EVENT PREDICTION DEVICE AND EVENT PREDICTION METHOD

DETAILED ACTION Claims 1 – 2, 4 and 6 have been presented for examination. Claim 3 and 5 are cancelled. Claims 1, 4 and 6 are currently amended. The instant Office Action is in response to the amendments dated 09/15/2026. 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 . Rejections under 35 U.S.C. § 112(b) (or pre-AJA, 35 U.S.C. § 112, second paragraph) Applicant’s amendments overcome the 112(b) rejection. Therefore, it is withdrawn. Response to Rejections under 35 USC § 101 Applicant’s arguments have been fully considered. However, the Office does not consider them to be persuasive. Applicant argues: “The final wherein clause of claim 1 is a limitation on the configuration of the claimed apparatus, requiring that the recited display of the event prediction apparatus be one that presents a visual representation of the generated prediction as the spatial density map with contour lines. This specifies the output the display must present rather than stating an intended use or a field of use, and the display is therefore not recited at the level of generality of a generic display. The "enabling resource allocation decisions" language is incidental to that requirement. This is not generic data outputting or an intended use of output data; it is a specific manner of presenting information on the interface … The claim does not merely recite displaying data on a generic display; it requires a display that presents the generated prediction as a spatial density map with contour lines, a specific manner of presenting the prediction that integrates the recited mathematical operations into a practical application … For the same reasons set forth above with respect to claim 1 as amended, claim 2 integrates any recited abstract idea into a practical application” (emphasis added) Examiner notes that the “display” is specific only with regard to what data is displayed, and is wholly generic with regard to how the data is tangibly presented. Further, nothing more than a general purpose computer display is required to display the recited output data, notwithstanding the amended contour lines. Considering the second display (i.e., of the remote terminal) does not add anything more than when considering the two displays separately since they do not explicitly interact in the claimed invention and are in separate locations, therefore, would not reasonably be usable by the same person at the same time. Further, there is no explicitly recited interaction with the displayed data beyond merely enabling downstream decisions, and which are not required to be undertaken. Further, the display with contour lines covers well-understood, routine, conventional activity (see Griffiths Page 2, Top “There are various methods in producing the contours, from hand-drawn to computer-generated contour maps. A common method for drawing computer generated contour maps is to interpolate a grid of uniformly spaced values and then contour this grid.”). Therefore, Applicant’s arguments are not persuasive. Applicant argues: “Furthermore, the Office has alleged that "the 'display automatically updates the density representation in real-time' amounts to insignificant data outputting since the 'resource allocation' itself is not explicitly recited being performed." Office Action, page 23. However, claim 4 recites that "the display automatically updates the density representation in real-time as new current event information is received, enabling dynamic resource allocation." This is not mere data outputting; it is a specific technological implementation in which the display automatically updates the density representation in real-time as new current event information is received. USPTO Subject Matter Eligibility Example 42 is instructive: there, a combination of storing information, providing remote access over a network, automatically generating a message whenever information is updated, and transmitting it to users in real time was held to integrate the exception into a practical application … The automatic real-time updating of the density representation as new event information is received is a specific manner of operating the claimed system that goes beyond generic data outputting and integrates any recited abstract idea into a practical application.” (emphasis added) Examiner notes that the examples are for training purposes only and do not have the force of legal precedent. This notwithstanding, Examiner notes that the abstract idea if Example 42 is a method of organizing human activity, and the argued steps of “storing information, providing remote access over a network, automatically generating a message whenever information is updated, and transmitting it to user in real time” were part of the abstract idea. In contrast, the claimed invention is to mathematical concepts, and the recited receiving and storing and displaying steps are additional elements. Examiner further notes that merely updating a display to reflect new data amounts to no more than repeating one or more displaying steps (and further any related receiving or storing steps), since the recited “dynamically” and “real-time” reasonably covers time scales achievable by generic computer hardware (i.e., at least as fast as human user time scales). Looking to the disclosure, there does not appear to be any further requirements or problems being overcome with regard to the display being in “real-time” or “enabling dynamic resource allocation”. Applicant argues: “The Office's citation to the court-recognized listing in MPEP § 2106.05(d)(Il)(i) speaks only to receiving, storing, and displaying data taken individually. No finding has been made as to whether the claimed combination of additional elements was well understood, routine, or conventional. For claims 1 and 2, that combination includes receiving the historical data from the remote terminal, storing it, the automated construction of the prediction formula G(t), and presentation of the generated prediction on the recited display as a spatial density map with contour lines. For claim 4, that combination includes storing the past and current event information, generating the event-specific prediction models automatically upon receipt of the current event information from the first remote device, and the density representation updating in real time on the user interface of the second remote device. Applicant respectfully submits that the Office has not provided the required evidentiary support for a finding that the specific limitations, in the specific combination claimed, are well-understood, routine, and conventional.” (emphasis added) Applicant asserts that evidentiary support is required for the combination of additional claim elements. Looking to the instant rejection, it explicitly concludes that the combination of additional elements does not add anything more than when considering them individually at least since they require no more than generic computer functions, and Applicant has not specifically argued this point. Therefore, the evidentiary request does not rest on any articulated effect of the combination which would be understood by one of ordinary skill in the art as being other than well-understood, routine, conventional activity. Therefore, Applicant’s arguments are not persuasive. Applicant argues: “Moreover, even if one or more additional elements are well-understood, routine, or conventional when considered individually, the combination of additional elements may amount to an inventive concept … Here, the arrangement of the additional elements - the server, the remote terminal or first and second remote devices, generation performed automatically upon receipt of new current event information, and the density display that refreshes in real time - is likewise non-conventional.” (emphasis added) As previous remarked, the “automatically” amounts to no more than repeating one or more displaying steps (and further any related receiving or storing steps), and the “real time” reasonably covers time scales achievable by generic computer hardware. Applicant argues: “The Office's own statement of reasons for allowance states that none of the eight references of record, alone or in combination, discloses the recited claim 1 and claim 2 structure or the claim 4 Laplace transformation of a Green's function transformation using a constant gamma of an inverse time-based matrix.” Examiner notes that novelty of the abstract idea does not transform the claimed invention into eligible subject matter (see MPEP 2106.04(I) “the novelty of the mathematical algorithm is not a determining factor at all")”). Claim Interpretation Claim 1 recites a “server configured to comprising a processor and memory configured to execute computational integration and Laplace transformation operations”. Applicant has previously argued that the “server” is the structure for performing the various functions disclosed in Figure 1 comprising a “history data receiving part” and “prediction part” (see Arguments dated 11/27/2023, Page 7). Therefore, the recited “in a history data receiving part” and “via a predict part” are interpreted as being implemented by server components. Further, the ordinary and customary meaning of a “server” performing calculations given by those of ordinary skill in the art comprises general purpose computer elements (see MPEP 2111.01). Allowable Subject Matter The following is an examiner’s statement of reasons for allowance, subject to overcoming the 101 rejections: None of the prior art of record take together or in combination with the prior art of record disclose the claim 1 (and similarly for claim 2) event prediction apparatus comprising; “ PNG media_image1.png 477 677 media_image1.png Greyscale PNG media_image2.png 360 703 media_image2.png Greyscale ”, in combination with the remaining elements and features of the claim. It is for these reasons that the applicant’s invention defines over the prior art of record. Cheng et al. (US 2019/0391574) teaches anomaly prediction on multi-variate time series data, and determining a precursor feature vector. However does not teach the recited equations in combination with the mathematical quantities. CN109117439 teaches a spatiotemporal co-occurrence pattern of public security events by establishing a probability model. However does not teach the recited equations in combination with the mathematical quantities. Harris et al. (US 2021/0176262) teaches computing a Laplacian matrix and an adjacency matrix for monitoring events for producing latent variables to train models to further improve predictions. However does not teach the recited equations in combination with the mathematical quantities. Kuecuekyan, H. (US 2011/0208681) teaches defined attribute relationships that correlate events and entities, collecting data, parsing the incoming data in the database front-end into structured metadata. However, does not appear to explicitly disclose the recited equations in combination with the mathematical quantities. Clark et al. “An Extended Laplace Approximation Method for Bayesian Inference of Self-Exciting Spatial-Temporal Models of Count Data” teaches self-exciting spatial-temporal model using a Laplace approximation. However, does not appear to explicitly disclose that the prediction model comprises a Laplace transformation of a Green's function transformation using a constant gamma of an inverse time-based matrix of the current event information and the past event information Li et al. (US 2020/0118017) teaches predicting occurrence of future events. However does not teach the recited equations in combination with the mathematical quantities. Bansal et al. (US 10671931) teaches predicting a time series over multiple time intervals based on external data. However does not teach the recited equations in combination with the mathematical quantities. meowoodie/Spatio-Temporal-Point-Process-Simulator [Code Repository] teaches a spatial-temporal point process simulation comprising a diffusion kernel ( PNG media_image3.png 225 624 media_image3.png Greyscale ). However does not teach the recited equations in combination with the mathematical quantities. Kajita et al. “Crime Prediction by Data-Driven Green’s Function method” teaches a data-driven Green’s function method comprising a green function and a Phi function for region k, and performing Laplace transform. However does not teach the Phi function based on matrix c(t), and does not teach the Green function without k. Kajita et al. (WO 2017/222030) teaches a green function (i.e. kernel function) and a Phi function for region k and the rho(t) assuming gamma=1 and delta_t = 1. However does not teach the Phi function based on matrix c(t), and does not teach the Green function without k. Nishiuma et al. (US 2005/0091176) teaches a forecasting apparatus for predicting future events includes a forecast processing data configuring section for configuring a data matrix including previously accumulated historical data and unknown forecast data. However does not teach the recited equations in combination with the mathematical quantities. Okawa et al. (US 2021/0209496) teaches a parameter estimation unit (16) estimates a set of parameters so as to optimize a likelihood function of a strength function expressing the event occurrence probability of a type m space-time event at a time t and a geospatial location s when the strength function is modelled with use of the occurrence probability of the type m space-time event at the time t and the geospatial location s. However does not teach the recited equations in combination with the mathematical quantities. None of the prior art of record taken individually or in combination discloses the claim 4 (and claim 6 by incorporation) method for predicting events, “wherein each of the event-specific prediction models comprises a Laplace transformation of a Green's function transformation using a constant gamma of an inverse time-based matrix of the current event information and the past event information”, in combination with the remaining elements and features of the claim. It is for these reasons that the applicant’s invention defines over the prior art of record. More specifically: Harris et al. (US 2021/0176262) teaches computing a Laplacian matrix and an adjacency matrix for monitoring events for producing latent variables to train models to further improve predictions. However, does not appear to explicitly disclose that the prediction model comprises a Laplace transformation of a Green's function transformation using a constant gamma of an inverse time-based matrix of the current event information and the past event information. Kuecuekyan, H. (US 2011/0208681) teaches defined attribute relationships that correlate events and entities, collecting data, parsing the incoming data in the database front-end into structured metadata. However, does not appear to explicitly disclose that the prediction model comprises a Laplace transformation of a Green's function transformation using a constant gamma of an inverse time-based matrix of the current event information and the past event information. Clark et al. “An Extended Laplace Approximation Method for Bayesian Inference of Self-Exciting Spatial-Temporal Models of Count Data” teaches self-exciting spatial-temporal model using a Laplace approximation. However, does not appear to explicitly disclose that the prediction model comprises a Laplace transformation of a Green's function transformation using a constant gamma of an inverse time-based matrix of the current event information and the past event information Li et al. (US 2020/0118017) teaches predicting occurrence of future events. However, does not appear to explicitly disclose that the prediction model comprises a Laplace transformation of a Green's function transformation using a constant gamma of an inverse time-based matrix of the current event information and the past event information Bansal et al. (US 10671931) teaches predicting a time series over multiple time intervals based on external data. However, does not appear to explicitly disclose that the prediction model comprises a Laplace transformation of a Green's function transformation using a constant gamma of an inverse time-based matrix of the current event information and the past event information. Kajita et al. “Crime Prediction by Data-Driven Green’s Function method” teaches a data-driven method of computing the g function of a self-exciting point process. However, does not appear to explicitly disclose that the prediction model comprises a Laplace transformation of a Green's function transformation using a constant gamma of an inverse time-based matrix of the current event information and the past event information 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.” 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 – 2, 4 and 6 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., an abstract idea) without significantly more. At Step 1, independent claim 1 recites a statutory category (i.e. a machine) event prediction apparatus predicting feature quantity vector ρ(t) at time t based on history data of specific past events, the event prediction apparatus configured to perform steps. At Step 2A, Prong I, the steps comprising: cause construction of a prediction formula G(t), for each location, by performing computational operations comprising: defining a matrix c(t) as an occurrence density of a specific event at time t from an initial time to, where t0 represents an infinitesimal time for computational integration requiring iterative numerical integration over the historical data, determining PNG media_image4.png 45 231 media_image4.png Greyscale by computing statistical averages across multiple initial states in the historical data, determining Laplace transform Φ(z) of Φ(t) using numerical transformation algorithms, and determining Green's function G(z) using a constant gamma as PNG media_image5.png 46 321 media_image5.png Greyscale , where Δt represents a change infinitesimal time for computational integration, wherein the constant gamma is determined from steady-state conditions of the historical data, and determining G(t) by applying an inverse Laplace transform to the Green’s function G(z) to convert from a frequency domain back to a time domain; and generate a prediction, for each location using G(t) and a feature quantity vector p(t) of the specific past events by inputting the future time t into PNG media_image6.png 33 404 media_image6.png Greyscale and computing a convolution operation between G(t) and historical event density At Step 2A, Prong I the recited limitations, alone or in combination, amount to steps that, under its broadest reasonable interpretation, cover mathematical concepts (see MPEP 2106.04(a)(2)(I)). For example, “computational operations” explicitly recites constructing a “formula”. The “defining” and “determining” and “generate a prediction” and “computing” amounts to generation, manipulation and/or usage of specific mathematical expressions. Accordingly, the claim recites an abstract idea. At Step 2A, Prong II, this judicial exception is not integrated into a practical application since the claimed invention further claims: a display of the event prediction apparatus configured to display a spatial density map with contour lines representing locations associated with a greater likelihood of occurrence of future events; a server comprising a processor and memory configured to execute computational integration and Laplace transformation operations, the server configured to perform steps; receive, from a remote terminal, historical data of past events at locations and a future time t; store, in a history data receiving part, the received historical data including the locations; that the computational operations are automated; and cause display, on a display of the remote terminal, the generated prediction across locations for the future time t; a prediction part; wherein the display of the event prediction apparatus presents a visual representation of the generated prediction as the spatial density map with contour lines, enabling resource allocation decisions based on predicted event density of the generated prediction. The “processor” and “memory” and “history data receiving part” and “prediction part” amount to general purpose computer elements (see Claim Interpretation). Further, the “automated” amounts to implementation the mathematical concepts using no more than general purpose computer elements. Further, the “display” and “remote terminal” and “display of the remote terminal” reasonably encompass no more than generic computer components. Therefore, they amount to no more than mere application of the judicial exception using generic computer components which does not amount to an improvement in computer functionality (see MPEP 2106.04(a)(I)). The “receive” and “store” and “cause display” and “display … presents a visual representation … with contour lines” amount to insignificant data gathering and outputting activity since they are recited at a high-level of generality with regarding how the receiving/storing/displaying is tangibly implemented and merely specifying which data is gathered or outputted (see MPEP 2106.05(g)). Examiner notes that the data gathering is wholly the same data which is used by the abstract idea steps, and the data outputted wholly flow from the results of the abstract idea steps. The claim is directed to an abstract idea. At Step 2B, the claim does not recite additional elements that, alone or in an ordered combination, are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the recited “processor“ and “memory” and “prediction part” and “history data receiving part” and “automated” and “display” and “remote terminal” and “display of the remote terminal” amount to no more than mere instructions to apply the judicial exception using generic computer components. The additional elements do not amount to a particulazr machine (see MPEP 2106.05(b)(I)). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The “receive” and “store” and “cause display” and “display … presents a visual representation … with contour lines” amounts to well-understood, routine, conventional activity due to the high-level generality for implementing the activity which reasonably encompasses receiving/storing/displaying using any desired electronic means (see MPEP 2106.05(d)(II)(i) “Receiving or transmitting data over a network … Storing and retrieving information in memory … Presenting offers and gathering statistics”). Further, the visual representation being “with contour lines” covers well-understood, routine, conventional activity (see Griffiths Page 2, Top “There are various methods in producing the contours, from hand-drawn to computer-generated contour maps. A common method for drawing computer generated contour maps is to interpolate a grid of uniformly spaced values and then contour this grid.”). Considering the additional elements in combination does not add anything more than when considering them individually since the “receive” and “store” and “cause display” and “display presents a visual representation” require no more than generic computer functions. Further, the data gathering is merely for data required by the abstract steps, and the data outputted is merely data generating by the abstract idea steps. For at least these reasons, the claim is not patent eligible. At Step 1, independent claim 2 recites a statutory category (i.e. a process) event prediction method predicting feature quantity vector ρ(t) at time t based on history data of specific past events. At Step 2A, Prong I the event prediction method comprising; causing the construction of a prediction formula G(t), for each location, by performing computational operations comprising: defining a matrix c(t) as an occurrence density of a specific event at time t from an initial time to, where t0 represents an infinitesimal time for computational integration requiring iterative numerical integration over the historical data, determining PNG media_image4.png 45 231 media_image4.png Greyscale by computing statistical averages across multiple initial states in the historical data, determining a Laplace transform Φ(z) of Φ(t) using numerical transformation algorithms, determining a Green's function G(z) using a constant gamma as PNG media_image5.png 46 321 media_image5.png Greyscale where Δt represents a change infinitesimal time for computational integration, wherein the constant gamma is determined from steady-state conditions of the historical data, determining G(t) by applying an inverse Laplace transform to the Green’s function G(z) to convert from a frequency domain back to a time domain; and generating a prediction, for each location via a prediction step using G(t) and a feature quantity vector p(t) of the specific past events by inputting the future time t into PNG media_image6.png 33 404 media_image6.png Greyscale and computing a convolution operation between G(t) and historical event density. At Step 2A, Prong I the recited limitations, alone or in combination, amount to steps that, under its broadest reasonable interpretation, cover mathematical concepts (see MPEP 2106.04(a)(2)(I)). For example, “computational operations” explicitly recites constructing a “formula”. The “defining” and “determining” and “generating a prediction” and “computing” amounts to the specific mathematical equations/expressions or mathematical transformations. Accordingly, the claim recites an abstract idea. At Step 2A, Prong II, this judicial exception is not integrated into a practical application since the claimed invention further claims: receiving, by a server comprising a processor and memory configured to execute computational integration and Laplace transformation operations and from a remote terminal, historical data of a past events at locations and a future time t; storing, in a history data receiving part, the received historical data including the locations; that the computation operations are automated; and causing display, on a display of the remote terminal configured to display a spatial density map with contour lines representing location associated with a greater likelihood of occurrence of future events, the generated prediction across locations for the future time t, wherein the display presents a visual representation enabling resource allocation decisions based on predicted event density of the generated prediction. The “processor” and “memory” and “remote terminal” and “history data receiving part” and “display of the remote terminal” reasonably encompass no more than generic computer components. Therefore, they amount to no more than mere application of the judicial exception using generic computer components which does not amount to an improvement in computer functionality (see MPEP 2106.04(a)(I)). Further, the “automated” amounts to implementation the mathematical concepts using no more than general purpose computer elements. Further, the “receiving” and “storing” and “causing display” and “display presents a visual representation” amount to insignificant extra-solution activity since they are recited at a high-level of generality with regard to how the receiving/storing/displaying is implemented (see MPEP 2106.05(g)). The claim is directed to an abstract idea. At Step 2B, the claim does not recite additional elements that, alone or in an ordered combination, are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the “processor” and “memory” and “remote terminal” and “history data receiving part” and “display of the remote terminal” amount to no more than mere instructions to apply the judicial exception using generic computer components. The additional elements do not amount to a particular machine (see MPEP 2106.05(b)(I)). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The “receiving” and “storing” and “causing display” and “display presents a visual representation” amounts to well-understood, routine, conventional activity due to the high-level generality for implementing the activity which reasonably encompasses receiving/storing/displaying using any desired electronic means (see MPEP 2106.05(d)(II)(i) “Receiving or transmitting data over a network … Storing and retrieving information in memory … Presenting offers and gathering statistics”). Considering the additional elements in combination does not add anything more than when considering them individually since the “receiving” and “storing” and “causing display” and “display presents a visual representation” require no more than generic computer functions. For at least these reasons, the claim is not patent eligible. At Step 1, independent claim 4 recites a statutory category (i.e. a process) method for prediction events comprising: generating, upon receiving the current event information and based on the current event information and the past event information, event-specific prediction models comprising function vectors, wherein each of the event-specific prediction models is specific to an event type identified in the current event type information and the event type indicator; computational algorithms that construct the event-specific prediction models through: iterative numerical integration over the past event information and current event information to generate time-correlation matrices, applying Laplace transformation algorithms to convert time-domain matrices to frequency-domain representations, and computing Green's function transformations of the frequency-domain representations; predicting, based on the request information and using a specific event-specific prediction model, a feature quantity vector for the request event time information and for a specific location identified by the event location information; and generating, based on a prediction of the feature quantity vector for the request event time information and across the all locations identified by the event location information, a density representation of the predicted feature quantity vector, wherein each of the event-specific prediction models comprises a Laplace transformation of a Green's function transformation using a constant gamma of an inverse time-based matrix of the current event information and the past event information. At Step 2A, Prong I the recited limitations, alone or in combination, amount to steps that, under its broadest reasonable interpretation, cover mathematical concepts (see MPEP 2106.04(a)(2)(I)). For example, “generating” relies on mathematical vectors to produce a model. The “computational algorithms” recites algorithms that rely on mathematical calculations. The “predicting” and “Laplace transformation of a Green's function transformation” recite generating a mathematical vector using a mathematical “event-specific prediction model”. The “iterative numerical integration” and “applying Laplace transformation algorithms” and “computing Green’s function transformation” recites specific mathematical transformations using further mathematical elements. Looking to the disclosure, there are specific forms disclosed of said transformations. Accordingly, the claim recites an abstract idea. At Step 2A, Prong II, this judicial exception is not integrated into a practical application since the claimed invention further claims: that the “generating” is automatically and by executing, without human intervention the computational algorithms; storing, in a memory of a server, past event information, wherein the past event information comprises: a plurality of history data for a specific event, an event type indicator and an index indicating a type of the specific event, past event time information, past event location information, and past event external factors; receiving, from a first remote device, current event information, wherein the first remote device comprises a terminal, and wherein the current event information comprises: current event type information based on the index of the type of the specific event, current event time information, current event location information, and current event external factors; storing, in the memory, the current event information with the past event information; receiving, from a second remote device, request information comprising: request event type information based on the index of the type of specific event, request event time information, request event location information, and request event external factors; and causing display of the density representation on a user interface of the second remote device configured to display locations associated with a greater likelihood of occurrence of future events, wherein the display automatically updates the density representation in real-time as new current event information is receiving, enable dynamic resource allocation. The “automatically” and “by executing, without human intervention” and “by the server” requires no more than implementing the generating using a server (see Figure 1 a server that implements various functions), and therefore amounts to no more than mere application of the judicial exception using generic computer components which does not amount to an improvement in computer functionality (see MPEP 2106.04(a)(I)). Further, the “memory of a server” and “first remote device” and “terminal” and “second remote device” and “user interface of the second remote device” reasonably encompass no more than generic computer components. Therefore, they amount to no more than mere application of the judicial exception using generic computer components which does not amount to an improvement in computer functionality (see MPEP 2106.04(a)(I)). The “receiving” and “storing” and “causing display” amount to insignificant extra-solution activity since they are recited at a high-level of generality with regard to how the receiving/storing/displaying is implemented apart from the specific data being displayed (see MPEP 2106.05(g)). The “display automatically updates the density representation in real-time” amounts to insignificant data outputting since the “resource allocation” itself is not explicitly recited being performed (i.e., merely enabled), and the “in real-time” appears to derive wholly from the updating being performed when the new data is received as contrasted with updates performed according to timescales relevant to the specific problem being modeled. The claim is directed to an abstract idea. At Step 2B, the claim does not recite additional elements that, alone or in an ordered combination, are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the recited “automatically” and “by executing, without human intervention” and “by the server” and “memory of a server” and “first remote device” and “second remote device” and “user interface of the second remote device” amount to no more than mere instructions to apply the judicial exception using generic computer components. The additional elements do not amount to a particular machine (see MPEP 2106.05(b)(I)). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The “receiving” and “storing” and “causing display” and “display automatically updates the density representation in real-time” amounts to well-understood, routine, conventional activity due to the high-level generality for implementing the activity which reasonably encompasses receiving/storing/displaying using any desired electronic means (see MPEP 2106.05(d)(II)(i) “Receiving or transmitting data over a network … Storing and retrieving information in memory … Presenting offers and gathering statistics”). Considering the additional elements in combination does not add anything more than when considering them individually since the “receiving” and “storing” and “causing display” and “display automatically updates the density representation in real-time” require no more than generic computer functions for operation within human user time scales. Further, the data gathering is merely for data required by the abstract steps, and the data outputted is merely data generating by the abstract idea steps. For at least these reasons, the claim is not patent eligible. Dependent claim 6 recite(s) the same statutory category as the parent claim(s), and further recite(s): claim 6 wherein each of the event-specific prediction models computationally integrates the event external factors as external factors vectors within the Green’s function transformation, such that the external factors modify the time-evolution of the predicted future quantity vector. At Step 2A, Prong I the recited limitations, alone or in combination, amount to steps that, under its broadest reasonable interpretation, cover mathematical concepts (see MPEP 2106.04(a)(2)(I)). For example, “each of the event-specific prediction models computationally integrates” further limits the parent claim “predicting” to recite further mathematical relationships. Accordingly, the claim recites an abstract idea. At Step 2A, Prong II, this judicial exception is not integrated into a practical application since the claimed invention does not further recite any limitations. The claim is directed to an abstract idea. At Step 2B, the claim does not recite additional elements that, alone or in an ordered combination, are sufficient to amount to significantly more than the judicial exception since there are no further recited limitations. For at least these reasons, the claim is not patent eligible. Conclusion THIS ACTION IS MADE FINAL. 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. 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 ALFRED H. WECHSELBERGER whose telephone number is (571)272-8988. The examiner can normally be reached M - F, 10am to 6pm. 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, Emerson Puente can be reached on 571-272-3652. 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. /ALFRED H. WECHSELBERGER/ExaminerArt Unit 2187 /EMERSON C PUENTE/Supervisory Patent Examiner, Art Unit 2187