Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This action is in response to the amendment and remarks filed July 1st, 2025. In the amendment, claims 1, 9, and 15 were amended, claims 5, 13, and 19 were cancelled, and claims 21- 23 were added. As such, claims 1-4, 6-12, 14-18 and 20-23 are presented for examination.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on April 14th, 2026, is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Response to Arguments
Applicant’s argument, see Page 11, filed June 23rd, 2026, with respect to the objected claims 9-14 has been fully considered and is persuasive. The objected claims 9-14 have been withdrawn.
Applicant’s arguments with respect to the rejection under 35 U.S.C. 101 are not persuasive for the following reasons:
35 U.S.C 101:
Applicant argues that Recentive Analytics, Inc. v. Fox Corp. is distinguishable because the claims reflect improvements to a particular type of machine learning model rather than the generic application of machine learning found ineligible in Recentive (Pages 11-14 of Remarks). The Examiner respectfully disagrees. The claims do not recite an improvement to the Bayesian model itself but instead recite generic training and updating operations that carry out the abstract ideas of determining a correlation, determining predictive scores, comparing the scores, and removing a predictor variable. The focus of the claims is generating an access permission key and controlling access based on the risk indicator generated from the model. As Recentive holds, applying a generic machine learning model to reach a result is not a technological improvement and naming the model Bayesian does not change that the claims apply the model to an abstract idea rather than improve it. The asserted benefit of fewer computation operations in less time is a result recited in paragraph 25 of the specification without any technical explanation of how the update achieves it. Claim 1 only recites that the parameters are updated in a manner that avoids regenerating the model and does not recite the mechanism by which regeneration is avoided. An asserted efficiency gain that is claimed as an outcome rather than as a specific technique does not reflect the disclosed improvement in the claim. Therefore, the claims remain directed to applying the exception rather than improving machine learning technology.
Applicant argues that the claims are directed to improvements in machine learning model performance and are therefore eligible under Enfish and Ex Parte Desjardins, relying on examples xiii and xiv added to MPEP § 2106.05(a) in view of Desjardins (Pages 15-17 of Remarks). The Examiner respectfully disagrees. Enfish and Desjardins require an improvement to the computer or to the technology itself, such as the self-referential data structure in Enfish. However, the claims are directed to using the Bayesian prediction model to determine whether access should be granted, not to improving the Bayesian model itself. This is different from Examples xiii and xiv, which are directed to improving the model or the computer system such as an improved way of training a model or improving system performance by adjusting model parameters, whereas here the model is simply used to produce a risk indicator and then an access permission key. Because the claim applies the model to reach an access decision rather than improving the model or improving how the computer functions, the Bayesian model is tied to the abstract idea and not to an improvement of the model or technology. Thus, the claims do not integrate the exception into a practical application.
Applicant argues that, like PEG Example 39, the claims cannot practically be performed in the human mind and therefore do not recite a mental process (Pages 17-19 of Remarks). The Examiner respectfully disagrees. The eligible claim in Example 39 recited operations such as applying transformations of mirroring, rotating, smoothing, and contrast reduction to digital facial images, which cannot be performed mentally. However, determining a correlation between two variables, determining a predictive score, comparing scores, and removing a variable are evaluations a person can perform with pen and paper. A claim does not leave the mental process grouping simply because it recites that the steps are carried out by a Bayesian model on generic processing devices.
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 1-4, 6-12, and 14-18, and 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1
Step 1: The claim recites a method; therefore, it is directed to the statutory category of a process.
Step2A Prong 1: The claim recites, inter alia:
determining… a risk indicator for the target entity from predictor variables associated with the target entity: This limitation recites a mental process because it involves evaluating information about a target entity and determining a risk based on the predictor variables.
determining a correlation between a first predictor variable and a second predictor variable in the plurality of predictor variables: This limitation is a limitation is a mental process because it involves comparing two variables based on their similarity.
determining a first predictive score for the first predictor variable and a second predictive score for the second predictor variable: This limitation recites a mental process because it involves the determination scores for the first and second predictor variables, which involves mental judgement to determine a score for a variable.
generating a refined training dataset by removing the first predictor variable from the plurality of predictor variables based at least in part on the correlation being greater than a threshold value of correlation, and further based at least in part on a comparison between the first predictive score and the second predictive score: This limitation is a mental process because it involves selecting or removing information from a dataset based on a comparison and threshold evaluation.
and generating an access permission key based on a value of the risk indicator: This limitation recites a mental process because it involves generating a value based on a previously determined risk indicator.
Step2A Prong 2: This judicial exception is not integrated into a practical application because the
additional elements are as follows:
receiving, from a user computing system, a risk assessment query for a target entity: Mere data gathering recited at a high level of generality, and thus are insignificant extra-solution activity (MPEP 2106.05(g)).
one or more processing devices performing operations comprising… using a Bayesian prediction model… wherein the Bayesian prediction model determines the risk indicator based on a set of parameters associated with the Bayesian prediction model that are calculated based on an initial training dataset and an additional training dataset, and wherein the Bayesian prediction model is configured by performing operations comprising: Adding the words “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 (MPEP 2106.05(f)).
receiving the initial training dataset for the Bayesian prediction model, the initial training dataset comprising a plurality of training records and a plurality of predictor variables: Mere data gathering recited at a high level of generality, and thus are insignificant extra-solution activity (MPEP 2106.05(g)).
generating the Bayesian prediction model by at least calculating the set of parameters based on the refined training dataset: Adding the words “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 (MPEP 2106.05(f)).
receiving the additional training dataset for the Bayesian prediction model, the additional training dataset containing an additional predictor variable or an additional training record: Mere data gathering recited at a high level of generality, and thus are insignificant extra-solution activity (MPEP 2106.05(g)).
dynamically updating the Bayesian prediction model by updating the set of parameters based on the set of parameters and the additional training dataset, wherein the set of parameters are updated based on the additional training dataset in a manner that avoids re-generating the Bayesian prediction model: Adding the words “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 (MPEP 2106.05(f)).
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly
more than the judicial exception because the additional elements are as follows:
receiving, from a user computing system, a risk assessment query for a target entity: The additional element of “receiving” does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of receiving steps amounts to no more than mere data gathering. This element amounts to receiving data over a network and are well-understood, routine, conventional activity. See MPEP 2106.05(d), subsection II (i). This cannot provide an inventive concept.
one or more processing devices performing operations comprising… using a Bayesian prediction model… wherein the Bayesian prediction model determines the risk indicator based on a set of parameters associated with the Bayesian prediction model that are calculated based on an initial training dataset and an additional training dataset, and wherein the Bayesian prediction model is configured by performing operations comprising: Adding the words “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 and cannot provide inventive concept (MPEP 2106.05(f)).
receiving the initial training dataset for the Bayesian prediction model, the initial training dataset comprising a plurality of training records and a plurality of predictor variables: The additional element of “receiving” does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of receiving steps amounts to no more than mere data gathering. This element amounts to receiving data over a network and are well-understood, routine, conventional activity. See MPEP 2106.05(d), subsection II (i). This cannot provide an inventive concept.
generating the Bayesian prediction model by at least calculating the set of parameters based on the refined training dataset: Adding the words “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 and cannot provide inventive concept (MPEP 2106.05(f)).
receiving the additional training dataset for the Bayesian prediction model, the additional training dataset containing an additional predictor variable or an additional training record: The additional element of “receiving” does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of receiving steps amounts to no more than mere data gathering. This element amounts to receiving data over a network and are well-understood, routine, conventional activity. See MPEP 2106.05(d), subsection II (i). This cannot provide an inventive concept.
dynamically updating the Bayesian prediction model by updating the set of parameters based on the set of parameters and the additional training dataset, wherein the set of parameters are updated based on the additional training dataset in a manner that avoids re-generating the Bayesian prediction model: Adding the words “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 and cannot provide inventive concept (MPEP 2106.05(f)).
The elements in combination as an ordered whole still do not amount to significantly more than the judicial exception (i.e., the abstract ideas of mental processes of evaluating predictor variables and determining a risk indicator). The claim merely describes a process of applying mental processes (determining correlations between predictor variables, determining predictive scores, comparing values, and removing variables from a dataset based on threshold and score comparisons) to analyze data and generate a risk indicator. The remaining steps recite conventional data processing operations such as receiving datasets, generating and updating a Bayesian prediction model based on calculated parameters, and transmitting a message. These elements merely implement the abstract idea on generic computer components and represent routine data gathering, analysis, and output operations without improving the functioning of a computer or technological field.
Therefore, the claim as a whole remains focused on the abstract idea and fails Step 2B of the eligibility analysis.
Claim 2
Step 1: A process, as above.
Step2A Prong 1: The claim recites, inter alia:
the set of parameters comprise a set of probabilities; the set of probabilities comprises a likelihood probability for a predictor variable of the plurality of predictor variables indicating a conditional probability of the predictor variable conditioned on a value of the risk indicator and a prior probability indicating a probability of the risk indicator taking the value: This limitation recites a mathematical concept because it involves using a statistical equations for generating the probabilities.
and determining the risk indicator based on the set of parameters comprises calculating a posterior probability from the set of probabilities: This limitation recites a mental process because it involves the determination of a risk based on the calculated posterior probability.
Step 2A Prong Two and Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under step 2B. Thus, the judicial exception is not integrated into a practical application (see MPEP 2106.04(d) I.), failing step 2A prong 2. The claim is ineligible.
Even when considered in combination, these additional elements represent mere instructions to apply
an exception and therefore do not provide an inventive concept. The claim is ineligible.
Claim 3
Step 1: A process, as above.
Step2A Prong 1: The claim recites, inter alia:
the additional training dataset comprises the additional predictor variable for each of the plurality of training records, and wherein updating the set of parameters comprises generating additional probabilities by calculating a likelihood probability for the additional predictor variable and generating an additional prior probability by taking a value of the posterior probability: This limitation recites a mathematical concept because it involves updating a set of parameters of a model based on the calculated probability.
Step 2A Prong Two and Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under step 2B. Thus, the judicial exception is not integrated into a practical application (see MPEP 2106.04(d) I.), failing step 2A prong 2. The claim is ineligible.
Even when considered in combination, these additional elements represent mere instructions to apply
an exception and therefore do not provide an inventive concept. The claim is ineligible.
Claim 4
Step 1: A process, as above.
Step2A Prong 1: The claim recites, inter alia:
the additional training dataset comprises the additional training record, and wherein updating the set of parameters comprises updating the prior probability using the prior probability and a number of training records in the additional training dataset having the value for the risk indicator: This limitation is a mathematical concept because it involves updating parameters of a model and updating the previously calculated probability.
Step 2A Prong Two and Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under step 2B. Thus, the judicial exception is not integrated into a practical application (see MPEP 2106.04(d) I.), failing step 2A prong 2. The claim is ineligible.
Claim 6
Step 1: A process, as above.
Step2A Prong 1: The claim recites, inter alia:
the correlation is a Spearman correlation and the first predictive score and the second predictive score are each a Kolmogorov-Smirnov (KS) score: This limitation recites a mathematical concept dealing with calculating correlations between scores.
Step 2A Prong Two and Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under step 2B. Thus, the judicial exception is not integrated into a practical application (see MPEP 2106.04(d) I.), failing step 2A prong 2. The claim is ineligible.
Even when considered in combination, these additional elements represent mere instructions to apply
an exception and therefore do not provide an inventive concept. The claim is ineligible.
Claim 7
Step 1: A process, as above.
Step2A Prong 1: The claim recites, inter alia:
prior to generating the Bayesian prediction model: dividing values of a predictor variable in the initial training dataset into a first set of bins: This limitation is a mental process because it involves dividing data into bins.
and generating a second set of bins by merging two or more bins in the first set of bins into one bin, wherein representative values of the predictor variable in the second set of bins are monotonic with respect to the risk indicator: This limitation recites a mental process because it involves merging two subsets of data (bins) together, which can be performed in the human mind.
Step 2A Prong Two and Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under step 2B. Thus, the judicial exception is not integrated into a practical application (see MPEP 2106.04(d) I.), failing step 2A prong 2. The claim is ineligible.
Even when considered in combination, these additional elements represent mere instructions to apply
an exception and therefore do not provide an inventive concept. The claim is ineligible.
Claim 8
Step 1: A process, as above.
Step2A Prong 1: This claim does not recite any abstract ideas but depends on claim 1 which does.
Step2A Prong 2: This judicial exception is not integrated into a practical application because the
additional elements are as follows:
the risk indicator comprises at least one of: a risk classification for the target entity; or a probability of the target entity being classified in the risk classification: The limitation amounts to merely indicating a field of use or technological environment in which to apply a judicial exception. This does not amount to significantly more than the exception itself (MPEP 2106.05(h)).
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly
more than the judicial exception because the additional elements are as follows:
the risk indicator comprises at least one of: a risk classification for the target entity; or a probability of the target entity being classified in the risk classification: The limitation amounts to merely indicating a field of use or technological environment in which to apply a judicial exception. This does not amount to significantly more than the exception itself which provide inventive concept (MPEP 2106.05(h)).
Even when considered in combination, these additional elements represent mere instructions to apply
an exception and therefore do not provide an inventive concept. The claim is ineligible.
Claim 9
Step 1: The claim recites a system; therefore, it is directed to the statutory category of a machine.
Step2A Prong 1: The claim recites, inter alia:
determining… a risk indicator for the target entity from predictor variables associated with the target entity: This limitation recites a mental process because it involves evaluating information about a target entity and determining a risk based on the predictor variables.
determining a correlation between a first predictor variable and a second predictor variable in the plurality of predictor variables: This limitation is a limitation is a mental process because it involves comparing two variables based on their similarity.
determining a first predictive score for the first predictor variable and a second predictive score for the second predictor variable: This limitation recites a mental process because it involves the determination scores for the first and second predictor variables, which involves mental judgement to determine a score for a variable.
generating a refined training dataset by removing the first predictor variable from the plurality of predictor variables based at least in part on the correlation being greater than a threshold value of correlation, and further based at least in part on a comparison between the first predictive score and the second predictive score: This limitation is a mental process because it involves selecting or removing information from a dataset based on a comparison and threshold evaluation.
and generating an access permission key based on a value of the risk indicator: This limitation recites a mental process because it involves generating a value based on a previously determined risk indicator.
Step2A Prong 2: This judicial exception is not integrated into a practical application because the
additional elements are as follows:
[a] system comprising: a processing device; and a memory device in which instructions executable by the processing device are stored for causing the processing device to… using a Bayesian prediction model… wherein the Bayesian prediction model determines the risk indicator based on a set of parameters associated with the Bayesian prediction model that are calculated based on an initial training dataset and an additional training dataset, and wherein the Bayesian prediction model is configured by performing operations comprising: Adding the words “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 (MPEP 2106.05(f)).
receive, from a user computing system, a risk assessment query for a target entity: Mere data gathering recited at a high level of generality, and thus are insignificant extra-solution activity (MPEP 2106.05(g)).
receiving the initial training dataset for the Bayesian prediction model, the initial training dataset comprising a plurality of training records and a plurality of predictor variables: Mere data gathering recited at a high level of generality, and thus are insignificant extra-solution activity (MPEP 2106.05(g)).
generating the Bayesian prediction model by at least calculating the set of parameters based on the refined training dataset: Adding the words “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 (MPEP 2106.05(f)).
receiving the additional training dataset for the Bayesian prediction model, the additional training dataset containing an additional predictor variable or an additional training record: Mere data gathering recited at a high level of generality, and thus are insignificant extra-solution activity (MPEP 2106.05(g)).
dynamically updating the Bayesian prediction model by updating the set of parameters based on the set of parameters and the additional training dataset, wherein the set of parameters are updated based on the additional training dataset in a manner that avoids re-generating the Bayesian prediction model: Adding the words “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 (MPEP 2106.05(f)).
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly
more than the judicial exception because the additional elements are as follows:
[a] system comprising: a processing device; and a memory device in which instructions executable by the processing device are stored for causing the processing device to: Adding the words “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 and cannot provide inventive concept (MPEP 2106.05(f)).
receive, from a user computing system, a risk assessment query for a target entity: The additional element of “receiving” does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of receiving steps amounts to no more than mere data gathering. This element amounts to receiving data over a network and are well-understood, routine, conventional activity. See MPEP 2106.05(d), subsection II (i). This cannot provide an inventive concept.
receiving the initial training dataset for the Bayesian prediction model, the initial training dataset comprising a plurality of training records and a plurality of predictor variables: The additional element of “receiving” does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of receiving steps amounts to no more than mere data gathering. This element amounts to receiving data over a network and are well-understood, routine, conventional activity. See MPEP 2106.05(d), subsection II (i). This cannot provide an inventive concept.
generating the Bayesian prediction model by at least calculating the set of parameters based on the refined training dataset: Adding the words “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 and cannot provide inventive concept (MPEP 2106.05(f)).
receiving the additional training dataset for the Bayesian prediction model, the additional training dataset containing an additional predictor variable or an additional training record: The additional element of “receiving” does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of receiving steps amounts to no more than mere data gathering. This element amounts to receiving data over a network and are well-understood, routine, conventional activity. See MPEP 2106.05(d), subsection II (i). This cannot provide an inventive concept.
dynamically updating the Bayesian prediction model by updating the set of parameters based on the set of parameters and the additional training dataset, wherein the set of parameters are updated based on the additional training dataset in a manner that avoids re-generating the Bayesian prediction model: Adding the words “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 and cannot provide inventive concept (MPEP 2106.05(f)).
Claim 10 recites similar limitations to claim 2. Therefore, claim 10 is rejected using the same rationale as claim 2.
Claim 11 recites similar limitations to claim 3. Therefore, claim 11 is rejected using the same rationale as claim 3.
Claim 12 recites similar limitations to claim 4. Therefore, claim 12 is rejected using the same rationale as claim 4.
Claim 14 recites similar limitations to claim 7. Therefore, claim 14 is rejected using the same rationale as claim 7.
Claim 15
Step 1: The claim recites a non-transitory computer medium; therefore, it is directed to the statutory
category of an article of manufacture.
Step2A Prong 1: The claim recites, inter alia:
determining… a risk indicator for the target entity from predictor variables associated with the target entity: This limitation recites a mental process because it involves evaluating information about a target entity and determining a risk based on the predictor variables.
determining a correlation between a first predictor variable and a second predictor variable in the plurality of predictor variables: This limitation is a limitation is a mental process because it involves comparing two variables based on their similarity.
determining a first predictive score for the first predictor variable and a second predictive score for the second predictor variable: This limitation recites a mental process because it involves the determination scores for the first and second predictor variables, which involves mental judgement to determine a score for a variable.
generating a refined training dataset by removing the first predictor variable from the plurality of predictor variables based at least in part on the correlation being greater than a threshold value of correlation, and further based at least in part on a comparison between the first predictive score and the second predictive score: This limitation is a mental process because it involves selecting or removing information from a dataset based on a comparison and threshold evaluation.
and generating an access permission key based on a value of the risk indicator: This limitation recites a mental process because it involves generating a value based on a previously determined risk indicator.
Step2A Prong 2: This judicial exception is not integrated into a practical application because the
additional elements are as follows:
[a] non-transitory computer-readable storage medium having program code that is executable by a processor device to cause a computing device to… using a Bayesian prediction model… wherein the Bayesian prediction model determines the risk indicator based on a set of parameters associated with the Bayesian prediction model that are calculated based on an initial training dataset and an additional training dataset, and wherein the Bayesian prediction model is configured by performing operations comprising: Adding the words “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 (MPEP 2106.05(f)).
receiving, from a user computing system, a risk assessment query for a target entity: Mere data gathering recited at a high level of generality, and thus are insignificant extra-solution activity (MPEP 2106.05(g)).
receiving the initial training dataset for the Bayesian prediction model, the initial training dataset comprising a plurality of training records and a plurality of predictor variables: Mere data gathering recited at a high level of generality, and thus are insignificant extra-solution activity (MPEP 2106.05(g)).
generating the Bayesian prediction model by at least calculating the set of parameters based on the refined training dataset: Adding the words “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 (MPEP 2106.05(f)).
receiving the additional training dataset for the Bayesian prediction model, the additional training dataset containing an additional predictor variable or an additional training record: Mere data gathering recited at a high level of generality, and thus are insignificant extra-solution activity (MPEP 2106.05(g)).
dynamically updating the Bayesian prediction model by updating the set of parameters based on the set of parameters and the additional training dataset, wherein the set of parameters are updated based on the additional training dataset in a manner that avoids re-generating the Bayesian prediction model: Adding the words “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 (MPEP 2106.05(f)).
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly
more than the judicial exception because the additional elements are as follows:
[a] non-transitory computer-readable storage medium having program code that is executable by a processor device to cause a computing device to… using a Bayesian prediction model… wherein the Bayesian prediction model determines the risk indicator based on a set of parameters associated with the Bayesian prediction model that are calculated based on an initial training dataset and an additional training dataset, and wherein the Bayesian prediction model is configured by performing operations comprising: Adding the words “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 and cannot provide inventive concept (MPEP 2106.05(f)).
receive, from a user computing system, a risk assessment query for a target entity: The additional element of “receiving” does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of receiving steps amounts to no more than mere data gathering. This element amounts to receiving data over a network and are well-understood, routine, conventional activity. See MPEP 2106.05(d), subsection II (i). This cannot provide an inventive concept.
receiving the initial training dataset for the Bayesian prediction model, the initial training dataset comprising a plurality of training records and a plurality of predictor variables: The additional element of “receiving” does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of receiving steps amounts to no more than mere data gathering. This element amounts to receiving data over a network and are well-understood, routine, conventional activity. See MPEP 2106.05(d), subsection II (i). This cannot provide an inventive concept.
generating the Bayesian prediction model by at least calculating the set of parameters based on the refined training dataset: Adding the words “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 and cannot provide inventive concept (MPEP 2106.05(f)).
receiving the additional training dataset for the Bayesian prediction model, the additional training dataset containing an additional predictor variable or an additional training record: The additional element of “receiving” does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of receiving steps amounts to no more than mere data gathering. This element amounts to receiving data over a network and are well-understood, routine, conventional activity. See MPEP 2106.05(d), subsection II (i). This cannot provide an inventive concept.
dynamically updating the Bayesian prediction model by updating the set of parameters based on the set of parameters and the additional training dataset, wherein the set of parameters are updated based on the additional training dataset in a manner that avoids re-generating the Bayesian prediction model: Adding the words “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 and cannot provide inventive concept (MPEP 2106.05(f)).
Even when considered in combination, these additional elements represent mere instructions to apply
an exception and therefore do not provide an inventive concept. The claim is ineligible.
Claim 16 recites similar limitations to claim 2. Therefore, claim 16 is rejected using the same rationale as claim 2.
Claim 17 recites similar limitations to claim 3. Therefore, claim 17 is rejected using the same rationale as claim 3.
Claim 18 recites similar limitations to claim 4. Therefore, claim 18 is rejected using the same rationale as claim 4.
Claim 20 recites similar limitations to claim 6. Therefore, claim 20 is rejected using the same rationale as claim 6.
Conclusion
Claims 21-23 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Claims 1-4, 6-12, and 14-18, and 20-23 overcome the prior art but are still rejected under 35 U.S.C. 101. The prior art of record fails to teach or suggest a feature selection technique that removes one of a plurality of predictor variables based on a correlation between the predictor variables and a comparison of predictive scores of the variables, and the correlation being greater than a threshold value based on the comparison between the scores. The combination of correlation and predictive evaluation for selecting between correlated variables and their respective threshold value is not taught by the prior art.
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 MAJD MAHER HADDAD whose telephone number is (571)272-2265. The examiner can normally be reached Mon-Friday 8-5 pm.
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) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kamran Afshar, can be reached at (571) 272-7796. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/M.M.H./Examiner, Art Unit 2125
/KAMRAN AFSHAR/Supervisory Patent Examiner, Art Unit 2125
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