DETAILED ACTION
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 01/20/2026 has been entered.
Claims 1 – 2, 4 and 6 have been presented for examination. Claim 3 and 5 are cancelled. Claims 1 – 2, 4 and 6 are currently amended.
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 .
Response to Objection to the Claims
Applicant’s amendments overcome the claim objection. Therefore, it is withdrawn.
Response to Rejection of Claims 1-2 under 35 USC § 112(b)
Applicant’s amendments overcome the 112(b) rejection. Therefore, it is withdrawn. However, new 112(b) rejection is included.
Response to Rejection of Claim 1-2 and 4-6 under 35 USC § 101
Applicant’s arguments have been fully considered. However, the Office does not consider them to be persuasive.
Applicant argues: “The specification describes, in paragraphs [0003]-[0004] and [0007], how conventional SEPP models and Expectation Maximization methods suffer from poor accuracy with small datasets. Claims 1, 2, and 4 solve a specific technical problem in computational prediction systems - accurately predicting spatiotemporal event density with limited training data…
Further, the claims relate to a non-abstract implementation of the prediction formula. The prediction formula construction describes actual computational integration over historical event data, not mere mathematical abstraction” (emphasis added)
Applicant argues that the problem being overcome relates to computing predictions, including computing integrals over data, as contrasted with mere mathematical abstraction. Examiner notes that the specificity of the recited mathematical concepts does not transform the claimed invention into eligible subject matter, and that mathematical concepts are explicitly one of the three grouping of “abstract idea” (see MPEP 2106.04(a)). To the extent that the computational prediction are part of “system”, their physical implementation requires no more than general purpose computer components.
Applicant argues: “The claims improve computer technology itself by providing a more computationally efficient prediction methodology … the system requires fewer computational cycles… the system efficiently updates the predictions upon receiving new data without complete retraining of the model … reduces memory requirements compared to storing individual event effects.” (emphasis added)
Applicant argues that the improvement derives from the “computationally efficient prediction methodology”. Examiner notes that merely operating a computer more efficiently due to an improved algorithm does not amount to a special purpose computer since it requires no more than general purpose computer functions.
Applicant argues: “Further, the application explicitly identifies a technical problem in the art: existing prediction models (like SEPP or Prospective Hotspot Method) fail to provide accurate results when the "number of data is small". See paragraphs [0007]-[0008] of the application as filed. The claims recite a specific non-conventional mathematical architecture … the equations are not merely "math on a computer"; they are the specific configuration of the prediction tool that solves the problem of sparse data analysis, which is a technical improvement in the field of data processing.” (emphasis added)
Applicant explicitly argues that the problem being overcome relates to the accuracy of a mathematical prediction model. Examiner notes that the claimed invention amounts to math on a computer since, although the recited mathematical concepts have a specific “mathematical architecture”, they tangible execution on the “server configured to” requires no more than general purpose computer elements. Further, the improvement is wholly deriving from the abstract idea.
Applicant argues: “The claims are patent-eligible because they require specific, unconventional computer processing steps that transform data in a meaningful way. Also, the specific mathematical framework (equations in claims) is not a general-purpose mathematical tool but a specialized computational method. As such, the claims do not relate to a generic, general-purpose computer.” (emphasis added)
Applicant argues that the recites steps are “unconventional computer processing steps”. Although the steps themselves are novel and non-obvious, their implementation requires no more than general purpose computer functions. Examiner notes that an abstract idea implemented using generic computer components does not amount to a particular machine (see MPEP 2106.05(b)(I) “The particularity or generality of the elements of the machine or apparatus, i.e., the degree to which the machine in the claim can be specifically identified (not any and all machines).”).
Applicant argues: “Further, the application describes how it produces a concrete result; the output is an actionable density representation displayed on specific interfaces for directing resource allocation (e.g., patrol activities, [0042]).”
Examiner notes that merely displaying results for potential future actions amounts to insignificant data outputting since said action are not positively recited. Looking to the instant claims, they merely recite enabling further “decisions” which covers mental processes.
Applicant argues: “The application describes how the claims are tied a practical use in that the result is used to "display locations associated with a greater to likelihood ... to direct attention," which facilitates real-world actions (e.g., police patrolling or resource allocation). … Further, the claims cannot be conventional under Berkheimer v. HP Inc. The Examiner stated in the Reasons for Allowance that the prior art does not teach the recited equations. Since the specific combination of mathematical steps is novel and non-obvious, it cannot be "well understood, routine, and conventional" under Berkheimer v. HP Inc.”
Examiner notes that merely displaying results for potential future actions amounts to insignificant data outputting since said action are not positively recited. Looking to the instant claims, they merely recite enabling further “decisions” which covers mental processes. Further, novelty in the abstract does not take away from any well-understood, routine, and conventional steps as the additional elements.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 4 and 6 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
With regard to claim 4, it recites the limitation “wherein the event-specific prediction model” in “wherein the event-specific 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”. There is insufficient antecedent basis for this limitation in the claim since there is previously recited “using a specific event-specific prediction model” from the previously recited “automatically generating … event-specific prediction models”. Therefore, the recited “the event-specific prediction model” could refer back to any of the automatically generated models, and not necessarily the specific one used. The limitation is interpreted for examination purposes as referring back to “a specific event-specific prediction model”.
With regard to claim 6, it is rejected by virtue of its dependency on a rejected parent claim, and without reciting additional limitations to overcome the deficiency.
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 112 and 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;
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, 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.
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 (
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). 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 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”, 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
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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
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, 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
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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 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 presents a visual representation 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 present 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 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 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 “receive” and “store” and “cause 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 “receive” and “store” and “cause 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 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
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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
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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
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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 event-specific prediction model 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 the event-specific 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. 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; that the “selecting” is by the server; 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 and selecting 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. 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. 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”). 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 event-specific prediction model 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 event-specific prediction model 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
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.
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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.
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/ALFRED H. WECHSELBERGER/ExaminerArt Unit 2187
/EMERSON C PUENTE/Supervisory Patent Examiner, Art Unit 2187