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 .
Notice to Applicant
Claims 1-5, 8-9, 11-15 and 18-19 were pending.
Claims 6-7, 10, 16-17 and 20 were cancelled.
Claims 1 and 11 are presently amended.
Claims 1-5, 8-9, 11-15 and 18-19 are rejected.
Response to Amendment
Applicant’s amendments are acknowledged.
Response to Arguments
Applicant' s arguments filed 5/21/2025 have been fully considered in view of further consideration of statutory law, Office policy, precedential common law, and the cited prior art as necessitated by the amendments to the claims, but are not persuasive for the reasons set forth below.
35 USC § 101 Rejections
First, Applicant argues that, “Claim 1, as amended, cannot be reasonably characterized as reciting a method of organizing human activity, particularly under the “managing personal behavior” category of the judicial exceptions. The claim is directed to a specific and technical process for generating a significance level using a trained neural network. This process involves a series of detailed computational steps that are inherently performed by a computer and cannot be carried out mentally or manually by a human. The computing device receives training data that associates event types of constitutional events with corresponding significance levels. It then trains a significance model using that data. The model includes an input layer of nodes for event types, one or more intermediate layers, and an output layer of nodes for significance levels… There is no aspect of the claim that involves directing human behavior, establishing personal relationships, or organizing interpersonal or commercial interactions. The process described does not resemble any human practice or long-standing societal activity. Instead, the claim describes how a machine learns from structured data to generate a significance level based on constitutional event types. This is a technological solution to a computational problem, not an abstract idea concerning how people interact or make decisions. Therefore, the claim falls outside the scope of the “organizing human activity” exception…” [Arguments, pages 10-12].
In response, Applicant’s arguments are considered but are not persuasive. Examiner respectfully disagrees and maintains that the present invention recites a judicial exception without significantly more. In particular, Examiner maintains that the amended claims recite certain methods of organizing human activity. Although the claims do involve a series of detailed computational steps, Examiner observes that the claims, when considered as a whole, describe steps for determining potential effects of constitutional events for human subjects, which is considered to amount to steps for managing personal behavior. Thus, Examiner respectfully maintains that the present claims recite certain methods for organizing human activity.
With regard to the assertion that “This process involves a series of detailed computational steps that are inherently performed by a computer and cannot be carried out mentally or manually by a human”, Examiner observes that the determination of whether or not the computational steps can be carried out mentally or manually by a human is a consideration for the abstract idea grouping of ”mental processes” rather than “certain methods of organizing human activity”.
Further, with regard to the assertion that “the process described does not resemble any human practice or long-standing societal activity…”, Examiner observes that the Supreme Court’s decisions make it clear that judicial exceptions need not be old or long-prevalent, and that even newly discovered or novel judicial exceptions are still exceptions. For example, the mathematical formula in Flook, the laws of nature in Mayo, and the isolated DNA in Myriad were all novel or newly discovered, but nonetheless were considered by the Supreme Court to be judicial exceptions because they were "‘basic tools of scientific and technological work’ that lie beyond the domain of patent protection." Myriad, 569 U.S. 576. As such, Examiner remains unpersuaded.
Second, Applicant argues that, “With reference to the July 2024 Subject Matter Eligibility examples, in Example 47, Claim 3, the eligible claim recites a method using an artificial neural network to detect malicious network packets. The claim integrates a judicial exception, such as mathematical operations and mental processes, into a practical application by combining steps that result in improved network security. Specifically, the claim includes steps like training the neural network with a backpropagation algorithm and gradient descent, detecting anomalies in network traffic, identifying the source of malicious packets, and taking automatic remedial actions such as dropping packets and blocking future traffic…
In the context of the present claims, a similar technical improvement is achieved. The amended claims recite that a significance level is generated by a computing device by receiving training data associating event types of constitutional events with significance levels from a database. The claims further recite training a significance model that is a neural network by applying the training data to an input layer of nodes, one or more intermediate layers, and an output layer of nodes. The model is trained by adjusting connections and weights between the layers and iteratively updating the model based on those adjustments. The trained model then generates a significance level as a function of the event type and the trained neural network. These steps are not routine or conventional, and instead represent a technical improvement in how constitutional event data is interpreted and evaluated. Just as the ANN in Example 47 improves the technical field of network security, the present claims improve the accuracy, efficiency, and automation of generating significance levels in response to complex event data…” [Arguments, pages 12-14].
In response, Applicant’s arguments are considered but are not persuasive. In Example 47, Claim 3, the eligible claim recites a method using an artificial neural network to detect malicious network packets. The claim integrates a judicial exception, such as mathematical operations and mental processes, into a practical application by combining steps that result in improved network security. Specifically, the claim includes steps like training the neural network with a backpropagation algorithm and gradient descent, detecting anomalies in network traffic, identifying the source of malicious packets, and taking automatic remedial actions such as dropping packets and blocking future traffic.
Similarly, with regard to Example 48, Claim 2, the claim as a whole integrates the judicial exception into a practical application by reciting steps for creating a mixed speech signal which excludes audio from an undesired source.
In contrast, Examiner observes that the present claims appear to be lacking a practical application, such as the step of dropping packets and blocking future traffic in Example 47, Claim 3, or the step of creating a mixed speech signal which excludes audio from an undesired source, as in Example 48, Claim 2. In other words, the recited steps of the present claims, including the machine learning and mathematical modeling steps and elements, culminate in the selection of potential cause and a potential effect of a constitutional event corresponding to a life expectancy and degree of disability. Examiner maintains that the mere selection of potential cause and a potential effect of a constitutional event amounts to gathering and analyzing information using conventional techniques and displaying the result, akin to TLI Communications, 823 F.3d at 612-13, 118 USPQ2d at 1747-48; (see MPEP § 2106.05(a)). Thus, Examiner respectfully maintains that the present invention recites an abstract idea without significantly more. As such, Examiner remains unpersuaded.
Examiner suggests amending the claims to include a step wherein the determined potential cause and potential effect of a constitutional event are utilized or applied in a meaningful way that demonstrates a practical application or an improvement to the field of technology.
Third, Applicant argues that, “In Berkheimer, the court recognized that improvements in the functioning of a computer or an improvement to other technology or technical fields might not be abstract if they involve a specific means or method that improves the relevant technology. Similarly, claim 1 as amended recites: “a significance level generated by the computing device by: receiving training data…”… This non-conventional and specific arrangement of steps provides a technical improvement in the field, aligning with the principles set forth in Berkheimer. These steps go beyond merely applying a known concept on a computer. They reflect a specific machine-learning implementation that is not generic or routine. As such, they cannot be dismissed as well-understood or conventional without a supporting factual showing…” [Arguments, pages 14-15].
In response, Applicant’s arguments are considered but are not persuasive. Examiner observes that when making a determination whether the additional elements in a claim amount to significantly more than a judicial exception, the examiner should evaluate whether the elements define only well-understood, routine, conventional activity. In this respect, the well-understood, routine, conventional consideration overlaps with other Step 2B considerations, particularly the improvement consideration (see MPEP § 2106.05(a)), the mere instructions to apply an exception consideration (see MPEP § 2106.05(f)), and the insignificant extra-solution activity consideration (see MPEP § 2106.05(g)).
The required factual determination must be expressly supported in writing, as discussed in MPEP § 2106.07(a). Appropriate forms of support include one or more of the following: (a) A citation to an express statement in the specification or to a statement made by an applicant during prosecution that demonstrates the well-understood, routine, conventional nature of the additional element(s); (b) A citation to one or more of the court decisions discussed in Subsection II below as noting the well-understood, routine, conventional nature of the additional element(s); (c) A citation to a publication that demonstrates the well-understood, routine, conventional nature of the additional element(s); and (d) A statement that the examiner is taking official notice of the well-understood, routine, conventional nature of the additional element(s). For more information on supporting a conclusion that an additional element (or combination of additional elements) is well-understood, routine, conventional activity, see MPEP § 2106.07(a), subsection III.
With respect to the additional elements claimed in the present invention, Examiner maintains that the apparatus, machine learning models and executable instructions are recited at a high-level of generality (see MPEP § 2106.05(a)), like the following MPEP example:
iii. Gathering and analyzing information using conventional techniques and displaying the result, TLI Communications, 823 F.3d at 612-13, 118 USPQ2d at 1747-48;
Furthermore, the computer implemented element is considered to amount to no more than mere instructions to apply the exception using a generic computer component (see MPEP 2106.05(f)), like the following MPEP example:
i. A commonplace business method or mathematical algorithm being applied on a general purpose computer, 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);
Accordingly, these additional elements do not integrate the abstract idea into a practical application or otherwise demonstrate more than well-understood, routine or conventional activity. As such, Examiner remains unpersuaded.
Claim Rejections - 35 USC § 101
Claims 1-5, 8-9, 11-15 and 18-19 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) without significantly more.
Step 1: Claims 1-5, 8-9, 11-15 and 18-19 are directed to statutory categories, namely a machine (claims 1-5 and 8-9), and a process (claims 11-15 and 18-19).
Step 2A, Prong 1: Claims 1 and 11 in part, recite the following abstract idea:
… for temporally sensitive causal heuristics.. populate, using…, a database with: a significance table identifying a significance of a plurality of constitutional event corresponding to a life expectancy and degree of disability; a frequency table describing relative frequency within one or more populations of potential effects of related to plurality of constitutional event in the significance table; provide a plurality of constitutional events and a plurality of potential effects relating to a human subject, wherein each constitutional event of the plurality of constitutional events includes: an event type; a significance level generated by …by: receiving training data associating event types of the constitutional events with significance levels from the database; training a significance model using the training data, wherein the significance model is… , wherein training the significance model further comprises: applying the training data to an input layer of nodes comprising the event types of the constitutional events, one or more intermediate layers of nodes, and an output layer of nodes comprising the significance levels from the database; adjusting one or more connections and one or more weights between nodes in adjacent layers of the significance model; iteratively updating the significance model as a function of the adjustments to the one or more connections and the one or more weights between the nodes in the adjacent layers of the significance model; and generating the significance level as a function of the event type of the constitutional event and the significance model; a time of occurrence, a temporal function; and at least a potential effect of the plurality of potential effects, wherein providing the plurality of constitutional events and a plurality of potential effects relating to the human subject further comprises: receiving training data associating event types with temporal functions; training a temporal model using the training data, wherein the temporal model comprises…; and generating the temporal function as a function of the temporal model and the event type of the constitutional event; generate a ranking of the plurality of constitutional events as a function of the significance level, time of occurrence, and temporal effect factor of each constitutional event; receive at least a current occurrence input from the human subject by receiving a transmission from …configured to detect an activity level; classify the at least a current occurrence input to an identified potential effect of the plurality of potential effects as a function of the ranking by: calculating a distance metric from the at least a current occurrence input to each potential effect of the plurality of potential effects; weighting the distance metric from the at least a current occurrence input to each potential effect of the plurality of potential effects by the ranking of corresponding constitutional events, wherein the ranking of corresponding constitutional events comprises identifying at least two constitutional events related to an identical potential event, and combining at least one inverse weighting and at least one proportional weighting by addition; and determining that the identified potential effect minimizes the weighted distance metric; output the identified potential effect; determine at least a potential cause as a function of the identified potential effect in order of greatest potential for impact on the human subject; receive an input indicating that the identified potential effect is incorrect; remove the identified potential effect; and select an alternative potential effect from the plurality of potential effects [Claim 1],
A method for temporally sensitive causal heuristics, the method comprising: populate… a database with: a significance table identifying a significance of a plurality of constitutional event corresponding to a life expectancy and degree of disability; a frequency table describing relative frequency within one or more populations of potential effects of related to plurality of constitutional event in the significance table; providing, by… a plurality of constitutional events and a plurality of potential effects relating to a human subject, wherein each constitutional event of the plurality of constitutional events includes: an event type, a significance level, a time of occurrence, a temporal function, and at least a potential effect of the plurality of potential effects, wherein providing the plurality of constitutional events and a plurality of potential effects relating to the human subject further comprises: receiving training data associating event types with temporal functions; training a temporal model using the training data, wherein the temporal model comprises…; and generating the temporal function as a function of the temporal model and the event type of the constitutional event; receiving training data associating event types of the constitutional events with significance levels from the database; training a significance model using the training data, wherein the significance model is… wherein training the significance model further comprises: applying the training data to an input layer of nodes comprising the event types of the constitutional events, one or more intermediate layers of nodes, and an output layer of nodes comprising the significance levels from the database;adjusting one or more connections and one or more weights between nodes in adjacent layers of the significance model;iteratively updating the significance model as a function of the adjustments to the one or more connections and the one or more weights between the nodes in the adjacent layers of the significance model; and generating the significance level as a function of the event type of the constitutional event and the significance model; generating… a ranking of the plurality of constitutional events as a function of the significance level, time of occurrence, and temporal effect factor of each constitutional event; receiving… at least a current occurrence input from the human subject by receiving a transmission from …configured to detect an activity level; classifying… the at least a current occurrence input to an identified potential effect of the plurality of potential effects as a function of the ranking by: calculating a distance metric from the at least a current occurrence input to each potential effect of the plurality of potential effects; weighting the distance metric from the at least a current occurrence input to each potential effect of the plurality of potential effects by the ranking of corresponding constitutional events, wherein the ranking of corresponding constitutional events comprises identifying at least two constitutional events related to an identical potential event, and combining at least one inverse weighting and at least one proportional weighting by addition; and determining that the identified potential effect minimizes the weighted distance metric; outputting… the identified potential effect; determining at least a potential cause as a function of the identified potential effect in order of greatest potential for impact on the human subject; receiving… an input indicating that the identified potential effect is incorrect; removing, at… the identified potential effect; and selecting… an alternative potential effect from the plurality of potential effects [Claim 11].
These concepts are not meaningfully different than the following concepts identified by the MPEP:
Concepts relating to certain methods of organizing human activity. The aforementioned limitations describe steps for managing personal behavior or relationships or interactions between people, including social activities, teaching, and following rules or instructions. Specifically, determining potential effects of constitutional events for human subjects is considered to describe steps for managing personal behavior. As such, claims 1 and 11 recite concepts identified as abstract ideas.
The dependent claims recite limitations relative to the independent claims, including, for example:
…generate, for a constitutional event of the plurality of constitutional events, the significance level of the constitutional event, wherein generating the significance level further comprises: receiving training data associating event types with significance levels; training a significance model using the training data; and generating the significance level as a function of the event type of the constitutional event and the significance model [Claim 2],
…wherein the plurality of constitutional events further includes at least a confirmed event. [Claim 3],
…wherein the plurality of constitutional events further includes at least a latent event [Claim 4],
…receive the at least a current occurrence input from the human subject by receiving at least a user entry [Claim 5].
The limitations of these dependent claims are merely narrowing the abstract idea identified in the independent claims, and thus, the dependent claims also recite abstract ideas.
Step 2A, Prong 2: This judicial exception is not integrated into a practical application. In particular, claims 1 and 11 only recite the following additional elements –
A system… the system comprising a computing device, the computing device designed and configured to… a form processing module and a language processing module…; … the computing device…; …a neural network…; … a machine-learning process; …a user-adjacent sensor comprising a wearable breath sensor… [Claim 1],
…by a computing device using a form processing module and a language processing module…; … a computing device…; …the computing device…; …the computing device…; …a neural network…; …a machine-learning process; …a user-adjacent sensor comprising a wearable breath sensor… …the computing device…;…the computing device…; …the computing device… [Claim 11].
The apparatus and executable instructions are recited at a high-level of generality (see MPEP § 2106.05(a)), like the following MPEP example:
iii. Gathering and analyzing information using conventional techniques and displaying the result, TLI Communications, 823 F.3d at 612-13, 118 USPQ2d at 1747-48;
Furthermore, the computer implemented element is considered to amount to no more than mere instructions to apply the exception using a generic computer component (see MPEP 2106.05(f)), like the following MPEP example:
i. A commonplace business method or mathematical algorithm being applied on a general purpose computer, 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);
Accordingly, these additional elements do not integrate the abstract idea into a practical application.
The remaining dependent claims do not recite any new additional elements, and thus do not integrate the abstract idea into a practical application.
Step 2B: Claims 1 and 11 and their underlying limitations, steps, features and terms, considered both individually and as a whole, do not include additional elements that are sufficient to amount to significantly more than the judicial exception for the following reasons:
Independent claims 1 and 11 only recite the following additional elements –
A system… the system comprising a computing device, the computing device designed and configured to… a form processing module and a language processing module…; … the computing device…; …a neural network…; … a machine-learning process; …a user-adjacent sensor comprising a wearable breath sensor… [Claim 1],
…by a computing device using a form processing module and a language processing module…; … a computing device…; …the computing device…; …the computing device…; …a neural network…; …a machine-learning process; …a user-adjacent sensor comprising a wearable breath sensor… …the computing device…;…the computing device…; …the computing device… [Claim 11].
These elements do not amount to significantly more than the abstract idea for the reasons discussed in 2A prong 2 with regard to MPEP 2106.05(a) and MPEP 2106.05(f). By the failure of the elements to integrate the abstract idea into a practical application there, the additional elements likewise fail to amount to an inventive concept that is significantly more than an abstract idea here, in Step 2B.
As such, both individually or in combination, these limitations do not add significantly more to the judicial exception.
The remaining dependent claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the dependent claims do not recite any new additional elements other than those mentioned in the independent claims, which amount to no more than mere instructions to apply the exception using a generic computer component (see MPEP 2106.05(f)). As such, these claims are not patent eligible.
Prior Art Considerations
Examiner conducted a thorough search of the body of available prior art (see attached documents regards PTO-892 Notice of Reference Cited and EAST Search History). Notably, Examiner discovered multiple patent literature documents that taught aspects of the invention, but no single disclosure taught “every element required by the claims under its broadest reasonable interpretation” [MPEP § 2131] to make a 35 USC § 102 rejection. Further, Examiner considered the individual elements of the recited claims taught across the prior art cited below, but did not find it obvious to combine such disclosures [MPEP § 2142] to make a 35 USC § 103 rejection.
In particular, Morturu et al., U.S. Publication No. 2016/0196389 [hereinafter Morturu] discloses a method for providing patient indication to an entity which “comprises: for each of a set of patients: by way of an application executing at a mobile computing device, accessing a sensor signal processor module and a log of use of a communication application executing on the mobile computing device S110; using the application, receiving a survey response dataset in association with a time period S120; generating a behavioral dataset, derived from the sensor signal processor module and the log of use, associated with the time period and derived from passive behavior S130; generating a predictive model derived from at least one of the log of use, the survey response dataset, and the behavioral dataset S140; generating a first comparison between the survey response dataset and a first threshold condition, a second comparison between the behavioral dataset and a second threshold condition, and a third comparison between an output of the predictive model and a third threshold condition S150; and generating an indication in response to at least one of the first, the second, and the third comparisons, thereby producing a set of indications corresponding to a subset of patients of the set of patients S160; ranking the set of indications according to a severity factor S170; and transmitting a portion of the set of indications to the entity according to a resource constraint of the entity S180.” (Morturu, ¶ 16). While Morturu discloses some aspects of the present invention including determining severity for constitutional events and training a temporal model, Morturu is silent with respect to “a significance table identifying a significance of a plurality of constitutional event corresponding to a life expectancy and degree of disability”, “a frequency table describing relative frequency within one or more populations of potential effects of related to plurality of constitutional event in the significance table”, “training a significance model using the training data”, and “generating the significance level as a function of the event type of the constitutional event and the significance model” (Claim 1), as recited in the amended independent claims of the present invention.
Cha et al., U.S. Publication No. 2020/0005900 [hereinafter Cha] discloses machine learning systems and methods for predicting risk of renal function decline which “employ machine learning techniques to assess a likelihood or risk that one or more patients will experience an adverse outcome, such as a decline in renal function, within one or more timeframes. The embodiments may utilize patient data relating to demographics, vital signs, diagnoses, procedures, diagnostic tests, biomarker assays, genetic tests, behaviors, and/or patient symptoms, to determine risk information, such as important predictive features and patient risk scores. And the embodiments may automatically execute patient workflows, such as providing treatment recommendations to providers and/or patients, based on determined risk scores” (Cha, Abstract). While Cha discloses some aspects of the present invention including calculating an occurrence input to an effect and minimizing a weighted distance metric, Cha does not disclose “a significance table identifying a significance of a plurality of constitutional event corresponding to a life expectancy and degree of disability”, “a frequency table describing relative frequency within one or more populations of potential effects of related to plurality of constitutional event in the significance table”, “training a significance model using the training data”, and “generating the significance level as a function of the event type of the constitutional event and the significance model” (Claim 1), as recited in the amended independent claims of the present invention.
Silver et al., U.S. Publication No. 2020/0097835 [hereinafter Silver] describes a device, system and method for assessing risk of variant-specific gene dysfunction, wherein “a method may include generating multiple virtual progenies from multiple first virtual gametes and multiple second virtual gametes. Each virtual progeny may combine one of the first virtual gametes and one of the second virtual gametes. A computing server may input, for each virtual progeny, data associated with the first virtual gamete of the virtual progeny to a machine learning model to determine a first variant-specific gene dysfunction score corresponding to a target allele site. The computing server may also input, for each virtual progeny, data associated with the second virtual gamete of the virtual progeny to the machine learning model to determine a second variant-specific gene dysfunction score corresponding to the target allele site. The computing server may derive, for each virtual progeny, a dysfunction likelihood score of the target allele site from the first variant-specific gene dysfunction score and the second variant-specific gene dysfunction score” (Silver, Abstract). While Silver discloses some aspects of the present invention including inverse weighting and proportional weighting, Silver is silent with respect to “a significance table identifying a significance of a plurality of constitutional event corresponding to a life expectancy and degree of disability”, “a frequency table describing relative frequency within one or more populations of potential effects of related to plurality of constitutional event in the significance table”, “training a significance model using the training data”, and “generating the significance level as a function of the event type of the constitutional event and the significance model” (Claim 1), as recited in the amended independent claims of the present invention.
Tran et al., U.S. Publication No. 2020/0251213 [hereinafter Tran] discloses a blockchain gene system wherein the “systems and methods disclosed for recommending lifestyle modification for a subject by using a DNA sequencer to generate genetic information; aggregating genetic information, environmental information, treatment data, and treatment response from a patient population; deep learning with a computer to generate at least one computer implemented classifier that predicts disease risks based on the aggregated genetic information, treatment data, and treatment response from a patient population; and recommending lifestyle modification to mitigate the disease risks” (Tran, ¶ 3). While Tran discloses a wearable breath sensor for detecting activity levels, in accordance with the present invention, Tran does not disclose “a significance table identifying a significance of a plurality of constitutional event corresponding to a life expectancy and degree of disability”, “a frequency table describing relative frequency within one or more populations of potential effects of related to plurality of constitutional event in the significance table”, “training a significance model using the training data”, and “generating the significance level as a function of the event type of the constitutional event and the significance model” (Claim 1), as recited in the amended independent claims of the present invention.
For the above reasons, Examiner determined the currently pending claims novel and non-obvious given the current search. Amendment to the claims and further search in reaction to such amendment may yield the claims anticipated or obvious in future prosecution, determined at that time. The currently pending claims would be considered allowable if written to overcome the 35 USC § 101 rejection.
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Kenedy et al., U.S. Publication No. 2008/0228824 discloses treatment determination and impact analysis.
Magent et al., U.S. Publication No. 2010/0076799 discloses a system and method for using classification trees to predict rare events.
Holmes et al., U.S. Publication No. 2011/0093249 discloses an integrated health data capture and analysis system.
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/NICHOLAS D BOLEN/Examiner, Art Unit 3624 /PATRICIA H MUNSON/Supervisory Patent Examiner, Art Unit 3624