DETAILED ACTION
This action is responsive to the Application filed 2/21/2024.
Accordingly, claims 1-20 are submitted for prosecution on merits.
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.
Claim 15 is rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Claim(s) 15 is/are directed to an Abstract Idea. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the following 2 steps Analysis.
Step I: the claim is directed to a method as a statutory category.
Step II, A, prong 1:
The recited steps of “monitoring” (requests); “filtering” (requests based on filters); “determining” (intent … requests); “generating” (multiple solutions); “identifying” (an optimized solution) are perceived as activities that can be performed by a mental process; notably when these activities are not depicted as being tied up to any special technical adaptation or unconventional equipment or means. As a whole, one human upon receiving information via a generic computer showing one or more requests content, would be able to “filter” the contents and derive therefrom a solution (as well as one particular version thereof) which the human deems most optimized using a well-known computer-based numerical or mathematical technique (cross validation tree). The method claim includes what amounts using known techniques for determining data that can be performed by (resulted from) a mental process (using a generic computer) typical to an Abstract Idea type of Judicial Exception. See MPEP 2106.04(a)
Step IIA, prong 2:
The recited steps of “generating a deployable component” and “executing” (the component) can be viewed as use a generic computer to complement effort of a human associated with deriving a component or executable component, which in all amount to a well understood implementation that do not integrate the judicial exception from prong 1 into a practical application. Nor do these extra-activities clearly evidence or suggest an inventive transformation – MPEP 2106.05c – over existing technologies in terms of effecting a much more concrete non-conventional involvement to solve a problem than a mere use of a well-known computer-based activity to aid the mental process.
Step II, B:
The additional elements are identified as “generating a deployable component” and “executing” (the component). The use of generic computer for a human to derive a component or executable component amounts to a well understood implementation associated with post-activity that makes use of information obtained from a mental process, thus the “generating” and “executing” are mere post-activities that rely of result from a mental process such as monitoring, filtering, determining and identifying a solution; and as such cannot render the Abstract Idea into non-conventional technique that signifies much more than a Judicial Exception. From the claim construed as a whole, no technical feature in the preamble is seen as directly involved with the mental process of step IIA. That is, mere use of mental derivation from presented data from a computer without showing how the post activities (additional elements as set forth above ) bring forth a defined instance of transforming an existing technology into a better one, of realizing a solution to an existing problem; that is, the additional elements thus identified cannot upconvert the method claim for it to amount to significantly much more than a Judicial Exception. See MPEP 2106.05 (II) c, d.
Claim 15 is rejected as a Judicial Exception of a Abstract Idea type.
Claims 1 and 8 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Claim(s) 1 and 8 is/are directed to an Abstract Idea. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the following 2 steps Analysis
For claim 1:
Step I: this is a system claim
Step IIA, prong 1:
The system of claim 1 includes instruction steps of “monitoring” (requests); “filtering” (requests based on filters); “determining” (intent … requests); “generating” (multiple solutions); “identifying” (an optimized solution); and as no details are provided for coordinating a processor with the above steps in a non-conventional way, the above steps are construed as being performed with use of a generic computer to provide information to the steps of monitoring, filtering, determining and identifying, similar to the scenario about a user processing information presented via a computer in order to identify, re-arrange or derive other information from the initial information, which fall under a deficiency of mental process or Abstract Idea type of Judicial Exception. See MPEP 2106.04 (a) to (d)
Step IIA, prong 2:
There are no sufficient details in claim 1 to demonstrate how processor and memory stored instructions can oblige the steps of monitoring, filtering, determining or identifying to necessarily be implemented with a particular machine or SW algorithm; nor is the intended use of artificial intelligence in the pre-amble integrates sufficient interaction with these mental process steps that would make it necessary to use a practical application to carry out execution result of these steps. In a whole, the system claim fails to provide sufficient teaching that would enable the mental process as identified in prong 1 to be integrated into a practical application. See MPEP 2106.05 b, c, or d
Step II B:
Claim 1 recites the same “additional elements” of generating a component and executing the component; and absent sufficient details showing how the “generating” and “executing” enable the mental processes of monitoring, filtering, determining or identifying to amount to much more than a mere use of computer to derive data mentally, it is deemed that the additional elements are non-significant, well-understood computer activities that rely on information obtained from activities of a mental process rather than describing a non-conventional way of transforming a technical problem into an improved version thereof; hence fail to upconvert the mental process in a manner that makes it amount to substantially much more than a judicial exception. MPEP 2106.05 (II) c, d.
For claim 8:
Step I: claim 8 is directed to a product category
Step IIA, prong 1:
The product of claim 8 includes instruction steps of “monitoring” (requests); “filtering” (requests based on filters); “determining” (intent … requests); “generating” (multiple solutions); “identifying” (an optimized solution); and as no details are provided for coordinating a processor with the above steps via a non-conventional technique, the above steps are construed as being performed with use of a generic computer to provide information to the steps of monitoring, filtering, determining and identifying, similar to the scenario about a user processing information presented via a computer in order to identify, re-arrange or derive other information from the initial information, which fall under a deficiency of mental process or Abstract Idea type of Judicial Exception. MPEP 2106.04 (a to d)
Step IIA, prong 2:
There are no sufficient details in claim 8 to demonstrate how processor and memory stored instructions can oblige the steps of monitoring, filtering, determining or identifying to necessarily be implemented with a particular machine or SW algorithm; nor is the intended use of artificial intelligence in the pre-amble integrates sufficient interaction with these mental process steps that would make it necessary to use a practical application to carry out execution result of these steps. In a whole, the system claim fails to provide sufficient teaching that would enable the mental process as identified in prong 1 to be integrated into a practical application. See MPEP 2106.05 b,c, and d
Step II, B:
Claim 8 recites the same “additional elements” of generating a component and executing the component of claim 1; and absent sufficient details showing how the “generating” and “executing” enable the mental processes of monitoring, filtering, determining or identifying to amount to much more than a mere use of computer to derive data mentally, it is deemed that the additional elements are non-significant or well-understood computer activities that rely on information obtained from activities of a mental process rather than describing a non-conventional way of transforming a technical problem into an improved version thereof; hence fail to upconvert the mental process in a manner that makes it amount to substantially much more than a judicial exception. MPEP 2106.05 (II) c, d.
It is deemed that both claims 1 and 8 are directed to a Abstract Idea without one or more additional elements capable of showing that the “system” or “product” amounts to significantly more than a Judicial Exception of a Abstract Idea type.
Analysis of Dependent claims under step II, B:
Claims 2, 9, 16 recite “determining” more channels for executing and selecting one for executing the component; but these can be viewed as generic technique of using computer support (post-activity) that makes use of information resulted from a mental process, hence cannot make the mental process to amount to significantly more than a judicial Exception.
Claims 3, 10, 17 recite “filtering’ as categorizing requests based on filters, and as such fail to integrate the mental process into a practical application that otherwise necessitates special machine and non-conventional software algorithms.
Claims 4, 11,18 recite instructions to generate filters and this can be viewed as pre-activity or preparation stage in place to support the mental activities; hence the “generating” fails to demonstrate how the mental process identified in step IIA perform a transformation or inventive step that improves upon an existing technological situation.
Claims 5, 12, 19 recite instructions to update dynamic filters based on executing a deployment; there is not sufficient teaching with the updating act to demonstrate that monitoring, filtering, determining or identifying of the base claim would actually cause transformation of an actual technology to arrive at a improved state thereof as result from the updating; nor is there clear relationship between the updating filters context with optimized solution and generating software component for addressing incoming requests as part of the adaptive intelligence set in the preamble; hence claims 5,12, 19 fail to transform the mental process of the base claim into a substantially much more significant inventive technology than a Judicial Exception
Claims 6, 13, 20 recite instructions to identify a optimized solution using a decision tree. Use of numerical technique or mathematical processing means as a validation technique are construed as well-understood technique to support analyses or derivation of solutions, and employ of mathematical techniques can fall under one category of Judicial Exception type infringement.
Claims 7, 14 recite generating a unique ID a link to the requests, but this processor action amounts to a pre-activity to the mental process of tracking and deriving data from the request, therefore cannot integrate this mental process into a practical application.
Claims 1, 8, 15 are therefore un-eligible subject matter for failing compliancy established under the USC 101 statute
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-20 is/are rejected under § 35 U.S.C. 103 as being unpatentable over Miller et al, USPubN: 2024/0354641 (herein Miller) in view of Loughmiller et al, USPubN: 2005/0076084 (herein Louchmiller), and Bollinger, III, USPubN: 2023/0171282 (herein Bollinger)
As per claim 1, Miller discloses a system (e.g. Fig .3-4) for generating deployable components associated with software applications for incoming requests via an adaptive zero-trust generative artificial intelligence engine (para 0070-0071), comprising:
at least one processing device; at least one memory device; and a module stored in the at least one memory device (para 0327-0328) comprising executable instructions that when executed by the at least one processing device, cause the at least one processing device to:
monitor (see tracking technology – para 0055; tracks interaction functions, posts the user like, share …spend on the platform – para 0072; interaction server - para 0031 ) one or more requests (interaction server, incoming network requests – para 0031; intent of the user and entities … identify and extract … important information … such as … a request – para 0110) entering an entity network (interaction server – Fig. 2; para 0027-0028) associated with an entity (see intent of the user and entities … identify and extract … important information … such as … question of the user or a request – para 0110 – para 0110);
filter (component 404 – Fig. 4) the one or more requests (see e.g. posts the user like, share …spend on the platform – para 0072; incoming network requests – para 0031) based on one or more filters (component 404 filters the raw content 420 based on a set of filtering criteria to eliminate … obscene words, images, or concepts or content that some may consider harmful – para 0121);
determine intent (AI agent system 400 determines the intent 422- para 0114; AI agent, understand and respond to an input … of the user – para 0119; content response component 412 to generate one or more content items using the intent 422 … component 412 communicates the content items to the response filter 404 – para 0120; filter component 404 generates an adjusted intent based on filtering the raw content – para 0121) associated with the one or more requests (e.g. posts the user like, share …spend on the platform – para 0072; incoming network requests – para 0031) based on filtering the one or more requests;
generate multiple solutions (generate one or more content items 410 using the intent 422 – para 0120; one or more content items 410 – para 0111; using deep learning techniques … more accurate and relevant content suggestions for users …enables content recommendations to be generated – para 0025; generative AI system 231 … inference data that is output include … translations, summaries, categorization … recommendation – para 0070;) to achieve the intent (extracted information from the intent 414 to determine the appropriate content items 410 – para 109; receive the intent 422 and generate a prompt based on the intent … generates the one or more content items 410 – para 0111) associated with the one or more requests;
generate a deployable component (interaction options that are displayed, options to suggest chat topics to the user, to guide the user … options comprise suggestions of conversations, or interaction steps - para 0123; receive output recommended content – para 0307; generate recommended content for the user – para 0315) associated with the solution (see recommendations and content items 410 from above); and
execute the deployable component (applying the recommended content to a first interaction client – para 0316; see applying the recommended content – para 0307; applying the recommended content – para 0307- claim 1, pg. 30; applying the recommended content – para 0315) via a channel (identifying a preferred communication channel … for the recommended content – para 0312).
Miller does not explicitly disclose determining intent associated with the requests
based on one or more dynamically changing filters
Loughmiller discloses use of neural network to classify incoming messages (e.g. mail – para 0014; incoming mail traffic – para 0037) in categories (para 0010) as part of identifying spams (“unwanted messages” – para 0023; “important message” – para 0025; spam filter 250 – Fig. 2; para 0047) via a dynamic message filtering (para 0025-0026; spam messages … are blocked – para 0119) approach, where a natural language processing of the incoming messages provides regular expressions (Fig. 4; para 0059) derived therefrom as input into the neural network algorithms (para 0013) to determine intent of the messages (para 0052, 0114) using classification (para 0087) underlying the artificial NN filtering or AI engine to categorize good/spam portion of the message (Fig. 3; para 0088; Fig. 5), the dynamic (message) filtering including manipulating rules (e.g. dynamically updated – para 0121) of the classification as input vector into a next instance of neural network (e.g. re-evaluated by the second neural network – para 0102) engine that implements the dynamic filtering, including provision of UI for users or administrators to interactively modify spam filtering preferences (para 0054, 0056), where learning and tagging (para 0026,0047) by the neural network can be adapted for generating defensive strategies against senders of spam (para 0117)
Hence, use of artificial intelligence as in Loughmiller for filtering received message content and determining of intent as part of the re-evaluation instance of a neural network via use of classification rules or filtering preferences that are dynamically modified to support the dynamic filtering aspect of the AI engine entails AI-based determination of intent associated with the requests based on one or more dynamically changing filters
Therefore, based on the highly adaptive aspect of AI inferencing functionality in regard to tailoring the analysis and AI recommendation to the user intent (Miller: para 0071) it would have been obvious before the effective filing date of the invention for one skill in the art to implement the modifiable aspect of the AI based analysis so that AI -based categorization of user content and intent would be supported by adaptive filtering implemented with artificial intelligence engine equipped with adaptive capability to alter classification rules in terms of adjusting and dynamic changing of filter settings associated with the AI(neural network) engine – as in per Loughmiller dynamic adjusting to support a neural network-based filtering of classification outcome – in accordance to finetuning aims of the AI engine in regard to classifying/reclassifying contents and determination of intent associated with tracking users messaging or incoming requests by Miller; because
combining AI type of classification and finetuning effect by one such AI engine via iteratively modifying filtering information -as set forth above – as reconfigurable input into different AI or classification engine to further refine the training analytics toward achieving the most optimal set of filtered outcome associated with monitoring of incoming NW data or requests and the underlying aim for determining user/request intent and categorizing the message or requests based thereon would enable the AI-based system to implement response that is commensurate with the identified intent, including a) issuing recommendation for application/business to conduct, b) proffering UI options to users to elect for resolving/repairing a solution/problem raised with the request; or c) dispatching countermeasure to mitigate, war- off threats envisioned from a impermissible, malicious intent detected from the monitoring; that is, a proper use of filter settings so that a modified and adjusted version thereof can be submitted into a respective run by the AI-based classification engine would highly enhance likelihood of the engine to derive the most optimal set of filtered outcome that is representative of the most accurate identification of the intent with which to classify a request, which in turn would facilitate the AI-based multi-modality analytic in Miller to generate recommendations or alternative solution set forth above as a) b) and c).
B) Nor does Miller explicitly disclose generating solutions that achieve the intent in terms of
identify an optimized solution from the multiple solutions via a cross-validation decision tree and generate a deployable component associated with the optimized solution.
Use of advanced modeling techniques such as artificial intelligence models or numerical methods such as decision tree (see Miller: para 0109, 0260, 0263, 0265) to resolve complexity among compared datasets; or cross-validation (see below) was known techniques to support simplification in selecting or identifying a most optimal set of configurations among comparable possibilities; and this is shown in Miller multi-modalities analytics where such techniques are part of machine learning or artificial intelligence in form of cross-validation as a means to compare input set effecting different training model contexts (para 0270) for reducing complexity in model selection (para 0283) and render the refinement for training more effective (para 0273, 0284), the refinement of the selected training model (Fig. 13) facilitating a corresponding deployment.
Bollinger discloses environment for identifying vulnerabilities and providing solution associated therewith (see claim 1, pg. 10) wherein cross-validation is applied to subgroups of training sets (para 0079) into a machine learning to generate the most optimum set among the performance scores for respective training procedure(s), where a trained model satisfying threshold of such score (para 0080) can be used for further observation, where the best overall cross-validation score combined with decision-tree algorithm (para 0081), enables the new observation to be set for target solution, according to which, applying a trained machine learning model to a new observation can predict update software for the variable of the solution (para 0085), e.g. in association with identifying anomalies and solutions for an environment (para 0083; determine asset vulnerabilities and the solution thereof – para 0086) in that the trained machine learning may predict target variable of a solution (Fig. 6B; target variable is a determination of whether a solution should be implemented – para 0072) to be implemented in conjunction with use of decision-tree algorithm (para 0076-0077) to help hyper-parameterization narrowing among a plurality of observations complicated by multitude branching possibilities. Hence, use of cross-validation to determine the highest score dataset to be selected for further application of machine learning geared to predict a target variable indicative of whether a solution should be implemented as part of the identification of vulnerabilities and seeking of solution assisted with (training) parameter simplification via decision-tree algorithms is recognized.
Thus, as AI techniques in Miller are geared for determination of intent from monitored requests or messages, it would have been obvious for one of ordinary skill in the art before the effective filing date of the invention to implement use of artificial intelligence in conjunction with cross-validation and decision tree support in Miller so that under the AI analytics, generating solutions to correspond to the (determined) intent would be finetuned to identify an optimized solution from the multiple solutions via a cross-validation decision tree - as shown in Bollinger seeking of solution for vulnerabilities - and generate a deployable component associated with the optimized solution, the latter based on finding a most optimum cross-validation score dataset with which to applying a predictive training instance that would yield a target variable indicative whether a solution among other possibilities should be implemented; because
intelligent techniques to finetune determination of request category and nature of intent can be indicative of component or applicable set to recommend to the requesting users or business entities of the provisioning platform, and artificial techniques to compute the most optimum set for the training via applying a cross-validation – and selecting of highest cross-validation score as set forth above- would enable applying the AI training to a most optimum input so to improve chance to predict/yield among multiple target solutions a best target considered the most optimal solution; and use of decision tree to narrow down parameterization of the training resulting from the cross-validated input would further improve chance that a most optimum variable or target output indicative of a most effective solution will be attained, thereby for the platform to convert this optimal solution into one or more actionable components (deployable solution) to recommend to the users whose requests have been tracked; or to implement it as countermeasure against undesirable intents such as vulnerability or malicious intrusion into assets of the system.
As per claim 2, Miller discloses system according to claim 1, wherein the executable instructions cause the at least one processing device to:
determine one or more channels (see below) for executing the deployable component; and
select the channel of the one or more channels (preferred communication channel … for the recommended content – para 0312) for executing the deployable component (applying the recommended content to a first interaction client – para 0316; see applying the recommended content – para 0307; applying the recommended content – para 0307- claim 1, pg. 30; applying the recommended content – para 0315) based on the intent (refer to claim 1; intent of the user and entities … identify and extract … important information … such as … a request – para 0110) associated with the one or more requests.
As per claim 3, Miller does not explicitly disclose system according to claim 1, wherein filtering the one or more requests comprises categorizing the one or more requests based on the one or more dynamically changing filters.
But use of machine learning techniques with classification function equipped with input filtering so to better categorize user message or communicated text on basis of a intent where the filtering of trained data is effectuated with dynamically modification of the filters prior to each classification run has been rendered obvious via rationale A in claim 1 via the teachings by Loughmiller.
Therefore, effect of filtering the one or more requests comprises categorizing the one or more requests based on the one or more dynamically changing filters would be deemed obvious for the same reasons set forth with rationale A from above.
As per claim 4, Miller does not explicitly disclose system according to claim 1, wherein the executable instructions cause the at least one processing device to generate the dynamically changing filters based on historical request processing data.
Loughmiller implements dynamic filtering of message in which the filtering preferences can be modified via a UI (para 0054-0056), where rules on blocking messages can be adjusted (dynamically updated – para 0121), as part of consolidation of filters or filter settings by administering effect of blocking and granting passage to messages, which is based in part of past or existing request processing instances recorded in form of blacklists or whitelists (para 0007, 0012) such that consulting these lists (para 0087) enable administrative action or re-evaluation thereby in regard to prioritize passing (good or important messages) or enforcing anti-spam to messages considered spams from the lists (para 0026), where maintaining the blacklists and whitelists is responsive to classification of the message (claim 12 pg. 9)
Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the invention to implement dynamic adaptation of filter setting in Miller’s AI approach so that adjusting the filtering or dynamically manipulation to filter setting is based in part on historical request processing shown by blacklist and whitelist being administratively maintained in Loughmiller; because
consulting past processing data – as set forth above - in evaluating incoming requests or messages for their intent and portent risk of intrusive spam would facilitate prompt administrative evaluation as to whether message or content belonging to a white-listed category be prioritized for quick pass-through, or else, content belonging to a black-listed category be turned away or inflicted with immediate anti-spam counter measures.
As per claim 5, Miller does not explicitly disclose system according to claim 4, wherein the executable instructions cause the at least
one processing device to update the dynamically changing filters based on outcomes associated with executing the deployable component.
However, deployable content, SW component or interactive options provided as recommendation (recommended content – para 0307; generate recommended content for the user – para 0315) using the intent-based and personalized AI analytics in Miller signifies by virtue of obviousness that any deployed application, SW component or contents will eventually return as contents (para 0059) messages, posting, queries or requests from the user (see Miller: message posted by a user to the interaction system – para 0060) using component or content from the recommendation, back to the recommendation provider system.
Therefore, as dynamically adjusting configuration of a security preventive rules or reconfiguring parameters of filters would be deemed obvious (refer to rationale of A in claim 1) so as to effectively meet complexity of activity types, user intents or disparate nature of vulnerability threats, expanding use of dynamically changing filters to the content, message or returning requests from the user application context that execute the deployable component originated from the Miller’s personalized AI or recommendation framework would be also deemed evident or obvious.
Thus, it would have been obvious for one of ordinary skill in the art before the effective filing date of the invention to implement tracking of incoming requests in support of the intent-based intelligent analytics and recommendation system in Miller, so that filters of incoming user messages/ requests is configured with dynamic readjust of the filtering rules and parameters as set forth in rationale A in claim 1( per dynamically changing filters by Loughmiller) so that the same effect of applying the dynamically changing filters would also be adapted towards all incoming data resulting from the users executing components or interacting with UI content resulting from one or more deployable components or recommended solutions provided by Miller’s intent-based analytics; because
scaling up effect of dynamically changing filters in just proportion to a) meet the ups and downs of demand for user request resulting from the user deploying the component recommended by the system and b) adapt a front-end prevention toward further confrontation with undesirable requests or intents, unverified/untrusted intrusions, or vulnerabilities attempts caused by augmented user activities resulting from deploying component recommended from above, would fall under the ambit of having adaptive filter capability configured at the entry points into the system, so that modifying setting or rules of the filter entities per a dynamic and extended basis would represent a self-adjusting or auto-improving front-end technique that not only fends off ill-intents or undesirable, malicious intrusions into the system in a scale commensurate with size and complexities of the incurred threat or challenge; but also would boost effectiveness of system’s front-end classification in regard to received data independent of the size of data and time of day, which in turn would further enhance throughput of the AI analytics and performance of the intent-driven solution recommendation system.
As per claim 6, Miller does not explicitly disclose system according to claim 1, wherein the executable instructions cause the at least one processing device to identify the optimized solution from the multiple solutions via the cross-validation decision tree based on performing impact analysis on the multiple solutions
But Bollinger solution seeking for resolving vulnerabilities discloses assessing as to whether a solution should be implemented via recursive training of highly score training set so that only the training set with the highest cross-validation score (para 0080-0081) would qualify to a next training refinement that yield the best variable representative of a target solution(para 0084-0085), in accordance with purport of a risk management framework (RMF) for establishing priorities and managing risk and organization security by monitoring (Fig. 1) and categorizing (of received input, internal asset) based on impact analysis (para 0030) by means of asset management, solution component (Fig. 4), where multiple solutions are subjected to evaluation on basis of observations run through intelligent models (Fig. 6A; para 0082) seeking acceptability of a applicable solution; which is indicative of sequence of artificial intelligence runs purported for assessing whether a deployable solution would yield either a favorable deployment impact or else constitute an impact of a deployment setback.
Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the invention to implement the intent-driven evaluation of solution or recommendation of content/components in Miller personalized AI system so that identifying the optimized solution (from the multiple solutions considered for a recommendation) via machine learning combined with cross-validation-decision tree as set forth above (refer to rationale B of claim 1) would be coupled with result from an impact analysis – as per Bollinger – as guidance in establishing and weighing applicability of a solution (for a target deployment) by means of assessing multiple solutions obtained from the iterative runs of training models - as shown in Bollinger; because
Coordinating of AI analytics in correlation with impact analysis suggestive of solution deployment feasibility so to further re-adapt training sets into a AI machine learning, on basis of refinement made to training sets via cross-validation, decision-tree by which the intelligent training can be recursively tuned to identify the most optimized solution – as set forth above- would enable multiple solutions resulting from cycles of AI training to be consolidated into one most optimum, in that the solution not only result from the most optimum set of training input which incorporates the most representative, significant and context relevant data from a filtering and classification stage; but would also benefit from one or more evaluation under impact analysis which pre-establishes measurable likelihood by which a given solution proposed from any stage of the AI analytics can be determined as secure, resource-compliant and fault-free for a final stage of deployment to the users.
As per claim 7, Miller does not explicitly disclose system according to claim 1, wherein the executable instructions cause the at least one processing device to:
generate a unique identifier (generate AR experience on the image currently being accessed, viewed by the user system … AI agent … generate the unique AR experience … activate the AR experience on the user system – para 0092) for the one or more requests; and
link the unique identifier (unique AR experience – para 0092) to the one or more requests (capture or access an image – para 0092) to allow tracking processing associated with the one or more requests (Note1: entity table storing instances of objects, events, individuals to which a access recognized as a AR experience – generated unique AR experience - by the AI agent can be linked to the table item – Table 808, Fig. 8; entity table … is linked referentially to an entity graph and profile data, each entity is provided with a unique identifier – para 0194 - in association with a given user attempt to access/view the entity - e.g. image – reads on generating a unique identifier to a user access request for facilitating linkage of the user request to the unique identifier).
As per claim 8, Miller discloses a computer program product for generating deployable components associated with software applications for incoming requests via an adaptive zero-trust generative artificial intelligence engine (refer to claim 1), comprising a non-transitory computer-readable storage medium having computer-executable instructions for:
monitoring one or more requests entering an entity network associated with an entity;
filtering the one or more requests based on one or more dynamically changing filters;
determining intent associated with the one or more requests based on filtering the one or
more requests;
generating multiple solutions to achieve the intent associated with the one or more
requests;
identifying an optimized solution from the multiple solutions via a cross validation
decision tree;
generating a deployable component associated with the optimized solution; and
executing the deployable component via a channel.
(all of which having been addressed in claim 1)
As per claim 9, Miller discloses computer program product according to claim 8, wherein the non-transitory
computer-readable storage medium comprises computer-executable instructions for:
determining one or more channels for executing the deployable component; and
selecting the channel of the one or more channels for executing the deployable
component based on the intent associated with the one or more requests.
(refer to claim 2)
As per claim 10, Miller discloses computer program product according to claim 8, wherein filtering the one or more requests comprises categorizing the one or more requests based on the one or more dynamically changing filters. (refer to rationale of claim 3)
As per claim 11, refer to rejection of claim 4.
As per claim 12, refer to claim 5.
As per claim 13, refer to claim 6
As per claim 14, refer to claim 7
As per claim 15, Miller discloses a computerized method for generating deployable components associated with software applications for incoming requests via an adaptive zero-trust generative artificial intelligence engine, the method comprising:
monitoring one or more requests entering an entity network associated with an entity;
filtering the one or more requests based on one or more dynamically changing filters;
determining intent associated with the one or more requests based on filtering the one or
more requests;
generating multiple solutions to achieve the intent associated with the one or more
requests;
identifying an optimized solution from the multiple solutions via a cross validation
decision tree;
generating a deployable component associated with the optimized solution; and
executing the deployable component via a channel.
(all of which having been addressed in claim 1)
As per claims 16-20, refer to rejection of claims 2-6 respectively.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Tuan A Vu whose telephone number is (571) 272-3735. The examiner can normally be reached on 8AM-4:30PM/Mon-Fri.
If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, Chat Do can be reached on (571)272-3721.
The fax phone number for the organization where this application or proceeding is assigned is (571) 273-3735 ( for non-official correspondence - please consult Examiner before using) or 571-273-8300 ( for official correspondence) or redirected to customer service at 571-272-3609.
Any inquiry of a general nature or relating to the status of this application should be directed to the TC 2100 Group receptionist: 571-272-2100.
/Tuan A Vu/
Primary Examiner, Art Unit 2193
February 19, 2026