CTNF 18/466,771 CTNF 79505 DETAILED ACTION Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 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 15-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claims fail to place the invention squarely within one statutory class of invention. The specification merely lists a number of possible hardware devices, i.e. ROM, RAM, hard disk, flash memory, etc., for the computer readable medium. Thus, the specification fails to provide a clear definition for a computer-readable medium. Given the broadest interpretation of a computer readable medium, an open-ended description of the computer readable medium does not EXECLUDE non-statutory subject matter, i.e. signals/transmission medium, from the claimed invention. Claim Rejections - 35 USC § 103 07-20-aia AIA 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. 07-21-aia AIA Claim (s) 1-2, 8-9 and 15-16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Socher et al. (“Socher”, Pub. No. US 2024/0311434) and Fabian et al. (“Fabian”, Pub. No. US 2024/0303440) . Per claim 1, Socher teaches an apparatus comprising: a display; a memory configured to store web pages with content about a software program ([0020]… The customized search system may in turn determine that the search query may be conducted through coding program related data sources such as Stack Overflow, W3Schools, Tutorials Point, or other well-known data sources for learning how to program and asking questions related to computer programming [0080] …The Extract module 232 may follow the links of search results and extract code snippets from the contents on the webpages or cloud files. ); and a processor coupled to the display and the memory configured to: receive a natural language input with a question about the software program from an input field of a user interface (fig. 8A. [0020]… The customized search system may in turn determine that the search query may be conducted through coding program related data sources such as Stack Overflow, W3Schools, Tutorials Point, or other well-known data sources for learning how to program and asking questions related to computer programming, and send the search query to relevant APIs corresponding to these data sources. In this way, a less-seasoned user, such as a rookie developer or even a layperson, may use the customized search system to enter a natural language query for a code project to search through these dedicated code databases, which are often scattered and difficult for a layperson to use, even without a certain level of professional knowledge to enter the most effective search strings. [0093] …As shown in FIG. 8A, in a search for “switch statement java,” the search result may be a list of code snippets presented on panels, rather than a direct link to a website, the search system extracts relevant code snippets and display the results in panels on the search screen. For instance, these code snippets may appear in a collection of panels from a tutorial-based website. A user can then scroll through the snippet panels to view different results or opt to see more information related to the snippet in a particular panel.) , generate an answer to the question about the software program based on execution of artificial intelligence models system on the question from the input field and the web pages stored in the memory (figs. 8A-8D; [0024] … The search system may then conduct a search on relevant coding data sources based on the code search code query and receive search results. The search system may further extract code snippets from webpages of content following the search result links and return the code snippets to the client component. The client component in turn displays the code snippet at a user interface within the IDE, which allows a user to elect to incorporate a code snippet into the current coding project within the IDE. [0031] …when a search query is entered through a search browser window, the search server 110 may engage neural network based AI models to predict relevant data sources for the search, such as coding related data sources. [0093] …As shown in FIG. 8A, in a search for “switch statement java,” the search result may be a list of code snippets presented on panels, rather than a direct link to a website, the search system extracts relevant code snippets and display the results in panels on the search screen. For instance, these code snippets may appear in a collection of panels from a tutorial-based website. A user can then scroll through the snippet panels to view different results or opt to see more information related to the snippet in a particular panel.) ; and display on the display the answer to the question via the user interface (fig. 8A-8D; [0093] …As shown in FIG. 8A, in a search for “switch statement java,” the search result may be a list of code snippets presented on panels, rather than a direct link to a website, the search system extracts relevant code snippets and display the results in panels on the search screen. For instance, these code snippets may appear in a collection of panels from a tutorial-based website. A user can then scroll through the snippet panels to view different results or opt to see more information related to the snippet in a particular panel. ) Socher does not specifically teach using a generative artificial intelligence (GenAI) model to generate answer. However, Fabian clearly teaches utilizing a generative artificial intelligence (GenAI) model to generate answer (figs. 5B and 5C; [0025] …Transformer models, of which LLMs are a type, are a class of deep learning models used in natural language processing (NLP) based on a neural network architecture which use self-attention mechanisms to process input data and capture contextual relationships between words in a sentence or text passage. Transformer models weigh the importance of different words in a sequence, allowing them to capture long-range dependencies and relationships between words. GPT, BERT, ERNIE (Enhanced Representation through kNowledge IntEgration), T5 (Text-to-Text Transfer Transformer), and XLNet models are types of transformer models which have been pretrained on large amounts of text data using a self-supervised learning technique called masked language modeling. This pretraining allows the models to learn a rich representation of language that can be fine-tuned for specific NLP tasks, such as text generation, language translation, or sentiment analysis. [0084] … Continuing with FIG. 5B, with the prompt configured, prompt engine 305 sends the prompt to LLM 330. LLM 330 generates a reply to the prompt and transmits the reply to prompt engine 305. Prompt engine 305 processes the reply, including extracting the natural language explanation enclosed in the appropriate tags, and generates a response for display by user interface 307 in task pane 503. ) Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the teaching of Fabian in the invention of Socher to include GenAI models, because doing so would enhance/improve efficiency by allowing the models to learn a rich representation of language that can be fine-tuned for specific NLP tasks Per claim 2, the modified Socher teaches the apparatus of claim 1, wherein the answer comprises a natural language output that is generated by the GenAI model based on text included in the web pages about the software program in the memory (Socher, fig. 8A-8D; [0092] In some embodiments, as shown in FIG. 7B, the search system use natural language processing to parse a comment (e.g., “#python open file”) inserted into the written code by the user and identify potential search queries that may be useful to the user, performing a search and showing search results on a side panel. [0093]…As shown in FIG. 8 A, in a search for “switch statement java,” the search result may be a list of code snippets presented on panels, rather than a direct link to a website, the search system extracts relevant code snippets and display the results in panels on the search screen. For instance, these code snippets may appear in a collection of panels from a tutorial-based website. A user can then scroll through the snippet panels to view different results or opt to see more information related to the snippet in a particular panel. ). Claims 8-9 are rejected under the same rationale as claims 1-2 respectively. Claims 15-16 are rejected under the same rationale as claims 1-2 respectively . 07-21-aia AIA Claim (s) 3, 7, 10, 14, and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Socher et al. (“Socher”, Pub. No. US 2024/0311434), Fabian et al. (“Fabian”, Pub. No. US 2024/0303440), and Ghosh et al. (“Ghosh”, Pub. No. US 2023/0161561) . Per claim 3, the modified Socher teaches the apparatus of claim 1, wherein the answer comprises code snipets included in the web pages about the software program in the memory as described above, does not specifically teach the answer comprises a diagram that is generated by the GenAI model based on diagrams included in the web pages about the software program in the memory. However, Ghosh teaches the answer comprises a diagram that is generated by the GenAI model based on diagrams included in the web pages about the software program in the memory ([0017] …The illustrative embodiments provide mechanisms for using artificial intelligence (AI) and machine learning (ML) to analyze various software requirements and types and thereby recommend in real time placement of components, connection types, data flow and pipeline sequences, and steps and sequences during creation of digital architectural diagram images aligning with software requirements so that the architectural diagram images are created in an optimized manner. The illustrative embodiments also provide mechanisms for validating software architectural diagrams and generating alerts or notifications when discrepancies or breaches are detected in the blocks or components of various architectural diagrams and models based on security, functionality, firewall, connection types, and services. [0019]… The smart recommendation engine of the illustrative embodiments may apply to any field or technology where AI and ML techniques can be used to ingest and understand textual design specifications and to recognize and classify design elements in a digital image. For example, the illustrative embodiments may aid a user in creating and validating architectural diagrams for a mechanical device based on design specifications. In addition, the illustrative embodiments may apply to architectural floor plans, electrical systems, plumbing, components of a business model, etc. ). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the teaching of Ghosh in the invention of the modified Socher because doing so would enhance software system for all stakeholders that can use the diagrams as a basis for understanding, negotiation, consensus, and communication between one another. Per claim 7, the modified Socher teaches the apparatus of claim 1, wherein the processor is further configured to train the GenAI model, prior to receipt of the natural language input ([0041] … the computing device 200 may receive the input 240 (such as a training dataset) from a networked database via a communication interface. Or the computing device 200 may receive the input 240, such as a user entered search query, from a user via the user interface. [0051] … the neural network based code search module 230 and one or more of its submodules 231-234 may be trained by iteratively updating the underlying parameters (e.g., weights 251, 252, etc., bias parameters and/or coefficients in the activation functions 261, 262 associated with neurons) of the neural network based on a loss objective. For example, during forward propagation, the training data such as past coding activities are fed into the neural network). The modified Socher does not teach wherein the processor is further configured to train the GenAI model based on text descriptions and diagrams of other software programs stored in the memory, prior to receipt of the natural language input. However, Goosh teaches the GenAI model based on text descriptions and diagrams of other software programs stored in the memory, prior to receipt of the natural language input ([0018] …The illustrative embodiments also create a corpus of digital diagram images and their corresponding correlated standard software requirement documents and classify them into standard templates (e.g., industry-based, technology, platform, business need, security, etc.) based on the depth and level of control, security, complexity, etc. The illustrative embodiments automatically create multifarious templated standard architectural diagrams and models using AI-based historical learning and corpus generation using transfer learning. The multifarious diagrams and models may include, for example, system context diagram, use case model, architectural overview diagram, static functional view, data model, interaction diagram, logical location view, zone model, logical operational model, reference architecture, sized and unsized physical operational model. [0019] …The smart recommendation engine of the illustrative embodiments may apply to any field or technology where AI and ML techniques can be used to ingest and understand textual design specifications and to recognize and classify design elements in a digital image. For example, the illustrative embodiments may aid a user in creating and validating architectural diagrams for a mechanical device based on design specifications. ) Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the teaching of Ghosh in the invention of the modified Socher because doing so would enhance efficiency by aiding a user in creating and validating architectural diagrams based on design specifications. Claims 10 and 14 are rejected under the same rationale as claims 3 and 7 respectively. Claim 17 is rejected under the same rationale as claim 3 . 07-21-aia AIA Claim (s) 4, 11 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Socher et al. (“Socher”, Pub. No. US 2024/0311434), Fabian et al. (“Fabian”, Pub. No. US 2024/0303440), Ghosh et al. (“Ghosh”, Pub. No. US 2023/0161561), Hawker (Pub. No. US 2024/0419575) . Per claim 4, the modified Socher teches the apparatus of claim 1, but does not teach wherein the GenAI model comprises a multi-modal large language model (LLM), and the processor is further configured to train the multi-modal large language model to understand a correlation between text descriptions of the software program and diagram components of the software program. However, Shosh teaches train the large language model to understand a correlation between text descriptions of the software program and diagram components of the software program ([0018] …The illustrative embodiments also create a corpus of digital diagram images and their corresponding correlated standard software requirement documents and classify them into standard templates (e.g., industry-based, technology, platform, business need, security, etc.) based on the depth and level of control, security, complexity, etc. The illustrative embodiments automatically create multifarious templated standard architectural diagrams and models using AI-based historical learning and corpus generation using transfer learning. The multifarious diagrams and models may include, for example, system context diagram, use case model, architectural overview diagram, static functional view, data model, interaction diagram, logical location view, zone model, logical operational model, reference architecture, sized and unsized physical operational model. [0019] …The smart recommendation engine of the illustrative embodiments may apply to any field or technology where AI and ML techniques can be used to ingest and understand textual design specifications and to recognize and classify design elements in a digital image. For example, the illustrative embodiments may aid a user in creating and validating architectural diagrams for a mechanical device based on design specifications. ) Hawker teaches a multi-modal large language model (LLM) ([0025] … One specific type of AI model is referred to as a large language model (LLM). An LLM is language model that includes a large number of parameters (often tens of billions or hundreds of billions of parameters). An LLM is often referred to as a generative AI model in that it receives, as an input, a prompt which may include data, and an instruction to generate a particular output. For instance, for such models a generative AI model may be asked to generate a suggested workaround, or generate a set of problematic code characteristics which, when present, cause an issue to manifest. Other types of AI models perform classification. For instance, for such models a prompt may be generated that inputs data that is to be classified into one of a plurality of different categories. The AI model generates an output identifying the classification for the input. Other AI models can include multimodal generative AI models. Such multimodal AI models may take different kinds of inputs (such as images and text) to infer an output. By taking images and text as an input the model may be able to process such things as screenshots of code, images of user interfaces, etc. These and other AI models can be used.) Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the teaching of Ghosh and Hawker in the invention of the modified Socher because doing so would enhance efficiency by aiding a user in creating and validating architectural diagrams based on design specifications, and by allowing AI models to take different kinds of inputs (such as images and text) to infer an output. Claim 11 is rejected under the same rationale as claim 4. Claim 18 is rejected under the same rationale as claim 4 . 07-21-aia AIA Claim (s) 5-6, 12-13 and 19-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Socher et al. (“Socher”, Pub. No. US 2024/0311434), Fabian et al. (“Fabian”, Pub. No. US 2024/0303440), and Podgorny et al. (“Podgorny”, Pub. No. US 2021/0065018) . Per claim 5, the modified Socher teaches the apparatus of claim 1, but does not teach wherein the processor is further configured to receive a response to the answer via the user interface, generate an additional answer based on execution of the GenAI model on the question, the answer, and the response to the answer, and display the additional answer via the user interface. However, Podgorny teaches receive a response to the answer via the user interface, generate an additional answer based on execution of the GenAI model on the question, the answer, and the response to the answer, and display the additional answer via the user interface ([0075] …the user interface provided to the user includes the option for the user to revise the question posted as well as request a new answer if the answer received does not actually answer the question or the use is not satisfied with the answer. If the user selects one of the actions depicted, the action is logged for training purposes. Further, if the user does not select an action and is satisfied with the answer displayed, then the satisfaction of the user (as indicated by not selecting an action) is also logged for training the generative answer model. ). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the teaching of Podgorny in the invention of the modified Socher because doing so would allow on-going training of the generative model and improve output. Per claim 6, the modified Socher teaches the apparatus of claim 1, but does not teach wherein the processor is further configured to receive feedback about the answer via the user interface, retrain the GenAI model based on execution of the GenAI model on the question, the answer, and the feedback, and store the retrained GenAI model in the memory. However, Podgorny teaches receive feedback about the answer via the user interface, retrain the GenAI model based on execution of the GenAI model on the question, the answer, and the feedback, and store the retrained GenAI model in the memory ([0045] … The user 106 can either accept or reject the automated answer and customized answer tips, and the action taken by the user 106 is logged for training the generative answer model 124. [0048]…. In some cases, the user 104 and the user 106 can provide feedback about their experience with the question optimizer 116 and the answer optimizer 122. Further, the users 104 and 106 can provide feedback about the generated question and answer. All of the feedback provided by the users 104 and 106 are shared between the question optimizer 116 and the answer optimizer 122 for improving how both operate going forward in the social computing system 102. [0060] … Further, after the generative answer model 124 is implemented, the generative answer model 124 is continually trained using feedback and logged user actions with the social computing system as part of the training data.) Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the teaching of Podgorny in the invention of the modified Socher because doing so would allow on-going training of the generative model and improve output. Claims 12-13 are rejected under the same rationale as claims 5-6 respectively. Claims 19-20 are rejected under the same rationale as claims 5-6 respectively . Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Turek et al. (Pub. No. US 2019/0317739) discloses Methods and apparatus to automatically generate code for graphical user interfaces are disclosed. An example apparatus includes a textual description analyzer to encode a user-provided textual description of a GUI design using a first neural network. Inquiries Any inquiry concerning this communication or earlier communications from the examiner should be directed to THANH T VU whose telephone number is (571)272-4073. The examiner can normally be reached M-F: 7AM - 3:30PM. 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, Fred Ehichioya can be reached at (571) 272-4034. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. 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If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /THANH T VU/Primary Examiner, Art Unit 2179 Application/Control Number: 18/466,771 Page 2 Art Unit: 2179 Application/Control Number: 18/466,771 Page 3 Art Unit: 2179 Application/Control Number: 18/466,771 Page 4 Art Unit: 2179 Application/Control Number: 18/466,771 Page 5 Art Unit: 2179 Application/Control Number: 18/466,771 Page 6 Art Unit: 2179 Application/Control Number: 18/466,771 Page 7 Art Unit: 2179 Application/Control Number: 18/466,771 Page 8 Art Unit: 2179 Application/Control Number: 18/466,771 Page 9 Art Unit: 2179 Application/Control Number: 18/466,771 Page 10 Art Unit: 2179 Application/Control Number: 18/466,771 Page 11 Art Unit: 2179 Application/Control Number: 18/466,771 Page 12 Art Unit: 2179 Application/Control Number: 18/466,771 Page 13 Art Unit: 2179 Application/Control Number: 18/466,771 Page 14 Art Unit: 2179
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