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
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 06/18/2026 has been entered.
Claims 1, 11 & 20 being independent have also been amended herein, and 2-9 & 12-19 dependent claims are in their original format.
The 35 U.S.C. § 103 rejection of claims 1-20 has been withdrawn in view of the claims as amended.
Claims 1-20 are currently pending and have been examined.
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 USC § 101 because the claimed invention is directed to an abstract idea without significantly more. When taking out all the additional elements from the claims, representative independent claim 1 recites:
obtain a plurality of electronic customer journey datasets, wherein at least one of the plurality of electronic customer journey datasets, corresponds to the electronic journey of the customer, corresponds to a transaction between the customer and the business, and indicates one or more transactional events associated with the transaction;
obtain friction identification rules used to identify the one or more friction events indicated in the at least one of the plurality of electronic customer journey datasets, wherein a friction event is an event that negatively impacts completion of the transaction;
analyze the at least one of the plurality of electronic customer journey datasets using the friction identification rules;
responsive to identifying a friction event, generate friction event information associated with the friction event;
determine one or more topics associated with the friction event,
determine one or more sentiments of the customer associated with the friction event, and
generate an indication of the one or more topics and/or the one or more sentiments in the friction event information.
Under the broadest reasonable interpretation, the limitations of claim 1 above merely recite analyzing information concerning a commercial interaction between a customer and a business, including identifying events negatively affecting completion of a transaction and determining topics and customer sentiment associated with such events. Accordingly, the claim recites a commercial interaction falling within certain methods of organizing human activity. Claims 11 and 20 recite similar limitations to claim 1 and are interpreted under the same premise.
The limitation that the fine-tuned LLM comprises a transformer architecture with self-attention mechanisms and has been fine-tuned using training data associated with detecting friction in electronic customer journeys further specifies the mechanism used to determine the claimed topics and customer sentiments associated with the friction event. Likewise, specifying that the LLM has been fine-tuned using training data associated with detecting friction in electronic customer journeys further specifies the LLM used to perform the claimed determination of topics and customer sentiments, but does not itself recite an improvement to the functioning of the LLM, computer, or other technology. Thus, the limitation further narrows the manner of performing the abstract information analysis rather than integrating the judicial exception into a practical application.
The judicial exception is not integrated into a practical application because the combination of additional elements, considered individually and in combination, including “one or more processors”, “one or more memories”, “fine-tuned large language model (LLM) comprising a transformer architecture with self-attention mechanisms and having been fine-tuned using training data associated with detecting friction in electronic customer journeys”, “a user interface”, “an electronic alert” and “computing device”, do not impose a meaningful limit on the judicial exception or improve the functioning of a computer or other technology. As discussed above, the fine-tuned LLM and its transformer architecture with self-attention mechanisms are used as the mechanism for determining the claimed topics and customer sentiments associated with the friction event, while the processors, memories, computing device, user interface, and electronic alert provide the computing environment and output mechanisms for implementing the claimed functions. These additional elements merely describe how to generally “apply” the judicial exception in a computer environment to automate an existing business process. A computer or additional elements in the claims are not necessary to perform the claimed steps but applied to automate an existing business process without significantly more. Simply implementing the abstract idea on a generic computer is not a practical application of the abstract idea, see MPEP 2106.05 (f).
The claims do not include additional elements individually or in combination that are sufficient to amount to significantly more than the judicial exception because, as discussed with respect to Step 2A Prong Two, the additional elements amount to no more than mere instructions to apply the exception using the claimed computer components and LLM implementation. In particular, the fine-tuned LLM comprising a transformer architecture with self-attention mechanisms and having been fine-tuned using training data associated with detecting friction in electronic customer journeys is used to perform the claimed determinations of topics and customer sentiments associated with the friction event and does not provide an inventive concept that transforms the judicial exception into patent-eligible subject matter. Further, when considered in combination with the processors, memories, computing device, user interface, and electronic alert, the claimed LLM implementation merely provides the computer-based mechanism for performing the claimed determinations and does not result in a non-conventional arrangement or technological improvement sufficient to provide an inventive concept.
The same analysis applies here in 2B, i.e., mere instructions to apply an exception using a generic computer component cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. Furthermore, the processors, memories, computing device, user interface, and electronic alert constitute well-understood, routine and conventional computing components performing generic functions, see specification at least paragraphs [0024-0025], [0030-0035], [0037], [0044]-[0047], [0051]-[0055], [0057-0067], [0077], [0083], [0085-0090], & [0106]-[0111] disclosing generic commercially available computing components carrying out well-understood, routine, and conventional computing functions, see MPEP 2106.05(d)(II). Therefore, the independent claims are ineligible.
The dependent claims 2-10 & 12-19 further narrow the abstract idea and do not provide any additional elements individually or in combination that amount to significantly more than the abstract idea. The dependent claims further limit the manner in which the abstract idea is implemented but do not integrate the judicial exception into a practical application or provide an inventive concept sufficient to amount to significantly more than the judicial exception. Mere instructions to apply an exception using a generic computer component cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. Claims 8 and 18 also include a database which does not amount to significantly more, because it is a generic component performing its basic function of storing data. Further narrowing of the independent claims does not make the dependent claims any less abstract. For the above reasons, dependent claims 2-10 & 12-19 are ineligible.
Response to Arguments
Applicants’ arguments filed 06/18/2026 have been fully considered but they are not persuasive.
With respect to Applicant’s arguments under rejections for 35 U.S.C. § 101 have been fully considered however the Examiner respectfully disagrees.
Applicant argues that the amended claims no longer recite a judicial exception because the fine-tuned LLM comprises a transformer architecture with self-attention mechanisms and has been fine-tuned using training data associated with detecting friction in electronic customer journeys. However, the rejection does not identify the transformer architecture or self-attention mechanisms themselves as the judicial exception. Rather, the claim recites analyzing information concerning a commercial interaction between a customer and a business, including identifying events negatively affecting completion of a transaction and determining topics and customer sentiment associated with such events. Such limitations concern a commercial interaction and therefore fall within certain methods of organizing human activity.
Applicant’s argument that the transformer architecture and self-attention mechanisms cannot practically be performed in the human mind does not alter this determination. The Examiner does not rely upon the transformer architecture with self-attention mechanisms itself as reciting a mental process. Rather, the fine-tuned LLM and its recited architecture are considered as additional elements in determining whether the judicial exception is integrated into a practical application.
Applicant further argues that the transformer architecture with self-attention mechanisms provides a technological improvement because Specification paragraph [0065] states that the transformer model may incorporate self-attention mechanisms to facilitate faster learning-training and/or more accurate output. This argument is not persuasive. Although the specification describes potential advantages associated with self-attention mechanisms, claims 1 does not recite using the self-attention mechanisms to achieve faster training, improved training efficiency, or improved accuracy of the LLM itself. Rather, the claimed LLM is used to determine one or more topics and one or more sentiments of the customer associated with the friction event. Thus, the claim uses the recited LLM architecture as the mechanism for performing the claimed topic and sentiment determinations, rather than reciting a technological improvement to the functioning of the LLM, computer, or another technology.
Applicant also argues that fine-tuning saves computing resources based on Specification paragraph [0063]. This argument is likewise not persuasive. While paragraph [0063] describes advantages that may result from fine-tuning a pre-trained model instead of creating a new model from scratch, the claims do not recite training a new model from scratch, comparing resource utilization between training techniques, reducing memory usage, reducing processor utilization, or reducing training time. The claims instead require that the LLM has been fine-tuned using training data associated with detecting friction in electronic customer journeys and thereafter use that LLM to determine topics and customer sentiments associated with the friction event. Accordingly, the alleged reduction in computing resources described in the specification is not reflected in the claimed invention as a technological improvement.
Applicant further argues that the LLM output drives concrete system actions that reduce wasted computing resources because the claim generates a user interface and/or electronic alert based upon the friction event information. This argument is not persuasive. Claim 1 does not recite mitigating a friction event, modifying or controlling the electronic transaction, preventing repeated transaction processing, reducing processor utilization, reducing network utilization, or reducing memory utilization. Rather, claim 1 recites generating at least one of a user interface including the friction event information or an electronic alert based upon the friction event information. Thus, the alleged downstream mitigation of friction events and corresponding reduction in computing resources relied upon by Applicant are not required by the claim. The claimed user interface and electronic alert constitute output mechanisms for presenting or communicating the results of the claimed information analysis and do not transform the commercial interaction into a technological improvement or otherwise integrate the judicial exception into a practical application.
Applicant further contends that the Office has not established that the specific ordered combination of the amended claims is well-understood, routine, and conventional. The argument is not persuasive. As discussed above, the Examiner does not rely upon finding that the newly recited fine-tuned LLM comprising a transformer architecture with self-attention mechanisms and having been fine-tuned using the claimed training data is itself well-understood, routine, and conventional. Rather, the processors, memories, computing device, user interface, and electronic alert constitute well-understood, routine, and conventional computing components performing generic computing functions, as evidenced by the portions of Applicant’s specification cited above.
The fine-tuned LLM limitation is separately considered both individually and in combination with the other additional elements. The fine-tuned LLM is used to perform the claimed determinations of topics and customer sentiments associated with the friction event. When considered in combination with the processors, memories, computing device, user interface, and electronic alert, the claimed LLM implementation provides the computer-based mechanism for performing the claimed determinations and does not result in a technological improvement or other inventive concept sufficient to transform the judicial exception into patent-eligible subject matter. Accordingly, the rejection does not depend upon a determination that the newly recited LLM implementation, either individually or as part of the claimed ordered combination, is itself well-understood, routine, and conventional. Rather, when the additional elements are considered as an ordered combination, the claimed LLM implementation performs the claimed topic and sentiment determinations, while the remaining computer components provide the computing environment and output mechanisms, without reciting a non-conventional arrangement or technological improvement sufficient to provide an inventive concept.
Applicant’s assertion regarding tokenization, embedding layers, feedforward layers, attention-weight computations, and recurrent layers do not alter the eligibility analysis. Claim 1 expressly recites a transformer architecture with self-attention mechanisms, but does not expressly require the particular internal operations identified by Applicant. Moreover, even accepting that certain such operations are performed in implementing the recited transformer architecture, the Examiner does not identify the transformer architecture or its self-attention mechanism as themselves reciting the judicial exception or as a mental process. Rather, as discussed above, the recited LLM architecture is considered as an additional element and is used as the computer-based mechanism for performing the claimed determinations of topics and customer sentiments associated with the friction event.
For the above reasoning, the 35 U.S.C. § 101 rejection of the claims is maintained.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TONY P KANAAN whose telephone number is (571)272-2481. The examiner can normally be reached Monday- Friday 7:30am - 3:30 pm EST.
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/T.P.K./Examiner, Art Unit 3696
/MATTHEW S GART/Supervisory Patent Examiner, Art Unit 3696
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