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 .
Claim Objections
Claims 1-20 are objected to because of the following informalities:
Claims 1, 10 and 19 recite on Lines 9, 5 and 5, respectively: “the technical foundation” which the Office believes should be “a [[the]] technical foundation”.
Claim 1, Line 13 recites: “the tech environment” which the Office believes should be “the technical environment”.
Claims 1, 10 and 19 recite on Lines 19, 13 and 12, respectively: “the ecosystem” which the Office believes should be “the tech ecosystem”.
Claims 9 and 18 recite on Lines 3 and 4, respectively: “the technological debt landscape” which the Office believes should be “the technological debt
Claims 10 and 19 recite on Lines 3 and 3, respectively: “the process information” which the Office believes should be “[[the]] process information”.
Claims 10 and 19 recite on Lines 5 and 5, respectively: “the technology information” which the Office believes should be “[[the]] technology information”.
Claims 10 and 19 recite on Lines 5 and 5, respectively: “the system” which the Office believes should be “a [[the]] system”.
Claims 10 and 19 recite on Lines 13 and 12, respectively: “the analyzed data” which the Office believes should be “[[the]] analyzed data”.
Claim 17 recites on Line 3: “summarization;, and” which the Office believes should be “summarization;[[,]] and”.
Claim 19, Line 8 recites: “the technical environment” which the Office believes should be “a [[the]] technical environment”.
Claims 2-9, 11-18 and 20 are also objected to since they depend from objected Claims 1, 10 and 19 respectively, and as such inherit the same deficiencies.
Appropriate correction is required.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f):
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f). The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f). The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) except as otherwise indicated in an Office action.
This application includes one or more claim limitations in Claims 1-9 that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitations are:
“a data acquisition system configured to acquire data,” “an analysis system configured to integrate and analyze the acquired data,” “an intelligent assessment system configured to synthesize the analyzed data” and “a management strategy system configured to generate actionable strategies” in Claim 1, and as further expanded on and defined in Claims 2-9.
Because these claim limitations are being interpreted under 35 U.S.C. 112(f) Claims 1-9 are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f).
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claim limitations “a data acquisition system configured to acquire data,” “an analysis system configured to integrate and analyze the acquired data,” “an intelligent assessment system configured to synthesize the analyzed data” and “a management strategy system configured to generate actionable strategies,” in Claim 1, invokes 35 U.S.C. 112(f). However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. The written description does not provide any corresponding structure, material, or acts for performing the entire claimed function regarding these claim limitations. Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b).
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f);
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
Claim Rejections - 35 USC § 103
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 1-5, 9-14 and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Fox et al. (US PGPUB 2022/0051162; hereinafter “Fox”) in view of Kanthan et al. (US PGPUB 2026/0050423; hereinafter “Kanthan”), Kosgi et al. (US Patent 12,197,313; hereinafter “Kosgi”) and Dziubinski et al. (US Patent 12,307,247; hereinafter “Dziubinski”).
Claim 1:
Fox teaches a system for evaluating and managing technological debt in a technical environment, the system comprising:
(a) a data acquisition system configured to acquire data from multiple sources including process information, technology information, governance information, and a tech ecosystem ([0074] “The connector component 204 of the dashboard framework system may be incorporated to gather data from multiple tools, systems and applications 130 in a way that the data is gathered via a predefined function initiated in a specified time period.” [0073] “Each of the sub-connectors 204a, 204b, 204c, 204d, . . . , 204n are respectively coupled to one of a plurality of third party program, project, or such source systems 130 or applications, such as 132a, 132b, 132c, 132d, . . . , 132n, such a JIRA, a Jenkins, a SonarQube, a ServiceNow, an Excel file programs.”);
(b) an analysis system configured to integrate and analyze the acquired data, wherein the analysis system is adapted for ([0074] “The analytical component 112 may be instructed to receive the data from the connector component 204, applying business formulae onto the data and deriving useful analytics… The analytical component 112 may further be facilitated to perform analytics in order to derive descriptive analytics, predictive analytics and prescriptive analytics from the data supplemented to the analytical component 112.”):
(i) evaluating operational procedures within the tech ecosystem based on the process information ([0108] “At step/block 402, receive, by a processor 140 of an application server 102, data from business attributes of at least one source program 130.” [0101] “Portfolio view provides a bird's-eye view at enterprise level of operational metrics.” [0059] “operational (e.g. productivity, predictive etc.) metrics providing transparency across all stakeholders throughout the process.” [0080] “the analytical component 112 derives analytics from the supplemented data mainly for the attributes including… productivity.”);
(ii) assessing the technical foundation of the system based on the technology information ([0080] “The analytical component 112 performs analytics, using specific calculations… while providing flexible and run-time view of information in terms of technical… metrics.” [0084] “The test case module 214 is a review module which reviews the work being performed by individuals in an organization. The test case module 214 of the analytical component 112 identifies test case scenarios based on multiple parameters including but not limited to coverage percentage, completion percentage, executed pass percentage and executed failure percentage which assist in analysing the performance of a project or program as being developed or worked upon.” [0059] “technical (e.g. code quality, technical debt etc.)…metrics providing transparency across all stakeholders throughout the process”);
(iii) incorporating and evaluating governance information to ensure compliance ([0039] “It is another object of the subject matter to provide agile and application service management (ASM) dashboards in a single tool and service level agreement (SLA) based tracking on volumetric & timeliness metrics for ASM projects.” [0101] “The ASM dashboard… showcases summarized visualization of… change request by service SLAs… Portfolio view provides a bird's-eye view at enterprise level of operational metrics including… SLA compliance.”);
(iv) analyzing the tech environment to understand its impact ([0082] “agile maturity index is calculated by an analytical component 112.” [0095] “Agile maturity, such as 304f features agile maturity index scores… This refers to details on five parameters organization culture, people, process, tools and technology, project management. An agile maturity index scores multiple parameters… Drilling down into each of the parameters further is possible for better clarity and more details on the current state versus set target state for continuous improvement steps and actions… The agile maturity index score indirectly impacts the performance of metrics in other categories like productivity, predictability, quality, and ROI,” see Chart 312 in Fig. 3.2.); and
(c) an intelligent assessment system configured to synthesize the analyzed data to assess technical debt within the ecosystem and identify areas for improvement ([0059] “A few of the important features, the solution offers are… set targets and thresholds for key metrics, send alerts when thresholds are not met… interfacing with a variety of application lifecycle management (ALM) tools/data sources in order to perform descriptive analytics, predictive & prescriptive analytics. Also, it's wide range of technical(e.g. … technical debt…)… metrics.” [0095] “An agile maturity index scores multiple parameters and showcases the agile health index… Drilling down into each of the parameters further is possible for better clarity and more details on the current state versus set target state for continuous improvement steps and actions,” wherein the “target state” serves to “identify areas for improvement,” i.e. shown in Chart 312 of Fig. 3.2. [0098] “the static code analysis measurements covering code complexity, code coverage, function count and technical debt.”).
With further regard to Claim 1, Fox does not teach the following, however, Kanthan teaches:
incorporating and evaluating governance information to ensure compliance with regulatory standards ([0176] “To begin, the analysis computing entity 106 receives one or more codes for analysis 202 and one or more corresponding parameters 203, which provide one or more objectives for enhancing their corresponding code… the parameters 203 includes purpose parameters.” [0177] “Examples of purpose parameters include… adherence to regulatory requirements.” [0453] “An AI code evaluation tool performs one or more evaluation functions from a list of evaluation functions. The list includes… compliance audits.” [0772] “compliance audits to ensure that a system, as a whole, is complying with laws such as GDPR, CCPA and other regional data regulations.”);
(v) incorporating industry knowledge to align the assessment with current trends and standards ([0453] “An AI code evaluation tool performs one or more evaluation functions from a list of evaluation functions. The list includes… compliance checklists… compliance testing.” [0773] “Compliance Testing (AI Evaluation Functions) refers to the process of assessing whether an AI system adheres to specific regulatory, legal, ethical, and industry standards.” [0775] “Compliance Checklist (AI Evaluation Functions) refers to the method of auditing a comprehensive audit of a software system to verify the systems adherence to regulatory requirements including but not limited to: organizational regulations, industry standards, and governmental regulations.”); and
(vi) evaluating new tools and technologies to identify opportunities for modernization ([0176] “To begin, the analysis computing entity 106 receives one or more codes for analysis 202 and one or more corresponding parameters 203, which provide one or more objectives for enhancing their corresponding code… the parameters 203 includes purpose parameters.” [0177] “Examples of purpose parameters include… adapt to new technologies.”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system as disclosed by Fox with the further compliance and new technology analysis as taught by Kanthan as this “improves the efficiency of software in a reliable and trustworthy manner” (Kanthan [0114]).
With further regard to Claim 1, Fox in view of Kanthan does not teach the following, however, Kosgi teaches:
(d) a management strategy system configured to generate actionable strategies based on the assessment of technical debt, wherein the strategies include at least a detailed report of technical debts by applications (Col. 16 ll. 37-40: “the method can further include displaying, by the user display device and via a heat map, respective technical debt scores per application, type of technical debt, remediation strategy, or combination thereof,” wherein the “technical debt scores per application” and “remediation strategy” are the claimed “technical debts by applications” and “actionable strategies” respectively.),
a report of technical debts by business functions (Col. 2 ll. 28-34: “the type of technical debt is at least one of: architecture debt, code debt, data debt, defect debt, design debt, documentation debt, governance debt, infrastructure debt, operations and maintenance debt, people debt, privacy protection debt, process debt, requirements debt, security debt, service debt, test automation debt, test debt, user experience debt, or a combination thereof,” wherein the different types of “technical debt” represent the “technical debts by business functions.” Col. 26 ll. 20-23: “FIG. 7 is a diagram depicting… a graphical user interface of a technical debt management machine.” Col. 26 ll. 25-30: “The search results page 704 can depict information describing a technical debt type 706. The search results page 704 can include information, per-technical debt type, describing: a weight 708, a score 710, a number of applicable objects 712 (i.e. a number of scanned objects), a number of technical debt objects 714…,” see Fig. 7 for further details.), and
a dashboard for visualizing tech debt data and progress in addressing identified debts (Col. 25 ll. 42-48: “The executive dashboard 504 can depict information describing a technical debt score for an enterprise (e.g. a numerical score for a plurality of software platforms of a company), a technical debt score heat map indicating details of a technical debt score, a list of technical debt by type and priority, a remediation plan, a list of remediation actions, or a combination thereof.”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system as disclosed by Fox in view of Kanthan with the management strategy system as taught by Kosgi in order to “advantageously mitigate technical debt and effects thereof” (Kosgi Col. 13 ll. 46-47).
With further regard to Claim 1, Fox in view of Kanthan and Kosgi does not teach the following, however, Dziubinski teaches:
an interactive chatbot for stakeholder engagement (Col. 37 ln. 58 – Col. 38 ln. 13: “Communication Module—Interactive Prompting Feature Capabilities is described. The AI-Enhanced Programming Code Value Assessment System includes an interactive prompting feature that enables users to engage directly with the Text-Based AI Models with Contextual Understanding (TBM-CUs) through a chat-like interface within the system's dashboard. This feature provides the following capabilities: Real-Time Dialogue: Users can ask questions and receive instant, context-aware responses from the TBM-CUs, allowing them to gain deeper insights into code impact, functionality, and the rationale behind specific evaluations. Context-Aware Responses: The TBM-CUs utilize the context of the entire project to provide precise and relevant responses, ensuring that communications are accurate and pertinent to the current project stage and user concerns. Plain Language Processing: The TBM-CUs articulate responses in plain language, making technical information accessible and understandable to non-technical stakeholders, bridging communication gaps within project teams.”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system as disclosed by Fox in view of Kanthan and Kosgi with the interactive chatbot as taught by Dziubinski as this “simplifies the interpretation of complex technical data, engages stakeholders directly, and enhances overall project communication and success” (Dziubinski Col. 38 ll. 15-17).
Claim 2:
Fox in view of Kanthan, Kosgi and Dziubinski teaches the system of Claim 1, and Fox further teaches wherein the data acquisition system further includes capabilities for interfacing with external sources to gather industry knowledge and information on new tools and technologies to receive latest advancements and trends ([0064] “the interface(s) 110 may enable the system 104 to communicate with other computing devices, such as web servers and external data servers, such as a DB server 120… The windows service 108 has a connector service 116… The internet information services (IIS) 106 communicates with a database (DB) server… The connector service 116 is further connected to and communicates with source systems 130… The source systems… together represented as a program are collections or a plurality of programs, projects, tools, systems, excel, enterprise solutions, databases, and such business tools that needs monitoring and are the source of all dashboard business attributes. These attributes/metrics are analysed to provide a basis of business decisions.”).
Claim 3:
Fox in view of Kanthan, Kosgi and Dziubinski teaches all the limitations of claim 1 as described above. Fox in view of Kosgi and Dziubinski does not teach the following, however, Kanthan teaches:
wherein the analysis system for evaluating operational procedures includes a generative AI model processing at least one of (i) images, (ii) text, (iii) audio, or (iv) video to generate an understanding of operational efficiencies and deficiencies ([0175] “the analysis computing entity 106 executes the intelligent software of the code enhancement system 104… Recall that intelligent software includes software that performs one or more of artificial intelligence (AI), machine learning (NIL), data processing, data storage, data analysis, etc.” [0117] “Such neural networks have a variety of applications, which include image and voice recognition, natural language processing.” [0179] “an AI tool is a tool that incorporates artificial intelligence, large language modeling (LLM), machine learning, natural language processing (NLP), and/or the like.” [0206] “An AI code enhancing tool generally functions to refactor code, optimize code for hardware efficiencies, accelerate code for software efficiencies, translate code, migrate code, generate new code, modify existing code, and/or simulate code.”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system as disclosed by Fox in view of Kosgi and Dziubinski with the AI model usage as taught by Kanthan as this in order “to enhance code and/or evaluate code in accordance with one or more parameters” (Kanthan [0175]).
Claim 4:
Fox in view of Kanthan, Kosgi and Dziubinski teaches all the limitations of claim 1 as described above. Fox in view of Kosgi and Dziubinski does not teach the following, however, Kanthan teaches:
wherein the analysis system for assessing the technical foundation includes one or more trained neural network models for data classification ([0117] “one or more computing entities are configurable to provide a neural network that includes an input layer, one or more intermediate layers, and an output layer. A neural network may be implemented in a variety of ways… Such neural networks have a variety of applications, which include image and voice recognition, natural language processing, large language modeling (LLM),” wherein an “LLM” is a type of “trained neural network model”. [0179] “an AI tool is a tool that incorporates artificial intelligence, large language modeling (LLM), machine learning, natural language processing (NLP), and/or the like.” [0197] “The code sectioning GUI allows the user to select which code sectioning tools to use (i.e., which AI code sectioning tools…).” [0198] “With the code sectioning tools selected, the code processing unit 220 sections the code using each of the selected AI code sectioning tool and/or selected proprietary AI code sectioning tool to produce one or more AI sectioned codes.” [0242] “As a further general example, code is divided based on one or more of header comments, functions & methods, object recognition, classes & objects, inheritance, encapsulation, modules & packets, regions, logical separation, configuration files, version control, framework-specific practices, modular recognition, micro service recognition, layer/component recognition, and namespace,” wherein the “sectioning” of code as described above is a type of “data classification”.) and
one or more generative AI models for identifying gaps and areas requiring technological updates or enhancements ([0179] “an AI tool is a tool that incorporates artificial intelligence, large language modeling (LLM), machine learning, natural language processing (NLP), and/or the like.” [0209] “code enhancement module 224 uses the selected AI code enhancement tool(s) and/or selected proprietary AI code enhancing tool(s) on an AI sectioned code or a sectioned code in light of the objective of the parameters to produce AI enhanced code.” [0427] “code enhancement module 224 determines whether any of the versions of sectioned code need refactoring… based on the parameters… update… improve software efficiencies, improve hardware efficiencies, expand functionality of code, add new code to existing code… add new features to existing code, improve data management, improved data analysis… adapt to new technologies…,” wherein Paragraph [427] of Kanthan discloses additional types of parameters that are indicative of “gaps and areas requiring technological updates or enhancements.” [0428] “When refactoring is needed, the method continues at step 508, where the code enhancement module 224 identifies a set of viable AI refactoring tools.”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system as disclosed by Fox in view of Kosgi and Dziubinski with the AI model usage as taught by Kanthan as this in order “to enhance code and/or evaluate code in accordance with one or more parameters” (Kanthan [0175]).
Claim 5:
Fox in view of Kanthan, Kosgi and Dziubinski teaches all the limitations of claim 1 as described above. Fox in view of Kosgi and Dziubinski does not teach the following, however, Kanthan teaches:
wherein the analysis system for incorporating and evaluating governance information includes one or more generative AI models to analyze regulatory requirements ([0179] “an AI tool is a tool that incorporates artificial intelligence, large language modeling (LLM), machine learning, natural language processing (NLP), and/or the like.” [0209] “code enhancement module 224 uses the selected AI code enhancement tool(s) and/or selected proprietary AI code enhancing tool(s) on an AI sectioned code or a sectioned code in light of the objective of the parameters to produce AI enhanced code.” [0427] “code enhancement module 224 determines whether any of the versions of sectioned code need refactoring… based on the parameters… adherence to regulatory requirements.” [0428] “When refactoring is needed, the method continues at step 508, where the code enhancement module 224 identifies a set of viable AI refactoring tools.” Further, [0177] “Examples of purpose parameters include… adherence to regulatory requirements.” [0453] “An AI code evaluation tool performs one or more evaluation functions from a list of evaluation functions. The list includes… compliance audits.” [0772] “compliance audits to ensure that a system, as a whole, is complying with laws such as GDPR, CCPA and other regional data regulations.) and
generate reports highlighting compliance gaps and recommended actions ([0209] “While the code is being enhanced, the dashboard processing module 230 displays and updates the code enhancing GUI 238 to enable the user to monitor the code enhancing and/or to participate by making decisions on code enhancing questions as they may arise.”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system as disclosed by Fox in view of Kosgi and Dziubinski with the AI model usage as taught by Kanthan as this in order “to enhance code and/or evaluate code in accordance with one or more parameters” (Kanthan [0175]).
Claim 9:
Fox in view of Kanthan, Kosgi and Dziubinski teaches all the limitations of claim 1 as described above. Fox in view of Kanthan and Kosgi does not teach the following, however, Dziubinski teaches:
wherein the management strategy system includes a chatbot including one or more natural language processing and machine learning models to facilitate dynamic interaction with stakeholders regarding the technological debt landscape and available mitigation strategies (Col. 37 ln. 58 – Col. 38 ln. 13: “Communication Module—Interactive Prompting Feature Capabilities is described. The AI-Enhanced Programming Code Value Assessment System includes an interactive prompting feature that enables users to engage directly with the Text-Based AI Models with Contextual Understanding (TBM-CUs) through a chat-like interface within the system's dashboard. This feature provides the following capabilities: Real-Time Dialogue: Users can ask questions and receive instant, context-aware responses from the TBM-CUs, allowing them to gain deeper insights into code impact, functionality, and the rationale behind specific evaluations.” Col. 39 ll. 28-33: “The TBM-CUs employ advanced natural language processing techniques… to deconstruct the code and extract meaningful insights. They assess various aspects of the code, including its structure, readability, complexity, performance, security, and adherence to industry-standard best practices.” Col. 26 33-41: “Adherence to Best Coding Practices (ABC Score Range: 0.0-0.1): This score represents how well the code follows established programming standards and best practices, which are essential for reducing technical debt and improving code reliability.” Col. 14 ll. 48-49: “Such capabilities position TBM-CUs as invaluable assets in assessing code value and automating peer review processes.” Col. 14 ll. 59-64: “Automated Peer Code Review… This feature… identifies potential issues, and suggests actionable improvements”).
Claims 10-14 and 18:
With regard to Claims 10-14 and 18, these claims are equivalent in scope to Claims 1-5 and 9 rejected above, merely having a different independent claim type, and as such Claims 10-14 and 18 are rejected under the same grounds and for the same reasons as discussed above with regard to Claims 1-5 and 9.
Claims 19-20:
With regard to Claims 19-20, these claims are equivalent in scope to Claims 1 and 3 rejected above, merely having a different independent claim type, and as such Claims 19-20 are rejected under the same grounds and for the same reasons as discussed above with regard to Claims 1 and 3.
With further regard to Claim 19, the claim recites additional elements not specifically addressed in the rejection of Claim 1. The Fox reference also anticipates these additional elements of Claim 19, for example, Fox teaches:
A non-transitory computer-readable medium having stored thereon computer-executable instructions that, when executed, cause a computer to perform operations ([0107] “a method 400 for a dashboard framework system is shown. The method may be described in the general context of computer executable instructions. Generally, computer executable instructions can include routines, programs, objects, components, data structures, procedures, modules, functions, etc., that perform particular functions or implement particular abstract data types… computer executable instructions may be located in both local and remote computer storage media, including memory storage devices.”).
Claims 6-8 and 15-17 are rejected under 35 U.S.C. 103 as being unpatentable over Fox in view of Kanthan, Kosgi and Dziubinski as applied to Claims 1 and 10 above, and further in view of Cella et al. (US PGPUB 2023/0173395; hereinafter “Cella”).
Claim 6:
Fox in view of Kanthan, Kosgi and Dziubinski teaches all the limitations of claim 1 as described above. Fox in view of Kanthan, Kosgi and Dziubinski does not teach the following, however, Cella teaches:
wherein the analysis system for incorporating industry knowledge includes one or more neural network models trained to extract relevant information from a plurality of sources, including web publications, domain-specific knowledge bases, and news outlets ([0170] “the artificial intelligence system… may make use of a training data set that may include… a set of outcomes (such as from additive manufacturing processes, from utilization of additive manufacturing outputs, from workflows and operations, and/or from related economic activities, including sales and service activities)… information from public information sources (such as search engine results, news feeds, website information, social media information, traffic data, weather data, climate data, demographic data, geospatial data, and many others); information from enterprise and other databases and information technology systems,” wherein the “set of outcomes (such as from additive manufacturing processes…)” and “information from enterprise and other databases” are types of “domain-specific knowledge bases.”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system as disclosed by Fox in view of Kanthan, Kosgi and Dziubinski with the different types of extracted data as taught by Cella in order “to optimize digital production processes and workflows” (Cella [0094).
Claim 7:
Fox in view of Kanthan, Kosgi and Dziubinski teaches all the limitations of claim 1 as described above. Fox in view of Kanthan, Kosgi and Dziubinski does not teach the following, however, Cella teaches:
wherein the analysis system for evaluating new tools and technologies comprises a generative AI model designed to summarize and categorize information from diverse inputs ([1343] “the RPA system 3442 may include or enable capabilities for machine learning on unstructured data 3909, such as learning on a training set of human labels, tags, or other activities that allow characterization of the unstructured data, extraction of content from unstructured data, generation of diagnostic codes or similar summaries from content of unstructured data, or the like. For example, the RPA system 3442 may include sub-systems or capabilities for processing PDFs (such as technical data sheets, functional specifications, repair instructions, user manuals and other documentation about financial entities 3330, such as machines and systems), for processing human-entered notes (such as notes involved in diagnosis of problems, notes involved in prescribing or recommending actions, notes involved in characterizing operational activities, notes involved in maintenance and repair operations, and many others), for processing information unstructured content contained on websites, social media feeds and the like (such as information about products or systems in an financial environment that can be obtained from vendor websites), and many others.”),
aiding in the identification of technological advancements suitable for integration into the technical environment ([0641] “The artificial intelligence system 13748 may also define the digital twin system 13720 to create a digital replica of one or more of the transaction entities… the machine learning model 13702 may automatically predict hypothetical situations for simulation with the digital replica, such as by predicting possible improvements to the one or more transaction entities, predicting when one or more components of the one or more transaction entities may fail, and/or suggesting possible improvements to the one or more transaction entities, such as changes to timing settings, arrangement, components, or any other suitable change to the transaction entities.”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system as disclosed by Fox in view of Kanthan, Kosgi and Dziubinski with the summarizing and categorizing information as taught by Cella in order “to optimize digital production processes and workflows” (Cella [0094), wherein a further advantage is that this “allows for invaluable analysis and simulation of the one or more transaction entities, by facilitating observation and measurement of nearly any type of metric” (Cella [0641]).
Claim 8:
Fox in view of Kanthan, Kosgi and Dziubinski teaches all the limitations of claim 1 as described above. Fox in view of Kanthan and Kosgi does not teach the following, however, Dziubinski teaches wherein the intelligent assessment system includes
a neural network trained to identify patterns (Col. 39 ll. 22-27: “The TBM-CUs, which form the core of the system's analytical capabilities, are invoked to perform a comprehensive examination of the submitted code. These models are trained on vast amounts of code data and possess the ability to understand the intricacies of… coding patterns”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system as disclosed by Fox in view of Kanthan and Kosgi with the identifying of patterns as taught by Dziubinski in order to “predict potential issues” (Dziubinski Col. 4 ln. 38).
With further regard to Claim 8, Fox in view of Kanthan, Kosgi and Dziubinski does not teach the following, however, Cella teaches:
one or more artificial intelligence models for summarization ([1343] “the RPA system 3442 may include or enable capabilities for machine learning on unstructured data 3909, such as learning on a training set of human labels, tags, or other activities that allow characterization of the unstructured data, extraction of content from unstructured data, generation of diagnostic codes or similar summaries from content of unstructured data, or the like. For example, the RPA system 3442 may include sub-systems or capabilities for processing PDFs (such as technical data sheets, functional specifications, repair instructions, user manuals and other documentation about financial entities 3330, such as machines and systems), for processing human-entered notes (such as notes involved in diagnosis of problems, notes involved in prescribing or recommending actions, notes involved in characterizing operational activities, notes involved in maintenance and repair operations, and many others), for processing information unstructured content contained on websites, social media feeds and the like (such as information about products or systems in an financial environment that can be obtained from vendor websites), and many others.”), and
a reinforcement learning with human feedback model to refine assessments and recommendations ([2522] “training may be done based on feedback received by the system, which is also referred to as ‘reinforcement learning.’ The artificial intelligence system 22312 may receive a set of circumstances that led to a prediction (e.g., attributes of part, attributes of a model, and the like) and an outcome related to the part and may update the model according to the feedback.” [2173] “the intelligent agent system 20210 and/or a client application 20312 can monitor outcomes related to the user's interactions and may reinforce the training of the intelligent agent based on the outcomes.” [2174] “the intelligent agent system 20210 receives feedback from users… a client application 20312 that leverages an intelligent agent may provide an interface by which a user can provide feedback regarding an action output by an intelligent agent… the user provides the feedback that identifies and characterizes any errors by the intelligent agent.” [2446] “a user interface wherein a user provides information regarding the success or failure of the 3D print. The data is then provided as feedback to the machine learning system 22310 which uses the feedback to train or improve the initial machine learning 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 have modified the system as disclosed by Fox in view of Kanthan, Kosgi and Dziubinski with the summarizing and reinforcement learning information as taught by Cella in order “to optimize digital production processes and workflows” (Cella [0094), wherein a further advantage is that this “allows for invaluable analysis and simulation of the one or more transaction entities, by facilitating observation and measurement of nearly any type of metric” (Cella [0641]).
Claims 15-17:
With regard to Claims 15-17, these claims are equivalent in scope to Claims 6-8 rejected above, merely having a different independent claim type, and as such Claims 15-17 are rejected under the same grounds and for the same reasons as discussed above with regard to Claims 6-8.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure is as follows:
Gunarathne et al. (US PGPUB 2025/0259127) discloses computer-implemented systems and methods for implementing an application modernization and migration tool which include the ability to use analyzed code from legacy systems and identify issues and opportunities for modernization.
Magnusson et al. (“Governing Technology Debt: Beyond Technical Debt,” 2018) discusses the concept of technology debt and how it affects information technology (IT) investment decision-making.
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/JOANNE G MACASIANO/ Examiner, Art Unit 2197
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