DETAILED ACTION
1. This communication is in response to the Application No. 18/409,017 filed on January 10, 2024 in which Claims 1-13 are presented for examination.
Notice of Pre-AIA or AIA Status
2. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
3. The information disclosure statement submitted on 01/10/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
4. The listing of references in the specification (See, for example, Applicant’s specification Pg. 16 lines 18-22) is not a proper information disclosure statement. 37 CFR 1.98(b) requires a list of all patents, publications, or other information submitted for consideration by the Office, and MPEP § 609.04(a) states, "the list may not be incorporated into the specification but must be submitted in a separate paper." Therefore, unless the references have been cited by the examiner on form PTO-892, they have not been considered.
Examiner’s Remarks
5. Examiner notes that Independent Claim 1 recites “a retraining system” and Independent Claim 10 recites “an extraction device” – however, the currently drafted device and system do not comprise corresponding structure to perform the claimed functions (See 35 U.S.C. 112(f) claim interpretation below). Applicant’s specification Pg. 16 lines 25-30 describe how the retraining system and inspection device may include a computer comprising processor and memory – Applicant is encouraged to amend the claims to add these details, in order to avoid any potential 35 U.S.C. 101 software per se rejection.
Claim Interpretation
6. The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
7. The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
8. This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier.
Such claim limitation(s) is/are:
“first extractor” in Claims 1-9
“clustering part” in Claims 1-9
“second extractor” in Claims 1-9
“updater” in Claims 1-9
“inspection device” in Claim 9
“extraction device” in Claim 10
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 101
9. 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.
10. Claims 1-13 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding Claim 1:
Step 1: Claim 1 is a system type claim. Therefore, Claims 1-9 are directed to either a process, machine, manufacture, or composition of matter.
2A Prong 1: If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation by mathematical calculation but for the recitation of generic computer components, then it falls within the “Mathematical Concepts” grouping of abstract ideas.
[…] extracting a plurality of first feature data from an intermediate layer of the neural network […] (mental process – other than reciting “a first extractor”, extracting a plurality of first feature data from an intermediate layer of the neural network may be performed manually by a user observing/analyzing the first training data from the intermediate layer and accordingly using judgement/evaluation to extract (with the aid of pen and paper) a plurality of first feature data based on said analysis. For example, with respect to image classification, a user may observe/analyze a relevant image (image of an animal) inputted into the network and accordingly use judgement/evaluation to extract visual features (species, anatomical features, color, etc.) based on said analysis)
[…] extracting a plurality of second feature data from the intermediate layer […] (mental process – other than reciting “a first extractor”, extracting a plurality of second feature data from an intermediate layer of the neural network may be performed manually by a user observing/analyzing the second training data from the intermediate layer and accordingly using judgement/evaluation to extract (with the aid of pen and paper) a plurality of second feature data based on said analysis. For example, with respect to image classification, a user may observe/analyze a relevant image (image of an animal) inputted into the network and accordingly use judgement/evaluation to extract visual features (species/sub species, anatomical features, color, etc.) based on said analysis)
[…] splitting the plurality of first feature data and the plurality of second feature data into a plurality of classes (mental process – other than reciting “a clustering part”, splitting the plurality of first feature data and the plurality of second feature data into a plurality of classes may be performed manually by a user observing/analyzing the first/second feature data and accordingly using judgement/evaluation to split/partition (with the aid of pen and paper) the plurality of first/second feature data into a plurality of classes. For example, in the preceding example of image classification with animal images, the user may observe/analyze the sets of features and accordingly use judgement/evaluation to split the data into classes (i.e., based on species, sub species, etc.) based on said analysis)
[…] extracting a portion of the plurality of first feature data and a portion of the plurality of second feature data from the plurality of classes to reduce differences between ratios of data quantities among the plurality of classes (mental process – other than reciting “ a second extractor”, extracting a portion of the plurality of first/second feature data from the plurality of classes to reduce differences between ratios of data quantities among the plurality of classes may be performed manually by a user observing/analyzing the first/second feature data and the relevant ratios of data quantities among the plurality of classes and accordingly using judgement/evaluation to extract a portion of the first/second feature data which reduces differences between ratios of data quantities among the plurality of classes)
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
a retraining system retraining a neural network, the neural network being trained by using a plurality of first training data as input data and by using a plurality of first output results as output data, the retraining system comprising: […] (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: high level recitation of training a machine learning model with previously determined data without significantly more)
a first extractor […] (recited at a high-level of generality (i.e., as a generic extractor without significantly more) such that it amounts to no more than mere instructions to apply the exception using generic computer components)
[…] inputting the plurality of first training data to the neural network […] (Adding insignificant extra-solution activity to the judicial exception – see MPEP 2106.05(g))
[…] inputting a plurality of second training data to the neural network […] the plurality of second training data being new (Adding insignificant extra-solution activity to the judicial exception – see MPEP 2106.05(g))
a clustering part […] (recited at a high-level of generality (i.e., as a generic part without significantly more) such that it amounts to no more than mere instructions to apply the exception using generic computer components)
a second extractor […] (recited at a high-level of generality (i.e., as a generic extractor without significantly more) such that it amounts to no more than mere instructions to apply the exception using generic computer components)
an updater updating the neural network by using a portion of the plurality of first training data and a portion of the plurality of second training data, the portion of the plurality of first training data corresponding to the portion of the plurality of first feature data, the portion of the plurality of second training data corresponding to the portion of the plurality of second feature data (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: high level recitation of training/retraining a machine learning model with previously determined data without significantly more)
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
a retraining system retraining a neural network, the neural network being trained by using a plurality of first training data as input data and by using a plurality of first output results as output data, the retraining system comprising: […] (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: high level recitation of training a machine learning model with previously determined data without significantly more. This cannot provide an inventive concept)
a first extractor […] (mere instructions to apply the exception using generic computer components cannot provide an inventive concept)
[…] inputting the plurality of first training data to the neural network […] (MPEP 2106.05(d)(II) indicates that merely “Receiving or transmitting data over a network” is a well-understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed limitation is well-understood, routine, conventional activity is supported under Berkheimer)
[…] inputting a plurality of second training data to the neural network […] the plurality of second training data being new (MPEP 2106.05(d)(II) indicates that merely “Receiving or transmitting data over a network” is a well-understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed limitation is well-understood, routine, conventional activity is supported under Berkheimer)
a clustering part […] (mere instructions to apply the exception using generic computer components cannot provide an inventive concept)
a second extractor […] (mere instructions to apply the exception using generic computer components cannot provide an inventive concept)
an updater updating the neural network by using a portion of the plurality of first training data and a portion of the plurality of second training data, the portion of the plurality of first training data corresponding to the portion of the plurality of first feature data, the portion of the plurality of second training data corresponding to the portion of the plurality of second feature data (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: high level recitation of training/retraining a machine learning model with previously determined data without significantly more. This cannot provide an inventive concept)
For the reasons above, Claim 1 is rejected as being directed to an abstract idea without significantly more. This rejection applies equally to dependent claims 2-9. The additional limitations of the dependent claims are addressed below.
Regarding Claim 2:
Step 2A Prong 1:
See the rejection of Claim 1 above, which Claim 2 depends on.
Step 2A Prong 2 & Step 2B:
the plurality of classes includes a first class and a second class (Field of Use – limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception does not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application; in this case specifying that the plurality of classes includes a first and second class does not integrate the exception into a practical application nor amount to significantly more – See MPEP 2106.05(h))
a ratio of a data quantity extracted from the second class by the second extractor to a data quantity extracted from the first class by the second extractor is closer to 1 than a ratio of a data quantity split into the second class to a data quantity split into the first class (Field of Use – limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception does not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application; in this case specifying that a ratio of data quantity extracted from the second class to a data quantity extracted from the first class is closer to 1 than a ratio of a data quantity split into the second class to a data quantity split into the first class does not integrate the exception into a practical application nor amount to significantly more – See MPEP 2106.05(h))
Accordingly, under Step 2A Prong 2 and Step 2B, these additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limits on practicing the abstract idea, as discussed above in the rejection of claim 1.
Regarding Claim 3:
Step 2A Prong 1: See the rejection of Claim 1 above, which Claim 3 depends on.
[…] extracts a uniform number of data from each of the plurality of classes (mental process – other than reciting “the second extractor”, extracting a uniform number of data from each of the plurality of classes may be performed manually by a user observing/analyzing the plurality of classes and accordingly using judgement/evaluation to extract (with the aid of pen and paper) a uniform number of data from each of the plurality of classes)
Step 2A Prong 2 & Step 2B:
the second extractor […] (mere instructions to apply the exception using generic computer components cannot provide an inventive concept)
Accordingly, under Step 2A Prong 2 and Step 2B, these additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limits on practicing the abstract idea, as discussed above in the rejection of claim 1.
Regarding Claim 4:
Step 2A Prong 1:
See the rejection of Claim 1 above, which Claim 4 depends on.
Step 2A Prong 2 & Step 2B:
the extracting of the portion of the plurality of first feature data and the portion of the plurality of second feature data by the second extractor and the updating of the neural network by the updater are alternately repeated (Field of Use – limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception does not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application; in this case specifying that the extracting and the updating are alternately repeated does not integrate the exception into a practical application nor amount to significantly more – See MPEP 2106.05(h))
Accordingly, under Step 2A Prong 2 and Step 2B, these additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limits on practicing the abstract idea, as discussed above in the rejection of claim 1.
Regarding Claim 5:
Step 2A Prong 1:
See the rejection of Claim 1 above, which Claim 5 depends on.
Step 2A Prong 2 & Step 2B:
the neural network performs one of classification, image generation, segmentation, object detection, or regression (Field of Use – limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception does not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application; in this case specifying that the neural network performs one of classification, image generation, segmentation, object detection, or regression does not integrate the exception into a practical application nor amount to significantly more – See MPEP 2106.05(h))
Accordingly, under Step 2A Prong 2 and Step 2B, these additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limits on practicing the abstract idea, as discussed above in the rejection of claim 1.
Regarding Claim 6:
Step 2A Prong 1:
See the rejection of Claim 1 above, which Claim 6 depends on.
Step 2A Prong 2 & Step 2B:
the neural network includes an input layer, the intermediate layer, and an output layer, the intermediate layer includes a plurality of layers, and the first extractor extracts the plurality of first feature data and the plurality of second feature data from a layer among the plurality of layers positioned at the output layer side. (Field of Use – limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception does not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application; in this case specifying the layers of the neural network and the extraction performed by the first extractor does not integrate the exception into a practical application nor amount to significantly more – See MPEP 2106.05(h))
Accordingly, under Step 2A Prong 2 and Step 2B, these additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limits on practicing the abstract idea, as discussed above in the rejection of claim 1.
Regarding Claim 7:
Step 2A Prong 1: See the rejection of Claim 1 above, which Claim 7 depends on.
[…] classifies the image into one of a plurality of classifications (mental process – other than reciting “neural network”, classifying the image into one of a plurality of classifications may be performed manually by a user observing/analyzing the image (for example, an image of an animal) and accordingly using judgement/evaluation to classify the image into one of a plurality of classifications (for example, dog, cat, bird, etc.))
[…] clusters the plurality of first training data and the plurality of second training data into the classifications (mental process – other than reciting “the clustering part”, clustering the plurality of first training data and the plurality of second training data into the classifications may be performed manually by a user observing/analyzing the first/second training data and accordingly using judgement/evaluation to cluster/group the training data into the various classifications based on said analysis)
Step 2A Prong 2 & Step 2B:
the neural network receives an input of an image […] (MPEP 2106.05(d)(II) indicates that merely “Receiving or transmitting data over a network” is a well-understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed limitation is well-understood, routine, conventional activity is supported under Berkheimer)
[…] the clustering part acquires a classification result of the plurality of first training data and a classification result of the plurality of second training data from the neural network […] (MPEP 2106.05(d)(II) indicates that merely “Receiving or transmitting data over a network” is a well-understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed limitation is well-understood, routine, conventional activity is supported under Berkheimer)
Accordingly, under Step 2A Prong 2 and Step 2B, these additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limits on practicing the abstract idea, as discussed above in the rejection of claim 1.
Regarding Claim 8:
Step 2A Prong 1:
See the rejection of Claim 7 above, which Claim 8 depends on.
Step 2A Prong 2 & Step 2B:
the neural network includes a convolutional layer and a fully connected layer, and the first extractor respectively extracts, as the plurality of first feature data and the plurality of second feature data, outputs from the fully connected layer when the plurality of first training data and the plurality of second training data are input to the neural network (Field of Use – limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception does not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application; in this case specifying the neural network includes a convolutional layer and a fully connected layer and the first extractor extracts outputs from the fully connected layer when the training data is input into the network does not integrate the exception into a practical application nor amount to significantly more – See MPEP 2106.05(h))
Accordingly, under Step 2A Prong 2 and Step 2B, these additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limits on practicing the abstract idea, as discussed above in the rejection of claim 1.
Regarding Claim 9:
Step 2A Prong 1: See the rejection of Claim 7 above, which Claim 9 depends on.
See the rejection of Claim 7 above, which Claim 9 depends on.
Step 2A Prong 2 & Step 2B:
imaging device acquiring an image of an article (MPEP 2106.05(d)(II) indicates that merely “Receiving or transmitting data over a network” is a well-understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed limitation is well-understood, routine, conventional activity is supported under Berkheimer)
inspection device inputting the image acquired by the imaging device to the neural network updated by the retraining system according to claim 7 […] (MPEP 2106.05(d)(II) indicates that merely “Receiving or transmitting data over a network” is a well-understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed limitation is well-understood, routine, conventional activity is supported under Berkheimer)
[…] inspecting the article based on a classification result output from the neural network (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner' s note: high level recitation of “inspecting an article” using “an inspection device” without significantly more. This cannot provide an inventive concept)
Accordingly, under Step 2A Prong 2 and Step 2B, these additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limits on practicing the abstract idea, as discussed above in the rejection of claim 1.
Regarding Claim 10:
Step 1: Claim 10 is a device type claim. Therefore, Claims 10 is directed to either a process, machine, manufacture, or composition of matter.
2A Prong 1: If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation by mathematical calculation but for the recitation of generic computer components, then it falls within the “Mathematical Concepts” grouping of abstract ideas.
extract a portion of the plurality of first feature data and a portion of the plurality of second feature data from the plurality of classes to reduce differences between ratios of data quantities among the plurality of classes (mental process – extracting a portion of the plurality of first feature data and a portion of the plurality of second feature data from the plurality of classes may be performed manually by a user observing/analyzing the plurality of classes and the ratios of data quantities among the plurality of classes and accordingly using judgement/evaluation to extract a portion of the plurality of first/second feature data from the plurality of classes to reduce differences between ratios of the data quantities, based on said analysis)
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
an extraction device extracting data for retraining a neural network, the neural network being trained by using a plurality of first training data as input data and by using a plurality of first output results as output data […] (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: high level recitation of training a machine learning model with previously determined data without significantly more)
acquire a result of splitting a plurality of first feature data and a plurality of second feature data into a plurality of classes, the plurality of first feature data being respectively extracted from an intermediate layer of the neural network when the plurality of first training data is input to the neural network, the plurality of second feature data being respectively extracted from the intermediate layer when a plurality of second training data is input to the neural network, the plurality of second training data being new (Adding insignificant extra-solution activity to the judicial exception – see MPEP 2106.05(g))
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
an extraction device extracting data for retraining a neural network, the neural network being trained by using a plurality of first training data as input data and by using a plurality of first output results as output data […] (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: high level recitation of training a machine learning model with previously determined data without significantly more. This cannot provide an inventive concept)
acquire a result of splitting a plurality of first feature data and a plurality of second feature data into a plurality of classes, the plurality of first feature data being respectively extracted from an intermediate layer of the neural network when the plurality of first training data is input to the neural network, the plurality of second feature data being respectively extracted from the intermediate layer when a plurality of second training data is input to the neural network, the plurality of second training data being new (MPEP 2106.05(d)(II) indicates that merely “Receiving or transmitting data over a network” is a well-understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed limitation is well-understood, routine, conventional activity is supported under Berkheimer)
For the reasons above, Claim 10 is rejected as being directed to an abstract idea without significantly more.
Independent Claim 11 recites substantially the same limitations as Claim 1, in the form of a method, including generic computer components. The claim is also directed to performing mental processes/mathematical calculations without significantly more, therefore it is rejected under the same rationale.
For the reasons above, Claim 11 is rejected as being directed to an abstract idea without significantly more. This rejection applies equally to dependent claims 12-13. The additional limitations of the dependent claims are addressed below.
Claim 12 recites substantially the same limitations as Claim 2, in the form of a method, including generic computer components/generic machine learning components. The claim is also directed to performing mental processes/mathematical calculations without significantly more, therefore it is rejected under the same rationale.
Claim 13 recites substantially the same limitations as Claim 11, in the form of a non-transitory computer-readable storage medium, including generic computer components/generic machine learning components. The claim is also directed to performing mental processes/mathematical calculations without significantly more, therefore it is rejected under the same rationale.
Claim Rejections - 35 USC § 103
11. 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.
12. Claims 1-13 are rejected under 35 U.S.C. 103 as being unpatentable over Kim et al. (hereinafter Kim) (US PG-PUB 20200193207), in view of Chakravorty et al. (hereinafter Chakravorty) (US PG-PUB 20230306079).
Regarding Claim 1, Kim teaches a retraining system retraining a neural network (Kim, Par. [0096-0097], “The device may train the at least one feature extraction layer 620 by applying images included in at least one similarity cluster in a database as input data to the neural network 600. For example, the device may obtain feature information of an object in an image by selecting at random and combining attribute information of the image, extracted from each of the first through fifth layers 611 through 615. The device may repeatedly train the at least one feature extraction layer 620 until the obtained feature information of the object satisfies a preset accuracy”, therefore, a training/retraining system for retraining a neural network is disclosed – See Par. [0088] which also describes how the training may be iterative/repeated), the neural network being trained by using a plurality of first training data as input data and by using a plurality of first output results as output data (Kim, Par. [0040], “Meanwhile, the device according to an embodiment may apply attribute information of the image, extracted from at least one of the plurality of layers 111 through 121, as input data of at least one feature extraction layer 140. The device may identify the object based on the feature information of the object, obtained as a result of inputting the attribute information of the image to the at least one feature extraction layer 140.”, thus, the neural network is trained by using a plurality of training data as input data (See Par. [0083]) and by using a plurality of first results as output data), the retraining system comprising:
a first extractor inputting the plurality of first training data to the neural network and respectively extracting a plurality of first feature data from an intermediate layer of the neural network (Kim, Par. [0040], “Meanwhile, the device according to an embodiment may apply attribute information of the image, extracted from at least one of the plurality of layers 111 through 121, as input data of at least one feature extraction layer 140. The device may identify the object based on the feature information of the object, obtained as a result of inputting the attribute information of the image to the at least one feature extraction layer 140. Herein, the feature information may be obtained in the form of a vector capable of indicating a representative attribute of the object.”, therefore, a first extractor (first feature extraction layer) inputs the plurality of first training data (images) to the neural network and respectively extracts a plurality of first feature data (attribute information) from an intermediate layer of the neural network), and inputting a plurality of second training data to the neural network and respectively extracting a plurality of second feature data from the intermediate layer (Kim, Par. [0105], “Meanwhile, in FIG. 7, the device may merge first feature information determined in the first feature extraction layer 742 based on the attribute information extracted from the fifth layer 715 with second feature information determined in the second feature extraction layer 744 based on the attribute information extracted from the ninth layer 719, and determine a merging result as the feature information of the object.”, therefore, a plurality of second training data (images) may be inputted to the neural network to respectively extract a plurality of second feature data from the intermediate layer (feature extraction layer)), the plurality of second training data being new (Kim, Par. [0081], “The training data selector 530 may select an image needed for learning from pre-processed data. The selected image may be provided to the model learner 540. The training data selector 530 may select an image needed for learning from pre-processed data, according to a preset criterion. For example, the training data selector 530 may select a first image and a second image that are included in a first similarity cluster and a third image included in a second similarity cluster.”, therefore, the plurality of second training data may be new data selected by the training data selector);
a clustering part splitting the plurality of first feature data and the plurality of second feature data into a plurality of classes (Kim, Par. [0019-0020], “According to an embodiment, the database may include a first similarity cluster and a second similarity cluster which are generated as a result of classifying the plurality of images according to similarity. According to an embodiment, the method may further include extracting a first image and a second image that are included in the first similarity cluster and a third image included in the second similarity cluster and training the feature extraction layer such that a difference between feature information of the first image and feature information of the second image is equal to or less than a first threshold value and a difference between the feature information of the first image and feature information of the third image is equal to or greater than a second threshold value.”, thus, a clustering part which splits the plurality of first and second feature data into a plurality of classes is disclosed);
a second extractor extracting a portion of the plurality of first feature data and a portion of the plurality of second feature data from the plurality of classes to reduce differences (Kim, Par. [0020], “According to an embodiment, the method may further include extracting a first image and a second image that are included in the first similarity cluster and a third image included in the second similarity cluster and training the feature extraction layer such that a difference between feature information of the first image and feature information of the second image is equal to or less than a first threshold value and a difference between the feature information of the first image and feature information of the third image is equal to or greater than a second threshold value.”, therefore, a second extractor may extract a portion of the plurality of first feature data and a portion of the plurality of second feature data from the plurality of classes, in order to reduce differences between classes. However, Kim does not explicitly disclose reducing the differences between ratios of data quantities among the plurality of classes) between ratios of data quantities among the plurality of classes (See introduction of Chakravorty reference below); and
an updater updating the neural network by using a portion of the plurality of first training data and a portion of the plurality of second training data, the portion of the plurality of first training data corresponding to the portion of the plurality of first feature data, the portion of the plurality of second training data corresponding to the portion of the plurality of second feature data (Kim, Par. [0118], “The model updater 950 may provide information about evaluation to the model learner 540 described with reference to FIG. 5 to update a species classification network included in the neural network or a parameter of at least one feature extraction layer based on evaluation with respect to an object identification result provided by the recognition result provider 940.”, thus, a model updater for updating the neural network by using a portion of first training data (first feature data) and a portion of the plurality of second training data (second feature data) is disclosed – see also Kim Figure 9 which depicts how the features are extracted and then the model is correspondingly updated based on said features).
As disclosed above, Kim teaches a second extractor extracting a portion of the plurality of first feature data and a portion of the plurality of second feature data from the plurality of classes to reduce differences. However, Kim does not explicitly disclose wherein the extractor performs the extracting to reduce differences between ratios of data quantities among the plurality of classes.
However, Chakravorty teaches a second extractor extracting a portion of the plurality of first feature data and a portion of the plurality of second feature data from the plurality of classes to reduce differences between ratios of data quantities among the plurality of classes (Chakravorty, Par. [0091], “The goal of down-sampling is to reduce the severity of class imbalance in a dataset by removing data representing the majority class. In the case of time-series data, a subset of time series that belong to the majority class may be removed from the dataset to thereby adjust the ratio of the number of time series that belong to the minority class to the number of time series that belong to the majority class.” & Par. [0093], “The tolerance value represents an acceptable difference between the actual ratio and the desired ratio. In other words, the objective of algorithm 400 is to adjust the dataset, such that the ratio of the number of time series that belong to the minority class to the number of time series that belong to the majority class is greater than the threshold value minus the tolerance value and less than the threshold value plus the tolerance value”, therefore, a portion of a plurality of first/second feature data (data representing the majority/minority class) is extracted to reduce differences between ratios of data quantities among the plurality of classes)
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the retraining system of claim 1, as disclosed by Kim to include a second extractor extracting a portion of the plurality of first feature data and a portion of the plurality of second feature data from the plurality of classes to reduce differences between ratios of data quantities among the plurality of classes, as disclosed by Chakravorty. One of ordinary skill in the art would have been motivated to make this modification to reduce the severity of class imbalance in a dataset, hence improving prediction accuracy and system performance (Chakravorty, Par. [0091], “The goal of down-sampling is to reduce the severity of class imbalance in a dataset by removing data representing the majority class. In the case of time-series data, a subset of time series that belong to the majority class may be removed from the dataset to thereby adjust the ratio of the number of time series that belong to the minority class to the number of time series that belong to the majority class.”).
Regarding Claim 2, Kim in view of Chakravorty teaches the retraining system according to claim 1, wherein
the plurality of classes includes a first class and a second class (Kim, Par. [0038], “The neural network 100 may include a species classification network 110 including a plurality of layers 111 through 121 and at least one feature extraction layer 140. Herein, the species classification network 110 may be used to determine a category of an object included in the image 10. Herein, the species classification network 110 may be described as a learning network model.”, thus, the plurality of classes may include a plurality of categories, including a first class and second class, used for object identification/classification), and
a ratio of a data quantity extracted from the second class by the second extractor to a data quantity extracted from the first class by the second extractor is closer to 1 than a ratio of a data quantity split into the second class to a data quantity split into the first class (Chakravorty, Par. [0097], “As a result of the removal of these time series, belonging to the majority class, the output dataset may be more balanced than the input dataset. For example, in the case of binary classes, the ratio of the number of time series that belong to the minority class to the number of time series that belong to the majority class in the output dataset will be closer to 1.0 than in the input dataset.”, therefore, a ratio of a data quality extracted from the second class to a data quantity extracted from the first class (extracted data) is closer to 1 than a ratio of a data quantity split into the second class to a data quantity split into the first class (input dataset)).
The reasons of obviousness have been noted in the rejection of Claim 1 above and applicable herein.
Regarding Claim 3, Kim in view of Chakravorty teaches the retraining system according to claim 1, wherein
the second extractor extracts a uniform number of data from each of the plurality of classes (Chakravorty, Par. [0005], “Accordingly, systems, methods, and non-transitory computer-readable media are disclosed for reducing class imbalance in a training dataset for machine learning. An objective of embodiments is to increase the proportion of time series of a minority class in a training dataset by generating synthetic time series of the minority class and/or reducing the number of time series of the majority class, to thereby reduce class imbalance in the training dataset (e.g., within a tolerance).”, thus, the number of data extracted from each of the plurality of classes is balanced, such that a uniform number of data is extracted from each class).
The reasons of obviousness have been noted in the rejection of Claim 1 above and applicable herein.
Regarding Claim 4, Kim in view of Chakravorty teaches the retraining system according to claim 1, wherein
the extracting of the portion of the plurality of first feature data and the portion of the plurality of second feature data by the second extractor and the updating of the neural network by the updater are alternately repeated (Kim, Par. [0097], “For example, the device may obtain feature information of an object in an image by selecting at random and combining attribute information of the image, extracted from each of the first through fifth layers 611 through 615. The device may repeatedly train the at least one feature extraction layer 620 until the obtained feature information of the object satisfies a preset accuracy. For example, the device may select a first image 10 and a second image 12 that are included in the first similarity cluster and a third image 14 included in the second similarity cluster. The device may train the at least one feature extraction layer 620 such that a difference between feature information of the first image 10 and feature information of the second image 12 is equal to or less than a first threshold value and a difference between the feature information of the first image 10 and feature information of the third image 14 is equal to or greater than a second threshold value.”, therefore, the feature extraction and the updating of the network may be alternately repeated, in order to train the neural network).
Regarding Claim 5, Kim in view of Chakravorty teaches the retraining system according to claim 1, wherein
the neural network performs one of classification, image generation, segmentation, object detection, or regression (Kim, Par. [0038], “The neural network 100 may include a species classification network 110 including a plurality of layers 111 through 121 and at least one feature extraction layer 140. Herein, the species classification network 110 may be used to determine a category of an object included in the image 10. Herein, the species classification network 110 may be described as a learning network model.”, thus, the neural network may perform one of image/object classification).
Regarding Claim 6, Kim in view of Chakravorty teaches the retraining system according to claim 1, wherein
the neural network includes an input layer, the intermediate layer, and an output layer, the intermediate layer includes a plurality of layers (Kim, Par. [0039], “The device may extract attribute information of an image by using the plurality of layers 111 through 121 included in the species classification network 110. The attribute information of the image may include a color, an edge, a polygon, a saturation, a brightness, a color temperature, blur, sharpness, contrast, etc., but these are merely examples, and the attribute information of the image is not limited thereto. The device may determine a category of the object based on attribute information 130 of the image, finally extracted from the last layer 121 among the plurality of layers 111 through 121.”, thus, the neural network includes an input layer, intermediate layer comprising a plurality of layers, and an output layer – this is better depicted by Figures 1 & 7), and
the first extractor extracts the plurality of first feature data and the plurality of second feature data from a layer among the plurality of layers positioned at the output layer side (Kim, Par. [0040], “Meanwhile, the device according to an embodiment may apply attribute information of the image, extracted from at least one of the plurality of layers 111 through 121, as input data of at least one feature extraction layer 140. The device may identify the object based on the feature information of the object, obtained as a result of inputting the attribute information of the image to the at least one feature extraction layer 140. Herein, the feature information may be obtained in the form of a vector capable of indicating a representative attribute of the object.”, thus, the first extractor may extract the plurality of first and second feature data from a layer among the plurality of layers positioned at the output layer side (feature extraction layer – depicted by Figure 1)).
Regarding Claim 7, Kim in view of Chakravorty teaches the retraining system according to claim 1, wherein
the neural network receives an input of an image and classifies the image into one of a plurality of classifications (Kim, Claim 1, “obtaining an image comprising an object; extracting at least one piece of attribute information of the image by using a plurality of layers included in a learning network model for determining a category of the object; […] identifying the object based on a result of comparing the obtained feature information with feature information of each of a plurality of previously stored images,”, thus, the neural network receives an input of an image and classifies the image into one of a plurality of classifications), and
the clustering part:
acquires a classification result of the plurality of first training data and a classification result of the plurality of second training data from the neural network (Kim, Claim 2, “The method of claim 1, wherein the database comprises a first similarity cluster and a second similarity cluster which are generated as a result of classifying the plurality of images according to similarity.”, thus, classification results of first/second training data are acquired), and
clusters the plurality of first training data and the plurality of second training data into the classifications (Kim, Claim 3, “extracting a first image and a second image that are included in the first similarity cluster and a third image included in the second similarity cluster; and training the feature extraction layer such that a difference between feature information of the first image and feature information of the second image is equal to or less than a first threshold value and a difference between the feature information of the first image and feature information of the third image is equal to or greater than a second threshold value.”, therefore, the plurality of first/second training data are clustered into the classifications).
Regarding Claim 8, Kim in view of Chakravorty teaches the retraining system according to claim 7, wherein
the neural network includes a convolutional layer and a fully connected layer (Kim, Par. [0066], “The neural network module may include a plurality of layers included in a species classification network and at least one feature extraction layer. The plurality of layers included in the species classification network may one or more instructions that detect at least one piece of attribute information from each image and abstract the detected at least one piece of attribute information. For example, the first through Nth layers 111 through 121 may include a convolutional layer including one or more instructions that extract attribute information of each image from the image and/or a pooling layer including one or more instructions that determine a representative value from an extracted image attribute.”, thus, the neural network includes a convolutional layer and fully connected layer (disclosed by Par. [0046] which describes how the second layer may be connected with the first)), and
the first extractor respectively extracts, as the plurality of first feature data and the plurality of second feature data, outputs from the fully connected layer when the plurality of first training data and the plurality of second training data are input to the neural network (Kim, Par. [0067], “The at least one feature extraction layer 140 may include a convolutional layer including one or more instructions that extract feature information representing an object in an image based on at least one piece of attribute information obtained from the species classification network 110 and/or a pooling layer including one or more instructions that determine a representative value from an extracted image attribute.”, therefore, the first extractor may extract, as the plurality of first feature data and plurality of second feature data, outputs from the fully connected layer (second layer) when the training data is input into the network).
Regarding Claim 9, Kim in view of Chakravorty teaches an inspection system, comprising:
an imaging device acquiring an image of an article (Kim, Claim 1, “obtaining an image comprising an object;”, thus, an image of an article (object) is acquired – Kim Par. [0042] also describes how the device may comprise a digital camera or cellular phone (imaging device)); and
an inspection device inputting the image acquired by the imaging device to the neural network updated by the retraining system according to claim 7 (See the rejection of Claim 7 above), and inspecting the article based on a classification result output from the neural network (Kim, Par. [0007], “The disclosure relates to a method, performed by a device, of identifying an object, the method including obtaining an image including an object, extracting at least one piece of attribute information of the image by using a plurality of layers included in a learning network model for determining a category of the object, obtaining feature information representing the object, by combining attribute information extracted from at least some layers among the plurality of layers by using at least one feature extraction layer, and identifying the object based on a result of comparing the obtained feature information with feature information of each of a plurality of previously stored images, wherein a parameter of each of the at least one feature extraction layer is configured according to a training result based on a database including a plurality of images.”, thus, an inspection device (computer, as supported by Applicant’s specification Pg. 16 lines 25-30) may input the image acquired by the imaging device to the neural network updated by the retraining system and correspondingly inspect/identify the article (object) based on a classification result output from the network).
Regarding Claim 10, Kim teaches an extraction device extracting data for retraining a neural network (Kim, Par. [0007], “The disclosure relates to a method, performed by a device, of identifying an object, the method including obtaining an image including an object, extracting at least one piece of attribute information of the image by using a plurality of layers included in a learning network model for determining a category of the object, obtaining feature information representing the object, by combining attribute information extracted from at least some layers among the plurality of layers by using at least one feature extraction layer, and identifying the object based on a result of comparing the obtained feature information with feature information of each of a plurality of previously stored images, wherein a parameter of each of the at least one feature extraction layer is configured according to a training result based on a database including a plurality of images.”, thus, an extraction device for extracting data for training/retraining a neural network is disclosed), the neural network being trained by using a plurality of first training data as input data and by using a plurality of first output results as output data (Kim, Par. [0040], “Meanwhile, the device according to an embodiment may apply attribute information of the image, extracted from at least one of the plurality of layers 111 through 121, as input data of at least one feature extraction layer 140. The device may identify the object based on the feature information of the object, obtained as a result of inputting the attribute information of the image to the at least one feature extraction layer 140.”, thus, the neural network is trained by using a plurality of training data as input data (See Par. [0083]) and by using a plurality of first results as output data), the extraction device being configured to:
acquire a result of splitting a plurality of first feature data and a plurality of second feature data into a plurality of classes (Kim, Par. [0019-0020], “According to an embodiment, the database may include a first similarity cluster and a second similarity cluster which are generated as a result of classifying the plurality of images according to similarity. According to an embodiment, the method may further include extracting a first image and a second image that are included in the first similarity cluster and a third image included in the second similarity cluster and training the feature extraction layer such that a difference between feature information of the first image and feature information of the second image is equal to or less than a first threshold value and a difference between the feature information of the first image and feature information of the third image is equal to or greater than a second threshold value.”, thus, a clustering part which splits the plurality of first and second feature data into a plurality of classes is disclosed), the plurality of first feature data being respectively extracted from an intermediate layer of the neural network when the plurality of first training data is input to the neural network (Kim, Par. [0040], “Meanwhile, the device according to an embodiment may apply attribute information of the image, extracted from at least one of the plurality of layers 111 through 121, as input data of at least one feature extraction layer 140. The device may identify the object based on the feature information of the object, obtained as a result of inputting the attribute information of the image to the at least one feature extraction layer 140. Herein, the feature information may be obtained in the form of a vector capable of indicating a representative attribute of the object.”, therefore, a first extractor (first feature extraction layer) inputs the plurality of first training data (images) to the neural network and respectively extracts a plurality of first feature data (attribute information) from an intermediate layer of the neural network), the plurality of second feature data being respectively extracted from the intermediate layer when a plurality of second training data is input to the neural network (Kim, Par. [0105], “Meanwhile, in FIG. 7, the device may merge first feature information determined in the first feature extraction layer 742 based on the attribute information extracted from the fifth layer 715 with second feature information determined in the second feature extraction layer 744 based on the attribute information extracted from the ninth layer 719, and determine a merging result as the feature information of the object.”, therefore, a plurality of second training data (images) may be inputted to the neural network to respectively extract a plurality of second feature data from the intermediate layer (feature extraction layer)), the plurality of second training data being new (Kim, Par. [0081], “The training data selector 530 may select an image needed for learning from pre-processed data. The selected image may be provided to the model learner 540. The training data selector 530 may select an image needed for learning from pre-processed data, according to a preset criterion. For example, the training data selector 530 may select a first image and a second image that are included in a first similarity cluster and a third image included in a second similarity cluster.”, therefore, the plurality of second training data may be new data selected by the training data selector); and
extract a portion of the plurality of first feature data and a portion of the plurality of second feature data from the plurality of classes to reduce differences (Kim, Par. [0020], “According to an embodiment, the method may further include extracting a first image and a second image that are included in the first similarity cluster and a third image included in the second similarity cluster and training the feature extraction layer such that a difference between feature information of the first image and feature information of the second image is equal to or less than a first threshold value and a difference between the feature information of the first image and feature information of the third image is equal to or greater than a second threshold value.”, therefore, a second extractor may extract a portion of the plurality of first feature data and a portion of the plurality of second feature data from the plurality of classes, in order to reduce differences between classes. However, Kim does not explicitly disclose reducing the differences between ratios of data quantities among the plurality of classes) between ratios of data quantities among the plurality of classes (See introduction of Chakravorty reference below).
As disclosed above, Kim teaches a extract a portion of the plurality of first feature data and a portion of the plurality of second feature data from the plurality of classes to reduce differences. However, Kim does not explicitly disclose extract a portion of the plurality of first feature data and a portion of the plurality of second feature data from the plurality of classes to reduce differences between ratios of data quantities among the plurality of classes.
However, Chakravorty teaches extract a portion of the plurality of first feature data and a portion of the plurality of second feature data from the plurality of classes to reduce differences between ratios of data quantities among the plurality of classes (Chakravorty, Par. [0091], “The goal of down-sampling is to reduce the severity of class imbalance in a dataset by removing data representing the majority class. In the case of time-series data, a subset of time series that belong to the majority class may be removed from the dataset to thereby adjust the ratio of the number of time series that belong to the minority class to the number of time series that belong to the majority class.” & Par. [0093], “The tolerance value represents an acceptable difference between the actual ratio and the desired ratio. In other words, the objective of algorithm 400 is to adjust the dataset, such that the ratio of the number of time series that belong to the minority class to the number of time series that belong to the majority class is greater than the threshold value minus the tolerance value and less than the threshold value plus the tolerance value”, therefore, a portion of a plurality of first/second feature data (data representing the majority/minority class) is extracted to reduce differences between ratios of data quantities among the plurality of classes)
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the extraction device of claim 10, as disclosed by Kim to include extract a portion of the plurality of first feature data and a portion of the plurality of second feature data from the plurality of classes to reduce differences between ratios of data quantities among the plurality of classes, as disclosed by Chakravorty. One of ordinary skill in the art would have been motivated to make this modification to reduce the severity of class imbalance in a dataset, hence improving prediction accuracy and system performance (Chakravorty, Par. [0091], “The goal of down-sampling is to reduce the severity of class imbalance in a dataset by removing data representing the majority class. In the case of time-series data, a subset of time series that belong to the majority class may be removed from the dataset to thereby adjust the ratio of the number of time series that belong to the minority class to the number of time series that belong to the majority class.”).
Regarding Claim 11, Kim in view of Chakravorty teaches a retraining method of retraining a neural network (Kim, Par. [0013], “FIG. 6 is a diagram for describing a method, performed by a device, of training a neural network to identify an object in an image, according to an embodiment.”, therefore, methods of training/retraining a neural network are disclosed), the neural network being trained by using a plurality of first training data as input data and by using a plurality of first output results as output data (Kim, Par. [0040], “Meanwhile, the device according to an embodiment may apply attribute information of the image, extracted from at least one of the plurality of layers 111 through 121, as input data of at least one feature extraction layer 140. The device may identify the object based on the feature information of the object, obtained as a result of inputting the attribute information of the image to the at least one feature extraction layer 140.”, thus, the neural network is trained by using a plurality of training data as input data (See Par. [0083]) and by using a plurality of first results as output data), the retraining method comprising: […]
The rest of the claim language in Claim 11 recites substantially the same limitations as Claim 1, in the form of a method, therefore it is rejected under the same rationale.
The reasons of obviousness have been noted in the rejection of Claim 1 above and applicable herein.
Claim 12 recites substantially the same limitations as Claim 2, in the form of a method, therefore it is rejected under the same rationale.
Regarding Claim 13, Kim in view of Chakravorty teaches a non-transitory computer-readable storage medium configured to store a program, the program causing a computer to execute the retraining method (Kim, Par. [0075], “Meanwhile, at least one of the data learner 410 or the data recognizer 420 may be implemented with a software module. When at least one of the data learner 410 and the data recognizer 420 may be implemented with a software module (or a program module including an instruction), the software module may be stored in a non-transitory computer-readable medium.”, therefore, a non-transitory computer-readable storage medium configured to store a program and cause a computer to execute the retraining method is disclosed) according to claim 11 (See the rejection of Claim 11 above).
Conclusion
13. The prior art made of record and not relied upon is considered pertinent to Applicant’s disclosure:
Chari et al. (US PG-PUB 20130097103) discloses generating balanced and class-independent training data from an unlabeled data set, in order to improve convergence of the iterative training process and reduce class imbalances.
14. Any inquiry concerning this communication or earlier communications from the examiner should be directed to Devika S Maharaj whose telephone number is (571)272-0829. The examiner can normally be reached Monday - Thursday 8:30am - 5:30pm.
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.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Alexey Shmatov can be reached at (571)270-3428. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/DEVIKA S MAHARAJ/Examiner, Art Unit 2123
Prosecution Insights