DETAILED ACTION
This action is in response to the filing of 9-10-2025. Claims 1-9 are pending and have been considered below:
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 1-9 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claims 1-9 represent method and system type claims. Therefore claims 1-9 are directed to either a process, machine, manufacture or composition of matter.
Regarding claim 1:
2A Prong 1:
calculating, by inputting training data to the inspector model, a first distance from the decision boundary to the training data on the feature space; calculating, by inputting a plurality of pieces of operation data to the inspector model, a second distance from the decision boundary to the operation data on the feature space;
As drafted, under the broadest reasonable interpretation, the claim covers mental processes (concepts performed in the human mind including an observation, evaluation, judgment, opinion-a user can calculate a distance from a boundary).
and detecting, when a difference between the first distance and the second distance is larger than or equal to a threshold, an accuracy degradation of the operation model caused according to the difference between the training data and the operation data.
As drafted, under the broadest reasonable interpretation, the claim covers mental processes (concepts performed in the human mind including an observation, evaluation, judgment, opinion-a user can determine distance from a boundary).
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
training a machine learning model by using a plurality of pieces of training data associated with a plurality of correct answer labels; training an inspector model for training a decision boundary that divides a feature space of data into two application areas based on an output result of the operation model, the inspector model being configured to calculate a distance from the decision boundary to input 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)).
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
training a machine learning model by using a plurality of pieces of training data associated with a plurality of correct answer labels; training an inspector model for training a decision boundary that divides a feature space of data into two application areas based on an output result of the operation model, the inspector model being configured to calculate a distance from the decision boundary to input 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)).
Regarding claim 2:
2A Prong 1:
calculating, by inputting the plurality of pieces of operation data to the inspector model, the second distance from the decision boundary to each of the plurality of pieces of operation data;
As drafted, under the broadest reasonable interpretation, the claim covers mental processes (concepts performed in the human mind including an observation, evaluation, judgment, opinion-a user can calculate a distance from a boundary).
and detecting, the operation data in which the second distance is less than a distance that is set in advance.
As drafted, under the broadest reasonable interpretation, the claim covers mental processes (concepts performed in the human mind including an observation, evaluation, judgment, opinion-a user can compare distance from a boundary).
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
using the processor;
(mere instructions to apply the exception using a generic computer component):
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
using the processor;
(mere instructions to apply the exception using a generic computer component):
Regarding claim 3:
2A Prong 1:
converting, the second distance to a certainty factor that takes a value larger than or equal to 0 but less than 1, wherein the detecting includes detecting the operation data in which the certainty factor is less than a value that is set in advance.
As drafted, under the broadest reasonable interpretation, the claim covers mental processes (concepts performed in the human mind including an observation, evaluation, judgment, opinion-a user can user can evaluate and convert data).
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
using the processor;
(mere instructions to apply the exception using a generic computer component):
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
using the processor;
(mere instructions to apply the exception using a generic computer component):
Claims 4-9 are similar in scope to claims 1-3, and are analyzed and rejected under the same rationale.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-9 is/are rejected under 35 U.S.C. 103 as being
unpatentable over Gou et al. (“Gou” 20200110982 A1) in view of Patel et al. (“Patel” 11868440 B1) and Tanimoto et al. (“Tanimoto” 20180075360 A1).
Claim 1: Gou discloses a computer-implemented detection method comprising:
training a an operation model by using a plurality of pieces of training data associated with a plurality of correct answer labels (Figure 3 and Paragraphs 131-132; true label used in training model);
training an inspector model for training a decision boundary that divides a feature space of data into two application areas based on an output result of the operation model, the inspector model being configured to calculate a distance from the decision boundary to input data (Figure 4b: 422 (boundary for feature space) Paragraphs 156-158, 164-165 and 214; selected data provides a layout from the boundary);
Gou may not explicitly disclose calculating, by inputting training data to the inspector model, a first distance from the decision boundary to the training data on the feature space;
calculating, by inputting a plurality of pieces of operation data to the inspector model, a second distance from the decision boundary to the operation data on the feature space;
and detecting, when a difference between the first distance and the second distance is larger than or equal to a threshold,
Patel is provided because it discloses training a model (abstract), further the model inputs training data and additional (first) data, the samples of data can be multiple forms (Column 10, Lines 56-61) and a threshold for a distance from the boundary is utilized for the data sets (Figure 5b-c (provides a feature space within SVM) and Column 13, Lines 24-45) the distance from the boundary of each sample determines an accuracy in classifying (Column 10, Lines 56-Column 11, Line 8 and Column 15, Lines 18-67).
Therefore it would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to apply a known technique to a known device ready for improvement and determine distances from a boundary in a model of Gou. One would have been motivated to provide the functionality because it improves the training efficiency by reducing use of resources and cost for the model (Patel: Column 9, Lines 35-40).
Gou as modified by Patel discloses a loss value/accuracy metric (Column 14, Lines 10-14), however may not explicitly disclose
an accuracy degradation of the operation model caused according to the difference between the training data and the operation data.
Tanimoto is provided because it discloses an accuracy degradation estimate between data sets for a model (Paragraphs 43 and 72).
Therefore it would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to apply a known technique to a known device ready for improvement and utilize accuracy degradation when evaluating a model of Gou. One would have been motivated to provide the functionality because it can properly determine the accuracy of a model (Tanimoto: Paragraph 8).
Claim 2: Gou, Patel and Tanimoto disclose a detection method according to claim 1, further comprising: calculating, using the processor, by inputting the plurality of pieces of operation data to the inspector model, the second distance from the decision boundary to each of the plurality of pieces of operation data; and detecting, using the processor, the operation data in which the second distance is less than a distance that is set in advance (Patel: Figures 5b and 7b and Column 24-38 and Column 15, Lines 18-67). The distance threshold from the boundary is set and data is passed through which complies with the threshold.
Claim 3: Gou, Patel and Tanimoto disclose a detection method according to claim 1, further comprising converting, using the processor, the second distance to a certainty factor that takes a value larger than or equal to 0 but less than 1, wherein the detecting includes detecting the operation data in which the certainty factor is less than a value that is set in advance (Patel: Column 13, Lines 24-45; threshold provides values greater than 0 but less than 1, this threshold provides a confidence factor (classifying) when analyzing data).
Claim 4 is similar in scope to claim 1 and therefore rejected under same rationale.
Claim 5 is similar in scope to claim 2 and therefore rejected under same rationale.
Claim 6 is similar in scope to claim 3 and therefore rejected under same rationale.
Claim 7 is similar in scope to claim 1 and therefore rejected under same rationale.
Claim 8 is similar in scope to claim 2 and therefore rejected under same rationale.
Claim 9 is similar in scope to claim 3 and therefore rejected under same rationale.
Response to Arguments
Applicant's arguments have been fully considered but they are not persuasive.
Regarding the 101, including language of a feature space does not overcome the 101 analysis applied to a calculating distance. The calculation distance within a space at the most basic level is still understood as being performed through a mental process with the utilization of pen and paper.
Regarding training a model, this is a feature performed by generic computer components and further the language does not provide any detailed methods of training that may amount to significantly more.
Regarding the remarks that the claims are directed to an entire model over time, that language is not presented in the claim language and therefore does not hold pertinent weight.
Regarding the remarks that Patel does not disclose “calculating, by inputting training data to the inspector model, a first distance from the decision boundary to the training data on the feature space;
calculating, by inputting a plurality of pieces of operation data to the inspector model, a second distance from the decision boundary to the operation data on the feature space;”
Examiner respectfully disagrees. Patel discloses a model with a first and second plurality of data provided within the feature space (Figure 5b-c). A distance is determined based on the understanding of a support vector machine.
Further, applicant argues that Tanimoto does not detect a difference between the first and second distance. Examiner respectfully disagrees. Applicant further argues that a different solution is provided, however this does not negate the functionality used to make a determination.
Last, applicant argues that one of ordinary skill would not have been motivated to provide the combination and further that the references teach away.
Gou, Patel and Tanimoto are analogous art because they come from the same field of art including machine learning and utilizing support vector machines. Therefore the combination of references can be utilized in a similar manner where multiple models are implemented in tandem to produce an outcome. Additionally, none of the references state that one method should not be used which would be considered teaching away.
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicant’s disclosure:
U.S. Pub No. 20210034985 A1: VONGKULBHISAL et al. teaches knowledge distillation.
U.S. Pub No. 20190205748 A1: Fukuda et al. teaches knowledge distillation.
U.S. Pub No. 20180268292 A1: Choi et al. teaches knowledge distillation and boundary learning.
Applicant is required under 37 C.F.R. § 1.111(c) to consider these references fully when responding to this action.
It is noted that any citation to specific pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck, 699 F.2d 1331, 1332-33, 216 U.S.P.Q. 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 U.S.P.Q. 275, 277 (C.C.P.A. 1968)).
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
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/SHERROD L KEATON/ Primary Examiner, Art Unit 2148
12-10-2025