DETAILED ACTION
This action is in response to the claims filed 02/23/2024 for Application number 18/585,588. Claims 1-12 are currently pending.
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 .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 02/23/2024, 09/05/2024 and 01/26/2026 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
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 1-12 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 Analysis: Claim 1 is directed to a process, which falls within one of the four statutory categories.
Step 2A Prong 1 Analysis: Claim 1 recites, in part, The limitations of:
[a feature calculating unit] that calculates or refers to a first-type feature of each of plural pieces of training data, and calculates or refers to a second-type feature of target data for detection can be considered to be an evaluation in the human mind,
[a first selecting unit] that, based on first-type attached information corresponding to each of the plural pieces of training data, selects at least one or more of plural first-type features can be considered to be an evaluation in the human mind
[an anomaly degree data calculating unit] that calculates anomaly degree data indicating a degree of anomaly in the target data for detection, using the selected first-type feature and using the second-type feature can be considered to be an evaluation in the human mind.
These limitations as drafted, are processes that, under broadest reasonable interpretation, covers performance of the limitation in the mind or with the aid of pen and paper which falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong 2 Analysis: This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements – “one or more hardware processors configured to function as: a feature calculating unit that calculates or refers…, a first selecting unit that, selects…, an anomaly degree data calculating unit that calculates…”. Thus, these elements in the claim are recited at a high level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component. Please see MPEP 2106.05(f). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim as a whole is directed to an abstract idea.
Step 2B Analysis: The claims do 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 elements of utilizing one or more hardware processors configured to function as… to perform the steps of the claimed process amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible.
Regarding claim 2, the rejection of claim 1 is further incorporated, and further, the claim recites: wherein the first selecting unit selects the first-type feature calculated or referred to from each of the plural pieces of training data corresponding to plural dissimilar pieces of the first-type attached information. This claim recites additional mental steps in addition to the judicial exception identified in the rejection of claim 1, thus recites a judicial exception.
The claim does not include any additional elements that amount to an integration of the judicial exceptions into a practical application, nor to significantly more than the judicial exceptions. The claim is not patent eligible.
Regarding claim 3, the rejection of claim 1 is further incorporated, and further, the claim recites: wherein the first selecting unit classifies plural first-type features into plural groups for each of which plural pieces of first-type attached information of the plural pieces of training data from which the plural first-type features are calculated or referred to, are similar to each other, and selects, for each of the plural groups, at least one or more of the first-type features belonging a concerned group. This claim recites additional mental steps in addition to the judicial exception identified in the rejection of claim 1, thus recites a judicial exception.
The claim does not include any additional elements that amount to an integration of the judicial exceptions into a practical application, nor to significantly more than the judicial exceptions. The claim is not patent eligible.
Regarding claim 4, the rejection of claim 1 is further incorporated, and further, the claim recites: wherein the first selecting unit selects, based on the first-type attached information corresponding to each of the plural pieces of training data, some of the plural first-type features. This claim recites additional mental steps in addition to the judicial exception identified in the rejection of claim 1, thus recites a judicial exception.
The claim does not include any additional elements that amount to an integration of the judicial exceptions into a practical application, nor to significantly more than the judicial exceptions. The claim is not patent eligible.
Regarding claim 5, the rejection of claim 1 is further incorporated, and further, the claim recites: wherein the one or more hardware processors are configured to further function as:
a second selecting unit that selects, from among the plural first-type features selected by the first selecting unit, the first-type feature satisfying at least either a condition of being similar to the second-type feature of the target data for detection or a condition of being calculated from each of the plural pieces of training data corresponding to the first-type attached information similar to second-type attached information corresponding to the target data for detection; and
the anomaly degree data calculating unit calculates anomaly degree data indicating the degree of anomaly in the target data for detection, using the first-type feature selected by the second selecting unit and using the second-type feature.
This claim recites additional mental steps in addition to the judicial exception identified in the rejection of claim 1, thus recites a judicial exception.
The claim does recite the additional element of “a second selecting unit”, however it does not amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception, for the reasons set forth in connection with the rejection of claim 1 above. The claim is not patent eligible.
Regarding claim 6, the rejection of claim 1 is further incorporated, and further, the claim recites: wherein the one or more hardware processors are configured to further function as: a display control unit that displays the anomaly degree data in a display unit. This limitation amounts to mere instructions to apply the judicial exception using a generic computer component. Please see MPEP 2106.05(f).
The claim does not include any additional elements that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 7, the rejection of claim 1 is further incorporated, and further, the claim recites: wherein the display control unit displays, in the display unit, the anomaly degree data in a display form that is in accordance with a degree of anomaly. This limitation amounts to mere instructions to apply the judicial exception using a generic computer component. Please see MPEP 2106.05(f).
The claim does not include any additional elements that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 8, the rejection of claim 1 is further incorporated, and further, the claim recites: wherein the display control unit displays, in the display unit, the anomaly degree data and at least either the target data for detection or the training data. This limitation amounts to mere instructions to apply the judicial exception using a generic computer component. Please see MPEP 2106.05(f).
The claim does not include any additional elements that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 9, the rejection of claim 1 is further incorporated, and further, the claim recites: wherein the target data for detection and the training data is image data or sound data. This limitation amounts to generally linking the judicial exception to a field of use or technological environment. Please see MPEP 2106.05(h).
The claim does not include any additional elements that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 10, the rejection of claim 5 is further incorporated, and further, the claim recites: wherein the first-type attached information indicates an acquisition condition of the training data, and the second-type attached information indicates an acquisition condition of the target data for detection. This limitation amounts to more specifics of the judicial exception identified in the rejection of claim 1 above.
The claim does not include any additional elements that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Regarding Claim 11, it recites features similar to claim 1 and is rejected for at least the same reasons therein.
Claim 12 recites features similar to claim 1 and is rejected for at least the same reasons therein. Claim 12 additionally requires analysis for “A computer program product having a non-transitory computer readable medium including an anomaly detection program, wherein the anomaly detection program, when executed by a computer, causes the computer to execute:” however this is an additional element that amounts to mere instructions to apply the judicial exception using a generic computer component. Please see MPEP 2106.05(f).
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-5 and 9-12 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Kimura et al. ("US 20220156529 A1", hereinafter "Kimura").
Regarding claim 1, Kimura teaches An anomaly detection device, comprising: one or more hardware processors (¶0005) configured to function as:
a feature calculating unit that calculates or refers to a first-type feature of each of plural pieces of training data (“In one embodiment, detection program 200 clusters a plurality of normal training images by using a plurality of sets of features of the normal training images.” [¶0022, “first-type feature”]), and calculates or refers to a second-type feature of target data for detection (“Also, detection program 200 calculates distance vectors for a test image (“target data”) by using the set of centroid information and the set of features of the test image. (“second-type feature”)” [¶0022; note: “for detection” is merely intended use and carries little to no patentable weight because the limitation, or portion thereof, does not claim the function(s) as being positively recited actions or functions, and or it does not add any meaning or purpose to the associated manipulative step(s).]);
a first selecting unit that, based on first-type attached information corresponding to each of the plural pieces of training data, selects at least one or more of plural first-type features (“In this example, detection program 200 extracts (corresponds to “selects”) a set of features (e.g., high-dimensional datasets) for each image of the set of training images, which includes the relevant information (“based on first-type attached information”) from the input data (e.g., input image) so that a desired task (e.g., anomaly detection) can be performed by using a reduced representation instead of the complete initial data (e.g., set of training images).” [¶0024]); and
an anomaly degree data calculating unit that calculates anomaly degree data indicating a degree of anomaly in the target data for detection, using the selected first-type feature and using the second-type feature. (“In step 212, detection program 200 classifies the test image. In one embodiment, detection program 200 utilize a score vector of a feature of a test image to detect an anomaly.” [¶0033; score vector corresponds to “degree of anomaly”. See further ¶0034, scores are used to determine anomalous images.])
Regarding claim 2, Kimura teaches The anomaly detection device according to claim 1, wherein the first selecting unit selects the first-type feature calculated or referred to from each of the plural pieces of training data corresponding to plural dissimilar pieces of the first-type attached information. (“Furthermore, detection program 200 utilizes distances of the training images of the k-means clusters to classify each image of the set of training images into groups that have similar properties and/or features and data points in different groups should have highly dissimilar properties and/or features.” [¶0025])
Regarding claim 3, Kimura teaches The anomaly detection device according to claim 1, wherein the first selecting unit classifies plural first-type features into plural groups for each of which plural pieces of first-type attached information of the plural pieces of training data from which the plural first-type features are calculated or referred to, are similar to each other (“For example, detection program 200 utilizes a machine learning technique (e.g., K-means clustering model), which identifies cluster centroids that minimize the distance between data points (e.g., training images) and the nearest centroid, to group a set of training images based on extracted high dimensional features as discussed in step 202… Furthermore, detection program 200 utilizes distances of the training images of the k-means clusters to classify each image of the set of training images into groups that have similar properties” [¶0025]), and selects, for each of the plural groups, at least one or more of the first-type features belonging a concerned group. (“Also, detection program 200 utilizes the generated score vector of the feature, which is based on a distance vector with respect to a cluster (e.g., group, class, etc.), to detect anomalies (e.g., outliers) in an unlabeled test data set of images by identifying features of the set of images that seem to fit least to feature data sets of respective clusters (e.g., centroids, classes, etc.).” [¶0033])
Regarding claim 5, Kimura teaches The anomaly detection device according to claim 1, wherein the one or more hardware processors are configured to further function as:
a second selecting unit that selects, from among the plural first-type features selected by the first selecting unit, the first-type feature satisfying at least either a condition of being similar to the second-type feature of the target data for detection or a condition of being calculated from each of the plural pieces of training data corresponding to the first-type attached information similar to second-type attached information corresponding to the target data for detection (“In one scenario, detection program 200 inputs a generated score vector for a data input (e.g., a feature of a set of features of a test image) of an image into a OCSVM to determine whether the generated score vector of the data input of the image within a range of distances of each centroid. Additionally, if detection program 200 determines that the generated score vector is within the range of distances, then detection program 200 assigns the feature a positive classification.” [¶0034; note: The claim recites “at least either… or” thus the examiner is only required to map to one of the recited elements and would correspond to “the first-type feature satisfying a condition of being similar to the second-type feature of the target data”. Note: The distance between the features of the test image and features of the training image would show the similarity between the features.]); and
the anomaly degree data calculating unit calculates anomaly degree data indicating the degree of anomaly in the target data for detection, using the first-type feature selected by the second selecting unit and using the second-type feature. (“In one embodiment, detection program 200 utilize a score vector of a feature of a test image to detect an anomaly… In this example, detection program 200 trains the machine learning algorithm utilizing one or more training sets of data, which can be comprised of a set of training images with no anomalies (as discussed in step 202) and/or a set of training images with anomalies, to classify the test image (i.e., identify images that include an anomaly).” [¶0033])
Regarding claim 9, Kimura teaches The anomaly detection device according to claim 1, wherein the target data for detection and the training data is image data or sound data. (“Embodiments of the present invention identify visual features of one or more images.” [¶0009])
Regarding claim 10, Kimura teaches The anomaly detection device according to claim 5, wherein the first-type attached information indicates an acquisition condition of the training data, and the second-type attached information indicates an acquisition condition of the target data for detection. (“The present invention may contain various accessible data sources, such as database 144, that may include personal data, content, or information the user wishes not to be processed. Personal data includes personally identifying information or sensitive personal information as well as user information, such as tracking or geolocation information.” [¶0014; accessible data sources correspond to acquisition condition of training/target data])
Regarding claim 11, it is substantially similar to claim 1 respectively, and is rejected in the same manner, the same art, and reasoning applying.
Claim 12 recites features similar to claim 1 and is rejected for at least the same reasons therein. Claim 12 additionally requires A computer program product having a non-transitory computer readable medium including an anomaly detection program, wherein the anomaly detection program, when executed by a computer, causes the computer to execute (Kimura, ¶0045 “computer readable storage medium”)
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 6-8 are rejected under 35 U.S.C. 103 as being unpatentable over Kimura in view of Balasubramanian et al. ("US 20220171995 A1", hereinafter "Balasubranmanian").
Regarding claim 6, Kimura teaches The anomaly detection device according to claim 1, however fails to explicitly teach wherein the one or more hardware processors are configured to further function as: a display control unit that displays the anomaly degree data in a display unit.
Balasubranmanian teaches wherein the one or more hardware processors are configured to further function as: a display control unit that displays the anomaly degree data in a display unit. (See Fig. 11 shows GUI that displays anomaly scores)
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Kimura’s teachings in order to display the anomaly scores on a GUI as taught by Balasubranmanian. One would have been motivated to make this modification in order to assist a user in performing anomaly detection and further train the anomaly detection ML models. [¶0042, Balasubranmanian]
Regarding claim 7, Kimura/Balasubranmanian teaches The anomaly detection device according to claim 6, Balasubranmanian teaches wherein the display control unit displays, in the display unit, the anomaly degree data in a display form that is in accordance with a degree of anomaly. (See Fig. 11)
Same motivation to combine the teachings of Kimura/Balasubranmanian as claim 6.
Regarding claim 8, Kimura/Balasubranmanian teaches The anomaly detection device according to claim 6, Balasubranmanian teaches wherein the display control unit displays, in the display unit, the anomaly degree data and at least either the target data for detection or the training data. (See Fig. 11, image data)
Same motivation to combine the teachings of Kimura/Balasubranmanian as claim 6.
Conclusion
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/MICHAEL H HOANG/PRIMARY EXAMINER, Art Unit 2122
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