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 .
Status of Claims
Claims 1-20 are pending of which claims 1, 9 and 17 are in independent form.
Claims 1-20 are rejected under 35 U.S.C. 101.
Claims 1-20 are rejected under 35 U.S.C. 103.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
The claim(s) recite(s) system and method for generating guardrail data structure in a LLM chatbot system.
With respect to step 1 of the patent subject matter eligibility analysis, the claims are directed to a process, machine, manufacture, or composition of matter.
Independent claims 1, and 17 are directed to a system, which comprises, one or more processors.
Independent claims 9 are directed to a method, which is a process.
Independent All other claims depend on claims 1, 9, and 17. As such, claims 1-20 are directed to a statutory category.
Regarding claims 1, 9 and 17:
With respect to step 2A, prong one (Judicial Exception), it is noted that the independent claims recite an abstract idea falling within the Mental Health grouping of abstract ideas. Specifically, the following limitations recite mathematical concepts and/or mental processes and/or certain methods of organizing human activity.
The claim recites the following limitations directed to an abstract idea:
“processing, by a large language model (LLM), the interaction data to determine an update to at least one cluster membership of a plurality of content clusters” the limitation as drafted recites a mental process involving evaluation and categorization of information (i.e. determining how data should be grouped based on analysis);
“instructing the remote computing device to update the guardrail data structure based on the update to the at least one cluster membership” the limitation as drafted recites a mental process of applying decision (i.e. updating rules/classification based on evaluation).
These limitations correspond to concept that can be performed in the human mind (Mental Process) and therefore fall within the mental process category of abstract idea (see MPEP 2016.04(a)(2))
The claims fall within:
Thus, the claims recite an abstract idea (mental process/mathematical concepts/information processing).
With respect to step 2A, Prong Two (Particular Application), the claims do not recite additional elements that integrate the judicial exception into a practical application. The following limitations are considered “additional elements” and explanation will be given as to why these “additional elements” do not integrate the judicial exception into a practical application.
The claims recite the use of:
“one or more processors; or more transitory or non-transitory computer-readable media storing instructions that are executable to cause the one or more processors to perform operations” as drafted recites generic computer performing generic computer functions (see MPEP 2106.05(f)(2)); and generic storage and execution of instructions, which is conventional and amounts to mere instructions to apply the abstract idea using a computer (see MPEP 2106.05(f)).
“receiving, from a first user of a plurality of users of a guardrail data structure, interaction data associated with chat data” recites data gathering, which is insignificant extra-solution activity (see MPEP 2106.05(g)).
“processing, by a large language model (LLM), the interaction data to determine an update to at least one cluster membership of a plurality of content clusters” recites the abstract idea itself, merely implemented using generic model, without specifying any technical improvement to the model.
“transmitting the update of the at least one cluster membership to a remote computing device” recites routine data transmission, which is conventional computer function and amounts to insignificant post-solution activity.
“instructing the remote computing device to update the guardrail data structure based on the update to the at least one cluster membership” recites application of the abstract idea, reflecting a result oriented functions limitations (update based on decisions), without specifying how the update is technically performed.
The LLM is used as a tool to perform the abstract classification and analysis. The guardrail data structure is simply a data representation of rule/policies. The computing device performs generic processing. The transmission step is routine data communication.
The claims do not:
Improve LLM architecture or training mechanism
Improve clustering algorithm technically,
Improve computer performance or memory
Provide a new data structure with technical functionality
Solve problem rooted in computer technology.
There are no improvements to computer functionality or any specific technical solution to a computer centric problem. Instead, the computer and semiconductor environment are used as tools to execute abstract mathematical encoding, data analysis, and decision making, with the result merely being applied in a generic manner.
There is no recitation of, a new data structure that changes computer operation, improved network functioning, an unconventional indexing technique, a specific hardware solution.
Instead, the claims recite conventional and generic computer functions performed in a routine manner, which does not amount to a practical application.
With respect to Step 2B. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The recited components are merely generic computer/database elements performing their routine, well-understood, and conventional functions. See Alive, MPEP 2016.05(d).
The steps mentioned in the independent claims are merely generic computing device, LLM performing conventional process, data structure representing rules, routine transmission. Courts have consistently helped such high-level information management operations are conventional.
The claims recite only functional, result oriented language (“detecting”, “propagating”, “transferring”,…), without specifying any technical mechanism for performing these operations in a non-conventional manner.
Considering claims as a whole, the ordered combination of elements also reflects nothing more than the typical workflow of distributed systems, and therefore DOES NOT add “significantly more” than the abstract idea.
Such generic, high‐level, and nominal involvement of a computer or computer‐based elements for carrying out the invention merely serves to tie the abstract idea to a particular technological environment, which is not enough to render the claims patent‐eligible, as noted at pg.74624 of Federal Register/Vol. 79, No. 241, citing Alice, which in turn cites Mayo. Further, See, e.g., Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 134 S. Ct. 2347, 2359‐60, 110 USPQ2d 1976, 1984 (2014). See also OIP Techs. v. Amazon.com, 788 F.3d 1359, 1364, 115 USPQ2d 1090, 1093‐94 (Fed. Cir. 2015) ("Just as Diehr could not save the claims in Alice, which were directed to 'implement[ing] the abstract idea of intermediated settlement on a generic computer', it cannot save O/P's claims directed to implementing the abstract idea of price optimization on a generic computer.") (citations omitted). See also, Affinity Labs of Texas LLC v. DirecTV LLC, 838 F.3d 1253, 1257‐1258 (Fed. Cir. 2016) (mere recitation of a GUI does not make a claimpatent‐eligible); Intellectual Ventures I LLC v. Capital One Bank, 792 F.3d 1363, 1370 (Fed. Cir. 2015) ("the interactive interface limitation is a generic computer element".).
The additional elements are broadly applied to the abstract idea at a high level of generality ("similar to how the recitation of the computer in the claims in Alice amounted to mere instructions to apply the abstract idea of intermediated settlement on a generic computer,") as explained in MPEP § 2106.05(f)) and they operate in a well‐understood, routine, and conventional manner.
MPEP § 2106.0S(d)(II) sets forth the following:
The courts have recognized the following computer functions as well-understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity.
• Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec ... ; TLI Communications LLC v. AV Auto. LLC ... ; OIP Techs., Inc., v. Amazon.com, Inc ... ; buySAFE, Inc. v. Google, Inc ... ;
• Performing repetitive calculations, Flook ... ; Bancorp Services v. Sun Life ... ;
• Electronic recordkeeping, Alice Corp ... ; Ultramercial ... ;
• Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc ... ;
• Electronically scanning or extracting data from a physical document, Content Extraction and Transmission, LLC v. Wells Fargo Bank ... ; and
• A web browser's back and forward button functionality, Internet Patent
• Corp. v. Active Network, Inc. ...
. . . Courts have held computer-implemented processes not to be significantly more than an abstract idea (and thus ineligible) where the claim as a whole amounts to nothing more than generic computer functions merely used to implement an abstract idea, such as an idea that could be done by a human analog (i.e., by hand or by merely thinking).
In addition, when taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements integrate the abstract idea into a practical application. Their collective functions merely provide conventional computer implementation. Therefore, when viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a practical application of the abstract idea or that the ordered combination amounts to significantly more than the abstract idea itself.
The dependent claims have been fully considered as well, however, similar to the findings for claims above, these claims are similarly directed to the “Mental Processes” grouping of abstract ideas set forth in the 2019 PEG, without integrating it into a practical application and with, at most, a general purpose computer that serves to tie the idea to a particular technological environment, which does not add significantly more to the claims. The ordered combination of elements in the dependent claims (including the limitations inherited from the parent claim(s)) add nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation. Accordingly, the subject matter encompassed by the dependent claims fails to amount to significantly more than the abstract idea.
Looking at the claim as a whole does not change this conclusion and the claim is ineligible.
Regarding claims 2, 3, 4, 13, and 18 (Flagging, Classification, and Content Moderation),
The claim recites:
Identifying flagged data (claim 2 and 18)
Classifying chat data into content cluster (claims 3 and 18)
Remove flagged data (claims 4 and 13)
This merely applies rules to: evaluate content, categorize content, take action (remove flag). There are no changes to: how classification is performed, how clusters are generated, or any underlaying technical mechanism. These fall under: Mental Process (evaluation/categorization), and Data Processing.
This does not change the nature of the abstract idea. It does not add a technical improvement to an abstract idea, such as improving computer functionality, data structure, or processing architecture.
There is no practical application, and no inventive step, the claims are still considered abstract.
Regarding claims 5, 6, 10, and 11 (Verification and Comparison Logic),
The claim recites:
Verification process to verify interaction data (claim 5 and 10)
Generating a vector, comparing to clusters, and verifying (claims 6 and 11)
This merely performs: data analysis, comparison, decision making. There are no changes to: how vectors are generated, how similarity is computed, or any technical improvement in comparison algorithms. These fall under: Mental Process (comparison and evaluation), and Mathematical Algorithm (vector representation and similarity).
This does not change the nature of the abstract idea. It does not add a technical improvement to an abstract idea, such as improving computer functionality, data structure, or processing architecture.
There is no practical application, and no inventive step, the claims are still considered abstract.
Regarding claims 7, and 12 (Notification/Output),
The claim recites:
Generating a notification base on guardrail updates
This merely reports results of the previous analysis. There are no changes to: how notifications are generated technically, any communication protocol, or system performance. These fall under: Insignificant Extra-Solution Activity.
This does not change the nature of the abstract idea. It does not add a technical improvement to an abstract idea, such as improving computer functionality, data structure, or processing architecture.
There is no practical application, and no inventive step, the claims are still considered abstract.
Regarding claims 8, 16, and 20 (Training Data Generation and Model Training),
The claim recites:
Generating training data from interaction data
Training the LLM using that data
This merely performs: data preparation, model training. There are no changes to: the LLM architecture, how training is technically performed, or any learning algorithm. These fall under: Mental Process (data preparation), and Mathematical Algorithm (model training).
This does not change the nature of the abstract idea. It does not add a technical improvement to an abstract idea, such as improving computer functionality, data structure, or processing architecture.
There is no practical application, and no inventive step, the claims are still considered abstract.
Regarding claim 14 (Query Expansion/Input Modification),
The claim recites:
Generating an expanded query database
Using a query expansion model
This merely reformulates input data. There are no changes to: how queries are processed technically, any search or retrieval mechanism, or system architecture. These fall under: Mental Process (reformulation of information), and Data Manipulation.
This does not change the nature of the abstract idea. It does not add a technical improvement to an abstract idea, such as improving computer functionality, data structure, or processing architecture.
There is no practical application, and no inventive step, the claims are still considered abstract.
Regarding claim 15 (Model Refinement/Fine-Tuning),
The claim recites:
Fine-tuning cluster membership using the LLM
Based on expanded query data
This merely refine prior results using additional data. There are no changes to: how the model in trained technically, any optimization algorithm, or computational efficiency. These fall under: Mental Process (refinement and adjustment), and Mathematical Algorithm (model updating).
This does not change the nature of the abstract idea. It does not add a technical improvement to an abstract idea, such as improving computer functionality, data structure, or processing architecture.
There is no practical application, and no inventive step, the claims are still considered abstract.
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(s) 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Taylor; Bret Steven et al. (US 20250258856 A1) [Taylor] in view of D'Agostino; Dino Paul et al. (US 20250307562 A1) [D'Agostino].
Regarding claim 1, Taylor discloses, a system for managing a [guardrail] data structure, comprising: one or more processors; and one or more transitory or non-transitory computer-readable media storing instructions that are executable to cause the one or more processors to perform operations (see Fig. 5), the operations comprising: receiving, from a first user of a plurality of users of a guardrail data structure, interaction data associated with chat data (receive user input conversation ¶ [0037]. Including inputs (e.g., user queries or session variables) and outputs (e.g., responses or actions) ¶ [0026], [0039], multi-user chat interaction/chatbots ¶ [0022], [0023], [0062]. the classification module may deploy guardrails when using the LLM to classify intent of an end-user ¶ [0033]-[0035], [0051]);
processing, by a large language model (LLM) (ML/LLM classification ¶ [0037], [0040]. Also see ¶ [0028], [0030]), the interaction data to determine an update to at least one cluster membership of a plurality of content clusters (comparing new input embedding vs existing cluster embeddings…similarity threshold…alights with new proposed category ¶ [0040]. Analyze and update the categories based on the most recent data…continuously updating and recalculating clusters/categories…¶ [0043]);
[instructing the remote computing device] to update the guardrail data structure based on the update to the at least one cluster membership (deploy guardrails…rules, constraints, safety mechanism ¶ [0033], rules define when guardrails override classification ¶ [0034], guardrails modify system behavior ¶ [0051]. Also see ¶ [0037]).
However, Taylor does not explicitly facilitate transmitting the update of the at least one cluster membership to a remote computing device; instructing the remote computing device.
D’Agostino discloses, transmitting the update of the at least one cluster membership to a remote computing device (tracking conversations across systems ¶ [0063], generating outputs/reports ¶ [0099], system processing and responses ¶ [0120]. Adaptation process may involve fine-tuning existing parameters, updating weights, or training new layers within the model to better capture the nuances of the conversation and generate more contextually appropriate responses ¶ [0162]. Also see ¶ [0098], [0135]);
instructing the remote computing device (system-level interactions across components ¶ [0063], [0120]).
It would have been obvious to one ordinary skilled in the art before the effective filing date of the claimed invention to combine the teachings of the cited references because D’Agostino’s system would have allowed Taylor to facilitate transmitting the update of the at least one cluster membership to a remote computing device; instructing the remote computing device. The motivation to combine is apparent in the Taylor’s reference, because there is a need to improve identifying subsets/contextual information in chatbot interactions.
Regarding claim 2, the combination of Taylor and D’Agostino discloses, wherein the operations further comprise instructing the remote computing device to identify, [using the updated guardrail data structure], flagged data based on the chat data (D’Agostino: detects violations…flags the non-compliant conversations ¶ [0098]. Applying compliance rules to identify violations ¶ [0120]).
using the updated guardrail data structure (Taylor: deploy guardrails…rules, constraints, safety mechanism ¶ [0033], rules define when guardrails override classification ¶ [0034], guardrails modify system behavior ¶ [0051]. Also see ¶ [0037]).
Regarding claim 3, the combination of Taylor and D’Agostino discloses, wherein identifying the flagged data comprises classifying (D’Agostino: detects violations…flags the non-compliant conversations ¶ [0098]. Applying compliance rules to identify violations ¶ [0120], [0122]. Grouping chat messages into subsets ¶ [0106], labeling based on contextual attributes ¶ [0175]), using the guardrail data structure, the chat data into one or more content clusters of the plurality of content clusters based on contextual data (Taylor: clustering/classification ¶ [0040], updating clusters ¶ [0043]).
Regarding claims 4 and 13, the combination of Taylor and D’Agostino discloses, wherein the operations further comprise instructing the remote computing device to remove the flagged data from the chat data (D’Agostino: detects violations…flags the non-compliant conversations ¶ [0098]. Applying compliance rules to identify violations ¶ [0120]).
Regarding claims 5 and 10, the combination of Taylor and D’Agostino discloses, wherein the operations further comprise verifying, by the LLM (Taylor: input…verify whether the user input meets predefined validation…LLM used to extract data and perform tasks related to validation ¶ [0045]. Rules and compliance triggers ¶ [0033]-[0034]), the interaction data using a verification process (D’Agostino: ensuring compliance with regulatory standards ¶ [0098], NLP and ML used to detect risks and identify compliance violations ¶ [0100]-[0101], verify user identity and validate transaction legitimacy ¶ [0123]).
Regarding claims 6 and 11, the combination of Taylor and D’Agostino discloses, processing the chat data associated with the interaction data to generate a vector (D’Agostino: contextual attributes…labels to the vector…stored within vector database ¶ [0079]. Transforming these communications into vector representation using NLP techniques ¶ [0100]);
comparing the vector to the plurality of content clusters (D’Agostino: vectors are compared to regulatory requirements and industry standards ¶ [0120], analyze…to identify patterns….grouping/categorization via ML models); and
verifying the interaction data based on the comparing (D’Agostino: ensure compliance ¶ [0098], identifying compliance related to issues, assessing compliance status ¶ [0100]. Also see ¶ [0123]).
Regarding claims 7 and 12, the combination of Taylor and D’Agostino discloses, wherein the operations further comprise generating a notification based on the update to the guardrail data structure (D’Agostino: generating reports, insights, alerts ¶ [0098]-[0102], outputs based on compliance analysis ¶ [0120]-[0124]).
Regarding claims 8, 16, and 20, the combination of Taylor and D’Agostino discloses, processing the interaction data to generate training data, wherein the training data comprises examples of chat data correlated to examples of cluster memberships (D’Agostino: collecting chat conversation…analyze to extract actionable insights ¶ [0100], communications transformed into vector representation…used to identify deviations/compliance issues ¶ [0120]); and
training the LLM using the plurality of training data (D’Agostino: feedback loop to refine models ¶ [0098], generating training material from detected issues ¶ [0135]).
Regarding claim 14, the combination of Taylor and D’Agostino discloses, wherein the method further comprises generating, using a query expansion model operating on the computing device, an expanded query dataset based on the flagged data (D’Agostino: generating a prompt including subset of vectors…based on contextual attributes…task description included ¶ [0162]. Detects violations…flags the non-compliant conversations ¶ [0098]. Applying compliance rules to identify violations ¶ [0120], [0122]. Grouping chat messages into subsets ¶ [0106], labeling based on contextual attributes ¶ [0175]).
Regarding claim 15, the combination of Taylor and D’Agostino discloses, wherein the method further comprises fine-tuning, using the LLM, one or more cluster memberships of one or more content clusters of the plurality of content clusters based on the expanded query dataset (D’Agostino: adjust parameters, weights, or architecture…fine-tuning existing parameters…updating weights ¶ [0162]. Assigning categories, compliance classes and risk levels ¶ [0100], [0120]).
Regarding claim 9, the combination of Taylor and D’Agostino clearly show a method for performing the process for the method in claims 1, 2 and 3. Therefore, the rejections of claims 1, 2 and 3 applies to claim 9.
Regarding claim 17, the combination of Taylor and D’Agostino clearly show a method for performing the process for the method in claims 1 and 8. Therefore, the rejections of claims 1 and 8 applies to claim 17.
Regarding claim 18, the combination of Taylor and D’Agostino clearly show a method for performing the process for the method in claims 2 and 3. Therefore, the rejections of claims 2 and 3 applies to claim 18.
Conclusion
The examiner requests, in response to this Office action, support be shown for language added to any original claims on amendment and any new claims. That is, indicate support for newly added claim language by specifically pointing to page(s) and line no(s) in the specification and/or drawing figure(s). This will assist the examiner in prosecuting the application.
When responding to this office action, Applicant is advised to clearly point out the patentable novelty which he or she thinks the claims present, in view of the state of the art disclosed by the references cited or the objections made. He or she must also show how the amendments avoid such references or objections See 37 CFR 1.111(c).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MOHAMMAD S ROSTAMI whose telephone number is (571)270-1980. The examiner can normally be reached Mon-Fri From 9 a.m. to 5 p.m..
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4/3/2026
/MOHAMMAD S ROSTAMI/ Primary Examiner, Art Unit 2154
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