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 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.
Claim 1 is rejected under 35 U.S.C. 101 because the claimed invention is directed to
an abstract idea (Mathematical Concept) without significantly more.
Regarding claim 1, in Step 1 of the 101 analysis set forth in MPEP 2106, the claim recites a system for determining a variable. A system is one of the four statutory categories of invention.
In Step 2a Prong 1 of the 101 analysis set forth in the MPEP 2106, the examiner has determined
that the following limitations recite a process that, under the broadest reasonable interpretation, covers
a mathematical concept but for recitation of generic computer components:
applying a cost function to each of the one or more simulated routes to determine the quality of the simulated routes, wherein the cost function reflects a time duration required for completion of the route; (using a cost function to determine route quality is an application of a mathematical formula to find a result and is therefore a mathematical concept.)
and computing, using one or more multi-dimensional scaling techniques and based on the distance matrix, one or more virtual locations; (computing a location is an application of a mathematical formula to find a result and is therefore a mathematical concept.)
If claim limitations, under their broadest reasonable interpretation, covers performance of the limitations as a mental process but for the recitation of generic computer components, then it falls within the mental process grouping of abstract ideas. According, the claim “recites” an abstract idea.
In Step 2a Prong 2 of the 101 analysis set forth in MPEP 2106, the examiner has determined that the following additional elements do not integrate this judicial exception into a practical application:
A system comprising: a processing device; and a memory coupled to the processing device and storing instructions that, when executed by the processing device, cause the system to perform operations comprising: initiating, using reinforcement learning techniques, a training phase to train a model, the training phase comprising: (Generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h))
receiving one or more synthetic requests, each of the one or more synthetic requests comprising one or more coordinates randomly generated within a defined first set of constraints, one or more time windows artificially generated within a defined second set of constraints, and one or more time-on- site intervals randomly generated within a defined third set of constraints; (Adding insignificant extra-solution activity (mere data gathering) to the judicial exception (MPEP 2106.05(g))
simulating one or more routes, each of the one or more routes comprising a navigation sequence that includes locations corresponding to each of the one or more synthetic requests; (In step 2A prong 2 simulating data is a mere application of a computer tool (M.L. Model), which is not indicative of integration into a practical application. In step 2B, merely applying a computer tool is not indicative of significantly more.)
and training the model to artificially generate routes based on the determined quality of the simulated routes; (In step 2A prong 2 training a model is a mere application of a computer tool (M.L. Model), which is not indicative of integration into a practical application. In step 2B, merely applying a computer tool is not indicative of significantly more.)
initiating an inference phase, the inference phase comprising: (Generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h))
receiving one or more real-world requests, each of the one or more real- world requests comprising one or more real-world coordinates, one or more real-world time windows, and one or more real-world time-on-site intervals; (Adding insignificant extra-solution activity (mere data gathering) to the judicial exception (MPEP 2106.05(g))
projecting the received one or more real-world requests onto a domain on which the model was trained by: (Adding insignificant extra-solution activity (mere data gathering) to the judicial exception (MPEP 2106.05(g))
generating a distance matrix that reflects a fully-connected graph representing travel times between respective geographic locations corresponding to the one or more real-world requests; using the model as trained based on the simulated routes, generating a route with respect to the one or more virtual locations; (In step 2A prong 2 generating data is a mere application of a computer tool (M.L. Model), which is not indicative of integration into a practical application. In step 2B, merely applying a computer tool is not indicative of significantly more.)
transforming the route, as generated using the model, into one or more real-world geographic coordinates; (In step 2A prong 2 transforming one type of data into another is a mere application of a computer tool (M.L. Model), which is not indicative of integration into a practical application. In step 2B, merely applying a computer tool is not indicative of significantly more.)
and initiating one or more actions with respect to the one or more real-world geographic coordinates. (In step 2A prong 2 performing an action is a mere application of a computer tool (M.L. Model), which is not indicative of integration into a practical application. In step 2B, merely applying a computer tool is not indicative of significantly more.)
Since the claim does not contain any other additional elements that are indicative of integration into a practical application, the claim is “directed” to an abstract idea.
In Step 2b of the 101 analysis set forth in the 2019 PEG, the examiner has determined that the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As discussed above, the additional elements (ii) recite generally linking the use of the judicial exception to a particular technological environment or field of use, (iii, vi, vii, viii) recites mere data gathering, (iv, ix, x, xi) recites a mere application of a computer tool, which is not indicative of significantly more. Considering the additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible.
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.
Claims 1, 15-28 is rejected under 35 U.S.C. 103 as being unpatentable over HAQUE (U.S. Pub. No. US 20200109952 A1) in view of QIN (U.S. Pub. No. US 20190340315 A1) in view of ALLEN (U.S. Pub. No. US 20110054770 A1)
Regarding claim 1, HAQUE substantially teaches the claimed invention, including:
A system comprising: a processing device; and a memory coupled to the processing device and storing instructions that, when executed by the processing device, cause the system to perform operations comprising: initiating, using reinforcement learning techniques, a training phase to train a model, the training phase comprising: ([0030] As noted above, in certain embodiments, the intelligent transportation routing system also inputs training route tiles into an artificial neural network to predict a route-accuracy metric for a region. The route-accuracy metric may take different forms to indicate a subset of training GPS locations (or a subset of training map-matched locations) for a particular region. For example, in some implementations, a route-accuracy metric may be an accuracy classifier that indicates a predicted noise level for a subset of training GPS locations corresponding to a region along the training route.
[0031] When training the artificial neural network, in some embodiments, the intelligent transportation routing system compares a ground-truth-route-accuracy metric to a predicted route-accuracy metric for a given region's training route tile. Based on the comparison between the ground-truth-route-accuracy metric and the predicted route-accuracy metric, the intelligent transportation routing system adjusts parameters of the artificial neural network to reflect the ground-truth-route-accuracy metric. Accordingly, the intelligent transportation routing system can utilize ground-truth-route-accuracy metric to train the artificial neural network to generate improved route accuracy predictions. (the metrics are used to either improve and reinforce the predicted route, acting as a ‘reward’ to train the model. Thus, the metrics used for training are used in such a way that the methodology is the same as reinforcement learning))
While HAQUE does teach utilizing reinforcement learning to train a model, it does not explicitly teach:
receiving one or more synthetic requests, each of the one or more synthetic requests comprising one or more coordinates randomly generated within a defined first set of constraints, one or more time windows artificially generated within a defined second set of constraints, and one or more time-on- site intervals randomly generated within a defined third set of constraints;
However, in analogous art that similarly teaches routing and using models to manage routes, QIN teaches:
receiving one or more synthetic requests, each of the one or more synthetic requests comprising one or more coordinates randomly generated within a defined first set of constraints, one or more time windows artificially generated within a defined second set of constraints, and one or more time-on- site intervals randomly generated within a defined third set of constraints; ([0022] The location may comprise GPS (Global Positioning System) coordinates of a vehicle. [0048] Algorithm 2 may correspond to a Take-1 Action (transporting 1 passenger group). That is, M=1. Given the initial state of S.sub.0, a transportation trip is assigned to the simulated vehicle for which the vehicle can reach the origin O.sub.1 of the transportation trip in a time less than the historical pick-up time of the passenger group. For example, referring to line 4 of Algorithm 2, a transportation request search area can be reduced by finding all historical transportation trips having pickup time in the range of to t.sub.0 (t.sub.0+T) irrespective of the origins of the historical trips, where T defines the search time window (e.g., 600 seconds). Referring to line 5 of Algorithm 2, the transportation trip search area can be further reduced by finding all historical vehicle trips where the simulated vehicle can reach before the historical pickup time from the simulated vehicle's initial state S.sub.0. Here, t(D.sub.0, O.sub.1) can represent the time for advancing from state D.sub.0 to state O.sub.1. Since the historical transportation data can represent when and where transportation demands arise, filtering the transportation request search by historical pick-up time in line 4 can obtain customer candidates matching a time window for potentially being picked up, while ignoring how far or close these customer candidates are. Additionally filtering the transportation request search by proximity to the location of the vehicle in line 5 can further narrow the group of potential customers who are mostly suitable to be picked up from reward maximization. Referring to lines 6-7 of Algorithm 2, if there is no such trip origin, similar to the Algorithm 1, the simulated vehicle continues waiting at its current location l.sub.0 but the time advance to (t.sub.0+t.sub.d) and the state of the vehicle becomes S.sub.1=(l.sub.0, t.sub.0+t.sub.d). And the reward for the waiting action is 0. (it should be noted that the request in this case is used in a simulation, and thus is ‘synthesized’))
It would have been obvious to a person skilled in the art before the effective filing date of the
invention to have combined with QIN‘s synthetic requests and, with HAQUE‘s training using reinforcement learning, with a reasonable expectation of success, a system that receives synthetic requests, as in QIN, used in reinforcement learning, as found in HAQUE. A person of ordinary skill would have been motivated to increase learning efficiency (QIN [0019]).
HAQUE further teaches:
simulating one or more routes, each of the one or more routes comprising a navigation sequence that includes locations corresponding to each of the one or more synthetic requests; ([0115] In addition to training and utilizing an artificial neural network, in certain embodiments, the intelligent transportation routing system 104 synthesizes data to train the artificial neural network. Indeed, as mentioned above, in some embodiments, the intelligent transportation routing system 104 can utilize millions of training route tiles to generate a trained artificial neural network. To reduce the time and expense associated with obtaining training data (e.g., ground-truth route accuracy metrics and training tiles), in one or more embodiments, the intelligent transportation routing system 104 generates synthetic training data.
[0116] In particular, as noted above, the intelligent transportation routing system 104 simulates route locations for a training route and transforms the simulated route locations into simulated training GPS locations. By simulating the training GPS locations along a training route, the intelligent transportation routing system can generate and use synthetic route tiles as training route tiles. FIG. 7 provides an example of the intelligent transportation routing system 104 generating training route tiles and ground-truth-route-accuracy metrics based on simulated route locations and simulated training GPS locations.)
QIN further teaches:
applying a cost function to each of the one or more simulated routes to determine the quality of the simulated routes, wherein the cost function reflects a time duration required for completion of the route; ([0020] The disclosed systems and methods provide algorithms for constructing a vehicle navigation environment (also referred to as a simulator) for training an algorithm or a model based on historical data (e.g., various historical trips and rewards with respect to time and location). From the training, the algorithm or the model may provide a trained policy. The trained policy may maximize the reward to the vehicle driver, minimize the time cost to the passengers, maximize the efficiency of the vehicle platform, maximize the efficiency of the vehicle service, and/or optimize other parameters according to the training. The trained policy can be deployed on servers for the platform and/or on computing devices used by the drivers.)
HAQUE further teaches:
and training the model to artificially generate routes based on the determined quality of the simulated routes; ([0123] After determining the ground-truth-route-accuracy metrics 712a-712c, the intelligent transportation routing system 104 can use the synthetic route tiles 710a-710c and the ground-truth-route-accuracy metrics 712a-712c to train an artificial neural network. Accordingly, in some embodiments, the training route tiles 402a-402n shown in FIG. 4 may be synthetic route tiles 710a-710c shown in FIG. 7. Similarly, the ground-truth-route-accuracy metrics 404a-404n shown in FIG. 4 may be the ground-truth-route-accuracy metrics 712a-712c.) initiating an inference phase, the inference phase comprising: receiving one or more real-world requests, each of the one or more real- world requests comprising one or more real-world coordinates, one or more real-world time windows, and one or more real-world time-on-site intervals; ([0054] In addition to communicating with the provider client device 116 to receive GPS locations, the intelligent transportation routing system 104 optionally stores data corresponding to each route traveled and each transportation request on a transportation routing database 106 accessed by the server(s) 102. Accordingly, the server(s) 102 may generate, store, receive, and transmit various types of data, including, but not limited to, GPS locations, map-matched locations, location information, price estimates, estimated times of arrival, pickup locations, dropoff locations, and other data stored in the transportation routing database 106. In some such embodiments, the intelligent transportation routing system 104 organizes and stores such data in the transportation routing database 106 by geographic area, requestor, and/or time period.) projecting the received one or more real-world requests onto a domain on which the model was trained by: generating a distance matrix that reflects a fully-connected graph representing travel times between respective geographic locations corresponding to the one or more real-world requests; ([0081] The intelligent transportation routing system 104 generates each image matrix by segmenting an estimated path into l×l regions and representing the data in an m×m image matrix, where m=l/g and g represents pixel granularity in meters per pixel. Accordingly, the first image matrix 322a represents an m×m image matrix for the first estimated path according to the GPS locations 318c in the region 306a, and the second image matrix 322b represent an m×m image matrix for the second estimated path according to the subset of map-matched locations 320c in the region 306b. Together, the route tile 324 includes pixels with m×m×2 matrices. In certain embodiments, the intelligent transportation routing system 104 generates a route tile with m×m×2 matrices for each of the regions 302a-316a and 302b-316b.) and computing, using one or more multi-dimensional scaling techniques and based on the distance matrix, one or more virtual locations; ([0081] In the embodiment shown in FIG. 3B, the first and third sets of pixels have one numeric value (shown as ones) to depict an estimated path of the client device. By contrast, the second and fourth sets of pixels have another numeric value (shown as zeros) to depict positions outside an estimated path of the client device. Continuing the example from above using l×l square meters for each region, in certain embodiments, each pixel includes either (a) the number one to represent the presence of the client device within the region or (b) zero to represent the absence of the client device. The intelligent transportation routing system 104 generates each image matrix by segmenting an estimated path into l×l regions and representing the data in an m×m image matrix, where m=l/g and g represents pixel granularity in meters per pixel. Accordingly, the first image matrix 322a represents an m×m image matrix for the first estimated path according to the GPS locations 318c in the region 306a, and the second image matrix 322b represent an m×m image matrix for the second estimated path according to the subset of map-matched locations 320c in the region 306b. Together, the route tile 324 includes pixels with m×m×2 matrices. In certain embodiments, the intelligent transportation routing system 104 generates a route tile with m×m×2 matrices for each of the regions 302a-316a and 302b-316b.) using the model as trained based on the simulated routes, generating a route with respect to the one or more virtual locations; ([0159] As noted above, the intelligent transportation routing system 104 trains an artificial network. In some such embodiments, generating the trained artificial neural network comprises generating a training route tile for a training region along a training route based on training GPS locations and corresponding training map-matched locations; utilizing an artificial neural network to predict a route-accuracy metric for the training region based on the training route tile; and generating the trained artificial neural network by comparing the route-accuracy metric to a ground-truth-route-accuracy metric for the training region.
[0160] Relatedly, in some implementations, generating the simulated training GPS locations comprises creating simulated route locations for the training route within a road network based on standard-traveling patterns of transportation vehicles;)
While HAQUE, as modified by QIN, does teach generating a route with simulated data, it does not explicitly teach:
transforming the route, as generated using the model, into one or more real-world geographic coordinates; and initiating one or more actions with respect to the one or more real-world geographic coordinates.
However, in analogous art that similarly calculates a route, ALLEN teaches:
transforming the route, as generated using the model, into one or more real-world geographic coordinates; ([0110] The wireless communication further comprises one or more processors and one or more memories configured at least in part to provide a processing module configured to process the route input with respect to a reference coordinate system, to generate processed route information indicative of the geographic route.) and initiating one or more actions with respect to the one or more real-world geographic coordinates. ( [0113] The receiving device may comprise a processing module configured to process the processed route information received from a transmitting device. Such processing may be performed at least in part based on the reference coordinate system and an output reference map to generate a route output indicative of a geographic route relative to the output reference map, the output user interface further configured to present the route output relative to the output reference map.)
It would have been obvious to a person skilled in the art before the effective filing date of the
invention to have combined with ALLEN‘s route transformation and, with HAQUE‘s, as modified by QIN, simulated routes, with a reasonable expectation of success, a system transforms routes into geographic coordinates, as in ALLEN, using simulated routes trained on synthetic data, as found in HAQUE, as modified by QIN. A person of ordinary skill would have been motivated to improve accuracy (ALLEN [0004]).
Regarding claim 15, it comprises of limitations similar to those of claim 1, and is therefore rejected for similar rationale.
Regarding claim 16, HAQUE further teaches:
The method of claim 15, wherein the training phase further comprises: simulating one or more routes, each of the one or more routes comprising a navigation sequence that includes locations corresponding to each of the one or more synthetic requests. ([0115] In addition to training and utilizing an artificial neural network, in certain embodiments, the intelligent transportation routing system 104 synthesizes data to train the artificial neural network. Indeed, as mentioned above, in some embodiments, the intelligent transportation routing system 104 can utilize millions of training route tiles to generate a trained artificial neural network. To reduce the time and expense associated with obtaining training data (e.g., ground-truth route accuracy metrics and training tiles), in one or more embodiments, the intelligent transportation routing system 104 generates synthetic training data.
[0116] In particular, as noted above, the intelligent transportation routing system 104 simulates route locations for a training route and transforms the simulated route locations into simulated training GPS locations. By simulating the training GPS locations along a training route, the intelligent transportation routing system can generate and use synthetic route tiles as training route tiles. FIG. 7 provides an example of the intelligent transportation routing system 104 generating training route tiles and ground-truth-route-accuracy metrics based on simulated route locations and simulated training GPS locations.)
Regarding claim 17, QIN further teaches:
The method of claim 16, wherein the training phase further comprises: applying a cost function to each of the one or more simulated routes to determine the quality of the simulated routes, wherein the cost function reflects a time duration required for completion of the route. ([0020] The disclosed systems and methods provide algorithms for constructing a vehicle navigation environment (also referred to as a simulator) for training an algorithm or a model based on historical data (e.g., various historical trips and rewards with respect to time and location). From the training, the algorithm or the model may provide a trained policy. The trained policy may maximize the reward to the vehicle driver, minimize the time cost to the passengers, maximize the efficiency of the vehicle platform, maximize the efficiency of the vehicle service, and/or optimize other parameters according to the training. The trained policy can be deployed on servers for the platform and/or on computing devices used by the drivers.)
Regarding claim 18, HAQUE further teaches:
The method of claim 17, wherein the training phase further comprises: training the model based on the determined quality of the simulated routes. ([0123] After determining the ground-truth-route-accuracy metrics 712a-712c, the intelligent transportation routing system 104 can use the synthetic route tiles 710a-710c and the ground-truth-route-accuracy metrics 712a-712c to train an artificial neural network. Accordingly, in some embodiments, the training route tiles 402a-402n shown in FIG. 4 may be synthetic route tiles 710a-710c shown in FIG. 7. Similarly, the ground-truth-route-accuracy metrics 404a-404n shown in FIG. 4 may be the ground-truth-route-accuracy metrics 712a-712c.)
Regarding claim 19, HAQUE further teaches:
The method of claim 15, wherein the inference phase further comprises: projecting the received one or more real-world requests onto a domain on which the model was trained by: generating a distance matrix that reflects a fully-connected graph representing travel times between respective geographic locations corresponding to the one or more real-world requests; ([0081] The intelligent transportation routing system 104 generates each image matrix by segmenting an estimated path into l×l regions and representing the data in an m×m image matrix, where m=l/g and g represents pixel granularity in meters per pixel. Accordingly, the first image matrix 322a represents an m×m image matrix for the first estimated path according to the GPS locations 318c in the region 306a, and the second image matrix 322b represent an m×m image matrix for the second estimated path according to the subset of map-matched locations 320c in the region 306b. Together, the route tile 324 includes pixels with m×m×2 matrices. In certain embodiments, the intelligent transportation routing system 104 generates a route tile with m×m×2 matrices for each of the regions 302a-316a and 302b-316b.) and computing, using one or more multi-dimensional scaling techniques and based on the distance matrix, one or more virtual locations; ([0081] In the embodiment shown in FIG. 3B, the first and third sets of pixels have one numeric value (shown as ones) to depict an estimated path of the client device. By contrast, the second and fourth sets of pixels have another numeric value (shown as zeros) to depict positions outside an estimated path of the client device. Continuing the example from above using l×l square meters for each region, in certain embodiments, each pixel includes either (a) the number one to represent the presence of the client device within the region or (b) zero to represent the absence of the client device. The intelligent transportation routing system 104 generates each image matrix by segmenting an estimated path into l×l regions and representing the data in an m×m image matrix, where m=l/g and g represents pixel granularity in meters per pixel. Accordingly, the first image matrix 322a represents an m×m image matrix for the first estimated path according to the GPS locations 318c in the region 306a, and the second image matrix 322b represent an m×m image matrix for the second estimated path according to the subset of map-matched locations 320c in the region 306b. Together, the route tile 324 includes pixels with m×m×2 matrices. In certain embodiments, the intelligent transportation routing system 104 generates a route tile with m×m×2 matrices for each of the regions 302a-316a and 302b-316b.)
Regarding claim 20, HAQUE further teaches:
The method of claim 19, wherein the inference phase further comprises:using the model as trained based on the simulated routes, generating a route with respect to the one or more virtual locations. ([0159] As noted above, the intelligent transportation routing system 104 trains an artificial network. In some such embodiments, generating the trained artificial neural network comprises generating a training route tile for a training region along a training route based on training GPS locations and corresponding training map-matched locations; utilizing an artificial neural network to predict a route-accuracy metric for the training region based on the training route tile; and generating the trained artificial neural network by comparing the route-accuracy metric to a ground-truth-route-accuracy metric for the training region.
[0160] Relatedly, in some implementations, generating the simulated training GPS locations comprises creating simulated route locations for the training route within a road network based on standard-traveling patterns of transportation vehicles;)
Regarding claim 21, ALLEN further teaches:
The method of claim 20, wherein the inference phase further comprises:transforming the route, as generated using the model, into one or more real- world geographic coordinates. ([0110] The wireless communication further comprises one or more processors and one or more memories configured at least in part to provide a processing module configured to process the route input with respect to a reference coordinate system, to generate processed route information indicative of the geographic route.)
Regarding claims 22-28, they comprise of limitations similar to those of claims 15-21, and are therefore rejected for similar rationale.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SKIELER A KOWALIK whose telephone number is (571)272-1850. The examiner can normally be reached 8-5.
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, Mariela D Reyes can be reached at (571)270-1006. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/SKIELER ALEXANDER KOWALIK/Examiner, Art Unit 2142
/Mariela Reyes/Supervisory Patent Examiner, Art Unit 2142