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 .
Response to Amendment
The Amendment filed 11/25/2025 has been entered. Claims 1-20 remain pending in the application.
Allowable Subject Matter
Claims 9, 12 and 15-16 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
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 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 of this title, 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.
Claims 1-7, 10, 13 and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Kumar et al. (US 11969764 B2 hereinafter Kumar) in view of Pruneri et al. (US 11898963 B2 hereinafter Pruneri)
As to independent claim 1, Kumar teaches a method comprising:
receiving, by one or more processors of one or more computing devices, [processors Col. 26 ln. 29-60] training data for training of a machine learning model, wherein the training data comprises: [knowledge base for training stage of neural network (model) Col. 18 ln. 1-11 "creating a knowledge base for later classification of a heterogeneous mixture of material pieces received by the system 100, which may then be sorted by desired classifications. Such a knowledge base may include one or more libraries, wherein each library includes parameters (e.g., neural network parameters) for utilization by the machine learning system in classifying material pieces. For example, one particular library may include parameters configured by the training stage to recognize and classify a particular type or class of material"]
the reference dataset is indicative of first results of a first polymer sample analysis, and [control samples from vision system or sensors of plastic (polymers) used as a library or knowledgebase col. 18 ln. 20-51 "during a training stage, a plurality of material pieces 201 of one or more specific types, classifications, or fractions of material(s), which are the control samples, may be delivered past the vision system and/or one or more sensor system(s) (e.g., by a conveyor system 203) so that the algorithms within the machine learning system detect, extract, and learn what features represent such a type or class of material"]
the sample dataset is indicative of second results of a second polymer sample analysis; and [measured data from multiple analysis for training col. 22 ln. 2-16"When the two objects look the same or very similar and have different chemical compositions, two or more sensor systems may be used to perform the classification (e.g., VIS plus XRF, etc.)."] [knowledge base has many libraries (datasets) Col. 18 ln. 1-11 "a knowledge base may include one or more libraries"]
an indication that features of the sample dataset and the reference dataset are a match; [classifications indicate matching features and are based on knowledge base and sample col. 23 ln. 56-67 "each material piece is identified/classified based on the sensed/detected features"…"assigns the classification with the highest match to each of the material pieces based on such a comparison]
training, by the one or more processors, the machine learning model based on the training data to determine matches between datasets; [trains based on control group and sensor systems col. 22 ln. 1-10 "A vision system (e.g., the vision system 110) may be used to train a machine learning system to identify those fractions. Using this method, the chemical data in the plastics is transferred to visual features, which the machine learning system can learn to classify."], [Col. 23 ln 61-65 "previously generated knowledge base (e.g., generated during a training stage), and assigns the classification with the highest match to each of the material pieces based on such a comparison. The algorithms of the machine learning system may process the captured information/data in a hierarchical manner by using automatically trained filters"]
receiving, by the one or more processors, a new sample dataset; and [extracts features from new piece (new sample dataset) Col. 22-23 ln. 67-65 "detect (extract) information of each of the material pieces (e.g., from the background (e.g., the conveyor belt)"…"compare the extracted features with those stored in a previously generated knowledge base"]
determining, by the one or more processors using the trained machine learning model, that the new sample dataset matches at least one of a new reference dataset, one of the reference datasets of the plurality of pairs of datasets in the training data, or one of the sample datasets of the plurality of pairs of datasets in the training data. [identifies or classifies new material based on matching a bin/class in knowledge base to current features Col. 23 ln. 56-65 "each material piece is identified/classified based on the sensed/detected features"… "a neural network employing one or more machine learning algorithms, which compare the extracted features with those stored in a previously generated knowledge base"]
Kumar does not specifically teach a plurality of pairs of datasets, wherein each of the pairs of datasets comprises a reference dataset and a sample dataset, and wherein: and an indication, for each of the pairs of datasets, that features of the sample dataset and the reference dataset are a match;
However, Pruneri teaches a plurality of pairs of datasets, wherein each of the pairs of datasets comprises a reference dataset and a sample dataset, and wherein: [Training data include pairs of matched (sample) and unmatched (reference) Col. 10-11 ln. 39-4 "neural network may be trained using pairs of spectral measurements, where each spectral measurement comprises spectra data including a plurality of spectral data points, the spectral data points representing relative intensity values at different wavelengths as discussed above. (58) It is advantageous for training purpose to use both similar pairs, i.e. spectral measurements derived from the same gemstone, and also dissimilar pairs"]
an indication, for each of the pairs of datasets, that features of the sample dataset and the reference dataset are a match; [label indicates match (similar) Col. 10-11 ln. 39-4 "Each similar pair (same gemstone) is given the label “1”, and each dissimilar pair (different gemstones) is given the label “0”. Thus, each training data item comprises a labelled measurement pair."]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the identifying systems by Kumar by incorporating the a plurality of pairs of datasets, wherein each of the pairs of datasets comprises a reference dataset and a sample dataset, and wherein: and an indication, for each of the pairs of datasets, that features of the sample dataset and the reference dataset are a match disclosed by Pruneri because both techniques address the same field of machine learning and by incorporating Pruneri into Kumar can reduce the number of measurements needed while improving identification accuracy [Pruneri Col. 1 ln. 22-35]
As to dependent claim 2, the rejection of claim 1 is incorporated. Kumar and Pruneri further teach wherein the new sample data set is determined to match the new reference dataset, the method further comprises receiving, by the one or more processors, the new reference dataset. [Kumar libraries are reference data sets for classifying different types Col. 18 ln. 1-10 " a knowledge base may include one or more libraries, wherein each library includes parameters (e.g., neural network parameters) for utilization by the machine learning system in classifying material pieces. For example, one particular library may include parameters configured by the training stage to recognize and classify a particular type or class of material, or one or more material that fall with a predetermined fraction"]
As to dependent claim 3, the rejection of claim 1 is incorporated. Kumar and Pruneri further teach wherein the new sample dataset is indicative of third results of a third polymer sample analysis. [Kumar sensors or labeling (sample analysis) on pieces to create library for use Col. 20 ln. 43-53 "a labeling/annotation technique whereby as data/information of material pieces are captured by a vision/sensor system, a user inputs a label or annotation that identifies each material piece, which is then used to create the library for use by the machine learning system when classifying material pieces within a heterogenous mixture of material pieces"]
As to dependent claim 4, the rejection of claim 3 is incorporated. Kumar and Pruneri further teach wherein the first polymer sample analysis, the second polymer sample analysis, and the third polymer sample analysis are each a same type of polymer sample analysis. [Kumar labeling Col. 18 ln. 1-10]
As to dependent claim 5, the rejection of claim 4 is incorporated. Kumar and Pruneri further teach wherein the same type of polymer sample analysis is at least one of an infrared spectroscopy analysis, a thermogravimetric analysis, or a differential scanning calorimetry analysis. [Pruneri infrared spectroscopy analysis Col. 5 ln. 40-52 " Fourier transform infrared spectroscopy (FTIR) systems "]
As to dependent claim 6, the rejection of claim 1 is incorporated. Kumar and Pruneri further teach pre-processing the training data prior to training the machine learning model. [Kumar Col. 22-23 ln. 66-5"perform pre-processing of the captured information, which may be utilized to detect (extract) information of each of the material pieces"]
As to dependent claim 7, the rejection of claim 6 is incorporated. Kumar and Pruneri further teach wherein the pre-processing comprises extracting features from the plurality of pairs of datasets. [Kumar extracts information Col. 22-23 ln. 66-5"perform pre-processing of the captured information, which may be utilized to detect (extract) information of each of the material pieces"]
As to dependent claim 10, the rejection of claim 9 is incorporated. Kumar and Pruneri further teach receiving, by the one or more processors, an input from a user via a user interface, wherein the input indicates whether matches for one or more of the sample datasets in the second portion of the total available training data match were successfully determined. [Kumar user input Col. 20 ln. 43-53]
As to independent claim 13, Kumar teaches a system comprising: [system Col. 26 ln. 29-60]
a memory; and [volatile memory Col. 26 ln. 29-60]
at least one processor coupled to the memory, the at least one processor configured to: [processors Col. 26 ln. 29-60]
store, on the memory, a trained machine learning model, wherein: [machine learning on computer system Col. 27 ln. 14-25]
the trained machine learning model was trained with training data comprising: [trained machine learning system (model) from features (training data) col. 22 ln. 1-10 "A vision system (e.g., the vision system 110) may be used to train a machine learning system to identify those fractions. Using this method, the chemical data in the plastics is transferred to visual features, which the machine learning system can learn to classify."], [knowledge base for training Col. 23 ln 61-65 "previously generated knowledge base (e.g., generated during a training stage), and assigns the classification with the highest match to each of the material pieces based on such a comparison. The algorithms of the machine learning system may process the captured information/data in a hierarchical manner by using automatically trained filters"]
the reference dataset is indicative of first results of a first polymer sample analysis, and [control samples from vision system or sensors of plastic (polymers) used as a library or knowledgebase col. 18 ln. 20-51 "during a training stage, a plurality of material pieces 201 of one or more specific types, classifications, or fractions of material(s), which are the control samples, may be delivered past the vision system and/or one or more sensor system(s) (e.g., by a conveyor system 203) so that the algorithms within the machine learning system detect, extract, and learn what features represent such a type or class of material"]
the sample dataset is indicative of second results of a second polymer sample analysis; and [measured data from multiple analysis for training col. 22 ln. 2-16"When the two objects look the same or very similar and have different chemical compositions, two or more sensor systems may be used to perform the classification (e.g., VIS plus XRF, etc.)."] [knowledge base has many libraries (datasets) Col. 18 ln. 1-11 "a knowledge base may include one or more libraries"]
an indication that features of the sample dataset and the reference dataset are a match; [classifications indicate matching features and are based on knowledge base and sample col. 23 ln. 56-67 "each material piece is identified/classified based on the sensed/detected features"…"assigns the classification with the highest match to each of the material pieces based on such a comparison]
receive a new sample dataset; and [extracts features from new piece (new sample dataset) Col. 22-23 ln. 67-65 "detect (extract) information of each of the material pieces (e.g., from the background (e.g., the conveyor belt)"…"compare the extracted features with those stored in a previously generated knowledge base"]
determine, based on the trained machine learning model, that the new sample dataset matches at least one of a new reference dataset, one of the reference datasets of the plurality of pairs of datasets in the training data, or one of the sample datasets of the plurality of pairs of datasets in the training data. [identifies or classifies new material based on matching a bin/class in knowledge base to current features Col. 23 ln. 56-65 "each material piece is identified/classified based on the sensed/detected features"… "a neural network employing one or more machine learning algorithms, which compare the extracted features with those stored in a previously generated knowledge base"]
Kumar does not specifically teach a plurality of pairs of datasets, wherein each of the pairs of datasets comprises a reference dataset and a sample dataset, and wherein: and an indication, for each of the pairs of datasets, that features of the sample dataset and the reference dataset are a match;
However, Pruneri teaches a plurality of pairs of datasets, wherein each of the pairs of datasets comprises a reference dataset and a sample dataset, and wherein: [Training data include pairs of matched (sample) and unmatched (reference) Col. 10-11 ln. 39-4 "neural network may be trained using pairs of spectral measurements, where each spectral measurement comprises spectra data including a plurality of spectral data points, the spectral data points representing relative intensity values at different wavelengths as discussed above. (58) It is advantageous for training purpose to use both similar pairs, i.e. spectral measurements derived from the same gemstone, and also dissimilar pairs"]
an indication, for each of the pairs of datasets, that features of the sample dataset and the reference dataset are a match; [label indicates match (similar) Col. 10-11 ln. 39-4 "Each similar pair (same gemstone) is given the label “1”, and each dissimilar pair (different gemstones) is given the label “0”. Thus, each training data item comprises a labelled measurement pair."]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the identifying systems by Kumar by incorporating the a plurality of pairs of datasets, wherein each of the pairs of datasets comprises a reference dataset and a sample dataset, and wherein: and an indication, for each of the pairs of datasets, that features of the sample dataset and the reference dataset are a match disclosed by Pruneri because both techniques address the same field of machine learning and by incorporating Pruneri into Kumar can reduce the number of measurements needed while improving identification accuracy [Pruneri Col. 1 ln. 22-35]
As to independent claim 18, Kumar teaches a non-transitory computer readable medium having instructions stored thereon that, upon execution by a computing device, cause the computing device to perform operations comprising: [medium with code Col. 27-28 ln. 64-3-]
receiving a dataset, wherein the dataset comprises: [knowledge base (dataset) for training stage of neural network (model) Col. 18 ln. 1-11 "creating a knowledge base for later classification of a heterogeneous mixture of material pieces received by the system 100, which may then be sorted by desired classifications. Such a knowledge base may include one or more libraries, wherein each library includes parameters (e.g., neural network parameters) for utilization by the machine learning system in classifying material pieces. For example, one particular library may include parameters configured by the training stage to recognize and classify a particular type or class of material"]
the reference dataset is indicative of first results of a first polymer sample analysis, and [control samples from vision system or sensors of plastic (polymers) used as a library or knowledgebase col. 18 ln. 20-51 "during a training stage, a plurality of material pieces 201 of one or more specific types, classifications, or fractions of material(s), which are the control samples, may be delivered past the vision system and/or one or more sensor system(s) (e.g., by a conveyor system 203) so that the algorithms within the machine learning system detect, extract, and learn what features represent such a type or class of material"]
the sample dataset is indicative of second results of a second polymer sample analysis; and [measured data from multiple analysis for training col. 22 ln. 2-16"When the two objects look the same or very similar and have different chemical compositions, two or more sensor systems may be used to perform the classification (e.g., VIS plus XRF, etc.)."] [knowledge base has many libraries (datasets) Col. 18 ln. 1-11 "a knowledge base may include one or more libraries"]
an indication that features of the sample dataset and the reference dataset are a match; [classifications indicate matching features and are based on knowledge base and sample col. 23 ln. 56-67 "each material piece is identified/classified based on the sensed/detected features"…"assigns the classification with the highest match to each of the material pieces based on such a comparison]
training the machine learning model based on the training data to determine matches between datasets; [trains based on control group and sensor systems col. 22 ln. 1-10 "A vision system (e.g., the vision system 110) may be used to train a machine learning system to identify those fractions. Using this method, the chemical data in the plastics is transferred to visual features, which the machine learning system can learn to classify."], [Col. 23 ln 61-65 "previously generated knowledge base (e.g., generated during a training stage), and assigns the classification with the highest match to each of the material pieces based on such a comparison. The algorithms of the machine learning system may process the captured information/data in a hierarchical manner by using automatically trained filters"]
receiving a new sample dataset; and [extracts features from new piece (new sample dataset) Col. 22-23 ln. 67-65 "detect (extract) information of each of the material pieces (e.g., from the background (e.g., the conveyor belt)"…"compare the extracted features with those stored in a previously generated knowledge base"]
determining, using the trained machine learning model, that the new sample dataset matches at least one of a new reference dataset, one of the reference datasets of the plurality of pairs of datasets in the training data, or one of the sample datasets of the plurality of pairs of datasets in the training data. [identifies or classifies new material based on matching a bin/class in knowledge base to current features Col. 23 ln. 56-65 "each material piece is identified/classified based on the sensed/detected features"… "a neural network employing one or more machine learning algorithms, which compare the extracted features with those stored in a previously generated knowledge base"]
Kumar does not specifically teach a plurality of pairs of datasets, wherein each of the pairs of datasets comprises a reference dataset and a sample dataset, and wherein: and an indication, for each of the pairs of datasets, that features of the sample dataset and the reference dataset are a match;
However, Pruneri teaches a plurality of pairs of datasets, wherein each of the pairs of datasets comprises a reference dataset and a sample dataset, and wherein: [Training data include pairs of matched (sample) and unmatched (reference) Col. 10-11 ln. 39-4 "neural network may be trained using pairs of spectral measurements, where each spectral measurement comprises spectra data including a plurality of spectral data points, the spectral data points representing relative intensity values at different wavelengths as discussed above. (58) It is advantageous for training purpose to use both similar pairs, i.e. spectral measurements derived from the same gemstone, and also dissimilar pairs"]
an indication, for each of the pairs of datasets, that features of the sample dataset and the reference dataset are a match; [label indicates match (similar) Col. 10-11 ln. 39-4 "Each similar pair (same gemstone) is given the label “1”, and each dissimilar pair (different gemstones) is given the label “0”. Thus, each training data item comprises a labelled measurement pair."]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the identifying systems by Kumar by incorporating the a plurality of pairs of datasets, wherein each of the pairs of datasets comprises a reference dataset and a sample dataset, and wherein: and an indication, for each of the pairs of datasets, that features of the sample dataset and the reference dataset are a match disclosed by Pruneri because both techniques address the same field of machine learning and by incorporating Pruneri into Kumar can reduce the number of measurements needed while improving identification accuracy [Pruneri Col. 1 ln. 22-35]
As to dependent claim 19, the rejection of claim 18 is incorporated. Kumar and Pruneri further teach wherein the first plastic sample analysis is an infrared spectroscopy analysis, a thermogravimetric analysis, or a differential scanning calorimetry analysis. [Pruneri infrared spectroscopy analysis Col. 5 ln. 40-52 " Fourier transform infrared spectroscopy (FTIR) systems "]
As to dependent claim 20, the rejection of claim 18 is incorporated. Kumar and Pruneri further teach wherein the first plastic sample analysis and the second plastic sample analysis are a same type of analysis. [Kumar labeling Col. 18 ln. 1-10]
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Kumar in view of Pruneri as applied in claim 3 above, and further in view of Guzman Cardozo (US 10955362 B2 hereinafter Guzman)
As to dependent claim 8, Kumar and Pruneri teach the method of claim 6 above that is incorporated,
Kumar and Pruneri do not specifically teach wherein the pre-processing comprises at least one of smoothing curves represented in the plurality of pairs of datasets, removing portions of the plurality of pairs of datasets in which events were not detected, or scaling of the plurality of pairs of datasets.
However, Guzman teaches wherein the pre-processing comprises at least one of smoothing curves represented in the plurality of pairs of datasets, removing portions of the plurality of pairs of datasets in which events were not detected, or scaling of the plurality of pairs of datasets. [Guzman scaling and removing outliers Col. 4 ln. 28-47 "input variables are typically scaled to get zero mean and standard deviation equal to one"…"identifying and removing outliers"]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the identifying systems by Kumar and Pruneri by incorporating the wherein the pre-processing comprises at least one of smoothing curves represented in the plurality of pairs of datasets, removing portions of the plurality of pairs of datasets in which events were not detected, or scaling of the plurality of pairs of datasets disclosed by Guzman because all techniques address the same field of machine learning and by incorporating Guzman into Kumar and Pruneri accelerates the quality control while reducing costs in testing polymer [Guzman Col. 1 ln. 18-33]
Claims 11. 14 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Kumar in view of Pruneri as applied in claim 1 and 13 above, and further in view of Riess et al. (US 7271388 B2 hereinafter Riess)
As to dependent claim 11, Kumar and Pruneri teach the method of claim 1 above that is incorporated,
Kumar and Pruneri do not specifically teach sending, by the one or more processors, to a display of a user computing device, data indicative of whether a match for the new sample dataset was identified.
However, Riess teaches sending, by the one or more processors, to a display of a user computing device, data indicative of whether a match for the new sample dataset was identified. [displays hit scores (data indicative of a match) Claim 2 "calculated first, second, and third hit scores are output in human readable form"]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the identifying systems disclosed by Kumar and Pruneri by incorporating the sending, by the one or more processors, to a display of a user computing device, data indicative of whether a match for the new sample dataset was identified disclosed by Riess because all techniques address the same field of polymers and by incorporating Riess into Kumar and Pruneri provides a less extensive and tedious sample preparation for detections [Riess Col. 1 ln. 49-67]
As to dependent claim 14, Kumar and Pruneri teach the method of claim 13 above that is incorporated,
Kumar and Pruneri do not specifically teach send to the display data indicative of whether a match for the new sample dataset was identified.
However, Riess teaches send to the display data indicative of whether a match for the new sample dataset was identified. [displays hit scores (data indicative of a match) Claim 2 "calculated first, second, and third hit scores are output in human readable form"]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the identifying systems disclosed by Kumar and Pruneri by incorporating the send to the display data indicative of whether a match for the new sample dataset was identified disclosed by Riess because all techniques address the same field of polymers and by incorporating Riess into Kumar and Pruneri provides a less extensive and tedious sample preparation for detections [Riess Col. 1 ln. 49-67]
As to dependent claim 17, Kumar and Pruneri teach the method of claim 13 above that is incorporated,
Kumar and Pruneri do not specifically teach wherein the processor is further configured to receive an input from a user via a user interface, wherein the input indicates whether the determination of a match for the new sample dataset is accurate.
However, Riess teaches wherein the processor is further configured to receive an input from a user via a user interface, wherein the input indicates whether the determination of a match for the new sample dataset is accurate. [Riess human verification Col. 3 ln. 63-66 "software packages with today's spectrophotometers provide an analog overlay of the unknown sample with the closest matches so the human user can further verify the match"],
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the identifying systems disclosed by Kumar and Pruneri by incorporating the wherein the processor is further configured to receive an input from a user via a user interface, wherein the input indicates whether the determination of a match for the new sample dataset is accurate disclosed by Riess because all techniques address the same field of polymers and by incorporating Riess into Kumar and Pruneri provides a less extensive and tedious sample preparation for detections [Riess Col. 1 ln. 49-67]
Response to Arguments
Applicant's arguments filed 11/25/2025. In the remark, applicant argues that:
With respect to 101, Claims are not directed to an abstract idea. Claims are integrated into a practical application.
With respect to 103, Kumar and Guzman fail to teach "an indication, for each of the pairs of datasets, that features of the sample dataset and the reference dataset are a match; " as recited by claim 1. See Kumar Col. 23 ln. 56-67.
With respect to 103, Kumar and Guzman fail to teach "determining, by the one or more processors using the trained machine learning model, that the new sample dataset matches at least one of a new reference dataset, one of the reference datasets of the plurality of pairs of datasets in the training data, or one of the sample datasets of the plurality of pairs of datasets in the training data." as recited by claim 1.
As to point (1), Applicant’s arguments with respect to 101, these rejections have been withdrawn.
As to point (2)-(3), Applicant’s arguments with respect to claim 1 have been considered but are moot in view of a new ground of rejection as set forth above of Kumar in view of Pruneri. Further, applicant appears to derive a distinction between the matching of features as recited in claim 1 vs the classifying based on features as in Kumar. Kumar compares extracted features to stored features and outputs a highest match which is an indication that features are a match. Knowledge base features are an example of a reference dataset and the sensed features are an example of a sample dataset. Both new art and the specification now both discuss labels to signify associations (see PGPUB ¶38) “single indication of whether reference and new sample products are a match. In such embodiments, the different analysis result data (e.g., TGA, IR, DSC analysis data) may be labeled”
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Applicant is required under 37 C.F.R. § 1.111(c) to consider these references fully when responding to this action.
Zhang et al. (US 20190265319 A1) teaches a Siamese network is a pair of identical networks that are trained with pairs of inputs that are mapped to a representational space (see ¶70)
It is noted that any citation to specific pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck, 699 F.2d 1331, 1332-33, 216 U.S.P.Q. 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 U.S.P.Q. 275, 277 (C.C.P.A. 1968)).
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/BEAU D SPRATT/Primary Examiner, Art Unit 2143