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 .
This Office Action is responsive to Applicant's amendment filed on 8 July 2026. Applicant’s amendment on 8 July 2026 amended Claims 1-3, 5-11, 18, and 20. Currently Claims 1-3, 5-13, and 15-22 are pending and have been examined. Claims 21 and 22 are newly presented. Claims 4 and 14 were previously canceled. The Examiner notes that the 101 rejection has been maintained.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 8 July 2026 has been entered.
Response to Arguments
The Applicant's argues on pages 13-14 that the claims are not practically capable of being performed in the human mind similar to PEG Example 39 and therefore do not recite mental process.
The Examiner respectfully disagrees.
With respect to the argument the Examiner notes that the amended claims are not directed to a mental process because they recite a "computer-implemented method" and involve machine-learning model architecture analogous to PEG Example 39 is not persuasive, and the 101 rejection is hereby maintained. As an initial matter, Applicant's characterization of Example 39 is not fully accurate. Example 39 (Method for Training a Neural Network for Facial Detection) was found eligible not simply because it recited a "computer-implemented method" or referenced hardware-level machine learning architecture in a general sense, but rather because the specific claimed operations collecting digital facial images, applying transformations, creating training sets, and training the neural network in multiple stages using those sets recited a particular sequence of steps tied to the technical process of building a trained neural network model that, taken together, could not practically be performed in the human mind. The mere addition of the label "computer-implemented method" to the preamble of the instant claims does not, under the broadest reasonable interpretation, preclude human mental performance of the core operative limitations.
More precisely, the critical distinction governing the mental process inquiry is not whether the claim recites a computer or machine learning model in some fashion, but whether the specific claim limitations themselves can practically be performed in the human mind. As confirmed by the August 4, 2025 Memorandum and MPEP 2106.04(a)(2)(III)(A), the mental process grouping applies on a limitation-by-limitation basis. This principle is directly illustrated by Example 47 (Anomaly Detection) from the July 2024 AI SME Update, which the August 2025 Memo expressly cites. In Example 47, Claim 2 recited the use of a trained artificial neural network to perform detecting and analyzing steps, yet those specific limitations were nonetheless found to fall within the mental process grouping because, under their broadest reasonable interpretation, detecting anomalies in a data set and analyzing detected anomalies to generate anomaly data encompass observations, evaluations, and judgments that can practically be performed in the human mind and the guidance expressly noted that "the recitation of a neural network in this claim does not negate the mental nature of these limitations because the claim here merely uses the neural network as a tool to perform the otherwise mental process." That holding controls here.
Applied to the instant claims, the core operative limitations of the independent claims generating a proficiency score by evaluating a user's request, assigning the proficiency score to the user, determining task complexity, determining dependencies between applications, and generating a response based on the proficiency score encompass evaluations, assessments, and judgments that, under their broadest reasonable interpretation, a skilled human trainer, project manager, or senior developer could practically perform by reading a user's query, assessing the user's apparent skill level, identifying application dependencies from documentation, and tailoring a response accordingly. The addition of the phrase "computer-implemented method" to claim 1, standing alone, does not transform these functionally-described mental steps into operations that cannot practically be performed in the human mind, because as established by the Federal Circuit and reflected in MPEP 2106.04(a)(2)(III)(A), the nominal recitation of a generic computer or the "computer-implemented" label does not remove limitations from the mental process grouping when those limitations, under their broadest reasonable interpretation, remain practically performable in the human mind. Accordingly, the mental process basis for the rejection is maintained, and the rejection under 35 U.S.C. 101 is not withdrawn. Applicant is invited to provide further arguments or amendments that specifically address the individual claim limitations that are identified as mental processes and demonstrate, on a limitation-by-limitation basis, why those specific operations cannot practically be performed in the human mind. The rejection is therefore maintained.
The Applicant's argues on pages 14-15 that the claims are not practically capable of being performed in the human mind similar to PEG Example 39 and therefore do not recite mental process.
The Examiner respectfully disagrees.
With respect to the argument the Examiner notes that the Applicant's argument that the claims are integrated into a practical application by improving user-interface experiences in a manner analogous to PEG Example 37 is not persuasive, and the 101 rejection is hereby maintained. The analogy to PEG Example 37 fails for a critical structural reason that Applicant's argument glosses over: the eligibility finding in Example 37 was grounded in the claim's recitation of a specific, concrete mechanism using a processor to track the amount of memory allocated to each application's icon over a predetermined period of time and then automatically moving the most-used icons to a defined position on the GUI closest to the start icon that directly and structurally reconfigured the interface itself based on computed usage data. That is, the claim in Example 37 did not merely describe a functional outcome of an improved interface; it recited the specific technical steps tracking memory allocation as a proxy for icon usage over a defined time window, and automatically repositioning icons based on that computed data that produced a concrete, structural change to the GUI. It was the specificity of those technical mechanisms, tied directly to the improved user-interface arrangement, that rendered the claim eligible under Step 2A Prong Two.
By contrast, the instant claims do not recite any specific structural improvement to the user interface itself. The chatbot interface recited throughout the claims is described in the specification generically as a conventional natural language text interface, and the claimed operations with respect to that interface consist solely of receiving text input and outputting text responses of varying complexity and detail calibrated to a user's assessed proficiency score. The variation in response complexity and detail is a change in the content of information delivered through a generic interface analogous to the type of content-tailoring that the Federal Circuit found insufficient in Trading Technologies Int'l v. IBG LLC, where a user interface that provided a trader with more information to facilitate market trades was found to improve the underlying business process rather than computer or interface technology itself. Moreover, the Desjardins Memorandum of December 5, 2025, which updated MPEP 2106.05(a), expressly reinforces that the claim itself must reflect the disclosed improvement; it is not sufficient for the specification to assert improvement in conclusory terms. The specification's statements at paragraphs [0002] and [0086] that prior interfaces required users to "sift through vast amounts of documentation" and that the instant system "intuitively adapts to the relative sophistication and expertise of the user" are precisely the type of bare assertions of improved user experience that, without specific claim limitations reflecting a concrete technical mechanism for achieving a structural change to the interface, do not satisfy the practical application requirement under Step 2A Prong Two. The claimed adaptive response engine modifies the content and complexity of a text response delivered through a generic chat interface it does not structurally reconfigure the interface, reposition displayed elements, or alter the interface's architecture in any technically specific, concrete way, as was the case in PEG Example 37 and Core Wireless. Accordingly, the practical application argument based on PEG Example 37 is unpersuasive, and the rejection is therefore maintained.
Applicant's arguments filed 8 July 2026 have been fully considered but they are moot in view of new grounds of rejection as necessitated by amendment.
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 1-3, 5-13, and 15-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea in the form of a mental process specifically, the concept of generating user training responses utilizing proficiency scores, task complexity assessments, and programmed response logic without significantly more. The judicial exception is not integrated into a practical application because the additional elements recited in the claims amount to no more than generic computer components (e.g., a processor, language learning model, vector store, and chatbot interface) performing their ordinary, expected functions of receiving, processing, and outputting data, and the data receiving, assigning, and outputting steps constitute insignificant extra-solution activity. The claims do not include additional elements sufficient to amount to significantly more than the judicial exception because the implementing components are described in the specification as well-known, conventional tools (including off-the-shelf NLP techniques and industry-standard project management platforms) and do not, individually or in combination, provide an inventive concept beyond the abstract idea itself.
STEP 1
Regarding Step 1 of the Subject Matter Eligibility Test for Products and Processes (from the January 2019 101 Examination Guidelines and 2024 AI-SME Update), claims 1-10 and 21-22 are directed to a computer-implemented method (process); claims 11-19 are directed to a system (machine); and claim 20 is directed to a non-transitory computer-readable medium (manufacture). Therefore, the claims fall within the statutory categories of invention.
STEP 2A Prong One
The claims recite an abstract idea. Specifically, independent claims 1, 11, and 20 recite the following limitations that constitute the identified judicial exception:
Identified Judicial Exception Limitations:
"tokenizing the training data with a language learning model into one or more vectors including a proficiency vector and a complexity vector, and storing the one or more vectors";
"determining a task complexity of the request by accessing one or more management data repositories and determining one or more dependencies between applications";
"generating a proficiency score by inputting the request into an adaptive response engine comprising a machine-learning model trained on the tokenized training data to process the proficiency vector and proficiency logic";
"assigning the proficiency score to the user";
"automatically modifying the baseline response based on the proficiency score"; and
"generating the response, wherein the response includes the projected resource expenditure and text output determined based on the proficiency."
Abstract Idea Grouping: Mental Process
These limitations, under their broadest reasonable interpretation (BRI), cover performance of the limitations in the human mind, including through observation, evaluation, judgment, and opinion, and therefore fall within the "mental processes" grouping of abstract ideas. See MPEP 2106.04(a)(2), subsection III.
Limitation (a) tokenizing training data into proficiency and complexity vectors encompasses the mental process of categorizing and organizing information about a user's skills and task requirements, which is an evaluation that a human trainer or project manager can perform mentally or with pen and paper by reviewing resumes, project documentation, and employee records and assessing relevant skill levels and task complexity accordingly.
Limitation (b) determining task complexity by accessing management data repositories and determining dependencies between applications encompasses the mental evaluation that a senior developer or project manager could perform by reviewing project management documentation (e.g., JIRA stories, Confluence pages) and mentally identifying which applications depend on one another. Under its BRI, this limitation covers any method of assessing task complexity and application dependencies, including purely mental analysis of project records.
Limitation (c) generating a proficiency score by inputting a request into an adaptive response engine encompasses the mental process of evaluating a user's written query and assessing the user's apparent level of expertise. A human reviewer, such as a senior developer or training coordinator, could read a user's request, assess the sophistication of the language and the nature of the question, and mentally assign a proficiency score based on that evaluation. This is precisely the type of observation, evaluation, and judgment that the courts and USPTO guidance identify as a mental process. See MPEP 2106.04(a)(2), subsection III; Electric Power Group v. Alstom, S.A., (collecting, analyzing, and displaying results of a data analysis performed at a high level of generality constitutes a mental process).
Limitation (d) assigning the proficiency score to the user encompasses the mental act of associating a determined level of proficiency with a particular user, which is a judgment a human trainer routinely performs. Limitation (e) automatically modifying the baseline response based on the proficiency score encompasses the mental process of deciding how to tailor the complexity, vocabulary, and level of detail of a response based on the assessed skill level of the recipient, which is a judgment any experienced instructor makes. Limitation (f) generating the response including resource expenditure and text output based on the proficiency is the culminating mental step of formulating a tailored answer based on all prior assessments, which is plainly within the capacity of a skilled human to perform.
The 2024 AI Subject Matter Eligibility Update (July 17, 2024) and the August 4, 2025 Memorandum both confirm that the mental process grouping applies on a limitation-by-limitation basis, and that the recitation of a machine learning model or neural network does not automatically remove a limitation from the mental process grouping when the underlying operation of that limitation such as detecting, analyzing, assessing, or determining can still practically be performed in the human mind. See July 2024 AI-SME Update, Example 47 (Anomaly Detection), Claim 2 (finding that "detecting" anomalies and "analyzing" detected anomalies fell within the mental process grouping even though a trained ANN was used, because the detecting and analyzing operations encompass mental observations and evaluations practically performed in the human mind, and noting that "the recitation of a neural network in this claim does not negate the mental nature of these limitations because the claim here merely uses the neural network as a tool to perform the otherwise mental process"). Precisely the same reasoning applies here: limitations (c) through (f) use the recited machine learning model as a tool to perform the mental processes of evaluating user proficiency, assigning a proficiency score, and tailoring a response operation that, under their BRI, remain practically performable in the human mind.
The nominal recitation of a processor, language learning model, adaptive response engine, and chatbot interface does not take the claim limitations out of the mental processes grouping. See MPEP 2106.04(a)(2), subsection III(C); Versata Dev. Group v. SAP Am., Inc., ("Courts have examined claims that required the use of a computer and still found that the underlying, patent-ineligible invention could be performed via pen and paper or in a person's mind."); Intellectual Ventures I LLC v. Symantec Corp., ("With the exception of generic computer-implemented steps, there is nothing in the claims themselves that foreclose them from being performed by a human, mentally or with pen and paper."). The claims, taken as a whole, recite the abstract idea of generating user training responses utilizing proficiency scores and response logic a concept that, with the exception of generic computer recitations, encompasses performance in the human mind.
STEP 2A Prong Two
The claims recite the following additional elements beyond the identified abstract idea:
A processor / one or more processors;
A language learning model (LLM) / training module;
A vector store for storing one or more vectors;
A chat-based interface / chatbot;
Receiving training data comprising software project management data;
Receiving a request from a user via a chat-based interface;
Generating a projected resource expenditure based on task complexity; and
Outputting the response via the chat-based interface.
Analysis of Additional Elements
Improvement to Technology or Technical Field (MPEP 2106.05(a))
The claim does not recite an improvement to the functioning of a computer or to any other technology or technical field. Per the December 5, 2025 Memorandum implementing Ex Parte Desjardins (Appeal No. 2024-000567) (precedential) and the updated MPEP 2106.04(d)(1), the specification must provide sufficient detail such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement in the functioning of a computer or an improvement to technology or a technical field, and the claims must reflect that improvement.
Here, the specification does not describe a technical improvement to how the machine learning model itself is trained, structured, or operates. The disclosure does not identify any specific technical problem with prior machine learning architectures, and the claims do not recite how the model is trained differently, what specific parameter adjustments are made, or how those adjustments produce a technical improvement to the model's functioning. This is in contrast to Ex Parte Desjardins, where the specification identified a concrete technical problem ("catastrophic forgetting" in continual learning systems) and the claims reflected the specific technical solution adjusting parameters to optimize performance on a new task while protecting performance on prior tasks. See December 5, 2025 Memorandum, pp. 2-5.
The improvement asserted in the specification delivering "personalized, context aware answers tailored to individual skill levels" (Spec. par. [0022]) and resolving "inefficiencies of previous user-training interfaces" (Spec. par. [0086]) is an improvement to the user experience and onboarding workflow, not to the machine learning model architecture, the computer, or any other technology. Such assertions, moreover, are stated in conclusory terms without the technical detail necessary for a person of ordinary skill in the art to recognize a concrete technical improvement. See MPEP 2106.05(a) (per December 5, 2025 Memorandum: "if the specification explicitly sets forth an improvement but only in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine that the claim improves technology or a technical field"). The claims therefore do not satisfy the improvement consideration under MPEP 2106.05(a).
Particular Machine (MPEP 2106.05(b))
The claims do not recite use of a particular machine that imposes meaningful limits on the claim. The recited "processor," "language learning model," "vector store," "adaptive response engine," and "chatbot interface" are recited at a high level of generality as generic computing components without any specific structural details that would limit the claim to a particular configuration. The specification describes these components as general-purpose or commercially available tools (e.g., JIRA, ServiceNow, Confluence, standard NLP models), confirming that no particular machine is specified. See MPEP 2106.05(b).
Mere Instructions to Apply the Exception (MPEP 2106.05(f))
The additional elements amount to no more than mere instructions to implement the abstract idea on a generic computer. Additional elements (1) through (4) the processor, LLM, vector store, and chatbot are generic computing components performing their ordinary expected functions. The recited LLM performs the generic computer function of tokenizing data; the adaptive response engine performs the generic function of generating a score from input data; and the chatbot performs the generic function of receiving and outputting text. There are no details in the claims as to how these components are specifically configured, how they interact in a non-routine way, or how they solve a technological problem through a particular technical mechanism. This is analogous to the claim in Example 47, Claim 2 (July 2024 AI-SME Update), where a DNN was found to constitute mere instructions to apply the abstract idea when the claim recited "no details about a particular DNN or how the DNN operates to derive the embedding vectors other than that it is being used to determine the embedding vectors" and "the limitation recites only the idea of determining embedding vectors using a DNN without details on how this is accomplished." The instant claims likewise recite that a machine-learning model generates a proficiency score from proficiency vectors and proficiency logic without any technical detail as to how this is accomplished beyond the abstract idea. See also MPEP 2106.05(f).
Insignificant Extra-Solution Activity (MPEP 2106.05(g))
Additional elements (5), (6), and (8) receiving training data, receiving a request from a user, and outputting the response constitute insignificant extra-solution activity. Receiving training data and receiving a user request are mere data gathering steps that are necessary precursors to performing the abstract idea itself and do not add any meaningful limitation. Outputting the response via the chat-based interface is a mere post-solution outputting step. These data gathering and outputting steps are incidental to the core mental process of generating user training responses utilizing proficiency scores and response logic, and do not, individually or in combination, integrate the judicial exception into a practical application. See MPEP 2106.05(g); Electric Power Group, 830 F.3d at 1355 (data gathering and display steps are insignificant extra-solution activity when they do not transform the underlying abstract process).
Considering all additional elements individually and in combination, the claim as a whole does not integrate the judicial exception into a practical application. The additional elements do not impose any meaningful limits on practicing the abstract idea, do not reflect a concrete technical improvement to any computer component or technical field, and amount to using generic computer components as tools to perform the mental processes of assessing user proficiency and generating tailored responses. The claims are directed to the abstract idea.
STEP 2B
As discussed with respect to Step 2A Prong Two, the additional elements in the claims amount to no more than mere instructions to apply the exception using generic computer components. The same analysis applies in Step 2B mere instructions to apply an exception using generic computer components cannot provide an inventive concept. See MPEP 2106.05(f); Alice Corp. Pty. Ltd. v. CLS Bank Int'l.
Well-Understood, Routine, and Conventional (WURC) Activity Analysis
The additional elements, considered individually and in combination, are well-understood, routine, and conventional activities in the field, as supported by the following evidence:
(A) Citation to the Specification: The specification itself describes the implementing components in conventional terms, which constitutes express evidentiary support for the WURC finding. See MPEP 2106.07(a), subsection III(A). Specifically:
The LLM is described as a "custom" model built on standard, well-known NLP techniques, including naive Bayes classifiers, Latent Dirichlet Allocation (LDA), Term Frequency-Inverse Document Frequency (TF-IDF) processing, and recursive neural networks (RNN). See Spec. par. [0024], [0034]-[0035]. The use of these NLP techniques as well-known tools is established by their explicit identification in the specification as known algorithmic approaches.
The management data repositories for task complexity determination are identified as off-the-shelf commercial platforms, including JIRA, ServiceNow, and Confluence. See Spec. par. [0018], [0021], [0031]-[0032], [0038]. The specification describes accessing these platforms generically via API without any particular non-conventional configuration.
The chatbot interface is described generically as a "natural language processing" based text interface for receiving queries and outputting responses. See Spec. par. [0026], [0043]. There is no description of any particular structural innovation to the chatbot or the interface itself.
The vector store is described as a database for storing embeddings. See Spec. par. [0031]-[0036]. Storing machine learning embeddings in a vector database is a well-known and conventional operation in the AI arts, and the specification treats it as such.
(B) Citation to Court Decisions: Courts have recognized as well-understood, routine, and conventional the additional elements of receiving/transmitting data over a network, storing and retrieving information in memory, and generating outputs via a generic user interface. See MPEP 2106.05(d)(II), citing: Symantec Corp.; TLI Communications LLC v. AV Auto. LLC; OIP Techs., Inc. v. Amazon.com, Inc.; Content Extraction & Transmission LLC v. Wells Fargo Bank, N.A. The additional elements of data ingestion, vectorization, and chatbot-based output in the instant claims are generic computer functions analogous to those recognized as WURC in these decisions.
The Federal Circuit and MPEP 2106.05(d) both recognize that a specification's own characterization of additional elements as well-known, commercially available, or conventional is strong evidence of conventionality that supports a WURC finding. See also Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d at 1316 (courts may rely on a specification's description of implementing elements as conventional to support a finding that they do not provide an inventive concept). Here, the specification repeatedly describes the implementing NLP techniques, data platforms, and processing components as industry-standard, commercially available tools, providing clear specification-based evidentiary support for the WURC finding at Step 2B.
Considered individually and in combination, the additional elements do not amount to significantly more than the identified abstract idea. The combination of these conventional components performing their expected generic functions receiving data, tokenizing data using standard NLP, generating scores from vectors, and outputting text responses does not constitute the type of non-conventional, non-generic arrangement that would qualify as an inventive concept. See BASCOM Global Internet Servs., Inc. v. AT&T Mobility LLC, (an inventive concept requires a specific, non-conventional arrangement of elements beyond a generic combination of known tools performing their ordinary functions).
The claims do not include additional elements sufficient to amount to significantly more than the recited judicial exception. The claims are not patent eligible.
Dependent Claims Analysis
The dependent claims do not add limitations that integrate the judicial exception into a practical application or provide an inventive concept, for the reasons set forth below.
Claims 2, 12: These claims further limit the response-generation process by reciting receiving a baseline response from programmed query response logic, determining a selected sophistication range associated with the proficiency score, modifying the baseline response based on the selected sophistication range, and outputting the modified baseline response. These limitations further specify the manner in which the abstract mental process of tailoring a response to a user's proficiency level is carried out, but do not add any element that integrates the abstract idea into a practical application or provides an inventive concept. Determining a "sophistication range" and "modifying a baseline response" based on that range are evaluations and adjustments that a human trainer could practically perform mentally, and specifying that a baseline response exists and is modified does not impose any meaningful technical limit on the abstract idea.
Claims 3, 13: These claims add the step of identifying effects of a request on a second software via a dependency impact engine comprising a dependency vector and dependency logic, and outputting an indication of the effect. Identifying dependencies between software applications by evaluating project documentation is a mental process that a senior developer could perform by reviewing application documentation and mentally tracing dependencies. The dependency impact engine and dependency vector are recited at a high level of generality without structural specificity, and the output of an indication of dependency effects is mere post-solution activity. These limitations narrow the abstract idea but do not add significantly more.
Claims 5, 15: These claims specify that the resource expenditure includes a size estimation of a task identified within the request. Including size estimation as a component of resource expenditure further specifies the content of the output but remains within the abstract idea of assessing and communicating task requirements. This is a narrowing of the mental process already identified and does not integrate the exception into a practical application.
Claims 6, 16: These claims add receipt of a projected due date from a compliance engine comprising a compliance vector and compliance logic, and outputting the projected due date. Determining regulatory or project timelines by evaluating compliance documentation is a mental process that compliance professionals routinely perform. The compliance engine is recited generically, and outputting a due date is insignificant post-solution activity.
Claims 7, 17: These claims add comparing the projected time for completion against the projected due date and outputting an alert if resources are insufficient. Comparing two time estimates and issuing an alert based on the comparison is a mental process (comparison and judgment) that a project manager could practically perform, and the alert output is mere post-solution activity.
Claims 8, 18: These claims specify that the proficiency score is additionally input into the predictive resource engine to determine task complexity. Inputting the proficiency score into the complexity assessment is a further elaboration of the mental process of adjusting resource estimates based on user skill level, which a human evaluator could perform mentally. This limitation does not add a meaningful technical improvement.
Claims 9, 19: These claims add identifying a software compliance requirement from a compliance engine and outputting the compliance requirement. Identifying applicable compliance requirements from regulatory documents by reviewing training data is a mental process a compliance professional could perform, and outputting the compliance requirement is insignificant extra-solution activity.
Claim 10: This claim adds identifying sensitive data within the training data via a sensitive data masker comprising a sensitivity vector and sensitivity logic, and automatically masking the sensitive data. While data masking operations may involve automated processes, as recited in this claim the sensitive data masker is described at a high level of generality without structural specificity, and the identification of sensitive data (such as PII) by scanning documents is an evaluation that a person could perform mentally or with pen and paper. The automatic masking does not introduce a specific technical mechanism that integrates the abstract idea into a practical application.
Claims 21, 22: These new claims add that the training data comprises application code and that tokenizing comprises tokenizing the application code into code-based or syntax-based embeddings (claim 21), and that receiving training data comprises executing retrieval of software application code (claim 22). These limitations further specify the type of training data and the tokenization output, but do not introduce any non-conventional technical mechanism. Tokenizing application code into code-based or syntax-based embeddings using an LLM is a generic machine learning operation that the specification describes as a standard technique. See Spec. par.[0034] ("Different embeddings may be generated for different components of the training data. For instance, application code may be tokenized into a code-based embedding and a syntax based embedding."). Retrieving software application code via an automated process is mere data gathering at a high level of generality. Neither limitation integrates the abstract idea into a practical application or provides an inventive concept.
None of the dependent claims remedy the deficiencies of the independent claims identified above. The dependent claims narrow the abstract idea but do not, individually or collectively, add elements that meaningfully limit the judicial exception in a manner that integrates it into a practical application or provides an inventive concept. For the foregoing reasons, claims 1-3, 5-13, and 15-22 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
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.
Claim 1, 11, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Guru et al. (U.S. Patent Publication 2022/0092514 A1) (hereafter Guru) in view of Nahamani et al. (U.S. Patent Publication 2021/0406973 A1) (hereafter Nahamani) in further view of Casa (U.S. Patent Publication 2023/0036730 A1).
Referring to Claim 1, Guru teaches a computer-implemented method, said method comprising:
tokenizing the training data with a language learning model into one or more vectors including a proficiency vector and a complexity vector, and storing the one or more vectors (see; par. [0015]-[0016] of Guru teaches using a LLM and machine learning to determine vector information regarding the training data including skills (i.e. proficiency) and job requirements (i.e. complexity)).
generating a response to the request through operations comprising (see; par. [0037] of Guru teaches an example of generating a response that a user matches the vectors’ requirements).
generating the response, wherein the response includes the projected resource expenditure and text output determined based on the proficiency (see; par. [0004] of Guru teaches taking into account role requirements (i.e. expenditure), par. [0039] communicating a skill gap (i.e. requirements vs skill)).
Guru does not explicitly disclose the following limitation, however,
Nahamani teaches determining a task complexity of the request by accessing one or more management data repositories and determining one or more dependencies between applications (see; par. [0099] of Nahamani teaches issue complexity derived from a data repository and includes the links to stored data and applications), and
generating a projected resource expenditure, based on the task complexity (see; par. [0052]-[0053] of Nahamani teaches that once an agents’ condition is determined a following determination is conducted to see if the issue can be solved by the agent taking into account complexity), and
assigning the proficiency score to the user (see; par. [0087] of Nahamani teaches assigning a capability score to a user based on their performance).
The Examiner notes that Guru teaches similar to the instant application teaches skill gap analysis for talent management. Specifically, Guru discloses the analyzing gap in between skill of the employee and the job description requirements it is therefore viewed as analogous art in the same field of endeavor. Additionally, Nahamani teaches intelligent inquiry resolution control system for agents using natural language communication and as it is comparable in certain respects to Guru which skill gap analysis for talent management as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. This provides support that it would be obvious to combine the references to provide an obviousness rejection.
Guru discloses the analyzing gap in between skill of the employee and the job description requirements. However, Guru fails to disclose determining a task complexity of the request by accessing one or more management data repositories and determining one or more dependencies between applications, generating a projected resource expenditure, based on the task complexity, and assigning the proficiency score to the user.
Nahamani discloses determining a task complexity of the request by accessing one or more management data repositories and determining one or more dependencies between applications, generating a projected resource expenditure, based on the task complexity, and assigning the proficiency score to the users.
It would be obvious to one of ordinary skill in the art to include in the task management
(system/method/apparatus) of Guru the determining a task complexity of the request by accessing one or more management data repositories and determining one or more dependencies between applications, generating a projected resource expenditure, based on the task complexity, and assigning the proficiency score to the user as taught by Nahamani since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Additionally, Guru, and Nahamani teach the collecting and analysis of data in order to manage training of employees and they do not contradict or diminish the other alone or when combined.
Guru in view of Nahamani does not explicitly disclose the following limitations, however,
Casa teaches receiving training data comprising software project management data (see; par. [0138] of Casa teaches using teaching the artificial intelligence to learn specific skills. Par. [0105] utilized for new projects), and
receiving, from a chat-based interface, a request corresponding to execution of a software task from a user (see; par. [0138] of Casa teaches using a chatbot interface, par. [0097] that is used to accomplish tasks), and
generating a proficiency score by inputting the request into an adaptive response engine comprising a machine-learning model trained on the tokenized training data (see; par. [0025] of Casa teaches a tokenized engagement with skill exerts for learning, par. [0105] & [0110] using a machine learning model and score on skills), and
determining a baseline response to the request (see; par. [0103] of Casa teaches a skill map (i.e. baseline) is updated in real time), and
automatically modifying the baseline response based on the proficiency score (see; par. [0150] of Casa teaches utilizing a measured proficiency score and monitoring the journey of the skill development, par. [0102]-[0105] and updating the skill map to refined the precise and is used to recommend future learning).
causing the response to be output via the chat-based interface, wherein the response comprises an automatic modification to the baseline response based at least in part on the proficiency score (see; par. [0103] of Casa teaches a skill map (i.e. baseline) is updated in real-time, par. [0100]-[0101] based on individual score, par. [0138] using a chatbot interface).
The Examiner notes that Guru teaches similar to the instant application teaches skill gap analysis for talent management. Specifically, Guru discloses the analyzing gap in between skill of the employee and the job description requirements it is therefore viewed as analogous art in the same field of endeavor. Additionally, Nahamani teaches intelligent inquiry resolution control system for agents using natural language communication and as it is comparable in certain respects to Guru which skill gap analysis for talent management as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. Additionally, Casa teaches tokenization of expert time for soft skill development and as it is comparable in certain respects to Guru and Nahamani which skill gap analysis for talent management as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. This provides support that it would be obvious to combine the references to provide an obviousness rejection.
Guru and Nahamani discloses the analyzing gap in between skill of the employee and the job description requirements. However, Guru and Nahamani fails to disclose receiving training data comprising software project management data, receiving, from a chat-based interface, a request corresponding to execution of a software task from a user, generating a proficiency score by inputting the request into an adaptive response engine comprising a machine-learning model trained on the tokenized training data, determining a baseline response to the request, and automatically modifying the baseline response based on the proficiency score.
Casa discloses receiving training data comprising software project management data, receiving, from a chat-based interface, a request corresponding to execution of a software task from a user, generating a proficiency score by inputting the request into an adaptive response engine comprising a machine-learning model trained on the tokenized training data, determining a baseline response to the request, and automatically modifying the baseline response based on the proficiency score.
It would be obvious to one of ordinary skill in the art to include in the task management (system/method/apparatus) of Guru and Nahamani receiving training data comprising software project management data, receiving, from a chat-based interface, a request corresponding to execution of a software task from a user, generating a proficiency score by inputting the request into an adaptive response engine comprising a machine-learning model trained on the tokenized training data, determining a baseline response to the request, and automatically modifying the baseline response based on the proficiency score as taught by Casa since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Additionally, Guru, Nahamani, and Casa teach the collecting and analysis of data in order to manage training of employees and they do not contradict or diminish the other alone or when combined.
Referring to Claim 11, Guru in view of Nahamani in further view of Casa teaches a system. Claim 11 recites the same or similar limitations as those addressed above in claim 1, Claim 11 is therefore rejected for the same reasons as set forth above in claim 1, except for the following noted exception.
One or more processors (see; par. [0004] and par. [0052] of Guru teaches a processor).
Referring to Claim 20, Guru in view of Nahamani in further view of Casa teaches a non-transitory computer readable medium. Claim 20 recites the same or similar limitations as those addressed above in claim 1, Claim 20 is therefore rejected for the same reasons as set forth above in claim 1.
Claim 2, 3, 12, and 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Guru et al. (U.S. Patent Publication 2022/0092514 A1) (hereafter Guru) in view of Nahamani et al. (U.S. Patent Publication 2021/0406973 A1) (hereafter Nahamani) in further view of Casa (U.S. Patent Publication 2023/0036730 A1) in further view of Van Hickman (U.S. Patent Publication 2023/0169268 A1).
Referring to Claim 2, see discussion of claim 1 above, while Guru in view of Nahamani in further view of Casa teaches the computer implemented method above, Guru in view of Nahamani in further view of Casa does not explicitly disclose a computer-implemented method having the limitations of, however,
Van Hickman teaches receiving a baseline response from the programmed query response logic (see; par. [0026] of Van Hickman teaches the assigning of an initial level of reading (i.e. baseline)),
determining a selected sophistication range associated with the proficiency score (see; par. [0088] of Van Hickman teaches the additional information regarding the student as a reading statistic (i.e. proficiency score)),
modifying the baseline response based on the selected sophistication range (see; par. [0003]-[0004] and par. [0024] of Van Hickman teaches incremental increase or decrease in levels that define the current level (i.e. baseline) of the user and can be revised), and
outputting the modified baseline response as the response (see; par. [0027] of Van Hickman teaches providing the initial level and par. [0028] adjusting the desired reading level).
The Examiner notes that Guru teaches similar to the instant application teaches skill gap analysis for talent management. Specifically, Guru discloses the analyzing gap in between skill of the employee and the job description requirements it is therefore viewed as analogous art in the same field of endeavor. Additionally, Nahamani teaches intelligent inquiry resolution control system for agents using natural language communication and as it is comparable in certain respects to Guru which skill gap analysis for talent management as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. Additionally, Casa teaches tokenization of expert time for soft skill development and as it is comparable in certain respects to Guru and Nahamani which skill gap analysis for talent management as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. Additionally, Van Hickman teaches textual adjustment to a target reading level and adjust training as necessary and as it is comparable in certain respects to Guru and Nahamani, and Casa which skill gap analysis for talent management as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. This provides support that it would be obvious to combine the references to provide an obviousness rejection.
Guru, Nahamani, and Casa discloses the analyzing gap in between skill of the employee and the job description requirements. However, Guru, Hahamani, and Casa fails to disclose receiving a baseline response from the programmed query response logic, determining a selected sophistication range associated with the proficiency score, modifying the baseline response based on the selected sophistication range, and outputting the modified baseline response as the response.
Van Hickman discloses receiving a baseline response from the programmed query response logic, determining a selected sophistication range associated with the proficiency score, modifying the baseline response based on the selected sophistication range, and outputting the modified baseline response as the response.
It would be obvious to one of ordinary skill in the art to include in the task management
(system/method/apparatus) of Guru, Hahamani, and Casa receiving a baseline response from the programmed query response logic, determining a selected sophistication range associated with the proficiency score, modifying the baseline response based on the selected sophistication range, and outputting the modified baseline response as the response as taught by Van Hickman since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Additionally, Guru, Nahamani, Casa, and Van Hickman teach the collecting and analysis of data in order to manage training of employees and they do not contradict or diminish the other alone or when combined.
Referring to Claim 3, see discussion of claim 1 above, while Guru in view of Nahamani in further view of Casa teaches the computer-implemented method above, Guru in view of Nahamani in further view of Casa does not explicitly disclose a computer-implemented method having the limitations of, however,
Van Hickman teaches identifying an effect of the request on a second software by inputting the request into a dependency impact engine, the dependency impact engine comprising the dependency vector and dependency logic (see; par. [0039] of Van Hickman teaches multiple data sources in connection with one another), and
outputting, as part of the response, an indication that a modification to the first software affects the second software (see; par. [0039] of Van Hickman teaches incorporating or integrated to other components).
The Examiner notes that Guru teaches similar to the instant application teaches skill gap analysis for talent management. Specifically, Guru discloses the analyzing gap in between skill of the employee and the job description requirements it is therefore viewed as analogous art in the same field of endeavor. Additionally, Nahamani teaches intelligent inquiry resolution control system for agents using natural language communication and as it is comparable in certain respects to Guru which skill gap analysis for talent management as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. Additionally, Casa teaches tokenization of expert time for soft skill development and as it is comparable in certain respects to Guru and Nahamani which skill gap analysis for talent management as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. Additionally, Van Hickman teaches textual adjustment to a target reading level and adjust training as necessary and as it is comparable in certain respects to Guru and Nahamani, and Casa which skill gap analysis for talent management as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. This provides support that it would be obvious to combine the references to provide an obviousness rejection.
Guru, Nahamani, and Casadiscloses the analyzing gap in between skill of the employee and the job description requirements. However, Guru, Hahamani, and Casa fails to disclose identifying an effect of the request on a second software by inputting the request into a dependency impact engine, the dependency impact engine comprising the dependency vector and dependency logic, and outputting, as part of the response, an indication that a modification to the first software affects the second software.
Van Hickman discloses identifying an effect of the request on a second software by inputting the request into a dependency impact engine, the dependency impact engine comprising the dependency vector and dependency logic, and outputting, as part of the response, an indication that a modification to the first software affects the second software.
It would be obvious to one of ordinary skill in the art to include in the task management
(system/method/apparatus) of Guru, Hahamani, and Casa identifying an effect of the request on a second software by inputting the request into a dependency impact engine, the dependency impact engine comprising the dependency vector and dependency logic, and outputting, as part of the response, an indication that a modification to the first software affects the second software as taught by Van Hickman since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Additionally, Guru, Nahamani, Casa, and Van Hickman teach the collecting and analysis of data in order to manage training of employees and they do not contradict or diminish the other alone or when combined.
Referring to Claim 12, see discussion of claim 11 above, while Guru in view of Nahamani in further view of Casa teaches the system above Claim 12 recites the same or similar limitations as those addressed above in claim 2, Claim 12 is therefore rejected for the same or similar limitations as set forth above in claim 2.
Referring to Claim 13, see discussion of claim 11 above, while Guru in view of Nahamani in further view of Casa teaches the system above Claim 13 recites the same or similar limitations as those addressed above in claim 3, Claim 13 is therefore rejected for the same or similar limitations as set forth above in claim 3.
Claim 5, 9, 10, 15, and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Van Guru et al. (U.S. Patent Publication 2022/0092514 A1) (hereafter Guru) in view of Nahamani et al. (U.S. Patent Publication 2021/0406973 A1) (hereafter Nahamani) in further view of Casa (U.S. Patent Publication 2023/0036730 A1) in further view of Van Hickman (U.S. Patent Publication 2023/0169268 A1) in view of Shear et al. (JP 2022183191 A) (hereafter Shear).
Referring to Claim 5, see discussion of claim 1 above, while Guru in view of Nahamani in further view of Casa teaches the computer-implemented method above, Guru in view of Nahamani in further view of Casa does not explicitly disclose a computer-implemented method having the limitations of, however,
Shear teaches the resource expenditure includes a size estimation of a task identified within the request (see; pg. 257, par. 15 – pg. 258 of Shear teaches estimating the resources need to complete the task including, but not limited to computational resources (i.e. size)).
The Examiner notes that Guru teaches similar to the instant application teaches skill gap analysis for talent management. Specifically, Guru discloses the analyzing gap in between skill of the employee and the job description requirements it is therefore viewed as analogous art in the same field of endeavor. Additionally, Nahamani teaches intelligent inquiry resolution control system for agents using natural language communication and as it is comparable in certain respects to Guru which skill gap analysis for talent management as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. Additionally, Casa teaches tokenization of expert time for soft skill development and as it is comparable in certain respects to Guru and Nahamani which skill gap analysis for talent management as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. Additionally, Shear teaches provide a system configured to facilitate a user purpose and as it is comparable in certain respects to Guru, Nahamani, and Casa which teaches textual adjustment to a target reading level as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. This provides support that it would be obvious to combine the references to provide an obviousness rejection.
Guru, Nahamani, and Casa discloses the helping a student learn how to read providing text at a reading level suitable for learning. However, Guru, Nahamani, and Casa fails to disclose the resource expenditure includes a size estimation of a task identified within the request.
Shear discloses the resource expenditure includes a size estimation of a task identified within the request.
It would be obvious to one of ordinary skill in the art to include in the task management
(system/method/apparatus) Guru, Nahamani, and Casa the resource expenditure includes a size estimation of a task identified within the request as taught by Shear since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Additionally, Guru, Nahamani, Casa and Shear teach the collecting and analysis of data in order to provide standardized resources, and they do not contradict or diminish the other alone or when combined.
Referring to Claim 9, see discussion of claim 1 above, while Guru in view of Nahamani in further view of Casa teaches the method above, Guru in view of Nahamani in further view of Casa does not explicitly disclose a method having the limitations of, however,
Shear teaches identifying a software compliance requirement from a compliance engine, the compliance engine comprising the compliance vector and compliance logic (see; pg. 118, pg. 9 of Shear teaches monitoring compliance and ensuring they are met using corrective actions), and
outputting, as part of the response, the software compliance requirement (see; pg. 184, par. 8 of Shear teaches the monitoring of compliance with the business agreement (i.e. output compliance)).
The Examiner notes that Guru teaches similar to the instant application teaches skill gap analysis for talent management. Specifically, Guru discloses the analyzing gap in between skill of the employee and the job description requirements it is therefore viewed as analogous art in the same field of endeavor. Additionally, Nahamani teaches intelligent inquiry resolution control system for agents using natural language communication and as it is comparable in certain respects to Guru which skill gap analysis for talent management as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. Additionally, Casa teaches tokenization of expert time for soft skill development and as it is comparable in certain respects to Guru and Nahamani which skill gap analysis for talent management as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. Additionally, Shear teaches provide a system configured to facilitate a user purpose and as it is comparable in certain respects to Guru, Nahamani, and Casa which teaches textual adjustment to a target reading level as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. This provides support that it would be obvious to combine the references to provide an obviousness rejection.
Guru, Nahamani, and Casa discloses the helping a student learn how to read providing text at a reading level suitable for learning. However, Guru, Nahamani, and Casa fails to disclose identifying a software compliance requirement from a compliance engine, the compliance engine comprising the compliance vector and compliance logic, and outputting, as part of the response, the software compliance requirement.
Shear discloses identifying a software compliance requirement from a compliance engine, the compliance engine comprising the compliance vector and compliance logic, and outputting, as part of the response, the software compliance requirement.
It would be obvious to one of ordinary skill in the art to include in the task management
(system/method/apparatus) Guru, Nahamani, and Casa identifying a software compliance requirement from a compliance engine, the compliance engine comprising the compliance vector and compliance logic, and outputting, as part of the response, the software compliance requirement as taught by Shear since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Additionally, Guru, Nahamani, Casa and Shear teach the collecting and analysis of data in order to provide standardized resources, and they do not contradict or diminish the other alone or when combined.
Referring to Claim 10, see discussion of claim 1 above, while Guru in view of Nahamani in further view of Casa teaches the method above, Guru in view of Nahamani in further view of Casa does not explicitly disclose a method having the limitations of, however,
Shear teaches identifying, by a sensitive data masker, sensitive data within the training data, the sensitive data masker comprising the sensitivity vector and sensitivity logic (see; pg. 340, par. 2 of Shear teaches using a sensitivity specification (i.e. training), in order to detect inconsistencies (i.e. data masker) and how the tool works to make corrections (i.e. vector and logic), and
automatically masking the sensitive data within the training data (see; pg. 119, par. 5-6 of Shear teaches mapping context sensitive information and applied based on weights (i.e. masks)).
The Examiner notes that Guru teaches similar to the instant application teaches skill gap analysis for talent management. Specifically, Guru discloses the analyzing gap in between skill of the employee and the job description requirements it is therefore viewed as analogous art in the same field of endeavor. Additionally, Nahamani teaches intelligent inquiry resolution control system for agents using natural language communication and as it is comparable in certain respects to Guru which skill gap analysis for talent management as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. Additionally, Casa teaches tokenization of expert time for soft skill development and as it is comparable in certain respects to Guru and Nahamani which skill gap analysis for talent management as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. Additionally, Shear teaches provide a system configured to facilitate a user purpose and as it is comparable in certain respects to Guru, Nahamani, and Casa which teaches textual adjustment to a target reading level as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. This provides support that it would be obvious to combine the references to provide an obviousness rejection.
Guru, Nahamani, and Casa discloses the helping a student learn how to read providing text at a reading level suitable for learning. However, Guru, Nahamani, and Casa fails to disclose identifying, by a sensitive data masker, sensitive data within the training data, the sensitive data masker comprising the sensitivity vector and sensitivity logic, and automatically masking the sensitive data within the training data.
Shear discloses identifying, by a sensitive data masker, sensitive data within the training data, the sensitive data masker comprising the sensitivity vector and sensitivity logic, and automatically masking the sensitive data within the training data.
It would be obvious to one of ordinary skill in the art to include in the task management
(system/method/apparatus) Guru, Nahamani, and Casa identifying, by a sensitive data masker, sensitive data within the training data, the sensitive data masker comprising the sensitivity vector and sensitivity logic, and automatically masking the sensitive data within the training data as taught by Shear since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Additionally, Guru, Nahamani, Casa and Shear teach the collecting and analysis of data in order to provide standardized resources, and they do not contradict or diminish the other alone or when combined.
Referring to Claim 15, see discussion of claim 11 above, while Guru in view of Nahamani in further view of Casa teaches the system above Claim 15 recites the same or similar limitations as those addressed above in claim 5, Claim 15 is therefore rejected for the same or similar limitations as set forth above in claim 5.
Referring to Claim 19, see discussion of claim 11 above, while Guru in view of Nahamani in further view of Casa teaches the system above Claim 19 recites the same or similar limitations as those addressed above in claim 9, Claim 19 is therefore rejected for the same or similar limitations as set forth above in claim 9.
Claim 6, 7, 8, 16, 17, and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Van Guru et al. (U.S. Patent Publication 2022/0092514 A1) (hereafter Guru) in view of Nahamani et al. (U.S. Patent Publication 2021/0406973 A1) (hereafter Nahamani) in further view of Casa (U.S. Patent Publication 2023/0036730 A1) in further view of Van Hickman (U.S. Patent Publication 2023/0169268 A1) in view of Shear et al. (JP 2022183191 A) (hereafter Shear) in further view of Van Hickman (U.S. Patent Publication 2023/0169268 A1).
Referring to Claim 6, see discussion of claim 5 above, while Guru in view of Nahamani in further view of Casa in further view of Shear teaches the method above, Guru in view of Nahamani in further view of Casa in further view of Shear does not explicitly disclose a method having the limitations of, however,
Van Hickman teaches the one or more vectors includes a compliance vector, the method further comprising: receiving a projected due date from a compliance engine, the compliance engine comprising the compliance vector and compliance logic (see; par. [0084] of Van Hickman teaches providing due dates of assignments in order to meet specific goals to stay on track (i.e. compliance with requirements)), and
outputting, as part of the response, the projected due date for the request (see; par. [0084] of Van Hickman teaches providing a score card to provided due dates and progress (i.e. outputting project due dates)).
The Examiner notes that Guru teaches similar to the instant application teaches skill gap analysis for talent management. Specifically, Guru discloses the analyzing gap in between skill of the employee and the job description requirements it is therefore viewed as analogous art in the same field of endeavor. Additionally, Nahamani teaches intelligent inquiry resolution control system for agents using natural language communication and as it is comparable in certain respects to Guru which skill gap analysis for talent management as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. Additionally, Casa teaches tokenization of expert time for soft skill development and as it is comparable in certain respects to Guru and Nahamani which skill gap analysis for talent management as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. Additionally, Shear teaches provide a system configured to facilitate a user purpose and as it is comparable in certain respects to Guru, Nahamani, and Casa which teaches textual adjustment to a target reading level as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. Additionally, Van Hickman teaches provide a system configured to facilitate a user purpose and as it is comparable in certain respects to Guru, Nahamani, Casa, and Shear which teaches textual adjustment to a target reading level as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. This provides support that it would be obvious to combine the references to provide an obviousness rejection.
Guru, Nahamani, Casa, and Shear discloses the helping a student learn how to read providing text at a reading level suitable for learning. However, Guru, Nahamani, Casa, and Shear fails to disclose the resource expenditure includes a size estimation of a task identified within the request.
Van Hickman discloses the resource expenditure includes a size estimation of a task identified within the request.
It would be obvious to one of ordinary skill in the art to include in the task management
(system/method/apparatus) Guru, Nahamani, Casa, and Shear the resource expenditure includes a size estimation of a task identified within the request as taught by Shear since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Additionally, Guru, Nahamani, Casa, Shear, and Van Hickman teach the collecting and analysis of data in order to provide standardized resources, and they do not contradict or diminish the other alone or when combined.
Referring to Claim 7, see discussion of claim 6 above, while Guru in view of Nahamani in further view of Casa in further view of Shear in further view of Van Hickman in further view of Shear teaches the method above, Guru in view of Nahamani in further view of Casa in further view of Shear does not explicitly disclose a method having the limitations of, however,
Van Hickman teaches the projected time for completion of the request exceeds the projected due date for the request (see; par. [0084] of Van Hickman teaches providing a scorecard with due dates and progress tracking that ensures a desired level is reached for the tasks), and
outputting an alert indicating that insufficient resources are allocated to complete the request prior to the projected due date (see; par. [0084] of Van Hickman teaches providing a scorecard with due dates and progress tracking that ensures a desired level is reached for the tasks).
The Examiner notes that Guru teaches similar to the instant application teaches skill gap analysis for talent management. Specifically, Guru discloses the analyzing gap in between skill of the employee and the job description requirements it is therefore viewed as analogous art in the same field of endeavor. Additionally, Nahamani teaches intelligent inquiry resolution control system for agents using natural language communication and as it is comparable in certain respects to Guru which skill gap analysis for talent management as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. Additionally, Casa teaches tokenization of expert time for soft skill development and as it is comparable in certain respects to Guru and Nahamani which skill gap analysis for talent management as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. Additionally, Shear teaches provide a system configured to facilitate a user purpose and as it is comparable in certain respects to Guru, Nahamani, and Casa which teaches textual adjustment to a target reading level as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. Additionally, Van Hickman teaches provide a system configured to facilitate a user purpose and as it is comparable in certain respects to Guru, Nahamani, Casa, and Shear which teaches textual adjustment to a target reading level as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. This provides support that it would be obvious to combine the references to provide an obviousness rejection.
Guru, Nahamani, Casa, and Shear discloses the helping a student learn how to read providing text at a reading level suitable for learning. However, Guru, Nahamani, Casa, and Shear fails to disclose the projected time for completion of the request exceeds the projected due date for the request, and outputting an alert indicating that insufficient resources are allocated to complete the request prior to the projected due date.
Van Hickman discloses the projected time for completion of the request exceeds the projected due date for the request, and outputting an alert indicating that insufficient resources are allocated to complete the request prior to the projected due date.
It would be obvious to one of ordinary skill in the art to include in the task management
(system/method/apparatus) Guru, Nahamani, Casa, and Shear the projected time for completion of the request exceeds the projected due date for the request, and outputting an alert indicating that insufficient resources are allocated to complete the request prior to the projected due date as taught by Shear since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Additionally, Guru, Nahamani, Casa, Shear, and Van Hickman teach the collecting and analysis of data in order to provide standardized resources, and they do not contradict or diminish the other alone or when combined.
Referring to Claim 8, see discussion of claim 5 above, while Guru in view of Nahamani in further view of Casa in further view of Shear teaches the method above, Guru in view of Nahamani in further view of Casa in further view of Shear does not explicitly disclose a method having the limitations of, however,
Van Hickman teaches the inputting the request into the predictive resource engine to determine a task complexity further includes inputting the proficiency score into the predictive resource engine (see; par. [0026] of Van Hickman teaches determining a reading level of a user and then providing a level to strive for (i.e. starting with a first score and determining how to get to a future point).
The Examiner notes that Guru teaches similar to the instant application teaches skill gap analysis for talent management. Specifically, Guru discloses the analyzing gap in between skill of the employee and the job description requirements it is therefore viewed as analogous art in the same field of endeavor. Additionally, Nahamani teaches intelligent inquiry resolution control system for agents using natural language communication and as it is comparable in certain respects to Guru which skill gap analysis for talent management as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. Additionally, Casa teaches tokenization of expert time for soft skill development and as it is comparable in certain respects to Guru and Nahamani which skill gap analysis for talent management as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. Additionally, Shear teaches provide a system configured to facilitate a user purpose and as it is comparable in certain respects to Guru, Nahamani, and Casa which teaches textual adjustment to a target reading level as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. Additionally, Van Hickman teaches provide a system configured to facilitate a user purpose and as it is comparable in certain respects to Guru, Nahamani, Casa, and Shear which teaches textual adjustment to a target reading level as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. This provides support that it would be obvious to combine the references to provide an obviousness rejection.
Guru, Nahamani, Casa, and Shear discloses the helping a student learn how to read providing text at a reading level suitable for learning. However, Guru, Nahamani, Casa, and Shear fails to disclose the inputting the request into the predictive resource engine to determine a task complexity further includes inputting the proficiency score into the predictive resource engine.
Van Hickman discloses the inputting the request into the predictive resource engine to determine a task complexity further includes inputting the proficiency score into the predictive resource engine.
It would be obvious to one of ordinary skill in the art to include in the task management
(system/method/apparatus) Guru, Nahamani, Casa, and Shear the inputting the request into the predictive resource engine to determine a task complexity further includes inputting the proficiency score into the predictive resource engine as taught by Shear since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Additionally, Guru, Nahamani, Casa, Shear, and Van Hickman teach the collecting and analysis of data in order to provide standardized resources, and they do not contradict or diminish the other alone or when combined.
Referring to Claim 16, see discussion of claim 11 above, while Guru in view of Nahamani in further view of Casa teaches the system above Claim 16 recites the same or similar limitations as those addressed above in claim 6, Claim 16 is therefore rejected for the same or similar limitations as set forth above in claim 6.
Referring to Claim 17, see discussion of claim 16 above, while Guru in view of Nahamani in further view of Casa in view of Shear in further view of Van Hickman teaches the system above Claim 17 recites the same or similar limitations as those addressed above in claim 7, Claim 17 is therefore rejected for the same or similar limitations as set forth above in claim 7.
Referring to Claim 18, see discussion of claim 15 above, while Guru in view of Nahamani in further view of Casa in view of Shear teaches the system above Claim 18 recites the same or similar limitations as those addressed above in claim 8, Claim 18 is therefore rejected for the same or similar limitations as set forth above in claim 8.
Claims 21 and 22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Van Guru et al. (U.S. Patent Publication 2022/0092514 A1) (hereafter Guru) in view of Nahamani et al. (U.S. Patent Publication 2021/0406973 A1) (hereafter Nahamani) in further view of Casa (U.S. Patent Publication 2023/0036730 A1) in further view of DOYLE (U.S. Patent Publication 2020/0057612 A1).
Referring to Claim 22, see discussion of claim 1 above, while Guru in view of Nahamani in further view of Casa teaches the method above, Guru in view of Nahamani in further view of Casa does not explicitly disclose a method having the limitations of, however,
Doyle teaches the training data comprises application code, and tokenizing the training data comprises tokenizing the application code into code-based embeddings or syntax based embeddings (see; par. [0027] of Doyle teaches generating software application code using tokenized rules, par. [0108] and par. [0113] embedding and conversion or transformation syntax process that may translate to code segments).
The Examiner notes that Guru teaches similar to the instant application teaches skill gap analysis for talent management. Specifically, Guru discloses the analyzing gap in between skill of the employee and the job description requirements it is therefore viewed as analogous art in the same field of endeavor. Additionally, Nahamani teaches intelligent inquiry resolution control system for agents using natural language communication and as it is comparable in certain respects to Guru which skill gap analysis for talent management as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. Additionally, Casa teaches tokenization of expert time for soft skill development and as it is comparable in certain respects to Guru and Nahamani which skill gap analysis for talent management as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. Additionally, Doyle teaches program product for automatic generation of software application code and as it is comparable in certain respects to Guru, Nahamani, and Casa which teaches textual adjustment to a target reading level as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. This provides support that it would be obvious to combine the references to provide an obviousness rejection.
Guru, Nahamani, and Casa discloses the helping a student learn how to read providing text at a reading level suitable for learning. However, Guru, Nahamani, and Casa fails to disclose the resource expenditure includes a size estimation of a task identified within the request.
Doyle discloses the resource expenditure includes a size estimation of a task identified within the request.
It would be obvious to one of ordinary skill in the art to include in the task management
(system/method/apparatus) Guru, Nahamani, and Casa the resource expenditure includes a size estimation of a task identified within the request as taught by Doyle since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Additionally, Guru, Nahamani, Casa and Doyle teach the collecting and analysis of data in order to provide standardized resources, and they do not contradict or diminish the other alone or when combined.
Referring to Claim 22, see discussion of claim 1 above, while Guru in view of Nahamani in further view of Casa teaches the method above, Guru in view of Nahamani in further view of Casa does not explicitly disclose a method having the limitations of, however,
Doyle teaches receiving the training data comprises executing, via one or more processors, retrieval of software application code (see; par. [0096] of Doyle teaches hosted software application (i.e. retrieval), par. [0101] formatted to training data sets. par. [0040] utilizing a processor).
The Examiner notes that Guru teaches similar to the instant application teaches skill gap analysis for talent management. Specifically, Guru discloses the analyzing gap in between skill of the employee and the job description requirements it is therefore viewed as analogous art in the same field of endeavor. Additionally, Nahamani teaches intelligent inquiry resolution control system for agents using natural language communication and as it is comparable in certain respects to Guru which skill gap analysis for talent management as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. Additionally, Casa teaches tokenization of expert time for soft skill development and as it is comparable in certain respects to Guru and Nahamani which skill gap analysis for talent management as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. Additionally, Doyle teaches program product for automatic generation of software application code and as it is comparable in certain respects to Guru, Nahamani, and Casa which teaches textual adjustment to a target reading level as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. This provides support that it would be obvious to combine the references to provide an obviousness rejection.
Guru, Nahamani, and Casa discloses the helping a student learn how to read providing text at a reading level suitable for learning. However, Guru, Nahamani, and Casa fails to disclose the receiving the training data comprises executing, via one or more processors, retrieval of software application code.
Doyle discloses receiving the training data comprises executing, via one or more processors, retrieval of software application code.
It would be obvious to one of ordinary skill in the art to include in the task management
(system/method/apparatus) Guru, Nahamani, and Casa receiving the training data comprises executing, via one or more processors, retrieval of software application code as taught by Doyle since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Additionally, Guru, Nahamani, Casa and Doyle teach the collecting and analysis of data in order to provide standardized resources, and they do not contradict or diminish the other alone or when combined.
Conclusion
The prior art made of record and not relied upon considered pertinent to Applicant’s disclosure.
Woo et al. (KR 102164769 B1) discloses a method for measuring competence using the number of inspection pass of crowdsourcing based project for artificial intelligence training data generation.
Cicio JR, Frank (WO 2014/022837 A1) discloses a skilled based, staffing system coordinated with communication based project, management application.
Gupta et al. (U.S. Patent Publication 2019/0102741 A1) discloses techniques for extraction and valuation of proficiencies for gap detection and remediation.
Fliess et al. (U.S. Patent 7,519,539 B1) discloses an assisted profiling of skills in an enterprise management system.
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/S.S.S/Examiner, Art Unit 3625 /BETH V BOSWELL/Supervisory Patent Examiner, Art Unit 3625
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