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 .
DETAILED ACTION
The present application is being examined under the pre-AIA first to invent provisions.
This Final Office Action is responsive to Applicant's amendment filed on 12 January 2026. Applicant’s amendment on 12 January 2026 amended Claims 1, 5, 11, 15, 16 and 20. Currently Claims 1-3, 5-13, and 15-20 are pending and have been examined. Claims 4 and 14 have been canceled. The examiner notes that the 101 rejection has been maintained.
Response to Arguments
Applicant's arguments filed 12 January 2026 have been fully considered but they are not persuasive.
The Applicant argues on pages 9-10 that “the claims are not practically capable of being performed in the human mind and therefore do not recite mental process. The Office Action generally alleges the claims recite a mental process. Office Action p. 3. Without conceding the propriety of the rejection and in order to advance prosecution, Applicant has amended each of the independent claim 1 to recite, in part:
Claims 11 and 20 are amended to recite substantially similar features. As shown, the claims are amended to illustrate that such operations are not practically capable of being performed in the human mind, and are therefore eligible as not directed to an abstract idea.
The M.P.E.P. at 2106.04(a)(2)(III)(A) indicates that a claim with limitation(s) that cannot practically be performed in the human mind does not recite a mental process. Examples of claims that cannot be performed in the human mind include those for training a neural network for facial detection, where operations included collecting training data, editing the training data, and training the neural network in multiple stages based on variations to the training data. M.P.E.P. 2106.049(a)(1)(ex. vii). Thus, operations which involve machine learning model architecture, where the machine learning model architecture can then be used to execute various tasks, are not practically capable of being performed in the human mind, and therefore not directed to abstract mental processes.
Similar to PEG Example, 39, the instant claims recite operations related to machine learning architecture, which are then used to implement downstream machine learning model tasks. The claims recite, inter alia, generating specific types of vectors (i.e., the proficiency vector and the complexity vector) via which the adaptive response engine machine learning model is trained to process to generate proficiency score. As described below, the proficiency score enables the adaptive response engine to "then adjust the complexity and detail of responses generated by a chatbot responding to a user" such that "[t]he adaptive response engine may thereby enhance user experiences by delivering personalized, context aware answers tailored to individual skill levels." Because the claims recite a specific machine learning model architecture, for implementation, the claims, like those in Example 39, are subject matter eligible as not directed to a mental process, nor any other abstract idea.
For at least this reason, the Office Action has failed to establish that claims 1-20 are not directed to an abstract idea. Because these claims are not directed to an abstract idea, the Office Action erred in rejecting the claims under 35 U.S.C. 101. Applicant respectfully requests withdrawal of the rejections under 35 U.S.C. 101”.
The Examiner Respectfully disagrees
Applicant's argument that the amended claims are not directed to a mental process because they recite machine-learning model architecture is not fully persuasive, and the 101 rejection should be maintained on this basis. While the guidance at MPEP 2106.04(a)(2)(III)(A) correctly states that limitations which cannot practically be performed in the human mind do not recite a mental process, that principle applies on a limitation-by-limitation basis under the broadest reasonable interpretation of the claim as a whole it does not immunize an entire claim from the mental process grouping simply because some limitations involve machine-learning operations. As demonstrated in the 2024 AI SME Update (Example 47, Claim 2), even claims that recite an artificial neural network can still recite mental processes when individual claim limitations such as "detecting," "analyzing," or "determining" encompass observations, evaluations, or judgments that can practically be performed in the human mind, and the guidance expressly notes 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." Applied here, the amended claims recite limitations such as "generating a proficiency score" by evaluating a user's request, "assigning the proficiency score to the user," "determining one or more dependencies between applications," and "generating a response… based on the proficiency score" all of which, under their broadest reasonable interpretation, encompass evaluations, judgments, and assessments that a human trainer, project manager, or senior developer could practically perform by reviewing a user's query, assessing their skill level, identifying application dependencies, and tailoring a response accordingly. Furthermore, Applicant's reliance on USPTO PEG Example 39 is misplaced, as that example involved specific hardware-level machine-learning architecture with dedicated structural components that precluded human mental performance of the claimed steps, whereas the instant claims recite functional outcomes proficiency scoring, dependency identification, and response generation at a level of generality that does not foreclose human mental performance of those steps. Accordingly, the mental process basis for the rejection remains proper, and the rejection is therefore maintained.
The Applicant argues on pages 11-13 that “the claims are integrated into a practical application. As noted above, the independent claims are patent eligible because they do not recite an abstract idea. But even if they did recite an abstract idea, the abstract idea is integrated into a practical application. When considering whether an abstract idea is integrated into a practical application, the Office considers the claim as a whole. Guidance of January 7, 2019, p. 10 ("That is, the limitations containing the judicial exception as well as the additional elements in the claim besides the judicial exception need to be evaluated together to determine whether the claim integrates the judicial exception into a practical application."). The additional limitations "should not be evaluated in a vacuum, completely separate from the recited judicial exception." Id. Instead, the analysis should "take into consideration all the claim limitations and how those limitations interact and impact each other when evaluating whether the exception is integrated into a practical application." Id.
i. The claims are integrated into a practical application under examples set forth by the Ex Parte Desjardins decision. Claimed improvements to computer functionality include improved machine learning architectures implemented in various tasks and workstreams. On December 5, 2025, the USPTO issued an advance notice of changes to the M.P.E.P. in view of the Ex Parte Desjardins decision noting, among other points, that the Ex Parte Desjardins decision is now precedential as of November 5th. See the USPTO December 5, 2025, Memorandum re:"Advance notice of change to the MPEP in light of Ex Parte Desjardins" ("The December Memo"). The December Memo revises the M.P.E.P. at 2106.05 to cite to the now precedential Ex Parte Desjardins decision. Among other revisions, the December Memo updates the M.P.E.P. at 2106.05 to clarify that inventions that improve technology "or a technical field" are patent eligible. December Memo pp. 2-4. The December Memo updates the second paragraph of the M.P.E.P. at 2106.05(a)(I) to add multiple additional examples in view of the Ex Parte Desjardins decision, including example xiv. Example xiv indicates that improvements to computer functionality include "improvements to computer component or system performance based upon adjustments to parameters of a machine learning model associated with tasks or workstreams." Id. p. 4
Here, the independent claims recite improvements to computer components (including chatbots and user interfaces), based upon adjustments to parameters of a machine learning model associated with the computer-based task of interfacing with users. The independent claims recite, in part: generating a proficiency score by inputting the request into an adaptive response engine comprising a machine-learning model trained to process the proficiency vector and proficiency logic. Such features describe a particular configuration of parameters as applied to a machine-learning model, where the parameters are implemented to improve computer performance in specific tasks and workstreams. The improved workstreams are described further below, as the claims are further eligible for reciting an improved user interface as in the examples set forth by the Core Wireless decision, described in the M.P.E.P. at 2106.5(a)(I)(ex. x).
The claims are integrated into a practical application for improving digital user interfaces as in Core Wireless. Claims directed to improved user interfaces have been found to be subject matter eligible for directing the claims to a practical application via improving computer functionality. M.P.E.P. 2106.5(a)(I)(ex. x)(citing Core Wireless Licensing S.A.R.L., v. LG Electronics, Inc.,. In Core Wireless claims were directed to an improved user interface for electronic devices that displays an application summary of unlaunched applications, where the particular data in the summary is selectable by a user to launch the respective application. The Federal Circuit, in reasoning that the claims were subject matter eligible, noted that the specification confirmed that the claims disclosed an improved user interface for electronic devices, where the specification noted that prior processes for displaying information could "'seem slow, complex, and difficult to learn, particularly to novice users."' Id. 1363 (internal citations omitted).
Similarly, here, the claims recite a particular implementation of an adaptive response engine comprising a machine learning model where the model can adapt to the level of sophistication of the users. As noted at [0022] of Applicant's specification "[t]he adaptive response engine may thereby enhance user experiences by delivering personalized, context aware answers tailored to individual skill levels." Such an approach is noted to apply "an intuitive, natural language processing and machine learning based system to intuitively adapt to the relative sophistication of and expertise of the user in providing a tailored-response" which "resolves inefficiencies of previous user-training interfaces." Applicant's Specification at [0086]. These are the same kind of benefits as those noted subject matter eligible in Core Wireless where prior user interfaces would be difficult to use, particularly with respect to novice users. Therefore, the claims here are eligible for integrating any alleged abstract idea into a practical application because the claims are directed to improvements in machine learning model-based systems for executing computer-based tasks, as in M.P.E.P. example xiv discussed above, and moreover because the improved computer-based task relates to improved user interfaces, as in M.P.E.P. example x.
Because the alleged abstract idea is integrated into a practical application, the independent claims are therefore patent eligible. For at least these reasons, the claims are integrated into a practical application and therefore are patent eligible. Applicant respectfully requests the withdrawal of the rejections under 35 U.S.C. 101”.
The Examiner respectfully disagrees.
With respect to the argument the Examiner notes that Applicant's argument that the claims are integrated into a practical application under Ex Parte Desjardins and Core Wireless is not persuasive, and the 101 rejection should be maintained, because the instant claims do not satisfy the key threshold requirements of either authority. With respect to Ex Parte Desjardins and the December 5, 2025 Memorandum, the updated MPEP example xiv recognizes as eligible improvements "to computer component or system performance based upon adjustments to parameters of a machine learning model associated with tasks or workstreams," but critically, the Desjardins decision itself was grounded in a specific, identified technical improvement to how the machine learning model itself functions namely, a method of training a model to learn new tasks while protecting knowledge about prior tasks to overcome the documented technical problem of "catastrophic forgetting" in continual learning systems. The claims in Desjardins were found eligible because the specification identified concrete technical improvements intrinsic to the operation of the machine learning model itself, including reduced storage capacity, reduced system complexity, and preservation of performance attributes, and the claims as a whole reflected those specific improvements. Here, by contrast, the specification does not identify a technical improvement to how the machine learning model itself is trained, structured, or operates. Instead, the claimed improvement is directed to the downstream outcome of delivering "personalized, context aware answers tailored to individual skill levels" to human users an improvement to the user experience and onboarding workflow, not to the machine learning model architecture or its technical performance. The mere recitation of generating a proficiency score via "a machine-learning model trained to process the proficiency vector and proficiency logic" does not, without more, constitute an adjustment to parameters of a machine learning model that improves the computer component or system performance in the manner recognized in Desjardins, because 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 they recite only that such a model is used as a tool to assess a user's proficiency and tailor a response.
Applicant's reliance on Core Wireless is similarly unavailing. In Core Wireless, the Federal Circuit found eligibility because the claims recited a specific, concrete improvement to the structure and display of a user interface namely, a summary window of a particular size that displayed specific types of data from unlaunched applications, selectable to launch those applications, thereby improving the speed and usability of navigating electronic devices in a structurally defined way. The court's holding was grounded in the particular structural configuration of the interface itself, not merely in the functional benefit of improved usability. Here, the claims do not recite any specific structural improvement to a user interface. The chatbot interface is described generically throughout the specification as a conventional natural-language text interface, and the claimed "improvement" consists of varying the complexity and detail of text responses based on a proficiency score which is a change in the content and tailoring of information delivered through a generic interface, not a structural or architectural improvement to the interface itself. As the December 5, 2025 Memorandum and the underlying Desjardins guidance make clear, examiners must ensure that the claim itself reflects the disclosed improvement and must not credit improvements that are merely asserted in conclusory terms in the specification. Here, characterizing the system as resolving "inefficiencies of previous user-training interfaces" at specification paragraph [0086] is precisely the type of bare assertion of improvement that, without specific claim limitations reflecting a concrete technical mechanism for achieving that improvement, does not satisfy the practical application requirement under Step 2A Prong Two. Accordingly, the rejection is therefore maintained.
The Applicant argues on pages 13 “The claims amount to significantly more than the judicial exception. The Office Action alleges the independent claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Office Action, p. 7. In view of the amended claims, Applicant respectfully disagrees. A claim includes "significantly more" if it recites an element or combination of elements that is unconventional. "Whether a particular technology is well-understood, routine, and conventional goes beyond what was simply known in the prior art. The mere fact that something is disclosed in a piece of prior art, for example, does not mean it was well-understood, routine, and conventional." Berkheimer v. HP Inc., 881 F.3d 1360, 1369 (Fed. Cir. 2018).
Amended claim 1 recites particular operations for responding to a request related to software tasks, the features including "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 "generating a proficiency score by inputting the request into an adaptive response engine comprising a machine-learning model trained to process the proficiency vector and proficiency logic." Applicant respectfully submits that the specific in which the described system responds to requests is unconventional”.
The Examiner respectfully disagrees.
With respect to the arguments the Examiner notes that Applicant's argument that the additional elements are unconventional and therefore amount to significantly more than the judicial exception under Step 2B is not persuasive, and the101 rejection should be maintained. While Applicant correctly cites Berkheimer for the proposition that the well-understood, routine, and conventional inquiry is a factual one and that prior art disclosure alone does not establish conventionality, Berkheimer equally makes clear that a factual determination of unconventionality must be supported by something beyond a bare assertion in the response and here Applicant provides no specific factual basis for the claim that the identified limitations are unconventional other than the conclusory statement that "the specific way in which the described system responds to requests is unconventional." A naked assertion of unconventionality, without pointing to specific evidence or specific claim language that demonstrates how these elements depart from what was well-understood, routine, and conventional in the AI and software engineering fields at the time of the invention, does not satisfy Applicant's burden under Berkheimer and MPEP 2106.05(d). Critically, and dispositive of this argument, the specification itself undermines any claim of unconventionality by repeatedly characterizing the implementing components in conventional terms: the LLM is described as a "custom" model built on well-known NLP techniques including naive Bayes classifiers, TF-IDF processing, and recursive neural networks; the management data repositories are identified as off-the-shelf platforms such as JIRA, Confluence, and ServiceNow; the dependency analysis and resource projection functions are described as leveraging historical project data in a manner consistent with existing project management practice; and the chatbot interface is described generically as a natural-language text interface. 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 that those elements do not provide an inventive concept, and that reliance may be placed on the specification to support a finding of conventionality without requiring a prior art search. Because the specification describes these elements at a high level of generality using industry-standard tools and techniques, and because Applicant has not identified any specific, non-conventional technical mechanism in the claims that departs from what those in the art would recognize as routine AI and software development practice, the additional elements whether considered individually or in combination do not amount to significantly more than the recited judicial exception. Accordingly, the rejection is maintained.
The Applicant argues on page 14 that “further, as explained below, the Office Action has not established that amended claim 1 is anticipated or obvious in view of the cited references. And even if it did, that still would not be sufficient, because "[t]he inventive concept inquiry requires more than recognizing that each claim element, by itself, was known in the art." Bascom Glob. Internet Servs., Inc. v. AT&T Mobility LLC, 827 F.3d 1341, 1350 (Fed. Cir. 2016). As the Federal Circuit explained "[w]hether a particular technology is well-understood, routine, and conventional goes beyond what was simply known in the prior art. The mere fact that something is disclosed in a piece of prior art, for example, does not mean it was well-understood, routine, and conventional." Berkheimer v. HP Inc., 881 F.3d 1360, 1369 (Fed. Cir. 2018) (emphasis added).
For at least these reasons, the amended claims amount to significantly more than any alleged abstract idea are therefore patent eligible. Accordingly, Applicant respectfully requests withdrawal of the rejections under 35 U.S.C. 101”.
The Examiner respectfully disagrees.
With respect to the argument the Examiner notes that Applicant's reliance on Berkheimer and BASCOM in Argument 4 does not overcome the 101 rejection because those authorities, properly applied, actually confirm rather than undermine the Examiner's position. Applicant correctly states that prior art disclosure alone does not automatically establish that a claim element is well-understood, routine, and conventional but this cuts both ways. Berkheimer requires that the well-understood, routine, and conventional determination be supported by a proper factual basis; it does not require that the Examiner abandon the inquiry or accept Applicant's bare assertion of unconventionality as sufficient to carry the day. Here, as discussed above, the primary factual basis for the conventionality finding is not the prior art rejections themselves but rather the specification's own repeated and explicit descriptions of the implementing components as well-known tools and techniques, which under MPEP 2106.05(d) and Intellectual Ventures v. Symantec constitutes proper evidentiary support for a conventionality finding entirely independent of any prior art reference. Furthermore, Applicant's invocation of BASCOM for the proposition that the inventive concept inquiry requires more than recognizing that each element was known in the art is also unavailing here, because BASCOM found an inventive concept based on a specific, non-conventional and non-generic arrangement of elements that produced a concrete technical improvement to Internet filtering technology a finding grounded in a specific technical configuration recited in the claims themselves. The instant claims do not recite any analogous non-conventional arrangement; they recite, at a high level of generality, the use of standard machine-learning components performing their ordinary and expected functions of receiving data, generating scores, and outputting tailored responses. The mere fact that multiple conventional elements are combined in a claim does not, without more, constitute the type of non-conventional, non-generic arrangement recognized as an inventive concept in BASCOM. Accordingly, the argument does not establish that the claims amount to significantly more than the recited judicial exception, and the 101 rejection will be maintained.
Applicant's arguments filed 12 January 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 15-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter because the claim(s) 1-3, 5-13, and 15-20 as a whole, considering all claim elements both individually and in combination, do not amount to significantly more than an abstract idea. The claim(s) 1-3, 5-13, and 15-20 is/are directed to the abstract idea of generating user training utilizing proficiency scores and response logic without significantly more than the judicial exception itself.
Step 1
Regarding Step 1 of the Subject Matter Eligibility Test for Products and Processes (from the January 2019 101 Examination Guidelines), claim(s) (1-3, and 5-10) is/are directed to a method, claim(s) (11-13, and 15-19) is/ are directed to a system, and claims(s) (20) is/are directed to a non-transitory computer readable medium and therefore the claims recites a series of steps and, therefore the claims are viewed as falling in statutory categories.
Step 2A Prong 1
The claimed invention is directed to an abstract idea without significantly more. The claim(s) recite(s) mental process. Specifically, the independent claims 1, 11, and 20 recite a mental process: as drafted, the claim recites the limitation of generating user training utilizing proficiency scores and response logic which is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting a processor, nothing in the claim precludes the determining step from practically being performed in the human mind. For example, but for the processor language, the claim encompasses the user generating user training utilizing proficiency scores and response logic. The mere nominal recitation of a generic processor does not take the claim limitation out of the mental processes grouping. It has been established by ongoing guidance that claims that contain a generic processor are still viewed as mental process when they contain limitations that can practically be performed in the human mind, however this is different for instance when the human mind is not equipped to perform the claim limitations (network monitoring, data encryption for communication, and rendering images). Therefore, these limitations are viewed a mental process. Additionally, with regard to the instant application the Examiner has reviewed the disclosure and determined that the underlying claimed invention is described as a concept that is performed in the human mind and/or with the aid of a pen and paper, and thus it is viewed that the applicant is merely claiming that concept performed 1) on a generic computer, 2) in a computer environment or 3) is merely using a computer as a tool to perform the concept, and therefore is considered to recite a mental process.
Step 2A Prong 2
Specifically, the determined judicial exception is not integrated into a practical application because the generically recited computer elements do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer and additionally that data receiving, assigning, and outputting steps required to use the tokenizing and generating do not add a meaningful limitation to the method as they are insignificant extra-solution activity (including post solution activity).
The claim recites the additional element(s): that a processor is used to perform both the tokenizing and generating steps. The processor in the steps are recited at a high level of generality, i.e., as a generic processor performing a generic computer function of processing data (generating user training utilizing proficiency scores and response logic). This generic processor limitation is no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to the abstract idea.
The claim recites the additional element(s): receiving training data, receiving a request, assigning the proficiency score, and outputting the response performs the tokenizing and generating steps. The receiving, assigning, and outputting steps are recited at a high level of generality (i.e., as a general means of managing data for use in the tokenizing and generating steps), and amounts to mere data management, which is a form of insignificant extra-solution activity. The processor that performs the tokenizing and generating steps are also recited at a high level of generality, and merely automates the tokenizing and generating steps. Each of the additional limitations is no more than mere instructions to apply the exception using a generic computer component (the processor).
The Examiner has further determined that the claims as a whole does not integrate a judicial exception into a practical application in order to provide an improvement in the functioning of a computer or an improvement to other technology or technical field. It has been determined that based on the disclosure does not provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. It has not been provided clearly in the disclosure that the alleged improvement would be apparent to one of ordinary skill in the art, but is instead 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, and therefore does not improve the technology. Second, in the instance, where it is not clear that the specification sets forth an improvement in technology, the claim must reflect the disclosed improvement (the claims must include components or steps of the invention that provide the improvement described in the specification).
For further clarification the Examiner points out that the claim(s) 1-20 recite(s) receiving training data, tokenizing the training data, receiving a request, generating a proficiency score, assigning the proficiency score, generating a response associated with the request and outputting the response which are viewed as an abstract idea in the form of a mental process. This judicial exception is not integrated into a practical application because the use of a computer for receiving, tokenizing, generating, assigning, and outputting which is the abstract idea steps of valuing an idea (generating user training utilizing proficiency scores and response logic) in the manner of “apply it”.
Thus, the claims recites an abstract idea directed to a mental process (i.e. to generating user training utilizing proficiency scores and response logic). Using a computer to receiving, tokenizing, generating, assigning, and outputting the data resulting from this kind of mental process merely implements the abstract idea in the manner of “apply it”.
The dependent claims recite elements that narrow the metes and bounds of the abstract idea but do not provide ‘something more’.
The dependent claims do not remedy these deficiencies.
Claims 2, 7, 8, 10, 12, 17, and 18 recite limitations which further limit the claimed analysis of data.
Claims 3, 4, 6, 9, 13, 14, 16, and 19 recites limitations directed to claim language viewed insignificantly extra solution activity.
Using a computer to perform the data processing as claimed is merely implementing the abstract idea in the manner of “apply it” and does not provide significantly more. Additionally with respect to the Berkheimer the Examiner points out that the steps of the claim are viewed to be to nothing more than spell out what it means to apply it on a computer and cannot confer patent-eligibility as there are no additional limitations beyond applying an abstract idea, restricted to a computer. As the claims are merely implementing the abstract idea in the manner of “Apply It” the need for a Berkheimer analysis does not apply and is not required. With respect to the currently filed claims the implementing steps can be found in Van Hickman which discloses how the claims alone and in combination are viewed to be well understood, routine and conventional based on point 3 of the Berkheimer memo and subsequent evidence, complying with and providing evidence.
Claims 5 and 15 recites limitations directed to claim language viewed non-functional data labels.
Thus, the problem the claimed invention is directed to answering the question based on gathered and analyzed information about generating user training utilizing proficiency scores and response logic. This is not a technical or technological problem but is rather in the realm of worker training and therefore an abstract idea.
Step 2B
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because as discussed with respect to Step 2A Prong Two, the additional element in the claim amounts to no more than mere instructions to apply the exception using a generic computer component.
The same analysis applies here in 2B, i.e., mere instructions to apply an exception using a generic computer component cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. This is the case because in order for the claims to be viewed as significantly more the claims must incorporate the integral use of a machine to achieve performance of a method, in contrast to where the machine is merely an object on which the method operates, which does not provide significantly more in order for a machine to add significantly more, it must play a significant part in permitting the claimed method to be performed, rather than function solely as an obvious mechanism for permitting a solution to be achieved more quickly. Whether its involvement is extra-solution activity or a field-of-use, i.e., the extent to which (or how) the machine or apparatus imposes meaningful limits on the claim. Use of a machine that contributes only nominally or insignificantly to the execution of the claimed method (e.g., in a data gathering step or in a field-of-use limitation) would not provide significantly more. Additionally, another consideration when determining whether a claim recites significantly more is whether the claim effects a transformation or reduction of a particular article to a different state or thing. "[T]ransformation and reduction of an article ‘to a different state or thing’ is the clue to patentability of a process claim that does not include particular machines. All together the above analysis shows there is not improvement in computer functionality, or improvement to any other technology or technical field. The claim is ineligible.
Additionally, with respect to the Berkheimer as noted above the same analysis applies to the 2B where the claims are viewed as applying it and as such no further analysis is required. However, with respect to the current claims receiving, assigning, and outputting that are viewed as extra solution or post solution activity the Examiner notes that the claims are viewed as well-understood, routine, and conventional because a citation to a publication that demonstrates the well-understood, routine, conventional nature of the additional element(s). An appropriate publication such as the currently cited prior art Van Hickman provides those extra solution activities and is viewed as a form of publication which also includes a book, manual, review article, or other source that describes the state of the art and discusses what is well-known and in common use in the relevant industry. The claim is ineligible.
The dependent claims recite elements that narrow the metes and bounds of the abstract idea but do not provide ‘something more’. Specifically, the dependent claims do not remedy these deficiencies of the independent claims.
With respect to the legal concept of prima facie case being a procedural tool of patent examination, which allocates the burdens going forward between the examiner and the applicant. MPEP 2106.07 discusses the requirements of a prima facie case of ineligibility. In particular, the initial burden was on the Examiner and believed to be properly provided as to explain why the claim(s) are ineligible for patenting because of the above provided rejection which clearly and specifically points out in accordance with properly providing the requirement satisfying the initial burden of proof based on the Guidance from the United States Patent and Trademark Office and the burden now shifts to the applicant.
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 QIN (U.S. Patent Publication 2024/0346256 A1) (hereafter QIN).
Referring to Claim 1, Guru teaches a method, said method comprising:
receiving training data (see; par. [0004] and par. [0015] of Guru teaches receiving personal data to be used as training data including, par. [0026] skill data).
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 a proficiency score by inputting the request into an adaptive response engine comprising a machine-learning model trained to process the proficiency vector and proficiency logic (see; Abstract of Guru teaches machine learning, par. [0038] an example of generating a score that takes into account the skill level score (i.e. proficiency) and the actual score equation (i.e. logic)).
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,
Qin teaches receiving a request corresponding to execution of a software task from a user (see; par. [0018]-[0019] of Qin teaches receiving a request of the query of the LLM model), and
outputting the response (see; Abstract of Qin teaches generating a response by an LLM model and, par. [0003] based on the vector analysis).
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, Qin teaches response generation using a retrieval augmented ai model to perform the execution of software tasks 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 a request corresponding to execution of a software task from a user and outputting the response.
Qin discloses receiving a request corresponding to execution of a software task from a user and outputting 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 and Nahamani receiving a request corresponding to execution of a software task from a user and outputting the response 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, Nahamani, and Qin 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 Qin 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 Qin 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 QIN (U.S. Patent Publication 2024/0346256 A1) (hereafter QIN) 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 Qin teaches the method above, Guru in view of Nahamani in further view of Qin does not explicitly disclose a 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, Qin teaches response generation using a retrieval augmented ai model to perform the execution of software tasks 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 Qin 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, ad Qin discloses the analyzing gap in between skill of the employee and the job description requirements. However, Guru, Hahamani, and Qin 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 Qin 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, Qin, 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 Qin teaches the method above, Guru in view of Nahamani in further view of Qin does not explicitly disclose a 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, Qin teaches response generation using a retrieval augmented ai model to perform the execution of software tasks 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, Nahamani, and Qin 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, ad Qin discloses the analyzing gap in between skill of the employee and the job description requirements. However, Guru, Hahamani, and Qin 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 Qin 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, Namamani, Qin, 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 Qin 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 Qin 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 QIN (U.S. Patent Publication 2024/0346256 A1) (hereafter QIN) 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 Qin teaches the method above, Guru in view of Nahamani in further view of Qin does not explicitly disclose a 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, Qin teaches response generation using a retrieval augmented ai model to perform the execution of software tasks 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 Qui 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 Qui discloses the helping a student learn how to read providing text at a reading level suitable for learning. However, Guru, Nahamani, and Qui 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 Qui 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, Qui 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 Qin teaches the method above, Guru in view of Nahamani in further view of Qin 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, Qin teaches response generation using a retrieval augmented ai model to perform the execution of software tasks 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 Qui 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 Qui discloses the helping a student learn how to read providing text at a reading level suitable for learning. However, Guru, Nahamani, and Qui fails to disclose the 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 Qui 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, Qui 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 Qin teaches the method above, Guru in view of Nahamani in further view of Qin 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, Qin teaches response generation using a retrieval augmented ai model to perform the execution of software tasks 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 Qui 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 Qui discloses the helping a student learn how to read providing text at a reading level suitable for learning. However, Guru, Nahamani, and Qui 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 Qui 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, Qui 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 Qin 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 Qin 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 QIN (U.S. Patent Publication 2024/0346256 A1) (hereafter QIN) 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 Qin in further view of Shear teaches the method above, Guru in view of Nahamani in further view of Qin 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, Qin teaches response generation using a retrieval augmented ai model to perform the execution of software tasks 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 Qui 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, Qui, 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, Qui, and Shear discloses the helping a student learn how to read providing text at a reading level suitable for learning. However, Guru, Nahamani, Qui, 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, Qui, 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, Qui, 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 Qin 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 Qin 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, Qin teaches response generation using a retrieval augmented ai model to perform the execution of software tasks 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 Qui 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, Qui, 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, Qui, and Shear discloses the helping a student learn how to read providing text at a reading level suitable for learning. However, Guru, Nahamani, Qui, 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, Qui, 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, Qui, 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 Qin in further view of Shear teaches the method above, Guru in view of Nahamani in further view of Qin 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, Qin teaches response generation using a retrieval augmented ai model to perform the execution of software tasks 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 Qui 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, Qui, 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, Qui, and Shear discloses the helping a student learn how to read providing text at a reading level suitable for learning. However, Guru, Nahamani, Qui, 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, Qui, 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, Qui, 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 Qin 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 Qin 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 Qin 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.
Conclusion
The prior art made of record and not relied upon considered pertinent to Applicant’s disclosure.
Kurjanowicz et al. (U.S. Patent 11,113,981 B2) discloses skill training system.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/S.S.S/Examiner, Art Unit 3625
/MUSTAFA IQBAL/Primary Examiner, Art Unit 3625
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