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 .
Notice to Applicant
The following is a Non-Final Office action. In response to Examiner’s Final Rejection of 7/7/2025, Applicant, on 10/7/2025, amended claims 1, 8 and 14. Claims 1-20 are pending in this application and have been rejected below.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this
application is eligible for continued examination under 37 CFR 1.114, and the fee set
forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action
has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on
10/7/2025 has been entered.
Response to Arguments
Applicant’s arguments filed October 7, 2025 have been fully considered but they are not persuasive and/or are moot In view of revised rejections. Applicant’s arguments will be addressed herein below in the order in which they appear in the response filed October 7, 2025.
On Pgs. 11-14 the Remarks, with respect to the claim rejection(s) under 35 U.S.C. § 101, Applicant claim 1 reflects an analogous improvement and recitation to that of Example 42 claim1 which was deemed patent eligible subject matter. Applicant states similar to Example 42 converting non-standardized updated information into a standardized format, the present claim 1 "match[es]... the project type extracted from the project description to the tagged requirements of project type.""[M]atching...the project type" is analogous to converting non-standardized information because both elements aggregate information for downstream processing that prepare the information so that the information is represented in "a consistent and unambiguous list" or "standardized." In both cases, the system transforms heterogeneous data sources into a uniform, comparable representation. This ensures the output is usable across multiple contexts and by different users, addressing the same interoperability problem identified in Example 42. In response, Examiner respectfully disagrees and finds the amended claims are tagging, tracking, analyzing, matching and reporting project data which are methods of organizing human activity and mental processes. The claims are utilizing computer components to perform each step. The use of the machine learning is solely used a tool to perform the instructions of the abstract idea. Please see updated 101 analysis for more detail.
On Pgs. 15-16 of the Remarks, with respect to the claim rejection(s) under 35 U.S.C. § 101, Applicant states Like McRO, present claim 1 recites "particular information and techniques and does not preempt approaches that use rules of a different structure or different techniques" (Id).Specifically, the claimed "learning engine" uses natural language processing in combination with a CNN and a SVM to extract requirement data correlated to project types and web-sourced best practices. This recitation imposes specificity, e.g., a defined model architecture and method that outlines the scope of the claim. For example, alternative approaches could employ different architectures (e.g., transformer-based encoders or clustering-based classifiers) or different learning paradigms altogether. Consequently, the claim does not monopolize all computerized requirement-generation methods, but only those using the specific combination of CNN-based feature extraction, SVM-based decision boundary classification, and tag-based ambiguity scoring, as recited. Applicant also states even if USPTO-issued guidance or McRO are deemed insufficient to overcome the rejection, DDRHoldings.LLCv.Hotels.com provides an additional rationale. In response, Examiner respectfully disagrees. The rules disclosed in McRO demonstrates improvements to a specific technological process (i.e., lip synchronization and manipulation of character facial expressions), thus improving computer animation without requiring an artist's constant intermediation with significant support in the specification. In contrast, the present claims contain improvements to the data analysis of an existing business process and not one of a technology or technological field. This is evident in the specification where it states on at least pg. 8, par. 29, “… Referring to FIG. 2, the natural language classifier 23 may include an ensemble of machine learning techniques. NLC models include multiple Support Vector Machines (SVMs) and a Convolutional Neural Network (CNNs).…”. Examiner respectfully reminds Applicant, regardless of the complexity and/or granularity of the type of data, computational data analysis without meaningful limitations within the claims that amount to significantly more than the abstract idea itself is a judicial exception (i.e. abstract idea). Applicants have not identified anything in the claimed invention that shows or even submits the technology is being improved or there was a problem in the technology that the claimed invention solves.
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 an abstract idea without significantly more. Claims 1-7 are directed to a method for creating a requirement document, Claims 8-13 are directed to a system for creating a requirements report and Claims 14-20 are directed to an article of manufacture for creating a requirements document.
Claim 1 recites a method for creating a requirement document, Claim 8 recites a system for creating a requirements report and Claim 14 recites an article of manufacture for creating a requirements document, which include receiving data feeds for requirement data correlated to historical projects describing organizations performing the historical projects; tagging and tracking the project types with the requirement data wherein tagging includes assigning a score of ambiguity ; analyzing a project description from a user entered description provided in paragraph form for a project type to be performed to extract the project type; generating a report of requirements including a consistent and unambiguous list of the tagged requirements matching the project type extracted from the project description on a display of a user performing the project in accordance with the requirements in the report; and performing at least one of the report requirements. As drafted, this is, under its broadest reasonable interpretation, within the Abstract idea grouping of “Methods of Organizing Human Activity”- managing interactions and Mental Processes- evaluation. The recitation of “system”, “web crawler”; “learning engine”; “learning receiver”; “tag writer”; “natural language classifier”; “update generator”; “requirement generator” , “comparator”; “report generator”; “display”; “computer readable storage medium”, “computer readable program” , “database”, “hardware processor” and “computer” does not take claims out of the certain methods of organizing human activity and mental processes grouping. Accordingly, the claim recites an abstract idea.
Furthermore, the claim 1, claim 8 and claim 14 recite using one or more natural language processing with convoluted neural networks and support vector machine techniques. The specification discloses the natural language processing analysis at a high-level of generality, providing examples of different techniques that may be applied. The general use of a natural processing analysis does not provide a meaningful limitation to transform the abstract idea into a practical application. Therefore, currently, the natural language processing is solely used a tool to perform the instructions of the abstract idea. This judicial exception is not integrated into a practical application. The claims primarily recite the additional element of using computer components to perform each step. The “system”, “web crawler”, “learning engine”; “learning receiver”; “update generator”; “tag writer”; “natural language classifier”; “requirement generator” , “comparator”; “report generator”; “display”; “computer readable storage medium”, “computer readable program”, “database”, “hardware processor” and “computer” is recited at a high-level of generality, such that it amounts no more than mere instructions to apply the exception using a computer component. See MPEP 2106.05(f). Additionally, the claim 1, claim 8 and claim 14 recite using one or more natural language processing with convolutional neural network and support machine vector techniques. The general use of natural language processing technique does not provide a meaningful limitation to transform the abstract idea into a practical application. Therefore, currently, the natural language processing is solely used a tool to perform the instructions of the abstract idea. The additional element of “web crawler” – field of use 2106.05(h). Accordingly, the additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims also fail to recite any improvements to another technology or technical field, improvements to the functioning of the computer itself, use of a particular machine, effecting a transformation or reduction of a particular article to a different state or thing, and/or an additional element applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception. See 84 Fed. Reg. 55. In particular, there is a lack of improvement to a computer or technical field in project management.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of “system”, “web crawler”, “learning engine”; “learning receiver”; “tag writer”; “natural language classifier”; “update generator”; “requirement generator” , “comparator”; “report generator”; “display”; “computer readable storage medium”, “computer readable program”, “database” ; “hardware processor” and “computer” is insufficient to amount to significantly more. (See MPEP 2106.05(f) – Mere Instructions to Apply an Exception – “Thus, for example, claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible.” Alice Corp., 134 S. Ct. at 235). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
The claim fails to recite any improvements to another technology or technical field, improvements to the functioning of the computer itself, use of a particular machine, effecting a transformation or reduction of a particular article to a different state or thing, adding unconventional steps that confine the claim to a particular useful application, and/or meaningful limitations beyond generally linking the use of an abstract idea to a particular environment. See 84 Fed. Reg. 55. Viewed individually or as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself. With regards to receiving data and step 2B, it is M2106.05(d)- Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information). Regarding Step 2B and the additional element of “natural language processing with convoluted neural network” it is MPEP 2106.05(f) – Mere Instructions to Apply an Exception. When the convoluted neural network has already been trained and is simply used to make a decision, then
it is just a complex mathematical exercise. As stated in the claim and specification the convoluted neural network is simply applied to return a result. Neither the result nor the rules (CNN) provide a practical application or significantly more than the identified abstract idea.
Examiner concludes that the additional elements in combination fail to amount to significantly more than the abstract idea based on findings that each element merely performs the same function(s) in combination as each element performs separately. The claim is not patent eligible. Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually.
Dependent Claims 2-7, 9-13 and 15-20 recite the additional elements storing tagged requirements to project type; receiving data feeds; updating tagged requirements to project type with requirement/project type pairs from the report of requirements; the requirements may be based on technology, industry, location; generating of the report of requirements including the tagged requirements matching the project type extracted from the project description includes considering an initial list of requirements from the project description, and providing tagged requirements to fill missing requirements; and further narrowing the abstract idea. These recited limitations in the dependent claims are mere instructions for applying the abstract idea on a computerized system which are operating such that they do not amount to significantly more than the above-identified judicial exceptions in Claims 1, 8 and 14. Regarding Claims 2, 5, 9, 12 , 13, 15 18 and 20 and the additional element of “database” and “update generator” and Claims 3, 10 and 16 recite the additional element of “webcrawler“ and it is M2106.05(d)- Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information) and MPEP 2106.05(d)(II) i. storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc..
Reasons Claims are Patentably Distinguishable from the Prior Art
Examiner analyzed Claims 1-20 in view of the prior art on record and finds not all claim limitations are explicitly taught nor would one of ordinary skill in the art find it obvious to combine these references with a reasonable expectation of success as discussed below.
In regards to Claim 1 (similarly Claim 8 or Claim 14), the prior art does not teach or fairly suggest:
“… employing a learning engine, wherein the learning engine uses natural language processing with a convolutional neural network to extract requirement data correlated to project type from the historical projects and learns relationships of internet based news publications and requirements; tagging the project types with the requirement data, wherein tagging includes assigning a score of ambiguity;… update and validate requirement data against industry standards and evolving project needs, the learning engine employing the web crawler to define new requirements from internet based requirement sources”.
Examiner finds that McLees et al. (U.S. PG Publication 20150066563 A1) teaches systems, methods, and computer program products are defined that provide for managing a technical project. In particular, embodiments provide for an end-to-end project delivery tool that includes planning, defining the requirements, designing, building, approving and providing communication/reporting of the technical project. The technical project management delivery tool herein described automates the capture of data and other functionality, such as identification of technical requirements, design infrastructure, build workflows and the like, to add increased efficiency as it relates to the design and build phases of a technical project. (see Abstract). In particular, McLees discloses determining the one or more technical requirements associated with the business requirement based on historical technical project business requirement-to-technical requirement associations (see par. 0011), and tagging project types (see par. 0018).
Peled et al. (U.S. PG Publication 20180276304A1) teaches systems and methods for relationship detection using a web crawling (see par. 0024-0025). In particular, Baughman discloses using a web crawler accessing standard best practices from internet based news publications describing organizations performing the historical projects (see par. 0124-0127, 0049).
Iqbal et al., "A Bird's Eye View on Requirements Engineering and Machine Learning," 2018 25th Asia-Pacific Software Engineering Conference (APSEC), 2018, pp. 11-20 teaches machine learning used in requirements engineering (see abstract). In particular, Iqbal discloses applying convolutional neural networks to automatically classify the content elements of natural language requirements specifications as "requirement" or "information". The authors claim their approach increases the quality of requirements specifications as it distinguishes content that is relevant for specific software construction activities. (see pg. 17).
Although McLees, Peled and Iqual teach analyzing requirements elements of the claim, none of the cited prior art, singularly or in combination, teach or fairly suggest, the combination of, the extracting and tagging by the convolutional network machine learning.
Additionally, Examiner finds Monk et al. (US9678949 ) teaches A Vital Text Analytics System (VTAS), incorporating a repository of enterprise terms or concepts, is one that improves the readability and fidelity of technical specifications, instructions, training manuals requirements engineering documents and other related engineering documents, typically from a single organization or workgroup. The system stresses ontological analysis of a corpus of related documents, and applies a suite of computational tools that supports the identification and assessment of risk in evaluating the content of the documents, as well as providing statistical measures reflecting the frequency and severity of document features that threaten comprehension. (see Abstract). In particular, Monk discloses ambiguity score. Specifically, VTQI score assists in identifying the inconsistencies and areas that may lead to the misinterpretation of similar concepts within a company or industrial area usage model and serves as a broad assessment of the clarity and intelligibility of a document or group of documents in a way that can be useful in project management, helping to determine for example, when a requirements document is mature enough to support the intended purpose or identifying the authors who most effectively achieve high quality documents. (see Col 5).
Hertz et al. (US20190354544) teaches Systems and techniques for determining relationships and association significance between entities are disclosed. The systems and techniques automatically identify supply chain relationships between companies based on unstructured text corpora. The system combines Machine Learning models to identify sentences mentioning supply chain between two companies (evidence), and an aggregation layer to take into account the evidence found and assign a confidence score to the relationship between companies. (Abstract). Therefore, for at least these reasons, Claim 1 (similarly Claim 8 and Claim 14) is eligible over the prior art.
The dependent claims 2-7, 9-13 and 15-20 are eligible under 35 U.S.C. 102 and 35 U.S.C. 103 because they depend on claim 1, 8 and 14 that is determined to be eligible.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure: US Patent Publication No. US 20210125124 A1 to Meharwade et al. - A project management platform may train a machine learning model with historical project data to generate a trained machine learning model that determines or analyzes a release schedule of a project. The project management platform may receive new project data identifying project information associated with a new project. The project management platform may perform natural language processing on the new project data to convert the new project data to processed new project data.”
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Chesiree Walton, whose telephone number is (571) 272-5219. The examiner can normally be reached from Monday to Friday between 8 AM and 5 PM. If any attempt to reach the examiner by telephone is unsuccessful, the examiner’s supervisor, Patricia Munson, can be reached at (571) 270-5396. The fax telephone numbers for this group are either (571) 273-8300 or (703) 872-9326 (for official communications including After Final communications labeled “Box AF”).
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Applicants are invited to contact the Office to schedule an in-person interview to discuss and resolve the issues set forth in this Office Action. Although an interview is not required, the Office believes that an interview can be of use to resolve any issues related to a patent application in an efficient and prompt manner.
Sincerely,
/CHESIREE A WALTON/ Examiner, Art Unit 3624