Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of the Claims
Claims 1-21 are currently pending. Additionally, Examiner notes that the present application is a Continuation-in-Part of applications 18/084156 and 16/425699 (and corresponding provisional application 62/693004), and patents 11,392,847 (and corresponding provisional application 63/009252), and 11,531,921. However, none of the aforementioned related applications/patents recite predicting a direct, first order contagion effect, and indirect, second and third order contagion effects. Hence, Claims 1-21 are afforded the effective priority date of the filing date of the present application of October 3, 2024.
Information Disclosure Statement
The information disclosure statement submitted on November 20, 2025 in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement has been considered by Examiner.
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 1-21 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 1-21 are within the four statutory categories. Claims 1-20 are drawn to a device for predicting events, which is within the four statutory categories (i.e. machine). Claim 21 is drawn to a method for predicting events, which is within the four statutory categories (i.e. process).
Prong 1 of Step 2A
Claim 1, which is representative of the inventive concept, recites: An early warning and event monitoring computer device for predicting events, the computer device comprising at least one processor in communication with at least one memory device, the at least one processor programmed to:
receive a plurality of events of interest, wherein each event of interest of the plurality of events of interest is represented by event data comprising at least one of occurrence, frequency, or magnitude;
derive interrelationships for the plurality of events of interest by computing directional and time-lagged relationship information for pairs of events from the event data;
generate pairwise event relationship metrics for the plurality of events of interest based upon the derived interrelationships, wherein the pairwise event relationship metrics comprising at least: (i) whether a relationship exists, (ii) a magnitude of the relationship, (iii) a direction of effect between events, and (iv) how the relationship changes over time;
generate an adjacency matrix for the plurality of events of interest from the pairwise event relationship metrics;
predict one or more future events at a future time period based upon the adjacency matrix;
predict a direct, first order contagion effect from a first event to a second event based upon a direct relationship in the adjacency matrix;
predict indirect contagion effects including:
a second order contagion effect comprising an indirect effect through an intermediate event from event X to event Y and from event Y to event Z; and
a third order contagion effect comprising an indirect cascade through two intermediate events from event A to event B, from event B to event C, and from event C to event D;
extract one or more variables from the events based upon strategies to change contagion effects.
The underlined limitations as shown above, given the broadest reasonable interpretation, cover the abstract idea of a mathematical concept and/or a certain method of organizing human activity because they recite mathematical relationships, formulas, equations, and/or mathematical calculations (in this case, the steps of generating an adjacency matrix, executing simulations to simulate how one or more selected events propagate through the adjacency matrix, and extracting one or more variables from the events based on strategies to change contagion effects recite at least mathematical relationships and/or calculations), and/or managing personal behavior or relationships or interactions between people (i.e. social activities, teaching, and following rules or instructions – in this case, the steps of receiving events, deriving interrelationships for the events, generating event relationship metrics, predicting future events based on the event metrics, predicting a direct, first order contagion effect, predicting indirect contagion events, and extracting variables from the events recite following rules or instructions to formulate strategies to change contagion effects), e.g. see MPEP 2106.04(a)(2). Any limitations not identified above as part of the abstract idea are deemed “additional elements,” and will be discussed in further detail below.
Furthermore, the abstract idea for Claim 21 is identical as the abstract idea for Claim 1, because the only difference between Claims 1 and 21 is that Claim 1 recites a device, whereas Claim 21 recites a method.
Dependent Claims 2-20 include other limitations, for example Claims 2-3 recite predicting one or more future events and indirect contagion effects by applying a regression model to the adjacency matrix, Claim 4 recites various mathematical techniques for computing directional and time-lagged relationship information for pairs of events, Claim 5 recites utilizing Monte Carlo stochastic optimization, Claims 6-8 recite features of the adjacency matrix and utilizing the adjacency matrix to evaluate events and predicting a first order contagion effect, Claims 9-11 recite the timing of the events, Claims 12-14 recite types of future events, Claims 15-16 recite conducting a simulation based on the first order contagion effect and extracting variables based on the simulation, Claim 17 recites determining important variables of the extracted variables, Claims 18-19 recite generating a strategy for risk mitigation based on the extracted variables, and Claim 20 recites types of events, but these only serve to further narrow the abstract idea, and a claim may not preempt abstract ideas, even if the judicial exception is narrow, e.g. see MPEP 2106.04. Hence dependent Claims 2-20 are nonetheless directed towards fundamentally the same abstract idea as independent Claim 1.
Hence Claims 1-21 are directed towards the aforementioned abstract idea.
Prong 2 of Step 2A
Claims 1 and 21 are not integrated into a practical application because the additional elements (i.e. the non-underlined limitations above – in this case, the computer device comprising the processor and memory device) amount to no more than limitations which:
amount to mere instructions to apply an exception – for example, the recitation of the computer device comprising any standard computing device, which amounts to merely invoking a computer as a tool to perform the abstract idea, e.g. see [0093]-[0096] of the as-filed Specification, and see MPEP 2106.05(f); and/or
generally link the abstract idea to a particular technological environment or field of use – for example, the claim language of the predicting of contagion effects, which amounts to limiting the abstract idea to the field of healthcare, e.g. see MPEP 2106.05(h).
Additionally, dependent Claims 2-20 include other limitations, but these limitations also amount to no more than generally linking the abstract idea to a particular technological environment or field of use (e.g. the types of event relationship metrics recited in dependent Claim 4, the types of future events and events recited in dependent Claims 12-14 and 20), and/or do not include any additional elements beyond those already recited in independent Claim 1, and hence also do not integrate the aforementioned abstract idea into a practical application.
Hence Claims 1-21 do not include additional elements that integrate the judicial exception into a practical application.
Step 2B
Claims 1 and 21 do not include additional elements that are sufficient to amount to “significantly more” than the judicial exception because the additional elements (i.e. the non-underlined limitations above – in this case, the computer device comprising the processor and memory device), as stated above, are directed towards no more than limitations that amount to mere instructions to apply the exception, generally link the abstract idea to a particular technological environment or field of use, and/or add insignificant extra-solution activity to the abstract idea, wherein the additional elements comprise limitations which:
amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields, as demonstrated by:
The present Specification expressly disclosing that the structural additional elements are well-understood, routine, and conventional in nature:
[0093]-[0096] of the as-filed Specification discloses that the additional elements (i.e. the computer device comprising the processor and memory device) comprise a plurality of different types of generic computing systems;
Dependent Claims 2-20 include other limitations, but none of these limitations are deemed significantly more than the abstract idea because the additional elements recited in the aforementioned dependent claims similarly amount to generally linking the abstract idea to a particular technological environment or field of use (e.g. the types of event relationship metrics recited in dependent Claim 4, the types of future events and events recited in dependent Claims 12-14 and 20), and/or the limitations recited by the dependent claims do not recite any additional elements not already recited in independent Claim 1, and hence do not amount to “significantly more” than the abstract idea.
Hence, Claims 1-21 do not include any additional elements that amount to “significantly more” than the judicial exception.
Thus, taken alone, the additional elements do not amount to significantly more than the abstract idea identified above. Furthermore, looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually, and there is no indication that the combination of elements improves the functioning of a computer or improves any other technology, and their collective functions merely provide conventional computer implementation.
Therefore, whether taken individually or as an ordered combination, Claims 1-21 are nonetheless rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
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 (i.e., changing from AIA to pre-AIA ) 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.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 4, 8-14, and 20-21 are rejected under 35 U.S.C. 103 as being unpatentable over Coffey (US 2020/0145447) in view of Razo (“Adjacency Matrix Deep Learning Prediction Model for Prognosis of the Next Event in a Process,” IEEE Access, Vol. 11, 2023).
Regarding Claim 1, Coffey teaches the following: An early warning and event monitoring computer device for predicting events, the computer device comprising at least one processor in communication with at least one memory device (The system comprises a processor and a memory, e.g. see Coffey [0016].), the at least one processor programmed to:
receive a plurality of events of interest, wherein each event of interest of the plurality of events of interest is represented by event data comprising at least one of occurrence, frequency, or magnitude (The system receives event data, for example an observed (i.e. occurrence) user behavior and/or contagion-based risk events, e.g. see Coffey [0023], [0025]-[0029], wherein the user behavior data may include frequency and/or force (i.e. magnitude) of the behavior, e.g. see Coffey [0078].);
derive interrelationships for the plurality of events of interest by computing directional and time-lagged relationship information for pairs of events from the event data (The system associates (i.e. derives an interrelationship for) a particular event with one or more user behaviors, e.g. see Coffey [0031] and [0036], wherein the behaviors associated with events may include determining a causal relationship, e.g. see Coffey [0059]. That is, an event determined to be a trigger event for a behavior (or vice versa) indicates a direction for the event and behavior, as well as a time-lag between the event and behavior because one causes the other.);
generate pairwise event relationship metrics for the plurality of events of interest based upon the derived interrelationships (The system associates a particular event with one or more user behaviors, e.g. see Coffey [0031], and the system further determines a causal sequence (i.e. a pairwise event relationship metric) from the event data, e.g. see Coffey [0059].), wherein the pairwise event relationship metrics comprising at least: (i) whether a relationship exists, (ii) a magnitude of the relationship, (iii) a direction of effect between events, and (iv) how the relationship changes over time (The system determines a causal sequence (i.e. a relationship exists, with “causal” being a “magnitude” and a “direction” for the event and behavior) for a behavior and event, e.g. see Coffey [0059], wherein user behavior may evolve (i.e. change) over time, e.g. see Coffey [0094]-[0095].);
predict one or more future events at a future time period (The system determines a risk assessment trigger event from the causal sequence, e.g. see Coffey [0059], wherein the risk assessment computes a security risk score for an event, e.g. see Coffey [0060]-[0061] and [0068].);
predict a direct, first order contagion effect from a first event to a second event (The system assigns or propagates the risk scores for events based on the computed risk probabilities for each event to individuals, e.g. see Coffey [0067]. Furthermore, the risk score for an event is assigned to a first individual, wherein the risk score may be modified by a scaling factor (i.e. the modified risk score is interpreted as a first order contagion effect) as it is applied to a first set of entities closely associated with the first individual, e.g. see Coffey [0067]-[0068].);
predict indirect contagion effects (The system assigns the event risk scores to a second and third set of entities which have smaller degrees of influence with respect to the first individual, and modify the event risk scores with second and third scaling factors (i.e. the modified risk scores are interpreted as second and third order contagion effects), e.g. see Coffey [0067]-[0068].); and
extract one or more variables from the events based upon strategies to change contagion effects (The system identifies security risks (i.e. one or more variables) based on the triggering threshold requirements, e.g. see Coffey [0068], wherein the system further employs various steps (i.e. strategies) to respond to contagion-based event risk scores to reduce operational overhead and improve system efficiency, e.g. see Coffey [0069].).
But Coffey does not teach and Razo teaches the following:
generate an adjacency matrix for the plurality of events of interest from the pairwise event relationship metrics (The system creates an adjacency matrix based on a plurality of events of an event log, e.g. see Razo section “V. Approach,” pg. 11950, wherein an event log comprises a multiset of sequences (i.e. pairwise event relationship metrics), e.g. see Razo section “III. Preliminaries,” pg. 11949.);
wherein the predicting of one or more future events is based upon the adjacency matrix (The system predicts next events using an Adjacency matrix deep learning prediction model (AXDP) that utilizes the adjacency matrix, e.g. see Razo section “I. Introduction,” pg. 11947, “V. Approach,” Figure 1, pg. 11950, section “VII. Discussion,” pgs. 11952-11954.);
wherein the predicting of the direct, first order contagion effect is based upon a direct relationship in the adjacency matrix (The system includes first order consecutive events (i.e. direct, first order contagion effects), e.g. see Razo section “III. Preliminaries,” pg. 11949, wherein the prediction of the next event by the AXDP is based on the first order consecutive events, e.g. see Razo section “V. Approach,” pg. 11950-11951, “VII. Conclusion,” pg. 11954.);
execute one or more simulations to simulate how one or more selected events propagate through the adjacency matrix (The adjacency matrix is fed into a neural network to generate the prediction (i.e. executing one or more simulations), e.g. see Razo section “V. Approach,” pg. 11950-1951.); and
wherein the indirect contagion effects include:
a second order contagion effect comprising an indirect effect through an intermediate event from event X to event Y and from event Y to event Z (The adjacency matrix includes a second order consecutive event, wherein a second order consecutive event occurs after a first event with an intervening event between the first event and the second order consecutive event, e.g. see Razo section “III. Preliminaries,” pg. 11949, “V. Approach,” pg. 1950-1951.); and
a third order contagion effect comprising an indirect cascade through two intermediate events from event A to event B, from event B to event C, and from event C to event D (The adjacency matrix includes a third order consecutive event, wherein a third order consecutive event occurs after a first event with a plurality of intervening events between the first event and the third order consecutive event, e.g. see Razo section “III. Preliminaries,” pg. 11949, “V. Approach,” pg. 1950-1951.).
Furthermore, before the effective filing date, it would have been obvious to one ordinarily skilled in the art of event prediction to modify Coffey to incorporate the adjacency matrix factoring in the first, second, and third order events as taught by Razo in order to improve event prediction by conserving the sequence of events, e.g. see Razo section “I. Introduction,” pg. 1947, “VII. Discussion,” pgs. 11952-19954.
Regarding Claim 4, the combination of Coffey and Razo teaches the limitations of Claim 1, and Coffey further teaches the following:
The computer device in accordance with Claim 1, wherein the at least one processor is further programmed to compute directional and time-lagged relationship information for pairs of events from the event data using at least one correlation, cointegration, mutual information, Granger causality, vector autoregression, vector error correction models, or convergent cross mapping (The system associates (i.e. derives an interrelationship for) a particular event with one or more user behaviors, e.g. see Coffey [0031] and [0036], wherein the behaviors associated with events may include determining a causal relationship, e.g. see Coffey [0059]. That is, a causal relationship comprises at least one of “correlation” and “mutual information.”).
Regarding Claim 8, the combination of Coffey and Razo teaches the limitations of Claim 1, and Razo further teaches the following:
The computer device in accordance with Claim 1, wherein the at least one processor is further programmed to predict a first order contagion effect based upon an interrelationship between two events of the plurality of events and the one or more future events using the adjacency matrix (The system predicts next events using an Adjacency matrix deep learning prediction model (AXDP) that utilizes the adjacency matrix, e.g. see Razo section “I. Introduction,” pg. 11947, “V. Approach,” Figure 1, pg. 11950, section “VII. Discussion,” pgs. 11952-11954, based on first, second, and third order consecutive events, e.g. see Razo section “III. Preliminaries,” pg. 11949, and “V. Approach,” pgs. 11950-11951.).
Furthermore, before the effective filing date, it would have been obvious to one ordinarily skilled in the art of event prediction to modify Coffey to incorporate the adjacency matrix factoring in the first, second, and third order events as taught by Razo in order to improve event prediction by conserving the sequence of events, e.g. see Razo section “I. Introduction,” pg. 1947, “VII. Discussion,” pgs. 11952-19954.
Regarding Claim 9, the combination of Coffey and Razo teaches the limitations of Claim 1, and Coffey further teaches the following:
The computer device in accordance with Claim 1, wherein the plurality of events of interest is received in regard to time t (The received event and behavior data includes time information such as timestamps and/or time intervals, e.g. see Coffey [0025], [0029]-[0030], and [0036].).
Regarding Claim 10, the combination of Coffey and Razo teaches the limitations of Claim 9, and Coffey further teaches the following: The computer device in accordance with Claim 9, wherein the at least one processor is further programmed to:
receive a plurality of events of interest for time t-k (The received event and behavior data includes time information such as timestamps and/or time intervals, e.g. see Coffey [0025], [0029]-[0030], and [0036]. That is, a time interval requires a start time and an end time, wherein a time “t” may be interpreted as an end time, and hence the time “t-k” may be interpreted as any time prior to the end time including the start time.);
derive interrelationships for the plurality of events of interest for time t-k (The system associates (i.e. derives an interrelationship for) a particular event with one or more user behaviors, e.g. see Coffey [0031].); and
generate pairwise event relationship metrics for the plurality of events of interest for time t-k based upon the derived interrelationships (The system associates a particular event with one or more user behaviors, e.g. see Coffey [0031], and the system further determines a causal sequence (i.e. a pairwise event relationship metric) from the event data, e.g. see Coffey [0059].).
Regarding Claim 11, the combination of Coffey and Razo teaches the limitations of Claim 9, and Coffey further teaches the following: The computer device in accordance with Claim 9, wherein the at least one processor is further programmed to:
receive a plurality of events of interest for time t+k (The received event and behavior data includes time information such as timestamps and/or time intervals, e.g. see Coffey [0025], [0029]-[0030], and [0036]. That is, a time interval requires a start time and an end time, wherein a time “t” may be interpreted as a start time, and hence the time “t+k” may be interpreted as any time after the start time including the end time.);
derive interrelationships for the plurality of events of interest for time t+k (The system associates (i.e. derives an interrelationship for) a particular event with one or more user behaviors, e.g. see Coffey [0031].); and
generate pairwise event relationship metrics for the plurality of events of interest for time t+k based upon the derived interrelationships (The system associates a particular event with one or more user behaviors, e.g. see Coffey [0031], and the system further determines a causal sequence (i.e. a pairwise event relationship metric) from the event data, e.g. see Coffey [0059].).
Regarding Claim 12, the combination of Coffey and Razo teaches the limitations of Claim 1, and Coffey further teaches the following:
The computer device in accordance with Claim 1, wherein the one or more future events include at least one of a predictive probability, a frequency, a magnitude, or a confidence level (The system determines a risk assessment trigger event from the causal sequence, e.g. see Coffey [0059], wherein the risk assessment computes a security risk score (i.e. a magnitude) for an event, e.g. see Coffey [0060]-[0061] and [0068]. Additionally, the system tracks data related to the user behaviors including a date/time/frequency, e.g. see Coffey [0078].).
Regarding Claim 13, the combination of Coffey and Razo teaches the limitations of Claim 1, and Coffey further teaches the following:
The computer device in accordance with Claim 1, wherein the one or more future events include a report detailing actors, event type, a geolocation, and causes associated with the one or more future events (The system determines a risk assessment trigger event from the causal sequence, e.g. see Coffey [0059].).
Regarding Claim 14, the combination of Coffey and Razo teaches the limitations of Claim 1, and Coffey further teaches the following:
The computer device in accordance with Claim 1, wherein the one or more future events include a probability of occurrence, a frequency, and a magnitude (The system determines a risk score for a risk assessment trigger event from the causal sequence, e.g. see Coffey [0059], wherein the risk score may be a probability, e.g. see Coffey [0061].).
Regarding Claim 20, the combination of Coffey and Razo teaches the limitations of Claim 1, and Coffey further teaches the following:
The computer device in accordance with Claim 1, wherein the plurality of events of interest include macro variables, meso variables, and micro variables, wherein the micro variables relate to actor specific data, wherein the meso variables relate to network structures between actors, and wherein the macro variables relate to structural political, business, military, economic, and social factors that encompass all events (The system tracks events and user behaviors (i.e. events of interest), e.g. see Coffey [0023] and [0025]-[0029], wherein the data pertaining to the events and user behaviors include context indicating the user (i.e. micro variables relating to actor specific data), e.g. see Coffey [0030], relationship information for different users (i.e. meso variables relating to network structures between actors), e.g. see Coffey [0055]-[0057], and organizational data including business groups (macro variables relating to business and/or economic factors that encompass all events), e.g. see Coffey [0068] and [0118].).
Regarding Claim 21, the limitations of Claim 21 are substantially similar to those claimed in Claim 1, with the sole difference being that Claim 1 recites a device whereas Claim 21 recites a method. Specifically pertaining to Claim 21, Examiner notes that Coffey teaches an apparatus, system, and method, e.g. see Coffey [0001], and hence the grounds of rejection provided above for Claim 1 are similarly applied to Claim 21.
Claims 2-3 are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Coffey and Razo in view of Philipp (“Analysis of Control Flow Graphs Using Graph Convolutional Neural Networks,” 2019 6th Intl. Conference on Soft Computing & Machine Intelligence).
Regarding Claim 2, the combination of Coffey and Razo teaches the limitations of Claim 1, but does not teach and Philipp teaches the following:
The computer device in accordance with Claim 1, wherein the at least one processor is further programmed to predict one or more future events based upon applying at least one network regression model to the adjacency matrix (The system constructs an adjacency matrix from an event log, and inputs the adjacency matrix into a graph convolutional neural network (GCN) which performs regression on the input adjacency matrix, e.g. see Philipp section “II. Fundamentals,” pgs. 73-74, section “IV. Modeling Approach,” pg. 74, Figure 2, wherein the output of the regression includes a prediction, for example a prediction of pathologies, e.g. see Philipp section “III. Related Work,” pg. 74.).
Furthermore, before the effective filing date, it would have been obvious to one ordinarily skilled in the art of event prediction to modify the combination of Coffey and Razo to incorporate the regression applied to the adjacency matrix as taught by Philipp in order to improve metrics such as productivity, throughput, or user experience, e.g. see Philipp section “I. Introduction,” pg. 73.
Regarding Claim 3, the combination of Coffey and Razo teaches the limitations of Claim 1, but does not teach and Philipp teaches the following:
The computer device in accordance with Claim 1, wherein the at least one processor is further programmed to predict indirect contagion effects using the adjacency matrix and at least one network regression model (The system constructs an adjacency matrix from an event log, and inputs the adjacency matrix into a graph convolutional neural network (GCN) which performs regression on the input adjacency matrix, e.g. see Philipp section “II. Fundamentals,” pgs. 73-74, section “IV. Modeling Approach,” pg. 74, Figure 2, wherein the output of the regression includes a prediction, for example a prediction of pathologies, e.g. see Philipp section “III. Related Work,” pg. 74.).
Furthermore, before the effective filing date, it would have been obvious to one ordinarily skilled in the art of event prediction to modify the combination of Coffey and Razo to incorporate the regression applied to the adjacency matrix as taught by Philipp in order to improve metrics such as productivity, throughput, or user experience, e.g. see Philipp section “I. Introduction,” pg. 73.
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over the combination of Coffey and Razo in view of King-Wilson (US 2016/0197953).
Regarding Claim 5, the combination of Coffey and Razo teaches the limitations of Claim 1, but does not teach and King-Wilson teaches the following:
The computer device in accordance with Claim 1, wherein one or more “what-if” simulations include Monte Carlo stochastic optimization (The system includes a stochastic model for predicting event probability distributions, wherein the model may be implemented using a Monte Carlo simulation, e.g. see King-Wilson [0173]-[0174].).
Furthermore, before the effective filing date, it would have been obvious to one ordinarily skilled in the art of event prediction to modify the combination of Coffey and Razo to incorporate the Monte Carlo simulation for the events as taught by King-Wilson in order to improve a potential threat assessment, e.g. see King-Wilson [0010].
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over the combination of Coffey and Razo in view of Kano (US 2024/0145090), further in view of Vepakomma (US 2015/0066828).
Regarding Claim 6, the combination of Coffey and Razo teaches the limitations of Claim 1, but does not teach and Kano teaches the following:
The computer device in accordance with Claim 5, wherein adjacency matrix is directed and weighted (The adjacency matrix layer is expressed by a graphical model representing a causal structure between multiple events, wherein the graphical model is defined by a directed graph, e.g. see Kano [0039], wherein the causal structure model may be trained by weighting data, e.g. see Kano [0065].).
Furthermore, before the effective filing date, it would have been obvious to one ordinarily skilled in the art of event prediction to modify the combination of Coffey and Razo to incorporate the directed and weighted adjacency matrix layer of the model as taught by Kano in order to more accurately predict downstream tasks, e.g. see Kano [0003].
But the combination of Coffey, Razo, and Kano does not teach and Vepakomma teaches the following:
wherein adjacency matrix is asymmetric (The system utilizes an asymmetric matrix to perform event prediction, e.g. see Vepakomma [0049]-[0051], and [0055].).
Furthermore, before the effective filing date, it would have been obvious to one ordinarily skilled in the art of event prediction to modify the combination of Coffey, Razo, and Kano to incorporate the asymmetric matrix as taught by Vepakomma in order to account for and/or correct potential inconsistencies in event data to make accurate predictions, e.g. see Vepakomma [0003].
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over the combination of Coffey and Razo in view of Kano.
Regarding Claim 7, the combination of Coffey and Razo teaches the limitations of Claim 1, but does not teach and Kano teaches the following:
The computer device in accordance with Claim 1, wherein the adjacency matrix is generated for all events for a particular time t (The system includes a causal structure model that determines the causal relationship between events, wherein the model includes an adjacency matrix layer, wherein the adjacency matrix layer analyzes data samples at a time t, e.g. see Kano [0038].).
Furthermore, before the effective filing date, it would have been obvious to one ordinarily skilled in the art of event prediction to modify the combination of Coffey and Razo to incorporate the sample time for the event data input into the adjacency matrix layer as taught by Kano in order to more accurately predict downstream tasks, e.g. see Kano [0003].
Claims 15-19 are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Coffey and Razo in view of Aston (US 2020/0090089).
Regarding Claim 15, the combination of Coffey and Razo teaches the limitations of Claim 1, but does not teach and Aston teaches the following:
The computer device in accordance with Claim 1, wherein the at least one processor is further programmed to conduct one or more simulations based upon the first order contagion effect (The system passes patient data through a classification model to identify high risk environments, and further may loop through a risk score/classification process to simulate a predefined action to identify which action would optimize the reduction of the risk of a hospital acquired condition (HAC), e.g. see Aston [0006] and [0044].).
Furthermore, before the effective filing date, it would have been obvious to one ordinarily skilled in the art of event prediction to modify the combination of Coffey and Razo to incorporate the simulation as taught by Aston in order to optimize the reduction of risk of a HAC, e.g. see Aston [0044].
Regarding Claim 16, the combination of Coffey, Razo, and Aston teaches the limitations of Claim 15, and Aston further teaches the following:
The computer device in accordance with Claim 15 wherein the at least one processor is further programmed to extract one or more variables based upon the one or more simulations (The system passes patient data through a classification model to identify high risk environments, and further may loop through a risk score/classification process to simulate a predefined action to identify which action (i.e. extracted one or more variables) would optimize the reduction of the risk of a hospital acquired condition (HAC), e.g. see Aston [0006] and [0044].).
Furthermore, before the effective filing date, it would have been obvious to one ordinarily skilled in the art of event prediction to modify the combination of Coffey and Razo to incorporate the simulation as taught by Aston in order to optimize the reduction of risk of a HAC, e.g. see Aston [0044].
Regarding Claim 17, the combination of Coffey and Razo teaches the limitations of Claim 1, but does not teach and Aston teaches the following:
The computer device in accordance with Claim 1, wherein the at least one processor is further programmed to determine one or more important variables for the extracted one or more variables of the plurality of events (The system passes patient data through a classification model to identify high risk environments, and further may loop through a risk score/classification process to simulate a predefined action to identify which action (i.e. important variables) would optimize the reduction of the risk of a hospital acquired condition (HAC), e.g. see Aston [0006] and [0044].).
Furthermore, before the effective filing date, it would have been obvious to one ordinarily skilled in the art of event prediction to modify the combination of Coffey and Razo to incorporate the simulation to identify the important variables as taught by Aston in order to optimize the reduction of risk of a HAC, e.g. see Aston [0044].
Regarding Claim 18, the combination of Coffey and Razo teaches the limitations of Claim 1, but does not teach and Aston teaches the following:
The computer device in accordance with Claim 1, wherein the at least one processor is further programmed to generate a strategy simulator for risk mitigation based upon the extracted one or more variables (The system passes patient data through a classification model to identify high risk environments, and further may loop through a risk score/classification process to simulate a predefined action to identify which action (i.e. a strategy simulator for risk mitigation) would optimize the reduction of the risk of a hospital acquired condition (HAC), e.g. see Aston [0006] and [0044].).
Furthermore, before the effective filing date, it would have been obvious to one ordinarily skilled in the art of event prediction to modify the combination of Coffey and Razo to incorporate the simulation to determine mitigation actions as taught by Aston in order to optimize the reduction of risk of a HAC, e.g. see Aston [0044].
Regarding Claim 19, the combination of Coffey, Razo, and Aston teaches the limitations of Claim 18, and Aston further teaches the following:
The computer device in accordance with Claim 18, wherein the strategy simulator is programmed to perform at least one of minimize contagion, dampen anticipated effects, and/or change risk interdependency networks (The system passes patient data through a classification model to identify high risk environments, and further may loop through a risk score/classification process to simulate a predefined action to identify which action (i.e. a strategy simulator) would optimize the reduction of the risk of a hospital acquired condition (HAC) (i.e. minimize contagion, dampen anticipated effects, and/or change risk interdependency networks), e.g. see Aston [0006] and [0044].).
Furthermore, before the effective filing date, it would have been obvious to one ordinarily skilled in the art of event prediction to modify the combination of Coffey and Razo to incorporate the simulation to determine mitigation actions as taught by Aston in order to optimize the reduction of risk of a HAC, e.g. see Aston [0044].
Response to Arguments
Applicant’s arguments, see Remarks, filed February 18, 2026, with respect to the rejections of Claims 1-21 under 35 U.S.C. 101 have been fully considered but are not persuasive.
Applicants first allege that the claimed invention is patent eligible because it is properly analogized to the inventions recited in Ex Parte Carmody and Ex Parte Desjardins, specifically because it recites an “extremely specific process of training and using a machine-learning model,” e.g. see pg. 13-14 of Remarks – Examiner disagrees.
As an initial matter, Examiner notes that Examiner evaluates the requirements of 35 U.S.C. 101 in view of the disclosures of MPEP 2106, rather than individual PTAB decisions, and hence Examiner declines to comment on the merits of Ex Parte Carmody. Regarding Ex Parte Desjardins, as Applicant notes, e.g. see pgs. 11-12 of Remarks, the MPEP incorporates language from Ex Parte Desjardins, and hence Examiner provides the following analysis regarding the claimed invention in view of the invention of Ex Parte Desjardins. The claimed invention is eminently distinguished from the invention of Ex Parte Desjardins. For example, the present claim language of Claims 1 and 21 recite generating an adjacency matrix from pairwise event relationship metrics, and using the adjacency matrix to predict future events and first, second, and third contagion effects. However, there is no recitation of any kind of training of any kind of machine learning model whatsoever, much less a particular improved method of training a machine learning model. Hence, the claimed invention is not properly analogized to the invention of Desjardins.
Applicants further allege that the claimed invention is patent eligible because it is properly analogized to the invention of Enfish, specifically because the claimed invention improves the speed and accuracy of prediction of future events and further provides increased efficiency and accuracy of machine-learning models, e.g. see pgs. 14-15 – Examiner disagrees.
As stated above, the present claim language does not recite any type of training or configuration of a machine learning and/or artificial intelligence model whatsoever, and hence the Claims do not recite any kind of improvement to the training of a machine learning model. Furthermore, as shown above, the use of an adjacency matrix to predict an event recites the use of a mathematical relationship, formula, equation, and/or mathematical calculations for a specific purpose (i.e. predicting an event). That is, Applicants do not purport to have invented the concept of an adjacency matrix, which itself comprises a mathematical construct, but instead claim a particular usage of the adjacency matrix, without claiming the particulars/details of how the matrix is actually generated and/or used in order to achieve the purported improvements. Additionally, the invention of Enfish recited a self-referential table which resulted in the improvements of increased flexibility, faster search times, and smaller memory requirements. In contrast, the claimed invention does not recite a particular data structure and/or configuration of a database that achieves similar improvements. Hence, the claimed invention is not properly analogized to the invention of Enfish.
For the aforementioned reasons, Claims 1-21 are rejected under 35 U.S.C. 101.
Applicant’s arguments, see Remarks, filed February 18, 2026, with respect to the rejections of Claims 1-4, 9-14, and 20-21 under 35 U.S.C. 102(a)(1) have been fully considered and, in combination with the claim amendments, are persuasive. The rejections of Claims 1-4, 9-14, and 20-21 under 35 U.S.C. 102(a)(1) have been withdrawn. However, for the reasons shown above Claims 1-21 are nonetheless presently rejected under 35 U.S.C. 103.
Applicant’s arguments, see Remarks, filed February 18, 2026, regarding the rejections of Claims 1-21 under 35 U.S.C. 103 have been considered but are moot because the arguments do not apply to any of the references being used in the current rejection. As stated above, the newly amended claim limitations recited in Claims 1-21 have necessitated the new grounds of rejection, and Razo, Philipp, and King-Wilson are now cited to address the newly amended claim limitations. Hence Claims 1-21 are rejected under 35 U.S.C. 103 for the reasons disclosed above.
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.
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/JOHN P GO/Primary Examiner, Art Unit 3681