Notice of Pre-AIA or AIA Status
The present application is being examined under the pre-AIA first to invent provisions. In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
DETAILED ACTION
This Final Office Action is in response Applicant communication filled on 03/30/2026.
Status of Claims
Claims 1,8,15 have been amended and Claims 2,7,9,14,16,20 canceled by Applicant.
Claims 1,3-6,8,10-13,15,17-19 are currently pending and have been rejected as follows.
Response to Arguments/ Amendments
Applicant’s 03/30/2026 amendment necessitated the new grounds of rejection in this action.
Response to the Applicant’s reply to the Double Patenting Rejection
The double patenting rejection is maintained.
Response to Applicant’s rebuttal of 35 USC 112 rejection
Remarks 03/30/2026 p.12 ¶1 argues that since Claims 1,8,15 were amended to remove statistically meaningful, the 112(a) rejection at Non-Final Act 12/29/2025 p.13 should be removed.
Examiner considered the argument which is found persuasive. 112(a) rejection is removed.
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Response to the Applicant’s rebuttal of the 35 USC 101 rejection
Remarks 03/30/2026 p.12 ¶2-p.13 ¶4 argues the first and second prediction model are tested against actual outcome to select one prediction model, which is then used to automatically reconfigure the self-learning predictive analysis engine, which is argued to be an improvement to the functioning of the computer, because the predictive analysis engine is automatically reconfigur[ed] to use a selected prediction model without additional intervention, which is interpreted by Applicant as the computer itself internally checking its logic, by selecting the prediction model based on actual outcomes so that computing resources are used for the most desirable model. As such, the functioning of the computer itself is allegedly improved, resulting in integration into a practical application. Thus, it is argued Claims 1,8,15 satisfy Step 2A, Prong 2.
Examiner fully considered the Applicant’s argument but respectfully disagrees because comparing among a plurality of options or solutions (here models) what has worked in the past against what has not worked in the past, or comparing what option or solution has worked better than another option or solution, still represents an abstract evaluation for a subsequent decision making or a judgement to select the better option in the future. These concepts fall squarely within the abstract observations and evaluations used for subsequent abstract judgments, cited by MPEP 2106.04(a)(2) III at ¶2. Moreover, MPEP 2106.04(a)(2) III C #2 clarifies that the execution of such abstract concepts (i.e. evaluation and decision making or judgments) do not preclude the claims to recite the judicial exception. Additionally, or alternatively1, Examiner notes that selection of a better or best option (i.e. solution) to be subsequently used in the future can also be viewed as a fundamental practice of selecting a best course of action or business practice (MEPP 2106.04(a)(2) II A]. It is perhaps worth noting that such concept of selecting the best option, asset or model to be used in the future appears also reminiscent to natural selection, as a law of nature, along with mathematical algorithms mimicking such law of nature such as survival of fitness as a genetic algorithm, race to the top, etc., equivalent to reinforcement learning used in machine learning, positive feedback loops used in cybernetics etc. Indeed, the selection of a better or best option (i.e. model, solution) to be subsequently used in the future is not meaningfully different than the organizing of information and manipulating it through mathematical correlations found by MPEP 2106.04(a)(2) I A as abstract mathematical relationships expressed in words, such as the iterative or repetitive algorithm found ineligible in Flook. No matter if considered as abstract mathematical relationship(s) expressed in words as per MPEP 2106.04(a)(2) I A, and/or abstract evaluations and subsequent judgements as per MPEP 2106.04(a)(2) III ¶2, executed in a computer environment, as per MPEP 2106.04(a)(2) III C #2, or fundamental best practices [MPEP 2106.04(a)(2) II A] etc., the end result of the prong one of the subject matter eligibility analysis is the same; namely, that the claims’ character as a whole remains directed to the judicial exception.
As corroborative evidence, the Examiner points to Brandan Artley, Training a Neural Network by Hand, towardsdatascience webpages, Jun 23, 2022, incorporated herein, who discloses the training of a neural network by hand to solve a regression problem where the model continually improves its predictions to arrive at a highly accurate model. Thus, it is clear that use of first and second prediction model as tested against actual outcome to select one prediction model, then reconfigure the self-learning predictive analysis engine to use the selected one prediction model, as argued by Applicant at Remarks 03/30/2026 p.12 ¶2-p.13 ¶4, does not preclude the claims to recite, describe or set forth the judicial (i.e. abstract) exception.
Even when more granularly testing the claims at the subsequent steps of the subject matter eligibility analysis, it becomes clear that comparison and selection among algorithmic models for reconsideration or reconfigur[ation] of a selected or surviving model between the first and second models at Claims 1,8,15 does, akin to section of ground truth, first principle(s), axiom, postulate, empirical baseline etc., still fall within the application of the judicial exception, such as monitoring of audit log data on a computer followed by application of a mathematical algorithm on such computer, which according to MPEP 2106.05(f)(2)(i) do not integrate the abstract exception into a practical application or provide significantly more. Further, the Non-Final Act 12/29/2025 p.23-p.24 found that the concept of comparison and selection of a model among algorithmic models for reconsideration or reconfiguration is not only abstract but also well-understood, routine or conventional, as evidenced by at least the following corroborative references:
* US 11341367 B1 column 9 lines 56-60 As is known in the art, when training a model, a training dataset may be split into a first subset for training and a second subset for validation—e.g., 80% for training, and 20% for validation. [bolded emphasis added]
* US 20180285691 A1 ¶ [0039] 2nd-3rd sentences: future data instances have unknown target values that cannot be used for checking accuracy of predictions of the model, some of the data from known data 405 for which we already know the answer of the prediction target for each row is used to evaluate the accuracy of the model (and underlying algorithm). For example, 70-80% of known data 405 may be used for training and 20-30% may be used for validation and testing.
* US 20180336369 A1 ¶ [0033] last sentence: If the conventional validation technique is used to partition the datasets, the training and testing datasets may be of unequal size, for example, 80% of the removed, hidden or masked dataset may be partitioned into the training dataset and 20% of the removed, hidden or masked dataset may be partitioned into the testing dataset, or 70% of the removed, hidden or masked dataset may be partitioned into the training dataset and 30% of the removed, hidden or masked dataset may be partitioned into the testing dataset.
Based on the preponderance of legal and factual evidence, and the prosecution history including the Non-Final Act 12/29/2025 p.3-p.9, p.13 last ¶-p.24¶2, the Examiner submits that use of first and second prediction models tested against actual outcome to select one prediction model, then automatically reconfigure the self-learning predictive analysis engine to use the selected one prediction model, as argued by Applicant at Remarks 03/30/2026 p.12 ¶2-p.13 ¶4
still does not preclude the claims from reciting, describing or setting forth the abstract exception, with no technological details presented to provide an actual technological improvement to demonstrate improvement in actual technology and hence integration of the judicial exception into a practical application or recitation of significantly more than the judicial exception itself.
Therefore, the argument is found unpersuasive and the claims parent ineligible.
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Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969). A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b). The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13. The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
- Claims 1,3-6,8,10-13,15,17-19 are rejected on the ground of nonstatutory double patenting unpatentable over:
= Claims 1-20 of U.S. Patent No 11568331 B2 in view of
= Showalter; Thomas US 20110270779 A1 hereinafter Showalter in view of
= Duta Nicolae US 8515736 B1 hereinafter Duta, and in further view of
= Sasagawa et al, US 20120005141 A1 hereinafter Sasagawa.
Although the claims at issue are not identical, they are not patentably distinct from each other because Claims 1-20 of U.S. Patent No 11568331 B2 recite substantially similar limitations as Claims 1,3-6,8,10-13,15,17-19 of the current Application, with the major difference being that independent Claims 1,8,15 of U.S. Patent No 11568331 B2, appears to recite similar subject matter as now spread throughout Claims 1,3-6,8,10-13,15,17-19 of the current Application. For example, independent Claims 1,8,15 of US 11568331 B2, recite, the narrower features of:
i. “retrieve from a predetermined minimum number of the plurality of cases or a predetermined minimum percentage of the plurality of cases, a first portion of the configuration data containing a plurality of the case attributes and a corresponding plurality of the performance criteria and associated outcomes”, [bolded emphasis added]
ii. “generate, from the retrieved correlation data, a first prediction model using a decision tree predictive algorithm”, [bolded emphasis added]
iii. “generate, from the retrieved correlation data, a second prediction model using a Naive Bayes prediction algorithm”, [bolded emphasis added]
iv. “test the first” [decision tree based] “and” “second” [Naïve Bayes based] “prediction models with an actual outcome to select one prediction model that best represents the defined case management data by detecting a same pattern between one of the case attributes and one of the projected outcomes, in two distinct portions of the defined case management data”.
Claims 1-20 of U.S. Patent No 11568331 B2 does not explicitly recite:
- “analyzing a minimum portion of the defined case management data associated with the case attributes”;
- “detecting a first pattern in a first portion of the minimum portion of case management data, the first pattern being detected between each of the case attributes and the corresponding one of the projected outcomes”; “and”
- “validating the correlation data using the pattern thus detected, the validating comprising: detecting a second pattern that is the same as the first pattern”;
- “automatically reconfigure the self-learning predictive analysis engine to use the selected one prediction model for subsequent received case data”;
- “continuously update or modify the selected one predictive model as the case management server receives incoming case management data for new or existing cases”; as amended at Claims 1,8,15.
Showalter however in analogous predictive data analytics models teaches or suggests:
- “analyzing a minimum portion of the defined case management data associated with the case attributes”; (Showalter ¶ [0094] 1st sentence: extracting at least one thousand metrics. Similarly, ¶ [0213] 4th sentence. ¶ [0230] 1st sentence minimizing severity)
- “detecting a first pattern in a first portion of the minimum portion of case management data, the first pattern being detected between each of the case attributes and the corresponding one of the projected outcomes”; (Showalter ¶ [0230] 2nd-3rd sentences: Loss severity is an amount of loss (given default) relative to the size of the unpaid principal balance. It is often expressed as a percentage. For example, 40% loss severity means that a defaulted loan of $200,000 incurred $80,000 in losses for a loss severity of 40% (80,000/200,000) mid ¶ [0231] a loan modification that reduces the borrower's 1st lien mortgage payment from $1,000 to $800 would be noted as a 20% payment reduction (post modification v pre modification). In turn, a loan modification with a 50% payment reduction reduces a $1,000 payment to $500. A 50% payment reduction is considered a more generous term than a 20% reduction) “and”
- “validating the correlation data using the pattern thus detected, the validating comprising: detecting a second pattern that is the same as the first pattern”;
(Showalter ¶ [0109] Each factor contains different information about a borrower. Each factor is statistically independent of the others or at least has low correlation (e.g., less than 0.5) with the other factors. The factors may include some correlation or statistical dependence on other factors. By having low correlation or statistical independence, a plurality of different behaviors may be modeled and treated separately. Although a loan may be within the borrower's capacity, the borrower's willingness or propensity for distress may indicate reasons not to make a loan. Unless the servicer takes into account the borrower's willingness and distress metrics, the future success of a loan modification is at risk. The profile of the borrower most likely to survive a loan modification is one with high capacity, very high willingness and low distress.
Showalter ¶ [0114] 4th-5th sentences: he factors are determined from factor analysis, such as selecting metrics with similar levels of correlation to a given result. Different factors may be determined for groupings of correlation for a same result. ¶ [0139] 5th-6th sentences: The matrix is trained to cluster based on the input features to provide relevant or correlated groups of borrowers by the desired score. Alternatively, the same clustering is used, but a different score is calculated from the sample data belonging in each cluster. ¶ [0202] 3rd-4th sentences: the machine training uses training data for a plurality of previous borrowers. Any number of borrowers may be included in the training data. The training data includes values for the variables (metrics) of the borrower, property, product, and real estate market. Data for a particular borrower included in the training data may include the same variables as others.
Showalter ¶ [0119] 5th-6th sentences As shown in Fig.8, 3 factors are origination amount, interest rate, and product type (ARM v Fixed). The 3 shown factors are the most heavily correlated with the clustering outcome. ¶ [0165] 6th sentence: The consistency and validity of the loan treatment decisions generated by the machine learned matrix using many training samples more likely ensures that borrowers with similar problems are treated alike and that the treatment offered to them generates mutual benefit or a best result to the lender. ¶ [0204] The trained classifier may be validated using the training data. Any validation may be used. For limited training data sets, random selection of training and testing data may be used with much iteration to create a more reliable model. A 5-fold or other cross validation is performed. A leave-one-out approach may be used. As another example with a large training data set, a group of borrowers may be used for validation and not for training. ¶ [0205] 1st sentence: In another embodiment, the trained classifier is validated using a separate set of training data. Also ¶ [0214]- ¶ [0215])
- “continuously update or modify the selected one predictive model as the case management server receives incoming case management data for new or existing cases”
(Showalter ¶ [0045] 5th-6th sentences: The model may be updated, such as controlling the time period over which the training data is collected. The retraining or updating allows for current conditions of the economy to more strongly influence the predications provided by the model. ¶ [0139] 2nd sentence: Due to the architecture of the matrix generated with machine learning, producing new scores is efficient & fast), at independent Claims 1,8,15
It would have been obvious to one skilled in the art, at the time of the invention to have modified U.S. Patent No 11568331 B2 to have included Showalter’s teachings in order to have more effectively handled different output situations according to different input modeling situations (Showalter ¶ [0045] 2nd - 3rd sentences, ¶ [0139] 2nd sentence, ¶ [0165] in view of MPEP 2143 G and/or F). The predictability of such modification would have been further corroborated by the broad level of skills of one of ordinary skills in the art as articulated by Showalter ¶ [0334]. Further, the claimed invention could have also been viewed as a mere combination of old elements in a similar field of endeavor dealing with predictive data analytics models. In such combination each element would have merely performed same analytical, modeling and predictive function as it did separately. Thus, one of ordinary skill in the art would have recognized that, given existing ability to combine the elements evidenced by U.S. Patent No 11568331 B2 in view of Showalter, the to be combined elements would have fitted together like puzzle pieces in a logical, complementary, technologically feasible and/or economically desirable manner. Thus, it would have been reasoned that the results of the combination would have been predictable (MPEP 2143 A). Further
Duta in analogous art of modeling or predictive analysis teaches or suggests: “wherein the second pattern is detected in another portion of the minimum portion of case management data”;
(Duta Figs7-12, column 9 lines 59-62: minimize cluster diameter based on semantic distance.
Duta column 5 lines 1-11: To find semantically similar utterances, a semantic distance is computed between each given target utterance and utterances present in the database. Then, the database utterances with the smallest semantic distance are returned or identified. The semantic distance is calculated by feeding or inputting the target utterance to each of the database routers and fusing the information returned by the routers (for example, the top 3 routes along their classification confidence) with the prior accuracy numbers. A level of confidence identifies a probability that the target utterance has been correctly labeled. column 15 lines 29-32: By way of a specific non-limiting example, the clustering process can be stopped when a cluster diameter (lowest semantic similarity between two samples in a cluster) becomes lower than a threshold (e.g. 0.1). column 15 lines 33-36: Fig.6 includes table 600, which illustrates some example sentences that appear in four semantic clusters 602, 604, 606, 608. Note that each cluster includes example sentences that are similar in meaning).
It would have been obvious to one skilled in the art, at the time of the invention to have further modified U.S. Patent No 11568331 B2 in view of Showalter’s teachings in order to have included the teachings or suggestions of Duta to have benefited from a more rigorous process of algorithmic identification or detection of target data while at the same time providing faster deployment and decreased costs (Duta column 4 lines 45-50 in view of MPEP 2143 G and/or F). The predictability of such modification would have been further corroborated by the broad level of skills of one of ordinary skills in the art as articulated by Duta column 20 lines 52-67.
Further, the claimed invention could have also been viewed as a mere combination of old elements in similar predictive analysis field of endeavor. In such combination each element would have merely performed same analytical, modeling and predictive function as it did separately. Thus, one of ordinary skill in the art would have recognized that, given existing ability to combine the elements evidenced by U.S. Patent No 11568331 B2 and Showalter in further view of Duta, the to be combined elements would have fitted together like puzzle pieces in a complementary, logical, technologically feasible and/or economically desirable manner. Thus, it would have been reasoned that the results of the combination would have been predictable (MPEP 2143 A). Also,
Sasagawa however in analogous mathematical matching or correlation teaches/suggests:
- “automatically reconfigure the self-learning predictive analysis engine to use the selected one prediction model for subsequent received case data”;
(Sasagawa ¶ [0160] a neural network system that can minimize circuit resources for constituting a self-learning mechanism and that can be reconfigured into network configurations suitable for various purposes. Further, since the von Neumann-type microprocessor is capable of performing sequential program processing, the process for which the neural network engine is unsuitable can be complemented. ¶ [0162] Therefore, minimizing the circuit resources for the self-learning mechanism, and making it possible to reconfigure the neural network into configurations suitable for various purposes and to perform sequential program processing enable fuzzy processes such as the user interface and the recognition and avoidance system for hazardous objects for vehicles to be performed at a high speed).
It would have been obvious to one skilled in the art, at the time of the invention to have further modified U.S. Patent No 11568331 B2 in view of Showalter and Duta, in order to have further included the teachings or suggestions of Sasagawa in order to have provided a more rigorous modeling algorithms while, at the same time, having mitigated the requirement of the desired degrees of freedom in a more time effective manner (Sasagawa ¶ [030] - ¶ [0040] in view of MPEP 2143 G). The predictability of such modification would have been further corroborated by the broad level of skills of one of ordinary skills in the art as articulated by Sasagawa ¶ [0163].
Further, the claimed invention could have also been viewed as a mere combination of old elements in a similar field of endeavor dealing with e data analytics models. In such combination, each element would have merely performed same analytical, modeling and predictive function as it did separately. Thus, one of ordinary skill in the art would have recognized that, given existing ability to combine the elements as evidenced by U.S. Patent No 11568331 B2 in view of Showalter and Duta, and in further view of Sasagawa, the to be combined elements would have fitted together like puzzle pieces in a logical, complementary, technologically feasible and/or economically desirable manner. Thus, it would have been reasoned that the results of the combination would have been predictable (MPEP 2143 A).
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Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), first paragraph:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1,3-6,8,10-13,15 and 17-19 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for pre-AIA the inventor(s), at the time the application was filed, had possession of the claimed invention. Claims 1,8,15 are independent and have each been amended to recite, among others:
- “detecting a second pattern that is the same as the first pattern, wherein the second pattern is detected in another portion of the minimum portion of case management data”;
- “automatically reconfigure the self-learning predictive analysis engine to use the selected one prediction model for subsequent received case data”;
Remarks 03/30/2026 p.11 ¶1 points as support to Original Specification ¶ [0040], ¶ [0043] and ¶ [0046]. Yet, none of Original Specification ¶ [0040], ¶ [0043] and ¶ [0046] provide clear, deliberate and sufficient support to show the Applicant had possession for the newly added matter
- “detecting a second pattern that is the same as the first pattern, wherein the second pattern is detected in another portion of the minimum portion of case management data”;
- “automatically reconfigure the self-learning predictive analysis engine to use the selected one prediction model for subsequent received case data”. [bolded emphasis added].
Applicant is reminded: “One shows that one is in possession of the invention by describing the invention, with all its claimed limitations, not that which makes it obvious” “Lockwood v. American Airlines, Inc., 41 USPQ2d 1961, No. 96-1168, 107 F3d 1565.
Claims 1,8,15 are thus rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement.
Claims 3-6 are dependent and rejected based on rejected parent Claim 1.
Claims 10-13 are dependent and rejected based on rejected parent Claim 8.
Claims 17-19 are dependent and rejected based on rejected parent Claim 15.
Examiner recommends Applicant amend Claims 1,8,15 as supported by the Original Disclosure.
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Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(B) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1,3-6,8,10-13,15 and 17-19 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention. Claim 1,8,15, as amended each recite the limitation: “
- “analyzing a minimum portion of the defined case management data associated with the case attributes the minimum portion of the defined case management data is derived from: at least a predetermined number of cases, at least a predetermined percentage of the total number of cases in the data store, or a combination thereof”;
- “determine an actual outcome for a selected case of the plurality of cases by retrieving, from the data store, case management data associated with the performance criteria for the selected case”;
Claims 1,8,15 are rendered vague and indefinite because there is insufficient antecedent basis for “the total number of cases”. Also, it is unclear if and how “case management data” as subsequently recited at the above determine, relates back to the antecedently recited “case management data”. Also, while there is antecedent basis for “performance criteria defining predetermined goals for associated ones of the case attributes”, as previously recited, there is insufficient antecedent basis for “the performance criteria for the selected case” at the “determine” limitation above.
Claim 1,8,15, are recommended to be amended to each recite, among others:
- analyzing a minimum portion of the defined case management data associated with the case attributes the minimum portion of the defined case management data is derived from: at least a predetermined number of cases, at least a predetermined percentage of a total number of cases in the data store, or a combination thereof;
- determine an actual outcome for a selected case of the plurality of cases by retrieving, from the data store, respective case management data associated with a particular performance criteria for the selected case;
Claims 3-6 are dependent and rejected based on rejected parent Claim 1.
Claims 10-13 are dependent and rejected based on rejected parent Claim 8.
Claims 17-19 are dependent and rejected based on rejected parent Claim 15.
Clarification and correction are required.
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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 1,3-6,8,10-13,15 and 17-19 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, here abstract idea) without significantly more. The claim(s) recite(s) describe, set forth the abstract grouping of computer-aided Mental Processes based on reliance on MPEP 2106.04(a)(2) III C, which states that: # 1. performing a mental process on a generic computer, #2. in a computer environment; and #3 using a computer as a tool to perform a mental process, are all examples of abstract mental processes.
Based on such findings, the Examiner finds that here performing a mental “analy[sis]” [of] “a first portion of the configuration data containing a plurality of the case attributes and a corresponding plurality of the performance criteria and associated outcomes in a first portion of the configuration data and generate a set of projected outcomes corresponding to the performance criteria”, [followed by] “generat[ing]” [a] “correlation data that establishes, for each of the case attributes, a relationship with a corresponding one of the projected outcomes based on at least one of the performance criteria corresponding to respective ones of the case attributes and the projected outcomes wherein generation of the correlation data comprises: analyzing a minimum portion of the defined case management data associated with the case attributes
the minimum portion of the defined case management data is derived from: at least a predetermined number of cases, at least a predetermined percentage of the total number of cases in the data store, or a combination thereof”; “detecting a first pattern in a first portion of the minimum portion of case management data, the first pattern being detected between each of the case attributes and the corresponding one of the projected outcomes; and validating the correlation data using the pattern thus detected, the validating comprising: detecting a second pattern that is the same as the first pattern, wherein the second pattern is detected in another portion of the minimum portion of case management data”, “identify[ing] values of the one or more attributes in the received case data”, “generat[ing], from the stored correlation data, a first predictive algorithm using a decision tree predictive algorithm and a second prediction model using a Naïve Bayes prediction algorithm which is different than the decision tree prediction algorithm”; “determine an actual outcome for a selected case of the plurality of cases by retrieving, from the data store, case management data associated with the performance criteria for the selected case”; “test[ing] the first prediction model that is based on the decision tree predictive algorithm and the second prediction model that is based on the Naïve Bayes prediction algorithm against the actual outcome to select one prediction model that best represents the defined case management data” “and” “identify”, “using the selected prediction model, for the corresponding specific case, ones of the projected outcomes in the stored correlation data that are associated with the identified case attribute values in the received case data”; (independent Claims 1,8,15) are representative of one or more of the examples #1, #2, #3 above, where the computer environment or the tool is set forth by the general recitation of “self-learning predictive analysis engine” at Claims 1,8,15. Such tool performs what would otherwise be within the practical cognitive capabilities of one of ordinary skills in the art. Specifically, when tested per MPEP 2106.04(a)(2) III, the Examiner finds that nothing would have precluded a skilled artisan from practically and cognitively self-learn through observation, (re)evaluation the “analy[sis]” [of] “first portion of the configuration data containing a plurality of the case attributes and a corresponding plurality of the performance criteria and associated outcomes in a first portion of the configuration data and generate a set of projected outcomes corresponding to the performance criteria”, [followed by] “generat[ing]” [a] “correlation data that establishes, for each of the case attributes, a relationship with a corresponding one of the projected outcomes based on at least one of the performance criteria corresponding to respective ones of the case attributes and the projected outcomes wherein generation of the correlation data comprises: analyzing a minimum portion of the defined case management data associated with the case attributes, the minimum portion of the defined case management data is derived from: at least a predetermined number of cases, at least a predetermined percentage of the total number of cases in the data store, or a combination thereof; detecting a first pattern in a first portion of the minimum portion of case management data, the first pattern being detected between each of the case attributes and the corresponding one of the projected outcomes; and validating the correlation data using the pattern thus detected
the validating comprising: detecting a second pattern that is the same as the first pattern, wherein the second pattern is detected in another portion of the minimum portion of case management data”, “identify[ing] values of the one or more attributes in the received case data”, (Claims 1,8,15) to came up with a judgment, namely: “generat[ing], from the stored correlation data, a first prediction model using a decision tree predictive algorithm and a second prediction model using a Navie Bayes prediction algorithm which is different than the decision tree prediction algorithm”,
“determine[ing] an actual outcome for a selected case of the plurality of cases by retrieving, from the data store, case management data associated with the performance criteria for the selected case”; “test[ing] the first prediction model that is based on the decision tree predictive algorithm and the second prediction model that is based on the Naïve Bayes prediction algorithm, against the actual outcome to select one prediction model that best represents the defined case management data”, [for ultimately] “using the selected prediction model, for the corresponding specific case, ones of the projected outcomes in the stored correlation data that are associated with the identified case attribute values in the received case data” (independent Claims 1,8,15).
Moreover, recitations of “the first prediction model is based on the decision tree predictive algorithm and the second prediction model that is based on the Naïve Bayes prediction algorithm” “wherein the first prediction model comprises correlations identified by the decision tree predictive algorithm and the second prediction model comprises correlations identified by the Naive Bayes prediction algorithm” (independent Claims 1,8,15) can also be viewed as examples of organizing information and manipulating information through mathematical correlations of the equally abstract mathematical relationships expressed in words as tested per MPEP 2106.04(a)(2) I A iv2. The Examiner’s rationales are corroborated by Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54,119 USPQ2d 1739,1741-42 (Fed Cir 2016), cited by MPEP 2106.04(a)(2) III. A., 5th bullet point, which states that combination of collecting information, analyzing it, and displaying certain results of the collection and analysis, still falls within the abstract mental processes.
- Here, the collecting is set forth by the “received case data” of independent Claims 1,8,15.
- Here, the analysis of information is set forth by: analy[sis]” [of] “a first portion of the configuration data containing a plurality of the case attributes and a corresponding plurality of the performance criteria and associated outcomes in a first portion of the configuration data and generate a set of projected outcomes corresponding to the performance criteria”, [followed by] “generat[ing]” [a] “correlation data that establishes, for each of the case attributes, a relationship with a corresponding one of the projected outcomes based on at least one of the performance criteria corresponding to respective ones of the case attributes and the projected outcomes wherein generation of the correlation data comprises: analyzing a minimum portion of the defined case management data associated with the case attributes the minimum portion of the defined case management data is derived from: at least a predetermined number of cases, at least a predetermined percentage of the total number of cases in the data store, or a combination thereof; detecting a first pattern in a first portion of the minimum portion of case management data, the first pattern being detected between each of the case attributes and the corresponding one of the projected outcomes; and validating the correlation data using the pattern thus detected the validating comprising: detecting a second pattern that is the same as the first pattern, wherein the second pattern is detected in another portion of the minimum portion of case management data”, “identify[ing] values of the one or more attributes in the received case data”, “generat[ing], from the stored correlation data, a first prediction model using a decision tree predictive algorithm and a second prediction model using a Naïve Bayes prediction algorithm which is different than the decision tree prediction algorithm”, “test[ing] the first prediction model that is based on the decision tree predictive algorithm and the second prediction model using Naïve Bayes prediction algorithm with an actual outcome to select one prediction model that best represents the defined case management data”, “and” “using the selected prediction model, for the corresponding specific case, ones of the projected outcomes in the stored correlation data that are associated with the identified case attribute values in the received case data”; (independent Claims 1,8,15)
Thus, there is a preponderance of legal evidence at Step 2A prong 1, that the claims recite, or at a minimum describe or set forth the abstract computer aided “Mental Processes” grouping.
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This judicial exception is not integrated into a practical application because per Step 2A prong two, the individual or combination of the additional, computer-based elements are found to merely apply the already recited abstract idea [MPEP 2106.05(f)] and/or narrow it to a field of use or technological environment [MPEP 2106.05(h)]. Here, the additional computer-based elements are “case management server”, “application protocol layer”, “network subsystem”, “remote computer devices”, “outgoing data handler” along with the “self-learning predictive analysis engine” at independent Claims 1,8,15 and “an incoming message handler” and “command handler” at Claims 4-6,10-13,17-19, “processor” at Claims 8, 10-13. Such additional elements when tested per MPEP 2106.05(f)(2), merely apply the business method and its algorithm on computer or other machinery3. For example, they perform economic tasks, and other tasks to receive, store and transmit data4 when considering the additional computer-based elements of “case management server”, “application protocol layer”, “network subsystem”, “remote computer devices”, “outgoing data handler” along with the “self-learning predictive analysis engine” at independent Claims 1,2,8, 9,15,16 and “an incoming message handler” and “command handler” at Claims 4-6,10-13,17-19.
Further, when more granularly testing the capabilities of “self-learning predictive analysis engine”, under MPEP 2106.05(f)(1), the Examiner finds that said “self-learning predictive analysis engine”, as broadly recited, now fails to provide the technological details as to how the technological solution to the technological problem is accomplished. For example, the general recitation of “automatically reconfigure the self-learning predictive analysis engine to use the selected one prediction model for subsequent received case data” at Claims 1,8,15 are tested and found to merely monitor audit log data executed on a computer5 [MPEP 2106.05(f)(2) iii] with subsequent application of an algorithm on the computer [MPEP 2106.05(f)(2) i] which are examples of applying the abstract idea that does not integrate the abstract idea to a practical application. Similarly, the Examiner tests the recitation “wherein the case management server is configured to: build a message based on the identified projected outcomes for the corresponding specific case, and provide the message to the application protocol layer; wherein the application protocol layer is configured to format the received message for delivery to the first remote computer device” of independent Claim 1 and similarly independent Claims 8,15, by pointing to MPEP 2106.05(f)(2) v. which cites Intellectual Ventures I LLC v Capital One Bank (USA),792 F3d 1363,1370-71,115 USPQ2d 1636,1642 Fed Cir 2015 to state that requiring use of software to tailor information and provide it to user on generic computer, is another example of applying the abstract idea, which again does not integrate it into a practical application. In a similar vein, Examiner points to MPEP 2106.05(h), which cites “Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016)” to state that limiting the combination of collecting information, analyzing it, and displaying certain results of the collection and analysis to data related to a technological environment, is an example of limiting the identified abstract idea to a field of use or technological environment which again does not integrate it into a practical application. Step 2A prong two. Such technological environment is represented here by the various computer components of “self-learning predictive analysis engine”, “incoming message handler” and “command handler”, “subsystem”, “handler” etc. to allow for the combination of collecting information, analyzing it, and displaying certain results of the collection and analysis, as mapped above, and which according to MPEP 2106.05(h) do not integrate the abstract idea into a practical application.
As per the capabilities of “self-learning predictive analysis engine” to generate correlation data comprising: “analyzing a minimum portion of the defined case management data associated with the case attributes the minimum portion of the defined case management data is derived from: at least a predetermined number of cases, at least a predetermined percentage of the total number of cases in the data store, or a combination thereof”; “detecting a first pattern in a first portion of the minimum portion of case management data, the first pattern being detected between each of the case attributes and the corresponding one of the projected outcomes”; “and” “validating the correlation data using the pattern thus detected, the validating comprising:
detecting a second pattern that is the same as the first pattern, wherein the second pattern is detected in another portion of the minimum portion of case management data”; “automatically reconfigure the self-learning predictive analysis engine to use the selected one prediction model for subsequent received case data”; “continuously update or modify the selected one predictive model as the case management server receives incoming case management data for new or existing cases” at Claims 1,8,15, the Examiner finds such features comparable with iterative or repeated calculation of the updated value for an alarm limit according to a mathematical formula as in Flook, which as cited by MPEP 2106.05(h), merely narrows the abstract idea to a field of use or technological environment and fails to integrate it into a practical application .
Examiner submits in the arguendo, that even if the “Naive Bayes” and “decision tree” prediction algorithms, would be considered, along with the automatically reconfigur[ing] the self-learning predictive analysis engine to use the selected one prediction model for subsequent received case data in combination as additional elements to the abstract idea they would still merely apply the abstract idea, because MPEP 2106.05(f)(2)(i) states that mathematical algorithms being applied on a general-purpose computer, represent mere invocation of computers or machinery as a tool, which does not integrate the abstract idea.
Thus here, there is a preponderance of legal evidence demonstrating that the additional, computer-based elements, merely apply the abstract idea [MPEP 2106.05(f)] and/or narrow it to a field of use or technological environment [MPEP 2106.05(h)], and thus not integrating the abstract exception into a practical application.
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The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because as shown above, the additional computer-based elements merely apply the already recited abstract idea and link use of abstract idea to a field of use or technological environment. MPEP 2106.05(f), (h). For these reasons, said additional elements similarly do not provide significantly more than the abstract idea itself.
Per MPEP 2106.05(d)(I)(2) Examiner points to the following publications showing the application of a combination of multiple machine learning models, being well-understood, routine, each classified by USPTO as part of ensemble learning in CPC G06N20/20 classification scheme:
* US 11037236 B1 column 9 lines 16-19: As noted above, the aforementioned adaptively-determined matching algorithm includes a machine learning algorithm, such as a rule ensemble algorithm known to those skilled in the art.
* US 20210065128 A1 ¶ [0007] 3rd sentence: The machine learning model may be an ensemble of decision trees or may be a neural network, among other implementations that are known
* US 20200160460 A1 ¶ [0070] 4th sentence: “the machine learning classifier may include any machine learning classifier known in the art including, but not limited to, a conditional generative adversarial network (CGAN), a convolutional neural network (CNN) (e.g., GoogleNet, AlexNet, and the like), an ensemble learning classifier”
* US 20200134716 A1 ¶ [0088] “Generally, the MLAs that are trained and/or deployed as described herein may comprise any one, or some combination (e.g., in an ensemble), of a number of known machine learning techniques, which may include, without limitation: linear/logistic regression models, classification models, time-series models, clustering algorithms, nearest neighbor methods, decision trees, support vector machines, graphical models, neural networks, boosting, bagging, random forests, other ensemble methods, and/or any other type of function, algorithm, and/or model. In certain implementations, some of the noted algorithms might not use specifically engineered features”.
* US 20190289826 A1 ¶ [0115] In another embodiment, system 100 may utilize one or more machine learning techniques to carry out one or more functions of the present disclosure. It is contemplated herein that system 100 may be configured to carry out any type of deep learning technique and/or machine learning algorithm/classifier known in the art including, but not limited to, a convolutional neural network, an ensemble learning classifier, a random forest classifier, an artificial neural network, and the like. In this regard, the one or more processors 12, 138, 146 may be configured to train one or more machine learning classifiers configured to carry out the one or more functions of the present disclosure.
* US 20190101908 A1 ¶ [0021] Fig.7 is a diagram illustrating the accuracy of the prediction result by a combination of the parametric model and the non-parametric model and the ensemble learning in accordance with an embodiment of the present disclosure, and the prediction result of the conventional method.
* US 20180253657 A1 ¶ [0068] 4th sentence: “The common machine learning algorithms include, but are not limited to: gradient boosting machine, random forest, support vector machine, deep learning, ensemble learning, etc.”
* US 20180218428 A1 ¶ [0050] 1st sentence: “In many embodiments, activity 410 can comprise determining the static features common to both the one or more first items and one or more second items using ensemble learning”.
* US 20180096362 A1 ¶ [0048] 1st sentence: “The machine learning model of the present invention may apply additional artificial intelligence techniques, such as a method of learning ensembles of decision trees known as random forests”.
* US 20140025354 A1 ¶ [0024] 3rd sentence: “The one or more variants of machine learning models can be generated using known ensemble techniques”.
* US 11138524 B2 column 3 line 25: conventional ensemble machine learning approaches.
* US 20210264320 A1 ¶ [0002]: “Boosting methods are generic methods for constructing an ensemble model from a set of base learners (also referred to as base regressors)”.
* US 20210248474 A1 ¶ [0002] 3rd sentence: “There are many well-known methods for building ensembles of machine learning systems”
* US 20210239828 A1 ¶ [0009] 1st sentence: “Ensemble learning is known”.
* US 20210192388 A1 ¶ [0070] last sentence: “There are several known techniques, including (but not limited to): convolutional neural networks (CNNs), recurrent neural networks (RNNs), ensemble learning methods such as adaptive boosting (e.g., Adaboost) learning, decision trees, support vector machines (SVMs), and other supervised learning techniques”.
* US 20210158197 A1 ¶ [0076] last sentence: “Using a common notation in ensemble modeling, the following levels of data and learners (see Fig.2) can be defined”.
* US 20210097446 A1 ¶ [0003] “Distributed classification is a type of machine learning method, known more generally as an ensemble method, in which the task of classification is performed jointly by multiple base classifiers whose output is aggregated by an aggregator to make a final classification decision”
* US 20210074425 A1 mid- ¶ [0033]: “It will be appreciated that many known methods of ensemble learning may be used in embodiments in which multiple alternative models are trained to make similar classification predictions using different supervised and/or unsupervised machine learning techniques”
* US 20210027163 A1 ¶ [0324] 2nd sentence noting “a conventional ensemble in the form of an extra cost function imposed by a learning coach”.
* US 20200410090 A1 ¶ [0117] 3rd sentence: “Any of many well-known methods for building and training ensembles of machine learning systems may be used in various embodiments of the invention to generate the base ensemble…”
* US 20200401951 A1 ¶ [0024] 5th sentence: “random forest algorithm (also known as “random decision forests”) is an ensemble learning method for classification, regression and other tasks that operates by constructing a multitude of decision trees at training time and outputting the class that is the mode of the classes (classification) or mean prediction (regression) of the individual trees”.
* US 20200387602 A1 ¶ [0021] 3rd sentence reciting “conventional approaches by providing a system for using an ensemble of machine learning models”
* US 20200349416 A1 ¶ [0003] 1st sentence: “Ensemble learning is a machine learning method in which a series of individual learners (or known as submodels) are used, and then the learning results are integrated to obtain a better learning effect than that of a single learner”
* US 20200327455 A1 ¶ [0031] last sentence: well-known techniques can be used to make the ensemble members learn to be different and to enhance the overall performance of the ensemble.
* US 20200250584 A1 ¶ [0052] last sentence: “ensemble learning are well-known to those skilled in the art and are therefore not described herein.
* US 20200210894 A1 ¶ [0089] “The creation method for the prediction model by formula (3) is one example, and the prediction model may be derived by a generally known method such as regularization, decision tree, ensemble learning, neural networks, and Bayesian networks”.
* US 20200210887 A1 ¶ [0040] last two sentences: “ensemble algorithms, and any other suitable machine-learning algorithms known to persons of ordinary skill in the art”
* US 20200143528 A1 ¶ [0051] 1st sentence: “It is further noted herein that the machine learning classifier generated in step 202 may include any type of machine learning algorithm/classifier and/or deep learning technique or classifier known in the art including, but not limited to, a random forest classifier, a support vector machine (SVM) classifier, an ensemble learning classifier, an artificial neural network (ANN), and the like”
* US 20200034668 A1 ¶ [0036]: “Random forest is an ensemble learning method for classification, regression, clustering, etc., which operates using many decision trees when trained, then outputting a classification that is the mode (most common) of the classes for classification, and a mean (average) prediction for regression. In effect, it is similar to a bootstrapping algorithm with a decision tree (such as the Classification and Regression Tree (CART) model)”.
* US 20190279112 A1 ¶ [0003] “Developers of artificial intelligence (AI) systems are constantly working to identify improvements in the performance (e.g., accuracy) of supervised machine learning (ML) techniques. For example, one of the most common approaches involves combining machine learning algorithms in a technique called ensemble learning.
* US 20190065859 A1 ¶ [0082] 2nd sentence: “various well-known techniques such as BrownBoost can be utilized for the branch function and learning of posterior probability by the decision tree execution unit 63, and ensemble learning by the probability integration unit 64”.
* US 20170091627 A1 ¶ [0072] 3rd sentence: “In alternate examples, other known ensemble machine learning techniques or data mining algorithms may be used as the plurality of models”.
* US 20150356461 A1 ¶ [0035] 2nd sentence: “Each of the machine learning models in the ensemble machine learning model 302 has been trained separately to generate scores that represent probabilities, e.g., using conventional machine learning training techniques”.
* US 20150100524 A1 mid-¶ [0025], mid-¶ [0032]: “In another embodiment of the smart selection technique the machine learning method that is used to train the ensemble model is a conventional gradient-boosted decision trees method. In yet another embodiment of the smart selection technique the machine learning method that is used to train the ensemble model is a conventional support vector machine method”.
* US 20140337269 A1 ¶ [0003] 2nd sentence: “Learning ensembles of decision trees is a popular method of machine learning, and one such implementation, known as Random Forests, are a combination of several decision trees”
* US 20120016816 A1 ¶ [0120] 2nd sentence: “An ensemble learning is known as one machine learning technique”.
* US 20090125463 A1 ¶ [0063] last sentence: method known as the so-called ensemble learning (see, for example, the following URL "http://www.hulinks.co.jp/software/randomforests/" for data mining package software using an ensemble learning technique)
Per MPEP 2106.05(d)(I)(2) Examiner also points to the following publications showing the continuous validation of the predictive algorithm, as being well-understood,
* US 11341367 B1 column 9 lines 56-60 As is known in the art, when training a model, a training dataset may be split into a first subset for training and a second subset for validation—e.g., 80% for training, and 20% for validation
* US 20180285691 A1 ¶ [0039] 2nd - 3rd sentences: future data instances have unknown target values that cannot be used for checking accuracy of predictions of the model, some of the data from known data 405 for which we already know the answer of the prediction target for each row is used to evaluate the accuracy of the model (and underlying algorithm). For example, 70-80% of known data 405 may be used for training and 20-30% may be used for validation and testing.
* US 20180336369 A1 ¶ [0033] last sentence: If the conventional validation technique is used to partition the datasets, the training and testing datasets may be of unequal size, for example, 80% of the removed, hidden or masked dataset may be partitioned into the training dataset and 20% of the removed, hidden or masked dataset may be partitioned into the testing dataset, or 70% of the removed, hidden or masked dataset may be partitioned into the training dataset and 30% of the removed, hidden or masked dataset may be partitioned into the testing dataset.
Also assuming arguendo, without conceding that considerations for “a minimum portion of the defined case management data associated with the case attributes” as amended at each of independent Claims 1,8,15 would be required at step 2B of the analysis, the Examiner would rely on MPEP 2106.05(d)(I)(2) Examiner and point to the following publications showing such concept not only abstract hut also well-understood, routine or conventional
* US 11341367 B1 column 9 lines 56-60 As is known in the art, when training a model, a training dataset may be split into a first subset for training and a second subset for validation—e.g., 80% for training, and 20% for validation. [bolded emphasis added]
* US 20180285691 A1 ¶ [0039] 2nd-3rd sentences: future data instances have unknown target values that cannot be used for checking accuracy of predictions of the model, some of the data from known data 405 for which we already know the answer of the prediction target for each row is used to evaluate the accuracy of the model (and underlying algorithm). For example, 70-80% of known data 405 may be used for training and 20-30% may be used for validation and testing.
* US 20180336369 A1 ¶ [0033] last sentence: If the conventional validation technique is used to partition the datasets, the training and testing datasets may be of unequal size, for example, 80% of the removed, hidden or masked dataset may be partitioned into the training dataset and 20% of the removed, hidden or masked dataset may be partitioned into the testing dataset, or 70% of the removed, hidden or masked dataset may be partitioned into the training dataset and 30% of the removed, hidden or masked dataset may be partitioned into the testing dataset.
In conclusion, Claims 1,3-6,8,10-13,15,17-19 although directed to statutory categories (system or machine, product or article, and method or process) they still recite or set forth the abstract idea (Step 2A prong one), with no additional, computer-based elements capable of not integrating the abstract idea into a practical application (Step 2A prong two) or providing significantly more than the abstract idea (Step 2B). Thus, the claims are believed to be ineligible.
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Reasons for allowance
- Reasons for Overcoming the Prior Art -
Examiner provides the following rationales for overcoming the prior art as stated in the parent Application. Specifically, the Examiner maintains that the closest prior art remains:
* Asgari et al, US 20060149608 A1 hereinafter Asgari, in view of
* Richter et al, US 20100100418 A1 hereinafter Richter, and in further view of
* Crivat et al, US 20080189237 A1 hereinafter Crivat.
Such references teach some elements of predictive analysis in case management.
Yet, each and all of the above prior art, does not teach either alone or together with adequate rationales, the technological details of: “case management server”, “application protocol layer”, “network subsystem”, “remote computer devices”, “network subsystem”, “outgoing data handler” along with the capabilities of the “self-learning predictive analysis engine” to:
i. “analyze a first portion of the configuration data containing a plurality of the case
attributes and a corresponding plurality of the performance criteria and
associated outcomes in a first portion of the configuration data and generate a
set of projected outcomes corresponding to the performance criteria”,
ii. “generate correlation data that establishes, for each of the case attributes, a relationship
with a corresponding one of the projected outcomes based on at least one of the performance criteria corresponding to respective ones of the case attributes and the projected outcomes, wherein generation of the correlation data comprises: analyzing a minimum portion of the defined case management data associated with the case attributes; detecting a first pattern in a first portion of the minimum portion of case management data, the first pattern being detected between each of the case attributes and the corresponding one of the projected outcomes; and validating the correlation data using the pattern thus detected; and store the correlation data in the data store the validating comprising: detecting a second pattern that is the same as the first pattern, wherein the second pattern is detected in another portion of the minimum portion of the case management data”;
iii. “receive case data including one or more attributes for a corresponding specific case
from the first remote computer device”,
iv. “identify values of the one or more attributes in the received case data”,
v. “generate, from the stored correlation data, a first prediction model using a decision tree predictive algorithm … and a second prediction model using a Naïve Bayes algorithm which is different than the first prediction algorithm”,
vi. “test the first prediction model that is based on the decision tree predictive algorithm and the second prediction model that is based on the Naïve Bayes prediction algorithm against the actual outcome to select one prediction model that best represents the defined case management data…, and”
vii. “identify, using the selected prediction model, for the corresponding specific case, ones of the projected outcomes in the stored correlation data that are associated with the identified case attribute values in the received case data”;
viii. “build a message based on the identified projected outcomes for the corresponding specific case, and provide the message to the application protocol layer; wherein the application protocol layer is configured to format the received message for delivery to the first remote computer device”; as explicitly recited in independent Claim 1 and similarly recited in each of sister independent Claims 8, 15.
Claims 3-6 are dependent and overcome the prior art by dependency to parent Claim 1.
Claims 10-13 are dependent and overcome the prior art by dependency to parent Claim 8.
Claims 17-19 are dependent and overcome the prior art by dependency to parent Claim 15.
To be clear, novelty (35 USC 102) and non-obviousness (35 USC 103) still pertain to features that are mostly abstract that do not render the claims patent eligible (35 USC 101). Simply said the novel and non-obviousness rationale above do not necessarily render the claims patent eligible. See for example MPEP 2106.04 I ¶5, 3rd sentence citing Mayo, 566 U.S. 71, 101 USPQ2d at 1965); Flook, 437 U.S. at 591-92, 198 USPQ2d at 198 "the novelty of the mathematical algorithm is not a determining factor at all”.
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Conclusion
The following art is made of record and considered pertinent to Applicant's disclosure:
* EP 1717735 A2 entitled Multi-objective Asset Optimization And Decision-making Method For Industrial Application, Involves Generating Pareto Frontier Including Optimal Input-output Vectors Using Multi-objective Optimization Algorithm, teaching as novelty the generating of Pareto Frontier including optimal input-output vectors using multi-objective optimization algorithm based on search constraints including upper and lower bounds for each input variable and tolerance levels representing range of values. Two predictive models are generated for an asset, by classifying the operational historical data based on the optimal input and output vectors and constraints.
* Huang et al, Comparing Naive Bayes, Decision Trees and SVM with AUC and Accuracy, IEEE International Conference on data mining, 2003
* US 20080120148 A1 reciting at ¶ [0334] 2nd sentence: “the user may configure the global objective S to represent a linear combination of the mean net present value for each asset A(k)”.
* US 20100131453 A1 teaching Hypothesis Selection And Presentation Of One Or More Advisories
* US 20120078825 A1 teaching Search result ranking using machine learning
* US 20130024408 A1 teaching Action execution based on user modified [akin here configured] hypothesis
* US 20060271210 A1 reciting at ¶ [0040] “Once these elements have been configured by the user, the process manager 120 identifies a corresponding Pareto Frontier at step 804 by applying a multi-objective optimization algorithm 106 to the predictive models 104”.
* US 7603326 B2 reciting at column 11 lines 56-59: “In a parallel embodiment configuration, for example, a user(s) can collectively evaluate the parallel populations and select one of the given parallel embodiments, while modifying objectives”. And at column 18 lines 43-49: “In this configuration of the system, an evolutionary algorithm using user-specified mutation and crossover operators will use the user-defined objective function as the fitness function and will execute automatically, without human intervention for a user-specified number of generations or until a stopping criterion is met (e.g see FIG. 2)”.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to OCTAVIAN ROTARU whose telephone number is (571)270-7950. The examiner can normally be reached on 571.270.7950 from 9AM to 6PM. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, PATRICIA H MUNSON, can be reached at telephone number (571)270-5396. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of an application may be obtained from Patent Center. Status information for published applications may be obtained from Patent Center. Status information for unpublished applications is available through Patent Center for authorized users only. Should you have questions about access to Patent Center, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) Form at https://www.uspto.gov/patents/uspto-automated- interview-request-air-form.
/Octavian Rotaru/
Primary Examiner, Art Unit 3624 A
April 18th, 2026
1 MPEP 2106.04(a): “examiners should identify at least one abstract idea grouping, but preferably identify all groupings to the extent possible”.
2 Digitech Image Techs., LLC v. Electronics for Imaging, Inc., 758 F.3d 1344, 1350, 111 USPQ2d 1717, 1721 (Fed. Cir. 2014)
3 Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 573 U.S. 208, 223, 110 USPQ2d 1976, 1983 (2014);
Gottschalk v. Benson, 409 U.S. 63, 64, 175 USPQ 673, 674 (1972);
Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015);
4 Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone);
TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016)
Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015).
5 FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016)”
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