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 Claims
This action is in response to the amendments filed 12/03/2025. Claims 1 and 4 have been amended, claims 1-7 are currently pending.
Response to Arguments
Applicant’s arguments regarding the 101 rejection have been fully considered but they are not persuasive. Applicant argues that at least the amended limitation wherein “the cost function used to measure distance within the time-specific population in the area is learned automatically” cannot be practically performed in the human mind. Examiner respectfully disagrees and notes that the claims do not explain how learning this cost function “automatically” provides a specific technical improvement rather than merely implementing the mental step of learning a cost function to measure distance using a generic computer. This limitation does not integrate the claimed judicial exceptions into a practical application or amount to significantly more than the claimed judicial exceptions (see MPEP 2106.05(f)). The 101 rejections have been updated to include the amended limitations and to clarify the reasoning given for the limitations that were not amended.
Applicant’s arguments regarding the prior art rejection have been fully considered but they are not persuasive. Applicant argues that the Sukenori reference does not teach the limitation wherein “the cost function used to measure distance within the time-specific population in the area is learned automatically”. Examiner respectfully disagrees and notes that at least figure 1 and page 2 paragraph 9 of Sukenori describe a computing device used to calculate the objective, or cost function as described on at least page 6 of Sukenori. The prior art rejections have been updated to include the amended limitations and to clarify the reasoning given for the limitations that were not amended.
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 1-7 are rejected under 35 U.S.C. 101. Claims 1-3 are directed to a method, claims 4-6 are directed to a system, and claim 7 is directed to a non-transitory computer readable storage medium; therefore, claims 1-7 fall within one of the four statutory categories (i.e., process, machine, manufacture, or composition of matter). However, claims 1-7 fall within the judicial exception of an abstract idea, specifically the abstract ideas of “Mental Processes” (including observation, evaluation, and opinion) and “Mathematical Concepts (including mathematical calculations and relationships)”.
Claim 1:
Claim 1 is directed to a method; therefore, the claim does fall within one of the four statutory categories (i.e., process, machine, manufacture, or composition of matter).
Claim 1 recites the following abstract ideas:
estimating a time-specific interareal movement probability, based on observed time-specific population in an area and a set of candidate areas for a movement from the area in a unit time (mental step directed to observation, evaluation – a person could estimate the probability of movement over a specific unit of time between a set of candidate areas based on observed population information at a specific time and for a set of candidate areas. Examiner notes that the broadest reasonable interpretation of estimating this time-specific interareal movement probability also includes a mathematical calculation as shown in Equations 1-7 from pages 4-7 of Applicant’s specification);
and estimating a population in the area at a time at which no observation is performed by using a cost function learned in the estimating of the time-specific interareal movement probability (mental step directed to evaluation – a person could estimate the population in a given area for which there is not observable data in their mind by using a cost function, potentially assisted by pen and paper (see MPEP 2106.04(a)(2)(III). Examiner notes that the broadest reasonable interpretation of using this cost function also includes a mathematical calculation as shown in paragraphs [0042]-[0046] of Applicant’s specification).
Claim 1 recites the following additional elements:
wherein the time-specific area population estimation method is executed by a computer, and wherein the cost function used to measure distance within the time-specific population in the area is learned automatically. These limitations are interpreted as merely implementing the recited abstract ideas using a generic computer and the technological environment in which the abstract ideas are implemented, which does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea (see MPEP 2106.05(f) and MPEP 2106.05(h)).
Claim 4 is a system claim and its limitation is included in claim 1. The only difference is that claim 4 requires a system. Therefore, claim 4 is rejected for the same reasons as claim 1.
Claim 7 is a non-transitory computer readable storage medium claim and its limitation is included in claim 1. The only difference is that claim 7 requires a non-transitory computer readable storage medium. Therefore, claim 7 is rejected for the same reasons as claim 1.
The independent claims are not patent eligible.
Dependent claims 2-3 and 5-6 when analyzed as a whole are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitations fail to establish that the claims are not directed to an abstract idea, as they recite further embellishment of the judicial exception.
Claim 2 recites wherein, in the estimating the time-specific interareal movement probability, the time-specific interareal movement probability is estimated by using a collective flow diffusion model. Estimating the time-specific interareal movement probability using a collective flow diffusion model is interpreted as a mathematical calculation in light of at least paragraphs [0019]-[0026] of Applicant’s specification.
Claim 3 recites wherein the population in the area at the time at which no observation is performed is estimated by computing a Wasserstein Barycenter while using the cost function. Estimating the population by computer a Wasserstein Barycenter while using the cost function is interpreted as a mathematical calculation in light of at least paragraphs [0042]-[0046] of Applicant’s specification. Examiner notes that paragraph [0045] of Applicant’s specification also states wherein the method of computing the Wasserstein Barycenter is well-known, understood, routine, conventional activity in the art (see MPEP 2106.05(d)).
Claim 5 is a system claim and its limitation is included in claim 2. Claim 6 is rejected for the same reasons as claim 2.
Claim 6 is a system claim and its limitation is included in claim 3. Claim 6 is rejected for the same reasons as claim 3.
Viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself. Therefore, the claims are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-2, 4-5, and 7 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Sukenori et al (JP 2018073236 A, herein Sukenori).
Regarding claim 1, Sukenori teaches a time-specific area population estimation method executed by a computer (pg. 14 para. 2 recites “the human flow prediction apparatus 100 according to the present embodiment is configured by a computer apparatus), the method comprising:
estimating a time-specific interareal movement probability, based on observed time-specific population in an area and a set of candidate areas for a movement from the area in a unit time (pg. 1 para. 2 - pg. 2 para. 2 recite “Collective data refers to data obtained by collecting samples with a certain granularity with respect to time and space. Specifically, the number of people who flowed out from a certain observation point i (flowing amount) and the number of people who flowed into a certain observation point i (flowing amount) at a certain observation time t are given. As a conventional technology, the connection between observation points where humans move is expressed in a graph, and the flow of people on the graph is expressed by a probability model, so that the number of people moving between observation points is estimated based on aggregate data, A method (Collective Flow Diffusion Model) for predicting future human flow has been proposed. in such a conventional technique, “the total number of people who flowed out from an arbitrary observation point at a certain observation time t is equal to the total number of people who flow into an arbitrary observation point at the next observation time (t + 1)”. An algorithm that can learn a probability model while estimating the number of people moving between observation points has been proposed by solving an optimization problem that takes into account the constraint of “preserving the number of people moving”. By using the learned probabilistic model, it is possible to predict the amount of inflow to each observation point at a future time t ' when the outflow amount from each observation point at the observation time (t'-1) is given” (i.e., estimating the probability of movement between a set of areas at a specific time based on observed input information related to the population in the areas and a set of candidate areas));
and estimating a population in the area at a time at which no observation is performed by using a cost function learned in the estimating of the time-specific interareal movement probability (pg. 4 para. 2 recites “The predictor flow rate calculation unit 10 is a parameter of a transition probability of moving from an observation point other than the observation point (target observation point to be predicted). The predicted inflow amount at the target observation point at the prediction time is calculated based on the outflow amount from the observation point other than the target observation point for the time before the prediction time”. Pg. 6 para. 4 recites “Estimate parameters of transition probability, number of people moving between observation points, and unknown variables of parameters representing the movement time between observation points. The log likelihood function logP (N .sup.OUT , N .sup.IN | Θ, ∑) is used as an objective function, and a parameter that maximizes the objective function is desired” (i.e., estimating the population in an area at a time where an observation was not recorded using an objective, or cost function)), wherein the cost function used to measure distance within the time-specific population in the area is learned automatically (pg. 2 para. 9 recites “As shown in FIG. 1, the human flow prediction device 100 includes an inflow amount storage unit 1, an outflow amount storage unit 2, an operation unit 3, a search unit 4, a human flow model learning unit 5, a parameter estimation unit 6, and a transition probability parameter storage. The unit 7 includes a moving number storage unit 8, a moving time parameter storage unit 9, a predicted person flow rate calculation unit 10, and an output unit 11”. Pg. 6 para. 4 recites “Estimate parameters of transition probability, number of people moving between observation points, and unknown variables of parameters representing the movement time between observation points. The log likelihood function logP (N .sup.OUT , N .sup.IN | Θ, ∑) is used as an objective function, and a parameter that maximizes the objective function is desired” (i.e., the cost function used to measure the time-specific population can be determined automatically using the human flow prediction device)).
Regarding claim 2, Sukenori teaches the time-specific area population estimation method according to claim 1, wherein, in the estimating the time-specific interareal movement probability, the time-specific interareal movement probability is estimated by using a collective flow diffusion model (pg. 1 para. 2 - pg. 2 para. 2 recite “Collective data refers to data obtained by collecting samples with a certain granularity with respect to time and space. Specifically, the number of people who flowed out from a certain observation point i (flowing amount) and the number of people who flowed into a certain observation point i (flowing amount) at a certain observation time t are given. As a conventional technology, the connection between observation points where humans move is expressed in a graph, and the flow of people on the graph is expressed by a probability model, so that the number of people moving between observation points is estimated based on aggregate data, A method (Collective Flow Diffusion Model) for predicting future human flow has been proposed. in such a conventional technique, “the total number of people who flowed out from an arbitrary observation point at a certain observation time t is equal to the total number of people who flow into an arbitrary observation point at the next observation time (t + 1)”. An algorithm that can learn a probability model while estimating the number of people moving between observation points has been proposed by solving an optimization problem that takes into account the constraint of “preserving the number of people moving”. By using the learned probabilistic model, it is possible to predict the amount of inflow to each observation point at a future time t ' when the outflow amount from each observation point at the observation time (t'-1) is given” (i.e., using a collective flow diffusion model to estimate the probability of movement at a specific time across a set of areas)).
Claim 4 is a system claim and its limitation is included in claim 1. The only difference is that claim 4 requires a system. Therefore, claim 4 is rejected for the same reasons as claim 1.
Claim 5 is a system claim and its limitation is included in claim 2. Claim 6 is rejected for the same reasons as claim 2.
Claim 7 is a non-transitory computer readable storage medium claim and its limitation is included in claim 1. The only difference is that claim 7 requires a non-transitory computer readable storage medium. Therefore, claim 7 is rejected for the same reasons as claim 1.
Claim Rejections - 35 USC § 103
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 3 and 6 are rejected under 35 U.S.C. 103 as being unpatentable over Sukenori et al (JP 2018073236 A, herein Sukenori) in view of Cuturi et al (“Fast Computation of Wasserstein Barycenters”, herein Cuturi).
Regarding claim 3, Sukenori teaches the time-specific area population estimation method according to claim 1, wherein the population in the area at the time at which no observation is performed is estimated [by computing a Wasserstein Barycenter] while using the cost function (pg. 4 para. 2 recites “The predictor flow rate calculation unit 10 is a parameter of a transition probability of moving from an observation point other than the observation point (target observation point to be predicted). The predicted inflow amount at the target observation point at the prediction time is calculated based on the outflow amount from the observation point other than the target observation point for the time before the prediction time”. Pg. 6 para. 4 recites “Estimate parameters of transition probability, number of people moving between observation points, and unknown variables of parameters representing the movement time between observation points. The log likelihood function logP (N .sup.OUT , N .sup.IN | Θ, ∑) is used as an objective function, and a parameter that maximizes the objective function is desired” (i.e., estimating the population in an area at a time where an observation was not recorded using an objective function)).
However, Sukenori does not explicitly teach computing a Wasserstein Barycenter to estimate a population in a given area.
Cuturi teaches computing a Wasserstein Barycenter to estimate a population in a given area (section 4 para. 1 recites “We propose in this section new approaches to compute Wasserstein barycenters when each of the N measures vi is an empirical measure, described by a list of atoms Yi ϵ Ωmi of size mi > 1, and a probability vector bi in the simplex ∑m”. Section 4.4 para. 5 and Algorithm 2 recite “We have proposed two original algorithms to compute Wasserstein barycenters of probability measures: one which applies when the support of the barycenter is fixed and its weights are constrained to lie in a convex subset of the simplex, another which can be used when the support can be chosen freely”. Fig. 3 and section 6.2 para. 2 recite “We illustrate the difference between looking for optimal centroids with “free” assignments, and looking for optimal “uniform” centroids with constrained assignments using US census data for income and population repartitions across 57.647 spatial locations in the 48 contiguous states” (i.e., computing a Wasserstein barycenter as part of a probability estimation to determine the population of an area)).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine these teachings by modifying the objective, or cost function, from Sukenori with the Wasserstein barycenter computation method from Cuturi. The methods taught by Sukenori and Cuturi can both be used as population estimation methods (see at least section 6.2 of Cuturi for this application). As such, one of ordinary skill in the art would recognize that the known objective function from Sukenori as related to population density and movement estimation may be modified by the computation of Wasserstein barycenters, or centroids, from Cuturi to yield a predictable result.
Claim 6 is a system claim and its limitation is included in claim 3. Claim 6 is rejected for the same reasons as claim 3.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
“Cluster-Based Crowd Movement Behavior Detection” (Yang et al) teaches a method for object detection, clustering of various groups of objects, characterizing the movement patterns of the various groups of objects, detecting group events, and finding the change point of group events.
“Advances in Crowd Analysis for Urban Applications Through Urban Event Detection” (Kaiser et al) teaches an overview of various data sources used for different urban crown analysis applications, the state-of-the-art on urban crowd data generation techniques and associated processing methods.
“DeepUrbanEvent: A System for Predicting Citywide Crowd Dynamics at Big Events” (Jiang et al) teaches a model for iteratively taking citywide crowd dynamics from the current one hour as input and reporting the prediction results for the next one hour as output.
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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/L.M.F./ Examiner, Art Unit 2147
/ERIC NILSSON/Primary Examiner, Art Unit 2151
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