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 .
Status of Claims
This is a Final Office Action for application Serial No. 16/620,786. Claims 1-11, 14- 17 and 19-22 have been examined and fully considered.
Claim(s) 1, 5, 6, 14, 17, and 19 have been amended.
Claim(s) 1-11, 14- 17 and 19-22 are pending in Instant Application.
Response to Arguments/Rejections
Applicant’s arguments, see Remarks, filed 04/24/2025, with respect to the rejection(s) of claim(s) 1, 14 and 17 under 35 USC § 103 have been fully considered and are persuasive. Therefore, the previous final rejection has been withdrawn. A new final office action is being issued, upon further consideration, a new ground(s) of rejection is made in view of Tashiro (Pub. No.: US 2005/0096842).
Information Disclosure Statement
The information disclosure statement(s) (IDS) submitted on 02/18/2025; 04/24/2025and 07/07/2025 is/are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement(s) is/are being considered if signed and initialed by the Examiner.
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 for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
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.
Claim(s) 1-6, 8, 10-11, 14-17 and 19-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Pichler (US 2013/0204529; previous recorded) in view of in view of Shinar et al. (US 8,938,358; previous recorded), hereinafter, referred to as “Shinar”, and in view of Tashiro (Pub. No.: US 2005/0096842) hereinafter, referred to as “Tashiro”.
Regarding claim 1: Pichler discloses a computer-implemented method for providing navigation directions (see at least Paragraph [0044]: “a method embodiment of providing addressable database objects for route calculation by the navigation device 100”), the method comprising:
receiving, by one or more processors, a request for navigation directions to a destination (see at least Paragraphs [0038]: “a processor 160 is provided that is adapted to control the operation of the navigation device 100 in accordance with the method and method aspects described herein based on program code stored in memory 110. The processor 160 has access to the memory 110 in general and the databases 120, 130 in particular via a memory interface not shown in FIG. 2. It should be noted that the map database 120 could also be located off-board the navigation device 100. In such a case, the processor 160 may be provided with access to the external map database 120 via a data interface conforming” and [0043]: “the destinations objects in the destinations database 130 have an object format that can be searched during a name search using, for example, the user interface illustrated in FIG. 1. For a specific destination object found during the name search and selected by the user, one or more associated destination points (typically their geographical coordinates) can then be determined by the navigation device 100 and used for route calculation” ***Examiner notes that the reference Pichler does not explicitly disclose “a request…”, however, discloses “navigation directions to a destination”, which is retrieved from a database by the user***);
identifying, by the one or more processors, a two-dimensional shape (see at least Paragraph [0046]: “a polygon representative of the geographical area. In the map database 120, the polygon is defined by a set of geographical coordinates that define the boundary of the geographical area and that can be interpreted as edges spanning the associated polygon. Additionally, each area object is associated with an area type designation (e.g., "Lake'). In a similar manner, each point object is associated with a point type (e.g., “Street'), a point name (e.g., "Am Wolfs mantel'), and one or multiple geographical coordinates defining one or more associated destination points”) enclosing a plurality of destination access points (see at least Paragraph [0051]: “the polygon P are maintained (as becomes apparent from a comparison of the original polygon illustrated in the upper portion of FIG. 7 and the thinned-out polygon illustrated in the lower portion of FIG. 7). Generally, the thinning-out is performed such that a sufficient number of edges E functioning as so-called access points APS is maintained. On the other hand, the thinning-out step can be omitted in case the original number of edges E already provides a desired number of access points APs. As understood herein, an access point AP represents geographical information that can be used by the navigation device 100 for best routes calculation from a given start point to the geographical area of interest”), to which the destination is logically mapped (see Paragraph [0053]-[0054]), including processing imagery of a geographic area that includes the destination to identify physical boundaries of the destination (see at least Figure 8 and Paragraph [0049]: “The upper portion of FIG. 7 illustrates for the retrieved area object the associated area name (“Broaches') and polygon (here a pentagon) P representative of the shape of the geographical area. The polygon P is defined in the map database 120 by a plurality of geographical coordinates representing polygon edges E. In their entirety, the edges span the polygon P and are thus representative of the shape of the geographical area underlying the retrieved area object”)…
selecting, by the one or more processors, a destination access point from the plurality of destination access points as a preferred destination (see at least Paragraphs [0052]: “The point objects may, for example, be representative of POIs, cities, streets (or street sections), and so on. The point objects thus determined for a particular access point AP may form the basis for determining at least one destination point for route calculation to the geographical area of interest. For example, the geographical coordinates of a selected point object may be defined as the destination point for the area object in general or one of its access points APs in particular” and [0061]: “the GUI 10 permits for a specific area destination (e.g., the area destination currently presented in the display section 40) a selection whether route calculation shall be based on a particular access point AP (e.g., the best access point AP with respect to a given start point) of the area destination or any children POI (e.g., of type POI, street or city) directly linked with the area object (see FIGS.5C and 8)”); and
generating, by the one or more processors, navigation directions to the preferred destination in response to the request (see at least Paragraph [0062]: “Once a user has selected a particular area destination, related access point AP or linked children POI by touching the corresponding proposal on the display section 40 of the touchscreen, the guidance component 170 determines the appropriate destination point, calculates an optimal route to the destination point and offers conventional guiding functionalities. AS has been explained above, route calculation may be based on either an access point AP suitably selected by the guidance component 170 or a children POI as destination point”).
Pichler does not explicitly disclose
receiving…a request for navigation directions to a destination;
…
…applying a contextual signal relating to a temporary event occurring at the destination;
…
However, in addition and/or in the alternative, Shinar teaches
receiving…a request for navigation directions to a destination (see at least col. 3, “location information associated with the travel patterns of the users after requesting driving directions to a given travel destination. For instance, after receiving a request from a user for directions to a particular travel destination, data regarding the user's movement may be collected and analyzed to deter mine if the user actually traveled to the requested destination or to a completely different destination”);
…
Accordingly, it would have been obvious to one of ordinary skill in the art before the filing of the invention to further modify Pichler by combining a computing device, a query from a client device associated with a requested travel destination as taught by Shinar. One would be motivated to make this modification in order to convey implemented to determine which combination of factors/criteria should be used to most accurately assess each potential alternative travel destination (see at least col. 11).
Neither Pichler nor Shinar teaches
…applying a contextual signal relating to a temporary event occurring at the destination;
…
However, Tashiro teaches
processing imagery of a geographic area that includes the destination to identify physical boundaries of the destination and applying a contextual signal relating to a temporary event occurring at the destination (see, Paragraphs [0019]: “Mobile navigation systems 14 are each operable to display or otherwise communicate to a user, such as a driver or passenger of a vehicle 12, route information, or directions, to a particular destination. Such display or other communication may occur through text, graphical images, audio and/or other suitable methods. The route information may be based upon a shortest distance or shortest time for travel from a particular origination location to the destination. The particular destination may be input and/or selected by the user at mobile navigation system 14 through a user interface 15, such as a keypad, of each mobile navigation system 14. In particular embodiments, a user may input and/or select a destination using another device, such as a personal digital assistant (PDA). In some embodiments, a user may input and/or select destination information using one or more spoken commands via speech recognition technology in mobile navigation system 14.; and [0024]: “While traveling along the route displayed to the user by the user's mobile navigation system 14, various incidents may arise that cause traffic jams or other delays that affect the ability of the user to easily or quickly reach the user's destination. For example, unforeseen construction or traffic accidents may occur that tie up traffic and delay vehicles whose navigation system routes take them through or near such construction or accidents. Areas that cause traffic delay may include areas where vehicular traffic is tied up, impassable or otherwise diverged from normal flow from construction, traffic accidents, obstacles or otherwise. Such areas may be referred to herein as "hot spots." Hot spots may cause drivers to diverge off of a route communicated by the drivers' mobile navigation systems 14 in order to decrease delay in reaching destinations. In particular embodiments, hot spots may be temporary and may be cleared up at some point in the future (e.g., in the case of traffic accidents).”);
…
Accordingly, it would have been obvious to one of ordinary skill in the art before the filing of the invention to incorporate an apparatus for use in a navigation system which is capable of predicting a travel time and a departure time based on past and present traffic information such as traffic incidents, histories of traffic incidents, distances from traffic incident, weather, time, road conditions, etc.(see, Tashiro [0001]).
Regarding claim 2: the combination of Pichler, Shinar and Tashiro teaches the computer-implemented method of claim 1. Shinar further teaching comprising:
training, by the one or more processors, a machine learning model that outputs an access point based on a destination, including applying training (see at least col. 11, “each factor/criteria may be given a certain weight depending on predetermined settings and/or user-defined settings. Alternatively, a suitable machine learning technique may be implemented to determine which combination of factors/criteria should be used to most accurately assess each potential alternative travel destination. For instance, a recent publication entitled "Large Scale Online Learning of Image Similarity Through Ranking" (Chechia et al) describes one example of a suitable learning algorithm and methodology that may be utilized to determine how to combine the various factors/criteria in the most effective and efficient manner”) data that includes (i) a plurality of destinations to which users requested navigation directions (see at least col. 12, “As shown, in response to the user submitting a query related to a specific travel destination, a plurality of alternative travel destinations may be presented to the user in the form of selectable interface elements 302 (e.g., with the elements 302 ordered randomly or according to their quality scores). By selecting one of the alternative travel destinations, the user may indicate that, instead of receiving query results for the requested travel directions, he/she would rather receive query results for the selected destination”) and (ii) for each of the plurality of destinations, respective locations to which the users travelled after completing respective navigation sessions to the corresponding destinations, wherein at least a subset of the locations defines the plurality of access points (see at least col. 3, “after requesting driving directions to a specific destination, a user may actually travel to a different destination. For example, as shown in FIG. 1, instead of traveling to the requested travel destination 12, the user deviated from the suggested route and completed his/her journey at an alternative destination 14. In many instances, this alternative destination 14 may be related to or associated with the requested travel destination 12. For example, in the illustrated embodiment, the requested travel destination 12 may correspond to a particular attraction (e.g., a sports arena, a concert hall, a shopping mall, etc.) and the alternative destination 14 may correspond to a location for parking near the attraction (e.g., a parking lot or parking garage) or a restaurant near the attraction”; and col. 9, “a geographic location(s) may also be identified as a potential alternative travel destination(s) based on its proximity to the requested travel destination (regardless of whether previous users have traveled to such geographic location(s)). For instance, in one embodiment, all businesses (e.g., restaurants, shops, etc.), potential parking areas and/or other points-of-interest (e.g., user-defined points-of-interest)
located within a given distance of the requested travel destination may be identified as potential alternative travel destinations. In another embodiment, any points-of-interest that are within a given distance from the requested travel destination and that also share a common feature with or have similar characteristics to the requested travel destination may be identified as potential alternative travel destinations”);
wherein the identifying and the selecting include applying the destination to the machine learning model (see at least col. 11, “a suitable machine learning technique may be implemented to determine which combination of factors/criteria should be used to most accurately assess each potential alternative travel destination”).
Accordingly, it would have been obvious to one of ordinary skill in the art before the filing of the invention to further modify Pichler by combining a computing device, a query from a client device associated with a requested travel destination as taught by Shinar. One would be motivated to make this modification in order to convey implemented to determine which combination of factors/criteria should be used to most accurately assess each potential alternative travel destination (see at least col. 11).
Regarding claim 3: the combination of Pichler, Shinar and Tashiro teaches the computer-implemented method of claim 2. Shinar further teaches wherein training the machine learning model includes applying boundary data that indicates boundaries of two-dimensional shapes associated with respective ones of the plurality of destinations (see at least col. 3, “by aggregating travel pattern data from a plurality of users that requested driving directions to the same travel destination, one or more alternative destinations may be identified as being associated with the requested travel destination. Thereafter, when another user enters a query related to the same travel destination ( e.g., a search query or a query for driving directions), the identified destination(s) may be suggested to the user as a potential alternative(s) to receiving query results for the requested travel direction”).
Accordingly, it would have been obvious to one of ordinary skill in the art before the filing of the invention to further modify Pichler by combining a computing device, a query from a client device associated with a requested travel destination as taught by Shinar. One would be motivated to make this modification in order to convey implemented to determine which combination of factors/criteria should be used to most accurately assess each potential alternative travel destination (see at least col. 11).
Regarding claim 4: the combination of Pichler, Shinar and Tashiro teaches the computer-implemented method of claim 2. Shinar further teaches wherein training the machine learning model includes applying trajectory data that indicates, for at least some of the navigation sessions, trajectories made up of position (see at least col. 3, “suggested routing information based on the origin and destination information and provide the routing information to the user (e.g., by mapping the suggested route onto a display screen of the device and/or by providing audible cues for navigating along the route). For example, as shown in FIG. 1, a suggested route may be displayed in response to a user's request for driving directions from his/her current location 10 to a nearby travel destination 12. The user may then, if desired, travel along the suggested route to the requested travel destination 12”) and time tuples (see at least col. 5, “Alternatively, the location monitoring component 118 may be any other suitable module, sensor and/or component that is capable of determining location information for the computing device 110. The location information may include, for example, time-stamped geographic coordinates for the computing device 110, which may, in turn, allow the travel velocity of the computing device 110 to be determined”).
Regarding claim 5: the combination of Pichler, Shinar and Tashiro teaches the computer-implemented method of claim 2. Shinar further teaches wherein training the machine learning model (see at least col. 11, “a suitable machine learning technique may be implemented to determine which combination of factors/criteria should be used to most accurately assess each potential alternative travel destination. For instance, a recent publication entitled "Large Scale Online Learning of Image Similarity Through Ranking" (Chechia et al) describes one example of a suitable learning algorithm and methodology that may be utilized to determine how to combine the various factors/criteria in the most effective and efficient manner”; and col. 5, “The computing device 110 may also be location-enabled and, thus, may include a location monitoring component 118 for generating location information for the computing device 110. For instance, the location monitoring component 118 may be a GPS module or sensor configured to determine location information for the computing device 110 based on signals received from one or more satellites”))….
applying secondary contextual signals for at least some of the navigation sessions (see, Paragraphs [0019]: “Mobile navigation systems 14 are each operable to display or otherwise communicate to a user, such as a driver or passenger of a vehicle 12, route information, or directions, to a particular destination. Such display or other communication may occur through text, graphical images, audio and/or other suitable methods. The route information may be based upon a shortest distance or shortest time for travel from a particular origination location to the destination. The particular destination may be input and/or selected by the user at mobile navigation system 14 through a user interface 15, such as a keypad, of each mobile navigation system 14. In particular embodiments, a user may input and/or select a destination using another device, such as a personal digital assistant (PDA). In some embodiments, a user may input and/or select destination information using one or more spoken commands via speech recognition technology in mobile navigation system 14.; and [0024]: “While traveling along the route displayed to the user by the user's mobile navigation system 14, various incidents may arise that cause traffic jams or other delays that affect the ability of the user to easily or quickly reach the user's destination. For example, unforeseen construction or traffic accidents may occur that tie up traffic and delay vehicles whose navigation system routes take them through or near such construction or accidents. Areas that cause traffic delay may include areas where vehicular traffic is tied up, impassable or otherwise diverged from normal flow from construction, traffic accidents, obstacles or otherwise. Such areas may be referred to herein as "hot spots." Hot spots may cause drivers to diverge off of a route communicated by the drivers' mobile navigation systems 14 in order to decrease delay in reaching destinations. In particular embodiments, hot spots may be temporary and may be cleared up at some point in the future (e.g., in the case of traffic accidents).”).
Accordingly, it would have been obvious to one of ordinary skill in the art before the filing of the invention to incorporate an apparatus for use in a navigation system which is capable of predicting a travel time and a departure time based on past and present traffic information such as traffic incidents, histories of traffic incidents, distances from traffic incident, weather, time, road conditions, etc.(see, Tashiro [0001]).
Regarding claim 6: the combination of Pichler, Shinar and Tashiro teaches the computer-implemented method of claim 5. Tashiro teaches wherein a-the secondary contextual signal for the navigation session includes at least one of: (i) requestor data identifying at least one of (i) a type of activity to which the navigation session pertains or (ii) user preferences, (ii) a time at which the navigation session occurred, (ii) weather during the navigation session, (iv) a temporary event occurring at the destination at a time of the navigation session, or (iv) a mode of transport to which the navigation session pertains (see, Paragraphs [0019]: “Mobile navigation systems 14 are each operable to display or otherwise communicate to a user, such as a driver or passenger of a vehicle 12, route information, or directions, to a particular destination. Such display or other communication may occur through text, graphical images, audio and/or other suitable methods. The route information may be based upon a shortest distance or shortest time for travel from a particular origination location to the destination. The particular destination may be input and/or selected by the user at mobile navigation system 14 through a user interface 15, such as a keypad, of each mobile navigation system 14. In particular embodiments, a user may input and/or select a destination using another device, such as a personal digital assistant (PDA). In some embodiments, a user may input and/or select destination information using one or more spoken commands via speech recognition technology in mobile navigation system 14.; and [0024]: “While traveling along the route displayed to the user by the user's mobile navigation system 14, various incidents may arise that cause traffic jams or other delays that affect the ability of the user to easily or quickly reach the user's destination. For example, unforeseen construction or traffic accidents may occur that tie up traffic and delay vehicles whose navigation system routes take them through or near such construction or accidents. Areas that cause traffic delay may include areas where vehicular traffic is tied up, impassable or otherwise diverged from normal flow from construction, traffic accidents, obstacles or otherwise. Such areas may be referred to herein as "hot spots." Hot spots may cause drivers to diverge off of a route communicated by the drivers' mobile navigation systems 14 in order to decrease delay in reaching destinations. In particular embodiments, hot spots may be temporary and may be cleared up at some point in the future (e.g., in the case of traffic accidents).”).
Accordingly, it would have been obvious to one of ordinary skill in the art before the filing of the invention to incorporate an apparatus for use in a navigation system which is capable of predicting a travel time and a departure time based on past and present traffic information such as traffic incidents, histories of traffic incidents, distances from traffic incident, weather, time, road conditions, etc.(see, Tashiro [0001]).
Regarding claim 8: the combination of Pichler, Shinar and Tashiro teaches the computer-implemented method of claim 1. Shinar further teaches wherein the destination is a first street address, and wherein selecting the access point includes selecting a second street address different from the first street address (see at least col. 6, “The map database 138 may also store street information. In addition to street images in the tiles, the street information can include the location of a street relative to a geographic area or other streets. For instance, the map database 138 may store. For instance, the map database 138 may store information indicating whether a user can access one street directly from another street. The street information may further include street names where available, and potentially other information, such as distance between intersections and speed limits. All or some of the foregoing can be used by processor(s) 132 to compute a route between an origin and a requested travel destination”).
Accordingly, it would have been obvious to one of ordinary skill in the art before the filing of the invention to further modify Pichler by combining a computing device, a query from a client device associated with a requested travel destination as taught by Shinar. One would be motivated to make this modification in order to convey implemented to determine which combination of factors/criteria should be used to most accurately assess each potential alternative travel destination (see at least col. 11).
Regarding claim 10: the combination of Pichler, Shinar and Tashiro teaches the computer-implemented method of claim 1. Pichler in view of Shinar, where Shinar further teaches wherein the destination is a set of geographic coordinates (see at least col. 5, “the location monitoring component 118 may be any other suitable module, sensor and/or component that is capable of determining location information for the computing device 110. The location information may include, for example, time-stamped geographic coordinates for the computing device 110”), and wherein selecting the access point includes selecting a street address (see at least col. 6, “the street information can include the location of a street relative to a geographic area or other streets. For instance, the map database 138 may store information indicating whether a user can access one street directly from another street. The street information may further include street names where available, and potentially other information”).
Accordingly, it would have been obvious to one of ordinary skill in the art before the filing of the invention to further modify Pichler by combining a computing device, a query from a client device associated with a requested travel destination as taught by Shinar. One would be motivated to make this modification in order to convey implemented to determine which combination of factors/criteria should be used to most accurately assess each potential alternative travel destination (see at least col. 11).
Regarding claim 11: the combination of Pichler, Shinar and Tashiro teaches the computer-implemented method of claim 1. Shinar further teaches wherein the destination is a geographic entity including multiple parking locations (see at least col. 3, “In many instances, this alternative destination 14 may be related to or associated with the requested travel destination 12. For example, in the illustrated embodiment, the requested travel destination 12 may correspond to a particular attraction (e.g., a sports arena, a concert hall, a shopping mall, etc.) and the alternative destination 14 may correspond to a location for parking near the attraction (e.g., a parking lot or parking garage)”), and wherein selecting the access point includes selecting one of the multiple parking locations (see at least col, 1, “It is often the case that a user of a computer-based mapping system desires to travel to a certain destination but actually issues a query related to a different destination. For example, a user may issue a query for a particular sports arena when in fact the user actually intends to travel to a parking lot or parking garage near the sports arena”; and col. 9, “For example, a geographic location(s) may also be identified as a potential alternative travel destination(s) based on its proximity to the requested travel destination (regardless of whether previous users have traveled to such geographic location(s )). For instance, in one embodiment, all businesses 15 (e.g., restaurants, shops, etc.), potential parking areas and/or other points-of-interest ( e.g., user-defined points-of-interest) located within a given distance of the requested travel destination may be identified as potential alternative travel destinations”).
Accordingly, it would have been obvious to one of ordinary skill in the art before the filing of the invention to further modify Pichler by combining a computing device, a query from a client device associated with a requested travel destination as taught by Shinar. One would be motivated to make this modification in order to convey implemented to determine which combination of factors/criteria should be used to most accurately assess each potential alternative travel destination (see at least col. 11).
Regarding claim 14: recites analogous limitations that are present in claim 1, therefore claim 14 would be rejected for the same reasons above. A client device (see at least col. 6, “a client device, such as the computing device 110”) comprising:
one or more processors (see at least col. 5, “computing device 110, the computing device
130 includes a processor(s) 132”);
a user interface (see at least col. 12, “the server device may be received by the client device and subsequently presented to the user. For example, FIG. 4 illustrates an example user interface 300 that may be utilized to display”);
a non-transitory computer-readable memory storing instructions that, when executed by the one or more processors (see at least col. 5, “the processor(s) 132 may be configured to execute instructions stored in the memory 134 to provide routing information to a user based at least in part on information stored in its various databases. For example, within the computing device 130, processor(s) 132 may compute a route in response to requests from a client device, such as the computing device 110”), cause the client device to:
transmit, to a server via a communication network (see at least col. 7, “client devices (e.g., computing devices 110, 160, 162, 164) may include location monitoring components 118 configured to generate location information related to the current location of each device, which may then be transmitted to the computing device 130 via the network 122”)…
Regarding claim 15: recites analogous limitations that are present in claim 8, therefore claim 15 would be rejected for the same reasons above.
Regarding claim 16: recites analogous limitations that are present in claim 10, therefore claim 16 would be rejected for the same reasons above.
Regarding claim 17: recites analogous limitations that are present in claim(s) 1 and 14, therefore claim 17 would be rejected for the same reasons above.
Regarding claim 19: recites analogous limitations that are present in claim 6, therefore claim 19 would be rejected for the same reasons above.
Regarding claim 20: recites analogous limitations that are present in claim 2, therefore claim 19 would be rejected for the same reasons above.
Claim(s) 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over the Pichler, Shinar and Tashiro, and in view of Levi Bellyache, hereinafter, referred to as “Levi” (EP3843002A1; previously recorded).
Regarding claim 7: the combination of Pichler, Shinar and Tashiro teaches the computer-implemented method of claim 2. Shinar teaches wherein training the machine learning model, however, neither Pichler nor Shinar or Kumar ...initializing the model using probabilities inversely proportional to distances between access points and geographic coordinates associated with the destinations.
However, in the same field of endeavor, Levi teaches
…initializing the model using probabilities inversely proportional to distances between access points (see at least Para. [0257], “The system may use a suitable thinning algorithm (e.g., an algorithm named "Zhang-Suen" thinning algorithm) to obtain the skeleton of the image. This skeleton may represent the underlying road structure, and junctions may be found using a mask (e.g., a point connected to at least three others). After the junctions are found, the segments may be the skeleton parts that connect them. To match the drives back to the skeleton, the system may use a Hidden Markov Model. Every GPS point may be associated with a lattice site with a probability inverse to its distance from that site. Use a suitable algorithm (e.g., an algorithm named the "Viterbi" algorithm) to match GPS points to lattice sites, while not allowing consecutive GPS points to match to non-neighboring lattice sites”) and geographic coordinates associated with the destinations (see at least Para. [0043], “map database 160 may include data relating to the position, in a reference coordinate system, of various items, including roads, water features, geographic features, businesses, points of interest, restaurants, gas stations, etc.”).
Accordingly, it would been obvious to one of ordinary skill in the art before the time of filing the invention to further modify the combination of Pichler, Shinar and Tashiro by combining generating, distributing, and using a sparse map and lane measurements for autonomous vehicle navigation as taught by Levi. One of ordinary skill in the art would have been motivated to make this modification in order to a variety of factors and make appropriate decisions based on those factors to safely and accurately reach an intended destination (see at least Para. [0003]).
Claim(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Pichler, Shinar, and Tashiro and in view of DeGrazia (US 2008/0046170; previously recorded).
Regarding claim 9: the combination of Pichler, Shinar and Tashiro teaches the computer-implemented method of claim 8. Shinar further teaches wherein the first street address and the second street address (see at least col. 6, “The map database 138 may also store street information. In addition to street images in the tiles, the street information can include the location of a street relative to a geographic area or other streets. For instance, the map database 138 may store. For instance, the map database 138 may store information indicating whether a user can access one street directly from another street”), however, Pichler nor Shinar explicitly teaches …correspond to two respective entrances to a building.
However, in the same field of endeavor, DeGrazia teaches
…correspond to two respective entrances to a building (see at least Para. [0020], “When arriving at the parking lot for the building, he user may then use the cell phone to send another navigation request over the cellular network to the server 22 to determine which building entrance to use. After entering the proper door, the user may then check out a portable navigation device that communicates wirelessly with the navigation server 22 through an access point in enterprise network 8”).
Accordingly, it would been obvious to one of ordinary skill in the art before the time of filing the invention to further modify as Pichler, Shinar and Tashiro by combining providing navigation directions taught by DeGrazia. One of ordinary skill in the art would have been motivated to make this modification in order accurately locate the user using the GPS (see at least Para. [0023]).
Claim(s) 21-22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Pichler, Shinar and Tashiro, and in view of Kumar et al., (US 2015/0066649; previously recorded), hereinafter, referred to “Kumar”.
As to [claim 21], the combination of Pichler, Shinar and Tashiro teaches the method of claim 1. While Pichler, Shinar and Tashiro teaches destination access point using data specific to a user, however, Kumar further teaches personalizing the selecting of the destination access point using data specific to a user associated with the request for the navigation directions (see at least Paragraphs [0046]: “server or identified by a user and transmitted by a client device. For example, a user may selector request POIs related to museums, such that the selected POIs will correspond to locations such as parks, museums, or hotels. In another example, a user may provide additional information specific to the POI in which the user is interested” [0049]: “Based on the rankings, the server may select the most relevant POIs. The number of POIs selected may be determined by selecting a default value, a value identified by the user, or determined by the server based on other input such as a maximum travel time identified by the user. Once the server has selected the POIs, server may generate a final touristic route based on the location set and the selected POIs.”), including: determining that the user previously accessed the destination via a first one of the plurality of destination access points different from a second one of the plurality destination access points used by most of the users see at least Paragraph [0058]-[0059]).
Accordingly, it would have been obvious to one of ordinary skill in the art before the filing of the invention to further modify Pichler, Shinar and Tashiro by combining a route between two locations which identifies one or more points of interest as taught by Kumar. One would be motivated to make this modification in order for systems may select the fastest route based on the shortest estimated time to travel along the route.
Regarding claim 22: recites analogous limitations that are present in claim 21 therefore claim 22 would be rejected for the same reasons above.
Conclusion
Applicant's submission of an information disclosure statement under 37 CFR 1.97(c) with the timing fee set forth in 37 CFR 1.17(p) on 02/18/2025; 04/24/2025; 07/07/202 prompted the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 609.04(b). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/B.U./Examiner, Art Unit 3663
/JAMES M MCPHERSON/Examiner, Art Unit 3663