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 .
This application has PRO 63/484,356 02/10/2023
Status of Claims
Claims 1-20, 23, and 24 are currently pending and rejected.
Claims 21 and 22 are cancelled.
Claim Rejection – 35 U.S.C. 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, 23, and 24 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The rationale for this finding is explained below. In the instant case, the claims are directed towards determining whether a transaction request can be adjudicated automatically using a model. The concept is clearly related to managing transactions between people, thus the present claims fall within the Certain Method of Organizing Human Activity grouping. The concept can also be performed mentally, thus the present claims also fall within the Mental Processes grouping. The claims do not include limitations that are “significantly more” than the abstract idea because the claims do not include an improvement to another technology or technical field, an improvement to the functioning of the computer itself, or meaningful limitations beyond generally linking the use of an abstract idea to a particular technological environment. Note that the limitations, in the instant claims, are done by the generically recited computer device. The limitations are merely instructions to implement the abstract idea on a computer and require no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry. Therefore, claims 1-20, 23, and 24 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Step 1: The claims 1-20, 23, and 24 are directed to a process, machine, manufacture, or composition matter.
In Alice Corp. Pty. Ltd. v. CLS Bank Intern., 134 S. Ct. 2347 (2014), the Supreme Court applied a two-step test for determining whether a claim recites patentable subject matter. First, we determine whether the claims at issue are directed to one or more patent-ineligible concepts, i.e., laws of nature, natural phenomenon, and abstract ideas. Id. at 2355 (citing Mayo Collaborative Servs. v. Prometheus Labs., Inc., 132 S. Ct. 1289, 1296–96 (2012)). If so, we then consider whether the elements of each claim, both individually and as an ordered combination, transform the nature of the claim into a patent-eligible application to ensure that the patent in practice amounts to significantly more than a patent upon the ineligible concept itself.
Step 2A: The claims are directed to an abstract idea.
Prong One
The present claims are directed towards determining whether a transaction request can be adjudicated automatically using a model. The concept comprises retrieving a transaction request, applying a training machine learning model to determine a likelihood that a retrieval of data from a data source over the network would result in the transaction request being automatically adjudicated, retrieving the data from the data source if the likelihood is above a threshold, and forwarding transaction request and one of the indication or the automatic adjudication over the network to an endpoint. Examiner notes that the model could simply be predefined rules, so the present claims do not necessarily require machine learning. The present claims are related to managing transactions between people, thus the present claims fall within the Certain Method of Organizing Human Activity grouping. Moreover, the claimed concept can be performed in the human mind, thus the present claims also fall within the Mental Processes grouping. The performance of the claim limitations using generic computer components (i.e., a processor and a memory) does not preclude the claim limitation from being in the certain methods of organizing human activity grouping or the mental processes grouping. Accordingly, this claim recites an abstract idea.
The amended feature reciting a machine learning model making determination on whether the transaction request can be automatically adjudicated, such feature does not render the claims any less abstract. In the recent Federal Circuit decision, Recentive Analytics v. Fox Corps, the court stated that “patents that do no more than claim the application of generic machine learning to new data environment without disclosing improvements to the machine learning models to be applied, are patent ineligible under 101”. Similarly, the amended claims and the specification do not provide any detail of what appears to be a generic machine learning model. There is no indication that the present claims improve the machine learning technology. Rather, the present claims merely apply a generic machine learning model trained with historical dataset to determine whether a transaction request can be adjudicated automatically. This step is part of processing transaction requests, which is related organizing human activities, and is analogous to a trained expert making determination by reading transaction data. Therefore, the features in question do not preclude the claims from being a mental process or organizing human activities.
Prong Two
The present claims recite a processor coupled with a memory as additional elements. The additional elements are claimed to perform basic computer functions, such as retrieving data, accessing model (e.g., rules or formula), determining a likelihood that the transaction request can be adjudicated automatically (i.e., performing calculation), generating an indication, and forwarding data over network. Under the broadest reasonable interpretation, the claimed concept can be performed mentally without computer. The recitation of the computer elements amounts to mere instruction to implement an abstract concept on computers. The present claims do not solve a problem specifically arising in the realm of computer networks. Rather, the present claims implement an abstract concept using existing technology in a networked computer environment. The present claims do not recite limitation that improve the functioning of computer, effect a physical transformation, or apply the abstract concept in some other meaningful way beyond generally linking the use of the abstract concept to a particular technological environment. As such, the present claims fail to integrate into a practical application.
Step 2B: The claims do not recite additional elements that amount to significantly more than the abstract idea.
As discussed earlier, the present claims recite a processor coupled with a memory as additional elements. The additional elements are claimed to perform basic computer functions, such as retrieving data, accessing model (e.g., rules or formula), determining a likelihood that the transaction request can be adjudicated automatically (i.e., performing calculation), generating an indication, and forwarding data over network. According to MPEP 2106.05(d), “performing repetitive calculations”, “receiving, processing, and storing data”, “electronically scanning or extracting data from a physical document”, “electronic recordkeeping”, “storing and retrieving information in memory”, and “receiving or transmitting data over a network, e.g., using the Internet to gather data” are considered well-understood, routine, and conventional functions of computer. The present claims do not improve the functioning of computer. Simply implementing the abstract idea on a generic computer or using a computer as a tool to perform an abstract idea cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. Therefore, the present claims are ineligible for patent.
Response to Remarks
Rejection under 35 U.S.C. 101
Applicant's arguments filed on 01/21/2026 have been fully considered but they are not persuasive.
Applicant argued that some of the limitations, such as filtering transactions over a network, applying a training machine learning model to determine a likelihood that a retrieval of data from a data source over the network would result in the transaction request being automatically adjudicated, retrieving the data from the data source if the likelihood is above a threshold, and forwarding transaction request and one of the indication or the automatic adjudication over the network to an endpoint, cannot cover performance in the human mind or organizing human activity. Applicant further argued the claim limitations “cover novel and non-obvious techniques for, among other things, training and applying a machine learning model in a particular fashion with a particular dataset to determine how to forward a transaction request over a network. Examiner disagrees, and points out that these limitations are extra-solution to the concept of determining whether a transaction request can be adjudicated automatically using a model, and processing the transaction request accordingly. Especially the amended feature reciting a machine learning model making determination a likelihood that a retrieval of data from a data source over the network would result in the transaction request being automatically adjudicated, such feature does not render the claims any less abstract. In the recent Federal Circuit decision, Recentive Analytics v. Fox Corps, the court stated that “patents that do no more than claim the application of generic machine learning to new data environment without disclosing improvements to the machine learning models to be applied, are patent ineligible under 101”. Similarly, the amended claims and the specification do not provide any detail of what appears to be a generic machine learning model. There is no indication that the present claims improve the machine learning technology. Rather, the present claims merely apply a generic machine learning model trained with historical dataset to determine whether retrieving additional data would result in a transaction request being adjudicated automatically. This step is part of processing transaction requests, which is related organizing human activities, and is analogous to a trained expert making determination by studying prior transaction data. Therefore, the features in question do not preclude the claims from being a mental process or organizing human activities.
Applicant mentioned Ex parte Hannun in attempt to argue certain machine learning applications cannot be considered mental processes or organizing human activities. Examiner points out that Ex parte Hannun is not a precedential decision. The Federal Circuit case Recentive Analytics v. Fox Corps, on the other hand is a precedential decision. As discussed earlier, the Federal Circuit stated that “patents that do no more than claim the application of generic machine learning to new data environment without disclosing improvements to the machine learning models to be applied, are patent ineligible under 101”. Moreover, Ex parte Hannun is directed to speech recognition by machine, a process that is entirely different from how human recognize speaking words. Claim 11 in Ex parte Hannun recites the steps of normalizing an input file, generating a jitter set of audio files, generating a set of spectrogram frames, obtaining predicted character probabilities from a trained neural network and decoding a transcription of the input audio using the predicted character probability outputs. These steps are not analogous to human processing of speaking words. On the other hand, the present claim 1 recites applying a generic machine learning model to transaction request to determine a likelihood that a retrieval of data from a data source over the network would result in transaction request being automatically adjudicated, and then act accordingly. The claim language does not specify how the determination is being made, and as such, it is unclear whether the machine learning model is merely automating mental processes. The claim language reads on the situation where an experienced worker can quickly decide whether missing data in the transaction request can be easily obtained from a source and route the transaction request to automatic adjudication. In such case, the machine learning model is merely training on past human decisions and automating the mental process in an analogous way. Applicant is advised to claim specific steps and/or rules that are different from human processing (see the decision of McRO v. Bandai).
Applicant further argued that claims 1 and 11 “integrate the ideas recited into a practical application and provide performance improvements – namely, training and applying a machine learning model in a specific fashion, and forwarding a transaction request over a network based on an output therefrom”. Examiner disagrees. The claim language does not provide any indication that the training of the machine learning model is any different from generic machine learning – historical prior authorizations are used as training data to teach the machine learning model to make authorization decision in the future. Nowhere in the claims or in the specification suggests the performance of the machine learning has been improved (i.e., in terms of higher efficiency, more secured data, etc.). The potential saving of network resources is a result of a decision that retrieving additional data may not be sufficient to allow the transaction request to be adjudicated automatically, a decision that can be made by an experience human staff. As such, the present claims do not recite feature that would lead to performance improvements. Therefore, the present claims fail to integrate the abstract concept into a practical application.
Applicant also argued the present claims, like claim 3 in Example 48, generally relate to utilization of a trained neural network, and that “the claims of the instant application relate to technical improvements and are integrated into a practical application (e.g., forwarding of transaction requests over a network based on the output of a specially trained machine learning model)”. Examiner disagrees. In claim 3 of Example 48, “the steps (b), (b), and (d) recite an abstract idea, the ordered combination of the steps of receiving a mixed speed signal, processing the speech signal to produce masked clusters, converting the masked clusters into a separate signals in time domain, extracting spectral features from one such converted signal, and generating a sequence of words from the extracted spectral features to produce a transcript reflects the technical improvement” and “the claim is directed to an improvement to existing speech-to-text technology, and the claim integrates the abstract idea recited in step (b), (c), and (d) into a practical application of speech-to-text conversion of a speech signal corresponding to one source of the mixed speech signal”. On the other hand, the amended claims in the present application merely recite “applying a trained machine learning model to the transaction request, the machine learning model configured to determine a likelihood that the transaction request can be automatically adjudicated” and “forwarding the transaction request and one of the indication or the automatic adjudication over the network to an endpoint”. The recitation of machine learning is highly generic, and such generic machine learning model is used to perform basic function of machine learning – using historical training dataset to make prediction of future events. There is no indication of technological improvement of machine learning in the present claims or in the specification. Therefore, the recitation of machine learning model cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. Therefore, the present claims are ineligible for patent.
Examiner maintains the ground of rejection under 35 U.S.C. 101.
Rejection under 35 U.S.C. 103
Applicant’s arguments, see Remarks, filed 01/21/2026, with respect to rejection under 35 U.S.C. 103 have been fully considered and are persuasive. The rejection of claims 1-20 and 23-24 under 35 U.S.C. 103 has been withdrawn.
Peterson et al. (Pub. No.: US 2002/0019754), Leszuk et al. (Pub. No.: US 2007/0282639), Zoldi et al. (Pub. No.: US 2020/0272853), Davidson et al. (Pub. No.: US 2008/0208638), and Khalak et al. (Patent No.: US 10,552,915) are the best references found by Examiner. However, whether individually or combined, these references do not teach the amended feature, “applying a trained machine learning model to the transaction request, the trained machine learning model configured to determine a likelihood that a retrieval of data from a data source over the network would result in the transaction request being automatically adjudicated, wherein the trained machine learning model is trained based on historical prior authorization; if the likelihood that the retrieval of data from the data source over the network would result in the transaction request being adjudicated automatically is above a threshold; retrieving the data from the data source over the network”, as recited in amended independent claim 1 and 11. Examiner has conducted updated search, but not relevant prior art was found. As such, no prior art rejection is cited in this Office. Nevertheless, the novelty is entirely in the realm of abstract idea. The present claims are still subject to rejection under 35 U.S.C. 101.
Conclusion
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/HAO FU/Primary Examiner, Art Unit 3695
FEB-2026