The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
This communication is responsive to the application filed on 01/29/2024.
Claims 1-20 are pending in this application. This action is made non-final.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1: Statutory Category
The claim is directed to a method, which is a statutory category of invention (process) under 35 U.S.C. §101.
Step 2A, Prong One: Does the Claim Recite a Judicial Exception?
Yes. The claim recites an abstract idea.
Specifically, the claim recites:
Obtaining a textual prompt
Generating relational data indicating relationships
Generating an implementation-specific representation based on that data
These limitations collectively amount to mental processes and data manipulation, which fall within the abstract idea groupings identified by the USPTO, including:
Mental processes (concepts that can be performed in the human mind, such as interpreting text and determining relationships)
Step 2A, Prong Two: Is the Abstract Idea Integrated Into a Practical Application?
No. The claim does not integrate the abstract idea into a practical application.
The claim merely applies the abstract idea using:
A generic NLP model
A generic graphical user interface (GUI) design tool
The claim does not:
Improve the functioning of a computer, NLP model, or GUI system itself
Specify any particular architecture, data structure, model configuration, or rendering technique
Instead, the claim uses generic computer technology as a tool to carry out the abstract idea of interpreting text and generating design information.
Thus, the claim fails to integrate the abstract idea into a practical application.
Step 2B: Does the Claim Recite Significantly More Than the Abstract Idea?
No. The claim does not recite an inventive concept sufficient to transform the abstract idea into patent-eligible subject matter.
The additional elements—such as:
“via at least a natural language processing (NLP) model”
“implementation-specific representation”
“compatible with a GUI design tool”
are generic, well-understood, routine, and conventional uses of computer technology in the field.
The claim does not recite:
A specific NLP technique or novel model architecture
A particular data structure for relational data
The additional elements merely implement the abstract idea on a generic computer and therefore do not amount to significantly more.
Accordingly, claims 1-20 are rejected under 35 U.S.C. 101.
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 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 1-2, 5, 7-10, 13-15, and 17-20 are rejected under 35 U.S.C. 102(2) as being anticipated by Zeng et al. (US 2024/0169623; Hereinafter Zeng).
Re claims 1, 14, and 20, Zeng teaches a method comprising:
obtaining a textual prompt (fig. 8 and [0066], encoding text prompt);
based on the textual prompt, generating, via at least a natural language processing (NLP) model, relational data indicating respective relationships between components of a graphical user interface (GUI) (fig. 8 and [0066], encodes the text prompt to obtain a text prompt embedding representing global information of the text prompt, where the image is generated based on the text prompt embedding. Also see [0068], natural language processing); and
generating, based on the relational data, an implementation-specific representation of the GUI that is compatible with a GUI design tool (fig. 8 and [0066], encodes the text prompt to obtain a text prompt embedding representing global information of the text prompt, where the image is generated based on the text prompt embedding … encoder 325 encodes each of the set of entities to obtain a set of entity embeddings, where the text feature map includes values from the set of entity embeddings at positions corresponding to the set of entities, respectively).
Re claim 2, the rejection of claim 1 is incorporated. Zeng teaches wherein the respective relationships between the components collectively represent an arrangement of the components within the GUI (fig. 8 and [0066], obtaining information and mapping them to the displayed image).
Re claim 5, the rejection of claim 1 is incorporated. Zeng teaches wherein generating the relational data includes invoking an embedding model that maps textual descriptions of the components to predefined specifications of the components (figs. 8-9 and [0066]-[0068], receiving the text prompt and map the information then generate the displayed content accordingly).
Re claims 7 and 15, Zeng teaches wherein generating the relational data includes invoking an embedding model that maps textual descriptions of one or more layouts related to the GUI to predefined specifications of the one or more layouts (figs. 8-9 and [0066]-[0068], receiving the text prompt and map the information then generate the displayed content accordingly).
Re claims 8 and 17, Zeng teaches wherein generating the relational data comprises a plurality of invocations of the NLP model that iteratively define the respective relationships between the components (figs. 8-9 and [0066]-[0068], receiving the text prompt and map the information then generate the displayed image containing what was received from the text prompt).
Re claims 9 and 18, Zeng teaches wherein the plurality of invocations include invoking the NLP model to:
generate a list of GUI features based on the textual prompt (figs 8-9 and [0066]-[0068], receiving information from a text prompt then generate and display the image which contains the information);
generate a list of GUI pages based on the list of GUI features (figs. 8-9, displaying content with requested information from the text prompt); and
generate a list of page titles, one for each of the GUI pages (figs. 8-9, Highway mountain car).
Re claim 10, the rejection of claim 1 is incorporated. Zeng teaches wherein the NLP model is a transformer-based large language model ([0078], A transformer or transformer network is a type of ANN used for natural language processing tasks. A transformer network transforms one sequence into another sequence using an encoder and a decoder. Encoder and decoder include modules that can be stacked on top of each other multiple times).
Re claims 13 and 19, Zeng teaches wherein generating the implementation-specific representation comprises applying a one-to-one mapping between the components and design artifacts supported by the GUI design tool (figs. 8-9 and [0066]-[0068], based on the text prompt, generate displayed content accordingly.
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 of this title, 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 3-4 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Zeng in view of Kumar et al. (US 2019/0250891; Hereinafter Kumar).
Re claim 3, the rejection of claim 1 is incorporated. Zeng does not explicitly teach wherein the components are represented in the relational data as nodes of a tree-like structure. However, it is taught by Kumar ([0012], the model may also include information about the structure of the GUI screen, such as information identifying a hierarchical organization of the user interface components and text content items on the GUI screen).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to add the teaching as seen in Kumar’s content into Zeng’s invention because it would reduce complexity and improve performance.
Re claim 4, the rejection of claim 3 is incorporated. Zeng does not explicitly teach wherein generating the relational data includes determining characteristics of the tree-like structure and the components. However, it is taught by Kumar ([0142], Based on the grouping at different levels, the clustering module may determine a hierarchy of the components in the image and an optimum layout for the GUI screen using the hierarchy and location information).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to add the teaching as seen in Kumar’s content into Zeng’s invention because it would reduce complexity and improve performance.
Re claim 11, the rejection of claim 1 is incorporated. Zeng teaches wherein generating the relational data comprises adding at least some of the textual prompt to a pre-defined NLP prompt (figs. 8-9 and [0066]-[0068], received information from a text prompt and mapped it to generate displayed content).
Claim 12 is rejected under 35 U.S.C. 103(a) as being unpatentable over Zeng in view of Wittekind (US 2025/0077902).
Re claim 12, the rejection of claim 1 is incorporated. Zeng does not explicitly teach wherein the relational data is specified in JavaScript Object Notation (JSON) metadata as a hierarchy of elements that define the components and the respective relationships. However, it is taught by Wittekind ([0044], feed raw data collected about an organization into the NLP 118 with a text-based prompt for the NLP 118 to convert the raw data into programmatic objects (e.g., JSON objects) that correspond to the application data 202(1)-202(N), the organizational data 204(1)-204(N), the tasks 206(1)-206(N), the plans 208(1)-208(N) and corresponding plan data, the classification hierarchies 210(1)-210(N)).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to add the teaching as seen in Wittekind’s content into Zeng’s invention because it would provide a way to store the extracted information such as relationship between components in defined fields.
Conclusion
The prior art made of record on form PTO-892 and not relied upon is considered pertinent to applicant's disclosure. Applicant is required under 37 C.F.R. § 1.111 ( c ) to consider these references fully when responding to this action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TOAN H VU whose telephone number is (571)270-3482. The examiner can normally be reached on PHP 9-5:30 PM.
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/TOAN H VU/Primary Examiner, Art Unit 2178