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 .
Information Disclosure Statement
Acknowledgment is made of the information disclosure statements filed March 24, 2023, which comply with 37 CFR 1.97. As such, the information disclosure statements have been placed in the application file and the information referred to therein has been considered by the examiner.
Response to Amendment
This office action is final and in response to the amendment filed on May 18, 2026, which was in response to the non-final office action mailed February 17, 2026.
Claims 11 and 12 are cancelled. Claims 1-10 and 13-22 are pending and have been examined. Claims 1-10 and 13-22 are rejected.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
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 non-obviousness.
Claim(s) 1-10 and 13-22 are rejected under 35 U.S.C. 103 as being unpatentable over Polleri et al., (U.S. Patent Application Publication No. US-11556862-B2 filed on 6/4/2020, hereinafter “Polleri”), in view of Anisingaraju et al., (U.S. Patent Application Publication No. US-20160203217-A1 filed on 12/19/2015, hereinafter “Anisingaraju”).
With respect to Claims 1, 8, and 17:
Polleri teaches:
“receiving, at an application platform associated with a computer system, an upload of the raw dataset;” (Column 8, Lines 29-34, discloses the receiving of a user input through an interface (application platform) where the user can identify one or more locations of data (i.e. a raw data set).)
“identifying, using a processor associated with the computer system, a trained machine-learning model by determining a context associated with the raw dataset and selecting the trained machine-learning model based on the determined context, wherein the trained machine-learning model is configured to process data associated with the determined context;” (Column 8, Lines 47-61, discloses a user inputting a specification for a type of problem they’d like to implement a machine learning solution for. The application can translate the native language inputted to understand the goals of the machine learning model. Such techniques can recognize keywords in the native language to recommend or select a particular machine learning algorithm. Column 64, Lines 63-67 and Column 65, Lines 1-3 further recite an example where the search-based adaptive engine can search the metadata of various machine learning models that would be effective in solving the given or identified problem, such as an image classifier used for detecting potential skin cancer that can provide the accuracy needed for detecting diabetic retinopathy. The technique would allow the appropriate model, required transformations, and particular pipelines to be selected. Under the broadest reasonable interpretation, this is akin to determining a context associated with a data input and selecting a machine learning model that is trained and configured to process data associated with the determined context.)
“applying, using the processor, the raw dataset to the trained machine-learning model to generate a structured output dataset comprising one or more extracted features, relationships, or trends not present in the raw dataset;” (Column 11, Lines 6-9, discloses the generated machine learning model using training data to train the machine learning model to the desired performance parameters (raw dataset). Column 62, Lines 4-12 further recite raw data being applied to a trained model, where the output is a numerical classification value indicating retinopathy severity, which is a feature that is not present in the raw data applied.)
“receiving, from the trained machine-learning model, an output result including the structured output dataset;” (Column 11, Lines 6-9, discloses the generated machine learning model using training data to train the machine learning model to the desired performance parameters (raw dataset). Column 62, Lines 4-12 further recite raw data being applied to a trained model, where the output is a numerical classification value indicating retinopathy severity, which is a feature that is not present in the raw data applied.)
Polleri does not appear to explicitly disclose:
“and presenting, subsequent to the receiving, the output result on the application platform by: generating a graphical user interface that presents a visualization of the structured output dataset;”
“receiving, via the graphical user interface, a user interaction specifying a display parameter associated with the visualization;”
“and automatically modifying, in response to the user interaction, an arrangement of the extracted features, relationships, or trends within the graphical user interface to emphasize a subset of the extracted features, relationships, or trends associated with the display parameter.”
However, Anisingaraju teaches:
“and presenting, subsequent to the receiving, the output result on the application platform by: generating a graphical user interface that presents a visualization of the structured output dataset;” (Paragraph 0047 discloses a data presentation subcomponent that relates to how to present the data to the user, which can include traditional or advanced visualization methods such as infographics, maps, and advanced charts (graphs).)
“receiving, via the graphical user interface, a user interaction specifying a display parameter associated with the visualization;” (Paragraph 0047 recites a data presentation tool may include a way for the user to filter and drill down the data, essentially allowing the user to explore the result.)
“and automatically modifying, in response to the user interaction, an arrangement of the extracted features, relationships, or trends within the graphical user interface to emphasize a subset of the extracted features, relationships, or trends associated with the display parameter.” (Paragraph 0047 recites a data presentation tool may include a way for the user to filter and drill down the data, essentially allowing the user to explore the result. Paragraph 0033 further recites the interactive user component may be through a webpage that allows the user to sort and organize the data in various ways.)
It would have been obvious to a person having ordinary skill in the art (PHOSITA) before the effective filing date of the present application to implement a claim that utilized the teachings of Polleri and the teachings of Anisingaraju, which are both in the same field of invention. A PHOSITA would have been motivated to combine Polleri’s method of receiving a dataset, applying a trained machine learning model to the dataset, and generating/presenting an output result through an application platform with Anisingaraju’s method of generating advanced graphical visualizations that illustrate the relationships found between data elements. This would provide users with a more visual and easier to understand way of comprehending analytical results using data visualization techniques.
With respect to Claims 2 and 9:
Polleri and Anisingaraju combined teach:
“wherein identifying the trained machine-learning model comprises receiving, from a user, a selection on the trained machine-learning model from a plurality of trained machine-learning models, wherein each of the plurality of trained machine-learning models is associated with a unique context” (Columns 8 and 9, Lines 62-67 and 1-3, from Polleri disclose a user can choose the type of problem they want to solve through a graphical user interface, where several generic machine learning models are then displayed back to the user for selection. The user can then select one of the generic models or a customer model to solve the problem received as the second input.)
With respect to Claims 3, 10, and 18:
Polleri and Anisingaraju combined teach:
“wherein identifying the trained machine-learning model comprises: deriving, upon an analysis of words contained in the raw dataset using the processor, the context associated with the raw dataset;” (Column 8, Lines 50-59, from Polleri discloses the input of the problem as native language text or speech, wherein the technique can decipher the native language to understand the goals (context) of the machine learning model associated with a wide variety of problem types, such as “classification, regression, product recommendations, medical diagnosis, financial analysis, predictive maintenance, image and sound recognition, text recognition, and tabular data analysis.”)
“automatically selecting, based on the deriving, the trained machine-learning model” (Column 8, Lines 59-61, from Polleri discloses the technique can then recognize one or more keywords in the native language to select or recommend a particular machine learning algorithm.)
With respect to Claims 4, 13, and 19:
Polleri and Anisingaraju combined teach:
“presenting, prior to application of the raw dataset to the identified trained machine-learning model, a template on the application platform;” (Column 8, Lines 24-33, from Polleri discloses an interface for the user to interact with, which can include a graphical user interface on a touchscreen display (application platform). The user can use the interface to identify the locations of the data that will be used for generating the machine learning model.)
“receiving, from a user, one or more contextual parameter designations for the raw dataset;” (Column 8, Lines 47-50, from Polleri discloses a second user input through the input of text via a user interface that can specify a type of problem that the user would like to implement the machine learning for.)
“applying, in conjunction with the raw dataset, the one or more contextual parameter designations to the trained machine-learning model” (Columns 11, Lines 6-9, from Polleri discloses a generated machine learning model that can use the training data to train the machine learning model to the desired performance parameters.)
With respect to Claims 5 and 14:
Polleri and Anisingaraju combined teach:
“wherein the output result is a graph illustrating a relationship between elements contained in the raw dataset” (Paragraph 0047 from Anisingaraju discloses a data presentation subcomponent that relates to how to present the data to the user, which can include traditional or advanced visualization methods such as infographics, maps, and advanced charts (graphs).)
With respect to Claims 6 and 15:
Polleri and Anisingaraju combined teach:
“wherein the graph is one of: a cluster graph, a choropleth graph, a bar graph, and a line graph” (Paragraph 0047 from Anisingaraju discloses how the data is presented to the user through traditional or advanced visualization methods such as infographics, maps, and advanced charts (graphs).)
With respect to Claims 7, 16, and 20:
Polleri and Anisingaraju combined teach:
“wherein the output result corresponds to a suggestion to adjust one or more activities of an organization that produces the raw dataset to improve an efficiency of the organization” (Paragraphs 0095 and 0096 from Anisingaraju disclose an Insight Generation Engine (IGE) that ingests data from various sources to create aggregated data, which is then processes using natural language processing (NPL) to attach attributes and contributors to the data. These attributes and contributors can be in the form of topics (i.e. topics specified to be important to an organization) and can further be processed to provide actionable insights and recommendations to improve the organization.)
With respect to Claims 21 and 22:
Polleri and Anisingaraju combined teach:
“wherein the output result comprises a representation of semantic relationships between two or more features of the raw dataset” (Column 63, Lines 18-28 from Polleri recite how semantic queries are used together with a graph representation to output information, where the nodes and edges represent relationships between two items (such as data feature). Paragraph 0047 from Anisingaraju discloses a data presentation subcomponent that relates to how to present the data to the user, which can include traditional or advanced visualization methods such as infographics, maps, and advanced charts (graphs).
Response to Arguments
Applicant’s arguments filed on May 18, 2026 have been fully considered, but the examiner believes that not all are fully persuasive.
Claim Rejections - 35 USC § 101
Applicant argues that the claims do not recite a judicial exception under Step 2A, Prong One,
integrate the judicial exception into a practical application under Step 2A, Prong Two, and recite significantly more than the judicial exception under Step 2B.
Applicant’s arguments have been considered and are persuasive. The previous 101 rejection is withdrawn.
Claim Rejections - 35 USC § 102
A. Applicant argues that Polleri does not teach the limitations of Claims 1-4, 8-13, and 17-19, specifically "identifying, using a processor associated with the computer system, a trained machine-learning model configured to process data that shares a context associated with the raw dataset;" from Claims 1, 18, and 17.
Applicant’s arguments have been considered, but are moot because the new ground of rejection does not rely solely on Polleri applied in the prior 102 rejection of record for any teaching or matter specifically challenged in the argument, with the exception of applicant’s argument that the feature "identifying, using a processor associated with the computer system, a trained machine-learning model configured to process data that shares a context associated with the raw dataset" is not disclosed in Polleri (Pages 19-21 of applicant’s remarks). Examiner respectfully disagrees.
The feature "identifying, using a processor associated with the computer system, a trained machine-learning model configured to process data that shares a context associated with the raw dataset;" is taught by Polleri. Column 8, Lines 47-61, from Polleri discloses a user inputting a specification for a type of problem they’d like to implement a machine learning solution for. The application can translate the native language inputted to understand the goals of the machine learning model. Such techniques can recognize keywords in the native language to recommend or select a particular machine learning algorithm. Column 64, Lines 63-67 and Column 65, Lines 1-3 from Polleri further recite an example where the search-based adaptive engine can search the metadata of various machine learning models that would be effective in solving the given or identified problem, such as an image classifier used for detecting potential skin cancer that can provide the accuracy needed for detecting diabetic retinopathy. The technique would allow the appropriate model, required transformations, and particular pipelines to be selected. Under the broadest reasonable interpretation, this is akin to determining a context associated with a data input and selecting a machine learning model that is trained and configured to process data associated with the determined context.
Claim Rejections - 35 USC § 103
Applicant argues that the combination of Polleri and Anisingaraju does not teach Claims 5-7,
14-15, and 20.
Examiner respectfully disagrees. Applicant does not provide specific arguments or reasons for why the claims are not taught under Anisingaraju. Please refer to the 103 rejections above for further details.
Conclusion
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 nonprovisional extension fee (37 CFR 1.17(a)) 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 mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Vibha Bhat whose telephone number is (571)-272-7091. The examiner can normally be reached on Monday – Thursday from 8:00 AM to 5:00 PM EST and every other Friday from 8:00 AM to 4:00 PM EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Mariela Reyes, can be reached at telephone number (571)-270-1006. The fax phone number for the organization where this application or proceeding is assigned is (571)-273-8300. Information regarding the status of an application may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or (572)-272-1000.
/Vibha Bhat/Examiner
Art Unit 2142
/HAIMEI JIANG/Primary Examiner, Art Unit 2142
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