstruction errors are indicative that the training samples are in the class (Nakachi et al. para[0150], uses reconstruction error determined by classifiers trained on input data and reconstructed datasets to determine a class).
It would have been obvious to one of ordinary skill in the art before the filing date of the invention to have combined the detection method of Wang et al. with the image recognition method of Nakachi et al. in order to improve computational efficiency in object recognition (Nakachi et al. para[0004]).
In regards to claim 44, Wang et al. as modified by Nakachi et al. discloses the method of claim 34, wherein each of the reconstruction errors is a value derived from an array of comparison values (Wang et al. section II.A para1, C is sample covariate matrix).
In regards to claim 56, Wang et al. as modified by Nakachi et al. discloses the method of claim 34, wherein step d) comprises compressing the input dataset in a plurality of compression stages to provide compressed data and then decompressing the compressed data in a plurality of stages to provide the reconstructed dataset (Wang et al. fig. 1 pg3 section III.A para2 the network architecture is composed of an encoder and a decoder).
In regards to claim 57, Wang et al. as modified by Nakachi et al. discloses the method of claim 34, wherein step d) comprises expanding the input dataset in a plurality of expansion stages to provide expanded data and then compressing the expanded data in a plurality of stages to provide the reconstructed dataset (Wang et al. pg5 section III.A para2, the reconstruction errors are fed backwards).
In regards to claim 62, Wang et al. as modified by Nakachi et al. discloses the method of claim 34, further comprising updating the trained AI model over time (Wang et al. pg3 section II.C para3, updates parameters).
In regards to claim 69, Wang et al. as modified by Nakachi et al. discloses the method of claim 36 wherein the input dataset comprises intensity values for a spectrum as a function of wavelength over a wavelength range (Wang et al. pg6 section IV.A.1 para1, The spectral range of this data set is 40-2500 nm).
In regards to claim 74, Wang et al. as modified by Nakachi et al. discloses the method of claim 34, wherein the characteristics associated with the sample comprise optical information harvested from the sample or a component thereof (Wang et al. pg2 section I para6, obtains optical information from convolutional image generator).
In regards to claim 75, Wang et al. as modified by Nakachi et al. discloses the method of claim 74, wherein the optical information comprises spectral characteristics (Wang et al. pg2 section II. Para7 1) original image contains spectral information).
In regards to claim 79, Wang et al. as modified by Nakachi et al. discloses the method of claim 34 wherein step d) comprises progressively compressing a data flow and then progressively decompressing the data flow (Wang et al. fig. 1 pg3 section III.A para2 the network architecture is composed of an encoder and a decoder).
In regards to claim 80, Wang et al. as modified by Nakachi et al. discloses the method of claim 34 wherein step d) comprises progressively expanding a data flow and then progressively compressing the data flow (Wang et al. pg5 section III.A para2, the reconstruction errors are fed backwards).
In regards to claim 81, Wang et al. as modified by Nakachi et al. discloses the method of claim 56,wherein the number of compression stages is different from the number of decompressing or compressing stages (Wang et al. pg3-4, section III.A. 1-2, encoder contains 15 convolution layers, decoder contains 11 convolution layers).
In regards to claim 82, Wang et al. as modified by Nakachi et al. discloses the method of claim 57, wherein the number of compressing stages is different from the number of decompressing or compressing stages (Wang et al. pg3-4, section III.A. 1-2, encoder contains 15 convolution layers, decoder contains 11 convolution layers).
Claim(s) 36 and 84 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. in view of Nakachi et al. and Rhoads (US11,769,241).
In regards to claim 36, Wang et al. as modified by Nakachi et al. discloses the method of claim 34. wherein the input dataset for each training sample characterizes an authentic taggant signature associated with the class, and wherein step d) comprises training the AI model to transform the input data sets into reconstructed datasets that match the input datasets within an error specification (Rhoads col71 ln 54-63, uses spectral data to identify taggant signature).
Wang et al. does not explicitly disclose wherein the input dataset for each training sample characterizes an authentic taggant signature associated with the class, and wherein step d) comprises training the AI model to transform the input data sets into reconstructed datasets that match the input datasets within an error specification.
However Rhoads discloses wherein the input dataset for each training sample characterizes an authentic taggant signature associated with the class, and wherein step d) comprises training the AI model to transform the input data sets into reconstructed datasets that match the input datasets within an error specification (Rhoads col71 ln 54-63, uses spectral data to identify taggant signature).
It would have been obvious to one of ordinary skill in the art before the filing date of the invention to have combined the detection method of Wang et al. with the identification method of Rhoads et al. in order to rapidly identify materials (Rhoads col2 ln44-58).
In regards to claim 84, Wang et al. discloses a method of making a system that determines information indicative of whether a sample is in a class associated with an authentic taggant system, comprising the steps of:
c) obtaining the spectral characteristics of the authentic spectral signature from each of the training samples (Wang et al. pg2 section I para6, obtains optical information from convolutional image generator);
c) providing an artificial intelligence (AI) model that compresses and decompresses a flow of data from each of the input datasets to provide an associated, reconstructed dataset, wherein a reconstruction error associated with each of the reconstructed data sets characterizes differences between each input dataset and the associated reconstructed dataset (Wang et al. fig. 1 pg2 section I para6, fully convolutional autoencoder (AI model) is used to encode (compress) and decode (decompress) data to identify anomalies (class) from reconstruction error).
Wang et al. does not explicitly disclose d) using the spectral characteristics obtained from the training samples to provide an input dataset for each of the training samples, wherein each of the input datasets comprises information indicative of the spectral characteristics exhibited by the authentic taggant system;
d) using information comprising the input datasets, the reconstructed datasets, and the reconstruction errors to train the AI model such that the reconstruction errors are indicative that the training samples are in the class.
However Nakachi et al. discloses d) using the spectral characteristics obtained from the training samples to provide an input dataset for each of the training samples, wherein each of the input datasets comprises information indicative of the spectral characteristics exhibited by the authentic taggant system (Nakachi et al. para[0120], provides training samples for L different classes);
d) using information comprising the input datasets, the reconstructed datasets, and the reconstruction errors to train the AI model such that the reconstruction errors are indicative that the training samples are in the class (Nakachi et al. para[0150], uses reconstruction error determined by classifiers trained on input data and reconstructed datasets to determine a class).
It would have been obvious to one of ordinary skill in the art before the filing date of the invention to have combined the detection method of Wang et al. with the image recognition method of Nakachi et al. in order to improve computational efficiency in object recognition (Nakachi et al. para[0004]).
Wang et al. does not explicitly disclose a) providing the authentic taggant system, wherein the authentic taggant system exhibits spectral characteristics associated with an authentic spectral signature;
b) providing a plurality of training samples, wherein each training sample comprises the authentic taggant system, and wherein the authentic taggant system exhibits spectral characteristics associated with an authentic spectral signature.
However Rhoads discloses a) providing the authentic taggant system, wherein the authentic taggant system exhibits spectral characteristics associated with an authentic spectral signature (Rhoads col31 ln17-35, receive spectral characterization of various materials and taggants);
b) providing a plurality of training samples, wherein each training sample comprises the authentic taggant system, and wherein the authentic taggant system exhibits spectral characteristics associated with an authentic spectral signature (Rhoads col31 ln17-35, trains machine learning model with library of feature vectors associated with certain families of materials).
It would have been obvious to one of ordinary skill in the art before the filing date of the invention to have combined the detection method of Wang et al. with the identification method of Rhoads et al. in order to rapidly identify materials (Rhoads col2 ln44-58).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Smith et al. (US 8,330,122) teaches using emission spectra to identify taggants and authenticate materials.
Sallee (US 2022/0129758) teaches using an autoencoder to classify content.
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/N.H/Examiner, Art Unit 2141
/MATTHEW ELL/Supervisory Patent Examiner, Art Unit 2141
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