Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This action is a responsive to the application filed on 12/11/2025.
Claims 2-21 are pending.
Claims 2-6, 9-13, and 16-20 have been amended.
Claim 1 has been previously canceled.
Response to Arguments
Applicant’s arguments, with respect to the Double Patenting rejections of claims 2-21, have been fully considered and are persuasive. Therefore, the rejections set forth in the previous office action have been withdrawn.
Applicant’s arguments, with respect to the rejection(s) of claim(s) 2-21 under 35 U.S.C. 101, have been fully considered and are persuasive. Therefore, the objections set forth in the previous office action have been withdrawn.
Applicant’s arguments, with respect to the rejection(s) of claim(s) 2, 9, and 16 under 35 U.S.C. 103, have been considered but are moot because the arguments do not apply to the current combination of references being used in the current rejection.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 2-3, 5-7, 9-10, 12-14, 16-17, and 19-21 are rejected under 35 U.S.C. 103 as being unpatentable over Nha et al (“A Comparative Study of Vehicles’ Routing Algorithms for Route Planning in Smart Cities”, 2012) hereinafter Nha, in view of Mohammad Mirzaei et al (US Pub 20160171390) hereinafter Mirzaei.
Regarding claims 2, 9, and 16, Nha teaches a method comprising; system comprising: one or more processors; and a non-transitory computer-readable medium including one or more sequences of instructions that, when executed by the one or more processors, cause the one or more processors to perform operations comprising; non-transitory computer-readable medium including one or more sequences of instructions that, when executed by one or more processors, cause the one or more processors to perform operations comprising (section 4 teaches simulating in a “program” via “code” on a computer system known to include one or more processors communicatively coupled to one or more memories for executing code to perform the embodiments of the disclosure):
executing, by a computing device, a mapping application an application component on the computing device (section 4 teaches simulating in a “program” via “code” on a computer system for vehicle route plotting);
transmitting, by the computing device to an Artificial Intelligence (AI) controller, state information relating to the mapping application component for selection of an Al algorithm from a plurality of Al algorithms for selecting a route to a destination location (section 3A teaches “information that can be used as input for vehicles routing algorithms, which are: Road information: current traffic conditions like congestion level, incidents and weather conditions etc. Destination information: the purpose of travel. Mobile information: such as, the remaining fuel, vehicle related conditions, traveler-related conditions etc. In addition to these inputs, we propose to incorporate the vehicles characteristics and road traffic prediction information since these two information will influence the best route selection.” Sections 3C-4 further teach “calculate an initial best route from the origin location of the vehicle to its desired destination according to a chosen algorithm (e.g. Dijkstra, Genetic Algorithm etc.)”, including hybrid algorithm combinations with a GA; wherein the input information is communicated to an executed algorithm SUMO simulation (AI controller) from a TRACI API on the computer system.);
configuring the mapping application to use the selected AI algorithm to select the route to the destination location (sections 3C-4 teach “calculate an initial best route from the origin location of the vehicle to its desired destination according to a chosen algorithm (e.g. Dijkstra, Genetic Algorithm etc.)” in the simulation software.); and
executing using, by the computing device, the mapping application configured in accordance with the identified Al algorithm for executing the application component to select the route to the destination location (sections 3C-4 further teach “calculate an initial best route from the origin location of the vehicle to its desired destination according to a chosen algorithm (e.g. Dijkstra, Genetic Algorithm etc.)”).
However, Nha does not explicitly teach wherein the Al controller selects the Al algorithm from the plurality of Al algorithms based at least on the state information; receiving, by the computing device from the Al controller, a communication identifying the selected Al algorithm.
Mirzaei teaches wherein the Al controller selects the Al algorithm from the plurality of Al algorithms based at least on the state information (paragraphs 0004, 0027, 0038-0041, and 0049-0050 teach computing cores communicating different computational parameters, including setting “static parameters for adapting the execution of the application and/or complex algorithms used by the application” and indications of levels of tolerance for program performance corresponding to “a change in a state of the application (state information)”. The adapting includes selecting/updating the proper “complex algorithm” from the complex algorithms (selects the Al algorithm from the plurality of Al algorithms), and these are further taught to be “machine learning algorithms”.);
receiving, by the computing device from the Al controller, a communication identifying the selected Al algorithm (paragraphs 0004, 0027, 0038-0041, and 0049-0050 teach computing cores communicating different computational and machine learning parameters (by the computing device from the Al controller), relating to “adapting the execution of the application and/or complex algorithms used by the application” and indications of levels of tolerance for program performance corresponding to “a change in a state of the application”. The adapting includes selecting/updating the proper “complex algorithm” from the complex algorithms (communication identifying the selected Al algorithm), and these are further taught to be “machine learning algorithms”.).
Further Nha at least implies transmitting, by the computing device to an Artificial Intelligence (AI) controller (see mappings above), however Mirzaei teaches transmitting, by the computing device to an Artificial Intelligence (AI) controller (paragraphs 0004, 0027, 0038-0041, and 0049-0050 teach computing cores communicating different computational and machine learning parameters for implementation and execution).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to implement machine learning algorithm selection/adjustment/execution based on monitoring/inferring user input data as taught by Mirzaei into Nha’s teaching of monitoring of optimal route mapping algorithm performance and choosing in order to improve complex model and application performance and selection according to user parameters (Mirzaei, paragraphs 0027, 0038-0041, and 0049-0050).
Regarding claims 3, 10, and 17, the combination of Nha and Mirzaei teach all the claim limitations of claims 2, 9, and 16 above; and further teach wherein, the state information comprises: a plurality of entities that are provided by the mapping application for interaction with by a user (Nha, sections 3A-B and 4 teach “information that can be used as input for vehicles routing algorithms, which are: Road information: current traffic conditions like congestion level, incidents and weather conditions etc. Destination information: the purpose of travel. Mobile information: such as, the remaining fuel, vehicle related conditions, traveler-related conditions etc. In addition to these inputs, we propose to incorporate the vehicles characteristics and road traffic prediction information since these two information will influence the best route selection”; further, that these include “user information” of driver preferences.);
a plurality of potential state changes that are available to each respective entity of the plurality of entities at a given time based on user input (Nha, sections 3B-3C teach calculating routes (potential state changes) for a vehicle (entity), including number of turns or stops on each route, and recalculating due to received updates including traffic conditions or driver preferences); and
metrics associated with at least one end condition (Nha, sections 3B-3C teach determining travel cost and fuel consumption for routes to a destination (end condition), including “route length but also by the number of stops during travelling, type of vehicle and the type of road. This metric is mainly useful for transport and freight companies as well as any other driver.”).
Regarding claims 5, 12, and 19, the combination of Nha and Mirzaei teach all the claim limitations of claims 3, 10, and 17 above; and further teach wherein the plurality of potential state changes includes an active potential state change where a representative entity changes a position with respect to an area and an inactive potential state change where a representative entity remains still with respect to the area (Nha, sections 3B-3C teach calculating and re-calculating routes (potential state changes) for a vehicle (entity) while it is traveling (active potential state change) and when the vehicle stops (inactive potential state change); since “whenever a vehicle reaches an intersection, the traffic conditions are checked for any update. If there is an update impacting at least on link in the best route, the affected links are removed from the map and the route planning algorithm is re-applied to calculate a new best route for the vehicle. Otherwise, the vehicle carries on its journey”).
Regarding claims 6, 13, and 20, the combination of Nha and Mirzaei teach all the claim limitations of claims 3, 10, and 17 above; and further teach wherein the at least one end condition comprises finding an optimal route to the destination location, and the plurality of potential state changes comprises route choices that each include a series of state changes (Nha, sections 3B-3C teach calculating and re-calculating routes (potential state changes) for a vehicle (entity) including finding a “best route” to a “destination location” (end condition); wherein the best route includes turns (alternative potential state changes) in the route and links (alternative potential state changes) corresponding to paths to the destination).
Regarding claims 7, 14, and 21, the combination of Nha and Mirzaei teach all the claim limitations of claims 2, 9, and 16 above; and further teach wherein the AI controller selects the AI algorithm from the plurality of AI algorithms based at least on the state information and a user-selected preference (Mirzaei, paragraphs 0004, 0027, 0039-0041, and 0049-0050 teach “Users may set static parameters (a user-selected preference) for adapting the execution of the application and/or complex algorithms used by the application” and indications of levels of tolerance for program performance corresponding to “a change in a state of the application”. The adapting includes selecting/updating the proper “complex algorithm” from the complex algorithms (selecting the Al algorithm from the plurality of Al algorithms), and these are further taught to be “machine learning algorithms”.).
Nha and Mirzaei are combinable for the same rationale as set forth above with respect to claim 2.
Claims 8 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Nha et al (“A Comparative Study of Vehicles’ Routing Algorithms for Route Planning in Smart Cities”, 2012) hereinafter Nha, in view of Mohammad Mirzaei et al (US Pub 20160171390) hereinafter Mirzaei, in view of Dotan et al (US Pub 20060025923) hereinafter Dotan.
Regarding claims 8 and 15, the combination of Nha and Mirzaei teach all the claim limitations of claims 2 and 10 above; however, the combination does not explicitly teach wherein the metrics are selected from a group comprising a relative set of scores and an absolute set of scores.
Dotan teaches wherein the metrics are selected from a group comprising a relative set of scores and an absolute set of scores (paragraphs 0013, 0024 and 0108 teach determining scores (metrics) of routes to a destination “based on complexity of the junction and/or the complexity of the maneuver that the user must perform at the junction (absolute set of scores)” or distances for inclusion of other roads (relative set of scores) in the vicinity of the junction).
Thus it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Nha’s teaching of monitoring of optimal route mapping algorithm performance and choosing, as modified by machine learning algorithm selection/adjustment/execution based on monitoring/inferring user input data as taught by Mirzaei, to include a navigation program to determining optimal and alternative routes as taught by Dotan in order to maintain a constant optimal route to a destination in regards to the user’s location (Dotan, Fig. 1 and paragraphs 0007, 0076 and 0094).
Allowable Subject Matter
Claims 4, 11, and 18 objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter: Claims 4, 11, and 18 are deemed allowable over prior art due to analogously reciting (or due to their dependency) “wherein the Al controller interprets the state information to generate interpreted state information, wherein the Al controller associates scores for each of a plurality of Al algorithms for use by the mapping application, each score being based on: the interpreted state information including the plurality of potential state changes that are available to each respective entity of the plurality of entities; and a computational complexity of simulating the mapping application using the state information for each of the plurality of Al algorithms including an estimated amount of time for simulation”.
Nha et al (“A Comparative Study of Vehicles’ Routing Algorithms for Route Planning in Smart Cities”, 2012), is deemed the closest art found that teaches choosing an algorithm for processing input road, destination, and vehicle information in a mapping program [sections 3A-4]. However, Nha et al does not explicitly teach assigning scores to AI algorithms via simulation of game state information and selecting one according to the current application state information the same way required by the claim.
Arif Ansari et al (WO03085545), teaches smart devices for adjusting game character actions based on user input and selecting algorithms to accomplish this [pages 214-216 and Figs. 37-38]. However, Arif Ansari et al does not explicitly teach assigning scores to AI algorithms via simulation of game state information and selecting one according to the current application state information the same way as required by the amended claim limitations.
Marwan Y. Ansari et al (US Pub 20110183739), teaches algorithm selection based on game state and transferring from a server to a local device for algorithm execution [paragraph 0021]. However, Marwan Y. Ansari et al does not explicitly teach assigning scores to AI algorithms via simulation of game state information and selecting and transferring an algorithm according to the current application state information the same way as required by the amended claim limitations.
Dotan et al (US Pub 20060025923), teaches a mapping application utilizing different algorithms for calculating multiple routes for a user including an optimal route [paragraphs 0072-0073, 0076, and Fig. 1]. However, Dotan et al does not explicitly teach assigning scores to AI algorithms via simulation of game state information and selecting one according to the current application state information the same way as required by the amended claim limitations.
Mohammad Mirzaei et al (US Pub 20160171390), teaches selecting machine learning algorithms for correcting program execution for meeting user program tolerance level indicators and user set parameters [paragraphs 0004, 0027, 0039-0041, and 0049-0050]. However, Mohammad Mirzaei et al does not explicitly teach assigning scores to AI algorithms via simulation of game state information and selecting one according to the current application state information in mapping or gaming applications the same way as required by the amended claim limitations.
Hsu-Hoffman et al (US Patent 9858832), teaches selection of algorithms based on scores from a simulation condition and the algorithm to determine a character condition in a simulated world [claim 16]. However, Hsu-Hoffman et al does not explicitly teach assigning scores to AI algorithms via simulation of game state information and selecting one according to the current application state information in mapping or gaming applications or transferring the algorithm between an AI controller and a device the same way as required by the amended claim limitations.
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 extension fee 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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/C.M./Examiner, Art Unit 2123
/ALEXEY SHMATOV/Supervisory Patent Examiner, Art Unit 2123
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