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 .
Status of Claim
This action is in response to application filed on 26 of April 2023.
Claims 1-20 are currently pending and are rejected as described below.
Allowable Subject Matter
Claims 1-20 are objected to as being currently rejected as below, but would be allowable if the independent claims were amended in such a way as to overcome the 35 USC 101 rejection set forth in the action. The prior art of record most closely resembling the applicant’s claimed invention includes Richens et. al. (US 11379747), Lublin et. al. (US 20230034198), Chan et. al. (US 20150254330), and Wu et. al. (NPL Methods and Applications of Causal Reasoning in Medical Field).
Richens teaches a method for providing a computer-implemented medical diagnosis includes receiving an input from a user comprising at least one symptom of the user. The method also includes providing the at least one symptom as an input to a medical model, the medical model being retrieved from memory. The medical model includes a probabilistic graphical model comprising probability distributions and relationships between symptoms and diseases. The method also includes performing inference on the probabilistic graphical model to obtain a prediction of the probability that the user has that disease. The method also includes outputting an indication that the user has a disease from the Bayesian inference, wherein the inference is performed using a counterfactual measure.
Lublin teaches a technique for dynamic data structure usage for storing data objects is described. In one example of the present disclosure, a system can receive a data object and properties associated with the data object. The system can determine, based on at least one of the properties and pre-defined rules for data objects and corresponding object types, an object type of the data object and a first data structure for storing the data object that is different from a second data structure currently storing data objects in the memory. The system can output a command for causing the first data structure to store the data object in the memory.
Chan teaches systems and methods for managing and processing large amounts of complex and high-velocity data by capturing and extracting high-value data from low value data using big data and related technologies. Illustrative database systems described herein may collect and process data while extracting or generating high-value data. The high-value data may be handled by databases providing functions such as multi-temporality, provenance, flashback, and registered queries. In some examples, computing models and system may be implemented to combine knowledge and process management aspects with the near real-time data processing frameworks in a data-driven situation aware computing system.
Wu teaches causal reasoning as an important component of explainable AI and has been a key research topic across domains, especially in the medical field. One of the core problems is to infer the causal effect of treatment from medical data. However, when the traditional methods of dealing with effect estimations are applied to medical cases, there are obstacles such as instability, incomprehensibility, and unexplainability, which may not be able to deal with special medical data. Furthermore, there is no thorough survey of causal reasoning methods for specific medical problems. Therefore, we present a comprehensive survey of causal reasoning methods in the context of medicine, combining the advantages of both the medical field and causal reasoning. And take specific examples t o s how the contribution of causal reasoning methods in disease prediction, diagnosis decision-making, treatment effect estimation, causal relationship mining, medical image analysis, and so on. This shows that causal reasoning methods have theoretical and practical significance in the medical field.
None of the above prior art explicitly teaches “generating, by the one or more processors, an optimization function using the causal relationship representation, wherein: the optimization function is configured to generate an optimal parameter occurrence set for a data object of the plurality of data objects, and the optimal parameter occurrence set is indicative of an optimal number of parameter occurrences for each of the plurality of outcome-influencing data types” and these are the reasons which adequately reflect the Examiner's opinion as to why Claims 1-20 are allowable over the prior art of record, and are objected to as provided below.
Claim Rejections - 35 USC § 101
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
When considering subject matter eligibility under 35 U.S.C. 101, it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machines, article of manufacture, or composition of matter. If the claim does fall within one of the statutory categories, it must then be determined whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea), and if so, it must additionally be determined whether the claim is a patent-eligible application of the exception. If an abstract idea is present in the claim, any element or combination of elements in the claim must be sufficient to ensure that the claim amounts to significantly more than the abstract idea. Alice Corporation Pty. Ltd. v. CLS Bank International, et al., 573 U.S. ____ (2014). See MPEP 2106.03(II).
The claims are then analyzed to determine if the claims are directed to a judicial exception. MPEP §2106.04(a). In determining, whether the claims are directed to a judicial exception, the claims are analyzed to evaluate whether the claims recite a judicial exception (Prong One of Step 2A), and whether the claims recite additional elements that integrate the judicial exception into a practical application (Prong Two of Step 2A). See 2019 Revised Patent Subject Matter Eligibility Guidance (“PEG” 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50-57 (Jan. 7, 2019)).
With respect to 2A Prong 1, claim 10 recites “a system comprising memory and one or more processors communicatively coupled to the memory, the one or more processors configured to: identify a plurality of outcome-influencing data types for a dataset comprising a plurality of data objects, wherein each of the plurality of data objects is associated with a number of parameter occurrences for each of the plurality of outcome-influencing data types; generate a causal relationship representation based on the dataset, wherein the causal relationship representation is indicative of a causal relationship between each of the plurality of outcome-influencing data types and a predictive outcome of the plurality of data objects; generate an optimization function using the causal relationship representation, wherein: the optimization function is configured to generate an optimal parameter occurrence set for a data object of the plurality of data objects, and the optimal parameter occurrence set is indicative of an optimal number of parameter occurrences for each of the plurality of outcome-influencing data types; and provide data indicative of the optimization function”. Claims 1 and 16 disclose similar limitations as Claim 10, as disclosed, and therefore recites an abstract idea.
With respect to 2A Prong 1, claim 7 recites “wherein generating the causal relationship representation comprises generating, by the one or more processors and using a non-parametric machine learning model, the causal relationship representation based on the dataset and a knowledge graph indicative of one or more relationships between the plurality of outcome-influencing data types and the predictive outcome”. Claim 17 discloses similar limitations as Claim 7, as disclosed, and therefore recites an abstract idea.
More specifically, claims 1, 7, 10, 16, and 17 are directed to “Mental Processes” such as “concepts performed in the human mind (including an observation, evaluation, judgment, opinion)” and “Mathematical Concepts” such as “mathematical calculations” as discussed in MPEP §2106.04(a)(2), and in the 2019-01-08 Revised Patent Subject Matter Eligibility Guidance. Accordingly, the claims recite an abstract idea. This is further supported by language found at least on ¶92 disclosing that “in some embodiments, the rate of change of the predictive outcome 414, or causal effect, is estimated using the causal relationship representation 410 and/or causal relationships 412 generated thereby. As described, the causal relationships 412 may indicate changes to a predictive outcome (e.g., causal effects) for parameter occurrence values of an outcome-influencing data type. In one example, each causal relationship 412 for each outcome-influencing data type 404 may include a nonlinear causal inference curve.”
Dependent claims 2-6, 8-9, 11-15, and 18-20 further recite abstract idea(s) contained within the independent claims, and do not contribute to significant more or enable practical application. Thus, the dependent claims are rejected under 101 based on the same rationale as the independent claims.
Under Prong Two of Step 2A of the Alice/Mayo test, the examiner acknowledges that Claims 1, 7, 10, 16, and 17 recite additional elements yet the additional elements do not integrate the abstract idea into a practical application. In order for the judicial exception to be “integrated into a practical application”, an additional element or a combination of additional elements in the claim “will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception.” PEG, 84 Fed. Reg. 54 (Jan. 7, 2019). The courts have identified examples in which a judicial exception has not been integrated into a practical application when “an additional element does no more than generally link the use of a judicial exception to a particular technological environment or field of use.” PEG, 84 Fed. Reg. 55 (Jan. 7, 2019); MPEP § 2106.05(h). The claims are directed to an abstract idea.
In particular, claims 1, 7, 10, 16, and 17 recite additional elements boldened and underlined above. These are generic computer components recited as performing generic computer functions that are mere instructions to apply an exception, because it does no more than merely invoke computers or machinery as a tool to perform an existing process. Accordingly, these additional elements do not integrate the abstract idea into a practical application. The claims are directed to an abstract idea.
With respect to step 2B, claims 1, 7, 10, 16, and 17 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea. The claim recites the additional elements described above. These are generic computer components recited as performing generic computer functions that are mere instructions to apply an exception, because it does no more than merely invoke computers or machinery as a tool to perform an existing process, as evidenced by at least ¶30 "FIG. 2 provides an example predictive data analysis computing entity 106 in accordance with some embodiments discussed herein. In general, the terms computing entity, computer, entity, device, system, and/or similar words used herein interchangeably may refer to, for example, one or more computers, computing entities, desktops, mobile phones, tablets, phablets, notebooks, laptops, distributed systems, kiosks, input terminals, servers or server networks, blades, gateways, switches, processing devices, processing entities, set-top boxes, relays, routers, network access points, base stations, the like, and/or any combination of devices or entities adapted to perform the functions, operations, and/or processes described herein. Such functions, operations, and/or processes may include, for example, transmitting, receiving, operating on, processing, displaying, storing, determining, creating/generating, monitoring, evaluating, comparing, and/or similar terms used herein interchangeably. In one embodiment, these functions, operations, and/or processes may be performed on data, content, information, and/or similar terms used herein interchangeably”.
Claims 2-6, 8-9, 11-15, and 18-20 do not disclose additional elements, further narrowing the abstract ideas of the independent claims and thus not practically integrated under prong 2A as part of a practical application or under 2B not significantly more for the same reasons and rationale as above.
After considering all claim elements, both individually and in combination, Examiner has determined that the claims are directed to the above abstract ideas and do not amount to significantly more. See Alice Corporation Pty. Ltd. v. CLS Bank International, No. 13–298.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MATHEUS R STIVALETTI whose telephone number is (571)272-5758. The examiner can normally be reached on M-F 8:30-5:30.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Rutao Wu can be reached on (571)272-6045. The fax phone number for the organization where this application or proceeding is assigned is 571-273-1822.
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/MATHEUS RIBEIRO STIVALETTI/Examiner, Art Unit 3623 5/20/2026
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