DETAILED ACTION
This Final Office Action is in response to the argument and amendment filed June 03, 2026.
Claims 1 and 10 are amended.
Claims 2-8 are originals.
Claims 9 is Canceled.
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 .
Claim Objections
Claim 2 is objected to because of the following informalities: "an area of a polygon or a length of a link" because claim 1 already recites an area of a polygon and area of a link. Claim 2 should recite “the” instead of “an” and “a”, respectively. Appropriate correction is required.
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 1-8 and 10 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more.
Step 1 (The Statutory Categories): Is the claim to a process, machine, manufacture, or composition of matter? MPEP 2106.03.
Per Step 1, claims 1-8 is to a device and Claim 10 is directed to a software.
Thus, the claims are directed to statutory categories of invention. However, the claims are rejected under 35 U.S.C. 101 because they are directed to an abstract idea, a judicial exception, without reciting additional elements that integrate the judicial exception into a practical application.
The analysis proceeds to Step 2A Prong One.
Step 2A Prong One: Does the claim recite an abstract idea, law of nature, or natural phenomenon? MPEP 2106.04.
The abstract idea of claim 1;
A population output device comprising processing circuitry configured to:
store an estimation model that receives an input of area information related to an area and including information related to a population of the area and information related to a summation value, for each type of a map element, related to one or more map elements constituting map data of the area, and outputs population information related to a population estimated for each type of the map element of the area;
acquire the area information related to a target area that is an area to be targeted; and
output the population information related to the target area, the population information being output by inputting the acquired area information related to the target area to the stored estimation model,
wherein the processing circuitry is further configured to:
convert map geometry data, which is an area of a polygon or a length of a link, into population information for each separate map element,
receive, via a network, positional data from a plurality of user terminals based on each user terminal sensing a position of the user terminal,
receive weather information corresponding to the timing of the positional data, and
train the estimation model based on the area information related to the area and information related to a population for each type of the map element of the area by aggregating the positional data,
wherein the processing circuitry is configured to store the trained estimation model.
The abstract idea steps italicized above are those which could be performed mentally, including with pen and paper. The steps describe, at a high level, storing an estimated information, receiving, acquiring, processing, converting, training and estimating using a device. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, including learning and estimating using a device, and/or opinions, then it falls within the Mental Processes – Concepts Performed in the Human Mind grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two: Does the claim recite additional elements that integrate the judicial exception into a practical application? MPEP 2106.04.
This judicial exception is not integrated into a practical application because the additional elements are merely instructions to apply the abstract idea to a computer, as described in MPEP 2106.05(f).
Claim 1 and 10 recites the following additional elements: population output device, an acquisition model, user terminals, trained model, estimation model, non-transitory computer readable medium and a neural network.
These elements are merely instructions to apply the abstract idea to a computer, per MPEP 2106.05(f). Applicant has only described generic computing elements in their specification, as seen in [0051] of applicant’s specification as filed, for example.
Further, the combination of these elements is nothing more than a generic computing system applied to the tasks of the abstract idea. Because the additional elements are merely instructions to apply the abstract idea to a generic computing system, they do not integrate the abstract idea into a practical application, when viewed in combination. See MPEP 2106.05(f).
Therefore, per Step 2A Prong Two, the additional elements, alone and in combination, do not integrate the judicial exception into a practical application. The claim is directed to an abstract idea.
Step 2B (The Inventive Concept): Does the claim recite additional elements that amount to significantly more than the judicial exception? MPEP 2106.05.
Step 2B involves evaluating the additional elements to determine whether they amount to significantly more than the judicial exception itself.
The examination process involves carrying over identification of the additional element(s) in the claim from Step 2A Prong Two and carrying over conclusions from Step 2A Prong Two pertaining to MPEP 2106.05(f).
The additional elements and their analysis are therefore carried over: applicant has merely recited elements that facilitate the tasks of the abstract idea, as described in MPEP 2106.05(f).
Further, the combination of these elements is nothing more than a generic computing system. When the claim elements above are considered, alone and in combination, they do not amount to significantly more.
Therefore, per Step 2B, the additional elements, alone and in combination, are not significantly more. The claims are not patent eligible.
The analysis takes into consideration all dependent claims as well:
Dependent claims 2 – 8 contain additional steps that further narrow the abstract idea above.
Claim 2-8 recites the following additional elements: population output device. Applicant has only described generic computing elements in their specification, as seen in {[0015 - 0018]} of applicant’s specification as filed. This does not integrate the abstract idea into practical application and/or add significantly more. The claim is ineligible. Refer to MPEP 2106.05(F).
Accordingly, claims 1-8 and 10 are rejected under 35 USC § 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-8 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Takashi et al [JP 2020155799 A], hereafter Takashi, in view of Motonari et al [JP 2013/12,1073A], hereafter Motonari, in view of Mann et al [KR 2019/ 010, 4822A], hereafter Mann, in further view of Yeon et al [KR 2020/002,5392], hereafter Yeon.
As per claim 1,
Takashi discloses the device and the storage unit;
A population output device comprising processing circuitry configured to:
store an estimation model that receives
{[Page 3, paragraph, 6] The server 100 is provided in the mobile communication network 1, and is composed of, for example, a single computer device or a plurality of computer devices or processors. By executing a predetermined program, the server 100 is at least one device of a mobile station number estimation device and a population estimation device.
[Page 5, Paragraph 6] Further, the control log information including the connection start time and the connection end time is collected by the server 100 and recorded in the information storage unit 102, but may be collected by a device different from the server 100. For example, the base station 20 may collect and record the data, transmit the data to the server 100 at a predetermined timing, and record the data in the information storage unit 102 of the server 100.}
Takashi discloses the population estimation unit area;
and outputs population information related to a population estimated for each type of the map element of the area;
{[Page 13, paragraph 4] In population estimation, it is often required to estimate the population in mesh units according to the method of dividing the area used in city planning and disaster prevention measures. Therefore, in the present embodiment, as an example, this mesh is used as a population estimation unit area, and a process of converting the population estimation value of each cell of each time window into the population estimation value of each mesh of each time window is performed (S6).)}
Takashi discloses the characteristic information of the population estimation unit area;
acquire the area information related to a target area that is an area to be targeted; and
{[Page 2, description] Further, in the information processing system, the population estimation unit has at least the ratio of the area of the overlapping portion between the cell and the population estimation unit area to the area of the cell and the population distribution characteristic information of the population estimation unit area. One of them may be used to correct the estimation result of the population of the population estimation unit area in the specific time zone.
[Page 3, Paragraph 1] The base station is formed at a specific time zone based on the acquisition of control log information of communication between the mobile station and the base station and the control log information including the connection start time and the connection end time. It includes estimating the number of mobile stations in the cell, and estimating the population of the population estimation unit area in the specific time zone based on the estimation result.}
Takashi discloses the information and characteristics of the estimation unit area;
output the population information related to the target area, the population information being output by inputting the acquired area information related to the target area to the stored estimation model,
{[Page 2, description] Further, in the information processing system, the population estimation unit has at least the ratio of the area of the overlapping portion between the cell and the population estimation unit area to the area of the cell and the population distribution characteristic information of the population estimation unit area. One of them may be used to correct the estimation result of the population of the population estimation unit area in the specific time zone.}
Takashi disclose the conversion of the estimation value of each cell time window into the population estimation value of each mesh, See [Page 13, paragraph 4]
wherein the processing circuitry is further configured to: convert map geometry data, which is an area of a polygon or a length of a link, into population information for each separate map element,
{[Page 13, paragraph 4] In population estimation, it is often required to estimate the population in mesh units according to the method of dividing the area used in city planning and disaster prevention measures. Therefore, in the present embodiment, as an example, this mesh is used as a population estimation unit area, and a process of converting the population estimation value of each cell of each time window into the population estimation value of each mesh of each time window is performed (S6).)}
Takashi does not explicitly disclose the neural network; However; Yeon discloses the model applying various analysis tools such as machine learning and neural network to acquire an area information;
{[Page 9, paragraph 3] Meanwhile, the weight value of the floating population integrated model is based on the relationship between the total floating population measured value for the plurality of target positions and the floating population by the same type of facility calculated by the second floating population calculating unit 30, and various regression analysis. It can be modeled by applying various analysis tools including machine learning through various algorithms such as statistical techniques, neural networks, and SVM.}
Takashi does not explicitly disclose the training and gathering of information by the information acquisition unit, however Yeon discloses training the floating population model using various methods such as regression analysis, algorithms and machine learning tools, to analyze correlation between population data and other statistical factors.
train the estimation model based on the area information related to the area and information related to a population for each type of the map element of the area by aggregating the positional data,
{[Page 6, paragraph 3] The floating population detailed model is based on actual floating population data, and includes various regression analysis methods to analyze the correlation between floating population number, facility factor and distance factor, various statistical techniques, neural network, support vector machine (SVM), etc. It can be modeled by applying various analysis tools including machine learning through various algorithms. As an example, the floating population detailed model may be expressed as a function of the facility and distance factors as independent variables, and the floating population by the facility as a dependent variable.}
Takashi does not explicitly disclose the detailed storage of information by the model, however; Yeon discloses the storage unit;
wherein the processing circuitry is configured to store the trained estimation model.
{[Page 5, paragraph 3] The detailed model storage unit 10 stores the detailed information on the floating population for estimating the floating population attracted by the facilities located around the target position with respect to the predetermined target position to be the floating population estimation target. The floating population detailed model models the floating population attracted by each facility among the floating population at the target location, and defines the correlation between the floating population, facility factor, and distance factor by each facility at the target location.}
Motivation: The combination would have been obvious because a person of ordinary skill in the art, since Takashi’s population output device with the inclusion of Yeon’s neural network to acquire the area information, modeling the floating population model using various tools such as machine learning and storing the information to determine the information related to a specific target area. See Yeon [Page 9, paragraph 3], [Page 5, paragraph 3] and [Page 6, paragraph 3].
The combination of Takashi and Yeon does not explicitly disclose the area information and the information related to a summation value or the total value, however; Motonari discloses the map elements such as the sum values, the totaling unit that aggregates the statistical value “summation value” of each map element, area distribution or cluster;
an input of area information related to an area and including information related to a population of the area and information related to a summation value, for each type of a map element, related to one or more map elements constituting map data of the area,
{[Page 3, paragraph 2, Description] Based on an area distribution unit that distributes area data to each cluster, and a plurality of area data distributed by the area distribution unit, By determining the position where the number of users in each cluster is concentrated based on a plurality of area data distributed by the totaling unit that aggregates the statistical value for each cluster and the area distribution unit, the position is determined for each cluster.
[Page 4, Paragraph 1] By determining each of the clusters to which the area corresponding to the plurality of area data belongs, An area distribution step for distributing data to each cluster, a totaling unit for totaling statistical values for each cluster based on a plurality of area data distributed by the area distribution unit, and a cluster center determining unit, A cluster center determining step for determining a position where the number of users in each cluster is concentrated based on a plurality of area data distributed by the area distributing unit to determine the position as the center position of each cluster; In the area distribution step, a plurality of area data are redistributed to each cluster with reference to the center position determined by the cluster center determination unit.
[Page 22, paragraph 1] Finally, the totaling unit 609 calculates the population for each cluster by counting the statistical value “population” for each cluster ID for the plurality of point data created in step S110. Furthermore, the totaling unit 609 determines the geographical range of each cluster based on the center position of each cluster (step S111).}
The combination of Takashi and Yeon does not explicitly disclose the collection of information by several mobile stations however Motonari discloses obtaining an area data from total number of registered locations or terminals using a GPS positioning system;
receive, via a network, positional data from a plurality of user terminals based on each user terminal sensing a position of the user terminal,
{[Page 7, paragraph 2] Further, as the position information to be totaled in the area data, GPS positioning data obtained by GPS positioning may be used. Further, the number of users to be aggregated may be the number of location registration signals or the number of terminals themselves, or may be the number of users estimated from the weight value calculated from the transmission density of location registration signals, or the weight value. It may be the number of users obtained from itself, or may be converted into a population for each area by performing various calculations such as multiplying the number of users by various coefficients.}
The combination of Takashi and Yeon does not explicitly disclose receiving a weather data and time intervals, however; Motonari discloses receiving and outputting a location information collected by a predetermined timing;
receive weather information corresponding to the timing of the positional data, and
{[Page 6, paragraph 3] Receive location information when The exchange 400 stores the received location information, and outputs the location information collected at a predetermined timing or in response to a request from the management center 500 to the management center 500 via the communication network….. The various processing nodes 700 acquire the location information of the mobile device 100 through the RNC 300 and the exchange 400, perform recalculation of the location in some cases, and collect at a predetermined timing or in response to a request from the management center 500 The obtained position information is output to the management center 500.
Motivation: The combination would have been obvious because a person of ordinary skill in the art, since the combination of Takashi and Yeon’s population output device with the inclusion of Motonari’s area information and the information related to a summation value or the total value, area data by GPS positioning system, and receiving a weather data and time intervals to determine the information related to a specific target area. See Motonari [Page 3, paragraph 2, Description], [Page 4, Paragraph 1], [Page 22, paragraph 1], [Page 7, paragraph 2] and [Page 6, paragraph 3].
As stated above, Takashi discloses the conversion of the estimation value of each cell of each time window into the population estimation value of each mesh, see [Page 13, paragraph 4] however, it does not explicitly disclose the area of the polygon using the five base stations and a living population at a specific point in time, However Mann discloses the five base stations representing an area of a polygon and the collection of information by several mobile stations;
{[Page 6, paragraph 1] Referring to FIG. 2B, the living population at a specific time point may be created based on the most recent signal and the base station where the signal is caught at a specific time point of the domestic communication subscriber. For example, a base station may be calculated as a base station A at a first time point, a base station B at a second time point, a base station C at a third time point, a base station C at a fourth time point, and a base station A at a fifth time point.}
Motivation: The combination would have been obvious because a person of ordinary skill in the art, since the combination of Takashi, Yeon and Motonari’s population output device with the inclusion of Mann’s five base stations representing an area of a polygon to determine the actual information collected by the base stations in a specific target area. See Mann [Page 6, paragraph 1]
As per claim 2;
The combination of Takashi, Yeon and Motonari does not explicitly disclose the summation of the map elements, however; Mann discloses the sum of the population related to the map elements; such as number of traffic passengers, the building type, the road type, and time;
The population output device according to The population output device according to wherein a summation target of the summation value related to the map element includes at least one of the number of the map elements, an area of a polygon indicating the map element, or a length of a link indicating the map element.
{[Page 2, paragraph 3] a weight allocation model is generated by combining the resident registration population, the number of business employees, the number of traffic passengers, the building type, the road type, and the area by use based on the day and time. can do. To represent the existing population, individual final positions for each time zone can be used to produce a sum of the population for a specific time zone similar to the total population. Based on the time of stay and the number of days of stay in the night time zone, it is possible to determine whether a person is staying or visiting and the number of days of stay.}
Mann discloses the five base stations which represents an area of a polygon and the collection of information by several mobile stations;
{[Page 6, paragraph 1] Referring to FIG. 2B, the living population at a specific time point may be created based on the most recent signal and the base station where the signal is caught at a specific time point of the domestic communication subscriber. For example, a base station may be calculated as a base station A at a first time point, a base station B at a second time point, a base station C at a third time point, a base station C at a fourth time point, and a base station A at a fifth time point.}
Motivation: The combination would have been obvious because a person of ordinary skill in the art, since the combination of Takashi, Yeon and Motonari’s population output device with the inclusion of Mann’s sum of map elements and five base stations representing an area of a polygon to determine the actual information collected by the base stations in a specific target area. See Mann [Page 6, paragraph 1].
As per claim 3;
The combination of Takashi, Yeon and Motonari does not disclose the following, however; Mann discloses the characteristics of the map such as children park, high school;
The population output device according to The population output device according to wherein the type of the map element includes at least one of a facility, a park, a station, a house, an office, a restaurant, an event venue, a lake, a river, a mountain, a road, or a railroad.
{[Page 7, paragraph 1] 3A is an exemplary view for explaining a process of verifying the accuracy of the estimation result of the living population for the grand park of the young according to an embodiment of the present invention. Referring to FIG. 3A, the children's grand park automatically counts the number of visitors using a sensor. At this time, the county district containing the Children's Grand Park has the characteristics that the area A and the Hwayoung Arts High School, B, are located in an area that is not recognized by the number of visitors. In the Children's Grand Park, the graph of the number of visitors and the population of living shows that the number of visitors in the operating hours and the population of living are significantly related.}
Motivation: The combination would have been obvious because a person of ordinary skill in the art, since the combination of Takashi, Yeon and Motonari’s population output device with the inclusion of Mann’s map characteristics to determine the actual information collected by the base stations in a specific target area. See Mann [Page 7, paragraph 1].
As per claim 4;
Takashi discloses the environmental characteristics of each mesh data;
The population output device according to wherein the area information further includes environmental data related to an environment.
{[Page 14, paragraph 1] In the present embodiment, the estimated population value for each cell area is distributed to the estimated population value for each mesh by area apportionment. However, in simple area apportionment, the population estimation value for each cell area is distributed to the population estimation value for each mesh according to the area ratio of each mesh in the cell. In this case, the characteristics of the basic population distribution for each mesh are not taken into consideration, and the accuracy of the estimated population value for each mesh is reduced. For example, meshes in water bodies such as the sea, rivers, lakes, and ponds are characterized by a significantly smaller or virtually zero population distribution ratio compared to meshes in the above-ground areas. In addition, even in the above-ground area, for example, a mesh that is an area such as a mountainous area or a forest (forest area) has a significantly smaller proportion of population distribution than a mesh in an area such as an urban area (other areas).}
As per claim 5;
Takashi discloses the he turning off of the base stations;
The population output device according to wherein the environment includes at least one of a timing at which the population is measured or weather of the area at the timing.
{[Page 4, paragraph 6] The timing at which the mobile stations 10A and 10B start connecting to the base station 20 is, for example, when the power of the mobile stations 10A and 10B is turned on (when the power is turned on) in the cell of the base station 20. When the idle mobile stations 10A and 10B disconnected from 20 are reconnected to the base station 20 (at the time of selection), the mobile stations 10A and 10B are in a communication state and the base station 20 is connected to the base station 20. This is when the states of the mobile stations 10A and 10B transition from the out-of-service state to the in-service state in the cell of the base station 20 (at the time of the in-service transition). In the present embodiment, a time indicating at least one of these timings is collected and accumulated as a connection start time to the base station 20.}
As per claim 6;
Takashi discloses the population ratio of an area and also discloses the characteristics of the estimated unit area;
The population output device according to wherein the population information is a population ratio estimated for each type of the map element, and the processing circuitry is configured to compute a population estimated for each type of the map element of the target area based on the population information related to the target area and a population of the target area, and further output the computed population.
{[Page 2, Description] Further, in the information processing system, the population estimation unit has at least the ratio of the area of the overlapping portion between the cell and the population estimation unit area to the area of the cell and the population distribution characteristic information of the population estimation unit area. One of them may be used to correct the estimation result of the population of the population estimation unit area in the specific time zone.}
As per claim 7;
The combination of Takashi, Yeon and Motonari does not disclose the following, however; Mann discloses the characteristics of the map such as children park, high school;
The population output device according to wherein the processing circuitry is configured to compute a population estimated for each map element based on the computed estimated population for each type of the map element of the target area and information related to the map element, and further output the computed population.
{[Page 7, paragraph 1] 3A is an exemplary view for explaining a process of verifying the accuracy of the estimation result of the living population for the grand park of the young according to an embodiment of the present invention. Referring to FIG. 3A, the children's grand park automatically counts the number of visitors using a sensor. At this time, the county district containing the Children's Grand Park has the characteristics that the area A and the Hwayoung Arts High School, B, are located in an area that is not recognized by the number of visitors. In the Children's Grand Park, the graph of the number of visitors and the population of living shows that the number of visitors in the operating hours and the population of living are significantly related.}
Motivation: The combination would have been obvious because a person of ordinary skill in the art, since the combination of Takashi, Yeon and Motonari’s population output device with the inclusion of Mann’s map characteristics to determine the actual information collected by the base stations in a specific target area. See Mann [Page 7, paragraph 1].
As per claim 8;
Takashi discloses the population estimation unit area;
The population output device according to claim 1, wherein the processing circuitry is configured to compute a population estimated for each map element based on the population information related to the target area and information related to the map element of the target area, and further output the computed population.
{[Page 13, paragraph 4] In population estimation, it is often required to estimate the population in mesh units according to the method of dividing the area used in city planning and disaster prevention measures. Therefore, in the present embodiment, as an example, this mesh is used as a population estimation unit area, and a process of converting the population estimation value of each cell of each time window into the population estimation value of each mesh of each time window is performed (S6).)}
As per claim 10;
Takashi discloses the device and the storage unit;
An non-transitory computer readable medium that stores estimation model
{[Page 3, paragraph, 6] The server 100 is provided in the mobile communication network 1, and is composed of, for example, a single computer device or a plurality of computer devices or processors. By executing a predetermined program, the server 100 is at least one device of a mobile station number estimation device and a population estimation device.
[Page 5, Paragraph 6] Further, the control log information including the connection start time and the connection end time is collected by the server 100 and recorded in the information storage unit 102, but may be collected by a device different from the server 100. For example, the base station 20 may collect and record the data, transmit the data to the server 100 at a predetermined timing, and record the data in the information storage unit 102 of the server 100.}
Takashi discloses the characteristic information of the population estimation unit area;
that is a trained model used by a population output device including processing circuitry configured to acquire area information related to an area and including information related to a population of the area
{[Page 2, description] Further, in the information processing system, the population estimation unit has at least the ratio of the area of the overlapping portion between the cell and the population estimation unit area to the area of the cell and the population distribution characteristic information of the population estimation unit area. One of them may be used to correct the estimation result of the population of the population estimation unit area in the specific time zone.
[Page 3, Paragraph 1] The base station is formed at a specific time zone based on the acquisition of control log information of communication between the mobile station and the base station and the control log information including the connection start time and the connection end time. It includes estimating the number of mobile stations in the cell, and estimating the population of the population estimation unit area in the specific time zone based on the estimation result.}
Takashi discloses the population estimation unit area;
and to output population information related to a population estimated for each type of the map element of the area,
{[Page 13, paragraph 4] In population estimation, it is often required to estimate the population in mesh units according to the method of dividing the area used in city planning and disaster prevention measures. Therefore, in the present embodiment, as an example, this mesh is used as a population estimation unit area, and a process of converting the population estimation value of each cell of each time window into the population estimation value of each mesh of each time window is performed (S6).)}
Takashi discloses the information and characteristics of the area;
the processing circuitry configured to output the population information related to a target area that is an area to be targeted, the population information being output by inputting the acquired area information related to the target area to the estimation model,
{[Page 2, description] Further, in the information processing system, the population estimation unit has at least the ratio of the area of the overlapping portion between the cell and the population estimation unit area to the area of the cell and the population distribution characteristic information of the population estimation unit area. One of them may be used to correct the estimation result of the population of the population estimation unit area in the specific time zone.}
Takashi disclose the conversion of the estimation value of each cell of each time window into the population estimation value of each mesh, See [Page 13, paragraph 4]
wherein the processing circuitry is further configured to: convert map geometry data, which is an area of a polygon or a length of a link, into population information for each separate map element,
{[Page 13, paragraph 4] In population estimation, it is often required to estimate the population in mesh units according to the method of dividing the area used in city planning and disaster prevention measures. Therefore, in the present embodiment, as an example, this mesh is used as a population estimation unit area, and a process of converting the population estimation value of each cell of each time window into the population estimation value of each mesh of each time window is performed (S6).)}
Takashi does not explicitly disclose the area information and the information related to a summation value or the total value however; Motonari discloses the sum values related to the map elements;
and information related to a summation value, for each type of a map element, related to one or more map elements constituting map data of the area,
{[Page 3, paragraph 2, Description] Based on an area distribution unit that distributes area data to each cluster, and a plurality of area data distributed by the area distribution unit, By determining the position where the number of users in each cluster is concentrated based on a plurality of area data distributed by the totaling unit that aggregates the statistical value for each cluster and the area distribution unit, the position is determined for each cluster.
[Page 4, Paragraph 1] By determining each of the clusters to which the area corresponding to the plurality of area data belongs, An area distribution step for distributing data to each cluster, a totaling unit for totaling statistical values for each cluster based on a plurality of area data distributed by the area distribution unit, and a cluster center determining unit, A cluster center determining step for determining a position where the number of users in each cluster is concentrated based on a plurality of area data distributed by the area distributing unit to determine the position as the center position of each cluster; In the area distribution step, a plurality of area data are redistributed to each cluster with reference to the center position determined by the cluster center determination unit.
[Page 22, paragraph 1] Finally, the totaling unit 609 calculates the population for each cluster by counting the statistical value “population” for each cluster ID for the plurality of point data created in step S110. Furthermore, the totaling unit 609 determines the geographical range of each cluster based on the center position of each cluster (step S111).}
Takashi does not explicitly disclose the collection of information by several mobile stations however Motonari discloses the obtaining an area data by GPS positioning system;
receive, via a network, positional data from a plurality of user terminals based on each user terminal sensing a position of the user terminal,
{[Page 7, paragraph 2] As this area, the serving sector of the BTS 200 may be used as it is, or an area divided into a predetermined shape such as a mesh shape may be adopted, or the serving sectors that overlap each other among a plurality of types of BTS 200 may be adopted. May be adopted as different areas. Further, as the position information to be totaled in the area data, GPS positioning data obtained by GPS positioning may be used. Further, the number of users to be aggregated may be the number of location registration signals or the number of terminals themselves, or may be the number of users estimated from the weight value calculated from the transmission density of location registration signals, or the weight value. It may be the number of users obtained from itself, or may be converted into a population for each area by performing various calculations such as multiplying the number of users by various coefficients.}
Takashi does not explicitly disclose receiving a weather data and time intervals, however; Motonari discloses;
receive weather information corresponding to the timing of the positional data, and
{[Page 6, paragraph 3] Receive location information when The exchange 400 stores the received location information, and outputs the location information collected at a predetermined timing or in response to a request from the management center 500 to the management center 500 via the communication network….. The various processing nodes 700 acquire the location information of the mobile device 100 through the RNC 300 and the exchange 400, perform recalculation of the location in some cases, and collect at a predetermined timing or in response to a request from the management center 500 The obtained position information is output to the management center 500.
Motivation: The combination would have been obvious because a person of ordinary skill in the art, since Takashi’s population output device with the inclusion of Motonari’s area information and the information related to a summation value or the total value, area data by GPS positioning system, and receiving a weather data and time intervals to determine the information related to a specific target area. See Motonari [Page 3, paragraph 2, Description], [Page 4, Paragraph 1], [Page 22, paragraph 1], [Page 7, paragraph 2] and [Page 6, paragraph 3].
The combination of Takashi and Motonari does not explicitly disclose the neural network; However; Yeon discloses the model applying various analysis tools such as a neural network to acquire the area information;
wherein the estimation model is configured by a neural network that has learned a weighting coefficient based on the area information related to the area and information related to a population for each type of the map element of the area, and
{[Page 9, paragraph 3] Meanwhile, the weight value of the floating population integrated model is based on the relationship between the total floating population measured value for the plurality of target positions and the floating population by the same type of facility calculated by the second floating population calculating unit 30, and various regression analysis. It can be modeled by applying various analysis tools including machine learning through various algorithms such as statistical techniques, neural networks, and SVM.}
The combination of Takashi and Motonari does not explicitly disclose the neural network; However; Yeon discloses the model applying various analysis tools such as a neural network to acquire the area information;
{[Page 9, paragraph 3] Meanwhile, the weight value of the floating population integrated model is based on the relationship between the total floating population measured value for the plurality of target positions and the floating population by the same type of facility calculated by the second floating population calculating unit 30, and various regression analysis. It can be modeled by applying various analysis tools including machine learning through various algorithms such as statistical techniques, neural networks, and SVM.}
The combination of Takashi and Motonari does not explicitly disclose the training and gathering of information by the information acquisition unit, however Yeon discloses modeling the floating population model using various tools such as machine learning;
train the estimation model based on the area information related to the area and information related to a population for each type of the map element of the area by aggregating the positional data,
{[Page 6, paragraph 3] The floating population detailed model is based on actual floating population data, and includes various regression analysis methods to analyze the correlation between floating population number, facility factor and distance factor, various statistical techniques, neural network, support vector machine (SVM), etc. It can be modeled by applying various analysis tools including machine learning through various algorithms. As an example, the floating population detailed model may be expressed as a function of the facility and distance factors as independent variables, and the floating population by the facility as a dependent variable.}
The combination of Takashi and Motonari does not explicitly disclose the storing detailed information by the model, however; Yeon discloses;
wherein the processing circuitry is configured to store the trained estimation model.
{[Page 5, paragraph 3] The detailed model storage unit 10 stores the detailed information on the floating population for estimating the floating population attracted by the facilities located around the target position with respect to the predetermined target position to be the floating population estimation target. The floating population detailed model models the floating population attracted by each facility among the floating population at the target location, and defines the correlation between the floating population, facility factor, and distance factor by each facility at the target location.}
Motivation: The combination would have been obvious because a person of ordinary skill in the art, since the combination of Takashi and Motonari population output device with the inclusion of Yeon’s neural network to acquire the area information, modeling the floating population model using various tools such as machine learning and storing the information to determine the information related to a specific target area. See Yeon [Page 9, paragraph 3], [Page 5, paragraph 3] and [Page 6, paragraph 3].
Response to Argument
In response to the argument filled on June 03, 2026, regarding the 101 rejections. Examiner withdraws the 112(f) and 101 (software per se), in view of the comment made on June 03, 2026.
Regarding the 101 rejection, Applicant argues that the amended claims recites that the processing circuitry is configured to "convert map geometry data, which is an area of a polygon or a length of a link, into population information for each separate map element, receive, via a network, positional data from a plurality of user terminals based on each user terminal sensing a position of the user terminal, receive weather information corresponding to the timing of the positional data, and train the estimation model based on the area information related to the area and information related to a population for each type of the map element of the area by aggregating the positional data, wherein the processing circuitry is configured to store the trained estimation model.”
Examiner Respectfully disagrees.
The Examiner notes that the aspect of converting, receiving, positioning, timing, aggregating and training the estimation model the Examiner viewed as steps of the identified abstract idea in the Step 2A Prong 1 Analysis and the Estimation model as an additional element in the Step 2A Prong 2 Analysis. Therefore, the Examiner maintains the Mental Processes – Concepts Performed in the Human Mind grouping of abstract idea.
Applicant argues that the amended claims clarify how the training of the model is explicitly based on capturing user positional data received via a network and aggregating the positional data and also a technological improvement.
Examiner Respectfully disagrees.
The Examiner notes that the training of the model is/are merely generic technology with no technical improvement rather an improvement to the abstract idea using generic technology. See Applicant specification [0051].
The courts consider a mental process (thinking) that "can be performed in the human
mind, or by a human using a pen and paper" to be an abstract idea. CyberSource Corp. v. Retail
Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011). As the Federal
Circuit explained, "methods which can be performed mentally, or which are the equivalent of
human mental work, are unpatentable abstract ideas the ‘basic tools of scientific and
technological work’ that are open to all.’" 654 F.3d at 1371, 99 USPQ2d at 1694 (citing
Gottschalk v. Benson, 409 U.S. 63, 175 USPQ 673 (1972)). See also Mayo Collaborative Servs.
v. Prometheus Labs. Inc., 566 U.S. 66, 71, 101 USPQ2d 1961, 1965 (2012) Mental processes [] and abstract intellectual concepts are not patentable, as they are the basic tools of scientific and
technological work’" (quoting Benson, 409 U.S. at 67, 175 USPQ at 675)); Parker v. Flook, 437
U.S. 584, 589, 198 USPQ 19 3, 197 (1978).
The Examiner maintains these claims recite an abstract idea.
Therefore, for the foregoing reasons the Examiner has maintained the 35 USC 101 rejection.
In terms of the 103 rejections, Applicant’s arguments with respect to claim(s) 1-8 and 10 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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.
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/VICTOR ESONU/
Examiner, Art Unit 3629
/SARAH M MONFELDT/Supervisory Patent Examiner, Art Unit 3629
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