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
Status of the Application
This action is in response to the Amendment filed on 7/29/2026, and is a Final Office Action. Claims 1-20 are pending in the application.
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.
Claim 1 is directed towards a method, thus meeting the Step 1 eligibility criterion. Claim 1 does recite the abstract concept of a commercial interaction/fundamental economic practice, which has been identified as an abstract idea by the MPEP. The relevant claimed limitations include: detecting the user interaction of a user / receiving a request for information from the user / providing the output.
This judicial exception is not integrated into a practical application. Claim 1 includes the additional elements of a processor , portal of an enterprise , portal processor, a machine learning chatbot and training/ using a ML chatbot to analyze and determine data (providing the request for the information to an ML chatbot / the ML chatbot is trained to generate a response based upon the user interaction of the user / the ML chatbot is trained using historical training data indicative of historical user interactions of a plurality of historical users / obtaining an output of the ML chatbot that is responsive to the request / determining a particular type of user activity using a trained classification model and based upon user interaction data associated with the user interaction/ the ML chatbot is based upon a fine-tuned ML model/ the fine-tuned ML model is associated with the particular type of user activity for the ML chatbot)/ user device including a processor. The processor/portal/portal processor/device represent generic computing elements. Using a ML chatbot, training the ML chatbot, and analyzing/determine data using the ML chatbot do no more than apply or link the use of the recited judicial exception to a particular technological environment. The additional elements do not, alone or in combination, improve the functioning of the computing device or another technology/technical field, nor do they apply or use the judicial exception in some other meaningful way beyond generally linking its use to a particular technological environment. The claim is directed to an abstract idea.
Claim 1 does not include additional elements that are sufficient to amount to significantly more than the judicial exception, because as noted above, the claimed computing elements represent generic computing elements that are recited at a high level of generality. Using a ML chatbot, training the ML chatbot, and analyzing/determine data using the ML chatbot do no more than apply or link the use of the recited judicial exception to a particular technological environment. The additional elements do not, alone or in combination, improve the functioning of the computing device or another technology/technical field, nor do they apply or use the judicial exception in some other meaningful way beyond generally linking its use to a particular technological environment. Therefore, Claim 1 does not amount to significantly more than the abstract idea itself. The claim is not patent eligible.
Independent claims 11, 20 are directed to a system and CRM for performing the method of claim 1, thus meeting the Step 1 eligibility criterion. Claims 11, 20 recite the same abstract idea as Claim 1. Claims 11, 20 perform the method of claim 1 using only generic components of a networked computer system. Therefore, claims 11, 20 are directed to an abstract idea without significantly more for the reasons given in the discussion of claim 1.
Remaining dependent claims 2-10, 12-19 further recite and narrow the abstract ideas of the independent claims. The claims further recite the additional elements of training/fine tuning a ML base model / using the ML chatbot to analyze and determine data, training a ML model, and an input device. The device represents a generic computing element that is recited at a high level of generality. Training/fine tuning a ML base model/ using the ML chatbot to analyze and determine data / training a ML model do no more than apply or link the use of the recited judicial exception to a particular technological environment/ field of use. The additional elements do not, alone or in combination with the other additional elements, improve the functioning of the computing device or another technology/technical field, or apply or use the judicial exception in some other meaningful way beyond generally linking its use to a particular technological environment. Therefore, the claims above do not amount to significantly more than the abstract idea itself. The claims are not patent eligible.
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, 2, 5, 6, 7, 8, 9, 10, 11, 12, 15, 16, 17, 18, 19, 20 are rejected under 35 U.S.C. 103 as being unpatentable in view of Mulligan (20240267344) in further view of Fong (20090248789) in even further view of Chakraborty (20190043483 ).
As per Claims 1, 11, 20, Mulligan teaches a method, system and CRM comprising:
One or more processors; one or more non-transitory memories storing processor-executable instructions that, when executed by the one or more processors, cause the system to: (the processor/memory represent generic computing elements that perform the claimed limitations. At least: para 95, 451)
based upon user interaction at a portal of an enterprise using machine learning (ML), the method comprising: detecting, by one or more processors, the user interaction of a user at the portal of the enterprise; receiving, by one or more processors via the portal, a request for information from the user; (at least fig3a-3c and associated/related text; para 93-94)
a portal of an enterprise using machine learning (ML) (para 88-90)
providing, by the one or more processors, the request for the information to an ML chatbot, (at last para 88-90, 95-97, 117-118, fig3a – 3c and associated/related text)
wherein: the ML chatbot is trained to generate a response based upon the user interaction of the user at the portal; (at least para 288, 87-91)
the ML chatbot is trained using historical training data indicative of historical user interactions of a plurality of historical users at the portal; (at least: para 118: “in some examples, the LLM 338 is continuously retrained or finetuned based on user interactions with the advertising content 358. For example, the interaction system 100 collects advertising content engagement metrics and stores the metrics in the advertisement analytics database 344. The metrics are then used to provide reinforcement to the LLM 338 when the LLM 338 provides a sequence of responses that lead to a successful user intent determination and consequently properly targeted advertising content 358 that the user interacted with.”, para 210)
obtaining, by the one or more processors, an output of the ML chatbot that is responsive to the request; (at last para 87, 95, 151, 121)
providing, by the one or more processors, the output to the user device. (at least: para 111 and 26 . the device represents a generic computing element that performs the claimed limitations- at least para 111-112 and 26)
the ML chatbot is based upon a fine-tuned ML model (at least para 273, 281)
Fong further teaches:
Determining, by the one or more processors, a particular type of user activity using a trained classification model and based upon user interaction data associated with the user interaction (at least para 38)
It would have been obvious for someone skilled in the art at the time of the filing of the
invention to modify Mulligan’s existing features, with Fong’s feature of determining, by the one or more processors, a particular type of user activity using a trained classification model and based upon user interaction data associated with the user interaction, to analyze user-specific data – Fong, abstract and para 38. Furthermore, 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.
Chakraborty further teaches:
The ML model is associated with the particular type of user activity for the ML chatbot (at least para 66, 68, 69, 70, 74)
It would have been obvious for someone skilled in the art at the time of the filing of the
invention to modify Mulligan’s existing features, combined with Fong’s existing feature, with Chakraborty’s feature above, to train the conversational agents using data – Chakraborty, para 69-70, abstract. Furthermore, 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.
As per Claims 2, 12, Mulligan in view of Fong in further view of Chakraborty teach:
training, by the one or more processors, a base ML model using historical enterprise data; (Mulligan, at least: para 31, 88-90, 85-96, 117-118)
fine-tuning, by the one or more processors, the base ML model based upon a plurality of user profiles associated with a plurality of types of user activity at the portal to generate a plurality of fine-tuned ML models associated with the plurality of types of user activity; (Mulligan, at least: para 35, 110, 31-32, 165-166)
storing, by the one or more processors, the plurality of fine-tuned ML models. (Mulligan, at least: para 267, 272-273, 35, 110)
As per Claims 5, 15, Mulligan in view of Fong in further view of Chakraborty teach:
The types of user activity include one or more of searching, an inquiry (Mulligan, at least: para 89, 222)
As per Claims 6, 16, Mulligan in view of Fong in further view of Chakraborty teach:
The portal includes one or more of a website, a mobile application…and/or a metaverse application. (Mulligan, at least: para 92, 148, 39-41: chatbot system is part of an interactive platform/social platform/AR platform/application/AR application/any other system , media or application with which a user can interact with)
As per Claims 7, 17, Mulligan in view of Fong in further view of Chakraborty teach:
To track the user interaction includes one or more of a session identifier, a user identifier and/or a user device identifier. (Mulligan, at least: 402, 437-438)
As per Claims 8, 18, Mulligan in view of Fong in further view of Chakraborty teach:
User interaction includes one or more of entering text , a signal from an input device (Mulligan, at least: para 41, 223)
As per Claims 9, 19, Mulligan in view of Fong in further view of Chakraborty teach:
Analyzing, by the one or more processors, the user profile to generate marketing content associated with the user; providing, by the one or more processors, the marketing content to the user device. (Mulligan, at least: para 9, 10, 26; 132 – using the user profile and intent to create the content)
As per Claim 10, Mulligan in view of Fong in further view of Chakraborty teach:
Generating, via the one or more processors, a request for the marketing content; inputting, via the one or more processors, the request into the ML chatbot; obtaining, via the one or more processors, an output of the ML chatbot that includes the marketing content. (Mulligan, at least: para 9, 10, 26; 219)
The prior art of record does not teach neither singly nor in combination the limitations of
claims 3, 4, 13, 14. The most relevant prior art identified, Mulligan (20240267344), teaches a chatbot for interactive platforms, comprising: receiving, by one or more processors, from a client system, a prompt of a user during an interactive session; filtering, by the one or more processors, the prompt of the user based on a set of platform policies; generating, by the one or more processors, a response based on the filtering of the prompt of the user; and communicating, by the one or more processors, to the client system, the response, as well as using an LLM to extract content from conversations with a chatbot to target users with customized content. However, it lacks the combination of claimed elements of pending claims 3, 4, 13, 14. Spiegel (20240249318) teaches determining user intent and providing targeted content using chatbot interactions, including using a LLM to improve intent modeling accuracy.
However, it lacks the combination of claimed elements of pending claims 3, 4, 13, 14.
When taken as a whole, claims 3, 4, 13, 14 are not rendered obvious as the available prior art
does not suggest or otherwise render obvious the noted features nor does the available prior art
suggest or otherwise render obvious further modification of the evidence at hand. Such modifications
would require substantial reconstruction relying solely on improper hindsight bias, and thus would not
be obvious.
Response to Arguments
Applicant’s arguments have been fully considered; Applicant argues with substance:
Applicant respectfully contends that amended claim 1 is allowable under 35 U.S.C. § 101 at least because claim 1 is not directed to an abstract idea under Step 2A, Prong 2 of the subject matter eligibility test. Under Prong 2, the Office evaluates claims that recite a judicial exception to determine whether the claims integrate the alleged judicial exception into a practical application, such that the claim "will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception." MPEP § 2106.04(d). In particular, "one way to demonstrate" that a claim as a whole integrates the exception into a practical application "is when the claimed invention improves the functioning of a computer or improves another technology or technical field." MPEP § 2106.04(d)(1). Applicant respectfully submits that claim 1 integrates any allegedly recited abstract ideas into a practical application thereof and is therefore eligible at least under Prong 2. Applicant respectfully contends that paragraph [0006] of the instant specification discusses a technical problem associated with conventional technology: "generic artificial intelligence and machine learning ... may lack personality, style and/or other attributes that ensure the customer experience is consistent with the brand identity, which may have deleterious effects for an enterprise." Applicant further respectfully contends that paragraphs [00168], [00169], and [00201] of the instant specification also disclose how the claimed invention provides a technical solution to the technical problem discussed above: [00168] ... The classification ML model 510 may receive as an input 530 data indicating user interactions at a portal 530B. The classification model may be trained to generate as an output 540 a type of user activity 540C associated with the user interaction. The ML model 510 may receive other types of suitable inputs 530 to generate associated suitable outputs 540 according to the training
of the ML model 510. [00169] ... In one example, an ML model 510 may be retrained based upon updated training data 520 or any other suitable data. In one example, an ML model 510 may be fine-tuned based upon user profile data 520D, other training data 520 associated with traits of a person, and/or any other suitable data. [00201] ... The server may identify a fine-tuned ML model associated with the user activity and load the identified fine-tuned ML model for the ML chatbot. In Jack's case, the classification model may determine Jack is trying to obtain information for new driver automobile insurance, such as a price quote ... For example, in response to the one or more Jack's requests for information, Jack's interactions, and/or Jack's user activity, the ML chatbot may load an appropriate fine-tuned ML model to generate the response in chat window 810 which indicates the ML chatbot may provide various new driver automobile insurance quotes ... Put another way, Applicant respectfully contends that conventional techniques are unable to generate responses for a user that are curated and tailored to a specific user interaction. The instant specification provides a technical solution to this technical problem by classifying a user interaction using a trained classification model and by fine-tuning a ML model associated with a type of user activity determined by the classification model. By using a classification model to determine a user interaction, the claimed invention allows for fine tuning of a ML model tailored to the specific user interaction. The fine-tuned ML model is then able to provide a response to the user specific to the user and the user interaction, thereby providing a curated and tailored response. As illustrated in paragraph [00201] above, by using both a trained classification model and a fine-tuned ML model, the claimed invention allows a system utilizing ML chatbots to provide improved responses that are curated and tailored to the specific user interaction, overcoming a limitation of conventional techniques. Applicant respectfully contends that amended claim 1 also recites a method for addressing such a technical solution by "determining ... a particular type of user activity using a trained classification model and based upon user interaction data associated with the user interaction" and "providing ... the request for the information to an ML chatbot" wherein "the ML chatbot is based upon a fine-tuned ML model" and "the fine- tuned ML model is associated with the particular type of user activity for the ML chatbot" and "obtaining ... an output of the ML chatbot that is responsive to the request" (emphasis added). Moreover, Applicant respectfully contends that amended claim 1 is similar to claim 3 of Example 47 of the Subject Matter Eligibility Examples. Example 47 discloses, at least in part: The claimed invention reflects this improvement in the technical field of
network intrusion detection. Steps (d)-(f) provide for improved network security using the information from the detection to enhance security by taking proactive measures to remediate the danger by detecting the source address associated with the potentially malicious packets. Specifically, the claim reflects the improvement in step (d), dropping potentially malicious packets in step (e), and blocking future traffic from the source address in step (f). These steps reflect the improvement described in the background. Thus, the claim as a whole integrates the judicial exception into a practical application such that the claim is not directed to the judicial exception. The additional elements in steps (d)-(f), when considered in combination, integrate the abstract idea into a practical application because the claim improves the functioning of a computer or technical field. See MPEP 2106.04(d)(1) and 2106.05(a). The claimed invention reflects this improvement in the technical field of network intrusion detection.
In particular, Applicant respectfully contends that the limitations of amended claim 1, "determining ... a particular type of user activity using a trained classification model and based upon user interaction data associated with the user interaction" and "providing ... the request for the information to an ML chatbot" wherein "the ML chatbot is based upon a fine- tuned ML model" are analogous to steps (d)-(f) of Example 47 claim 3. Specifically, the above-recited limitations of amended claim 1 reflect the improvement to a computing system implementing ML models that interact with a user by generating curated and tailored responses to the user. Accordingly, Applicant respectfully submits that amended claim 1 overcomes the rejection under 35 U.S.C. § 101 at least because amended claim 1 is not directed to an abstract idea. Further, each of claims 11 and 20 are rejected and amended similarly, and the remaining claims depend from one of claims 1, 11, or 20. Therefore, Applicant respectfully submits that all pending claims 1-20 overcome the 35 U.S.C. § 101 rejection for at least the reasons set forth above.
The pending claims do recite an abstract idea, and the additional elements do not, alone or in combination, integrate the recited judicial exception into a practical application, nor do they represent significantly more than the abstract idea itself, as noted above. The pending claims, when implemented, do not improve the functioning of the computing device itself or other technology/technical field; the pending claims do not recite an improvement to the technical field of machine learning – i.e. they do not recite a novel machine learning architecture or improved training method(s).
The pending claimed invention and USPTO Example 47, Claim 3, have different fact patterns and different claim sets , and thus the two are not analogous. Furthermore, in Example 47, Claim 3, the claim was deemed patent eligible since it provides an improvement in the technical field of network intrusion detection – i.e. the claims provide for improved network security using the information from the detection to enhance security by taking proactive measures to remediate the danger by detecting the source address associated with the potentially malicious packets. Specifically, the claim reflects the improvement in step (d), dropping potentially malicious packets in step (e), and blocking future traffic from the source address in step (f). These steps reflect the improvement 12 described in the background. Thus, the claim as a whole integrates the judicial exception into a practical application such that the claim is not directed to the judicial exception. Contrary to Example 47, Claim 3, the pending instant claims do not provide an improvement in the technical field of network intrusion detection; they do not provide for improved network security.
There is no technical support/technical evidence in the Spec. , including the paras noted by the Applicant, that the pending instant claims, when implemented, improve the functioning of the computing device itself or other technology/technical field. See Office Action above for the detailed, reasoned 35 USC 101 analysis.
The 35 USC 112 rejection has been overcome
Examiner agrees; the 35 USC 112 rejection has been overcome and has been withdrawn.
Remaining arguments: Applicant’s remaining arguments have been considered but are moot in view of the new grounds of rejection.
Conclusion
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 extension fee 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALEXANDRU CIRNU whose telephone number is (571)272-7775. The examiner can normally be reached on M-F 9:00am-5pm. If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, Ilana Spar can be reached on (571) 270-7537. The fax phone number for the organization where this application or proceeding is assigned is 571- 273-8300. Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
Sincerely,
/Alexandru Cirnu/
Primary Patent Examiner, Art Unit 3622
8/12/2026
Prosecution Insights