Claims 1-7, 10-19 and 21-23 are presented for examination. This is a Non- Final 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. DETAILED ACTION Claim Rejections - 35 U.S.C. §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 1-2 0 are rejected under 35 USC 101 as directed to an abstract idea without significantly more. With respect to independent claims 1 , 10 and 16 specifically claim 1 recites “ generate the content label for the interface element according to the contextual label data, the set of label generation rules and the label generation prompt” , which corresponds to evaluating information (contextual label data, rules and prompts) and determining a label. Such evaluation and determination can be performed in the human mind or using pen and paper, and therefore falls within the mental process category of abstract idea. This judicial exception is not integrated into a practical application. At step 2A, prong two, claim(s) 1 , 10 and 16 recites the additional elements of “ a memory component” , “one or more processing device coupled to the memory component and one or more processing device to perform operations”, “non-transitory computer readable medium comprising instructions that, when executed by at least one processor”, receiving a label generation instruction from a client device…, providing the instruction to a label generator neural network…, receiving the generated content label at the client device…, a graphical interface. The label generator neural network is recited at a high level of generality and merely serves as a tool to perform the abstract idea (i.e. generating a label based on input data). The claim does not recite any specific improvement to NN architecture, training or operation. Instead, the NN is used as a generic computing component to execute the mental process. Similarly, the client device and graphical interface are generic components used to receive input and display output. Furthermore, the additional elements perform routine data processing functions (receiving data, processing data, outputting results). These functions are insignificant extra-solution activity and do not impose a meaningful limit on the abstract idea. The claims do not recite any specific improvement to computer technology, a particular machine implementing the process in a non-generic manner, a transformation of an article to a different state or thing, or any other meaningful limitation that applies the abstract idea in a manger that imposes a meaningful limit on the claim. Instead, the additional element simply applies the abstract idea using generic data processing operations, which amounts to implementing the mental processes using a computer environment. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea. The claims 1 , 10 and 16 at step 2B do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As explained with respect to Step 2A Prong Two , the additional elements as recited in step 2A prong 2 recite “ a memory component” , “one or more processing device coupled to the memory component and one or more processing device to perform operations”, “non-transitory computer readable medium comprising instructions that, when executed by at least one processor”, receiving a label generation instruction from a client device…, providing the instruction to a label generator neural network…, receiving the generated content label at the client device…, a graphical interface . No elements individually or in combination adds “significantly more” than the abstract idea hence are no more than well-understood, routine and conventional computer functions that merely apply the abstract idea on a generic computer. When viewed as an ordered combination, these additional elements do not integrate the abstract idea into a practical application and do not add significantly more than the abstract idea itself. According, claim 1 is ineligible under 101. Claims 2-7 are dependent claims and do not recite any additional elements that would amount to significantly more than the abstract idea. Specifically, Claim 2. With respect to step 2A prong 2 “ receiving, from the client device, a label modification prompt comprising an update to at least one of the contextual label data, the set of label generation rules, or the label generation prompt; providing, to the label generator neural network, an updated label generation instruction comprising the update to at least one of the contextual label data, the set of label generation rules, or the label generation prompt; and receiving, at the client device, a modified content label from the label generator neural network ” recites additional elements of insignificant extra solution activity. With respect to step 2B the recited insignificant extra solution activity is recited at a high level of generality which are well-understood, routine and conventional as taught by the prior art of records. Claim 3. With respect to step 2A prong 2 “ wherein the contextual label data comprises context information for the content label comprising one or more of a coordinate location within a graphical interface, a border configuration of the graphical interface, a label category within the graphical interface, a graphical interface element size, or a character font. ” recites additional elements of insignificant extra solution activity. With respect to step 2B the recited insignificant extra solution activity is recited at a high level of generality which are well-understood, routine and conventional as taught by the prior art of records. Claim 4 . With respect to step 2A prong 2 “ wherein: the contextual label data comprises a content hierarchy profile, the content hierarchy profile defining a structured representation of different content categories associated with content labels for the graphical interface; and the label generation prompt comprises a content category from the content hierarchy profile. ” recites additional elements of insignificant extra solution activity. With respect to step 2B the recited insignificant extra solution activity is recited at a high level of generality which are well-understood, routine and conventional as taught by the prior art of records. Claim 5 . With respect to step 2A prong 2 “ wherein the set of label generation rules comprise parameters for one or more of label size constraints, label placement constraints, label color constraints, or label phrasing constraints for the content label. ” recites additional elements of insignificant extra solution activity. With respect to step 2B the recited insignificant extra solution activity is recited at a high level of generality which are well-understood, routine and conventional as taught by the prior art of records. Claim 6 . With respect to step 2A prong 2 “ wherein the set of label generation rules comprise design requirements indicating a plurality of tones associated with content labels for the graphical interface. ” recites additional elements of insignificant extra solution activity. With respect to step 2B the recited insignificant extra solution activity is recited at a high level of generality which are well-understood, routine and conventional as taught by the prior art of records. Claim 7 . With respect to step 2A prong 2 “ wherein the label generation prompt comprises a selected tone from the plurality of tones that is associated with a visual presentation of the graphical interface for an organization. ” recites additional elements of insignificant extra solution activity. With respect to step 2B the recited insignificant extra solution activity is recited at a high level of generality which are well-understood, routine and conventional as taught by the prior art of records. Claim 8 . With respect to step 2A prong 2 “ wherein the label generation prompt comprises a natural language text string indicating a purpose for the content label. ” recites additional elements of insignificant extra solution activity. With respect to step 2B the recited insignificant extra solution activity is recited at a high level of generality which are well-understood, routine and conventional as taught by the prior art of records. Claim 9 . With respect to step 2A prong 2 “ wherein: the label generation prompt comprises a natural language text string indicating a graphical interface element; and receiving the content label comprises receiving a preview of the content label shown in combination with the graphical interface element. recites additional elements of insignificant extra solution activity. With respect to step 2B the recited insignificant extra solution activity is recited at a high level of generality which are well-understood, routine and conventional as taught by the prior art of records. Claims 10 and 16 are similar to claim 1 hence rejected similarly. Claims 11 and 17 are similar to claim 2 hence rejected similarly. Claim 1 2. With respect to step 2A prong 1 “ generating, the content label based on historical training data and utilizing a measure of loss between the historical training data and the content label. ” recites evaluating information (i.e. historical training data and generated content labels) and determining or refining an output based on comparison (loss). Such evaluation and comparison of information to determine an outcome can be performed mentally or with pen and paper and therefore falls within the category of mental processes, which is abstract idea. With respect to step 2A prong 2 “ utilizing the label generator neural network ” , recites additional elements of insignificant extra solution activity. With respect to step 2B the recited insignificant extra solution activity is recited at a high level of generality which are well-understood, routine and conventional as taught by the prior art of records. Claim 13 is similar to claim 3 hence rejected similarly. Claim 14 is similar to claim 8&9 hence rejected similarly. Claim 1 5 . With respect to step 2A prong 1 “ generating an additional content label based on the content label and historical content label generation requests ” recites evaluating information (i.e. historical training data and generated content labels) and determining an additional label based on that information. Such evaluation and determination can be performed in the human mind or using pen and paper and therefore falls within the category of mental processes, which are abstract idea. With respect to step 2A prong 2 “ and providing the additional content label for presentation within the graphical interface. ” recites additional elements of insignificant extra solution activity. With respect to step 2B the recited insignificant extra solution activity is recited at a high level of generality which are well-understood, routine and conventional as taught by the prior art of records. Claim 18 is similar to claims 5&6 hence rejected similarly. Claim 19 & 20 are similar to claim 4 hence rejected similarly. 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 1 - 20 rejected under 35 U.S.C. 103 as being unpatentable over Pasternack et al. (US 20180276561) in view of Block (US 9,684,479 ) further in view of Radmilac et al. (US 2024/0411751) 1. Pasternack teaches, A method comprising: receiving, from a client device, a label generation instruction (Fig 1:110 – user device, Fig 4:406 - receive a request for topic labels and request for topic label – discloses receiving request from client device for generating label, Pasternack) comprising contextual label data ( Fig 4: 408 - teaches request includes contextual information – discloses contextual info corresponds to contextual label data, Pasternack) , … for generating a content label ( Fig 4:410 - teaches determine a set of topic labels – disclosing labels correspond to claimed content label, Pasternack) corresponding to an interface element within a graphical interface ( Fig 2: 120 and 214 - teaches provide topic labels to a composer interface – discloses labels displayed in UI, Pasternack) ; providing the label generation instruction to a label generator neural network (Fig 4:408 – teaches provide content and contextual information to one or more machine learning models, Pasternack) to generate the content label for the interface element according to the contextual label data (Fig 4:408 $ 410– teaches contextual information to machine learning models thereby determine a set of topic labels, Pasternack) ; and receiving, at the client device, the content label from the label generator neural network ( Fig 4:414 - provide topic labels to… interface , Pasternack) . Pasternack does not explicitly disclose, .. the set of label generation rules, and the label generation prompt ; and providing the label generation instruction to a label generator neural network to generate the content label for the interface element according to the contextual label data . However, Block teaches, ... the set of label generation rules ( Col 10: last paragraph, Col 11: first paragraph – teaches determining one or more properties of the label, Block ) It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which said subject matter pertains to incorporate the label property parameterization of Block into the label generation system of Pasternack in order to control and refine the characteristics of generated labels. Pasternack teaches generating labels based on contextual information using ML models, while Block teaches, determining and controlling properties of labels, including text properties such as font, size and color, to influence label appearance and presentation. A POSITA would have recognized that applying such configurable label properties to the labels generated in Pasternack would have been a predictable use of known techniques to improve usability, readability and customization of labels within a graphical interface, yielding no more than predictable results. Radmilac teaches, … the label generation prompt (Abstract – model processes data prompts – disclosing the prompt influences output, Radmilac) It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which said subject matter pertains to incorporate the prompt based input of Radmilac into the label generation system of Pasternack, as enhanced by the label property parameterization of Block, in order to provide additional flexible input mechanism for controlling label generation. Radmilac teaches providing prompts as input to model, which influence generated output. Pasternack teaches generating labels based on contextual information using ML models, a n d Block teaches configuring properties of labels to control their appearance and presentation. A POSITA would have recognized that allowing prompt based input to be provided alongside contextual information and configurable label properties represents a predictable use of known input techniques to refine and control generated labels, improving flexibility and user control within a graphical interface, and yielding no more than predictable results. 2. The combination of Pasternack, Block and Radmilac teach , The method of claim 1, further comprising: receiving, from the client device ( Paragraph 53, Fig 4:404 – teaches receive user input, Pasternack) , a label modification promp t ( “Label modification prompt” is interpreted under BRI as a type of input provided to ML system to influence output. Radmilac explicitly teaches input data such as data prompts thereby establishing that prompts are a form of input. Pasternack teaches receiving user input (fig 4:404 and processing such input to generate labels. Accordantly, receiving input as taught by Pasternack encompasses receiving prompt based input as taught by Radmilac ) comprising an update to at least one of the contextual label data ( F ig 3B:356 – teaches updated topic prediction based on change and content update , the set of label generation rules, or the label generation prompt; providing, to the label generator neural network, an updated label generation instruction (Fig 4:408 – teaches provide content and contextual information to one or more ML models, Pasternack) comprising the update to at least one of the contextual label data (Fig 4: 408 – provide context and contextual information; Fig 3B – teaches content update, Pasternack) , the set of label generation rules (Fig 2B – teaches generate data quality rules, Joyce) , or the label generation prompt (Abstract – input data such as data prompt, Radmilac) ; and receiving, at the client device, a modified content label from the label generator neural network (Fig 3B – teaches updated topic Prediction, and Fig 4:414 – teaches provide topic labels to composer interface) . 3. The combination of Pasternack, Block and Radmilac teach , The method of claim 1, wherein the contextual label data comprises context information (Fig 4:408 – teaches provide context and contextual information to one or more ML models – discloses contextual information is input used to generate labels, Pasternack) for the content label comprising one or more of a coordinate location within a graphical interface a border configuration of the graphical interface, a label category within the graphical interface ( Paragraphs 38-41, Fig 4:410 – teaches determine a set of topic labels, Pasternack) , a graphical interface element size, or a character font. 4. The combination of Pasternack, Block and Radmilac teach , The method of claim 1, wherein: the contextual label data comprises a content profile, the content profile defining a structured representation of different content categories associated with content labels for the graphical interface (Paragraph s 38- 4 1 – teaches interface generator 214 – determines contextual information further defining topic labels, topic applied or removed previously determined further implying structured representation because labels are maintained and organized as part of contextual information , Pasternack ) ; and the label generation prompt comprises a content category from the content profile (Abstract - teaches input data… such as data prompts, disclosing the prompt is model input, Radmilac; Paragraph 40 – teaches content category– topic labels… topics, Pasternack) . The combination of Pasternack, Joyce and Radmilac, do not explicitly recite, “ content hierarchy profile” However, Pasternack teaches, determining contextual information including topic labels associated with content (paragraphs 38-41), which represents context categories and are maintained as part of contextual data, Under BRI, such organized categories correspond to a content hierarchy profile. Radmilac teaches providing prompts as input to a ML model (Abstract, input data… such as data prompts). Because Pasternack teaches content categories (topic labels), a POSITA would understand that such categories can be included as part of input provided to the model. Accordingly, the prompt comprises a content category from the content hierarchy profile is taught. 5. The combination of Pasternack, Block and Radmilac teach , The method of claim 1, wherein the set of label generation rules ( Col 10: last paragraph, Col 11: first paragraph - teaches determining one or more properties of the label, Block ) comprise parameters for one or more of label size constraints, label placement constraints, label color constraints (Col 10: last paragraph, Col 11: first paragraph – teaches object’s visual properties may include text properties (size, color, font… etc.), Block) , or label phrasing constraints for the content label . 6. The combination of Pasternack, Block and Radmilac teach , The method of claim 1, wherein the set of label generation rules comprise design requirements indicating a plurality of tones (Col 10: last paragraph, Col 11: first paragraph – teaches determining one or more properties of the label and object’s visual properties including text properties like font, size color, etc. – thus teaching the recited “tones” under BRI as stylistic characteristics of labels. Block’s control of text properties directly defines different visual and textual styles corresponding to different tones, Block) associated with content labels for the graphical interface (Fig 4:410 – teaches determines a set of topic labels – disclosing Labels in GUI, Pasternack) . 7. The combination of Pasternack, Block and Radmilac teach , The method of claim 6, wherein the label generation prompt comprises a selected tone from the plurality of tones that is associated with a visual presentation of the graphical interface for an organization (Radmilac teaches providing prompts as input to a model (input data… such as data prompts) (Abstract). Block teaches determining label properties including text properties including text properties such as font, size and color which defines stylistic variations of labels (Col 10: last paragraph, Col 11: first paragraph) These variations correspond to a plurality of tones, and selection of such properties correspond to selecting a tone. Because prompts represent input to the model, inclusion of selected stylistic parameters corresponds to a prompt comprising a selected tone. Pasternack teaches generating label within a graphical interface (Fig 4:410) and such label properties define the visual presentation of the interface. Accordingly the combination teaches the limitation) . 8. The combination of Pasternack, Block and Radmilac teach , The method of claim 1, wherein the label generation prompt comprises a natural language text string indicating a purpose for the content label (Radmilac teaches providing prompts as input to a model (“input data… such as data prompts”) (Abstract)(, which are textual inputs that influence generated output. Under BRI, such prompts correspond to natural language text strings . Pasternack teaches generating labels corresponding to content categories (fig 4:410), which indicate the purpose of the label. Accordingly, a prompt including text that guides generation of such labels corresponds to a natural language text string indicating a purpose for the content label) . 9. The combination of Pasternack, Block and Radmilac teach , The method of claim 1, wherein: the label generation prompt comprises a natural language text string indicating a graphical interface element; and receiving the content label comprises receiving a preview of the content label shown in combination with the graphical interface element (Radmilac teaches providing prompts as input to a model (Abstract), which are textual inputs. Under BRI, such prompts correspond to natural language text that may reference elements relevant to output generation. Pasternack teaches generating labels and providing them to a GUI (Fig 4:414), where the label are displayed in conjunction with UI (Fig 2, paragraphs 38-41). Displaying generated labels within the interface corresponds to presenting a preview of the content label in combination with the GUI element) . Claims 10 and 16 are similar to claim 1 hence rejected similarly. Claims 11 and 17 are similar to claim 2 hence rejected similarly. 12. The combination of Pasternack, Block and Radmilac teach , The system of claim 10, further comprising generating, utilizing the label generator neural network (Fig 4:408 – teaches provide to one or more ML models, wherein ML models corresponds to NN under BRI, Pasternack) , the content label based on historical training data and utilizing a measure of loss between the historical training data and the content label (Abstract – teaches generating labels using ML models and further teaches training such models (“the online system trains the machine learning models… employs various machine learning model training methods such as … gradient training”. Training ML models inherently involves the use of training data. Moreover, gradient-based training requires computation of a loss function that measures a difference between predicted outputs and training data and is minimized during training. Accordingly, Pasternack teaches generating labels based on training using historical data and utilizing a measure of loss, Pasternack) . Claim 13 is similar to claim 3 hence rejected similarly. Claim 14 is similar to claim 8&9 hence rejected similarly. 15. The combination of Pasternack, Block and Radmilac teach , The system of claim 10, further comprising: generating an additional content label (Fig 3B – teaches updated topic predictions (new/updated label generated), Pasternack) based on the content label (Paragraphs 38-41 – teaches topic labels are applied or removed based on previous determinations, Pasternack) and historical content label generation requests (Paragraph 38-41 – teaches maintain a record of content with which the user interacted, Pasternack – The recited “historical content label generation requests” are interpreted under BUI as prior inputs and interactions associated with label generation. Pasternack teaches maintaining records of user interactions and previously determined to pic labels which corresponds to historical label generation activity) ; and providing the additional content label for presentation within the graphical interface (Fig 4:414 – teaches provide topic labels to a composer interface, Fig 2 – composer interface, Pasternack. Pasternack teaches generating undated labels (fig 3B – teaches updated topic prediction) based on existing labels (Paragraph 40 – teaches topic labels previously determined) . Pasternack further teaches maintaining records of user interactions with content (paragraph 39), which corresponds to historical inputs associated with label generation. Under BRI, such prior interactions and previously determined labels correspond to historical content label generation requests. Pasternack also teaches providing labels to GUI (Fig 4:414)) Claim 18 is similar to claims 5&6 hence rejected similarly. Claim 19 & 20 are similar to claim 4 hence rejected similarly. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to FILLIN "Examiner name" \* MERGEFORMAT AMRESH SINGH whose telephone number is FILLIN "Phone number" \* MERGEFORMAT (571)270-3560 . The examiner can normally be reached FILLIN "Work Schedule?" \* MERGEFORMAT Monday-Friday 8am-5pm . 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, FILLIN "SPE Name?" \* MERGEFORMAT Ann J. Lo can be reached at FILLIN "SPE Phone?" \* MERGEFORMAT (571) 272-9767 . 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