DETAILED ACTION
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 .
Continued Examination under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 2/27/2026 has been entered.
Claim Interpretation
Several of the claims use conditional elements. However, this is most important to note for method claim 9 (“…when the user operation for changing the label given to the selected data displayed on the display unit is input…”) since a method can be interpreted to not activate the condition, thereby representing a broader scope than other identical claims from different statutory categories. See MPEP 2111.04(II) for more information.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1, 3, 5, 7-10 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claim 1 is a device claim. Claim 8 is a device claim. Claim 9 is a method claim. Therefore, claims 1, 8, and 9 are directed to either a process, machine, manufacture or composition of matter.
With respect to Claim 1:
Step 2A Prong 1:
derive, for each training data, a first feature quantity of the training data represented in a feature space having predetermined dimensions, on the basis of a model trained using the training data so that target data is classified into any one of the first label and the second label, and the training data, and derive, for each training candidate data, a second feature quantity of the training candidate data represented in the feature space, on the basis of the model and the at least one training candidate data (mental process – user can manually derive a feature quantity of the training data represented in a feature space having predetermined dimensions and derive a feature quantity of the training candidate data represented in the feature space)
calculate, for each training candidate data, at least one of a first distance, the first distance being a distance between the training candidate data and the first data in the feature space, and a second distance, the second distance being a distance between the training candidate data and the second data in the feature space, on the basis of the first feature quantity of the training data and the second feature quantity of the at least one training candidate data (mental process – user can manually calculate a first distance and a second distance)
calculate an evaluation value by using the first distance and the second distance for each training candidate data (mental process – user can manually calculate an evaluation value by using the first distance and the second distance for each training candidate data)
select data to be added as the training data from among the at least one training candidate data, on the basis of the evaluation value for each piece of training candidate data (mental process – user can manually select data to be added as the training data from among the at least one piece of training candidate data)
change a label given to the selected data when the user operation for changing the label given to the selected data displayed on the display unit is input (mental process – user can manually change a label given to the selected data)
Step 2A Prong 2: This judicial exception is not integrated into a practical application. Additional elements:
a central processing unit (CPU) (mere instructions to apply the exception using a generic computer component)
one or more memories storing a program for execution by the CPU, wherein the program is configured to cause the CPU to (mere instructions to apply the exception using a generic computer component)
acquire training data having first data given a first label and second data given a second label, wherein the first label corresponds to a good product label and the second label corresponds to a defective product label (Adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g))
acquire at least one training candidate data given any one of the first label and the second label, wherein a plurality of training candidate data comprises the at least one training candidate data (Adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g))
display the selected data on a display unit (mere instructions to apply the exception using a generic computer component)
receive an input of a user operation (Adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g))
wherein the program is further configured to cause the CPU to (mere instructions to apply the exception using a generic computer component): train an updated model with the selected data having the changed label or a maintained label (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)), and obtain recognition results using the updated model (Adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g))
Step 2B: The claim does not include additional elements considered individually and in combination that are sufficient to amount to significantly more than the judicial exception. Additional elements:
a central processing unit (CPU) (mere instructions to apply the exception using a generic computer component)
one or more memories storing a program for execution by the CPU, wherein the program is configured to cause the CPU to (mere instructions to apply the exception using a generic computer component)
acquire training data having first data given a first label and second data given a second label, wherein the first label corresponds to a good product label and the second label corresponds to a defective product label (MPEP 2106.05(d)(II) indicate that merely “storing and retrieving information in memory” or “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 storing step is well-understood, routine, conventional activity is supported under Berkheimer)
acquire at least one training candidate data given any one of the first label and the second label, wherein a plurality of training candidate data comprises the at least one training candidate data (MPEP 2106.05(d)(II) indicate that merely “storing and retrieving information in memory” or “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 storing step is well-understood, routine, conventional activity is supported under Berkheimer)
display the selected data on a display unit (mere instructions to apply the exception using a generic computer component)
receive an input of a user operation (Adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g))
wherein the program is further configured to cause the CPU to (mere instructions to apply the exception using a generic computer component): train an updated model with the selected data having the changed label or a maintained label (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), and obtain recognition results using the updated model (MPEP 2106.05(d)(II) indicate that merely “storing and retrieving information in memory” or “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 storing step is well-understood, routine, conventional activity is supported under Berkheimer)
Conclusion: The claim is not patent eligible.
Claims 8 and 9 are rejected on the same grounds as claim 1.
Regarding Claim 3: The limitation(s), as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation(s) in the mind. That is, other than the additional elements, nothing in the claim limitation(s) precludes the step from practically being performed in the mind.
The limitation(s) encompasses the user manually increase a probability of the training candidate data being selected from the at least one training candidate data such that the smaller the second distance, the higher the probability of selection.
The limitation(s) includes the additional elements of wherein the program is further configured to cause the CPU to.
These judicial exceptions are not integrated into a practical application. The additional element(s) of wherein the program is further configured to cause the CPU to are recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element(s) of wherein the program is further configured to cause the CPU to amount to no more than mere instructions to apply the exception using a generic computer component or operation. Mere instructions to apply an exception using a generic computer component or operation cannot provide an inventive concept. Accordingly, the claims are not patent eligible.
Regarding Claim 5: The limitation(s), as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation(s) in the mind. That is, other than the additional elements, nothing in the claim limitation(s) precludes the step from practically being performed in the mind.
The limitation(s) includes the additional elements of a graphic controller configured to, based on the program, display the data selected.
These judicial exceptions are not integrated into a practical application. The additional element(s) of a graphic controller configured to, based on the program, display the data selected are recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element(s) of a graphic controller configured to, based on the program, display the data selected amount to no more than mere instructions to apply the exception using a generic computer component or operation. Mere instructions to apply an exception using a generic computer component or operation cannot provide an inventive concept. Accordingly, the claims are not patent eligible.
Regarding Claim 7: The limitation(s), as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation(s) in the mind. That is, other than the additional elements, nothing in the claim limitation(s) precludes the step from practically being performed in the mind.
The limitation(s) encompasses the user manually when it is determined that data to be added as the training data from among the at least one training candidate data does not exist, on the basis of the first distance and the second distance.
The limitation(s) includes the additional elements of wherein the CPU causes the graphic controller to display a determination result.
These judicial exceptions are not integrated into a practical application. The additional element(s) of wherein the CPU causes the graphic controller to display a determination result recite adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g). Accordingly, this does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element(s) of wherein the CPU causes the graphic controller to display a determination result recite merely “storing and retrieving information in memory” or “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) (MPEP 2106.05(d)(II)). Thereby, a conclusion that the claimed storing step is well-understood, routine, conventional activity is supported under Berkheimer. Accordingly, the claims are not patent eligible.
Regarding Claim 10: The limitation(s), as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation(s) in the mind. That is, other than the additional elements, nothing in the claim limitation(s) precludes the step from practically being performed in the mind.
The limitation(s) includes the additional elements of a non-transitory computer-readable media including a training support program for causing a computer to function as the training support device according to claim 1.
These judicial exceptions are not integrated into a practical application. The additional element(s) of a non-transitory computer-readable media including a training support program for causing a computer to function as the training support device according to claim 1 are recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element(s) of a non-transitory computer-readable media including a training support program for causing a computer to function as the training support device according to claim 1amount to no more than mere instructions to apply the exception using a generic computer component or operation. Mere instructions to apply an exception using a generic computer component or operation cannot provide an inventive concept. Accordingly, the claims are not patent eligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1, 3, 5, 7-10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Avasarala et al. (hereinafter Avasarala) U.S. Patent 10,025,950 in view of Rousseeuw, Silhouettes: a graphical aid to the interpretation and validation of cluster analysis, further in view of Wu et al (hereinafter Wu), Refinement Approach to Handling Model Misfit in Text Categorization, further in view of Zhou et al. (hereinafter Zhou), CLASSIFICATION OF SURFACE DEFECTS ON STEEL SHEET USING CONVOLUTIONAL NEURAL NETWORKS.
Regarding Claim 1, Avasarala discloses a training support device comprising:
a central processing unit (CPU) [“Processor” Fig. 3]; and
one or more memories storing a program for execution by the CPU [“Memory” Fig. 3],
wherein the program is configured to cause the CPU to:
acquire training data [“collects images 410” col. 7 line 50; Fig. 4] having first data given a first label and second data given a second label ["supervised learning processes, based on sets of labeled ground truth data" col. 7-8 lines 67-1; "cluster labeling engine 445 labels the images associated with the feature vectors in the identified clusters" col. 9 lines 3-5], wherein the first label corresponds to a good product label and the second label corresponds to a defective product label;
acquire at least one training candidate data given any one of the first label and the second label, wherein a plurality of training candidate data comprises the at least one training candidate data ["cluster labeling engine 445 labels the images associated with the feature vectors in the identified clusters" col. 9, lines 3-5; "cluster labeling engines in accordance with several embodiments of the invention use the labeled images as a part of an expanded training dataset to re-train or tune the weights of a feature extraction engine to allow for tighter and more compact clusters" col. 9 lines 23-27];
derive, for each training data, a first feature quantity of the training data represented in a feature space having predetermined dimensions ["analyze one or more of image files 334 to extract feature vectors" col. 7, lines 5-6; Examiner Note: specification paragraph 21 states, "The feature quantity is a vector representing a feature of an image."; "two clusters of images based on a distance between the feature vectors of the images in a feature space" col. 8, lines 36-37; Fig. 4], on the basis of a model trained using the training data so that target data is classified into any one of the first label and the second label, and the training data ["Image processing application" Fig. 4; "cluster labeling engine 445 labels the images associated with the feature vectors in the identified clusters" col. 9, lines 3-5], and derive, for each training candidate data, a second feature quantity of the training candidate data represented in the feature space ["analyze one or more of image files 334 to extract feature vectors" col. 7, lines 5-6; Examiner Note: specification paragraph 21 states, "The feature quantity is a vector representing a feature of an image."; "two clusters of images based on a distance between the feature vectors of the images in a feature space" col. 8, lines 36-37; Fig. 4], on the basis of the model and the at least one training candidate data ["Image processing application" Fig. 4];
calculate, for each training candidate data, at least one of a first distance, the first distance being a distance between the training candidate data and the first data in the feature space, and a second distance, the second distance being a distance between the training candidate data and the second data in the feature space, on the basis of the first feature quantity of the training data and the second feature quantity of the at least one training candidate data ["analyze one or more of image files 334 to extract feature vectors" col. 7, lines 5-6];
select data to be added as the training data from among the at least one training candidate data ["cluster labeling engines in accordance with several embodiments of the invention use the labeled images as a part of an expanded training dataset to re-train or tune the weights of a feature extraction engine to allow for tighter and more compact clusters" col. 9, lines 23-27], on the basis of the evaluation value for each training candidate data;
display the selected data on a display unit ["Client device 200 includes an image gathering module 210, annotation engine 220, and display 230." Col. 6, lines 32-34; "user interfaces are generated in which at least one image representative of each similar cluster is displayed and a user is asked to confirm that the images are images of the previously annotated object or person" col. 4, lines 26-30];
receive an input of a user operation ["the client devices receive the clustered images to present a user interface that allows the client device to receive, from a human annotator, annotations that identify the entities within the images." Col. 6, lines 57-60] and
change a label given to the selected data when the user operation for changing the label given to the selected data displayed on the display unit is input ["labeling of the neighboring clusters in accordance with several embodiments of the invention is performed through interactions with the user, such as through a user interface" col. 15, lines 32-35; “shows selection controls 710 that allow a user to select and/or deselect images to be associated with the recommended primary identity” col. 14 lines 44-46; Fig. 7].
However, Avasarala fails to explicitly disclose calculate, for each training candidate data, at least one of a first distance, the first distance being a distance between the training candidate data and the first data in the feature space, and a second distance, the second distance being a distance between the training candidate data and the second data in the feature space, on the basis of the first feature quantity of the training data and the second feature quantity of the at least one training candidate data;
calculate an evaluation value by using the first distance and the second distance for each training candidate data;
select data to be added as the training data from among the at least one training candidate data, on the basis of the evaluation value for each training candidate data.
Rousseeuw discloses calculate, for each training candidate data, at least one of a first distance, the first distance being a distance between the training candidate data and the first data in the feature space ["compute the Euclidean distance d(i, j) between any objects i and j" §1 12; Fig. 1], and a second distance, the second distance being a distance between the training candidate data and the second data in the feature space ["compute the Euclidean distance d(i, j) between any objects i and j" §1 12; Fig. 1], on the basis of the first feature quantity of the training data and the second feature quantity of the at least one training candidate data;
calculate an evaluation value by using the first distance and the second distance for each training candidate data [“A” for object I in Fig. 1];
select data to be added as the training data from among the at least one training candidate data, on the basis of the evaluation value for each training candidate data ["compute the Euclidean distance d(i, j) between any objects i and j" §1 12; Fig. 1; "-1 ≤ s(i) ≤ 1" §2 14; Note: s(i) defines the dissimilarity between objects].
It would have been obvious to one having ordinary skill in the art, having the teachings of Avasarala and Rousseeuw before him before the effective filing date of the claimed invention, to modify the device of Avasarala to incorporate the distance measurements of Rousseeuw.
Given the advantage of determining the similarity/dissimilarity between objects to determine if an object has been misclassified, one having ordinary skill in the art would have been motivated to make this obvious modification.
However, Avasarala fails to explicitly disclose wherein the program is further configured to cause the CPU to:
train an updated model with the selected data having the changed label or a maintained label, and
obtain recognition results using the updated model.
Wu discloses wherein the program is further configured to cause the CPU to:
train an updated model with the selected data having the changed label or a maintained label ["learning algorithm to concentrate on documents that have been misclassified most often previously" §2 14], and
obtain recognition results using the updated model ["refined model is able to improve the naïve Bayesian or Rocchio classifier's prediction performance" Abstract].
It would have been obvious to one having ordinary skill in the art, having the teachings of Avasarala, Rousseeuw, and Wu before him before the effective filing date of the claimed invention, to modify the combination to incorporate adaptive sampling for training data of Wu.
Given the advantage of improved classification accuracy, one having ordinary skill in the art would have been motivated to make this obvious modification.
However, Avasarala fails to explicitly disclose wherein the first label corresponds to a good product label and the second label corresponds to a defective product label.
Zhou discloses wherein the first label corresponds to a good product label and the second label corresponds to a defective product label ["As shown in Figure 5, the dataset of surface defects of hot-rolled steel sheet comprises seven kinds of typical surface defects (Crazing, Folding, Inclusion, Patch, Pitted Surface, Rolled-in Scale and Scratch) and one kind of zero surface defects (Original)." §3.1 1; Fig. 5].
It would have been obvious to one having ordinary skill in the art, having the teachings of Avasarala, Rousseeuw, Wu, and Zhou before him before the effective filing date of the claimed invention, to modify the combination to incorporate the field of use for healthy and defective products of Zhou.
Given the advantage of accurately detecting product defects at manufacturing, one having ordinary skill in the art would have been motivated to make this obvious modification.
Regarding Claim 3, Avasarala, Rousseeuw, Wu, and Zhou disclose the training support device according to claim 1.
However, Avasarala fails to explicitly disclose wherein the program is further configured to cause the CPU to increase a probability of the training candidate data being selected from the at least one training candidate data such that the smaller the second distance, the higher the probability of selection.
Rousseeuw discloses wherein the program is further configured to cause the CPU to increase a probability of the training candidate data being selected from the at least one training candidate data such that the smaller the second distance, the higher the probability of selection [“When s(i) is at its largest (that is, s(i) close to 1) this implies that the 'within' dissimilarity a(i) is much smaller than the smallest 'between' dissimilarity b(i). Therefore, we can say that i is 'well-clustered'”.” §2 ¶7; Note: one having ordinary skill in the art knows that properly classified data should be used to retrain in order to define the class].
It would have been obvious to one having ordinary skill in the art, having the teachings of Avasarala, Rousseeuw, Wu, and Zhou before him before the effective filing date of the claimed invention, to modify the combination to incorporate the selection of misclassified data of Rousseeuw.
Given the advantage of correcting misclassifications and improving model accuracy, one having ordinary skill in the art would have been motivated to make this obvious modification.
Regarding Claim 5, Avasarala, Rousseeuw, Wu, and Zhou disclose the training support device according to claims 1. Avasarala further comprising a graphic controller configured to, based on the program, display the data selected [“Client device 200 includes an image gathering module 210, annotation engine 220, and display 230.” Col. 6, lines 32-34; “user interfaces are generated in which at least one image representative of each similar cluster is displayed and a user is asked to confirm that the images are images of the previously annotated object or person” col. 4, lines 26-30].
Regarding Claim 7, Avasarala, Rousseeuw, Wu, and Zhou disclose the training support device according to claim 5. Avasarala further discloses wherein the CPU causes the graphic controller to display a determination result [““Client device 200 includes an image gathering module 210, annotation engine 220, and display 230.” Col. 6, lines 32-34].
However, Avasarala fails to explicitly disclose when it is determined that data to be added as the training data from among the at least one training candidate data does not exist, on the basis of the first distance and the second distance.
Rousseeuw discloses when it is determined that data to be added as the training data from among the at least one training candidate data does not exist, on the basis of the first distance and the second distance [“The worst situation takes place when s(i) is close to -1. Then a(i) is much larger than b(i), so i lies on the average much closer to B than to A. Therefore it would have seemed much more natural to assign object i to cluster B, so we can almost conclude that this object has been 'misclassified'.” §2 ¶8].
It would have been obvious to one having ordinary skill in the art, having the teachings of Avasarala, Rousseeuw, Wu, and Zhou before him before the effective filing date of the claimed invention, to modify the combination to incorporate the selection of misclassified data of Rousseeuw.
Given the advantage of correcting misclassifications and improving model accuracy, one having ordinary skill in the art would have been motivated to make this obvious modification.
Claim 8 is rejected on the same grounds as claim 1.
Claim 9 is rejected on the same grounds as claim 1.
Claim 10 is rejected on the same grounds as claim 1. Avasarala further discloses a non-transitory computer-readable media including a training support program [“Client devices 110-114 in this example include a mobile phone 110, a desktop computer 112, and a smart TV 114. Client devices may refer to any of a number of devices associated with a user including mobile devices, laptop computers, desktop computers, storage devices, smart appliances, and/or any other device as appropriate to the requirements of a given application.” Col. 5, lines 12-18].
Examiner’s Note
The Examiner respectfully requests of the Applicant in preparing responses, to fully consider the entirety of the reference(s) as potentially teaching all or part of the claimed invention. It is noted, REFERENCES ARE RELEVANT AS PRIOR ART FOR ALL THEY CONTAIN. “The use of patents as references is not limited to what the patentees describe as their own inventions or to the problems with which they are concerned. They are part of the literature of the art, relevant for all they contain.” In re Heck, 699 F.2d 1331, 1332-33, 216 USPQ 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 USPQ 275, 277 (CCPA 1968)). A reference may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art, including non-preferred embodiments (see MPEP 2123). The Examiner has cited particular locations in the reference(s) as applied to the claim(s) above for the convenience of the Applicant. Although the specified citations are representative of the teachings of the art and are applied to the specific limitations within the individual claim(s), typically other passages and figures will apply as well.
Additionally, any claim amendments for any reason should include remarks indicating clear support in the originally filed specification.
Response to Arguments
Regarding the §101 rejections, Applicant's arguments have been fully considered but have been found unpersuasive. Applicant argues that the claims provide a technical improvement and therefore a practical application. Examiner disagrees for at least the following reasons.
Any alleged improvement is to the abstract idea. The functionality of the model does not change. Rather, only better data is given to the model. This better data is derived from the abstract idea consisting of derivations and calculations. In order for a practical application to be found, the additional elements must integrate the judicial exception into the practical application. As outlined in the rejection above, this is not the case with the current claims. Accordingly, the rejections are maintained.
Regarding the prior art rejections, Applicant's arguments have been fully considered but have been found unpersuasive. Applicant argues that 1) Avasarala does not disclose select data to be added as the training data from among the at least one training candidate data, on the basis of the evaluation value for each training candidate data; display the selected data on a display unit; receive an input of a user operation; and change a label given to the selected data when the user operation for changing the label given to the selected data displayed on the display unit is input; wherein the program is further configured to cause the CPU to: train an updated model with the selected data having the changed label or a maintained label, and obtain recognition results using the updated model, and 2) there is no motivation to combine the references. Examiner disagrees for at least the following reasons.
First, in response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986). That group of limitations is not rejected by only Avasarala. Those limitations are rejected by the combination of Avasarala, Rousseeuw, and Wu as explained in the rejection above.
Second, Applicant’s conclusory argument against the combination does not provide an actual reason why the motivation to combine outlined in the rejections is incorrect.
Accordingly, the rejections are maintained.
Conclusion
Any prior art made of record and not relied upon is considered pertinent to Applicant's disclosure. Applicant is reminded that in amending in response to a rejection of claims, the patentable novelty must be clearly shown in view of the state of the art disclosed by the references cited and the objections made. Applicant must also show how the amendments avoid such references and objections. See 37 CFR §1.111(c). Additionally when amending, in their remarks Applicant should particularly cite to the supporting paragraphs in the original disclosure for the amendments.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ROBERT H BEJCEK II whose telephone number is (571)270-3610. The examiner can normally be reached Monday - Friday: 9:00am - 5:00pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Michelle T. Bechtold can be reached at (571) 431-0762. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/R.B./ Examiner, Art Unit 2148 /MICHELLE T BECHTOLD/ Supervisory Patent Examiner, Art Unit 2148
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