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 Provisional 63/177,810 filed on 04/21/2021.
Status of Claims
Claims 1-4, 6-20, 22-25 are currently pending and rejected.
Claims 5 and 21 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 1-4, 6-20, 22-25 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 a process of predicting an occurrence of an event based on interaction data, comprising generating an input dataset based on the interaction data characterizing the occurrence of the event, applying a trained artificial intelligence process to the input dataset to generate output data representative of a predicted likelihood of an event, and transmitting a portion of output data to a computing system which is configured to person one or more operations associated with a reduction in the predicted likelihood of the event. The language itself is unclear with regards to what the input or the output is, or what the prediction is for. But according to the Background section of the specification, the claimed invention is intended to predict human behavior in financial scenarios. For example, the claimed invention can predict the occurrence of delinquency and send notifications related entities to lower the risk. As such, the present claims fall within the Certain Method of Organizing Human Activity grouping. Moreover, the present claims are similar to the ineligible claims in Electric Power Group v. Alstom, because they are directed to a process of obtaining data, analyzing data, and providing result of the analysis. Without any limitation on the amount of data being processed and the difficulty of calculations needed, the artificial intelligence is merely an extra-solution and the claimed invention could be performed in the human mind under the broadest reasonable interpretation. As such, 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 or artificial intelligence 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 1-4, 6-20, 22-25 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Step 1: The claims 1-4, 6-20, 22-25 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.
Claims 1-4, 6-12, and 22-25 are directed to a machine (apparatus claims).
Claims 13-19 are directed to a process (method claims).
Claim 20 is directed to a manufacture (non-transitory computer readable medium claim).
Step 2A: The claims are directed to an abstract idea.
Prong One
The present claims are directed towards a process of predicting an occurrence of an event based on interaction data, comprising generating an input dataset based on the interaction data characterizing the occurrence of the event, applying a trained artificial intelligence process to the input dataset to generate output data representative of a predicted likelihood of an event, and transmitting a portion of output data to a computing system which is configured to person one or more operations associated with a reduction in the predicted likelihood of the event. The language itself is unclear with regards to what the input or the output is, or what the prediction is for. But according to the Background section of the specification, the claimed invention is intended to predict human behavior in financial scenarios. For example, the claimed invention can predict the occurrence of delinquency and send notifications related entities to lower the risk. As such, the present claims fall within the Certain Method of Organizing Human Activity grouping. Moreover, the present claims are similar to the ineligible claims in Electric Power Group v. Alstom, because they are directed to a process of obtaining data, analyzing data, and providing result of the analysis. Without any limitation on the amount of data being processed and the difficulty of calculations needed, the artificial intelligence is merely an extra-solution and the claimed invention could be performed in the human mind under the broadest reasonable interpretation. Distributed processing in artificial intelligence is well-understood and conventional. Artificial intelligence process in the present claims is recited in high level of generality. The claim language does not specify how input is transformed into output. As such, the artificial intelligence process could be merely automating mental processes. As such, the present claims also fall within the Mental Processes grouping.
The performance of the claim limitations using generic computer components (i.e. a processor, a memory, and a communication interface) does not preclude the claim limitation from being in the certain methods of organizing human activity grouping. The use of a trained artificial intelligence process to predict a likelihood of an event does not render the claims less abstract, because using artificial intelligence/machine learning/neural network was well-known in computer art. The claimed invention does not use artificial intelligence any differently. Furthermore, according to the specification, the present claims utilize existing and commercially available parallelized, fault-tolerant distributed and analytical processes (see paragraph 0030 of the specification). The specification discloses Apache SparkTM (open source) and DatabrickTM (made by San Francisco based software company) as examples, and neither was invented by the applicant. In other words, the present claims are not directed to improve artificial intelligence or parallelized, fault-tolerant distributed and analytical processes. Accordingly, this claim recites an abstract idea.
Prong Two
The present independent claims 1, 13, and 20 recite a processor, a memory, and a communication interface as additional elements. Dependent claims 2-4, 6-12, 14-19, and 22-25 do not recite other additional elements. The additional elements are claimed to perform basic computer functions, such as generating data input data from interaction data, ingesting the input data and performing calculation to generate output data, and transmitting output data over network. The use of a trained artificial intelligence process to predict a likelihood of an event does not render the claims less abstract, because using artificial intelligence/machine learning/neural network was well-known in computer art. The claimed invention does not use artificial intelligence any differently. In a recent precedential ruling, Recentive Analytics v. Fox Corp, the Federal Circuit found that “patents that do no more than claim the application of generic machine learning to new data environments, without disclosing improvements to the machine learning models to be applied, are patent ineligible under 101”. 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 artificial intelligence 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 only recite a processor, a memory, and a communication interface as additional elements. The additional elements are claimed to perform basic computer functions, such as generating data input data from interaction data, ingesting the input data and performing calculation to generate output data, and transmitting output 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 use of artificial intelligence technology to analyze data and to make prediction is also not inventive. The present claims do not improve the functioning of computer or artificial intelligence technology. The amended feature, “perform operations, in parallel across a plurality of distributed computing components, that apply a trained artificial intelligence process to the input database”, does not improve existing computer tool. Zhang (CN 109472462 A) teaches neural network model has parallel distributed processing capability (see parge 7), for example. Distributed processing in artificial intelligence is well-understood and conventional. 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.
Claims 24 and 25 recite elaborate parallel processing across a plurality of distributed computing components and parallelized, fault-tolerant distributed computing and analytical processes. According to the specification, parallelized, fault-tolerant distributed computing and analytical processes were commercially available at the time of filing of the present application (see paragraph 0030 of the specification). The specification discloses Apache SparkTM (open source) and DatabrickTM (made by San Francisco based software company) as examples, and neither was invented by the applicant. Siebel et al. (Pub. No.: US 2015/0120224) teaches “making a batch parallel processing analytic services module to automatically perform the tasks of parallelization, fault-tolerance, and load balancing, thus improving the performance and reliability of processing-intensive tasks, thus fully utilizing the computational power of the cluster by distributed processing to ensure that calculations are completed quickly and efficiently” (see abstract). It is evident that parallelized, fault-tolerant distributed computing and analytical processes were readily available and their benefits were well-understood prior to the filing of the present application. Implementing existing and readily available technology cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. Therefore, claims 24 and 25 are ineligible for patent.
Response to Remarks
Rejection under 35 U.S.C 112
Applicant’s arguments, see Remarks, filed on 07/30/2025, with respect to rejection under 35 U.S.C. 112 (a) and (b), have been fully considered and are persuasive. The rejection under 35 U.S.C. 112(a) and (b) has been withdrawn.
Rejection under 35 U.S.C. 101
Applicant's arguments filed 07/30/2025 with respect to rejection under 35 U.S.C. 101 have been fully considered but they are not persuasive.
The present claims recite an abstract idea
Applicant argued that the Office Action has failed to provide sufficient reasoning as to why the present claims recite an abstract idea. Examiner has already provided rationale as to why the present claims fall under the organizing human activity grouping and mental processes group of abstract concepts. The present claims are directed towards a process of using machine learning in general, comprising generating an input dataset, generate output data representative of a predicted likelihood of an event based on an application of a trained artificial intelligence process to the input dataset, and transmitting a portion of output data to a computer. The language itself is unclear with regards to what the input or the output is, or what the prediction is for. But according to the Background section of the specification, the claimed invention is intended to predict human behavior in financial scenarios. The amended limitations, “the elements of first interaction data characterizing an occurrence of a first event having a pendency period that fails to exceed a first threshold duration during a first temporal interval” and “generate output data comprising a numerical value representative of a predicted likelihood that the pendency period of the first even exceeds a second threshold duration” and “one or more operations being associated with a reduction in the predicted likelihood that the pendency period of the first event exceeds the second threshold duration”, are related to predict a delinquency event (see paragraph 0022, 0060, 0096-098 of the specification) and perform operations that, based on the parsed elements of processed output data, identify and apply one or more treatment or remediation processes (see paragraph 0120 of the specification). The claimed invention analyzes data related to human behavior, and identifies and applies remediation operation to influence human activity. As such, the present claims fall within the Certain Method of Organizing Human Activity grouping.
Moreover, the present claims are similar to the ineligible claims in Electric Power Group v. Alstom, because they are directed to a process of obtaining data, analyzing data, and providing result of the analysis. Without any limitation on the amount of data being processed and the difficulty of calculations needed, the claimed invention could be performed in the human mind under the broadest reasonable interpretation. The amended feature, “perform operations, in parallel across a plurality of distributed computing components, that apply a trained artificial intelligence process to the input database”, does not improve existing computer tool. Zhang (CN 109472462 A) teaches neural network model has parallel distributed processing capability (see parge 7), for example. Distributed processing in artificial intelligence is well-understood and conventional. Artificial intelligence process in the present claims is recited in high level of generality. The claim language does not specify how input is transformed into output. As such, the artificial intelligence process could be merely automating mental processes. Therefore, the amended claims also fall within the Mental Processes grouping.
Examiner points out that the amended claims do not provide sufficient details to make it impossible for human to perform. For example, claim 1 recites generating an input based on interaction data (note: it is unclear what formula or algorithm is being used to generate input from interaction data), applying a trained AI process to the input data to generate output (note: again, it is unclear what formula or algorithm is being used to generate output from input; trained AI process is also unspecified and could be interpreted as having an experienced human to process the data), and transmitting first identifier and at least a portion of output data to a computer system to perform an unspecified operation (note: this could be interpreted as writing down data on paper and thinking about policy that could reduce the likelihood of defaults). In a recent precedential ruling, Recentive Analytics v. Fox Corp, the Federal Circuit found that “patents that do no more than claim the application of generic machine learning to new data environments, without disclosing improvements to the machine learning models to be applied, are patent ineligible under 101”. Clearly, using machine learning to process data does not preclude the steps from practically being performed in the human mind.
The Office has established that the present claims are directed to ineligible abstract idea
Applicant argued that the independent claims, when considered as a whole, provide “specific, technological improvement to an existing technology or technical field”. Examiner disagrees and points out that claim 1, for example, recites generating input data based on unspecified “interaction data” from an unspecified interval, using an unspecified AI process to transform the input data into unspecified output data for predicting the likelihood of event occurrence in a later interval, and transmitting the result to a computer to perform unspecified operation. Such generic process reads on most “machine learning” applications. Applicant did not provide persuasive explanation with regards to the actual improvement to existing machine learning technology. In a recent precedential ruling, Recentive Analytics v. Fox Corp, the Federal Circuit found that “patents that do no more than claim the application of generic machine learning to new data environments, without disclosing improvements to the machine learning models to be applied, are patent ineligible under 101”. Clearly, using machine learning model on a generic computer to process unspecified data to generate unspecified output is also patent ineligible.
Applicant argued that the recited elements in independent claims, “facilitate, among other things, an application, in parallel across a plurality of distributed computing components, of a trained artificial intelligence process to an input dataset associated with an occurrence, during a first temporal interval, of a first event associated with a pendency period that fails to exceed a first threshold duration, a dynamic generation of output data indicative of a predicted likelihood that the pendency period of the first event exceeds a second threshold duration during a second temporal interval, and a transmission of at least a portion of the output data to a computing system for the performance of operations that reduce the predicted likelihood, represent a specific, technological improvement to existing computer-implemented predictive processes that ingest, operate on, and process increasingly large volumes of interaction data”. Examiner disagrees and points out that analyzing interaction data to predict an unspecified event occurrence and performing unspecified operation to reduce the likelihood of the unspecified event occurrence is an abstract concept. The claimed concept could be as simple as analyzing user interaction data to predict a delinquency event, and sending a notification the user and/or the lender. Artificial intelligence is recited in high level of generality – a trained artificial intelligence ingests input data and generates numerical value output that is presentative of a predicted likelihood of occurrence. The amended feature, “perform operations, in parallel across a plurality of distributed computing components, that apply a trained artificial intelligence process to the input database”, does not improve existing computer tool. Zhang (CN 109472462 A) teaches neural network model has parallel distributed processing capability (see parge 7), for example. Distributed processing in artificial intelligence is well-understood and conventional. The claim language does not recite any feature or step that could improve the functionality of artificial intelligence itself. Similar to the ineligible claims in Recentive Analytics v. Fox Corp, the present claims merely apply a generic machine learning model to a new data environment without disclosing any improvement to machine learning itself.
The claims do not amount to “significantly more” than an abstract idea
In Step 2B, the evaluation of the insignificant extra-solution activity consideration takes into account whether or not the extra-solution activity consideration takes into account whether or not the extra-solution activity is well understood, routine, and conventional in the field. In the Office Action, Examiner has performed this analysis in Step 2A Prong Two to strengthen the argument the recited elements do not improve computer functions, since they are considered well-understood computer functions according to MPEP 2106.05(g). Here, similar to the analysis in Step 2A Prong Two, the limitations in claim 1 of the present applications provide nothing more than mere instructions to implement an abstract idea on a generic computer. Even when considered in combination, these additional elements represent mere instructs to implement an abstract idea or other exception on a computer and insignificant extra-solution activity, which do not provide an inventive concept. 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.
The Office Action did analyze the dependent claims
Examiner stated that the dependent claims do not recite any other additional element that would integrate the abstract idea into a practical application in Step 2A or amount to significantly more than an abstract concept in Step 2B. The dependent claims recite additional steps of data processing using machine learning. However, these limitations do not improve existing machine learning technology.
Examiner maintains the ground of rejection under 35 U.S.C. 101.
New Claims 24 and 25
Claims 24 and 25 recite elaborate parallel processing across a plurality of distributed computing components and parallelized, fault-tolerant distributed computing and analytical processes. According to the specification, parallelized, fault-tolerant distributed computing and analytical processes were commercially available at the time of filing of the present application (see paragraph 0030 of the specification). The specification discloses Apache SparkTM (open source) and DatabrickTM (made by San Francisco based software company) as examples, and neither was invented by the applicant. Siebel et al. (Pub. No.: US 2015/0120224) teaches “making a batch parallel processing analytic services module to automatically perform the tasks of parallelization, fault-tolerance, and load balancing, thus improving the performance and reliability of processing-intensive tasks, thus fully utilizing the computational power of the cluster by distributed processing to ensure that calculations are completed quickly and efficiently” (see abstract). It is evident that parallelized, fault-tolerant distributed computing and analytical processes were readily available and their benefits were well-understood prior to the filing of the present application. Implementing existing and readily available technology cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. Therefore, claims 24 and 25 are ineligible for patent.
Rejection under 35 U.S.C. 103
Applicant’s arguments, see Remarks, filed on 07/30/2025, with respect to rejection under 35 U.S.C. 103, have been fully considered and are persuasive. Examiner has conducted updated search, but could not find prior art that addresses every limitation in the claims. The rejection under 35 U.S.C. 103 has been withdrawn.
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 nonprovisional extension fee (37 CFR 1.17(a)) 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.
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/HAO FU/Primary Examiner, Art Unit 3697
NOV-2025
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