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 .
Claims 1-17, filed on 1/15/2025, is acknowledged and considered.
Claims 1, 8, and 12 are independent claims. Claims 1-17 are pending.
Information Disclosure Statement
3. The information disclosure statement (IDS) submitted on 1/15/2025 and 1/20/2026 was filed after the mailing date of the claims on 1/15/2025. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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.
5. Claim(s) 1-17 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Frattura, et al. [US 20240265124].
As per claim 1: Frattura, et al. teaches a thesis plagiarism detection method performed by a computing system, the thesis plagiarism detection method comprising:
acquiring figure data for a target thesis, the figure data including images and text; [Frattura: para 0198-0200; an insight may be defined as a finding gained through data analytics or through the discovery of patterns and/or relationships amongst any given assortment of data/information. An insight may take form through any existing data/information format, examples include tabular data (e.g., a dataset), text, a data graphic (e.g., a chart) visualizing tabular data, an image, an audio track, and a video clip. Para 0417; a given asset (e.g., a research journal article, a research white paper, a research dissertation or thesis, or any other form of information describing one or more research subareas), where the representative asset catalog entry store metadata for, or information descriptive of, the given asset]
acquiring first feature data for the figure data by applying the figure data to a first machine learning model; [Frattura: para 0057; Digitally-assisted organization strategy, meanwhile, references the scheming and/or implementation of organization strategy through insights distilled by artificial intelligence. The insight service employ artificial intelligence to ingest assets maintained across various data sources. Para 0197; an artificial intelligence and/or machine learning based inference computer program, a text editor, a spreadsheet editor, a presentation editor, an integrated development environment (IDE), an audio editor, a video editor, and an image and/or graphic editor]
determining whether a thesis associated with second feature data having a similarity above a predetermined threshold with the acquired first feature data is found; and [Frattura: para 0243; a performance score (or metric) refer to a measurement for evaluating ML model performance. Any given performance score may measure performance relatable to the form of data analysis (e.g., regression, classification, ranking, computer vision, natural language processing, deep learning, etc.) being performed by the given ML model being evaluated. The claimed feature data with “similarity above a predetermined threshold”, is relative to what constitutes as above or a predetermined threshold. Thus, any degree or level of similarity set or determined may be similarity above a predetermined threshold. More examples on para 0253-0254, 0261, 0277, 0327]
determining the target thesis as a plagiarized thesis when it is determined that the thesis associated with the second feature data is found. [Frattura: para 0441; any time the traceable insight may be re-used, an original state of the traceable insight retained and thus used by a plagiarist. Any time the traceable insight may be re-used, a plagiarist modify the original state of the traceable insight to obtain a variation of the traceable insight. Moreover, whether the traceable insight retains its original state or is modified, any re-use (or plagiarism) of the traceable insight may be incorporated into one or more new assets authored/created by any plagiarist]
Claim 2: Frattura: para 0064, 0200 [automated ML service automatically identify one or more optimal ML algorithms from which one or more ML models constructed and fit to a submitted dataset in order to best achieve any given set of tasks]; discussing the thesis plagiarism detection method of claim 1, wherein the acquiring the figure data includes acquiring the figure data, including the images included in the target thesis and the text associated with the images, by applying the target thesis to a second machine learning model.
Claim 3: Frattura: para 0156, 0208 [an asset (e.g., a text document, multimedia, a presentation deck, an audio book or podcast, or any other forms of digital learning materials) is extracted. Machine learning models suggest machine learning functions that involves records with metadata or descriptive information]; discussing the thesis plagiarism detection method of claim 2, wherein the second machine learning model is configured to: extract the images included in the target thesis, captions for the images, and descriptions associated with the images; and output figure data including the text containing the captions and the descriptions, and the images.
Claim 4: Frattura: para 0447, 0635; discussing the thesis plagiarism detection method of claim 1, wherein the first machine learning model is configured to output the first feature data based on the images included in the figure data and the text.
Claim 5: Frattura: para 0441, 0597; discussing the thesis plagiarism detection method of claim 1, further comprising: after the determining the target thesis as a plagiarized thesis, acquiring a plagiarism type of the target thesis by applying the first feature data and the second feature data to a third machine learning model.
Claim 6: Frattura: para 0441 [obtain a variation of the traceable insight, any re-use (or plagiarism) of the traceable insight may be incorporated into one or more new assets authored/created by any one or more plagiarist(s). Suggest there are more than one thesis to identify variations of plagiarism]; discussing the thesis plagiarism detection method of claim 5, wherein the third machine learning model is configured to output a plagiarism type between a first thesis and a second thesis based on the first feature data related to the first thesis and the second feature data related to the second thesis.
Claim 7: Frattura: para 0419, 0493 [similarity calculation and measurement for evaluating ML model performance, relatable to the form of data analysis (e.g., regression, classification, ranking, computer vision, natural language processing, deep learning, etc.) being performed by the given ML model being evaluated]; discussing the thesis plagiarism detection method of claim 1, further comprising: after the determining the target thesis as a plagiarized thesis, transmitting the similarity between the first feature data and the second feature data, and information related to the found thesis, to a user terminal.
As per claim 8: Frattura, et al. teaches a thesis plagiarism detection method performed by a computing system, the thesis plagiarism detection method comprising:
acquiring first feature data for a first thesis and second feature data for a second thesis; and [Frattura: para 0417-0419; each node pertain to a given asset (e.g., a research journal article, a research white paper, a research dissertation or thesis, or any other form of information describing one or more research subareas), where the representative asset catalog entry thereof may store metadata for, or information descriptive of, the given asset. The asset metadata graph is filtered based on the research subarea. Filtering of the asset metadata graph entail topic matching (e.g., case-insensitive word or phrase matching) and/or semantic similarity calculation between the research subarea and the asset metadata for assets catalogued in the asset catalog entries of which nodes of the asset metadata graph are representative. By each node with a given asset (i.e. thesis) and metadata associated to an image and text, suggest there are more than one thesis and each thesis has its own feature data per se. More examples on para 0487, 0531-0534]
determining a plagiarism type between the first thesis and the second thesis by applying the first feature data and the second feature data [Frattura: para 0441; any time the traceable insight may be re-used, an original state of the traceable insight retained and thus used by a plagiarist. Any time the traceable insight may be re-used, a plagiarist modify the original state of the traceable insight to obtain a variation of the traceable insight. Moreover, whether the traceable insight retains its original state or is modified, any re-use (or plagiarism) of the traceable insight may be incorporated into one or more new assets authored/created by any plagiarist] to a machine learning model. [Frattura: para 0197; an artificial intelligence and/or machine learning based inference computer program, a text editor, a spreadsheet editor, a presentation editor, an integrated development environment (IDE), an audio editor, a video editor, and an image and/or graphic editor]
Claim 9: Frattura: para 0419, 0493 [similarity calculation and measurement for evaluating ML model performance, relatable to the form of data analysis (e.g., regression, classification, ranking, computer vision, natural language processing, deep learning, etc.) being performed by the given ML model being evaluated]; discussing the thesis plagiarism detection method of claim 8, wherein a similarity between the first feature data and the second feature data is equal to or greater than a predetermined threshold.
Claim 10: Frattura: para 0441 [obtain a variation of the traceable insight, any re-use (or plagiarism) of the traceable insight may be incorporated into one or more new assets authored/created by any one or more plagiarist(s). Suggest there are more than one thesis to identify variations of plagiarism]; discussing the thesis plagiarism detection method of claim 8, further comprising: after the acquiring the plagiarism type between the first thesis and the second thesis, transmitting information related to the acquired plagiarism type to a user terminal.
Claim 11: Frattura: para 0441 [obtain a variation of the traceable insight, any re-use (or plagiarism) of the traceable insight may be incorporated into one or more new assets authored/created by any one or more plagiarist(s). Suggest there are more than one thesis to identify variations of plagiarism]; discussing the thesis plagiarism detection method of claim 8, wherein the acquiring the first feature data for the first thesis and the second feature data for the second thesis includes acquiring the first feature data and the second feature data by applying the first thesis and the second thesis to different machine learning models. [Frattura: para 0064, 0200; automated ML service automatically identify one or more optimal ML algorithms from which one or more ML models constructed and fit to a submitted dataset in order to best achieve any given set of tasks]
As per claim 12: Frattura, et al. teaches a method for training a machine learning model, performed by a computing system, the method comprising:
acquiring a training dataset including a plurality of training figure data [Frattura: para 0239; a ML algorithm may be perceived as a default template of a learning process attempting to perform a set of tasks, whereas the ML model represents a trained, tested, and/or otherwise, optimized, version of the ML algorithm on which the ML model is based. See also para 0244, 0595], wherein the plurality of training figure data include text and images; and [Frattura: para 0198-0200; an insight may be defined as a finding gained through data analytics or through the discovery of patterns and/or relationships amongst any given assortment of data/information. An insight may take form through any existing data/information format, examples include tabular data (e.g., a dataset), text, a data graphic (e.g., a chart) visualizing tabular data, an image, an audio track, and a video clip. Para 0417; a given asset (e.g., a research journal article, a research white paper, a research dissertation or thesis, or any other form of information describing one or more research subareas), where the representative asset catalog entry store metadata for, or information descriptive of, the given asset]
applying each of the plurality of training figure data included in the training dataset to the machine learning model to train the machine learning model to output feature data associated with thesis images based on the plurality of training figure data. [Frattura: para 0244-0245; receiving the given ML job at least specifying an input matrix and a label vector, where the input matrix represents a S×F dataset (S for samples and F for features) that specifies the input data for ML model training and testing purposes. The label vector represents a 1×S (S for samples) array that specifies a desired/target output (e.g., continuous/numerical or categorical) for each data sample reflected in the input matrix. Selecting one or more ML algorithms (i.e., considered ML algorithm(s) that can handle the input matrix as input(s) and produce output(s) that can be assessed against the label vector]
Claim 13: Frattura: para 0156, 0208 [an asset (e.g., a text document, multimedia, a presentation deck, an audio book or podcast, or any other forms of digital learning materials) is extracted. Machine learning models suggest machine learning functions that involves records with metadata or descriptive information]; discussing the method of claim 12, wherein the acquiring the training dataset includes: collecting original theses; extracting figure data included in the original theses; augmenting the extracted figure data into a plurality of figure data [Frattura: para 0441; augmenting by modification or update of the original state]; and generating the plurality of training figure data based on the plurality of figure data.
Claim 14: Frattura: para 0441 [augmenting by modification or update the original state]; discussing the method of claim 13, wherein the augmenting the extracted figure data into the plurality of figure data includes: augmenting text included in the figure data into a plurality of text data; and augmenting images included in the figure data into a plurality of images.
Claim 15: Frattura: para 0441 [augmenting by modification or update of the original state to obtain a variation of the traceable insight (or modified traceable insight). Whether the traceable insight retains its original state or is modified, any re-use (or plagiarism) of the traceable insight is incorporated into one or more new assets authored/created by any one or more plagiarist(s)]; discussing the method of claim 13, wherein the augmenting the extracted figure data into the plurality of figure data includes: selecting test data and source data from the augmented plurality of figure data, and the method further comprises: after the training the machine learning model, evaluating the machine learning model by applying the selected test data and source data to the machine learning model. [Frattura: para 0323, 0549]
Claim 16: Frattura: para 0419, 0493 [similarity calculation and measurement for evaluating ML model performance, relatable to the form of data analysis (e.g., regression, classification, ranking, computer vision, natural language processing, deep learning, etc.) being performed by the given ML model being evaluated]; discussing the method of claim 15, wherein the evaluating the machine learning model includes: identifying first feature data for the test data; identifying second feature data for the source data; and evaluating the machine learning model based on a similarity between the first feature data and the second feature data.
Claim 17: Frattura: para 0441, 0455 [omitting the identified previously produced insight(s) from the training dataset(s) employed in a training of prospective insight generating model. Data of the original state]; discussing the method of claim 15, wherein the source data includes at least one of original images or original text, and the source data and the test data are not included in the training dataset.
Conclusion
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Leynna Truvan
Examiner
Art Unit 2435
/L.TT/Examiner, Art Unit 2435
/EDWARD ZEE/Primary Examiner, Art Unit 2435
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