DETAILED ACTION
This office action is responsive to communication(s) filed on 1/2/2026.
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 Status
Claims 1-11 and 21-29 are pending and are currently being examined.
Claims 1, 21 and 25 are independent.
Claims 12-20 are newly canceled.
Claims 21-29 are newly added.
Claims 1-11 are newly amended.
Specification
The new title of the invention filed on 1/2/2026 is acceptable.
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 1-11 and 21-29 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Representative claim 1 recite(s) A method, comprising: determining a hypergraph model trained on a corpus of hypertext markup language (HTML) documents, wherein the hypergraph model comprises nodes and hyperedges, and each node corresponds to a style element of a plurality of style elements and each hyperedge corresponds to one of a plurality of fragments in the HTML documents; identifying one or more candidate style elements for a candidate fragment of an HTML document based on the hypergraph…model; scoring the candidate fragment with each of the one or more candidate style elements based on embeddings of the hypergraph…model; determining the candidate fragment with a candidate style element of the one or more candidate style elements having a highest score..
This method a method that falls under the “Mental processes” grouping of Abstract ideas. Note that it has been recognized by the courts that claims that recite mental processes include: a claim to "collecting information, analyzing it, and displaying certain results of the collection and analysis," where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016). Note that, assuming a small dataset, a hypergraph model can be determined, style elements identified and selected, the candidate fragment determined using the human mind with the aid of pen and paper, e.g., by representing its vertices and hyperedges through various graphical methods. Here, the claim involves collecting and/or analyzing hypergraph model information and then presenting the information. However, each step in the claimed method corresponds to logical reasoning and information processing tasks that a human can naturally perform with or without aids, like pen and paper. Mapping Document Structure: A human can mentally organize an HTML document by identifying its "fragments" (paragraphs, headers, or buttons) as nodes and recognizing their relationships (e.g., "these three paragraphs belong to the same section") as hyperedges. Contextual Brainstorming: When looking at a specific fragment, a human can naturally identify "candidate style elements"—such as color, font size, or bolding—that might fit based on their understanding of how similar documents are structured. Intuitive Evaluation: Instead of mathematical "embeddings," a human uses mental heuristics and past experience, which serving as mental “embeddings”, to "score" how well a style fits. For example, you can mentally judge if a specific header looks better in a larger font or a different color based on its surrounding context. Selection of Optimal Style: A human can compare these mental "scores" and decide which style element is the most appropriate or visually appealing for that specific fragment, effectively selecting the "highest score" option.
Accordingly, the claim recites the judicial exception of an abstract idea grouped under “mental processes”.
This judicial exception is not integrated into a practical application because the claim doesn’t recite any other additional elements that would preclude the method from being performed in the mind or integrate the claims into a practical application, expect for adding language “machine language”. Here, the claim includes that the hypergraph model is a “machine learning” model. However, applying “machine learning” to the hypergraph model is not a practical application of the abstract idea because it generally links the abstract idea the use in a particular technological environment or field of use (machine learning) – see MPEP 2106.05(h), and/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).
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 lack of integration of the abstract idea into a practical application, generally linking the abstract idea the use in a particular technological environment or field of use (machine learning) and/or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea are not reflective of meaningful limitations. The same cannot provide “significantly more” than the abstract idea. As such, the claim is not patent eligible under 101.
Claims 2-3 and 8-10 include limitations that restrict the data of the graph to specific types and/or purpose of the data. E.g., “wherein the one or more candidate style elements comprise at least one style element having a node in the hypergraph machine learning model” (claim 2), and “wherein the one or more candidate style elements comprise at least one style element not represented in the hypergraph machine learning model” (claim 3). Here, these are reflective of additional elements that do generally link the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h), and are not sufficient under Step 2A Prong B or Step 2B, and these claims also ineligible similar to claim 1.
Claims 4-7 and 11 do not go beyond simply further reciting the abstract idea. Assuming a very small dataset, each of the steps of claim 4-7 and 11 are possible and feasible in the human mind, or in human mind with the aid of pen and paper. A such, the additional elements are not sufficient under Step 2A Prong B or Step 2B, and these claims also ineligible similar to claim 1.
Claims 21 and 25 are directed to a storage medium and system for accomplishing the steps in the method of claim 1 and are ineligible for similar reasons. Furthermore these claims have limitations that such as “A non-transitory computer-readable storage medium, the computer-readable storage medium including instructions that when executed by one or more processors, cause the one or more processors to perform operations comprising” (claim 21) and “A system comprising: a memory component; and one or more processing devices coupled to the memory component, the one or more processing devices to perform operations” (claim 25), which are also not a practical application or significantly more as they are directed to 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).
Additionally claim 21 includes “presenting the candidate fragment with the candidate style element having the highest score”. However, the step of presenting can be done by the human mind, e.g., with the aid of pen and paper, so it doesn’t make the abstract idea any less abstract.
Claim 29 as similar limitation “presenting the candidate fragment with the candidate style element having the highest score as a design recommendation for an HTML document” and is ineligible for similar reasons.
Claims 22 and 26 are directed to a storage medium and system for accomplishing the steps in the method of claim 2 and are ineligible for similar reasons.
Claims 23 and 27 are directed to a storage medium and system for accomplishing the steps in the method of claim 7 and are ineligible for similar reasons.
Claims 24 and 28 are directed to a storage medium and system for accomplishing the steps in the method of claim 8 and are ineligible for similar reasons.
This 101 rejection(s) can be overcome by incorporating limitations that are reflective of a practical application. E.g., improvements in the efficiency of creating new HTML documents beyond what is already known, expected in the art (beyond what is expected by implementing abstract concepts using a typical machine learning model).
No Prior Art Rejection
This office action doesn’t include a prior art rejection, because despite the abovementioned issues, the prior art(s), individually and in combination, fail to fairly teach all the limitations of the claim, as a whole. E.g., Asente; Paul et al. (hereinafter Asente – US 20170169340 A1) is pertinent to claim 1 for disclosing providing design recommendations for new objects based on a machine learning model, ¶¶ 11 and 39, but does teach all of the limitations of the claim, e.g., that the model is a trained hypergraph model with each hyperedge corresponding to one of a plurality of fragment of an HTML document.
Response to Arguments
Previous claim objection(s) and 112(b) rejections(s) have been overcome by claim amendment.
Applicant's 101 arguments have been fully considered but they are not persuasive.
First, the applicant alleges ‘that the claimed step of "determining a hypergraph machine learning model trained on a corpus of hypertext markup language (HTML) documents, wherein the hypergraph machine learning model comprises nodes and hyperedges," as recited in amended claim 1, cannot practically be performed in the human mind, or by a human using pen and paper… Applicant submits that a human mind, by thinking, cannot practically process thousands of HTML documents containing tens of thousands of fragments and even more style elements to train a hypergraph machine learning model that captures the relationships between the fragments and style elements. The sheer volume of data involved in training the hypergraph machine learning model (i.e., thousands of documents, tens of thousands of fragments, and an even greater number of style elements) is far beyond what a human mind can practically store, process, and analyze, even with the aid of pen and paper. " (Applicant's Specification, [0058])’, Remarks Pages 12-13.
The examiner respectfully disagrees at least because:
Humans can process vast amounts of documents, fragments, and styles through semantic understanding and contextual synthesis. Still, while humans excel at understanding context and complex information, our limited memory and slower processing speed create bottlenecks when managing high-volume data.
However, it is noted that the features upon which applicant relies (e.g., processing “thousands” of documents and “tens of thousands of fragments” and even greater number of styles) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
The claim steps are reasonably performed in the human mind, as explained in the 101 rejection section, minus the “machine learning” language (further discussed in the 101 rejection and response(s) below in regard to “practical application”.
Second, the applicant alleges that ‘since a human mind does not include and/or execute learned embeddings from a trained hypergraph machine learning model, scoring candidate fragments with candidate style elements "based on embeddings of the hypergraph machine learning model" is not performable in the human mind’, Remarks ¶ 13.
The examiner respectfully disagrees because:
while it is true the that human mind is limited, here, machine learning is specifically used as a tool to implement the abstract idea.
concerning scoring based on “embeddings”, the human mind is capable of intuitive evaluation. Namely, instead of mathematical "embeddings," a human uses mental heuristics and past experience, which serving as mental “embeddings”, to "score" how well a style fits. For example, you can mentally judge if a specific header looks better in a larger font or a different color based on its surrounding context.
Third, the applicant alleges that the claimed method includes a practical application of the abstract idea because it enhances HTML design technology using an inductive hypergraph machine learning model to recommend high-quality, unseen design fragments as comprehensive style patterns, Instant Specification ¶¶ 39 and 41, addressing the constraints of traditional rule-based methods. See Remarks Pages 14-15.
The examiner respectfully disagrees because:
As claimed, the invention merely uses machine learning as a tool to implement the abstract idea.
The benefits described by the applicant—learning to recommend styles from a corpus, using hyperedges for encoding, and inductive inference of unseen data—represent expected, inherent advantages of using a machine learning model rather than a concrete, practical application that transforms the abstract idea into a patent-eligible invention. Machine learning is known to solve the issue of complex and large scale calculations. The core expectation of using machine learning is to achieve high efficiency and handle massive volumes of data that cannot be managed manually. Essentially, machine learning automates the laborious task of capturing these intricate relationships, turning high-volume data into actionable insights much faster than a human could.
As claimed, machine learning, generally links the abstract idea the use in a particular technological environment or field of use (machine learning) and/or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, as explained in the 101 rejection section above.
Fourth, the applicant relies on argument(s) above to allege patentability for remaining claims.
The examiner respectfully disagrees for reason(s) provided above.
Conclusion
THIS ACTION IS MADE FINAL. 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.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Below is a list of these references, including why they are pertinent:
Asente; Paul et al. (hereinafter Asente – US 20170169340 A1) is pertinent to claim 1 for disclosing providing design recommendations for new objects based on a machine learning model, ¶¶ 11 and 39, based but does teach all of the limitations of the claim, e.g., that the model is a trained hypergraph model with each hyperedge corresponding to one of a plurality of fragment of an HTML document.
Yang; Tsun-Yu et al. (US 20180150585 A1) is pertinent to claim 1 for teach a method for generating a hypergraph for fabricating an integrated circuit, but doesn’t cure the deficiencies of Asente.
Erten, Gamze et al. (US 20010030668 A1) is pertinent to claim 1 for disclosing that the concept of edge detection for an image has been well-known even before 2001, see ¶ 56.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GABRIEL S MERCADO whose telephone number is (408)918-7537. The examiner can normally be reached Mon-Fri 8am-5pm (Eastern Time).
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kieu Vu can be reached at (571) 272-4057. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Gabriel Mercado/Primary Examiner, Art Unit 2171