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 .
Claims 1-20 are pending in this office action.
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 1-10 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.
the claimed invention is directed to an abstract Idea without significantly more.
Claim 1 recites:
“…and one or more processors configured to execute computer-readable instructions that cause the one or more processors to create a generative model using the plain-text requirements and the formatted requirements in the requirements database, wherein the generative model is trained to generate additional formatted requirements from user-provided plain-text requirements….”;
that are certainly a mental process that a person can carry out mentally through observation, evaluation, judgment and/or opinion, or even with the aid of pen and paper.
Claim 1 additionally recite:
“… a memory configured to store a requirements database comprising plain-text requirements and formatted requirements, wherein the formatted requirements are requirements associated with the plain-text requirements that are formatted to a standard…”.
The additional elements “ memory” are directed to generic computer components which are recited at a high level of generality, but to nothing more than an instruction implement “to apply” the abstract idea using a generic computer. See MPEP 2106.05(f).
The additional elements “…configured to store a requirements database comprising plain-text requirements and formatted requirements, wherein the formatted requirements are requirements associated with the plain-text requirements that are formatted to a standard…” are directed to storing, retrieving and manipulating data, that is mere data gathering/storing and does nothing more than adding insignificant extra solution activity to the judicial exception, that is a mere data gathering. See MPEP 2106.05(g).
Claim 1 additional elements do not add meaningful limits to practicing the abstract idea, but to nothing more than an instruction to apply the abstract idea using a generic computer. Thus, the additional elements fail to integrate the judicial exception into a practical application.
Claim 1 does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with regard to integration of the abstract idea into a practical application, the additional elements “memory” are generic computer component used as a tool to perform the abstract idea.
With regard to the additional element “…configured to store a requirements database comprising plain-text requirements and formatted requirements, wherein the formatted requirements are requirements associated with the plain-text requirements that are formatted to a standard…” the court have found and identified retrieving/storing/manipulating information as well understood, routine conventional activity in the art. See MPEP 2106.05(d).
Accordingly, the additional elements do not provide an inventive concept, thus claim 1 is not patent eligible.
-Dependents claims 2-10:
Claim 2 recite “…wherein the plain-text requirements and the formatted requirements are arranged in sets comprised of training data, validation data, and testing data...” ,
Claim 6 recites “wherein the plain-text requirements and the formatted requirements are preprocessed before being arranged into the training data, the validation data, and the testing data”, and
claim 9 recites a description of the retrieved data,:” wherein the deployed training data comprises at least one of: input plain-text requirements provided to the generative model within one or more deployed environments; output formatted requirement generated by the generative model within the one or more deployed environments; and revisions to the output formatted requirements made by users within the one or more deployed environments”,
that all data describing the data information and data manipulation , and as discussed above it fails to integrate the judicial exception into a practical application nor sufficient to amount to significantly more than the judicial exception.
Claims 3, 4, 5 recite “ validating, testing and training” that is a mental process.
Claim 7 recites: “wherein the standard is at least one of: easy approach to requirements syntax (EARS); and constrained language enhanced approach to requirements (CLEAR)” that is data description and as discussed above it fails to integrate the judicial exception into a practical application nor sufficient to amount to significantly more than the judicial exception.
Claim 8 recites: “wherein the memory stores deployed training data received from other systems that implemented the generative model within a deployed environment” that is data retrieving and storing and as discussed above it fails to integrate the judicial exception into a practical application nor sufficient to amount to significantly more than the judicial exception.
Claim 10 recites” additional training” that is a mental process
Dependents claims 2-10 are not patent eligible.
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.
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.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Arora et al (NPL: Advancing Requirements Engineering Through Generative AI: Assessing the Role of LLMs) in view of Raman et al US20250124389A1.
With respect to claim 1, Arora discloses a system comprising:
section 7 “We used ChatGPT to simulate the initial stages of requirements elicitation, wherein requirements engineers acquire project knowledge from stakeholders, review existing documentation, and formulate user requirements and core functionalities………Guided by the project brief, they interacted with ChatGPT to elicit user-story-style requirements over a 45-minute session. Subsequently, Katelyn examined the requirements generated by the participants using ChatGPT against the actual project requirements.”;
wherein the formatted requirements are requirements associated with the plain-text requirements that are formatted to a standard:
section 4.1 “Requirements Specification translates the raw, elicited requirements information into structured and detailed documentation, serving as the system design and implementation blueprint. LLMs can contribute to this process by helping generate well-structured requirements documents that adhere to established templates and guidelines, e.g., the “shall” style requirements, user story formats, EARS template ”;
and one or more processors configured to execute computer-readable instructions that cause the one or more processors to create a generative model using the plain-text requirements and the formatted requirements in the requirements database:
section 3.2: “LLMs help identify unknowns by analyzing existing documentation and high lighting areas of ambiguity or uncertainty. LLMs can help with the completion or suggest alternative ideas that the requirements analysts might have otherwise missed, drawing on their large corpus of training data and connections”.
wherein the generative model is trained to generate additional formatted requirements from user-provided plain-text requirements:
page 130 second paragraph : “ LLMs are advanced AI models designed to process and generate human language by learning patterns and structures from vast amounts of text data.”;
section 4.2” LLMs can streamline the specification process. The unstructured requirements from the elicitation stage can be automatically formatted into structured templates like EARS or user stories (see the example prompt below for EARS and the example for user stories)”;
but not explicitly:
a memory configured to store a requirements database comprising plain-text requirements and formatted requirements:
Raman discloses:
a memory configured to store a requirements database comprising plain-text requirements and formatted requirements:
[0057] “the server devices 204(1)-204(n) hosts the databases 206(1)-206(n) that are configured to store data that relates to requests, search requests, tasks, raw data, natural language data, structured data sets, predetermined parameters, machine learning models, task scores, confidence scores, predictive outputs, and adjacency probability values.
[0077] “At step S406, structured data sets may be generated from the raw data based on predetermined parameters. In an exemplary embodiment, the structured data sets may relate to data that have been processed for consumption by the disclosed invention.
It would have obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to combine the teachings of cited references. One of ordinary skill in the art before the effective filling date of the claimed invention would have been motivated to incorporate the teachings of Raman into teachings of Arora to facilitate automated scoring of software development tasks by using predictive analytics using computer system. The project planning and execution is enhanced by leveraging artificial intelligence to facilitate automated scoring of development tasks. The project management tools are efficiently utilized because accurate estimation of effort required for each of the development tasks is crucial for effective project planning.[Raman 0050].
With respect to claim 2, the rejection of claim 1 is incorporated and furthermore Arora discloses:
wherein the plain-text requirements and the formatted requirements are arranged in sets comprised of training data, validation data, and testing data:
section 2:” In this chapter, we specifically focus on prompting by requirements analysts or other stakeholders directly on generative AI agents, e.g., ChatGPT or fine-tuned LLMs RE agents built on top of these agents. One would generate multiple agents based on LLMs for interaction (via prompting) with the stakeholders (e.g., domain experts, engineering teams, clients, requirements engineers, and end users) and potentially with each other for eliciting, specifying, negotiating, analyzing, validating requirements, and generating other artefacts for quality assurance. Prompting is a technique to perform generative tasks using LLMs [11].Prompts are short text inputs to the LLM that provide information about the task the LLM is being asked to perform. Prompt engineering is designing and testing prompts to improve the performance of LLMs and get the desired output quality. Prompt engineers use their knowledge of the language, the task at hand, and the capabilities of LLMs to create prompts that are effective at getting the LLM to generate the desired output [26].”;
With respect to claim 3, the rejection of claim 2 is incorporated and furthermore Arora discloses:
wherein the one or more processors use the training data to train the generative model
Introduction :” When a user provides input to ChatGPT or Bard, the model processes the text and generates a contextually appropriate response based on the patterns learned during the training process.”;
With respect to claim 4, the rejection of claim 2 is incorporated and furthermore Arora discloses:
wherein the one or more processors use the validation data to validate the generative model:
Section 6.2 “LLMs can assist in the validation phase in several nuanced ways. As highlighted in the Analysis phase, LLMs can aid in the manual review and inspections by flagging potential ambiguities, inconsistencies, or violations based on predefined validation heuristics. LLMs can be utilized to simulate stakeholder perspectives, enabling analysts to anticipate potential misinterpretations or misalignments.”;
With respect to claim 5, the rejection of claim 2 is incorporated and furthermore Arora discloses:
wherein the one or more processors use the testing data to test the generative model.
Sections 2: “Prompt engineering is designing and testing prompts to improve the performance of LLMs and get the desired output quality”;
With respect to claim 6, the rejection of claim 2 is incorporated and furthermore Arora discloses:
wherein the plain-text requirements and the formatted requirements are preprocessed before being arranged into the training data, the validation data, and the testing data.
Section2 : “Prompt engineering involves selecting appropriate prompt patterns and prompting techniques [26]. Prompt patterns refer to different templates targeted at specific goals, e.g., Output Customization pattern focuses on tailoring the format or the structure of the output by LLMs…. Other generic templates include formatting your prompts consistently in “Context, Task and Expected Output” format. Other generic templates include formatting your prompts consistently in “Context, Task and Expected Output” format.”;
With respect to claim 7, the rejection of claim 1 is incorporated and furthermore Arora discloses:
wherein the standard is at least one of: easy approach to requirements syntax (EARS); and constrained language enhanced approach to requirements (CLEAR):
section 4.1 “Requirements Specification translates the raw, elicited requirements information into structured and detailed documentation, serving as the system design and implementation blueprint. LLMs can contribute to this process by helping generate well-structured requirements documents that adhere to established templates and guidelines, e.g., the “shall” style requirements, user story formats, EARS template ”;
With respect to claim 8, the rejection of claim 1 is incorporated and furthermore Arora does not explicitly disclose:
wherein the memory stores deployed training data received from other systems that implemented the generative model within a deployed environment:
Raman discloses:
wherein the memory stores deployed training data received from other systems that implemented the generative model within a deployed environment:
[0089] “ In another exemplary embodiment, the training model may be operable, i.e., actively utilized by an organization, while continuing to be trained using new data. In another exemplary embodiment, the models may be generated using at least one from among an artificial neural network technique”;
It would have obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to combine the teachings of cited references. One of ordinary skill in the art before the effective filling date of the claimed invention would have been motivated to incorporate the teachings of Raman into teachings of Arora to facilitate automated scoring of software development tasks by using predictive analytics using computer system. The project planning and execution is enhanced by leveraging artificial intelligence to facilitate automated scoring of development tasks. The project management tools are efficiently utilized because accurate estimation of effort required for each of the development tasks is crucial for effective project planning.[Raman 0050].
With respect to claim 9, the rejection of claim 8 is incorporated and furthermore Arora does not explicitly disclose: wherein the deployed training data comprises at least one of: input plain-text requirements provided to the generative model within one or more deployed environments; output formatted requirement generated by the generative model within the one or more deployed environments; and revisions to the output formatted requirements made by users within the one or more deployed environments.
Raman discloses:
wherein the deployed training data comprises at least one of: input plain-text requirements provided to the generative model within one or more deployed environments; output formatted requirement generated by the generative model within the one or more deployed environments; and revisions to the output formatted requirements made by users within the one or more deployed environments.
[0075]” In another exemplary embodiment, the raw data may be aggregated from a plurality of sources such as, for example, first-party data sources as well as third party data sources. The raw data may be automatically aggregated via an application programming interface as well as manually inputted by a user via the graphical user interface.”
It would have obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to combine the teachings of cited references. One of ordinary skill in the art before the effective filling date of the claimed invention would have been motivated to incorporate the teachings of Raman into teachings of Arora to facilitate automated scoring of software development tasks by using predictive analytics using computer system. The project planning and execution is enhanced by leveraging artificial intelligence to facilitate automated scoring of development tasks. The project management tools are efficiently utilized because accurate estimation of effort required for each of the development tasks is crucial for effective project planning.[Raman 0050].
With respect to claim 10, the rejection of claim 1 is incorporated and furthermore Arora discloses:
wherein the generative model is trained to generate additional formatted requirements that conform to domain-specific terminologies:
section 3.2 “LLMs can address numerous key challenges in the elicitation phase, including domain analysis. LLMs can rapidly absorb vast amounts of domain-specific literature, providing a foundational structuring and acting as a proxy for domain knowledge source [15]”;
As per claim 11, Arora discloses 11. A method comprising:
preparing data for training of a generative model from the plain-text requirements and the associated requirements formatted according to the standard;
section 4.1 “Requirements Specification translates the raw, elicited requirements information into structured and detailed documentation, serving as the system design and implementation blueprint. LLMs can contribute to this process by helping generate well-structured requirements documents that adhere to established templates and guidelines, e.g., the “shall” style requirements, user story formats, EARS template ”;
training the generative model using the prepared data to convert input plain-text software requirements into output requirements formatted according to the standard;
page 130 second paragraph : “ LLMs are advanced AI models designed to process and generate human language by learning patterns and structures from vast amounts of text data.”;
section 4.2” LLMs can streamline the specification process. The unstructured requirements from the elicitation stage can be automatically formatted into structured templates like EARS or user stories (see the example prompt below for EARS and the example for user stories)”;
but not explicitly:
creating a requirement database, wherein the requirement database contains plain-text requirements and associated requirements formatted according to a standard;
and deploying the generative model for use within one or more deployed environments.
Raman discloses:
creating a requirement database, wherein the requirement database contains plain-text requirements and associated requirements formatted according to a standard:
[0057] “the server devices 204(1)-204(n) hosts the databases 206(1)-206(n) that are configured to store data that relates to requests, search requests, tasks, raw data, natural language data, structured data sets, predetermined parameters, machine learning models, task scores, confidence scores, predictive outputs, and adjacency probability values.
[0077] “At step S406, structured data sets may be generated from the raw data based on predetermined parameters. In an exemplary embodiment, the structured data sets may relate to data that have been processed for consumption by the disclosed invention.
and deploying the generative model for use within one or more deployed environments.
[0088]”Once the training model has been sufficiently trained, the training model may be deployed onto various connected systems to be utilized”;
It would have obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to combine the teachings of cited references. One of ordinary skill in the art before the effective filling date of the claimed invention would have been motivated to incorporate the teachings of Raman into teachings of Arora to facilitate automated scoring of software development tasks by using predictive analytics using computer system. The project planning and execution is enhanced by leveraging artificial intelligence to facilitate automated scoring of development tasks. The project management tools are efficiently utilized because accurate estimation of effort required for each of the development tasks is crucial for effective project planning.[Raman 0050].
As per claim 15, the rejection of claim 11 is incorporated and furthermore Arora discloses:
wherein training the generative model comprises varying system and system responses for domain-specific terminologies:
Section 3.1“The key tasks in elicitation include domain analysis, as is analysis, stakeholders analysis, feasibility analysis, and conducting elicitation sessions with the identified stakeholders using techniques such as interviews and observations”;
Section 3.2 “In addition to stakeholder communication, leveraging LLMs would require other inputs such as existing domain or project-specific documentation (e.g., fine-tuning LLMs) and regulations (e.g., GDPR).”;
As per claim 18, the rejection of claim 16 is incorporated and furthermore Arora discloses:
performing additional training of the generative model using the deployed training data.
Raman discloses:
performing additional training of the generative model using the deployed training data.
[0088] In another exemplary embodiment, the model may include training models such as, for example, a machine learning model which is generated to be further trained on additional data. Once the training model has been sufficiently trained, the training model may be deployed onto various connected systems to be utilized”;
It would have obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to combine the teachings of cited references. One of ordinary skill in the art before the effective filling date of the claimed invention would have been motivated to incorporate the teachings of Raman into teachings of Arora to facilitate automated scoring of software development tasks by using predictive analytics using computer system. The project planning and execution is enhanced by leveraging artificial intelligence to facilitate automated scoring of development tasks. The project management tools are efficiently utilized because accurate estimation of effort required for each of the development tasks is crucial for effective project planning.[Raman 0050].
Claims 12, 13, 14, 16, 17 are the method claim corresponding to system claims 7, 2 ( 3 and 4 and 5) 8, 9, and rejected under the same rational set forth in connection with the rejection of claims 7, 2 ( 3 and 4 and 5) 8, 9, above.
As per claim 19, Arora discloses a method comprising:
dividing data in the requirement database into training data, validation data, and testing data:
section 2:” In this chapter, we specifically focus on prompting by requirements analysts or other stakeholders directly on generative AI agents, e.g., ChatGPT or fine-tuned LLMs RE agents built on top of these agents. One would generate multiple agents based on LLMs for interaction (via prompting) with the stakeholders (e.g., domain experts, engineering teams, clients, requirements engineers, and end users) and potentially with each other for eliciting, specifying, negotiating, analyzing, validating requirements, and generating other artefacts for quality assurance. Prompting is a technique to perform generative tasks using LLMs [11].Prompts are short text inputs to the LLM that provide information about the task the LLM is being asked to perform. Prompt engineering is designing and testing prompts to improve the performance of LLMs and get the desired output quality. Prompt engineers use their knowledge of the language, the task at hand, and the capabilities of LLMs to create prompts that are effective at getting the LLM to generate the desired output [26].”;
training a generative artificial intelligence (AI) model using the training data in the requirement database and generative artificial intelligence techniques to convert plain-text software requirements into requirements formatted according to the standard:
page 130 second paragraph : “ LLMs are advanced AI models designed to process and generate human language by learning patterns and structures from vast amounts of text data.”;
section 4.2” LLMs can streamline the specification process. The unstructured requirements from the elicitation stage can be automatically formatted into structured templates like EARS or user stories (see the example prompt below for EARS and the example for user stories)”;
wherein training the generative AI model comprises varying system and system response for domain-specific terminologies to get accurate generative AI models;
section 3.2 “LLMs can address numerous key challenges in the elicitation phase, including domain analysis. LLMs can rapidly absorb vast amounts of domain-specific literature, providing a foundational structuring and acting as a proxy for domain knowledge source [15]. They can assist in drawing connections, identifying gaps, and offering insights based on the existing literature and based on automated tasks such as as-is analysis, domain analysis, and regulatory compliance. In addition to stakeholder communication, leveraging LLMs would require other inputs such as existing domain or project-specific documentation (e.g., fine-tuning LLMs) and regulations (e.g., GDPR).’;
validating the trained generative AI model with the validation data;
Section 6.2 “LLMs can assist in the validation phase in several nuanced ways. As highlighted in the Analysis phase, LLMs can aid in the manual review and inspections by flagging potential ambiguities, inconsistencies, or violations based on predefined validation heuristics. LLMs can be utilized to simulate stakeholder perspectives, enabling analysts to anticipate potential misinterpretations or misalignments.”;
Sections 2: “Prompt engineering is designing and testing prompts to improve the performance of LLMs and get the desired output quality”;
testing the generative AI model with the testing data;
Sections 2: “Prompt engineering is designing and testing prompts to improve the performance of LLMs and get the desired output quality”;
but not explicitly:
creating a requirement database, wherein the requirement database contains plain-text software requirements and associated requirements formatted according to a standard;
deploying the generative AI model; and training the generative AI model using additional training data derived from information created by the deployed generative AI model.
Raman discloses:
creating a requirement database, wherein the requirement database contains plain-text software requirements and associated requirements formatted according to a standard:
0057] “the server devices 204(1)-204(n) hosts the databases 206(1)-206(n) that are configured to store data that relates to requests, search requests, tasks, raw data, natural language data, structured data sets, predetermined parameters, machine learning models, task scores, confidence scores, predictive outputs, and adjacency probability values.
[0077] “At step S406, structured data sets may be generated from the raw data based on predetermined parameters. In an exemplary embodiment, the structured data sets may relate to data that have been processed for consumption by the disclosed invention.
deploying the generative AI model;
[0088]”Once the training model has been sufficiently trained, the training model may be deployed onto various connected systems to be utilized”;
and training the generative AI model using additional training data derived from information created by the deployed generative AI model.
[0088] In another exemplary embodiment, the model may include training models such as, for example, a machine learning model which is generated to be further trained on additional data. Once the training model has been sufficiently trained, the training model may be deployed onto various connected systems to be utilized”;
It would have obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to combine the teachings of cited references. One of ordinary skill in the art before the effective filling date of the claimed invention would have been motivated to incorporate the teachings of Raman into teachings of Arora to facilitate automated scoring of software development tasks by using predictive analytics using computer system. The project planning and execution is enhanced by leveraging artificial intelligence to facilitate automated scoring of development tasks. The project management tools are efficiently utilized because accurate estimation of effort required for each of the development tasks is crucial for effective project planning.[Raman 0050].
Claim 20 is the method claim corresponding to system claim 2 and rejected under the same rational set forth in connection with the rejection of claim 2 above.
Pertinent arts:
US 20250103471 A1:
A first user input can provide descriptions of a software feature to be tested and various use cases for the software feature. Accordingly, the probabilistic model generates a plurality of test cases for evaluating various aspects of the software feature in accordance with the first user input. The natural language descriptions can be accompanied by a contextual input to constrain the probabilistic model and ensure consistent outputs.
US 20180341645 A1:
to translate and/or support the translation of a natural language description of a software requirement. In some aspects herein, a user may provide an indication or request for one or more software requirement translations;
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRAHIM BOURZIK whose telephone number is (571)270-7155. The examiner can normally be reached Monday-Friday (8-4:30).
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, Wei Y Mui can be reached at 571-270-2738. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications 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://www.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 571-272-1000.
/BRAHIM BOURZIK/ Examiner, Art Unit 2191
/WEI Y MUI/ Supervisory Patent Examiner, Art Unit 2191
Prosecution Insights