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 .
Response to Amendment
Applicant's amendment filed on June 24, 2026 has been entered. Claims 1, 2, 7 – 11, 14 – 16 and 19 – 20 have been amended. No claims have been canceled. No claims have been added. Claims 1 – 20 are still pending in this application, with claims 1, 10 and 15 being independent.
Response to Arguments
Applicant's arguments filed June 24, 2026 have been fully considered.
Regarding rejections under 35 U.S.C. §101
The Applicant alleges: “
Legal precedent and the MPEP only require that an inventive concept, such as that recited in the pending claims, result in improvements to pre-existing technology in order to satisfy the "integration into a practical application." As provided in further detail below, pending claims, when considered as a whole, provide meaningful improvements to the state-of-the-art technologies.
The technical improvements that result from the application of the claimed methods and systems are disclosed and supported in detail in the Specification. It is respectfully submitted that the Office Action's characterization of the claims, without taking into account the resulting practical improvements and the details recited in the claims as supported by the specification, oversimplifies the claimed subject matter. In doing so, the Office Action disregards and misconstrues the claim language as a whole.
The explicitly claimed technical features and improvements cannot be reasonably deemed to be abstract ideas devoid of any practical application or technological integration. To the contrary, as provided in further detail below, the claimed features represent or result in concrete advancements and improvements in computing technology consistent with Enfish, LLC v. Microsoft Corp., 822 F.3d 1327 (Fed. Cir. 2016), McRO, Inc. v. Bandai Namco Games America Inc., 837 F.3d 1299 (Fed. Cir. 2016), and most recently, Ex parte Desjardins, Appeal 2024-000567 (ARP 2025).
The Alice framework requires determining (1) whether the claim is "directed to" a judicial exception and, if so, (2) whether the claim includes additional elements that, when considered as a whole, integrate that exception into a "practical application." Here, both steps favor eligibility.
1. Step 2A, Prong 1: Not Directed to an Abstract Idea
The Office Action erroneously isolates the claimed technical operations as belonging purely to organizing human activity or mental processes and alleges that the claims are directed to these alone. Office Action page 3. Yet the Federal Circuit in Enfish cautioned that the focus of the inquiry must be on the claimed advance over the prior art.
When properly construed, the claims are directed to a specific improvement in computer functionality. The amended claims recite, at least in part:
…
In Enfish, the Federal Circuit noted that in determining whether a claim is directed to an abstract idea, it is not sufficient to simply ask whether the claim involves a patent-ineligible concept. Because all patent-eligible claims involve, at some level, a law of nature, natural phenomena or abstract idea, per the Court, the claims must be "considered in light of the specification, based on whether 'their character as a whole is directed to excluded subject matter."' Enfish reinforces the point, also expressed in recent USPTO guidance, that the determination of whether a claim is directed to an abstract idea should be framed by the idea as it is recited in the claim limitations. Moreover, the Court noted that "[m]uch of the advancement made in computer technology consists of improvements to software that, by their very nature, may not be defined by particular physical features but rather by logical structures and processes."5
Similar to Enfish, the pending claims are directed to an improvement in the functionality of a narrative generation platform for explainable predictive classifiers. Referring to 1 [0002]- [0004] of the specification, the improvements are not possible in the prior art because the conventional technology is limited to generating a discrete set of reason codes without a coherent, human-readable narrative explaining the output of the predictive classifier. More specifically, the claimed combination, when considered as a whole, is directed to a technological system that converts heterogeneous structured transaction data and classifier outputs into a standardized token format processable by a narrative generation natural language processing (NLP) module and derives temporal and quantitative information used to generate an accurate, human-readable narrative for the classifier output. As recited in the pending claims, as amended, the combination of the recited hardware/software features and functions (whether these functions are performed either purely in software or purely in hardware) also improves the accuracy and operational reliability of the narrative generation system and the efficient use of the underlying processing resources.
The Enfish court gave specific guidance that, as here, "benefits over conventional databases, such as increased flexibility, faster search times, and smaller memory requirements," may be determinative that the claims are directed to improvement in existing computer technology and not an abstract idea. Pending claims herein, likewise, are directed to improvements in the function of computers-specifically, the generation of accurate, coherent, human-readable narratives explaining the output of a predictive classifier, and the reason-code-driven execution of calculation-engine functions that conserves processing resources by computing only the functions warranted by the classifier's reason codes-which cannot be implemented without the recited combination of functions and features. Because these details are inextricably related to enhancing the functional efficiency and accuracy of the recited narrative generation platform and its calculation engine, the claimed subject matter cannot be abstract.
The pending claims, as amended, further recite specific technical and standardization advantages and improvements over the conventional art. That is, the claims recite the conversion of structured transaction data and classifier outputs into a standardized token format suitable for input to a narrative generation NLP module, and the execution, by a calculation engine, of one or more functions selected based on the reason codes of the predictive classifier output to derive temporal and quantitative information that improves the accuracy and completeness of the generated narrative. These improvements over the older technology result from the integration of the claimed technical features into a practical application and provide non-abstract and meaningful practical improvements to the performance of conventional technologies that suffer from inaccuracies and inefficiencies.
The explicitly recited claim language specifically and in detail covers the steps and processes that are implemented by way of the claimed narrative generation platform, including its token conversion architecture, calculation engine, and narrative generation NLP module. These steps and processes cannot possibly be performed in the human mind. Further, if the claimed subject matter is fairly and objectively considered, the claimed systems and methods are directed to more than mere generic computer functionality and are not simple data gathering and data storage activities. The recited combination of features and the resulting improvements are neither conclusory in nature, nor can they be reasonably considered as simply automating a well-known programming technique.
Examiner’s response:
The Examiner respectfully disagrees.
Enfish claimed a self-referential table for a computer database benefits over conventional databases, such as increased flexibility, faster search times, and smaller memory requirements. Thus, it provides improvement in computer capabilities.
Unlike Enfish, the claims in the instant application, read as whole, merely disclose to generate a narrative by using statistical function (predictive classifier) and other mathematical function based on the temporal information and the quantitative information. Thus, it is mainly mathematical concepts and mental processes.
Using different function may improve the functionality of prediction (predictive classifier). However, it is still in the category of mathematical calculations, mathematical relationships, and/or mathematical formulas or equations. Thus, it is still mathematical concepts.
The Applicant further alleges: “
The technical improvements of the claimed system, in addition to improving the accuracy and reliability of the generated narrative and conserving processing resources, include the capability for:
(1) converting heterogeneous structured data sources, such as transaction data and classifier outputs in CSV or JSON formats, into a standardized token format amenable to input to a narrative generation NLP module ([0025], [0035]);
(2) selecting, based on one or more reason codes output by the predictive classifier, one or more transaction-related functions of the calculation engine to derive temporal and quantitative information relevant to the classifier output ( [0028]-[0030]); and
(3) executing the selected functions to generate additional textual features that are integrated into the narrative to provide a more accurate and detailed human-readable explanation of the classifier's output ( [0031], [0037]).
All the above technological improvements are specific (not abstract) and are implemented by way of utilizing the claimed combination of features. Further, the improvements are not to a business method, but are actual technical improvements to computing technology itself (i.e., explainable predictive classifiers that automatically convert structured classifier outputs and transaction data into NLP-processable tokens and compute temporal and quantitative features used to generate accurate human-readable narratives), which cannot be implemented in the human mind or using pen and paper.
Because the Office is required to examine the claim as a whole, any contention in the Office Action that a certain line in the claim refers to a mathematical concept and therefore the entire claim is abstract is unfair and untenable. It is well-settled that a claim that "involves" the use of mathematical formulas cannot be deemed abstract if additional meaningful details are included in the claims, as here. More specifically, the claims do not recite any mathematical relationships, formulas, or calculations. While some of the limitations may be based on mathematical concepts, mathematical concepts are not recited in the claims.”
Examiner’s response:
The Examiner again respectfully disagrees.
Regarding “converting the transaction data from the structured format into a standardized format for input to a narrative generation natural language processing (NLP) module”, it can be interpreted as the transaction data can be converted from the structured format into a standardized format by using some formula. Parsing CSV or JSON schemas and mapping them to tokens is viewed as collecting and manipulating data. It also can be considered as human mind can convert transaction data format by using pen and paper. Thus, it can be categorized as mathematical concepts or mental processes.
Regarding (2) and (3), The claim just stated “a calculation engine to executes one or more functions based on one or more reason codes associated with the output of the predictive classifier to improve the narrative generated by the narrative generation platform”. It can be interpreted as using a calculation algorithm to improve the narrative based on one or more reason codes. Thus, it can be categorized as mathematical concepts.
As the Examiner mentioned above, the claims, read as whole, merely disclose to generate a narrative by using statistical function (predictive classifier) and other mathematical function based on the temporal information and the quantitative information. Thus, it is mainly mathematical concepts and mental processes.
The Applicant still further alleges: “
Further, the claim does not recite any method of organizing human activity such as a fundamental economic concept or managing interactions between people. That is, the recited subject matter is not related to managing relationships, social activities, human behavior analysis, or advertising, which are the main categories that have been traditionally associated with organization of human activity in the abstract. The claimed subject matter does not match any of the mentioned categories. Even if the claimed subject matter can be deemed associated with organization of human activity, "not all methods of organizing human activity are abstract ideas."
As in Enfish, the claims here "are directed to specific improvements to the way computers operate," rather than to the abstract idea itself. The improvements are technological solutions to a computer-centric problem-the inability of conventional predictive classifiers to generate accurate, coherent, human-readable narratives explaining their outputs in a manner sufficient to satisfy regulatory transparency requirements. This focus on improved narrative accuracy and the efficient use of computing resources demonstrates that the claims are directed to an improvement in computer capabilities, not to a process that merely uses a computer as a tool.
Similarly, McRO held claims patent-eligible because the recited rules improved the process of animating 3D characters-a specific technical improvement in how computers generate outcomes." The present claims analogously improve how the computing system operates by the conversion of structured transaction data into a standardized format for input to a narrative generation NLP module, and the execution by a calculation engine of one or more functions selected based on the reason codes of the predictive classifier output.
More particularly, [0025]-[0037] of the Specification provide that the structured transaction data and classifier outputs are converted into a standardized token format suitable for input to a narrative generation NLP module, and that the calculation engine selects and executes one or more functions based on the reason codes to derive the temporal and quantitative information that is integrated into the narrative. These are not generic computer functions. Rather, they are concrete improvements in how the underlying data store and processor convert heterogeneous data and execute calculation-engine functions to generate accurate, human-readable narratives.
As amended, the claims expressly recite that the transaction data is converted from a structured format into a standardized format for input to a narrative generation NLP module, that a narrative generation platform generates a narrative for the output of the predictive classifier based on the derived temporal and quantitative information, and that a calculation engine executes one or more functions based on one or more reason codes associated with the classifier output to improve the generated narrative. Each of these claimed features maps directly to a specific technological improvement disclosed in the specification, not to any business or organizational concept.
For example, the amended feature that the transaction data be converted from a structured format into a standardized format for input to a narrative generation NLP module ( [0025], [0035]) maps to the technological improvement of enabling heterogeneous, machine-generated data to be processed by an NLP module to produce a coherent, human-readable narrative-an improvement in the operation of the narrative generation system itself rather than any organizational or economic practice.”
Examiner’s response:
McRO discloses an application for automatically animating lip synchronization and facial expression of three dimensional characters. It is a real computer technology for automatic animation. It also focused on a specific asserted improvement in computer animation. Thus, it provides Improvements to the functioning of a computer, or to any other technology or technical field - see MPEP 2106.05(a).
Unlike McRO or Enfish, the instant application does not provide Improvements to the functioning of a computer, or to any other technology or technical field.
As the Examiner mentioned above, “converting the transaction data from the structured format into a standardized format for input to a narrative generation natural language processing (NLP) module”, is interpreted as the transaction data can be converted from the structured format into a standardized format by using some formula. Parsing CSV or JSON schemas and mapping them to tokens is viewed as collecting and manipulating data. It also can be considered as human mind can convert transaction data format by using pen and paper. Thus, it can be categorized as mathematical concepts or mental processes.
The Applicant still further alleges: “
Moreover, the amended claims tie the calculation engine to the reason codes output by the predictive classifier, such that the calculation engine executes one or more functions selected based on those reason codes to derive temporal and quantitative information that is integrated into the narrative to improve its accuracy ( [0028]-[0031], [0037]). These are all functional, structural, and practical improvements that avoid the inaccurate outputs and eliminate or minimize resource- intensive, error-prone processing used in older or conventional fraud detection models and technologies.
Even further, the amended claims recite that the generated narrative is produced by a narrative generation platform based on the temporal and quantitative information derived from the dataset, and that the calculation engine executes one or more functions based on the reason codes to improve that narrative ( [0026]-[0031], [0037]). Taken together, the claimed features recite a tightly coupled architecture-a list of selectable transaction-related functions, a token conversion architecture, a calculation engine, and a narrative generation NLP module-that delivers the specific technological improvements (improved narrative accuracy and completeness and conservation of processing resources) as disclosed in the specification and claimed.”
Examiner’s response:
As the Examiner mentioned above, the claimed limitation “a calculation engine to executes one or more functions based on one or more reason codes associated with the output of the predictive classifier to improve the narrative generated by the narrative generation platform,” can be interpreted as selecting and using a calculation algorithm to improve the narrative based on one or more reason codes (e.g. one of the algorithm in a list can more suitable to improve the narrative). Thus, it can be categorized as mathematical concepts.
Regarding “a narrative generation platform generates a narrative for the output of the predictive classifier based on the temporal information and the quantitative information”, it can be interpreted as using a statistical algorithm to generates a narrative based on the temporal information and the quantitative information. For example, based on frequency and amount value of transaction, statistically calculate and generate a narrative for features of the account. Thus, it can be categorized as mathematical concepts.
Combined in general, it merely discloses to generate a narrative by using statistical function (predictive classifier) and other mathematical function based on the temporal information and the quantitative information. Thus, it is mainly mathematical concepts and mental processes.
The Applicant still further alleges: “
The claims are analogous to USPTO Subject Matter Eligibility Example 39 (Method for Training a Neural Network for Facial Detection), in which the claims were held not to recite a judicial exception because, although some limitations were based on mathematical concepts, the claimed steps were not practically performed in the human mind and did not recite a method of organizing human activity. Like the claims in Example 39, the present claims recite concrete data- conversion, calculation-engine, and narrative-generation steps that cannot practically be performed in the human mind and that improve the operation of a predictive classification system.
The Desjardins ARP panel reaffirmed this principle, finding claims to be eligible where the claimed framework as a whole improved model retention and computational efficiency, even though mathematical formulas were used internally. Critically, Desjardins confirms that computing software and computer-implemented models are themselves patent-eligible subject matter where, as here, they are claimed in a manner that improves the operation of the underlying computing system or technology. The Desjardins ARP panel rejected the notion that the mere internal use of mathematical formulas or distance metrics renders such claims abstract.
Instead, the panel held that the training framework as a whole improved model retention and computational efficiency and that the claims do not merely invoke computers as tools. The same is true here: the amended claims recite that a calculation engine executes one or more functions selected based on the reason codes associated with the output of the predictive classifier, and that the resulting temporal and quantitative information is used by the narrative generation platform to generate a more accurate, human-readable narrative explaining that output. This reason-code-driven selection and execution of particular calculation-engine functions improves computing efficiency by confining the computationally expensive derivation of temporal and quantitative features to the specific functions warranted by the classifier's reason codes, rather than indiscriminately computing every available function.
As such, processing resources are conserved while the accuracy and regulatory sufficiency of the generated narrative are improved. Just as in Desjardins, the claimed framework as a whole- the reason-code-conditioned calculation engine cooperating with the narrative generation NLP module-delivers a concrete improvement to how the computer itself operates and does not merely invoke a generic computer to perform an abstract mental task. The presence of any internal mathematical operations within the calculation engine therefore cannot, under Desjardins, defeat eligibility of a computing software or model claim that, taken as a whole, delivers concrete improvements to how the computer itself operates.”
Examiner’s response:
Example 39 claimed a two-phase neural network training. It is eligible because of the two-phase training technique in combination with the transforming of the facial images.
In a contrast, the instant Application does not any neural network training, nor a specialized training technology. It merely discloses using a mathematical way to generate the result. Thus, it is an abstract idea.
The Applicant still further alleges: “
2. Step 2A, Prong 2: Integration Into a Practical Application
Even assuming arguendo that aspects of the invention could be characterized as abstract, the claims recite a specific implementation that integrates those aspects into a practical, real-world application improving how fraud detection technologies operate.
The pending claims are directly analogous to USPTO Subject Matter Eligibility Example 42 (Method for Transmission of Notifications When Medical Records Are Updated), in which the claims were held to integrate an abstract idea into a practical application because they recited converting information input in a non-standardized format into a standardized format and transmitting it to provide a specific improvement over prior art systems. Here, the claimed conversion of heterogeneous structured transaction data and classifier outputs into a standardized token format suitable for input to a narrative generation NLP module, followed by generation of a human-readable narrative for the classifier output, similarly provides a specific improvement over prior systems that merely output reason codes. The claims therefore integrate any alleged abstract idea into a practical application under Step 2A, Prong 2.
Furthermore, under DDR Holdings, claims that are "necessarily rooted in computer technology in order to overcome a problem specifically arising in the realm of computer networks" are patent-eligible. The same is true here: the claimed subject matter is necessarily rooted in a technical problem that arises only in the computing environment. More particularly, conventional classifiers output only a discrete set of reason codes and scores, which do not, standing alone, constitute the regulator-ready explanation that automated decision-making systems are required to provide. The amended claims solve this computer-centric problem with a specific technical solution.
As claimed, the recited process and system use the classifier's reason codes to select and execute particular calculation-engine functions. Temporal and quantitative information are then derived from a relevant dataset that has been converted into a standardized token format, where a narrative generation platform is utilized to transform that machine-generated output into an accurate, human-readable narrative. As noted earlier, the generated result cannot be determined in the human mind and has no meaningful analog to pen-and-paper computation, because it depends on the reason-code-driven coordination of the predictive classifier, the calculation engine, and the narrative generation NLP module operating on standardized and tokenized machine data.
Referring back to Desjardins, the claimed subject matter does not merely invoke computers as tools, but instead changes how the computing system itself operates-using the classifier's reason codes to determine which calculation-engine functions to execute and integrating the resulting textual features into the generated narrative-to achieve the improved outcomes and technological advances enumerated earlier. The specification provides detailed data structures and implementation guidance showing how reason codes are parsed to select calculation-engine functions, how structured transaction data and classifier outputs are converted into a standardized token format suitable for the narrative generation NLP module, and how the derived temporal and quantitative information is incorporated into an accurate, human-readable narrative, satisfying MPEP § 2106.05(a) (i.e., improvements to the functioning of a computer or to another technology or technical field).”
Examiner’s response:
The Examiner respectfully disagrees.
As the Examiner explained above, in Step 2A prong 1 analysis, the claims in the instant application, read as whole (including “reason-codes, converting … for input to a narrative generation natural language processing (NLP) module, and generating”), merely disclose to generate a narrative by using statistical function (predictive classifier) and other mathematical function based on the temporal information and the quantitative information. Thus, it is mainly mathematical concepts and mental processes.
The additional elements left after Step 2A prong 1 analysis are “system, programmable processor, calculation engine, generation platform and non-transient machine-readable medium.” However, they are considered as to use a general computational hardware/software. It is considered as “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f).
They are mere instructions to implement an abstract idea uses a computer as a tool to perform an abstract idea.
Moreover, the claim limitations that are not indicative of integration into a practical application.
Thus, the recited generic additional element (e.g., system, programmable processor, calculation engine, generation platform and non-transient machine-readable medium) perform no more than their basic computer function. Generic computer-implementation of a method is not a meaningful limitation that alone can amount to significantly more than an abstract idea. Moreover, when viewed as a whole with such additional element considered as an ordered combination, claims modified by adding a computational algorithm, a generic memory and processor are nothing more than a purely conventional computerized implementation of an idea in the general field of computer processing and do not provide significantly more than an abstract idea.
Accordingly, the claims are directed to an idea of itself, and therefore not patent eligible.
The Applicant still further alleges: “
Step 2B: Significantly More Than a Generic Computer Implementation
Even under Alice Step 2B, the claims contain an inventive concept that transforms any abstract idea into a patent-eligible application. The ordered combination of claimed processes is neither routine nor conventional.
In BASCOM,15 the Federal Circuit held that an inventive concept can be found in the non-conventional and non-generic arrangement of known elements. Here, the amended claims recite a specific, ordered combination-identifying the reason codes associated with the output of the predictive classifier, using those reason codes to select and execute one or more particular functions of the calculation engine, deriving temporal and quantitative information from the relevant dataset, and generating an accurate, human-readable narrative for the classifier's output based on that information. Even if certain individual components (a predictive classifier, a calculation engine, or an NLP module) were known in isolation, the claimed standardization or the particular reason-code-driven arrangement is neither routine nor conventional.
When properly considered as an ordered combination, the recited claim features, such as selecting a transaction-related function from a list of available functions based on the predictive classifier output and its reason codes, retrieving and converting the relevant structured transaction data into a standardized token format, analyzing the dataset to derive temporal and quantitative information, generating a narrative based on that information, and executing one or more calculation-engine functions based on the reason codes to improve the narrative. These features, when considered in combination, cannot be construed as "generic steps" of a mental process or a method of organizing human activity as alleged in the Office Action.
Rather, the claims as amended recite a specific, sequenced architecture in which one claimed process forms the basis for the next. For example, the output of the predictive classifier, including its reason codes, determines which transaction-related function is selected from the list of available functions; the selected function governs which dataset is retrieved and converted into the standardized token format; the converted dataset is analyzed to derive the temporal and quantitative information; that information is used by the narrative generation platform to generate the narrative; and the calculation engine then executes one or more functions based on the reason codes to refine and improve that narrative. This is precisely the type of non-conventional and non- generic arrangement of known elements that the Federal Circuit held to supply an inventive concept in BASCOM, and it provides significantly more than the abstract idea of generating a narrative for a predictive classifier's output on a generic computer.”
Examiner’s response:
The Examiner respectfully disagrees.
As the Examiner explained above, in Step 2A prong 1 analysis, the claims in the instant application, read as whole (including “reason-codes, converting … for input to a narrative generation natural language processing (NLP) module, and generating”), merely disclose to generate a narrative by using statistical function (predictive classifier) and other mathematical function based on the temporal information and the quantitative information. Thus, it is mainly mathematical concepts and mental processes.
The additional elements left after Step 2A prong 1 and prong 2 analysis are “system, programmable processor, calculation engine, generation platform and non-transient machine-readable medium.”
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception such as improvements to another technology or technical field, or other meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment.
Moreover, the claim language that may be separate from the abstract idea (i.e., additional elements) include computer processors, computer-readable storage media.
The additional element (e.g., system, programmable processor, calculation engine, generation platform and non-transient machine-readable medium) simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception (WURC) - see MPEP 2106.05(d) and 2106.07(a)III.
Thus, the recited generic additional elements (e.g., system, programmable processor, calculation engine, generation platform and non-transient machine-readable medium) perform no more than their basic computer function. Generic computer-implementation of a method is not a meaningful limitation that alone can amount to significantly more than an abstract idea. Moreover, when viewed as a whole with such additional element considered as an ordered combination, claims modified by adding a generic memory are nothing more than a purely conventional computerized implementation of an idea in the general field of computer processing and do not provide significantly more than an abstract idea.
Consequently, the identified additional elements taken into consideration individually or in combination fails to amount of significantly more than the abstract idea above.
The Applicant still further alleges: “
4. Supporting Policy and Agency Authority
The Ex parte Desjardins decision reinforces that § 101 is not a substitute for §§ 102 and 103, noting that eligibility should not be denied merely because a claim involves mathematics. The proper focus is on whether the claimed subject matter applies mathematical calculations or data analysis in a way that improves technology. This aligns with the USPTO's April 2024 memorandum, which instructs that such inventions are not per se abstract and that claims should be evaluated based on specific technological improvements described in the specification.
Therefore, the Examiner's reliance on the presence of mathematical operations as dispositive is inconsistent with current PTO policy and binding Federal Circuit precedent.
The Office is requested to consider the pending claims and the proposed amendments under the principles established in Alice, Enfish, McRO, DDR Holdings, BASCOM, and Ex parte Desjardins. The Office is also requested to provide a reply to all of Applicant's arguments provided herein, including why the established legal precedent is inapplicable.
It is respectfully submitted that, when properly considered, the pending claims are directed to a specific improvement in computer functionality through a novel framework that reduces computational costs and conserves processing resources by limiting calculation-engine execution to the functions warranted by the classifier's reason codes. Accordingly, the claims are not directed to an abstract idea, and even if they were, they recite significantly more than any abstract concept. Applicant respectfully requests that the § 101 rejection be reversed and withdrawn.
No amendment made was related to the statutory requirements of patentability unless expressly stated herein; and no amendment made was for the purpose of narrowing the scope of any claim, unless Applicant has expressly argued herein that such amendment was made to distinguish over a particular reference or combination of references.
Examiner’s response:
The Examiner respectfully disagrees.
The Examiner analyzed the claimed subject matter applies mathematical calculations or data analysis.
However, it does not provide improvements to the functioning of a computer, or to any other technology or technical field - see MPEP 2106.05(a); nor applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition – see MPEP 2106.04(d)(2); nor applying the judicial exception with, or by use of, a particular machine - see MPEP 2106.05(b); nor effecting a transformation or reduction of a particular article to a different state or thing - see MPEP 2106.05(c); nor applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception - see MPEP 2106.04(d)(2) and 2106.05(e).
Therefore, it is still an abstract idea without a significantly more. Thus, it is still rejected under 35 USC § 101.
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. an abstract idea) without significantly more.
Regarding claims 1, 10 and 15:
Step 1:
Claims 1, 10 and 15 are directed towards a process, machine, manufacture or composition of matter which is/are statutory subject matter.
Step 2A:
Prong 1:
Claims 1, 10 and 15 are directed an idea for generating a narrative for output of a predictive classifier which is an abstract idea.
Consideration of the claimed elements:
Regarding claims 1, 10 and 15:
The claims in the instant application include:
selecting, from a list of available functions associated with an entity's recorded transaction history, a transaction-related function based on an output of a predicative classifier;
retrieving a dataset relevant to the selected transaction-related function, wherein the dataset includes transaction data in a structured format;
converting the transaction data from the structured format into a standardized format for input to a narrative generation natural language processing (NLP) module, and
analyzing the dataset, in accordance with the selected transaction-related function to derive temporal information and quantitative information associated with the dataset;
wherein a narrative generation platform generates a narrative for the output of the predictive classifier based on the temporal information and the quantitative information; and
wherein a calculation engine to executes one or more functions based on one or more reason codes associated with the output of the predictive classifier to improve the narrative generated by the narrative generation platform.
Regarding “selecting, from a list of available functions associated with an entity's recorded transaction history, a transaction-related function based on an output of a predicative classifier”, it can be interpreted as a human can select, from a list of available functions associated with an entity's recorded transaction history, a transaction-related function based on an output of a predicative classifier (for example, analyzing bank transaction). The claimed limitation can be broadly read as a concept performed by a human mind, thus categorized as mental processes.
Regarding “retrieving a dataset relevant to the selected transaction-related function, wherein the dataset includes transaction data in a structured format,” it can be interpreted as the human can retrieve a dataset relevant to the selected transaction-related function. The claimed limitation can be broadly read as a concept performed by a human mind, thus categorized as mental processes.
Regarding “converting the transaction data from the structured format into a standardized format for input to a narrative generation natural language processing (NLP) module”, it can be interpreted as the transaction data can be converted from the structured format into a standardized format by using some formula, or human mind can convert transaction data format by using pen and paper. Thus, it can be categorized as mathematical concepts or mental processes.
Regarding “analyzing the dataset, in accordance with the selected transaction-related function to derive temporal information and quantitative information associated with the dataset”, it can be interpreted as using the selected transaction-related function to derive temporal information and quantitative information associated with the dataset. Thus, it can be categorized as mathematical concepts.
Regarding “wherein a narrative generation platform generates a narrative for the output of the predictive classifier based on the temporal information and the quantitative information”, it can be interpreted as using a statistical algorithm to generates a narrative based on the temporal information and the quantitative information. Thus, it can be categorized as mathematical concepts.
Regarding “wherein a calculation engine to executes one or more functions based on one or more reason codes associated with the output of the predictive classifier to improve the narrative generated by the narrative generation platform”, it can be interpreted as using a calculation algorithm to improve the narrative based on one or more reason codes. Thus, it can be categorized as mathematical concepts.
As analyzed above, the above claimed limitations are mental processes or mathematical concepts.
Prong 2:
The claims include additional elements of
system, programmable processor, calculation engine, generation platform and non-transient machine-readable medium.
Regarding “system, programmable processor, calculation engine, generation platform and non-transient machine-readable medium”, they are considered as to use a general computational hardware/software. It is considered as “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f).
They are mere instructions to implement an abstract idea uses a computer as a tool to perform an abstract idea.
Moreover, the claim limitations that are not indicative of integration into a practical application.
Thus, the recited generic additional element (e.g., system, programmable processor, calculation engine, generation platform and non-transient machine-readable medium) perform no more than their basic computer function. Generic computer-implementation of a method is not a meaningful limitation that alone can amount to significantly more than an abstract idea. Moreover, when viewed as a whole with such additional element considered as an ordered combination, claims modified by adding a computational algorithm, a generic memory and processor are nothing more than a purely conventional computerized implementation of an idea in the general field of computer processing and do not provide significantly more than an abstract idea.
Accordingly, the claims are directed to an idea of itself, and therefore not patent eligible.
Step 2B:
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception such as improvements to another technology or technical field, or other meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment.
Moreover, the claim language that may be separate from the abstract idea (i.e., additional elements) include computer processors, computer-readable storage media.
The additional element (e.g., system, programmable processor, calculation engine, generation platform and non-transient machine-readable medium) simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception (WURC) - see MPEP 2106.05(d) and 2106.07(a)III.
Thus, the recited generic additional elements (e.g., system, programmable processor, calculation engine, generation platform and non-transient machine-readable medium) perform no more than their basic computer function. Generic computer-implementation of a method is not a meaningful limitation that alone can amount to significantly more than an abstract idea. Moreover, when viewed as a whole with such additional element considered as an ordered combination, claims modified by adding a generic memory are nothing more than a purely conventional computerized implementation of an idea in the general field of computer processing and do not provide significantly more than an abstract idea.
Consequently, the identified additional elements taken into consideration individually or in combination fails to amount of significantly more than the abstract idea above.
Regarding claims 2 – 9, 11 – 14 and 16 – 20, the rejection is based on the same rationale described for claims 1, 10 and 15 because the claims include/inherit the same/similar type of problematic limitation(s) as claims 1, 10 and 15, wherein limitations regarding additional aspect for process; "comprising ... ", “is …”, “refining …”, “converted …”, “determining …”, “executing …” and “integrating …”, is/are of sufficient breadth that it would be substantially directed to or reasonably interpreted as a part of the “mental processes” as the abstract idea (similar to claim as stated above). It is noted that further additional limitation is merely generic/conventional computer component/steps to implement the abstract idea, which is, individually or in combination, not sufficient to amount to significantly more than the judicial exception. Therefore, the claimed invention as a whole is directed to an ineligible subject matter.
Claim Rejections - 35 USC § 103
The claims amendment overcomes the prior art of the record. After a further search, no reference is found to teach, or in a combination to teach, the claimed limitations for the independent claims.
The closest reference Purang et al. (“Practical Reasoning and Plan Execution with Active Logic") discloses a method for integrating aspects of theoretical reasoning practical reasoning and reasoning about and executing plans (abstract).
However, Purang, alone or in combination, does not teach all the limitations recited in the independent claims of the instant application.
Conclusion
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/QIAN YANG/
Primary Examiner, Art Unit 2677
