DETAILED ACTION
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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 10/10/2025 has been entered.
Claim(s) 1, 5-8, 12-15, and 18 have been amended. Claim(s) 1-20 are pending examination.
Response to Arguments
Applicant presents the following argument(s) regarding the previous office action:
Applicant asserts that the 35 USC 101 rejection of independent claims 1, 15, and 18 is improper. Applicant asserts that in light of the amended claim limitations the claims are no longer directed to an abstract idea and are integrated into a practical application.
Applicant asserts that the 35 USC 103 rejection of independent claims 1, 15 ,and 18 is improper. Applicant asserts that in light of the amendment the claims overcome the teachings of the prior art. In particular the new limitation reciting, “processing the navigation data and the navigation request from the user, by one or more machine-learned models trained to extract semantic content and contextual information from navigation requests, to obtain: a first determination regarding one or more travel times associated with fulfilling the navigation request from the user and whether the navigation request from the user indicates a deferred travel time,” is not taught by the prior art
Applicant’s arguments, see Pages 11-13, "I. Rejections under 35 U.S.C. 101", filed 10/10/2025, with respect to claims 1, 15, and 18 have been fully considered and are persuasive. The 35 USC 101 rejection of claims 1-20 has been withdrawn.
Regarding applicant’s argument A, the examiner finds it persuasive. The inclusion of the control step wherein the computer actively controls the vehicle systems would remove the independent claims from an abstract idea and incorporate them into a practical application. Therefore the 35 USC 101 rejection of claims 1-20 would be removed as the claims are no longer directed to an abstract idea.
Applicant’s arguments with respect to claim(s) 1-20 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.
Regarding applicant’s argument B, the examiner finds it moot. After further search and consideration the examiner would rely on the newly cited teachings of Bellegarda, (US PG Pub 2019/0355346), which broadly teaches extraction of semantic word embeddings and context using bi-directional recurrent neural networks. Using these networks the system can receive user inputs including “providing navigation instructions,” [0222]. This system can interpret the user input in order to determine a location, a time, and other relevant semantic information from a user request in order to provide assistance to the user. Additionally, Koo (US PG Pub 2018/0283889) teaches broadly a system that can use user constraints in order to provide the best travel route to a user. In light of Tashiro, Bellegarda, and Koo; independent claims 1, 15, and 18 would be seen as obvious. Accordingly dependent claims 2-14, 16-17, and 19-20 would remain rejected at least due to their dependence on rejected claims. Further detailed mapping and explanation can be found below in the section titled, “Claim Rejections – 35 USC 103.”
Claim Rejections - 35 USC § 103
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claim(s) 1-3, 8, and 11-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Tashiro (US PG Pub 2005/0096842) in view of Bellegarda (US PG Pub 2019/0355346) and Koo (US PG Pub 2018/0283889).
Regarding claim 1, Tashiro teaches a computer-implemented method of navigation for a vehicle, the computer-implemented method comprising: ([0036] teaches a vehicle based navigation system, “FIG. 2 is a block diagram showing an example of structure of a vehicle navigation system implementing the present invention.”)
accessing, by a computing system comprising one or more processors, ([0039] teaches the system having processors that can be used by the computer to access navigation program data) navigation data comprising information associated with a navigation request from a user; (Figs. 11A-11H and [0074] teaches the user inputting a destination)
accessing, by the computing system, event data comprising information associated with a schedule of the user or travel history of the user; ([0074] teaches the system accessing computing data that shows previous destinations of a user, i.e. travel history, specifically it recites, “the user selects the "Previous Destination" menu to specify the destination from the past destinations recorded in the navigation system”)
processing the navigation data and the navigation request from the user, ([0039] teaches a processor “for controlling an overall operation of the navigation system” which would be understood to process the navigation request. This request is input by the user, [0074] recites “the navigation system displays a "Find Destination By" screen in FIG. 11B which shows various input methods for defining the destination”)
a first determination regarding one or more travel times associated with fulfilling the navigation request from the user ([0070]-[0073] teach the user inputting information that allows the system to determine that the navigation request comes with a range of possible travel times, this allows for the user to determine whether they want to leave now or later) and whether the navigation request from the user indicates a deferred travel time, ([0072] teaches the user inputting a deferred departure time) and
a second determination regarding one or more locations associated with fulfilling the navigation request from the user and whether the navigation request from the user indicates a specific location or includes information from which a plurality of alternative locations that satisfy the navigation request are determined; ([0070]-[0074] teaches determining the locations and potential travel times that the navigation system can recommend; this includes that the destination is a specific destination)
wherein the one or more travel times comprise one or more deferred travel windows determined based the navigation request indicating flexibility in the schedule of the user, ([0071] teaches the user selecting a “range” of departure times, this indicates the user’s flexibility as it shows the time a user can wait to ensure they reach their destination when needed)
generating, by the computing system, an output comprising one or more indications associated with navigation of the vehicle to at least one of the one or more locations during a time window comprising at least one of the one or more travel times (Figs. 1A-1H; [0005]-[0008], [0052], and [0077]-[0079] teach an output of travel indication including a route/graphic data/ and turn-by-turn navigation data) and
Tashiro does not teach by one or more machine-learned models trained to extract semantic content and contextual information from navigation requests; the one or more deferred travel windows do not conflict with any time that the user is not available to travel based on the event data; and controlling, by the computing system, one or more vehicle systems of the vehicle to guide the vehicle to the at least one of the one or more locations during the time window.
However, Bellegarda teaches “one or more machine-learned models trained to extract semantic content and contextual information from navigation requests;” ([0210] teaches the use of various neural networks to process language inputs; [0228] teaches these networks can be implemented to interpret user inputs through the use of “natural language processing;” at least [0251] teaches this neural network can understand semantic and contextual information, further details can be found in [0253]-[0258] as to how this is done. [0222] teaches that this system can be used to “provide navigation instructions” and “find directions” when a user inputs the proper information)
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date, to incorporate the teachings of Tashiro and Bellegarda; and have a reasonable expectation of success. Both relate to navigation control devices that navigate a user to a destination. The use of a machine learning algorithm allows for the determination of destinations for the user and/or deferred travel times to be more accurate than a normal computing system. The machine learned algorithm can better guess and predict where the user would want to travel and guide them more effectively. As taught in [0006] the use of such a network allows for full contextual understanding of the words the user says. The system can fully interpret what the request of the user is. As further taught in [0230] a user inputting limited speech allows the computer to fully understand and satisfy a user request. Additionally, the use of a neural network to understand this information would fall under MPEP 2144.04.III. “Automating a manual activity.” As understood from In re Venner, 262 F.2d 91, 95, 120 USPQ 193, 194 (CCPA 1958) the court held that broadly providing an automatic or mechanical means to replace a manual activity which accomplished the same result is not sufficient to distinguish over the prior art. The prior art can find a user destination and user travel window based on user input. The use of a neural network, or machine learned model, to accomplish the same feature would be an obvious automation of such a process.
The combination of Tashiro and Bellegarda does not teach the one or more deferred travel windows do not conflict with any time that the user is not available to travel based on the event data; and controlling, by the computing system, one or more vehicle systems of the vehicle to guide the vehicle to the at least one of the one or more locations during the time window.
However, Koo teaches “the one or more deferred travel windows do not conflict with any time that the user is not available to travel based on the event data” ([0061]-[0062] teaches the system determining a travel route including times to travel and windows during which travel is an option. This is including the use of various constraints set by the navigation system which alter the way the route is constructed. [0100]-[0102] teaches the system having a series of user constraints including meetings during which the user cannot travel. ) and “controlling, by the computing system, one or more vehicle systems of the vehicle to guide the vehicle to the at least one of the one or more locations during the time window.” ([0111] teaches the vehicle to be autonomously guided to a destination based on user input)
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date, to incorporate the teachings of Tashiro, Bellegarda, and Coughlin with Koo; and have a reasonable expectation of success. All relate to the input of user destination/intentions and the system acting on those inputs. As Koo teaches in [0038]-[0040] the use of user constraints ensures that the system does not merely send a user to a location. The system can understand the user’s calendar and make sure that the user is where they are when they need to be there and not travelling when it would be inappropriate. Also, as taught by In re Venner, the use of user constraints to determine that a user needs to be at a location would be automating of the known user constraints. A user would be able to check their calendar and determine whether or not they can go somewhere at a given time. The use of a neural network/machine learned model, is merely automating this feature.
Claims 15 and 18 are substantially similar and would be rejected for the same rationale as above.
Regarding claim 2, Tashiro teaches the computer-implemented method of claim 1, further comprising: receiving, by the computing system, one or more inputs from the user, wherein the one or more inputs comprise a user selected location and a user selected time range, wherein the user selected location is selected from the one or more locations, (Fig. 11C and [0074] teaches user selecting a destination from a series of possible destinations) and wherein the user selected time range is selected from the one or more travel times; (Figs. 11E-11H; and [0072] teach the user inputting a selection of a time from the presented recommended times) and
generating, by the computing system, one or more navigation indications comprising a route from a current location of the user to the user selected location within the user selected time range. (Figs. 1A-1H; [0005]-[0008], [0052], and [0077]-[0079] teach an output of travel indication including a route/graphic data/ and turn-by-turn navigation data)
Regarding claim 3, Tashiro teaches the computer-implemented method of claim 1, further comprising: determining, by the computing system, a combination of the location of the one or more locations and the travel time of the one or more travel times that is associated with a shortest travel duration within the time window of the one or more travel times, ([0054] teaches determining a combination of route/travel time that makes the user travel the shortest time to the destination) wherein the output comprises the combination of the location and the travel time that is associated with the shortest travel duration. (Figs. 1A-1H; [0005]-[0008], [0052], and [0077]-[0079] teach an output of travel indication including a route/graphic data/ and turn-by-turn navigation data)
Regarding claim 8, Tashiro teaches The computer-implemented method of claim 1.
Tashiro does not teach wherein the navigation request from the user indicates a product or service, and the second determination regarding the one or more locations associated with fulfilling the navigation request from the user comprises associating the product with one or more locations that sell the product or associating the service with one or more locations that provide the service.
However, Bellegarda teaches “wherein the navigation request from the user indicates a product or service, and the second determination regarding the one or more locations associated with fulfilling the navigation request from the user comprises associating the product with one or more locations that sell the product or associating the service with one or more locations that provide the service” ([0230] teaches the user indicating their desire to go to a restaurant, and the system using the user’s current location to determine one or more locations “nearby” that would satisfy the requirement of the user request)
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date, to incorporate the teachings of Tashiro and Bellegarda; and have a reasonable expectation of success. Both relate to navigation control devices that navigate a user to a destination. The use of a machine learning algorithm allows for the determination of destinations for the user and/or deferred travel times to be more accurate than a normal computing system. The machine learned algorithm can better guess and predict where the user would want to travel and guide them more effectively. As taught in [0006] the use of such a network allows for full contextual understanding of the words the user says. The system can fully interpret what the request of the user is. As further taught in [0230] a user inputting limited speech allows the computer to fully understand and satisfy a user request. Additionally, the use of a neural network to understand this information would fall under MPEP 2144.04.III. “Automating a manual activity.” As understood from In re Venner, 262 F.2d 91, 95, 120 USPQ 193, 194 (CCPA 1958) the court held that broadly providing an automatic or mechanical means to replace a manual activity which accomplished the same result is not sufficient to distinguish over the prior art. The prior art can find a user destination and user travel window based on user input. The use of a neural network, or machine learned model, to accomplish the same feature would be an obvious automation of such a process.
Regarding claim 11, Tashiro teaches the computer-implemented method of claim 1, further comprising: determining, by the computing system, based on traffic data, ([0042]-[0045] teaches the determination of current and historic traffic data for a route) the one or more travel times associated with less than a threshold amount of road traffic at the one or more locations, ([0051]-[0053] teaches the determination of traffic information including estimated recovery times, speeds, and historical traffic. This information is used to determine a series of weights and comparisons between travel times) wherein the traffic data comprises information associated with road traffic in a geographic region associated with the navigation request, ([0043]-[0045] teaches the traffic data being selected for a specific road segment and travel path based on user navigation) and wherein the output comprises the one or more travel times associated with less than the threshold amount of road traffic at the one or more locations. (Figs. 11e-11H and [0083]-[0060] teach outputting the travel times and routes based on traffic information received by the system)
Regarding claim 12, Tashiro teaches the computer-implemented method of claim 1, further comprising: determining, by the computing system, a combination of the location of the one or more locations and the travel time of the one or more travel times that are associated with a shortest travel distance, ([0065] teaches the system determining the shortest travel distance to the destination based on the destination/time) wherein the output comprises the combination of the location and the travel time that is associated with the shortest travel distance. (Figs. 1A-1H; [0005]-[0008], [0052], and [0077]-[0079] teach an output of travel indication including a route/graphic data/ and turn-by-turn navigation data)
Regarding claim 13, Tashiro teaches the computer-implemented method of claim 1, further comprising, determining, ([0077]-[0080] teaches the system as able to determine whether or not the use of a deferred travel time would provide a shorter travelling time than leaving right now)
Tashiro does not teach via the one or more machine-learned models.
However, Bellegarda teaches “via the one or more machine-learned models” ([0210] teaches the use of various neural networks to process language inputs; [0222] teaches that this system can be used to “provide navigation instructions” and “find directions” when a user inputs the proper information)
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date, to incorporate the teachings of Tashiro and Bellegarda; and have a reasonable expectation of success. Both relate to navigation control devices that navigate a user to a destination. The use of a machine learning algorithm allows for the determination of destinations for the user and/or deferred travel times to be more accurate than a normal computing system. The machine learned algorithm can better guess and predict where the user would want to travel and guide them more effectively. As taught in [0006] the use of such a network allows for full contextual understanding of the words the user says. The system can fully interpret what the request of the user is. As further taught in [0230] a user inputting limited speech allows the computer to fully understand and satisfy a user request. Additionally, the use of a neural network to understand this information would fall under MPEP 2144.04.III. “Automating a manual activity.” As understood from In re Venner, 262 F.2d 91, 95, 120 USPQ 193, 194 (CCPA 1958) the court held that broadly providing an automatic or mechanical means to replace a manual activity which accomplished the same result is not sufficient to distinguish over the prior art. The prior art can find a user destination and user travel window based on user input. The use of a neural network, or machine learned model, to accomplish the same feature would be an obvious automation of such a process.
Regarding claim 14, Tashiro teaches the computer-implemented method of claim 13.
Tashiro does not teach wherein the navigation request from the user is obtained via one or more audible inputs, and the one or more machine-learned models are configured to process the navigation request from the user based on an application of one or more natural language processing techniques.
However, Bellegarda teaches “wherein the navigation request from the user is obtained via one or more audible inputs,” ([0186] teaches the use of a microphone to accept and audible input to the machine learned model) and “the one or more machine-learned models are configured to process the navigation request from the user based on an application of one or more natural language processing techniques.” ([0207]-[0210], [0216]-[0218] and [0226]-[0231] teach the machine learned model has a natural language processing module to process the user request based on audio input)
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date, to incorporate the teachings of Tashiro and Bellegarda; and have a reasonable expectation of success. Both relate to navigation control devices that navigate a user to a destination. The use of a machine learning algorithm allows for the determination of destinations for the user and/or deferred travel times to be more accurate than a normal computing system. The machine learned algorithm can better guess and predict where the user would want to travel and guide them more effectively. As taught in [0006] the use of such a network allows for full contextual understanding of the words the user says. The system can fully interpret what the request of the user is. As further taught in [0230] a user inputting limited speech allows the computer to fully understand and satisfy a user request. Additionally, the use of a neural network to understand this information would fall under MPEP 2144.04.III. “Automating a manual activity.” As understood from In re Venner, 262 F.2d 91, 95, 120 USPQ 193, 194 (CCPA 1958) the court held that broadly providing an automatic or mechanical means to replace a manual activity which accomplished the same result is not sufficient to distinguish over the prior art. The prior art can find a user destination and user travel window based on user input. The use of a neural network, or machine learned model, to accomplish the same feature would be an obvious automation of such a process.
Regarding claim 16, Tashiro teaches the one or more tangible non-transitory computer-readable media of claim 15, wherein the output comprises one or more visual indications, and wherein the one or more visual indications are presented on a map of a geographic area comprising a portion of the one or more locations. (Figs. 11E-11H and 90073] teach the output of visual information to a map of the geographic area the user is in)
Regarding claim 17, Tashiro teaches the one or more tangible non-transitory computer-readable media of claim 15, wherein the output comprises one or more audible indications, and wherein the one or more audible indications indicate the time window of the one or more travel times for the user to travel to at least one of the one or more locations. ([0039] teaches the system providing an audible output to the user regarding route guidance based on selected destination and travel time)
Regarding claim 19, Tashiro teaches the computing system of claim 18, wherein the output comprises one or more turn-by-turn directions for navigation to the one or more locations. (Figs. 1E-1H; 11E-11H; [0004] and [0073] teach the output of visual information to a map of the geographic area the user is in that allows for turn-by-turn navigation)
Regarding claim 20, Tashiro teaches the computing system of claim 18, wherein the navigation request is based on one or more interactions of the user, and wherein the one or more interactions comprise one or more tactile inputs to a graphical user interface of a navigation application, one or more textual inputs to the navigation application, or one or more spoken statements by the user. ([0039] teaches the user providing a series of inputs whether hard keys, joysticks, or the like)
Claim(s) 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Tashiro, Bellegarda, and Koo in view of Schilling (EP 2,746,727).
Regarding claim 4, Tashiro teaches the computer implemented method of claim 1.
The combination of Tashiro, Bellegarda, and Koo does not teach determining, by the computing system, one or more time savings for the user based on one or more differences in travel duration between the user travelling to the one or more locations immediately and the user travelling to the one or more locations at the one or more travel times, wherein the output comprises the one or more time savings.
However, Schilling teaches “determining, by the computing system, one or more time savings for the user based on one or more differences in travel duration between the user travelling to the one or more locations immediately and the user travelling to the one or more locations at the one or more travel times, wherein the output comprises the one or more time savings.” (Fig. 29 and [0366] teach determining and presenting potential time savings to the user of a navigation system)
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date, to incorporate the teachings of Tashiro, Bellegarda, and Koo in view of Schilling; and have a reasonable expectation of success. All relate to navigation device controls and finding the most efficient route based on a possible deferred travel time. Presenting potential time savings to the user in a clear and efficient manner would be obvious to try. Tashiro presents the total travel times that different routes and differed travel times may take, Figs. 11E-11H. But the express sharing of a time savings such as Fig. 29 of Schilling presents a much clearer and precise possible time savings that would allow for a much more informed decision.
Claim(s) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Tashiro, Bellegarda, and Koo in view of Ozawa (US PG Pub 2010/0036601).
Regarding claim 5, Tashiro teaches the computer-implemented method of any of claim 1, further comprising: based on the second determination determining the navigation request from the user indicates the specific location, the output comprises a request for confirmation from the user that the user indicated the specific location; (Fig. 11D and [0074] teach confirming the user has selected the destination input) or
The combination of Tashiro, Bellegarda, and Koo does not teach based on the second determination determining the navigation request from the user includes information from which a plurality of alternative locations that satisfy the navigation request are determined determining, by the computing system, a location type associated with the navigation request; and determining, by the computing system, the plurality of alternative locations that match the location type associated with the navigation request, wherein the output comprises the plurality of alternative locations.
However, Ozawa teaches “based on the second determination determining the navigation request from the user includes information from which a plurality of alternative locations that satisfy the navigation request are determined determining, by the computing system, a location type associated with the navigation request;” (Fig. 11 and [0141]-[0147] teach identifying the category of a user search result) and “determining, by the computing system, the plurality of alternative locations that match the location type associated with the navigation request, wherein the output comprises the plurality of alternative locations.” (Fig. 11 and [0141]-[0147] teach identifying the category of a user search result and presenting alternative search results for the user to select from)
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date, to incorporate the teachings of Tashiro, Bellegarda, and Koo with Ozawa; and have a reasonable expectation of success. All relate to the control of navigational devices. By the way the claim is written Tashiro would teach the claim due to the inclusion of the or statement. However, including Ozawa would present a much clearer rejection. The inclusion of specific vs alternative places allows for a route generation system to get a user to a location at the appropriate time and if it is unable to do that an alternate may remedy that situation. As Ozawa teaches in [0146]-[0147] the use of alternate locations for navigation allows a user to reach a type of location by an approved time. This would improve the system to ensure that is a user will not reach a destination in time, there may be some kind of alternate that would work.
Claim(s) 6 and 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Tashiro, Bellegarda, and Koo in view of Os (US PG Pub 2019/0137293).
Regarding claim 6, Tashiro teaches the computer implement method of claim 1.
The combination of Tashiro, Bellegarda, and Koo does not teach wherein the output comprises a request for feedback from the user with respect to whether the user prefers to defer navigation, and further comprising: receiving, by the computing system, the feedback from the user; and performing, by the computing system, one or more operations based on the feedback from the user, wherein the one or more operations comprise modifying one or more heuristics associated with determining whether the navigation request specifies a specific location or a deferred travel time to travel to the location, or training the one or more machine-learned models.
However, Os teaches “wherein the output comprises a request for feedback from the user with respect to whether the user prefers to defer navigation, and further comprising: receiving, by the computing system, the feedback from the user;” ([0551] teaches requesting user feedback based on the travel system usage and method) and “performing, by the computing system, one or more operations based on the feedback from the user, wherein the one or more operations comprise modifying one or more heuristics associated with determining whether the navigation request specifies a specific location or a deferred travel time to travel to the location, or training the one or more machine- learned models.” ([0551] teaches using the user feedback to modify the search results in the future by using the feedback to determine if the user prefers certain destinations etc.)
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date, to incorporate the teachings of Tashiro, Bellegarda, and Koo with Os; and have a reasonable expectation of success. All relate to navigation devices that present a series of results based on inputs. As Os teaches in [0551] the use of feedback in a computer system allows for a better user experience. By requesting feedback the system can determine if the results presented as optimal and can tweak the results accordingly. By editing the internal data it can present the best output to the user based on their traveling usage.
Regarding claim 7, Tashiro teaches the computer implemented method of claim 1.
The combination of Tashiro, Bellegarda, and Koo does not teach wherein the output comprises a request for feedback from the user with respect to the one or more locations or the one or more travel times further comprising receiving, by the computing system, the feedback from the user; and performing, by the computing system, one or more operations based on the feedback from the user, wherein the one or more operations comprise modifying one or more heuristics associated with determining whether the navigation request specifies a specific location or a deferred travel time to travel to the location, or training one or more machine-learned models.
However, Os teaches “wherein the output comprises a request for feedback from the user with respect to the one or more locations or the one or more travel times further comprising receiving, by the computing system, the feedback from the user;” ([0551] teaches requesting feedback from the user related to the destinations presented based on a search result) and “performing, by the computing system, one or more operations based on the feedback from the user, wherein the one or more operations comprise modifying one or more heuristics associated with determining whether the navigation request specifies a specific location or a deferred travel time to travel to the location, or training one or more machine-learned models.” ([0551] teaches using the user feedback to modify the search results in the future by using the feedback to determine if the user prefers certain destinations etc.)
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date, to incorporate the teachings of Tashiro, Bellegarda, and Koo with Os; and have a reasonable expectation of success. All relate to navigation devices that present a series of results based on inputs. As Os teaches in [0551] the use of feedback in a computer system allows for a better user experience. By requesting feedback the system can determine if the results presented as optimal and can tweak the results accordingly. By editing the internal data it can present the best output to the user based on their traveling usage.
Claim(s) 9 and 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Tashiro, Bellegarda, and Koo in view of Coughlin (US PG Pub 2008/0167937).
Regarding claim 9, Tashiro teaches the computer-implemented method of claim 1.
The combination of Tashiro, Bellegarda, and Koo does not teach determining, by the computing system, based on the event data, that the one or more locations do not comprise any location greater than a threshold distance from a location where the user is scheduled to be present during the one or more travel times.
However, Coughlin teaches “determining, by the computing system, based on the event data, that the one or more locations do not comprise any location greater than a threshold distance from a location where the user is scheduled to be present during the one or more travel times.” (Figs. 10A-10C and [0109]-[0113] teach determining that the user does not deviate by a large distance from a calendar event when they will not have enough time to return to said route/make the calendar appointment)
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date, to incorporate the teachings of Tashiro, Bellegarda, and Koo with Coughlin; and have a reasonable expectation of success. All relate to navigation devices and ensuring navigation can occur in a timely manner. Ensuring that a travel path does not deviate from a destination by too much is a smart way to ensure that the user reaches a destination in an approved timeframe. It would be obvious to try to keep a user on a path/route and alert them if they deviate beyond an acceptable limit based on some kind of distance threshold.
Regarding claim 10, Tashiro teaches the computer-implemented method of claim 8.
The combination of Tashiro, Bellegarda, and Koo does not teach wherein the output comprises a notification that is generated within a predetermined amount of time of the earliest travel time of the one or more travel times that the user is available to travel.
However, Coughlin teaches “wherein the output comprises a notification that is generated within a predetermined amount of time of the earliest travel time of the one or more travel times that the user is available to travel.” (Fig. 8B and [0100] teach generating the earliest time a user should leave as presented by an alert when road conditions change that may delay the full travel time)
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date, to incorporate the teachings of Tashiro, Bellegarda, and Koo with Coughlin; and have a reasonable expectation of success. All relate to navigation devices and ensuring navigation can occur in a timely manner. Ensuring a user leaves as early as needed is essential to maintaining a user’s on time arrival to a destination. By presenting the earliest possible leaving time it ensures a user does not delay and run into a traffic or otherwise slow road condition that prevents them from making it to a meeting, date, or other timely event.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NICHOLAS STRYKER whose telephone number is (571)272-4659. The examiner can normally be reached Monday-Friday 7:30-5:00.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Christian Chace can be reached at (571) 272-4190. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/N.S./Examiner, Art Unit 3665 /CHRISTIAN CHACE/Supervisory Patent Examiner, Art Unit 3665