DETAILED ACTION
1. Claims 1-16 have been presented for examination.
Notice of Pre-AIA or AIA Status
2. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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.
3. Claims 1-16 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e. abstract idea) without anything significantly more.
i) In view of Step 1 of the analysis, claim(s) 1 is directed to a statutory category as a process and claim 9 is directed to an article of manufacture as a non-transitory computer readable medium, which each represent a statutory category of invention. Therefore, claims 1-16 are directed to patent eligible categories of invention.
ii) In view of Step 2A, Prong One, claims 1 and 9 recite the abstract idea of calculating flow demand based on data and calculations by a user which constitutes an abstract idea based on Mental Processes based on concepts performed in the human mind, or with the aid of pencil and paper as well as and alternatively as Mathematical Concepts including mathematical formulas or equations as well as calculations.
As per claim 1, and similarly recited in claim 9, the limitation of “processing the people flow data to generate movement trajectories; mapping the movement trajectories to existing transportation network;” would be analogous to a person determining the movement of people based on data provided and aligning that with a map of given locations and thus fall under Mental Processes. In addition, the steps would constitute Mathematical Concepts including mathematical formulas or equations as well as calculations.
As per claim 1, and similarly recited in claim 9, the limitation of “aggregating the movement trajectories associated with the network of the plurality of sensors to estimate movement flow density; for the people flow data representing less than entire population of the area of interest, performing data simulation to rescale the movement flow density;” would be analogous to a person calculating the movement of a group of people and calculating a scaled version and thus fall under Mental Processes. In addition, the steps would constitute Mathematical Concepts including mathematical formulas or equations as well as calculations.
As per claim 1, and similarly recited in claim 9, the limitation of “classifying the people flow data into different transportation types; and generating flow-demand matching by matching current demand with transportation resource information.” would be analogous to a person classifying the flow data and calculating the demand based on given information and thus fall under Mental Processes. In addition, the steps would constitute Mathematical Concepts including mathematical formulas or equations as well as calculations.
Dependent claims 2-8 and 10-16 further narrow the abstract ideas, identified in the independent claims.
iii) In view of Step 2A, Prong Two, the judicial exception is not integrated into a practical application. Claim 9 recites “non-transitory computer readable medium” merely uses a computer device as a tool to perform the abstract idea. (MPEP 2106.05(f)) The limitation in claim 1, and similarly recited in claims 9 of “collecting people flow data from a network of a plurality of sensors within an area of interest, wherein each sensor of the plurality of sensors is associated with a user device;” are mere instructions to implement an abstract idea using a computer in its ordinary capacity, or merely uses the computer as a tool to perform the identified abstract idea. See MPEP (2106.05(f)) Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a mental process) does not integrate a judicial exception into a practical application. (MPEP 2106.05(f)(2)) Additionally the limitation of “collecting people flow data from a network of a plurality of sensors within an area of interest, wherein each sensor of the plurality of sensors is associated with a user device;” in claims 1 and 9, alternatively can be viewed as insignificant extra-solution activity, specifically pertaining to mere data gathering/output necessary to perform the abstract idea (MPEP 2106.05(g)) and is not sufficient to integrate the judicial exception into a practical application. This is akin to selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display, which has been identified as extra solution activity. Therefore, the judicial exception is not integrated into a practical application.
Dependent claims 2-8, 10-16 further narrow the abstract ideas, identified in the independent claims and do not introduce further additional elements for consideration beyond those addressed above.
iv) In view of Step 2B, claims 1 and 9 do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Claim 9, the additional element of “non-transitory computer readable medium” merely uses a computer device as a tool to perform the abstract idea. (MPEP 2106.05(f)) The limitation in claim 1, and similarly recited in claim 9 of “collecting people flow data from a network of a plurality of sensors within an area of interest, wherein each sensor of the plurality of sensors is associated with a user device;” are mere instructions to implement an abstract idea using a computer in its ordinary capacity, or merely uses the computer as a tool to perform the identified abstract idea. See MPEP (2106.05(f)) Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a mental process) does not integrate a judicial exception into a practical application. (MPEP 2106.05(f)(2)) Additionally the limitation of “collecting people flow data from a network of a plurality of sensors within an area of interest, wherein each sensor of the plurality of sensors is associated with a user device;” in claims 1 and 9, alternatively can be viewed as an insignificant extra-solution activity, specifically pertaining to mere data gathering/output necessary to perform the abstract idea (MPEP 2106.05(g)) and is not sufficient to integrate the judicial exception into a practical application. This is akin to selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display, which has been identified as extra solution activity. Therefore, the claim as a whole does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, when considered alone or in combination, do not amount to significantly more than the judicial exception. As stated in Section I.B. of the December 16, 2014 101 Examination Guidelines, “[t]o be patent-eligible, a claim that is directed to a judicial exception must include additional features to ensure that the claim describes a process or product that applies the exception in a meaningful way, such that it is more than a drafting effort designed to monopolize the exception.”
The dependent claims include the same abstract ideas recited as recited in the independent claims, and merely incorporate additional details that narrow the abstract ideas and fail to add significantly more to the claims.
Dependent claims 2 and 10 further define the trajectory calculation to account for errors which merely narrows the abstract idea identified as a mental process and/or mathematical concepts including mathematical formulas or equations as well as calculations.
Dependent claims 3 and 11 further define the steps involved in people flow calculation which merely narrows the abstract idea identified as a mental process and/or mathematical concepts including mathematical formulas or equations as well as calculations.
Dependent claims 4 and 12 further define scaling calculations which merely narrows the abstract idea identified as a mental process and/or mathematical concepts including mathematical formulas or equations as well as calculations.
Dependent claims 5 and 13 further define demand calculations which merely narrows the abstract idea identified as a mental process and/or mathematical concepts including mathematical formulas or equations as well as calculations.
Dependent claims 6 and 14 further define mode calculations which merely narrows the abstract idea identified as a mental process and/or mathematical concepts including mathematical formulas or equations as well as calculations.
Dependent claims 7 and 15 further define map calculations which merely narrows the abstract idea identified as a mental process and/or mathematical concepts including mathematical formulas or equations as well as calculations.
Dependent claims 8 and 16 can be viewed as an insignificant extra-solution activity, specifically pertaining to mere data gathering/output necessary to perform the abstract idea (MPEP 2106.05(g)) and is not sufficient to integrate the judicial exception into a practical application.
v) Accordingly, claims 1-16 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e. an abstract idea) without anything significantly more.
Appropriate correction is required.
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.
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 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.
4. Claims 1-16 are rejected under 35 U.S.C. 102(a)(1) as being clearly anticipated by Hadjidimitriou, Natalia Selini, Marco Lippi, and Marco Mamei. "A data driven approach to match demand and supply for public transport planning." IEEE Transactions on Intelligent Transportation Systems 22.10 (2020): 6384-6394, hereafter H.
Regarding Claim 1: The reference discloses A method for demand estimation, the method comprising:
collecting people flow data from a network of a plurality of sensors within an area of interest, wherein each sensor of the plurality of sensors is associated with a user device; (H. Abstract, “The estimation of OD flows from mobile phone and GPS positioning data is an important application that can naturally support urban and transport studies. In this work, we first present an approach to generate OD matrices from mobile phone positioning and GPS data, and scale them with traffic counts.”)
processing the people flow data to generate movement trajectories; (H. Page 6386, Section III(A), “OD matrices describing people movements are estimated in two steps. First, the trips of a sample of individual users are identified on the basis of measured movements between cells (time-based trips). Second, individual trips are aggregated to create the OD matrix. The same algorithm can be applied to both GPS and CDR data (see Section V). The aggregation of travels between pairs of grid cells provides information on the demand for transport between ODs.”)
mapping the movement trajectories to existing transportation network; (H. Figure 1 showing movement trajectories of users and buses)
aggregating the movement trajectories associated with the network of the plurality of sensors to estimate movement flow density; (H. Figure 2 and Table 2)
for the people flow data representing less than entire population of the area of interest, performing data simulation to rescale the movement flow density; (H. Figure 4, showing scaled flows)
classifying the people flow data into different transportation types; and (H. Page 6386, top left, “Finally, [39] proposes a two-phases method for flexible bus routes. In the first phase, origins and destinations are clustered using agglomerative clustering. Then, taxi trajectories are used in a routing algorithm that identifies the optimal route of a flexible transport system.”)
generating flow-demand matching by matching current demand with transportation resource information. (H. Page 6386, bottom right, “The result is that, on the one hand, the traffic assignment generated from the OD matrix matches the road-level counts obtained by sensors (where they are available) and forecasts traffic in all the other segments.”)
Regarding Claim 2: The reference discloses The method of claim 1, further comprising: performing trajectory preprocessing to the movement trajectories to compensate for global positioning system (GPS) errors and provide data smoothing to the movement trajectories. (H. Page 6385, bottom left, “Finally, k-means algorithm has been applied by [30] to cluster GPS transit data with the aim to improve their accuracy and remove outliers.”)
Regarding Claim 3: The reference discloses The method of claim 1, wherein the classifying the people flow data into different transportation types comprises:
reading spatial requirements, wherein the spatial requirements comprise geographical constraints; (H. Page 6385, right middle, “The bus network design can be formulated as an optimization problem in which the resources and the costs are minimized, while a set of feasibility constraints are satisfied (i.e., length of the route, frequency, capacity, etc.).”)
reading temporal requirements, wherein the temporal requirements comprise operational time associated with transits and time flexibility; (H. Page 6385, right middle, “The authors underline that the future research direction of transit network problems will take into account more realistic information on passenger behaviour and real time transit information. A methodology to design night bus routes based on GPS coordinates of taxis has been proposed by [28]. The authors identify pick-up and drop-off locations to set the candidate bus stops and propose a heuristic to select k possible routes between the two locations with the maximum number of passengers under time constraints.”)
aggregating the people flow data over spatial segments; (H. Page 6386, Section III(A), “OD matrices describing people movements are estimated in two steps. First, the trips of a sample of individual users are identified on the basis of measured movements between cells (time-based trips). Second, individual trips are aggregated to create the OD matrix. The same algorithm can be applied to both GPS and CDR data (see Section V). The aggregation of travels between pairs of grid cells provides information on the demand for transport between ODs.”)
applying distance filters to filter out local movements, (H. Page 6389, left first paragraph, “The GPS coordinates that are deployed in this work are those that have origin and destination included in the provinces of Modena, Reggio Emilia and Bologna (a spatial filtering process allowed to select such travels) from September to October 2012.”) wherein local movements comprise displacements not associated with transportations and displacements within buildings. (H. Page 6387, bottom left, “Furthermore, we focus the analysis on movements larger than the typical walking distance, and we assume that in our region cycling represent a negligible fraction of movements.”)
reading data on modes of transportation specifications; and (H. Page 6390, left middle, “In general, these two datasets are representative of two opposite trends in big data for mobility. On the one hand, it is possible to have very precise data from a small-medium size population (GPS Data). This kind of data offer a precise estimation of transportation mode and of the path being followed.”)
classifying the people flow data into the different transportation types based on the data on modes of transportation specifications. (H. Page 6385, bottom right paragraph, “Taxi data have been deployed to optimize bus routes in [37]. The authors predict the probability of taking a bus with logistic regression and support vector machines, based on which the optimized bus routes are defined. The authors point out the need to take into account different transport modes.”)
Regarding Claim 4: The reference discloses The method of claim 1, wherein the performing data simulation to rescale the movement flow density comprises: performing spatial indexing and temporal indexing to rescale the movement flow density, wherein the spatial indexing involves generating hash for location referencing, and wherein the temporal indexing involves timestamping. (H. Page 6389, bottom left paragraph, “Each record comprises a user (hashed) id, the MCC (Mobile Country Code) representing the country where the SIM card has been registered, the timestamp of the CDR, the code of the cell tower and the coordinates and coverage radius of the cell tower. Thus, the spatial resolution of CDR localization is the cell radius.”)
Regarding Claim 5: The reference discloses The method of claim 1, further comprising: generating demand-supply matching to estimate demand based on the generated flow- demand matching. (H. Page 6386, Section III(A), “OD matrices describing people movements are estimated in two steps. First, the trips of a sample of individual users are identified on the basis of measured movements between cells (time-based trips). Second, individual trips are aggregated to create the OD matrix. The same algorithm can be applied to both GPS and CDR data (see Section V). The aggregation of travels between pairs of grid cells provides information on the demand for transport between ODs.”)
Regarding Claim 6: The reference discloses The method of claim 5, further comprising: performing mode classification and latent demand estimation on the generated flow- demand matching to generate demand-supply matching. (H. Page 6390, left middle, “In general, these two datasets are representative of two opposite trends in big data for mobility. On the one hand, it is possible to have very precise data from a small-medium size population (GPS Data). This kind of data offer a precise estimation of transportation mode and of the path being followed.”)
Regarding Claim 7: The reference discloses The method of claim 1, further comprising: performing map matching by mapping observed behaviors of the people flow data from the network of the plurality of sensors over edges of existing transportation network based on timestamps. (H. Page 6388, Section IV(A), 2nd paragraph edges with respect to graphs. Page 6389, bottom left, last paragraph, CDR data including timestamps)
Regarding Claim 8: The reference discloses The method of claim 1, wherein the people flow data comprises global positioning system (GPS) data. (H. Abstract, “The estimation of OD flows from mobile phone and GPS positioning data is an important application that can naturally support urban and transport studies. In this work, we first present an approach to generate OD matrices from mobile phone positioning and GPS data, and scale them with traffic counts.”)
Regarding Claim 9: The reference discloses A non-transitory computer readable medium, storing instructions for demand estimation, the instructions comprising: collecting people flow data from a network of a plurality of sensors within an area of interest, wherein each sensor of the plurality of sensors is associated with a user device; processing the people flow data to generate movement trajectories; mapping the movement trajectories to existing transportation network; aggregating the movement trajectories associated with the network of the plurality of sensors to estimate movement flow density; for the people flow data representing less than entire population of the area of interest, performing data simulation to rescale the movement flow density; classifying the people flow data into different transportation types; and generating flow-demand matching by matching current demand with transportation resource information. (See rejection for claim 1)
Regarding Claim 10: The reference discloses The non-transitory computer readable medium of claim 9, further comprising: performing trajectory preprocessing to the movement trajectories to compensate for global positioning system (GPS) errors and provide data smoothing to the movement trajectories. (See rejection for claim 2)
Regarding Claim 11: The reference discloses The non-transitory computer readable medium of claim 9, wherein the classifying the people flow data into different transportation types comprises: reading spatial requirements, wherein the spatial requirements comprise geographical constraints; reading temporal requirements, wherein the temporal requirements comprise operational time associated with transits and time flexibility; aggregating the people flow data over spatial segments; applying distance filters to filter out local movements, wherein local movements comprise displacements not associated with transportations and displacements within buildings; reading data on modes of transportation specifications; and classifying the people flow data into the different transportation types based on the data on modes of transportation specifications. (See rejection for claim 3)
Regarding Claim 12: The reference discloses The non-transitory computer readable medium of claim 9, wherein the performing data simulation to rescale the movement flow density comprises :performing spatial indexing and temporal indexing to rescale the movement flow density, wherein the spatial indexing involves generating hash for location referencing, and wherein the temporal indexing involves timestamping. (See rejection for claim 4)
Regarding Claim 13: The reference discloses The non-transitory computer readable medium of claim 9, further comprising: generating demand-supply matching to estimate demand based on the generated flow- demand matching. (See rejection for claim 5)
Regarding Claim 14: The reference discloses The non-transitory computer readable medium of claim 13, further comprising: performing mode classification and latent demand estimation on the generated flow- demand matching to generate demand-supply matching. (See rejection for claim 6)
Regarding Claim 15: The reference discloses The non-transitory computer readable medium of claim 9, further comprising: performing map matching by mapping observed behaviors of the people flow data from the network of the plurality of sensors over edges of existing transportation network based on timestamps. (See rejection for claim 7)
Regarding Claim 16: The reference discloses The non-transitory computer readable medium of claim 9, wherein the people flow data comprises global positioning system (GPS) data. (See rejection for claim 8)
Conclusion
5. All Claims are rejected.
6. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
i) Chen, Xiaoxuan, et al. "Data‐Driven Prediction System of Dynamic People‐Flow in Large Urban Network Using Cellular Probe Data." Journal of Advanced Transportation 2019.1 (2019): 9401630.
ii) Ali, Akbar, et al. "Mobile crowd sensing based dynamic traffic efficiency framework for urban traffic congestion control." Sustainable Computing: Informatics and Systems 32 (2021): 100608.
iii) U.S. Patent Publication No. 20040088392 which teaches [0007] “Putting together consistent, accurate transportation improvement plans requires models and tools that incorporate an analytical capability that properly accounts for travel demand, human behavior, traffic and transit operations, major investments, and environmental effects. Modeling further benefits from simulated interactions between travelers.”
7. Any inquiry concerning this communication or earlier communications from the examiner should be directed to Saif A. Alhija whose telephone number is (571) 272-8635. The examiner can normally be reached on M-F, 10:00-6:00.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Renee Chavez, can be reached at (571) 270-1104. The fax phone number for the organization where this application or proceeding is assigned is (571) 273-8300. Informal or draft communication, please label PROPOSED or DRAFT, can be additionally sent to the Examiners fax phone number, (571) 273-8635.
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SAA
/SAIF A ALHIJA/Primary Examiner, Art Unit 2186
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